Aligning Design and Development
Processes for Additive Manufacturing
By
MASSACHU SETT WST TUTE
01
Michael L. Stern
C.HNOLOLC3Y
JUL 3 0 2015
S.B. Mechanical Engineering
LIBRARIES
Massachusetts Institute of Technology, 2009
Submitted to the Department of Mechanical Engineering in
Partial Fulfillment of the Requirements for the Degree of
Master of Science in Mechanical Engineering
at the
Massachusetts Institute of Technology
June 2015
2015 Massachusetts Institute of Technology. All rights reserved
redacted
Signature
-- - ----------- . .. ... .....
.
Sign ature o f Auth or ....................................
Michael L. Stern
Department of Mechanical Engineering
May 21, 2015
Certified by .......................................
Signature redacted
Maria
Yang
Associate Professor of Mechanical Engineering
Thesis Supervisor
Accepted by..........................
Signature redacted
David E. Hardt
Professor of Mechanical Engineering
Chairman, Department Committee on Graduate Studies
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Aligning Design and Development
Processes for Additive Manufacturing
By Michael Stern
Submitted to the Department of Mechanical Engineering
on May 21, 2015 in Partial Fulfillment of the
Requirements for the Degree of Master of Science in
Mechanical Engineering
Abstract:
Rapid Prototyping has transitioned from only being able to produce delicate
prototypes to being capable of producing robust production parts. As part of this transition,
it has been renamed Additive Manufacturing (AM). As a true manufacturing technology, it
has become important to deliberately design parts for Additive Manufacturing, and research
has begun in how best to achieve this. This thesis explores the background of Additive
Manufacturing, the growth in its use as a manufacturing technology, and the advantages and
challenges of the technology. Following background information, this thesis progresses to
different design approaches and technologies that promise to be effective when paired with
AM. A design methodology using topology optimization is proposed, detailed and then
tested on two case studies. The first case is a high-speed mirror for imaging and the second
case is an aircraft bracket for the 2013 GrabCAD-GE design challenge. This thesis also
includes an examination of the implementation of the proposed methodology on these case
studies and the resulting designs. The design from both case studies achieved a greater than
60% weight reduction through the use of design methodology tailored for AM.
The final section of the thesis shifts from the design process to the development
process where the impact of AM is examined. In order to gain an understanding of the
influence that Additive Manufacturing has on production, this thesis includes a synthesis of
the literature from Additive Manufacturing as well as Design and Management. The benefits
are approached from an economic perspective, reviewing the first order benefits that have
been extensively studied and then progressing to the second order benefits, and indirect
benefits, which have not been examined in detail before. To understand the full effect of
Additive Manufacturing on product development, the consideration of advantages such as
high fidelity prototypes, decreased risk, faster time to market and late stage design flexibility
are assessed.
Thesis Supervisor: Maria C. Yang
Tide: Associate Professor of Mechanical Engineering
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4
Acknowledgments
I would like to thank those who were instrumental in my education. You have helped me reach the
MIT Graduate School of Mechanical Engineering, aided me in the pursuit of meaningful research
and guided me in writing a coherent thesis.
Maria Yang, Lois Feldman, Fred Stern, Kaidyn Becker, Laura Brennan, Nathaniel Lubin, Jim
Ingraham, Scott VanBroekhoven, Eli Niewood, Ken Estabrook and all of the students in the
Ideation Lab.
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Glossary
3-Matic-STL
3-Matic-STL is a software tool for directly editing STL meshes produced by
Materialise.
Additive Manufacturing (AM)
American Society for Testing and Materials (ASTM) F42 definition: "a process of
joining materials to make objects from 3D model data, usually layer upon layer, as
opposed to subtractive manufacturing methodologies [1]"
AlSi1OMg
AlSil 0Mg is an aluminum alloy used in metallic powder bed fusion it is 10% silicon
and roughly 0.5% magnesium by weight.
CBush
CBush is a type spring element form the Nastrtran FEA system (used in Optistruct).
They have independent coefficients of translational and rotational stiffness and join
two nodes to each other.
Computer Aided Design (CAD)
Computer Aided Design is a software tool that is used for creating 2D or 3D digital
models.
Computer Based Design (CBD)
Computer Based Design is a term coined by Mistree and Muster that defines a
category of design tool where the user does not directly model features but instead
builds models generatively or with the assistance of the computer [2].
Design for Additive Manufacturing (DFAM)
Design for Additive Manufacturing is an area of study that examines how best to
create parts and assemblies for Additive Manufacturing.
Design for Manufacturing and Assembly (DFMA)
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Design for Manufacturing and Assembly is a term coined by Boothroyd and
Dewhurst for a process focusing on the redesign of parts to maximize the efficiency
of manufacturing and assembly.
ElectroOptical Systems (EOS)
Electro Optical Systems GmbH is a manufacturer of Additive Manufacturing
powder bed fusion systems.
Fused Deposition Modeling (FDM)
Fused Deposition Modeling is a term coined by Stratasys LTD. for a plastic material
extrusion process.
Meshmixer
Meshmixer is a software tool for directly editing STL meshes produced by Autodesk.
Optistruct
Optistruct is a finite element solver produced by the Altair company. It has the
capability to run SIMP topology optimization.
OSS Smooth
OSS Smooth is a thresholding and smoothing tool. It is also capable of creating a
reanalysis model where the thresholded geometry is returned as a new finite element
model with the original loading and boundary conditions applied to it.
Powder Bed Fusion (PBF)
An Additive Manufacturing process in which thermal energy selectively fuses regions
of a powder bed.
Rapid Manufacturing (RM)
Rapid Manufacturing is "the use of a CAD-based automated Additive Manufacturing
process to construct parts that are used directly as finished products or components
[3]."
RBE 3
A RBE 3 is the type of rigid body element (used in Optistruct), created as part of the
NASTRAN FEA system. It is often used to connect constraints to a set of elements.
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It can be used in a wagon wheel configuration typically join elements of a circular
feature to a central node, such as in a bolt constraint.
Selective Laser Melting (SLM)
Selective Laser Melting is a term for a powder bed fusion process typically applied to
metallic processes.
Self-weighting
Self-weighting is a term describing the loading of optical components. It is the
concept that the mass of an optical component drives its own inertial loading.
Solid Isotropic Material with Penalization (SIMP)
SIMP is a methodology for TO developed by Bendsoe and Kikuchi that utilizes
element weightings and finite element analysis to optimize problems [4].
TI6AL4V
Ti6A14V is a titanium alloy with 6% aluminum and 4% vanadium by weight as
alloying elements. It is commonly used in metallic powder bed fusion systems.
Topology Optimization (TO)
Topology optimization is an optimization method that redistributes material to
create an optimized layout for a given objective while subject to a set of constraints.
Most topology optimization solvers use the SIMP algorithm.
Unmanned Air Vehicle (UAV)
An unmanned air vehicle is a remotely or autonomously controlled air vehicle.
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Table of Contents
Chapter 1: Background on A dditive M anufacturing...........................................................
16
1.1
Introduction:.........................................................................................................................
16
1.2
A dditive M anufacturing Term inology .........................................................................
16
1.2.1
Types of M anufacturing Processes.........................................................................
16
1.2.2
Com puter-Controlled A dditive M anufacturing....................................................
17
1.2.3
U se of A dditive M anufacturing ..............................................................................
18
1.3
1.2.3.1
Four Categories of U tilization.........................................................................
18
1.2.3.2
Visual M odels...................................................................................................
19
1.2.3.3
Fit-Check Prototypes .......................................................................................
19
1.2.3.4
Fixtures and Patterns .......................................................................................
20
1.2.3.5
End-U se Parts ...................................................................................................
20
1.2.3.6
Influences on Part Utilization.........................................................................
20
Rapid M anufacturing......................................................
E rror! B ookm ark not defined.
1.3.1
D efinition of Rapid M anufacturing.......................................................................
21
1.3.2
Adoption of Rapid M anufacturing.......................................................................
22
1.3.3
Advantages of Rapid M anufacturing ....................................................................
22
G eom etric Freedom ..............................................................................
1.3.3.2
M old-Free M anufacturing ..............................................................................
23
1.3.3.3
D evelopm ent benefits ....................................................................................
23
1.3.3.4
Supply Chain, D istribution and Inventory ...................................................
23
1.3.4
1.4
..... 22
1.3.3.1
Challenges......................................................................................................................
24
1.3.4.1
M aterials.................................................................................................................
24
1.3.4.2
Layer Based Process..........................................................................................
24
1.3.4.3
Process Lim itations .........................................................................................
24
1.3.4.4
Part Q ualification ............................................................................................
25
1.3.4.5
Inspection ..........................................................................................................
25
1.3.4.6
Process K now ledge..........................................................................................
25
Conclusion :............................................................................................................................
26
Chapter 2: The A doption of Rapid M anufacturing...........................................................
27
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2.1.1
Tipping the Balance ................................................................................................
27
2.1.2
Trend Setting Exam ples.........................................................................................
28
2.1.3
Economics of Rapid Manufacturing and the Direct Substitution Fallacy .......... 29
2.1.4
Research Q uestions ..................................................................................................
29
2.1.5
Value Proposition ....................................................................................................
30
Chapter 3: Breaking outo
3.1
...................................................................................
31
Fighting Against O ur D esign Instincts.........................................................................
31
3.1.1
The D esign Paradox ................................................................................................
31
3.1.2
Breaking O ut of D esign Fixation ..........................................................................
31
3.2
D esign for M anufacturing ..............................................................................................
3.2.1
3.3
G uidelines for D esign .............................................................................................
32
32
3.2.1.1
Part O rientation................................................................................................
33
3.2.1.2
D igital D esign ..................................................................................................
33
3.2.1.3
Support M aterial Plan ......................................................................................
33
3.2.1.4
O verhanging Structures..................................................................................
34
D esign Tools and D esign Fram ew orks ........................................................................
35
3.3.1
Com puter Aided D esign .........................................................................................
35
3.3.2
Anim ation Softw are................................................................................................
35
3.3.3
Com plexity, Sim plicity and Biomim icry ..............................................................
36
3.3.4
Com puter-Based D esign.........................................................................................
38
3.3.5
Function Based D esign ...........................................................................................
38
3.4
Structural O ptim ization ..................................................................................................
39
3.4.1
H istory of Structural O ptim ization ......................................................................
39
3.4.2
O ptimization Engine ..............................................................................................
39
3.4.3
Types of O ptim ization ...........................................................................................
40
3.4.3.1
Size O ptim ization..............................................................................................
40
3.4.3.2
Shape O ptimization .........................................................................................
40
3.4.3.3
Topology O ptim ization..................................................................................
41
Beam Bending Exam ple..............................................................................................
42
3.4.4
3.4.4.1
Problem Form ulation ..........................................................................................
42
3.4.4.2
Thresholding .....................................................................................................
43
Design Methods Utilizing Topology Optimization:..........................................
45
3.4.5
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3.4.5.1
Inspiration Based Design ................................................................................
45
3.4.5.2
Traditional Manufactured Design..................................................................
46
3.4.5.3
Additive Manufactured Design ......................................................................
48
3.4.6
P roposed Process:...................................................................................................
49
C o nclu sio n ............................................................................................................................
51
Chapter 4: Applying an AM Aligned Design Methodology..................................................
52
3 .5
4 .1
C ase S tu dies ..........................................................................................................................
52
4.1.1
Spinning M irror ........................................................................................................
52
4.1.2
GrabCAD GE Engine Bracket..................................................................................
59
4.1.3
Methodology Implementation Challenges ..........................................................
65
4.1.3.1
Difficulties Associated with Function-Based Design ................................
65
4.1.3.2
Software Limitations .......................................................................................
65
4.1.4
4 .2
Adapting Topology Optimization for AM...........................................................
66
4.1.4.1
Lattice-Based Topology Optimization Interpretation.................................
66
4.1.4.2
Lattice Generation and Sizing .........................................................................
67
4.1.4.3
Manufacturing Constraint Integration ..........................................................
67
4.1.4.4
Interactive Optimization ................................................................................
67
C o n clu sio n ............................................................................................................................
4.2.1
Effective Methodology............................................................................................
P ro cess .................................................................................................................................................
68
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69
5.1
Design Process Versus Development Process...........................................................
69
5.2
Economics of Rapid Manufacturing............................................................................
70
5.2.1
Economics of Tool-Free Production.....................................................................
70
5.2.2
Supply Chain, Distribution and Inventory...........................................................
70
5.2.3
The Effect of Rapid Manufacturing on Product Development .......................
71
5.2.4
The Elimination of the Manufacturing Cycle ......................................................
71
5.2.5
Benefits of the Perfect Prototype .........................................................................
72
Evaluating the Magnitude of the Benefits of Rapid Manufacturing........................
73
5.3
5.3.1
Economic Impact on Product Development Process .......................................
5.3.1.1
5.3.2
Quantifying the Benefit of Prototype Fidelity ............................................
Impact on the Development Process ....................................................................
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5.3.2.1
E ffect of N on-Frozen D esign.........................................................................
75
5.3.2.2
H ardw are Follow s Softw are ...........................................................................
75
5.4
Innovative Product D evelopm ent................................................................................
76
5.5
Conclusion: ...........................................................................................................................
77
Chapter 6: C onclusion ................................................................................................................
79
6.1
Future w ork ..........................................................................................................................
79
6.2
Fusion of D esign and D evelopm ent .............................................................................
80
Appendix: GE- GrabCA D Contest Info.............................................................................
