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TABLE OF CONTENTS
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Cover Sheet (NSF Form 1207 proposal only)
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Project Summary (NSF Form 1358 (not to exceed 1 page))
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Table of Contents
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Project Description (NSF Form 1360) (including Results from Prior
(not to exceed 15 pages) (Exceed only if approved in advance of
prosal submission by NSF Assistant Director or Program
Announcement/Solicitation)
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Bibliography (NSF Form 1361)
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Biographical Sketches
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Summary Proposal Budget
(NSF Form 1030, including up to 3 pages of budget justification.)
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Current and Pending Support (NSF Form 1239)
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Facilities, Equipment and Other Resources (NSF Form 1363)
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Appendix (List below)
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RAPID DESIGN THROUGH VIRTUAL AND PHYSICAL PROTOTYPING
1
2
1
2
David Baraff , Mark R. Cutkosky , Susan Finger , Fritz B. Prinz ,
1
1
1
3
Daniel P. Siewiorek , Lee E. Weiss , Andrew Witkin , Paul K. Wright
1
Carnegie Mellon University
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Stanford University
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University of California - Berkeley
PROJECT SUMMARY
Berkeley, Carnegie Mellon, and Stanford in collaboration with their
industrial and government partners have joined in a consortium for
rapid design and generation of parts and assemblies through the
transformation of virtual prototypes into physical prototypes. They
are building an experimental system using the Internet to enable
students in design courses and engineers at partner companies to use
rapid prototyping services. They will bring together rapid virtual
and physical prototyping technologies to create a network of
interconnected services to support the rapid design, test, and
manufacture of mechanical, electro-mechanical, and electronic
products.
With the proposed prototyping environment, a user will be able to
design, test, and debug a product before it is built. Once a virtual
prototype is finished, the design can be sent directly for
manufacturing on one or more of the available and developing rapid
prototyping technologies. Initially, the research will focus on
designing and manufacturing mechanical parts such as those that would
be designed by students in a senior-level design class. Building on
the expertise and facilities of the participants, the network will
later be expanded to include electro-mechanical and electronic
designs. The long term research goal is to create a prototyping
environment that integrates traditional electronic simulation and
software prototyping environments with the mechanical prototyping
environment.
One goal of this research in prototyping is to allow automatic, rapid
generation of parts by exploring the mapping from the design
description to the manufacturing plan; that is, the transformation
from the description of the virtual prototype to a plan for
manufacturing the physical prototype. To test the level of process
understanding, the rapid prototyping services will be made available
remotely over the Internet. If designers from remote sites can use
the rapid prototyping services with confidence, the research goals
will have been achieved.
The following results are anticipated from the proposed research:
- A deeper understanding of the relationship between virtual
and physical prototyping; for example, what behaviors can be
simulated effectively and how manufacturing processes
constrain the geometry and material in a design.
- More capable, reliable, and predictable (better documented)
rapid prototyping processes and a comprehensive
infrastructure that supports their use.
- Increased understanding of the new rapid prototyping
processes -- both virtual and physical -- How they perform,
what characteristics they impart, what their economics are,
how to use them, what they are best for, what their niche
is, and how to present them to professional design
engineers.
- A better understanding of the role of rapid prototyping in
collaborative design and how best to support that role.
- Results of experiments from each semesters' design students
use of rapid prototyping to determine the information that
must be made available and in what forms.
- A community of graduating engineers with a clear
understanding of how to use rapid prototyping services in
design and of how to collaborate over the Internet.
--------------------------------------------------------------------------RAPID DESIGN THROUGH VIRTUAL AND PHYSICAL PROTOTYPING
1
2
1
2
David Baraff , Mark R. Cutkosky , Susan Finger , Fritz B. Prinz ,
1
1
1
3
Daniel P. Siewiorek , Lee E. Weiss , Andrew Witkin , Paul K. Wright
1
Carnegie Mellon University
2
Stanford University
3
University of California - Berkeley
1. Introduction
Berkeley, Carnegie Mellon, and Stanford in collaboration with our
industrial and government partners have joined in a consortium for
rapid design and generation of parts and assemblies through the
transformation of virtual prototypes into physical prototypes. In
support of our collaborative effort, Carnegie Mellon will cost share
the amount of the overhead for the subcontracts to Berkeley and
Stanford.
We propose to create an experimental system using the Internet that
will allow students in design courses and engineers at participating
partner companies to make use of rapid prototyping services. Each of
the participants has developed individual, promising technologies for
virtual and physical prototyping; however, these technologies
currently stand alone. We believe that bringing these technologies
together will result in exciting new capabilities. We propose to
address several key issues in virtual and physical prototyping and to
create a network of interconnected services to support the rapid
design, test, and manufacture of mechanical, electro-mechanical, and
electronic products.