81
Bibliography .................................................................................................................................
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List of Figures
Figure 1: Breakdown of utilization of AM in 2014. Based off of data from the Wohlers
Rep o rt 20 14 [7]..........................................................................................................................
19
Figure 2: The relationship between utilized part strength, complexity and function of AM
p arts.............................................................................................................................................
21
Figure 3: The increase in spending at service bureaus on end-use parts. Source: Wohlers
R ep o rt 2 0 14 [7]..........................................................................................................................
27
Figure 4: Effect of orientation on support structure. Blue is the part material and yellow is
support material. Note the effect digital of design in the representation of the fillet,
outlined with red, and of the sensitivity to overhanging structures and the required
sup p ort material.........................................................................................................................
34
Figure 5: Natural root system compared to engineering run and shaft design........... 37
Figure 6: (a) size optimization, (b) shape optimization, (c) topology optimization. Source:
B en d so e [4 5]...............................................................................................................................
40
Figure 7: Beam bending problem formulation: design region, loads and boundary conditions.
.....................................................................................................................................................
42
Figure 8: Topology optimization output as a function of penalty value with virtual density
shown as a grayscale and compliance reported -
3..................
Figure 9: Beam bending results thresholded at 0.3 with varying penalty weighting............ 44
Figure 10: Inspired design based off topology optimization...................................................
45
Figure 11: Flow diagram for inspiration based use of TO...........................................................
46
Figure 12: Traditionally manufacturable design based on TO..............................................
47
Figure 13: Traditional manufactured design topology optimization flow chart.................. 47
Figure 14: AM manufacturable design based on TO ...................................................................
48
Figure 15: AM topology optimization flow chart. ........................................................................
49
Figure 16: Mirror bounding volume in cross section: Red portions define mechanical
interface regions that cannot be modified and blue portions are mirror faces and cannot
be altered. The central void houses the drive motor and is unavailable. Green
represents the available design volume..............................................................................
Figure 17: Optistruct finite element model of one-eighth mirror. ........................................
53
54
14
Figure 18: Top view of with symbolic boundary conditions; disconnected hub (left),
modified load case and connected hub (right). ...............................................................
55
Figure 19: One-quarter mirror model showing displacements normal to the x-face (right); red
regions do not meet specifications. Left, initial design directly from thresholding; right,
post processed resulting in 0.79% weight increase..........................................................
56
Figure 20: One-quarter model of final mirror design displacements normal to the x-face
(right); faces meet surface figure specifications...............................................................
Figure 21: Addition of a boss to accommodate a balancing setscrew in 3-Matic STL .....
57
58
Figure 22: Traditional mirror design on left, additive mirror design on right. ...................... 58
Figure 23: AM Optimization flow chart, with backward red arrows representing required
reform ulation experim entation. ..........................................................................................
59
Figure 24: Four load conditions for the bracket. Source [54]. ...............................................
60
Figure 25: Resulting geometry from TO optimization.................................................................
61
Figure 26: Isometric view of the final design with VonMises stress displayed. Maroon
colored elem ents represent yielded material......................................................................
62
Figure 27: Bottom view of the final design with VonMises stress displayed. Maroon colored
elem ents represent yielded m aterial....................................................................................
62
Figure 28: Comparison of topology optimized design with top three designs from the
competition, displaying percent weight reduction. .........................................................
63
Figure 29: Comparison between TJ2 and T20 design showing stresses viewed from the
bottom. Maroon colored elements represent yielded material in upper two images. Red
stress represents 500 MPa in the lower two images. ......................................................
Figure 30: Density versus Young's modulus plot. Source: Bendsoe [45].............................
64
66
Figure 31: Development process for both AM and conventionally manufactured parts.
Unlike traditional manufacturing, AM does not require a separate manufacturing cycle.
S o urce: [3 5].................................................................................................................................
72
Figure 32: Prototype Fidelity verus number, holding savings near constant. Source: Thomke
an d B ell, [6 7]..............................................................................................................................
74
Figure 33: Left: Radiolaria an interactive design tool to create necklace [68]. Right: Hyphae
Lam p 132 a generative designed lamp [69]........................................................................
76
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Chapter 1: Background on Additive
Manufacturing
1.1 Introduction:
This thesis will explore how design and development processes can be modified to
better harmonize with Additive Manufacturing (AM). Additive Manufacturing will be
discussed in four categories based on utilization: visual model, fit check prototype, pattern or
fixture and end-use part. There will be an additional focus on Rapid Manufacturing (RM);
the practice of using AM to fabricate end-use parts. RM is particularly interesting, because
when fabrication technologies transition to use in manufacturing, the investment of time to
optimize design and development becomes important. Finally, a function-based design
methodology for RM design based on structural optimization will be presented, formalized
and then applied to two case studies.
This thesis is organized into five chapters. The first chapter examines important
terminology of AM, how it has been used historically and how it is currently being used. The
second chapter focuses on the growth in the use of RM, the significance of this growth, and
includes a formal statement of the thesis's research questions. The third chapter presents
background on design and optimization relevant to the thesis' proposed methodology. The
methodology is applied to two case studies and results are reviewed in detail in Chapter 4.
Chapter 5 examines the higher-level effects that AM and particularly RM can have on the
development process.
1.2 Additive Manufacturing Terminology
1.2.1 Types of Manufacturing Processes
Manufacturing can be divided into three categories: subtractive, formative, and
additive. Subtractive manufacturing comprises any process in which parts of a larger section
of material are removed to produce an end product. Examples are numerous, reaching as far
16
back as the fabrication of arrowheads in the Stone Age and extending through modern
diamond grinding with accuracy on the order of angstroms [5]. Subtractive manufacturing
serves as the benchmark for all manufacturing and, although it has the greatest precision, it is
time consuming and requires more starting material than the final product.
Formative manufacturing is usually mold-based or pattern-based with the final parts
produced indirectly. Examples of formative manufacturing processes include casting,
injection molding and forging. A critical advantage of this method is its efficiency for largescale production: a single mold or pattern can be used to produce very large numbers of
parts.
An Additive Manufacturing process is one in which material is selectively placed to
form the final part. An example of this type process is brick and mortar construction. A key
advantage in an additive process is the ability to create structures of much greater complexity
than could be created by subtractive or formative manufacturing. This complexity is enabled
by the fact that internal features are accessible during fabrication.
1.2.2 Computer- Controlled Additive Manufacturing
Charles Hull's 1984 patent filed for photopolymeric construction of 3D parts and
marked the beginning of Additive Manufacturing [6]. Terminology has evolved with the
technology, first coalescing around Rapid Prototyping (RP), and then in an effort not to
pigeonhole the technology for developing prototypes, the term Additive Manufacturing
(AM) was born. It is important to distinguish this AM terminology from the previouslydiscussed Additive Manufacturing processes. The distinction is made in addition to selective
material placement already mentioned the process be driven by 3D modeled data. The
American Society for Testing and Materials (ASTM) F42 committee, the recognized body
for material, manufacturing and testing standardization, defined Additive Manufacturing as
"a process of joining materials to make objects from 3D model data, usually layer upon layer,
as opposed to subtractive manufacturing methodologies [1]." While industry has adopted
AM, the media has used 3D printing (3DP) as the terminology of choice. Originally a
descriptor for a specific process, 3D printing evokes for the layperson the concept of the
technology and has gained widespread acceptance. Recently, Terry Wohlers, a key industry
analyst, declared in his annual report of the industry that Additive Manufacturing and 3D
17
printing can be used synonymously [7]. Similarly, this thesis will use AM and 3DP
interchangeably.
In addition to the various names for Additive Manufacturing as a whole, there is
terminology for each specific technology or class of technologies within AM. This plethora
of terms is further complicated by the fact that almost every company has a trademarked
name for their specific process. These processes may not necessarily be unique, resulting in
the creation of many names with the same meaning. In an effort to create standard
terminology to reduce confusion, the ASTM organized and defined seven categories that
encompass every current methodology for 3D printing [1]. These various approaches rely on
different physical phenomena and, as a result, often work with different materials that
exhibit both strengths and weaknesses across cost, rate, quality or material availability.
Chapter 4 describes two case studies in which the technology used is metal Powder
Bed Fusion (PBF) an Additive Manufacturing process in which thermal energy selectively
fuses regions of a powder bed.
1.2.3 Use of Additive Manufacturing
1.2.3.1 Four Categories of Utilization
Additive Manufacturing has been employed in a wide range of applications. By
teLguiizing hese applicationis, we can gain insight into its historical use and how that use is
evolving. Here we propose that the use of Additive Manufacturing can be broken into four
different categories: visual models, fit check prototypes, fixtures and patterns, and end-use
parts. Based on data from an organizational survey by Wohlers Associates, utilization by the
four proposed categories is captured in Figure 1.
18
8
17
w Visual Models
I Fit-Check
29
Fixtures & Patterns
20
F
End-Use Parts
SOther
26
Figure 1: Breakdown of utilization of AM in 2014. Based off of data from the Wohlers Report 2014 [7].
The utilization is split quite evenly between visual models, fit check prototypes, fixtures and
patterns and end-use parts. An expansion of the description of each of the use categories are
presented below.
1.2.3.2
Visual Models
The earliest use of Additive Manufacturing was for the creation of physical models
designed from computer aided design (CAD) files. A visual model has the form but not
necessarily the function of the final part. As such, the value of a model is to provide a
mechanism for tactile representation that can be visualized and handled, helping gain
management or customer approval of a design in a medium that can be easily evaluated. And
for engineers, it offers an opportunity to get a physical sense of dimensions and an intuitive
sense of the design [7].
1.2.3.3 Fit-Check Prototypes
Fit-check prototypes give the user an opportunity to establish appropriate part fit
and to assess whether there is sufficient space and access for assembly. This application can
be useful both for ensuring that tolerances are correctly accounted for and, in the case of
mating to preexisting parts, that interfaces and fits are correct. Frequently, fit-check
prototypes will reveal errors that would be expensive in production but can be costeffectively addressed during this early design stage.
19
1.2.3.4 Fixtures and Patterns
For fixtures and patterns, Additive Manufacturing is used to generate the geometry
but not the final part. Due to limitations in materials available for Additive Manufacturing, it
is often desirable to prototype or manufacture in a standard engineering material instead. In
these cases, parts can be printed and then, using an intermediate process, transformed into a
final part. For example, wax positives can be printed and then processed by lost wax casting
into metallic parts. Fixtures for assembly or fabrication can also be made to speed up
alignment of parts during assembly or to provide a template for manual fabrication
procedures.
1.2.3.5 End-Use Parts
For end-use parts, Additive Manufacturing generates both the geometry and material
for the final part. After removal from the printer, the part will be utilized directly. End-use
part production was the last category of use to be developed but is now exhibiting the most
rapid growth, as will be discussed in Chapter 2. In addition to being used for prototypes,
end-use parts can be manufactured in quantity as the final product.
1.2.3.6 Influences on Part Utilization
The material and machine properties in Additive Manufacturing have advanced
tremendously since the 1980s when brittle plastic was the standard material [6]. At the time,
there was a widespread anecdote that models passed around a boardroom rarely made it
back to the presenter in one piece. Research since then has led to increasingly durable and
diverse materials. Now, parts are made with super alloys, engineering grade thermoplastics,
and even living cells [8]. From a design perspective, great improvements have swept the
computer industry, transforming the capabilities of CAD and thereby allowing greater
complexity and sophistication of part design. By looking at the material requirements and
part complexity of AM, we can get a sense of how the utilization has evolved. A conceptual
plot of part function based on complexity and utilized strength is shown in Figure 2.
20
E
Models
Fit Check
Fixture / Pattern
End Use Part
Utilized Part Strength
Figure 2: The relationship between utilized part strength, complexity and function of AM parts.
Through this lens of part utilization we see that the evolution of part use is a
function of both the complexity that could be effectively captured digitally via software and
the part strength or material properties that could be fabricated. The least rigorous
requirements for both strength and complexity only allow for the generation of models of
which the primary goal is to physically produce a digital concept. For this purpose material
properties are not critical and the value is a function of the complexity and realism that can
be captured. For fit-check parts, complexity is not as great since all features need not be
captured and the geometry will be limited by traditional manufacturing. However, there is
still some level of handling strength required for fit check parts. For fixtures, the material
properties become much more important as parts will now be handled aggressively during
assembly or production; for patterns, the material properties are critical for processes such as
investment casting where material must be melted for removal. End-use parts utilize the
most strength and can leverage the greatest complexity providing the opportunity to create
parts with the most significant impact.
1.3 Rapid Manufacturing
1.3.1 Definition of Rapid Manufacturing
Rapid Manufacturing is a term that was popularized in the early 2000s and developed
from "rapid prototyping [9]." RM can be defined as "the use of a . . CAD-based automated
21
Additive Manufacturing process to construct parts that are used directly as finished products
or components [3]."
1.3.2 Adoption of Rapid Manufacturing
In many cases, Rapid Manufacturing has grown organically from the use scenarios in
which a user, under time pressure, took a model that was designed for fit-check purposes
and tested if it had the strength to be functional. These tests were often successful, which
demonstrates the utility of RM. Yet, to better understand the current level of adoption of
Rapid Manufacturing, it is useful to further examine its advantages and challenges.
1.3.3 Advantages of Rapid Manufacturing
A great deal of literature on Additive Manufacturing has focused on the advantages
and disadvantages of the process. A concept has arisen based on the characteristics of AM:
Design For Additive Manufacturing (DFAM), which is central to utilizing the advantages
and minimizing the disadvantages of the AM process. The comprehensive list of the benefits
and drawbacks RM has been informed by my own personal experience with AM tools,
discussions with experts and many valuable sources [3,9-21].
1.3.3.1 Geometric Freedom
Geometric freedom is one of the most far-reaching and significant advantages of
AM because, for the first time, form can be decoupled from fabrication. While specific
additive processes may have manufacturing restrictions, they are dramatically less
constraining than any previous manufacturing technology. And with this geometric freedom
comes a host of benefits that have profound implications:
*
Static assemblies can be reduced to single parts.
*
Single-Part Assemblies can be fabricated-an assembly grown as a single part with
moving subcomponents.
*
Mass customization is possible, and large numbers of similar parts can be created in
which each part is unique.
22
*
Parts can be optimized for functionality instead of manufacturability, enabling higher
performance.
1.3.3.2 Mold-Free Manufacturing
Since the part geometry is defined by digital files and realized with an Additive
Manufacturing system, molds are not required and therefore manufacturing is scale invariant.
New parts can be created more quickly than previously possible and changes can be made
inexpensively and expeditiously. As a result:
*
The time to reach market is faster.
*
Designs do not need to be frozen after tools are made.
*
Factories become more agile since part mix can be varied without retooling.
1.3.3.3 Development benefits
An important and often overlooked impact of using RM is the effect that it has on
the development process. More detail on these claims will be presented in the fourth
chapter. Generally the advantage of using RM for development can be described as:
e
Faster development times of products.
*
Perfect fidelity prototypes result when using Rapid Manufacturing.
*
Late stage changes can be made without excessive costs.
1.3.3.4 Supply Chain, Distribution and Inventory
Rapid Manufacturing enables a leaner supply chain since goods can now be
fabricated on-site and on demand. Transport is simplified, requiring only the distribution of
raw materials.
e
Reduced inventory since there is no need to store a diversity of parts.
*
Faster distribution since parts can be built on site.
*
Supply chain can be simplified since only raw material is required.
23
1.3.4 Challenges
As a young manufacturing technology, the challenges of Additive Manufacturing are
just beginning to be understood well, and solutions or mitigation techniques are under
development. This section below will enumerate these challenges.
1.3.4.1 Materials
The process requirements for Additive Manufacturing and the specific requirements of
the wide range of different AM technologies used have given rise to extensive materials
research. Different processes are often suited to specific materials or generate material
microstructures with different properties than previously encountered. This has given rise to:
*
Requirement for process specific material development.
*
The qualifications of novel materials.
e
Experimentation into process-material interactions.
1.3.4.2 Layer Based Process
Due to the layer-by-layer process employed by Additive Manufacturing, bonding
within layers and between layers may be different, leading to anisotropic materials.
Additionally, point-wise manufacturing means that a region may have different properties
than its neighbors. More specifically:
*
Parts built will have anisotropic material properties requiring more detailed
characterization.
*
Defects can occur at interior locations of a part, making them difficult to detect.
*
The stair-stepping, also described as figure error, effect prevents smooth profiles
from being generated in the z-axis due to discontinuities between layer profiles.
1.3.4.3 Process Limitations
The limitations that will be the most difficult to mitigate are those linked to process
physics itself. As a result, these limitations may force us to adapt the design and adjust our
requirements.
*
Dimensional accuracy is generally worse than subtractive processes.
24
*
Surface finish is typically worse than traditionally manufactured parts.
*
Process times are usually slower than traditional manufacturing.
*
Support material required for complex overhanging shapes.
1.3.4.4 Part Qualification
Qualification of parts has been difficult given the unknowns in materials, process
parameters and machine reliability. As a result, users have been reluctant to put AM parts
into critical applications. Recent application of AM parts for end-use has lead to focus on:
*
The development of qualification standards and procedures.
*
Study of the variability of material properties with as a result of geometry.
*
Lack of repeatability between parts.
1.3.4.5
Inspection
The benefits that Additive Manufacturing provides from a geometric freedom
standpoint also create challenges from an inspection standpoint. Every detail is another
feature to measure. The need to inspect parts has typically been focused on the exterior of a
part where the limits for traditional manufacturing serve to ensure access for measurement.
Verifying the detail of internal features is limited by the resolution of the most advanced
inspection technologies and the geometric freedom of AM enables the creation of parts that
exceed that inspection resolution. Recent developments in Computed Tomography (CT) are
helping study certain material parts [22]. White light imaging is similarly helpful for analyzing
complex 3D shapes, but state of the art measurement of internal and complex features is still
trailing the technology that can fabricate it [23]. In situ part monitoring is under
development as an alternative to inspection after fabrication, this holds great promise
because it allows for inspection of the interior of complex parts during fabrication [24,25].
1.3.4.6 Process Knowledge
Additive Manufacturing is still in its infancy. Processes are being studied and new
features continue to emerge; practitioners have yet to fully understand how to leverage all of
the positive elements and how to mitigate the impact of the challenging features. Rapid
25
Manufacturing has been most successful as engineers have learned how to target some of the
design and performance benefits while solving certain qualification issues.
1.4 Conclusion:
The DFAM framework of leveraging the pros and limiting exposure to the cons of
AM processing will be critical to understanding the current transformation of AM from
rapid prototyping to Rapid Manufacturing. Additionally, the framework provides perspective
for examining the design methodology and development process presented in the following
chapters. Many of the concepts presented will add another layer to the benefits and
drawbacks presented above.
26
Chapter 2: The Adoption of Rapid
Manufacturing
2.1.1 Tipping the Balance
Until recently, Rapid Manufacturing for end-use parts was an option only for a small
niche group of AM users. Continued improvements in materials, machines, and testing
methodology have helped drive down the impact of some of the challenges, thereby
reshaping the cost-benefit tradeoff. The resulting shift has been captured by data collected in
the Wohlers report, shown in Figure 3, showing how utilization of end-use parts has grown
over the past decade.
40
35
+0
1
------------------------ - - - -
(A
cd
----
30
-------------------------------------
25-
--------------------------------- -------
20
----------------------------
28.3
4.4
--
-
4.0
-----19.6
17.2
1510
-- -- --------- 9.
- - -- - - --
- - --
-
-
4.0
8.3
2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Year
Figure 3: The increase in spending at service bureaus on end-use parts. Source: Wohlers Report 2014 [7].
Currently, end-use parts represent more than one-third of the business for Additive
Manufacturing service bureaus.
27
2.1.2 Trend Setting Examples
The most effective utilization of Rapid Manufacturing has been within low-volume
manufacturing: taking advantage of the mold-free economics and geometric complexity
available through this method. Two industries in particular have successfully utilized these
benefits: the medical device market in which prostheses, surgical guides and implants can be
customized for customers' specific profiles, and the aerospace industry in which the
geometric freedom has enabled higher performance parts to be developed. In each of these
cases, value has not been derived by delivering lower cost manufactured parts but rather
through an increase in part performance.
A striking example of the adoption of RM for medical manufacturing has been the
fabrication of hearing aids of which more than ten million of which have been designed and
produced to fit into individual patients' ears [26]. The added value of a customized fit and
the ability to fabricate replacement units without holding inventory or re-measuring a
person's ears outweighs any additional manufacturing costs. For these reasons it is estimated
that 90% of hearing aids are now made with RM, more than two million per year [7].
Rapid manufactured acetabular cups for hip replacements demonstrate another
example of geometric freedom providing critical advantages. One of the key benefits of this
design over those made via conventional manufacturing is the ability to create a range of
densities within the same part. Varying density enables the porous side to nrovide an ontimal
site for Osseo-integration (bone growth), while the polished high density side creates an
efficient ball and socket joint. Additive manufactured accetabular cups have been implanted
in more than 90,000 patients in Europe and recently the FDA has allowed implantation of
similar devices in the United States [7,27].
Within the aerospace industry, one of the most notable parts being rapidly
manufactured is a fuel injector nozzle for the LEAP engine scheduled to enter service in
2016 on Boeing 737 and Airbus A380 airplanes. The utilization of RM to make this part has
received a great deal of press due to the scale of manufacturing and the increased
performance it provides. It will be manufactured in quantities between 20,000 and 50,000
units per year. The highlight of this project is that the new fuel injector, by utilizing
geometric freedom of AM, has reduced an assembly of 20 parts to a single part, resulting in a
reduction of mass by 25% and a fivefold increase in durability [28].
28
2.1.3 Economics of Rapid Manufacturing and the Direct
Substitution Fallacy
Though the following academic research suggests that RM is not cost effective in
large quantities, the examples of the fuel injector nozzle and hearing aid have demonstrated
that manufacturing products on a scale of tens of thousands to millions can be profitable. A
number of studies have explored the manufacturing cost tradeoffs between choosing to
&
manufacture a part by injection molding or Rapid Manufacturing. Two studies: Hopkins
Dickens and Ruffo et al. state that the economical break-even volume between AM and
injection molding ranges from roughly 1,000 to 10,000 based on part size [9,19]. More
recently, Atzeni et al. conducted similar research focusing on the effect of redesigning the
RM part to be better suited for Additive Manufacturing. Their results show that the breakeven point for a small part rose to roughly 90,000, demonstrating a substantial shift when the
design is tailored for AM [10]. A key limitation of these studies is their assumption that new
parts will ultimately perform identically to traditional parts; as we have seen in the preceding
examples, this generally is not the case.
Let us formalize this concept that we can define as the Direct Substitution Fallacy, as
the belief that Additive Manufacturing can be substituted to create end-use parts without
altering designs tailored for traditional manufacturing. Without accounting for this fallacy,
analysis is almost without exception a disappointment. Typically, material properties are
inferior, costs are higher and tolerances are worse [13,12]. To make up for these deficiencies
the part must be redesigned expressly for RM to have a more efficient geometry or an
otherwise increased value [11].
2.1.4 Research Questions
This thesis examines the impact that Rapid Manufacturing can have on both the design
and development processes. The research into this was motivated by the desire to gain a
better understanding in how these two processes can be aligned to the advantages of AM.
Specifically, this thesis sets out to answer two key questions:
1.
How does one leverage the benefits of RM during the design process?
29
2.
Can the unique characteristics of RM be utilized to enhance the development
process?
The third and fourth chapters will focus on answering the first of the questions and
will explore how to design parts for RM. A methodology will be proposed that leverages
benefits while minimizing the impact of the challenges of AM. Typically, to justify the
increased cost of AM parts, a design must outperform conventionally-manufactured designs.
In chapter four, two case studies are explored to illustrate the proposed design methodology
and the benefits of its utilization.
The second question will be answered in the fifth chapter, which offers an approach
for how one can leverage AM to improve the product development process. We will explore
how the development process for Rapid Manufacturing is fundamentally different than that
for conventional manufacturing. And it will be argued that these key differences, when
leveraged correctly, can be of substantial financial benefit.
2.1.5 Value Proposition
Research is underway to improve upon current systems to minimize or eliminate the
challenges that have restricted the use of Additive Manufacturing technology. Improved
material properties, qualification procedures, robust AM systems, and closed loop control
systems are all under development. Together, these improvements will shift the value
proposition for Rapid Manufacturing and, as a result, pressure will rise to leverage this
increasingly robust technology.
A McKinsey & Company survey highlights the lack of organizational readiness for
adoption. The report states, "In essence, more than half of the market has yet to grasp AM's
applications [29]." Currently, AM is being heralded as an important new technology based
solely on its manufacturing freedom. Yet there is a lack of literature on exploring how best
to reshape the design process and the product development process to amplify the benefits.
Due to the similarities between AM processes, increased knowledge in this area will apply to
all of the various AM processes and will engender increasing relevance as the technology
becomes more mature.
30
Chapter 3: Breaking out of the Mold
3.1 Fighting Against Our Design Instincts
This chapter is divided into two sections and sets out to answer how we can best
leverage the benefits of RM during the design process. The first section explores the tools
we use for design and how those contribute to the design process. Based on the advantages
and disadvantages of the different tools examined, we propose a methodology of design for
AM.
3.1.1 The Design Paradox
For the first time, design has become more challenging than manufacturing. Design
and manufacturing have historically been intertwined, typically with the designer pushing the
manufacturer to implement increasingly complex designs. Now, that paradigm is reversed.
With the development of Additive Manufacturing, it has become possible to fabricate
objects of virtually unlimited intricacy leading to the slogan that "complexity is free." But
this is only half of the creation process and designing has not become any easier. The
reduction of constraints on the manufacturing process shifts the limitations from
manufacturing to design. We are now in a design-limited paradigm where we could make
increasingly complex objects if only we could design them. This transition is well captured
by Reeves: "Most products are not optimised, as they are 'designed-for-manufacture' rather
than 'manufactured-for-design [30]."'
3.1.2 Breaking Out of Design Fixation
Currently, a significant limitation in learning to design for Additive Manufacturing is
an understanding of how to distance ourselves from past designs. Since design always has to
be manufacturable, we design within the regime of the possible as defined by what can be
manufactured. In traditional design, consideration is paid to tool access, wall thickness
variations, undercuts, draft angles, and a myriad of other factors that are not relevant for 3D
31
printing. While manufacturing is less restrictive with Additive Manufacturing, assembly
constraints remain of critical importance. By carefully considering how something would be
made and assembled, Boothroyd and Dewhurst established that savings of 50% over a
project life cycle could be realized [31]. Their work demonstrated a significant
interdependence between design, manufacturing, and assembly that has led to greater focus
on their interaction.