Our hypothesis is that integrating virtual and physical prototyping
for mechanical and electro-mechanical products will shorten the design
cycle time and improve the quality of our designs. This belief is
supported by the experience in VLSI circuit design. Over the last
decade, VLSI circuit design has shifted from using physical to virtual
prototypes. Physical prototyping has been reduced from 70% to 30% of
the total effort (Siewiorek et al., 1984). Changes can be made more
quickly and cheaply in virtual prototypes, so more alternatives can be
tested and design mistakes are exposed before they reach
manufacturing. We are cognizant of the many differences between VLSI
design and mechanical design, particularly the difference in the role
of geometry and functional coupling; however, we believe that by
combining virtual and physical prototyping, mechanical designers will
be able to achieve a similar reduction in cycle time and a similar
increase in the complexity of their designs.
Our research goal is to create a spectrum of virtual and physical
prototyping technologies integrated so that designers can rapidly
design, simulate, debug, and manufacture new products. These products
will include electro-mechanical devices such as wearable computers and
smart consumer products. For example, at Carnegie Mellon,
interdisciplinary design teams of up to twenty students each have
designed and fabricated four generations of wearable computers.
Designing a wearable computer requires integration of industrial
design, thermal analysis, structural analysis, mechanical assembly,
electronic design, and software engineering. The design teams use
rapid physical prototyping technologies like stereolithography to
produce the housing. Each new generation has taken less than four
months from start to finish (Siewiorek et al., 1994). Our long term
goal is to create a prototyping environment that integrates
traditional electronic simulation and software prototyping
environments with the mechanical prototyping environment.
The research programs at Carnegie Mellon, Stanford, and Berkeley
collectively represent the state-of-the-art in rapid prototyping
across a range of technologies. These technologies include Layered
Shape Deposition, precision machining, rapid computer design and
fabrication, and interactive physical simulation. We will build on
our experience from previous and on-going projects including MOSAIC-PM
(Wright and Greenfield, 1990), Layered Shape Deposition Manufacturing
(Hartmann et al., 1994), The Navigator (Siewiorek et al., 1994),
interactive simulation (Baraff and Witkin, 1992), ACORN (Coyne et al.,
1994), SHARE (Toye et al., 1994), and SHADE (Gruber et al., 1992).
Much of the basic research work on the individual technologies will
continue under the various funding arrangements; this proposal
focusses on the research necessary to create new products through
virtual and physical prototyping. The research to achieve this goal
falls into two broad categories: research on advancing the
state-of-the-art in the individual rapid prototyping technologies and
research on the fundamental issues involving geometry and process
constraints that link design and manufacturing. Our work will
include:
- Design and Virtual Prototyping
* expanding the design and analysis capabilities of the
virtual prototyping system to ensure that virtual
prototypes can be transformed into physical prototypes.
* incorporating continuous, incremental analysis and
process planning into the virtual prototyping
environment.
* creating interactive simulation environments that can
accurately model the behavior of complex parts and
assemblies, including the effects of contacts and
friction.
* developing robust process planners for the physical
prototyping technologies so that a part description can
be transformed automatically and reliably into
manufacturing plans.
* exploring the interactions between mechanical and
electronic design in order to create an integrated
rapid prototyping environment for electro-mechanical
design.
- Physical Prototyping
* deepening our understanding of the underlying physical
phenomena that govern the manufacturing processes in
order to develop robust models of the processes and
facilities.
* expanding the variety and performance of materials
available for use in physical prototypes, focusing on
classes of materials useful to design students and
identified as crucial by our industrial team members.
* exploring embedding rapidly fabricated printed circuit
boards into physical prototypes to create electromechanical devices.
* improving the reliability of the facilities and
processes so they can be used remotely with confidence.
With the proposed prototyping environment, a user will be able to
design, test, and debug a product before it is built. Once a virtual
prototype is finished, the design can be sent directly for physical
prototyping on one or more of the available and developing rapid
prototyping technologies. Initially, we plan to focus our experiment
on designing and manufacturing mechanical parts such as those that
would be designed by students in a senior-level design class; however,
building on the expertise and facilities of the participants, we plan
to expand the experiment to include electro-mechanical and electronic
designs. For example, one electro-mechanical product of interest is
the head-mounted screen display for the wearable computers being
developed at Carnegie Mellon (Smailagic and Siewiorek, 1993). Boeing,
one of our industrial partners, plans to use these wearable computers
on the shop floor to superimpose manufacturing instructions onto the
wearer's workspace.
We propose to create a prototyping environment in which designers can
retrieve prior successful designs for re-use and modification.
Designers will be able to manipulate their designs using destructive
and constructive geometric operators that incorporate the geometric
constraints of the manufacturing processes. For manufacturing
processes such as machining in which material is removed, destructive
geometric operators have been created (Cutkosky and Tenenbaum, 1990;
Sarma et al., 1993). We believe that we can find equivalent
constructive geometric operators for manufacturing processes such as
shape deposition in which material is deposited rather than removed.
Giving the designer operators that enforce the geometric constraints
of the manufacturing processes is the first step to ensuring that the
virtual prototypes can be transformed into physical prototypes.
Building on our existing simulation capabilities (Baraff and Witkin,
1992), we propose to develop an interactive simulation environment in
which designers can manipulate virtual parts and assemblies in real
time, observing the effects of forces, joint connections, collisions,
and contacts. Our goal is to let designers change part geometries,
subject to manufacturing constraints, within the running simulation,
so that the effects of these changes on behavior can be observed
immediately. The manufacturing constraints, such as minimum wall
thickness and minimum distance between holes, will be modelled in a
manner analogous to the way we model the constraints imposed by
external forces. We propose to expand and integrate the ideas on
incremental analysis and process planning that have been developed at
Berkeley (Hayes and Wright, 1989) and Stanford (Kambhampati et al.,
1993), so that designers receive immediate feedback on the feasibility
of their designs. As the design progresses, the designer will be able
to simulate the behavior of the design and to feel the forces
constraining the design. Once the design is complete and the designer
is satisfied with its behavior, it can be sent to one or more of the
physical prototyping systems on the experimental network.
The physical prototyping services are distributed across the United
States. Some of the rapid prototyping processes, such as the
precision machining service at Berkeley, are mature; others, such as
Layered Shape Deposition at Stanford and Carnegie Mellon, are far less
mature.
One goal of our research in prototyping is to allow automatic, rapid
generation of parts by exploring the mapping from design descriptions
to manufacturing plans; that is, the transformation from the
description of the virtual prototype to a plan for manufacturing the
physical prototype. At Berkeley, we have been developing an open
architecture controller for precision machining that will enable the
generation of a wide variety of machined components. Similarly, at
Carnegie Mellon, we have been developing a planner to generate parts
automatically from CAD descriptions for Layered Shape Deposition. To
test the level of our process understanding, we propose to make the
rapid prototyping services available remotely over the Internet. If
designers from remote sites can use the rapid prototyping services
with confidence, we will have achieved our goal.
Our first experiment will be to create an environment for the virtual
prototyping of mechanisms using precision machining to create the
physical prototypes. We will then expand this experiment to include
other types of mechanical designs and to include other manufacturing
processes, including those available from our industrial and
government partners. For example, ALCOA provides a stereolithographic
service through the World Wide Web. Sandia provides a variety of
specialized machining and welding services including gas-tungsten arc
welding, pinch welding and ultra-sonic welding, and their agile
manufacturing cell can be accessed over the Internet. Our government
and industrial partners will participate as providers and users of
these rapid prototyping technologies.
We anticipate the following results from the proposed research:
- A deeper understanding of the relationship between virtual
and physical prototyping -- what behaviors can be simulated
effectively and how manufacturing processes constrain the
geometry and material in a design.
- More capable, reliable, and predictable (better documented)
rapid prototyping processes and a comprehensive
infrastructure that supports their use.
- Increased understanding of the new rapid prototyping
processes -- both virtual and physical. How they perform,
what their economics are, what they are best for, what their
niche is, and how to present them to professional design
engineers.
- A better understanding of the role of rapid prototyping in
collaborative design and how best to support that role.
- Findings of experiments from each semesters' design students
use of rapid prototyping. Through these experiments we will
learn what information must be made available and in what
forms.
- A community of graduating engineers with a clear
understanding of how to use rapid prototyping services in
design and of how to collaborate over the Internet.
To aid in evaluating the direction and results of our work, we propose
to create an industrial advisory board composed of representatives of
the separate industrial consortia that have been formed for each of
the individual rapid prototyping manufacturing technologies. This
board will meet with the academic, government, and industrial
participants to provide guidance through annual technical reviews. To
evaluate the rapid prototyping service network itself, we propose to
perform experiments with our design classes in which some teams have
access to the network and others do not. We will seek to enlist small
design teams at partner companies who would be willing to experiment
with our new technologies.
2. Engineering and Educational Impact
2.1. Engineering Impact
Rapid prototyping is a key element of U.S. industries are to produce
higher quality products with faster times to market; however, no
single prototyping process meets all the requirements for rapid
product development. Success requires innovation and incorporation of
a broad range of technologies covering materials (metal to plastic),
domains (electrical and mechanical), media (physical and virtual) and
resources (local and distributed). To maintain a competitive edge,
companies and universities need access to a complete spectrum of both
existing and emerging rapid prototyping technologies.
For rapid prototyping processes to be used in practice, the design and
manufacturing communities must have access to them and must be able to
acquire knowledge about them rapidly. One of our goals is to compress
the period between the time a new manufacturing process becomes viable
and when it achieves widespread use. When designers are unfamiliar
with a process, they hesitate to use it; if few designers use a
process, the experience base grows slowly. We need to address this
cycle at both ends: providing an infrastructure that makes it easy
for designers to become familiar with new processes and capturing
their experiences so that other designers can re-use it. The key
impact of our work will be a reduction in product realization time,
reduced costs, yet higher quality because the link between design and
manufacturing has been strengthened.
2.2. Educational Impact
The primary educational impact of the proposed work will be through
direct participation by students in graduate design courses at
Berkeley, Carnegie Mellon, and Stanford. The students in these
courses range from Masters students with one to two years of
industrial experience to undergraduate students encountering design,
physical prototyping, and manufacturing for the first time.