While 3D printing is not free from restrictions, it has many fewer manufacturing
constraints -- and those it has serve mainly as guidelines. Guidelines do not strictly prohibit
features but instead increase the cost of the feature, typically driven by build time, post
processing and material waste.
To generate complex engineering systems, we often build off past experiences:
engineers are taught not to "reinvent the wheel." While such an approach is often beneficial,
one often inherits obsolete assumptions tied to traditional manufacturing methods. That
phenomenon is referred to as design fixation: when designers are primed with specific
concepts that result in a restricted ability to conduct novel design generation. With this
fixation, the framing of design problems influences designers,
limiting the range of their
ideas and ability to think creatively [32,33]. And this phenomenon of design fixation is
particularly impactful in Additive Manufacturing, where conventional manufacturing
experience restricts novel designs that leverage the advantages brought by AM [34].
3.2 Design for Manufacturing
3.2.1 Guidelines for Design
To design effectively for RM, engineers must have an intimate understanding of AM
technology and its specific constraints. Due to the recent development of AM technology
and the diversity of the systems and software available, users must often experimentally
determine many of the system constraints. The case studies examined here for this research
involve manufacturing using metal powder bed fusion, more specifically Selective Laser
Melting (SLM) with an EOS system.
The following set of guidelines for AM are based on authors previous work on the
development of an AM Unmanned Air Vehicle (UAV) built with FDM [35]. The design
32
guidelines were synthesized to assist designers in understanding the fundamental differences
between AM design and traditional design. These guidelines have been adapted from FDM
to metal PBF, many of the following considerations apply to other AM processes as well:
3.2.1.1 Part Orientation
-
The orientation of a part during the printing process impacts many of its final
characteristics; the layer deposition process creates an inherent anisotropy in the part,
which may adversely affect its strength properties. In metal, these effects are quite
small. For example, Ti6AL4V yield strength differs by only 2%, for FDM ABS-M30
it is 20% and some processes more than 50% [36,37,20].
-
Depending on orientation, some features require support material.
e
At small scales, print orientation can affect figure error on shapes, such as circles.
3.2.1.2 Digital Design
-
Every part has a resolution of a discrete size that is dictated by a physical constant,
such the diameter of the laser.
-
For very small-scale or thin-walled parts, the dimensions of a sub element must be a
multiple of the laser beam spot size.
-
The vertical build direction resolution is limited by the layer height.
3.2.1.3 Support Material Plan
*
Metallic support material is wasteful; it takes additional material and time to create
and must be removed and thrown away after fabrication, requiring handing and
extensive processing time.
-
The further above the build plane a supported feature is located, the more costly it is
since support material propagates through the entire height of the print.
-
Depending on geometry, support material may not be required to bridge small gaps.
For example, a typically-supported feature, such as a horizontal hole, often can be
built support-free with only minor distortion.
33
..
.....
..
...
3.2.1.4 Overhanging Structures
-
The machine can create self-supporting overhangs up to a specific angle; in the case
of metallic PBF systems, this angle is roughly 45 degrees from the vertical axis.
-
The part may be reoriented to eliminate problematic overhangs. Here, the tradeoff
between the two potential configurations must be evaluated -- changes may not
necessarily reduce support but instead shift it to another location. Alternatively, the
support may be reduced but the build may take longer.
The behavior of Fused Deposition Modeling (FDM) is quite similar to that of PBF in
metal. Figure 4 shows the effects of part orientation, digital design, support structures and
overhanging structures.
Figure 4: Effect of orientation on support structure. Blue is the part material and yellow is support material.
Note the effect digital of design in the representation of the fillet, outlined with red, and of the sensitivity to
overhanging structures and the required support material.
In this example, we see the dramatic effect of reorienting the part. In doing so there is a
decrease in the overhanging structures and thus in required support structure. The effect of
digital design, the discretization of material, is exemplified by the poor representation of the
fillet on the left. Finally, the difference between a fillet and chamfer results in dramatic
difference in support structure required.
34
3.3 Design Tools and Design Frameworks
The software tools we use have a strong effect on the types of designs we create.
This section examines two different categories of design tools to understand some of the
fundamental differences that exist. Two main categories of tools are: Computer Aided
Design tools, where the user models every detail of the design with his or her own hand, and
generative Computer Based Design (CBD) tools, where users create computer interfacing
rules that in turn generate the parts. An explanation of how bioinspired design and function
based design tools fit into the generative category of design tools will also be examined.
3.3.1 Computer Aided Design
Computer Aided Design has advanced dramatically from its origins as a drafting aid,
but its fundamental role has always been to help engineers design objects to be physically
realized [38,39]. The implication is that there is no need to be able to model structures that
cannot be built. The result has been to develop features that enable engineers to build parts
digitally in a way that reflects their physical counterparts and resultant traditional
manufacturing methods [8]. In the common design software, Solidworks, an example of
manufacturing influencing a digital tool, the extrude function largely reflects milling
operations; revolve similarly reflects turning operations, etc. Historically, the reflection of
manufacturing constraints in digital design software has not been problematic since it has
not added further restrictions. As we focus on design for Additive Manufacturing, the
connection between software and traditional manufacturing greatly restricts our ability to
design novel shapes and therefore to think in ways that capture the potential of the new
tools. The following subsections represent a survey of design tools and frameworks for
addressing the software challenges of AM.
3.3.2 Animation Software
The manufacturing bias in software described above is a product of the
fundamentals of engineering; animation software, on the other hand, which considers design
without the intent of fabrication. Animation software enables us to investigate design tools
35
free from the influence of manufacturing bias. This different end-goal has resulted in the
creation of a dramatically different tool, one that has a great deal more flexibility but typically
creates only the surface meshes required to produce animations. An example of this freedom
can be seen in Autodesk's Maya software. Within Maya, the this is particularly apparent in
the XGen tool, which uses rules and guide curves to build arrays of objects on top of
existing surfaces [40]. The XGen is often used for the creation of hair or foliage but could be
envisioned as a method to generate surface textures on a part for printing or as a framework
for lattice generation. While there are a lot of important elements that can be taken from
animation software as a class of design tools, the use of surface meshes can present
problems when creating models for 3D printing.
3.3.3 Complexity, Simplicity and Biomimicry
"Complexity is free," an often-espoused mantra for AM, may be true from a
manufacturing perspective, but it is not from a design perspective. Each detail costs
computation power, yielding models that become burdensome during design and sometimes
too complex for converting the file from geometry to tool paths for printing. We can expect
in the future that software will more efficiently address complex 3D structures and that
computer power will continue to increase, lessening these effects. Beyond the increased
processing power required for traditional CAD, each detail requires time by the designer to
create it.
Simple designs are faster to create, cheaper to make, easier to understand, and
quicker to communicate. They are developed more quickly since creators can explain the
designs to peers more effectively, interface with analysis teams more easily, create drawings
for manufacturing teams more swiftly, and create tooling that will be easier to design and
fabricate. As a result, most designed objects have straight lines and simple curves, and
deviate from this form only when there is a design constraint. Yet each of these ideals is
founded in a paradigm of traditional manufacturing where each detail, if removed, will create
a simpler, cheaper and often more effective design.
Over the last 70 years there has been a push to explore nature as inspiration for
engineering through bionics, biomimicry and biomimetics as a way to leverage the
evolutionary advantages that nature has developed [41]. This discipline evokes the
36
comparison of human and natural design, one in which we see fundamental differences. As
we compare man-made designs to those of nature, we find that the simplistic straight lines
and right angles common to human design are a rarity in nature. When analyzing the design
of natural systems, we quickly reach our limits of understanding. The systems are often so
complex and made up of so many different elements that we struggle to grasp the design
intent and sometimes even the environmental pressures that guided it. As a result, we often
implicitly accept that evolution has demonstrated the functional efficacy of the organism or
feature even though we may not completely understand its function. Frequently, in bioinspired design, the most challenging step is to understand the significance of the
mechanisms of the natural system so that they can then be distilled and translated into a
mechanical design [42].
As an example, let us consider a design exercise asking us to create a foundation for
a tall vertical cantilever beam subject to wind loading that uses a minimum of material.
Nature grows a tree with a complex root system that anchors it into the earth. As a human
designer we might extend the cantilever beam deep into the ground and affix it with four
fins to increase its exposed surface area, a depiction of this is shown in Figure 5.
Figure 5: Natural root system compared to engineering run and shaft design.
Clearly, in this problem there is a tradeoff between design complexity and performance. As
designers, if we assume that the root system requires half as much material as the finned
beam design, we find ourselves in a bind: we would like to take advantage of the increased
efficiency of the root system but are unable to create it. There is no technology that can
fabricate a root system, and this represents the point at which the discussion usually ends.
Although no current Additive Manufacturing system could create such a large-scale
37
structure, AM introduces fabrication capabilities for detail on the level of a root system. The
greater challenge that still remains, yet is rarely discussed: design the root system.
If we consider the future feasibility of manufacturing, we must consider how design
may work. Programming could be used as a tool for design; we can envision that a person
could design a fractal-based algorithm that would generate a custom sized "root" system for
the cantilever beam. Computers could be used as part of the creative design process, a
fundamentally different usage from how we have designed historically. In this generative
paradigm we can create rules for a system that governs its behavior rather than explicitly
defming it.
3.3.4 Computer-Based Design
Let us shift our focus from computer-aided design to computer-based design, which
is a less well-known term in the field. CAD tools primarily focus on creating a more
streamlined design process and increasing efficiency [38]: the goal is to realize the designer's
intent more efficiently than manual drafting, akin to how a word processor improves upon
the efficiency of a typewriter. The term Computer Based Design, coined by Mistree and
Muster, focuses on utilizing computers differently for design [2]. In computer-based design,
substantially more work is shifted from the designer to the computer. Now, not only can
design software aid in realization of concepts, it can be one of the drivers in creating
increasingly complex and efficient designs. Implementing CBD is an active research area.
Structural optimization is one form of CBD and will be examined later in this chapter.
3.3.5 Function Based Design
To create a framework for generating more complex designs, we need to shift how
we define design problems such that computers can be more integrated in the process.
During the design process, we generate lists of functional requirements, but those lists are
not necessarily comprehensive and, more insidiously, they are often arbitrary. This lack of
rigor in framing the design problem functionally is mitigated by two factors; the designer's
interpretation of the requirements and the explicit generation of forms by the designer. If we
move towards a function-based design, we must instead strive to carefully understand the
needs of the design problem rather than an explicit vision of its potential form.
38
If the input of the design process can subtly shift from explicit forms to
requirements, the opportunity exists to envision a much more powerful parametric design
tool. This tool, instead of being driven by variable dimensions, could be driven by variable
function requirements, such as forces on the structure and boundary conditions. Here, the
user would proceed in a similar fashion to how a designer now parametrically adjusts
dimensions. With this methodology, design would be explored by investigating how changes
to loading, objective, and constraints on the problem affect the design's form. While CAD
tools focus on increasing designer productivity, they do little to alter the type of output.
CBD tools not only begin to increase productivity but also foster a new design methodology
and a path for the generation of new forms. Topology optimization is a CBD tool that is
currently gaining traction in structural optimization.
3.4 Structural Optimization
3.4.1 History of Structural Optimization
Structural optimization is a function-based design approach driven by a set of
constraints and an objective function. The optimization process centers on minimizing the
objective without violating the constraints. A key contribution to the field of structural
optimization came in 1904 when Michell published "The Limits of Economy of Material in
Frame-structures." This work laid the foundation for structural optimization by
demonstrating several analytically-optimized structures and developing some rules which
govern optimized structures [43]. His work on analytical optimums of structures were
limited by the complexity of hand calculations, but could be built upon to explore a more
diverse range of problems with the introduction of computers. The initial examples that
Mitchell produced remain relevant today as benchmarks for many algorithmic solvers.
3.4.2 Optimization Engine
To conduct structural optimization, two distinct modules of software are required.
The first is an analysis module, typically a finite element analysis (FEA) tool that is capable
of determining the behavior of a structure. The second is an optimization module that uses
39
the data created by the finite element analysis tool to modify the design and submit it for
reanalysis. The optimizer often takes advantage of a subroutine that conducts a sensitivity
analysis of the problem to guide the software as to what variables to modify [44].
3.4.3 Types of Optimization
For structural optimization, it is common to break the field into three distinct
categories: size, shape, and topology. The following diagram shows schematics for the three
main optimization techniques as illustrated by Bendsoe.
(a)
(b)
(c)
Figure 6: (a) size optimization, (b) shape optimization, (c) topology optimization. Source: Bendsoe [45].
3.4.3.1 Size Optimization
Size optimization is the simplest type of optimization; it centers on the relative sizing
of specific structural members. In the case of a truss, the cross section of each
member is adjusted to match the load passing through it. The result is that the
original layout or shape of the truss remains, but individual elements are resized
creating a system that best fulfills the optimality criteria.
3.4.3.2 Shape Optimization
Shape optimization enables changes to the form of an object within certain
boundaries. For this process, the topology of the part, the edges, and holes remain constant
while the position and dimensions of the design are adjusted and optimized. This process is
achieved by adjusting the surfaces of a 3D model or the edges of a 2D model (as seen in
Figure 6 above).
40
3.4.3.3 Topology Optimization
Topology optimization (TO) has the most flexibility of these three methods enabling
its use for a wider range of applications. Typically, for topology optimization, a design space
is defined and subdivided into small sections that are used as the basis for finite elements.