At Berkeley, the participating course will be ME22, in which students
engage in projects that lead them to explore emerging design and
manufacturing technologies. At Stanford, the relevant courses are
ME210, "Mechatronic Systems Design" and ME217, "Design for
Manufacturability." The students in these courses work on
industry-sponsored projects that involve a mix of mechanical design,
sensors, and software. At Carnegie Mellon, the courses are 99-600 and
39-648 which use large interdisciplinary teams of engineering and
design undergraduates and graduate students to design and fabricate
electromechanical devices such as wearable computers. The students
explore a range of issues from marketing to product maintenance to
environmental disposal. Students in each of these courses will have
the opportunity to explore and evaluate our rapid and virtual
prototyping services in the context of collaborative design. We will
develop projects in which teams at each site cooperate to produce and
integrate the subsystems of electro-mechanical products. The products
will be selected in consultation with industrial sponsors and partners
in this proposal and will be chosen partly on the basis of their
potential to benefit from rapid prototyping capabilities and/or novel
manufacturing processes such as Layered Shape Deposition.
The distributed design collaborations will serve several purposes.
They will:
- expose students to the problems associated with
collaborative engineering projects that cross institutional
and geographic boundaries, as well as exposing them to the
solutions for overcoming these problems.
- expose students to the roles of virtual and rapid
prototyping for reducing design cycle times and promoting a
concurrent engineering approach.
- allow us to evaluate the effectiveness of virtual and
physical prototyping for capturing design history as each
new generation of students creates, iterates, updates, and
refines their designs.
- familiarize students with using the Internet for multimedia
communications and accessing tools and services for design,
analysis, simulation, and manufacturing.
- expose students to using rapid and virtual prototyping as a
way to communicate design ideas among geographically
separated design teams and with industrial clients.
The students will assist us in assessing the virtual and physical
rapid prototyping capabilities. At Carnegie Mellon, we will conduct
comparative studies of team behavior and performance with and without
access to the rapid prototyping services. At Stanford, where
industrial sponsors provide teams with a substantial budget for
prototype fabrication, we will conduct studies to determine what
factors cause teams to use on-line rapid prototyping services in favor
of working directly with local job shops. We will also conduct formal
evaluation sessions with students and industry clients at the close of
each academic year in which we attempt to answer questions such as:
How should libraries of previous examples be organized and presented?
Under what conditions are virtual and physical rapid prototyping
capabilities most effective? What front-ends are needed for designers
to use remote prototyping services?
3. Background and Related Work
Our work builds on many diverse areas of research. For example, some
of the complementary projects to our work are the ongoing research on
collaboration, networking, and manufacturing for the Agile
Manufacturing and the ARPA/MADE initiatives, the mobile computing work
at Carnegie Mellon, and the SHARE project at Stanford. The background
section will cover only those areas of research in which we expect to
make substantial contributions. We will not discuss in detail the
complementary research; however, we will take advantage of advances in
areas like Agile Manufacturing so that our work builds on, but does
not duplicate, the research of our colleagues.
3.1. Kinematic and Dynamic Simulation
Physical simulation has a long history in computer science and in all
fields of engineering. By far, the most common type of physical
simulation uses a model, such as a Finite Element Model, designed to
analyze an object or a configuration of objects for a particular
behavior under a given set of external conditions. To simulate the
behavior of mechanisms, simulation systems are built by formulating a
mathematical model of the physics of the desired real-world objects.
In the mechanical engineering domain, two notable examples of large,
complex simulation systems are the ADAMS system (Chace, 1984),
originally developed at the University of Michigan, and the multi-body
simulation work at the University of Iowa (Shu and Haug, 1993). These
two approaches offer sophisticated treatments of three-dimensional
dynamic simulation, such as the use of efficient formulations for
motion dynamics, sparse-matrix techniques, and automatic reformulation
of constraints based on kinematic relationships. Several companies
also offer software simulation packages for mechanisms. All of these
simulation environments model simple mechanical linkages, constraints
between rigid bodies, and in some cases, flexible bodies. Geometric
considerations, contact, impact, and friction are not modeled in
detail.
Newton (Cremer and Steward, 1989) and its extension, SIMLAB (Palmer
and Cremer, 1991), combine these dynamical capabilities with a more
comprehensive treatment of contact and collision. The capabilities of
these systems include particle dynamics, rigid body dynamics (without
contact), and electrical circuit simulations; however neither is
intended to support interactive simulation. Recent work by Sacks and
Joskowicz (Sacks and Joskowicz, 1993) on qualitative kinematic
analysis of physical mechanisms uses an off-line algorithm to infer
the allowable motions of objects from an initial configuration. To
reduce the computational burden, the algorithm examines only behaviors
that are realizable from the starting configuration assuming a
constant environment. Because of this, the analysis is inherently not
interactive.
3.2. Process Planning
Although commercial CAD systems have some post-processing software
packages that link design to generic manufacturing instructions that
can be post-processed into NC commands, there are still walls between
each of these sub-systems. These walls create many inefficiencies,
such as when designers have to reevaluate their work after a faulty
manufacturing operation has begun. The generation of an efficient
process plan is a time consuming task even for an expert. (Wang and
Li, 1991) note the following reasons for the slow evolution of
computer-aided process planning:
- Process planning is a highly subjective skill and has not
yet been organized into an objective and goal oriented
methodology. The selection of processes by an expert is
rarely done through precise scientific analysis, but rather
by rule of thumb and experience.
- There exists a high degree of variability in human process
planning. One study found that in a sample of 425
relatively simple gears, 377 different process plans and 54
different type of machine requirements were generated when
the planning was done manually.
General process planning was among the first of the problems tackled
by AI researchers in the 60's. The size of the search space for any
realistically complex planning problem renders brute-force techniques
useless and general applicable heuristic search techniques have proven
difficult to define and implement. Despite continued efforts of AI
researchers over the past thirty years, general purpose planning
systems have yet to become practically useful planning tools for
engineers (Dym and Levitt, 1991). In manufacturing, the complexity of
the planning task can be attributed to manufacturing concerns like
tool accessibility, fixturing, etc.
3.3. Intelligent Machining
Manufacturing tools are equipped with a wide variety of
architecturally closed controllers, so machine tool programs have to
be locally reconfigured and sensors for fixturing and inspection
cannot be accommodated on standard NC machines. Despite the quality
of systems like Pro-Engineer, commercial CAD systems are not supported
by manufacturing knowledge bases. There is no standard mapping
between design definition and manufacturing realization; there is no
infrastructure to implement open architecture manufacturing; and there
is a lack of open architecture control.
The research activity in intelligent machining is increasing as the
need for integrated manufacturing systems emerges. Recently, several
reviews of current research in machining have appeared. For example,
(Tlusty and Andrews, 1983) review sensors for unmanned machining. The
most comprehensive survey is (Tonshoff et al., 1988), which is updated
in (Dornfeld, 1992). Dornfeld identifies twelve research efforts
addressing various aspects of intelligent machining. However, most of
the work focuses on one or two aspects of the problem, such as on
closed loop control for untended machining. The work of Chryssolouris
(Chryssolouris and Domroese, 1987), with its focus on sensor
information and the statistical basis for information fusion is of
great interest to us. The work of Chang (Chang, 1990) at Purdue
University's Engineering Research Center is relevant. We believe the
integration depends on new approaches such as those described in this
proposal, namely: open-system computer platforms, integration of
knowledge-bases and open network access.
3.4. Layered Manufacturing
For centuries the majority of manufactured goods have all been built
first by shaping the components and then assembling them. Individual
components are cast, molded, stamped, milled, or turned, and then
assembled into functional products. In contrast to traditional
manufacture, the integrated circuit (IC) fabrication industry
assembles logic circuits based on a different idea. Thin layers of
metal and ceramic materials are sequentially deposited and shaped.
Deposition is done with techniques such as vapor deposition and
sputtering. Shaping occurs through various lithographic, masking and
etching techniques. The properties of individual layers are further
modified by oxidation, doping, and heat treatment. The key difference
from mechanical manufacturing is that, in IC fabrication, shaping and
assembling occur simultaneously and incrementally.
Only during the last decade has the mechanical manufacturing community
started to introduce techniques analogous to VLSI fabrication. In
particular, solid freeform fabrication through layered material
deposition is an attractive method for 3-D object generation (Bourell
et al., 1990) (Marcus and Bourell, 1993), (Weiss et al., 1992). Three
dimensional mechanical structures are generated by decomposing CAD
models into thin cross sectional slices and then building one cross
section on top of the other. Various embodiments of this method are
commercially available and well-publicized. See for example (Ashley,
1994). Like VLSI fabrication, objects are built by incremental
material deposition and simultaneous shaping of layers. However, the
range of feature sizes which can be built in layered manufacturing
today is typically hundreds of microns to hundred thousands of microns
versus the micron or sub-micron structures which are characteristic of
VLSI components.
Layered manufacturing offers the possibility of expanding the design
space with respect to geometric complexity, material composition, and
traditional cost and time constraints. However, building up materials
in layers poses significant challenges from a metrology, materials
science, heat transfer, and applied mechanics viewpoint. For example,
layered metal parts with dense, metallurgically-bonded microstructure
has been achieved only recently for a limited family of materials
(Hartmann et al., 1994).
4. Rapid Design through Virtual and Physical Prototyping
Through a collaboration of Berkeley, Stanford, and Carnegie Mellon, we
propose to create a networked environment for rapid design through
virtual and physical prototyping. The participants have developed
rapid prototyping facilities with complementary strengths. Each of
the participants has been creating design environments, all with
similar goals, based on similar views of the world. By working
together, we will be able to create a more extensive and robust rapid
design environment than if we each worked alone. Stanford, Berkeley
and Carnegie Mellon have already established several bilateral
collaborations. For example, Carnegie Mellon and Stanford are
collaborating on the ACORN project (Coyne et al., 1994); Berkeley and
Carnegie Mellon are collaborating on a parts-on-demand system (Hansen
et al., 1993); and Stanford and Berkeley are collaborating with Sandia
on an Agile Manufacturing Cell.
We believe that virtual prototyping and rapid physical prototyping
will support design collaboration. In particular, when teams of
designers are geographically dispersed, virtual prototypes become an
important means of communication for achieving a shared understanding
of the design. Physical prototypes are equally important for design
collaboration, as they augment even the best virtual prototypes with
sensory qualities that ultimately can be obtained only through
hands-on exploration and testing. In essence, a designer can send a
three dimensional fax of a design idea to another team member.