An optimal distribution of material is then solved for. Topology optimization solves for the
global optimum provided the problem is convex and the constraint objective and design
space have been appropriately modeled. This is not typically the case since most solvers used
for this type of analysis are gradient-descent-based solvers that for non-convex problems are
capable of finding only local optima. Yet while local optima are not ideal, they still frequently
provide very high performance solutions.
There are many implementations of Topology optimization, but the most broadly
used methodology is called Solid Isotropic Material with Penalization (SIMP). Ideally, TO
searches for a discrete solution in which the material either is present or void for every
element of the model. This is prohibitively expensive computationally, and therefore
continuous formulations of the problem have been developed. Bendsoe and Kikuchi in 1988
created the method called SIMP which tackles the topology optimization problem by
creating a continuous design variable for each element called virtual density, designated rho
[4]. This variable represents the influence of that element on the design problem and is a
multiplier that scales both the stiffness imparted into the structure by that element as well as
the mass of the element. The penalization comes in by making the stiffness contribution of
that element nonlinear. The governing function is shown in Equation 1,
Ei = pi - E 0
Where:
i is the element index
Ei is the virtual stiffness of element 1
pi is the virtual density of element 1
p is the penalization factor
Eo is the material stiffness
Equation 1: Element stiffness definition as a function of virtual density and Young's modulus
The penalization factor helps to guide the optimization to create fewer intermediate density
regions yet keeps the problem formulated in a continuous fashion.
41
3.4.4 Beam Bending Example
3.4.4.1 Problem Formulation
To illustrate this topology optimization process more clearly, we can work through a
design exercise based on a three point bending example. First, the initial conditions are
captured in the solver. In Figure 7 we see that the design space is now set and that the loads
and boundary conditions have been inputted.
Figure 7: Beam bending problem formulation: design region, loads and boundary conditions.
For this problem, we will formulate the objective as a minimization of the global compliance
of the structure subject to a given load and constrained by a maximum quantity of material.
In symbolic terms:
Find P = (P1,P2 ... Pi ...PN)
Minimize H(P) = compliance
_U(P)
-
F
F
N
0.3
pi -V i
Subject to g(P) =
t=1
0
pi
1
Where:
i is the element index
N is the number of elements
pi is the virtual density of element i
P is the virtual density vector
F is the force vector
U is the displacement vector
Now we can apply this problem to the optimization software and produce the raw results.
42
p=1, compliance =74.1
p=1.5, compliance =80.7
p=2, compliance =84.9
p=3, compliance =85.2
Figure 8: Topology optimization output as a function of penalty value with virtual density shown as a grayscale
and compliance reported.
The output at this stage still contains intermediate densities which are represented in Figure
8 in greyscale. White represents zero density and black is full density. Additionally, the
significance of the penalization factor ranging from one (no penalization) to three
(significant penalization) on the solution is evident. As penalization increases a more discrete
solution is generated, but at the cost of performance. In this problem, we see that the
compliance increases by 15%. In return for this decrease in performance, in the topology
optimization problem, the un-realizable intermediate densities are minimized. Finally, the last
step is to apply a filter to remove any remaining intermediate densities.
3.4.4.2 Thresholding
The process of removing intermediate densities is typically referred to as
thresholding. The simplest and most common approach is to set an arbitrary density as a
cut-off such that any value greater than this density will become full density and any value
less than this will become a void. A typical threshold is 0.3. We will use this typical threshold
43
for our design exercise. The results are displayed in Figure 9. For the low penalization
solutions, the effect of thresholding is quite substantial and much of the elegance and
performance of the design is forfeited. The higher the penalization, the smaller the effect
thresholding will have on the design. Therefore the designs generated with higher
penalizations will more likely represent the original design well.
p=1 Threshold = 0.3
p=1.5 Threshold = 0.3
p=2 Threshold = 0.3
p=3 Threshold = 0.3
Figure 9: Beam bending results thresholded at 0.3 with varying penalty weighting.
From this example it is clear that many variables contribute to how a problem is
formed and also how it is processed and thus what sort of output is generated. It is
important to note that, although a problem may have been optimized, there is no guarantee
that it was formulated correctly or processed correctly. If we examine the top design from
Figure 9 we see a relatively low performing design, here we have a problem that although
formulated correctly was not processed effectively ultimately resulting in a suboptimal
solution.
44
3.4.5 Design Methods Utilizing Topology Optimization:
Topology optimization can be utilized in multiple different ways to support the
design process. Here, we examine a few of the common ways that it is used: first, instances
where it is used as inspiration; second, detail design for conventional manufacturing; and
third, final part generation for Additive Manufacturing.
3.4.5.1 Inspiration Based Design
One typical application of topology optimization occurs during the concept phase of
a project. This is the simplest method of use, requiring only the boundary geometry and a
load case, since the objective is typically to minimize compliance for a given volume fraction.
For simple problem formulations, material properties need not even be introduced. As in the
prior example, the volume fraction is limited, and a stiff structure is generated. The engineer
or designer then examines the output and gleans insight from the load paths that have taken
shape. Utilizing these generated forms, a design for a structure is developed including any
additional necessary criteria for the design or intuitive engineering changes. Extending the
example above we can apply the inspiration methodology the output from Figure 9 the
results are shown in Figure 10.
b)
Figure 10: Inspired design based off topology optimization.
Figure 10 a) shows a sample thresholded topology optimization output and how it might be
converted to b) an interpretation of design into largely straight sections that could be easily
constructed. We might expect in this example that further sizing could be achieved manually.
45
To summarize the inspiration utilization of TO, Figure 11 shows a flow diagram of
the steps required for this utilization of topology optimization, including the interpretation
mentioned above.
Topology Optimization Phase
P.
Figure 11: Flow diagram for inspiration based use of TO.
This method is highly effective because it avoids a number of the limitations of the TO
process. First, it does not require complete formulation of the problem from an analytical
perspective. Additionally, not all load cases are needed and post processing is limited only to
visual "inspiration" for the designer who tunes the general form with the help of additional
modeling.
3.4.5.2 Traditional Manufactured Design
The second way TO is often used is to create a more refined design for traditional
manufacture of a complex component. For this process, the designer must be much more
comfortable with the use of both the TO software and an understanding of FEA. The
design must be well defined in terms of functional requirements and an objective function.
This TO will be used to create a rough model of the final part. It will be subject to redesign
by the user to ensure that all features are manufacturable, and to convert the voxelated
design into something that conforms to standard engineering practices. During post
processing, this is very much like reverse engineering. The designer carefully creates a
46
classical mechanical design utilizing a traditional CAD package [46-48]. The design is
recreated to envision the part as a person would design it with extruded features, cuts
revolves, fillets etc. For our example part in Figure 12 there is very little pixilation (in 3D
voxelation) given the large number of elements that were used. Therefore, the primary task
was to create normal geometric shapes and ensure manufacturability.
a)
b)
Figure 12: Traditionally manufacturable design based on TO
For this example the design was treated as a part that would be milled, and to aid in ease of
fabrication the design was limited to a discrete set of three corner radii. The process
explained here is summarized in Figure 13 in a flow chart.
Topology Optimization Phase
Figure 13: Traditional manufactured design topology optimization flow chart.
There are two key differences between this mode of use and the previous one. The first, is
the required pre-optimization fidelity that must be understood about formulation of
47
functional requirements and then generation of boundary conditions, objective and design
volume. The second, is the care with which the design is reverse engineered after
optimization during its conversion to CAD.
3.4.5.3 Additive Manufactured Design
Additive Manufacturing TO methodology is similar to the pre-processing of the
traditional manufacturing TO methodology. In keeping with the detail required for
traditional manufacturing TO methodology, we must have a comprehensive understanding
of the problem from a functional standpoint in order to capture the requirements and
objectives. The major difference in process occurs during the post-processing phase in
which the user normally follows the same reverse engineering process, substituting AM
design guidelines for the traditional manufacturing constraints. We will argue, however, that
this methodology is not particularly well suited to design for AM. This is because, as
previously explained, humans are easily overwhelmed by extreme complexity and thus driven
to simplify the design.
The reverse engineering step will have the adverse effect of removing complexity
from the design and also transforming it into traditional geometries that are easy to model.
We propose that it is instead better to eliminate this step. Rather than taking the output
model from topology optimization and transforming into a parametric CAD model, the
designer should manipulate the design directly, working with the output mesh, smoothing it
and making small modifications as needed. The output of the beam example is shown in
Figure 14 transitioning from the topology optimization output to a finalized smoothed
design.
a)
b)
Figure 14: AM manufacturable design based on TO
48
One benefit of this direct mesh editing approach is that no functional complexity is removed
that would cost the design performance. Additionally, less time is required to update the
design to its final form. The flow chart of the AM optimization workflow is shown in Figure
15.
Topology Optimization Phase
Figure 15: AM topology optimization flow chart.
The methodology demonstrated through this beam bending example uses TO for the direct
generation of geometry. We will expand on this below.
3.4.6 Proposed Process:
An outline of the implementation of the Additive Manufacturing TO process from
Figure 15 is presented below in greater detail. The case studies in the following chapter will
be approached using an instantiation of the TO for AM methodology described here.
1.
Formulation of Functional Requirements
The designer must fully capture the characteristics of the part as specific functional
requirements. This is a challenging process because many design requirements are
hard to formulate, and instead the design falls back on the engineering equivalent of
"I'll know it when I see it." During the conventional design process, the common
sense of the designer tempers the results, preventing trivial outcomes. For TO, the
requirements are mathematically operated by the solver and thus trivial edge cases
are both common and problematic.
2.
Generation of Boundary Conditions, Loads, Objective, and Design Volume
49
This second step is the mathematical distillation of the previous set of functional
requirements. Here, the boundary conditions, loads, objective and design volume
must be reformulated to be mathematically rigorous. A boundary volume must be
generated that constitutes the maximum allowable space that the design can exist
within. Next, the design space must be split into design and non-design regions.
Design regions are volumes in which the optimizer can modify the element densities
and non-design regions are "keep-out" zones where the density cannot be adjusted
such as mating areas. A mathematical objective function and a set of load cases must
also be defined.
3.
TO Software Execution
This phase is an involved process of converting the problem formulation from step
one and step two into a format for the optimization software. After running the
topology optimization routine, the user must interpret the results. This step involves
checking to see that the results make intuitive sense (confirming the problem
formulation) and thresholding the design at an appropriate level. Finally, the
geometry must be captured again as a finite element model for additional analysis
and then exported.
4. Analysis
Now that a specific geometry has been generated, the user runs an analysis on the
design to gain an understanding of how the thresholding and smoothing has affected
the design. Optistruct has a thresholding tool: OSS Smooth can be used along with
thresholding and smoothing, to map the original boundary conditions and load cases
to the new geometry. This analysis will demonstrate any deficiencies or excess
structure in the design that should be modified. Two different methods for this
modification can be explored; the first and simplest option is to choose a new
threshold value. The new value should be lower if the design lacks strength, or
higher if the design has excess structure. Once the optimal threshold has been
determined, the results can be exported for post processing. The surface boundary of
the geometry is captured, and then an STL file is written from the surface triangles.
5.
Post Processing
For this step, the STL file is imported into a software tool for direct modification of
the mesh. The part design is finalized and readied for manufacturing. In many cases,
50
the symmetries that are taken advantage of in order to simplify the problem must
now be used to regenerate the full part. Additionally, smoothing operations must be
conducted to eliminate coarse triangulation of the topology optimization. Also, any
features based on the previous analysis that are ineffective may be directly modified.
Examples of this include kinked beams, vestigial growths or overly thin sections.
Additionally, the non-design regions of the geometry are often most effectively
smoothed and repaired by directly substituting STL's generated from their original
parametric geometry. For this project, two different tools were used in concert:
Autodesk Meshmixer and Materialise 3-Matic STL. Meshmixer has very effective
features for intuitive local smoothing and 3-Matic STL has effective tools for
mirroring and joining multiple STL's into a final part. At the conclusion of this step
the design should be smooth and ready for fabrication.
6.
Down Sampling and Remeshing
In this step, the mesh is readied for 3D printing and prepared for a final validation
FEA. To do this, the mesh must be down sampled to a resolution that can be
effectively analyzed. This lower resolution mesh is saved and then sent back for
analysis as in step four.
7. Export STL for 3D printing
After the final analysis has verified the performance of the design and the user is
satisfied, the STL can be exported for 3D printing. The export will be of the file
generated at the end of step 5, not the down sampled design used for reanalysis.
3.5 Conclusion
This section has outlined a function based design methodology that utilizes TO as a
computer based design tool. We have seen that using a functional description for a problem
can lead to complex, novel, high performing designs and can be readily manufactured with
AM. This methodology will next be applied to two case studies in Chapter 4, which will
further explore the intricacies of this process as well as its efficacy applied to real world
problems.
51
Chapter 4: Applying an AM Aligned
Design Methodology
4.1 Case Studies
Chapter 3 introduced a design process: CBD-function based design methodology that
utilizes TO to generate designs tailored to AM. In an effort to better understand this process
we examine its efficacy when applied to real world problems. The following case studies
explore the inner workings of this methodology, its applicability to 3D designs and expose
the challenges of its implementation.
The two studies we will investigate include a high-speed spinning mirror and the
GrabCAD GE Bracket Challenge. The spinning mirror design focuses on validation of the
methodology and the development of the post processing technique. The same process was
then applied to the second case, the GrabCAD GE Bracket Challenge, results from which
were benchmarked against the collection of other entries to the challenge.