4.1. Design and Virtual Prototyping
With virtual prototyping, a user will be able to design, test, and
debug a product before it is built. Once the virtual prototype is
finished, the design can be sent directly for physical prototyping on
one or more of the available and developing rapid prototyping
technologies. Our goal is to create an design environment in which a
designer can create the geometry and material description of the
artifact, simulate its behavior, and generate the process plan.
4.1.1. Design (Geometric Construction)
All of the participants in this collaboration have been working on
variations of feature-based design environments to ensure
manufacturability of parts (Cutkosky and Tenenbaum, 1987; Finger and
Safier, 1990; Sarma et al., 1993). Much of this research on design
was done under NSF sponsorship. We propose to take the fundamental
ideas from these design systems and to create a new design environment
in which designers will be able to create and modify their designs
using either destructive or constructive geometric operators that
incorporate the constraints of the manufacturing processes.
For manufacturing processes such as machining, in which material is
removed, destructive geometric operators have been created. Each
operator basically corresponds to the material removed during one
machining operation (e.g., drilling a hole or milling a slot). At
Berkeley, this idea has been extended so that the application of an
operator is constrained to avoid creating parts in which the machine
tool cannot access a feature. We propose to create analogous
constructive geometric operators for manufacturing processes such as
shape deposition in which material is deposited rather than removed.
The mapping from geometric features to manufacturing primitives and
constraints is less straightforward than with machining, but our
experience with processes such as injection molding (Cutkosky,
1991;Hanada-89) gives us confidence that it can be done. And, based
on our experience with shape deposition processes, we believe that the
number of geometric constraints will be small and their effect will be
local. Restricting the designer to geometric operators that enforce
the geometric constraints of the manufacturing processes is the first
step to ensuring that virtual prototypes can be transformed into
physical prototypes. We propose to incorporate manufacturing
constraints, such as minimum wall thickness and minimum distance
between holes, which arise due to interactions between features.
These constraints will be simulated as forces using the same
algorithms as the interactive mechanical simulation (see Section
4.1.3). Other constraints that must be incorporated include machine
tool accessibility for precision machining.
In this environment, designers can retrieve prior successful designs
for re-use and modification. Building, maintaining, and using design
libraries present a number of research problems. Conventional library
approaches employing keyword and regular expression searching are not
adequate. Ultimately, design libraries will be effective only if they
can be built as a by-product of the design process. Consequently, we
need to research methods for extracting the information necessary to
produce good libraries. This work builds directly upon work initiated
in the n-dim (Konda, 1992) and SHARE (Toye et al., 1994) projects.
The Dedal project (Baudin et al., 1993) has shown that a high level
model of design function can efficiently guide query-based navigation
of design documentation. We also need to explore how to capture and
use the context that is developed during the course of a human-guided
browsing session (Bradley and Agogino, 1993).
Where possible, we will retrieve elements of previously prototyped
designs which already encapsulate the process constraints; however, we
must also address the problem of translating from design geometry into
representations suitable for physical prototyping. Rather than
attempt to perform automated translation and process planning
directly, we will explore a two-step approach in which geometries are
converted via a (initially human-assisted) operation to equivalent
descriptions that incorporate the constraints and characteristics of
the manufacturing process. This translation is not a simple mapping
from one geometric model to another; it is essentially a planning
exercise and will be done with the help of the incremental planning
tools discussed in Section 4.1.2.
Currently both Carnegie Mellon and Berkeley use the Noodles geometric
modeller (Gursoz, 1990) which was developed at Carnegie Mellon,
partially under NSF. We are planning to switch to ACIS which is an
open, commercially-available geometric modeller based on the same
non-manifold representation as Noodles and which also supports
Non-Uniform Rational B-Spline (NURBS) representation of surfaces. For
the purposes of this proposal, in which communication between virtual
and physical prototyping systems is essential, we propose to use a
common representation for the geometry of the parts and assemblies,
their behaviors, their materials, their process plans, etc.
Establishing this common description language will be one of the first
research tasks. The transformations between descriptions discussed
above is a longer-term research issue.
4.1.2. Process Planning
We will provide design rules and process simulators so that designers
can explore how to use the rapid prototyping processes for best
results and can submit designs with confidence. To achieve this, we
must improve our process models, process planning, and process
control.
The evolution of feature-based design reflects a continuing attempt to
embody manufacturing constraints in a formal grammar. The ideal
design language would be one in which all parts described by the
language would be manufacturable. Unfortunately, it is not possible
to capture all the manufacturing constraints in a geometric grammar or
with design rules. Therefore, we must evaluate the manufacturability
of the design directly, using process planning and evaluation during
design. Ultimately, the most reliable way to assess the manufacturing
ramifications of each addition or alteration to a design is to create
a process plan. However, to provide immediate feedback to designers,
the planning must be done incrementally and interactively, reusing
previous results where applicable for speed (Kambhampati et al., 1993;
Hayes and Wright, 1989). Incremental planning exploits several
techniques to obtain fast response, including hierarchical process
representations that localize change, a least-commitment nonlinear
planning approach that preserves options and minimizes backtracking,
and the automatic generation of validation or dependency structures to
keep track of which portions of a previous plan are unaffected by
particular design additions or alterations. We also note that when
incremental planning is adopted, plan re-use becomes closely connected
with design re-use.