4.1.1 Spinning Mirror
A case study investigating the development of a low-weight spinning mirror for
imaging was enabled by a novel post-processing technique for AM. Topology optimization
was used for the design, which was fabricated in aluminum, AlSil 0Mg, by metallic powder
bed fusion. This approach enables RM metallic mirrors to be fabricated by growing an
additive manufactured blank, post processing the faces, coating them with electroless nickel,
and diamond turning [49].
The system performance drives a requirement for minimal face-deformations of the
mirror while under inertial loading from high-speed rotation. This methodology is
particularly well suited to this problem because the primary loading of the mirror is driven by
its own weight. As mass is removed from the design, loading is also reduced, providing
positive feedback that helps dramatically reduce weight.
52
In engineering system design, there is a persistent desire to reduce the SWaP (Size,
Weight, and Power) requirements of a system. This goal is of paramount importance in the
development of aircraft and spacecraft, where every additional pound of payload increases
the cost of operation over the 30-year life of the aircraft by $40,000 or a spacecraft launch by
$10,000 [50,51]. The use of aircraft and spacecraft for surveillance creates a demand for
lightweight optics. The benefit of lightweight optics is compounded when the optical
component is dynamically actuated in the system, causing the mass of the component to
drive its own loading. This phenomenon is called self-weighting. As a result, light-weight
optimized mirrors can lead to great reductions of full system mass since they require less
support structure and lower power drive systems [52,53].
The geometry available for the design is constrained by the requirement for the four
optical surfaces as well as mechanical interface with the motor. The optical surfaces must
have a range of deflections no greater than 500 nm perpendicular to the face while rotating
at a constant rate of 21 Hz about its center axis. For system packaging efficiency, the motor
is installed inside of the mirror body and interfaces with the hub. A cross section of the
mirror is shown in Figure 16.
Figure 16: Mirror bounding volume in cross section: Red portions define mechanical interface regions that
cannot be modified and blue portions are mirror faces and cannot be altered. The central void houses the drive
motor and is unavailable. Green represents the available design volume.
The geometry shown in Figure 16 was then converted into a finite element model in
Figure 17. It was decomposed by symmetry into a one-eighth model and imported into
Optistruct. The geometry was then meshed with 3mm, second order (ten node) tetrahedral
53
elements, resulting in a model of approximately 20,000 elements. The model was divided
into design and non-design regions where the red interface region and blue mirror face
region as depicted in Figure 16 were both considered non-design and the green region was
the available design space.
Figure 17: Optistruct finite element model of one-eighth mirror.
An unexpected result occurred when this model was run through the topology
optimization solver. It was discovered that the solver had disconnected the inner hub from
the outer faces. For clarity, the design for a one-quarter model is shown in Figure 18.
54
Figure 18: Top view of with symbolic boundary conditions; disconnected hub (left), modified load case and
connected hub (right).
At first, the disconnected hub appeared to be an error resulting from the topological
optimization, but further investigation demonstrated that the error was actually in the
problem statement. The error results from the simplified load case that is being analyzed
with constant rotation about the central axis. In the simplified load case, there is no start up
torque, no gravity, no vibration and no air resistance and thus no need to connect the hub to
the faces. For this free-floating condition, supporting structures can be created between
faces more efficiently than spokes to the hub, the faces enabling a "higher performance"
design.
Here are two ways to address this disconnected hub problem. First, a non-design
region can be included, generating spokes and directing the optimization to the type of
solution desired. Second, the boundary conditions or load cases can be modified to include
conditions that ensure an appropriate optimized design would be generated. The second
method was used since the shape of the spokes could not be determined a priori and thus
could not be added optimally. The correction made to the model was an additional load case.
This vertical load applied to the top face of the mirror creates the structural need for
connection between the internal hub and the face of the mirror.
55
Using this updated problem formulation, topology optimization was executed in 80
design iterations over approximately 37 minutes.' The resulting output was thresholded and
run through OSS Smooth at various values; a density of 20% was ultimately selected. This
design, shown in the left image of Figure 19, was selected because of its low mass and the
expected potential to meet the necessary performance requirements. While the performance
of the original thresholded solution did not meet the performance specification, as can be
observed in the large red regions of the design, good performance in the top half of the
design and the presence of suboptimal features in the bottom half provided a map for how
to selectively modify the design rather than increase the threshold.
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Figure 19: One-quarter mirror model showing displacements normal to the x-face (right); red regions do not
meet specifications. Left, initial design directly from thresholding; ight, post processed resulting in 0.79%/
weight increase.
Post processing was then performed, starting with a reflection of the one-eighth
model in 3-Matic STL. This created a one-quarter model to enable smoothing at the center
interface. The quarter model was imported into Meshmixer where it was manually modified
and locally smoothed, focusing on one side and the transition region. After smoothing, the
finished side was reflected to replace the other side, ensuring symmetry and generating a
uniformly smoothed quarter model. These small modifications increased the mass of the
mirror by 0.79%. This design, as shown in the right-hand image of Figure 19, was imported
1 Desktop PC with 2.13Ghz Intel Xeon with 12GB RAM.
56
back into Optistruct for re-analysis, and at this point it became clear that the design could
meet the design requirements with only minor adjustments. The only region that did not
meet the specifications was the lower section, and therefore, further post processing was
conducted to enlarge the diameter of both the bottom and middle gussets. With these minor
modifications, the design was brought within the deformation specifications, Figure 20,
thereby verifying the functionality of the design.
Contour Plot
Displacement(X)
Global System
6.000E-04
5.301E-04
-
4.601 E-04
3902E-04
32026 04
2.503E-04
1.804E-04
1.1 04E-04
4.047E-05
-2.947E-05
Max = 5.347E-04
Grids 67936
Min = -2.947E-05
Grids 225558
x
z
Figure 20: One-quarter model of final mirror design displacements normal to the x-face (right); faces meet
surface figure specifications.
The model has a final weight increase of 1.32% over the initial TO output; far less than
would have been required to generate a design that met specifications through thresholding
alone.
Next, a small but important feature was added to the model: weighting lands were
drilled and tapped to accept setscrews that can be used to balance the mirror. Figure 21
shows the insertion of a land for a setscrew.
57
Figure 21: Addition of a boss to accommodate a balancing setscrew in 3-Matic STL
While these lands could have been programmed as non-design regions a priori, the
locations of minimal impact could only be determined after topology optimization was
performed. Regions furthest from the center with areas near the required land size were
selected and enlarged as necessary for the setscrews. This added only 0. 18% to the total
weight of the mirror and did not noticeably alter its performance.
The final post processing was completed to transform the quarter model into the full
model. The non-design regions, the hub and faces, were then deleted and original STL's
generated from the native CAD were substituted in. Finally, the quarter design was reflected
two more times and then joined and smoothed in 3-matic STL.
The final result of the topology optimization design process is shown in Figure 22,
beside the conventional design.
Figure 22: Traditional mirror design on left, additive mirror design on right.
58
The topology optimized design is 62% lighter while still meeting the structural requirements
for the part.
A key lesson from the mirror case is that the design process, while presented earlier
as a mostly linear-flow diagram in Figure 15, failed to capture much of the cyclic
reformulation of the functional requirements. To better represent the methodology used in
this case study, the flow diagram has been updated in Figure 23.
Topology Optimization Phase
condtionExportiv
MmirZ Miinr
Figure 23: AM Optimization flow chart, with backward red arrows representing required reformulation
experimentation.
The example of the disconnected hub demonstrates precisely the cyclic nature of this
process. Frequently, a subtle problem in the formulation manifests itself as a substantial
error after optimization. This can actually be very helpful in understanding the functional
definition of a part. Our inexperience and lack of education regarding function-based design
further aggravates this problem, leading us to otherwise avoidable pitfalls.
4.1.2 GrabCAD GE Engine Bracket
The second case study examined was a GrabCAD challenge that was posted in 2013
as a collaboration with General Electric (GE) with a purse totaling $20,000. This contest
received a great deal of attention because it promoted the fusion of structural optimization
and Additive Manufacturing. It attracted a large group of participants, culminating with 640
submissions spanning an extensive range of tools and tactics [54].
A problem statement was presented online to create a lightweight bracket. The full
problem statement is included in the Appendix the highlights of which include a material
59
definition of Ti-6A1-4V, geometric constraints and the load conditions for the part shown in
Figure 24.
Load Conditions
1
Load Conditions 2
Static
Static
Vertical
Horizontal
5000 lbs up
8500 lbs out
Load Condition 3
Load Condition 4
Static
Static Torsional
42 degrees from
vertical.
Horizontal plane at
centerline of clevis.
9500 lbs out
5000 lb-in
Load Interfaces
Interface 1
Interface2
InterfaceS
InterfIcen4e
Interfoce 5
Figure 24: Four load conditions for the bracket. Source [54].
After some initial feedback from participants, the problem was clarified on GrabCAD and
additional critical information was provided. Explicit material properties were added to use
for the 3D printed titanium, which was specified to be treated as isotropic with a Young's
Modulus of 110 GPa, Poisson's Ratio of 0.31 and a yield stress of 903 MPa. It was also
specified that the FEA used for evaluation of the designs would be ANSYS and the results
would be processed with a linear-elastic material model and the bolts, pin and mounting
surface would be treated as rigid bodies [54]. Finally, it was also clarified that the objective
was to minimize the mass of the part.
A few key interpretations of the problem statement were made to clarify the
problem. First, the safety factor was assumed to be equal to 1 since it is carefully explained
that the maximum allowable stress is the yield stress of 903 MPa, not some fraction thereof.
This standard assumes that the load cases included safety factors. Second, from a topology
optimization standpoint, a maximum displacement for the entire structure was inputted to
ensure connectivity. This value was chosen to be 1mm.
Using these assumptions and the information provided by GE, a model was created
in Optistruct and optimized. The results of this optimization are shown in Figure 25.
60
Figure 25: Resulting geometry from TO optimization.
Further ambiguity arose after topology optimization when it became clear that the highest
stresses that occur in the design do so in the areas surrounding the bolted boundary
conditions. As a result, the potential qualification or disqualification of a part was highly
dependent on how the FEA model was created and not solely on the behavior of the part.
These maximal stresses at the boundary conditions create difficult results to interpret. Within
the FEA community, specific interpretations become very sensitive to the modeling
subtleties. Often, results that occur from the elements connected to boundary conditions are
considered to be artificially elevated.
A study on the impact of boundary conditions would have been useful, but each
optimization took almost five days to complete. After consulting with a couple of FEA
experts, a decision was made to use specific constraint strategy, a RBE 3 wagon wheel
connected via a CBush to a fixed node for each bolt connection, additional information on
these finite element components can be found in the Glossary. These results are presented
here. Given the ambiguity around the precise definition of boundary conditions, these results
cannot be fully validated, and we are hoping to initiate future collaboration with GE to
better understand how the problem was modeled during the competition.
After careful exploration of different threshold values, a 20% threshold was selected.
This included the assumption that the set of elements directly touching the boundary
condition could be ignored. Based on that assumption, the maximum stress within the bulk
of the part was kept below the required limits. Filtering the results for local stress-related
anomalies, the maximum VonMises stress for the four load cases are as follows: 800 MPa,
61
700 MPa, 670 MPa, and 700 MPa, providing a safety factor of roughly 10%. Isometric and
bottom views of the design are shown with VonMises stress mapping in Figure 26 and
Figure 27 respectively.
Load Case 1
Load Case 2
Load Case 3
Load Case 4
Figure 26: Isometric view of the final design with VonMises stress displayed. Maroon colored elements
represent yielded material.
IL
'4.
Load Case 3
Load Case 4
Figure 27: Bottom view of the final design with VonMises stress displayed. Maroon colored elements represent
yielded material.
62
This 20% threshold design results in an 84.8% weight reduction from the original design.
Had our design been submitted in 2013, it would have placed first in the competition with
the greatest mass reduction, beating the top designs by a small margin. The top three designs
along with the proposed design are presented in Figure 28 along with their weight
reductions.
TEUB
80.7%
T_20
84.8%
Figure 28: Comparison of topology optimized design with top three designs from the competition, displaying
percent weight reduction.
When comparing these designs, we can see distinct differences between each of them and
the solution generated during this case study. Revisiting the discussion about design fixation
from Chapter 3.1.2, it is clear that a methodology for design using topology optimization
helps avoid the generation of similar non-optimal solutions.
To build confidence in the solution, a qualitative comparison was made between the
well-presented results from Thomas Johannson (TJ2) and those generated here. In Figure 29
similar behavior can be seen in each of the load cases with major differences existing
primarily in the loads exhibited near the boundary condition locations.
63
tr-
f::E=
~-P:
Figure 29: Comparison between TJ2 and T20 design showing stresses viewed from the bottom. Maroon
colored elements represent yielded material in upper two images. Red stress represents 500 MPa in the lower
two images.
Based on the results from this optimization, the design generated with Altair
Optistruct is very high performing. With the assumption that stresses near the constraints
are artificially elevated, it is the top design yet seen, serving as a good benchmark for the
process.
While working on the bracket case study, the dependence of topology optimization
on FEA became abundantly clear. Although the functional definition of this problem was
well formed, there were many difficulties in transforming that definition into a mathematical
formulation that could be usefully processed with finite elements. This underscores the
wisdom of GE in having two stages of their design competition: a first evaluation based
solely on computer simulations, for expediency, and a second conducted through real world
testing, thereby eliminating the risk including an assumption by omission. It is also important
to note that even something as seemingly well understood as FEA can still give rise to very
diverse (and therefore many incorrect) solutions.