In the completed system, the designer will be guided by manufacturing
knowledge bases that constantly evaluate the manufacturability of the
design. The phrase we use for this is incremental process planning
during design (Kambhampati et al., 1993; Hayes and Wright, 1989).
This process planning interface will advise the designer of the
correctness of the design. Our experience indicates that it is
important to make the designer a participant in the planning process.
To make this possible, the traditional, time-consuming batch process
approach to computing process plans must be replaced by fast,
incremental and interactive planning that keeps pace with the evolving
design. One of our research tasks is to integrate the manufacturing
constraints into the interactive behavioral simulation discussed in
Section 4.1.3.
4.1.3. Interactive Simulation
Mechanical simulation has traditionally been a batch-oriented analysis
tool that enters late in the design cycle, if at all. At Carnegie
Mellon, we are creating an interactive hands-on simulation environment
that can be used in the early stages of conceptual design. This
environment is accessible to a broad population of designers and
engineers, not just simulation experts. By interactive simulation we
mean more than just a simulation that runs fast. Rather, the designer
is a full participant in the running simulation by using a mouse (or
more exotic input device) to apply forces, and as the hardware becomes
available, to feel forces in return. Moreover, we seek simulation
techniques that are flexible enough to allow contact relationships to
change freely and to allow objects to be added, deleted, and reshaped
interactively. We contend that these capabilities will lead to a
range of powerful virtual prototyping tools, for example tools to
design mechanisms or to solve layout and packaging problems, as well
as being tools for design education.
We have been working for some time on the basic technology for
interactive simulation. Although much work remains, we have solved
many of the key numerics, modelling, software, and interaction
problems. Our current testbed system allows the user to build and
manipulate two and half-dimensional models (two-dimensional objects in
multiple layers) with collisions, contact, joint constraints, and
realistic friction. Performance on mid-range workstations is
sufficient to allow assemblies with dozens of parts to be simulated
interactively. In experiments with the system, we have been able to
simulate existing mechanisms and reproduce their behavior accurately.
Perhaps of greater interest, we have been able to gain insight into
their operation by modifying them interactively and observing the
change in behavior. Now, we can initiate virtual prototyping
experiments, in which we design on the simulator, physically
instantiate the design, and directly compare the behavior of the
physical and virtual versions. To progress beyond fairly simple
examples, however, we will need to overcome a number of hurdles:
- Full 3-D simulation and interaction: From the standpoint of
pure simulation, no real obstacle exists to performing
interactive 3-D simulations: we have performed complex 3-D
simulations off-line and done some specialized on-line
interactive 3-D simulations. The biggest problem is that
currently available tools for direct 3-D interaction are
limited. We have developed a set of techniques called
through-the-lens control that make it possible to manipulate
3-D models using conventional 2-D pointing devices.
- Complex object geometry: We have good techniques to handle
collision detection and contact analysis for concave
polyhedral objects. Objects with curved surfaces pose more
of a problem. We have developed methods for curved-surface
contact analysis, but to handle collision and contact for
non-convex curved surfaces, currently we must resort to
polygonization, which results in accuracy and efficiency
problems. We intend to develop collision detection and
contact analysis methods that handle non-convex curved
surface directly.
- Nonrigid Objects, buckling, etc.: Our current interactive
simulator handles rigid objects only, without buckling or
deformation. Large-scale finite element simulations are
infeasible at interactive speeds. We have experimented with
simplified nonrigid models, based on low-order global
deformations that are within reach of interactive speed. We
plan to test the hypothesis that even these simple models
will be useful at the conceptual stages of design.
- Product Complexity: We can handle models of nontrivial
complexity now, but to simulate many engineering designs, we
need to increase the complexity of models we can simulate.
In part, faster computers will solve this problem because
our algorithms are low-order polynomial time. To
dramatically improve performance, we plan to make better use
of sparsity information, and to investigate hierarchic
partitioning and approximation methods.
4.2. Physical Prototyping
Mechanical designers often make rough prototypes out of readily
available materials such as balsa wood, foam core, and plexiglass,
because interacting with the physical artifact is an important part of
the design creation and evaluation process (Allen, 1989). We propose
to improve the quality of physical prototypes by at least an order of
magnitude by providing designers with rapid, functional prototypes
with behaviors that correspond to the behaviors that were simulated in
the virtual prototypes. These physical prototypes will be useful not
only to designers but also for producing low-volume parts and for
producing the custom tooling required for high-volume production.
The Precision Machining facility offers a reliable, well understood
process with predictable results in terms of material properties,
stresses, achievable tolerances, process limitations, etc. In
addition, it is integrated with metrology for automated inspection of
prototypes giving the designer assurance that specified tolerances are
achieved. On the other hand, Layered Shape Deposition is a new
process that addresses the challenge of direct fabrication of
functional shapes in metals, multi-material and multi-component
assemblies, and functionally graded structures in complex geometries.