64
4.1.3 Methodology Implementation Challenges
4.1.3.1 Difficulties Associated with Function-Based Design
The first challenge of integrating topology optimization in an engineering workflow
is the necessity for creating a set of concrete functional specifications early in the design
process. This requirement can be challenging in its own right. It is made more difficult,
however, because engineering education and practice usually do not require a formal
description of a product early in the design process, making engineers unfamiliar with this
process.
4.1.3.2 Software Limitations
The design methodology presented in this thesis is limited in its application by the
challenge required to implement it. This is a result of the expertise required, the limited
distribution and large expense of the requisite software, the cumbersome workflow and,
finally, the difficulty to implement a function-based design methodology. The topology
optimization software was built upon the FFA software which was developed to primarily
serve as an analytical tool, only rarely intended to function as a design tool. The result is a
degree of formalism to the software that makes it difficult to conduct design experiments or
to make simple modifications. In many cases, an engineer must make incremental changes
manually and rerun time consuming FEA. Currently, the case studies presented in this
chapter require a complex workflow spanning multiple different software tools. This results
in a time consuming process that inhibits creativity. Furthermore, it is rare that problems
can be defined as having a solely structural requirement; many problems are multidisplinary,
requiring modeling and optimization over a multi-objective space.
Additionally, given the small market for this software at present, little time has been
spent focusing on user interface development that is specifically tuned for topology
optimization. With the increased focus that TO is receiving, additional resources and time
are being invested on usability for designers. The development of less powerful but simple
to use software such as Inspire from Altair shows the beginning of this transition. In the
future, we can expect the fusion of the capabilities of software like Optistruct with the ease
of use of Inspire. Improved usability and functionality in future versions will likely draw
65
more users to the technology. Lastly, since this methodology is contingent on the RM of the
designed parts it is important that there be further development to bring down the cost and
increase both the reliability and accessibility of AM technologies.
4.1.4 Adapting Topology Optimization for AM
There is ongoing research in structural optimization with a focus around pairing it
with AM. Specifically, there are a few methodologies that hold particular promise, which are
worth describing here.
4.1.4.1 Lattice-Based Topology Optimization Interpretation
Sigmund and Bendsoe proposed the expansion of the topology optimization method
in 1999, exploring the concept of mapping densities to different cellular structures. Figure 30
shows the Young's Modulus versus Density for a series of lattices that can fit a p 3 function.
1.0
0.8
0.6
0.2
0.0
0.0
0.2
0.4
0.6
Density, p
0.8
1.0
Figure 30: Density versus Young's modulus plot. Source: Bendsoe [45].
This type of mapping takes the raw output of a SIMP topology optimization and assigns it
directly to a series of different lattices, thereby removing the need for thresholding. This
concept coupled with AM provides a pathway to directly utilize and manufacture the results
from topology optimization [48].
66
4.1.4.2 Lattice Generation and Sizinp
Another approach for creating optimized structures for AM is to generate a lattice
structure and then perform sizing optimization, as described in Chapter 3.4.3.1, Size
Optimization. A number of groups are currently pursuing methods of this type [55-57]. This
sizing approach is attractive because the geometry can be directly fabricated with AM
without requiring any interpretation if a self-supporting lattice is used.
4.1.4.3 Manufacturing Constraint Integration
Another method under consideration is the direct generation of manufacturable
results from a topology optimization program by implementing more manufacturing
constraints directly into the optimization software. This process avoids painstaking post
processing by including manufacturing requirements during the problem formulation. It is
particularly valuable because changes made before the TO run are able to capitalize on the
benefits that are generated by the optimizer. Given the value of this potential workflow,
there are a number of groups that are working towards developing algorithms that can
identify overhanging structures and penalize their formation during the optimization.
Thereby leading to more manufacturable structures that require less post processing [48].
4.1.4.4 Interactive Optimization
Optimization has generally focused on the development of a closed process where
the starting point and objective are programmed and an optimal solution is found through
extensive computation. In most optimization processes the user contributes to the problem
formulation, but does not guide the optimization itself. This does not utilize the value of
human intellect or allow for evaluation of subjective objectives during the optimization. A
growing sub-field of optimization is exploring how to involve human interaction [58,59].
Interactive optimization research has the potential to be applied to a wide range of different
optimization methodologies.
67
4.2 Conclusion
4.2.1 Effective Methodology
This chapter set out to answer the research question, "How does one leverage the
benefits of RM during the design process?"
The case studies above have demonstrated that a methodology built upon functionbased design is very effective. The methodology presented in this thesis utilizes computerbased design, specifically topology optimization. It provides a way to counter the natural
human instinct for overly simplistic design and prevents design fixation, allowing designers
to approach solutions with a level objectivity that is otherwise challenging.
This approach was employed in both of case studies presented: a high speed spinning mirror
and the GrabCAD GE bracket challenge. In both cases, high-performing, non-obvious
solutions were generated, illustrating the value of this methodology. Specifically, greater than
60% weight savings, the primary metric of evaluation, was realized in both cases, the fusion
of TO and AM provide the opportunity to generate complex shapes from simple functional
definitions without concern for fabrication. By deviating from the common practice of
reparameritizing and simplifying the TO result and instead directly editing the mesh, we have
enabled the preservation of a more complex and diverse set of solutions.
Greater adoption of this efficient design methodology for RTM will help grow the
utilization of the technology. It will also help create a larger user group, which will provide
software companies a market for tailored design tools. Improved computing, refined TO
user interfaces, and increased familiarity with function-based design will contribute to the
further adoption of similar methodologies to the ones presented in the case studies above.
68
Chapter 5: The Potential Impact of
Rapid Manufacturing on the Product
Development Process
5.1 Design Process Versus Development Process
This chapter will explore how Rapid Manufacturing can impact not only the design
process, as explored in Chapter 3 and 4, but also the development process. In particular, we
will consider how the affordances of RM affect development and, based on these results, we
will pursue the research question: "Can the unique characteristics of RM be utilized to
enhance the development process?"
The methodology explored here governs the development of a project from idea
generation to manufacturing. We believe that Rapid Manufacturing has the potential to
dramatically alter how products are developed through small modifications that can enable a
cheaper and lower-risk product development cycle. Many of these subtle changes to the
development process stem from a property of RM, which we will refer to as the Perfect
Prototype. The name is characterized by the ability to create a prototype that is of complete
fidelity, thus eliminating any rework associated with transitioning from prototype to
manufacturing.
This chapter we will first conduct a literature review examining material from the
Additive Manufacturing community, the design community and the management
community. Then, we propose a synthesis of ideas from these three different fields, the goal
being to understand the impact of Additive Manufacturing on the traditional development
process.
69
5.2 Economics of Rapid Manufacturing
5.2.1 Economics of Tool-Free Production
In manufacturing, economics frequently serves as a means to evaluate the viability of
a novel production method. Extensive research has been done to explore the conditions
under which Rapid Manufacturing can be applied cost-effectively. One approach, discussed
in Chapter 2.1.3 has been to compare the use of Rapid Manufacturing as a substitute for
Injection Molding, examining the break-even point where the two methods have equivalent
costs. These studies have shown that the production volume at which the breakeven point
can be reached is relatively low: equivalency is reached in a range from 1,000-10,000.
Variance in equivalency is determined largely based on part size, where the higher cost per
printed part equals the mold fabrication cost [9,19]. More recently, Atzeni et al. conducted
similar research focusing on the effect of redesigning the RM part to leverage the advantages
of Additive Manufacturing. Redesign raises the break-even point; for the part they studied,
to roughly 90,000 parts, demonstrating how the application of design for Additive
Manufacturing can increase the efficiency of RM [10].
5.2.2 Supply Chain, Distribution and Inventory
Relative to traditional methods, Rapid Manufacturing creates a radically different
supply chain where only two inputs must be present in the factory: raw material and digital
files. The raw material need not be specific to each part and can instead be used for a wide
range of parts, and the digital files can be transferred instantly. As such, this fundamentally
simplified supply chain can provide substantial savings from reduced waste and complexity
of inventory. Currently, due to the limited availability of RM facilities, centralized production
has been found to be the most economical distribution model for RM. However, experts
have predicted that, with higher production volumes and lower cost, as well as more
prevalent RM centers, distributed manufacturing will likely become economically feasible
[16,15].
This simplified supply chain and the resultant flexibility of Rapid Manufacturing
enables factories to alter their product schedules. Because parts can be produced on demand,
70
there is far less incentive to maintain a stored inventory. Estimates show that for long lead
times typical with traditional manufacturing, every 5% reduction of inventory enabled by ondemand printing could reduce return on invested capital by 3%. Considering that typically
half of a manufacturer's working capital is tied up in inventory, this is a very substantial
efficiency improvement [29].
5.2.3 The Effect of Rapid Manufacturing on Product
Development
To more accurately assess the value Rapid Manufacturing has, it is also important to
consider prototyping implications. When Rapid Manufacturing is used, an obvious choice
for prototyping parts is to fabricate them with the same process and cquipment as
production. Traditionally, it has been financially prohibitive to use the same equipment for
prototyping and production although it would ensure a smooth transition between the two
fabrication scales. Wall et al. created a framework to evaluate prototyping technologies,
highlighting the value of unifying the fabrication process between prototyping and full-scale
manufacturing. Wall defines the performance vector, a measure of a prototype's fidelity to
the final manufactured part. The vector contains attributes that can be tailored to a specific
application but typically include strength, stiffness, material properties, dimensional accuracy,
feel, and appearance. For each dimension, metrics are evaluated from zero (no similarity to
the final part) to 1 (perfectly representing the final part [60].) By applying this scoring
method to the prototypes created during the Rapid Manufacturing process, every attribute
of' the prototype will be a perfect representation of the final part. Thus, each prototype
made will be the Perfect Prototype.
5.2.4 The Elimination of the Manufacturing Cycle
The potential for unification in prototyping and manufacturing described in the
previous section leads to the benefit that effort invested during the prototype phase around
manufacturability, assembly, and analysis also applies directly to the production parts,
eliminating the need for additional design and testing to facilitate final production.
71
BUILD
MANUFACTURE
DESIGN
PROCESS
MANUFACTURING
CYCLE
VALIDATE
DESIGN
I
TEST
REDESIGN
FINA
T
Figure 31: Development process for both AM and conventionally manufactured parts. Unlike traditional
manufacturing, AM does not require a separate manufacturing cycle. Source: [35].
As indicated in Figure 31, once the design process produces a prototype that passes all
performance testing and is deemed complete, it instantly becomes the first production part
[35]. Designers can also more confident in the scaling process with the knowledge that no
unexpected changes will arise from manufacturing or assembly requirements. Furthermore,
modifications can easily be implemented to meet customer needs or updated design
specifications without retooling or restructuring the manufacturing process.
5.2.5 Benefits of the Perfect Prototype
The benefits of the perfect fidelity prototype can be divided into four facets. The
first is the elimination of the manufacturing cycle, something we can quantify in a
straightforward manner as being equal to the cost of manufacturing development and
tooling costs. Second, perfect fidelity reduces risk by enabling faster and more accurate
performance testing, customer interaction, and streamlined manufacturing processes. Third,
the development process is shorter, resulting in faster time to market. Fourth, the designer
can remain in development for both the design cycle and manufacturing cycle. This fourth
component may produce the most significant effect, both reducing costs and improving
quality.
72
5.3 Evaluating the Magnitude of the Benefits of
Rapid Manufacturing
In this section, we explore the relative magnitude of the four benefits described
above. Benefits can be divided into two varieties: first and second order effects. The first
order effects from the elimination of the manufacturing cycle (as depicted in Figure 31) and
performance gains made through improved design can be quantified, a framework for this is
examined in Chapter 5.2.1 for eliminating mold costs, and for the spinning mirror Chapter
4.1.1 respectively. The methodology for this evaluation is well developed. Academia and
industry have focused on the economics of tool-free production, analyzing supply chain and
inventory holding costs and the improved performance of the parts (which justify the added
expense). This has given rise to the current successful utilization of Additive Manufacturing
in applications such as the production of hearing aids or jet engine fuel injectors described in
Chapter 2 or the case studies in Chapter 4.
Evaluating second order benefits -
such as decreased risk or the ability to remain in
the design cycle longer with faster time to market -
has largely been overlooked because
they are difficult to quantify. Unlike mold-free attributes, their economic impact does not
affect a baseline cost but rather that of the whole development process [29]. As a result,
benefits such as these are often categorized as strategic rather than economic. Currently, the
challenges of qualifying machines and materials have outweighed most first-order benefits,
often resulting in the decision not to use Additive Manufacturing. In many of these
calculations, the second-order strategic benefits-the benefits that might tip the balanceare not included. They are not included because management typically demands concrete
cost savings to justify their initial investment. I believe this has led to an under-utilization of
RM.
Research in management and operations has shown that evaluations of advanced
manufacturing technologies often become focused on conventional financial assessments.
Generally, this has led to unwillingness to adopt new technologies that focus on
improvements other than immediate financial gain. A survey conducted by Millen and Sohal
found that "Although competitive advantage was the most important motivation,
conventional financial investment criteria were applied [61]." This has the effect of
73
undervaluing the new technologies. Additionally, research has shown that adopting advanced
manufacturing technologies and their associated benefits can require organizational shifts
and retraining, both substantial hurdles [61,62]. These management biases against advanced
manufacturing technologies have likely hindered adoption of Rapid Manufacturing for a
wider range of applications.[18] By further examining the second order strategic benefits of
Additive Manufacturing, we are able to get a holistic picture of the economic situation and
more accurately weigh the advantages and disadvantages of its use for a specific project.
5.3.1 Economic Impact on Product Development Process
The development process plays a very large role in the total cost of the traditional
-
product design process. It has been shown that early design decisions typically control 70%
85% of the total project costs [63-65]. It has also been shown that more frequent
prototyping is key to product development leading to better products, faster time to market,
and cheaper development by discovering unknown risks early in the process. Development
projects that proceed with few or low fidelity prototypes often miss key details - including
crucial customer feedback, assembly issues, or manufacturing pitfalls [66].
5.3.1.1 Quantifying the Benefit of Prototype Fidelity
Thomke & Bell created a mathematical model to evaluate an optimal rate of
prototyping during the development process. One of the key variables that they studied was
how prototype fidelity affects avoidable costs. The insight provided by this calculation can
help us quantify the value of the Perfect Prototype. In Figure 32, Thomke and Bell show the
number of prototypes that are needed to maintain savings at roughly 60% as a function of
prototype fidelity.
Table 3
n
Savings
f
Optimal But Conutant Fidelity as a Function of n, Given A=5
5
6
7
8
9
10
11
12
60.0%
1.00
60.2%
0.67
60.5%
0.77
61.0%
0.70
61.5%
0.64
61.9%
0.60
62.4%
0.56
62.9%
0.52
Figure 32: Prototype Fidelity verus number, holding savings near constant. Source: Thomke and Bell, [67].
The clear trend based on this mathematical model is that increasing the fidelity of the
prototype decreases the number needed to maintain a similar avoidable cost. Specifically, we
74
see from this chart that increasing the fidelity of the prototypes from 52% to 100% results in
the need for fewer than half as many prototypes to identify the same number of flaws in the
design. This analysis is a quantitative way to assess some of the value of a RM prototype
having 100% fidelity.
5.3.2 Impact on the Development Process
5.3.2.1 Effect of Non-Frozen Design
The freedom Rapid Manufacturing provides from tooling and production scale up
enables designers to improve designs much later in the development process. Design
modifications must now be evaluated with respect to the engineering specifications rather
than the cost of tooling changes. Consequently, Rapid Manufacturing creates an
environment in which designs do not need to be frozen [9,17]. In this new paradigm,
decisions about design will no longer be restricted by the cost of the change but by risks
associated with the change and with any testing needed to validate it.
5.3.2.2 Hardware Follows Software
The effects of decoupling design and testing from manufacturing on the
development process are quite similar to those of software development. When creating
software, there is no manufacturing process. Therefore, the design process is able to remain
in constant development, as previously described, for the entire design process. Building a
prototype or manufacturing a new version of software requires compiling a new version of
the code, only. Design changes involve targeted testing and validation to demonstrate that an
update is an improvement worth distributing to the customer. With Rapid Manufacturing
having been freed from tooling costs, updating a mechanical design can now be done for a
dramatically lower cost -- one driven by redesign and not manufacturing.
One key difference between software and hardware development, however, is that
new software designs can be disseminated virtually free of cost to users via online updates.
This dissemination is still an expensive process for mechanical parts since it requires
additional manufacturing and physical distribution.
75
. ........
...
..........
5.4 Innovative Product Development
RM provides the opportunity to break away from traditional development strategies.
There are three standout companies that are utilizing both the design and development
benefits of RM to create a novel product through an innovative process.
First, GE is pushing boundaries in the RM aeronautics industry. Described in
Chapter 2, the creation of the LEAP engine fuel nozzle provides a textbook example of
using conventional tools, with great expertise, to generate a progressive high performance
output.
Second, Nervous Systems Inc. has developed a model of product design in which
they deliver a parametric range of designs rather than a single, fixed design. Instead of the
normal sequence, they have created design tools that enable the customer to be part of the
process, adjusting parameters to tailor the output to his or her personal preferences. Figure
33 presents the Radiolaria app for designing custom earrings that can be then made to the
customer specifications.
Figure 33: Left: Radiolaria an interactive design tool to create necklace [68]. Right: Hyphae Lamp 132 a
generative designed
lamp [69].
Interactive design provides the opportunity to capture a new market. Supported by a
guiding design tool, customers are able to create a bounded yet infinite array of different
designs. And with the evolution of the product, it is possible to update the software if there
are functional issues with any of the generated designs. They have also been developing
more complex generative tools that cannot operate in real-time such as the Hyphae Lamp,
right image in Figure 33. Here, a trade-off is apparent between simple and fast user
76
interactivity in Radiolaria and the complexity but offline generation required of the Hyphae
Lamp. These examples demonstrate a new type of parametric product generation, one that
interfaces with the customer to deliver personalized unique products.
The third exemplary company is Bhold, who have taken the non-frozen capability of
RM product development and put it into practice. The founder and CEO describes this as
having taken some of the software mentality and applied it to physical product design. She
calls this "responsive product design [70]." Susan Taing explains, "At Google we learned to
launch and iterate to get the best possible product for the consumer, which is fairly easy to
do in a digital space. At Bhold, we're taking this same concept to the physical world [71]."
This process is closely tied to the perfect prototype concept presented here: Bhold's model
leverages the ability to create large numbers of prototypes-50-100 versions of a designbefore release. Bhold also enables designers to incorporate feedback from the wider public
even after releasing a product [72]. Taing says, "the process is fast enough to allow a weekly
turnaround for new product iterations based on user feedback [71]." That represents an
unheard-of standard of late stage control in the product design world, and it is exciting to see
a real-world example of the theoretical benefits described in this chapter realized in a
marketable way.
5.5 Conclusion:
It is critical to expand our understanding of the benefits of Rapid Manufacturing to
include considerations not just at the part level but considerations at the organization level as
well. With a better understanding of the complete impact of Rapid Manufacturing, we can
make more informed and effective decisions when selecting a manufacturing method during
product development. In summary, the advantages of Rapid Manufacturing, in addition to
those at a part level, include: (1) no expense or time spent manufacturing tooling; (2)
decreased time to market; (3) decreased product risk through testing with the perfect
prototype; and (4) the opportunity to continually refine the design since the design need not
be frozen.
Research has shown the strong impact, 70%-85%, of early stage design on product
life cycle cost [63-65]. As a result, small improvements in the prototyping process generate
an outsized reduction in required rework and redesign which often dramatically reduces the
77
total cost of a project. Analytically, we have seen that prototype fidelity can dramatically
affect the number of prototypes needed during development of a project. If we look to the
DFMA movement to gauge how changes in the design process that directly couple with
manufacturing can alter the total cost of a project, we find that cost savings can be 50% or
more of a total project [31]. We expect the benefits of RM and the perfect prototype to the
development process may be similar in magnitude to the introduction of DFMA principles
and therefore the impact may reach a similarly large scale.
A key limitation of this work and an area for further research is the lack of
quantitative data examining these effects in industry. There are two main reasons for this
limitation: the data is difficult to obtain, spanning long time scales and many different
departments in a company; and there are few practitioners working at production scales,
limited by part validation methods that are under development.
78
Chapter 6: Conclusion
This thesis has explored both the design and development for Additive
Manufacturing while focusing on the particular relevance of Rapid Manufacturing. Two
research questions helped guide the exploration of the topic throughout the thesis.
1. "How does one leverage the benefits of RM during the design process?"
In response to this question we presented a methodology centered on function-driven
design, exploring computer-based design and topology optimization. Two case studies
demonstrated the efficacy of the methodology, demonstrating it as an effective means to
generate out of the box solutions to complex structural problems and create lightweight,
high performance designs. In both case studies we saw weight savings of over 60% that were
realized with equivalent performance to the original designs.
2.
"Can the unique characteristics of RM be utilized to enhance the development
process?"
We examined how research in design, management and AM could be synthesized to assess
the impact of using RM for the manufacturing of products. Literature across each of these
three disciplines indicated that sizable benefits could be realized through the utilization of a
RM development process. Lastly, three companies, GE, Nervous Systems and Bhold, were
presented as examples of industry leaders in the adoption of AM for manufacturing; these
companies are innovating in RM, the development process and generative user interactive
design.
6.1 Future work
Future work in this area is needed to further investigate the expansive range of
applications to which this RM design methodology could be applied and continue to explore
the fusion of Additive Manufacturing and topology optimization. Additional case studies of
an increased range of applications will continue to enhance our understanding of the efficacy
of this design methodology.
Further study of development processes using RM is an exciting field that merits
additional attention. Through a combination of factors, increased use of AM and improved
79
and more intuitive software, the design methodology presented here will hopefully become
more accessible and widely adopted. We expect this to lead to an increase in the number of
companies leveraging the advantages of RM in their development process. Given the
growing number of such companies, we expect to find an opportunity to collaborate on the
redesign and documentation of the transition from traditional manufacturing to RIM.
6.2 Fusion of Design and Development
There is still a great deal to explore in this area in which we have only scratched the
surface of applications of computer-based generative design and AM. The synthesis of the
two disciplines, itself, is a very rich area for study. This thesis has demonstrated optimism
that design and development with AM can each individually provide substantial benefits to
streamline how we design and manufacture products. This fusion of the two provides the
opportunity to revolutionize how we make products and what they look like.
80
Appendix: GE- GrabCAD Contest
Info
This information is directly from the GE Jet Engine Bracket Challenge webpage [54].
All aircraft engines require the use of efficient and cost effective brackets.
Additive Manufacturing creates opportunities to build unique and highly
efficient bracket-like structures. Here is an opportunity to show your best
design.
When designing critical components for aircraft engines, today's designers are constantly
challenged with the tradeoff between performance requirements for strength and stiffness
on one hand and size and weight on the other. Recently, software tools have been developed
to aid designers in optimizing their part designs. However, today's manufacturing methods
restrict designers ability to take advantage of these optimized structures beyond a certain
level of complexity. Additive Manufacturing is lifting the constraints of traditional
manufacturing processes, giving designers the ability to grow practically any shape, enabling
the use of fully optimized lightweight designs that do not sacrifice performance.
Participants in this challenge will use Additive Manufacturing as the basis for optimizing an
existing aircraft engine bracket.
The designs submitted will be analyzed and evaluated via simulation, and the top ten designs
will be selected for fabrication and testing. These optimized engine bracket designs will be
additively manufactured and subjected to a given loading scenario. The winning entries will
best satisfy all of the performance criteria with the lowest mass.
The part
Loading brackets on jet engines play a very critical role: they must support the weight of the
engine during handling without breaking or warping. The brackets may be used only
periodically, but they stay on the engine at all times, including during flight. But these
brackets aren't the only parts on an engine that offer weight-reduction opportunities. There
are many similar load-carrying parts on the engine that, because they were designed for
conventional manufacturing technologies, are not fully optimized for both performance and
weight. By substantiating Additive Manufacturing in this particular case, we will enable
significant weight savings throughout the engine.
The test section
The designs entered in this competition that meet the weight and size envelope requirements
will be evaluated by simulation for performance based on the load conditions given in
Requirements Section below. The top ten designs will be manufactured using Additive
81
Manufacturing and subjected to the defined load cases. To simulate engine transport and
demonstrate maximum load carrying capacity, the part will be subjected to the individual
load conditions shown in the Requirements Section below with the specified load being
applied to 3/4" diameter pin at interface 1 while interfaces 2-5 are fixed. For designs that
meet the performance criteria, the static loading will be increased to show ultimate capability.
Designs that do not match the interface dimensions will be disqualified.
This Challenge will have TWO phases:
PHASE I:
Submit an improved design based on the provided diagram and specs from June 12 to
August 9 (note: extended on July 19, 2013). These designs will be analyzed and evaluated via
simulation with the top ten designs awarded $1,000 each.
PHASE II:
The top ten optimized engine bracket designs from Phase I will then be additively
manufactured and subjected to a given loading scenario. The top 8 designs will receive
awards from a total prize pool of $20,000. Phase II will run from September 17 to
November 15 (note: dates changed after Phase I extension on July 19, 2013). The final
announcement is set for mid-December.
*
You can design your entry in any CAD software as long as STEP or IGES file is
submitted.
The optimized geometry must fit within the original part envelope. STEP file will be
available via the Download specifications button, soon.
e
Material: Ti-6Al-4V
-
Service Temperature: 75 F
Minimum material feature size (wall thickness): 0.050 in.
Interface 1: 0.75 inch diameter pin. The pin is to be considered infinitely stiff.
*
Interfaces 2 - 5: 0.375-24 AS3239-26 machine bolt. Nut face 0.405 in. max ID and
-
-
0.558 in. min OD. The bolts are to be considered infinitely stiff.
Load Conditions: 1. Max static linear load of 8,000 lbs vertical up. 2. Max static
linear load of 8,500 lbs horizontal out. 3. Max static linear load of 9,500 lbs 42
degrees from vertical. 4. Max static torsional load of 5,000 lb-in horizontal at
intersection of centerline of pin and midpoint between clevis arms.
Added based on Community questions (6/11/2013): - Assume yield strength is 131
ksi. - Participants should target the lightest weight designs.
Please post your mass or volume reduction compared to the original part envelope in
your entry description. This will make it easier for the judges to sort the entries at the
end of the Challenge. Thanks! (Added 06/14/2013)
82
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