While initial experimentation has demonstrated the feasibility of
building such structures, we have yet to build industrial-quality
parts due to poor dimensional control arising from internal residual
stresses. We believe that through appropriate alloying and
post-processing, these limitations can be overcome.
For a designer, part of the value of a rapid prototyping service is
knowing the answers to questions like:
- How soon I can get my parts? The answer will depend on my
design as well as on the facility and the answer may be
subject to tolerances or uncertainties, e.g. there is an 80%
likelihood that we can produce a batch of 10 of your parts
within 2 days and a 99.9% likelihood within a week.
- What tolerances can be achieved for specified design
features? How are the tolerances correlated with cost?
- What will be the material properties, internal stresses,
surface finish, crystalline structure, etc.?
- What is the status of the facility? How busy is it? How
long is the job queue? How is the present job going?
One of the goals of the proposed research is to improve our
understanding of the rapid prototyping processes so that we can answer
questions like these with confidence. To provide such information
requires a level of process modelling and process control that is
beyond current capabilities. For example, most manufacturing cell
controllers will not allow an external user to query their status.
Using the Berkeley open architecture controller such inquiries are
possible.
The research in rapid physical prototyping technologies will focus on
Precision Machining and Layered Shape Deposition. However, in
addition, the consortium will have access to a facility for the rapid
design and fabrication of printed circuit boards (PCBs). Our research
on PCBs will involve their use in electro-mechanical devices, rather
than on the fabrication process itself.
4.2.1. Precision Machining
The Precision Machining facility, based at Berkeley, is the result of
a continuing collaboration between Berkeley and Carnegie Mellon.
Sandia National Laboratories have also joined this collaboration. We
have performed preliminary experiments in which parts were designed at
one node of the Internet and then machined at another node (Hansen et
al., 1993). The Precision Machining facility serves two key purposes:
it is a manufacturing research platform for feature-driven and
sensor-based precision machining, and it is a prototype of the
next-generation machine tool controller for US industry. It provides
all the functionality of a conventional machine tool having the
ability to run G&M code and APT-like programs, but with the following
advantages:
- allows bi-directional upstream communication with planning
and design; it has the ability to interact with the process
planner and manufacturing knowledge base over a network.
- can perform sensor integration at any level in the control
loop. Instead of using sensors just for monitoring and
stopping a machining process, the sensors can also be used
to adjust the machining process in real time.
- allows programming changes at all levels.
Instead of being
restricted to down-loading G&M code commands, any algorithm
written in the C language can be used.
All machining control functions are incorporated including: tool
changing, operator input, spindle control, e-stops, and hardware
monitoring. This results in a fully functional machine tool that is
open to program changes, sensor integration, and communication to
higher level systems. The VME bus architecture acts as the machine
equivalent of a spinal cord, allowing fast knowledge exchange between
subsystems of design, planning, and fabrication. Robust processes,
on-machine acceptance, and feedback control are enhanced because the
open-architecture controller constitutes an intelligent machine that
can incorporate a wide variety of sensor-based control techniques.
The research issues for Precision Machining concern the design and
development of open-architecture machine tools that can receive the
information and rapidly manufacture the parts.
By connecting all prototyping functions using the same computational
languages, operating systems, and unified data structures, the
opportunities for knowledge exchange, diagnosis, and refinement grow.
The manufacturing enterprise becomes a self-diagnosing and
self-learning environment. By using a standard architecture for
product and process models, designers can efficiently and rapidly
obtain prototypes and manufacture components from any manufacturing
job shop with an open architecture controller.
The Precision Machining facility is already in the early stages of
development. The design interface, process planner, and open
architecture machine tool have been proven in their rudimentary forms.
Concept demonstrations of design-for-manufacturability and rapid
prototyping have been carried out on simple components. This new
program will result in an operational rapid prototyping system. One
key focus will be improving the complexity of parts that can be
processed.
The capabilities of the Precision Machining facility reach beyond
rapid prototyping. Although our system is designed for rapid
prototyping, the fact that it has been built on a high-quality milling
machine enables the fabrication of the production component. These
activities are partially underway, yet need considerable expansion to
cope with the wide variety of geometries that can be generated by
designers.
4.2.2. Shape Deposition Manufacturing
We are developing a system called Layered Shape Deposition
Manufacturing that combines the benefits of layered manufacturing
(i.e., rapidly planned, independent of part complexity with the
capability of multi-material layers) and machining (i.e., accuracy and
precision with good surface quality). Our strategy is to slice the
CAD model of the shape to be fabricated into layers while maintaining
the corresponding outer surface geometry information. Each layer
consists of primary material, which becomes the part when the process
is complete, and complementary shaped sacrificial support material,
which is removed when the process is complete. The layer thickness
varies depending on the part geometry. The materials in each layer
are deposited as a near-net shape using either thermal deposition,
such as welding, or non-thermal deposition, such as ultraviolet
curing. The material is then shaped with a 5-axis CNC milling machine
to net shape before proceeding with the next layer. Shape deposition
eliminates the stair-step surface appearance common to conventional
layered prototyping technologies. The sequence for depositing the
primary and support materials depends on the local geometry. For
example, when undercut features are being formed, the support material
is deposited first and then machined. The primary material is then
deposited into the support feature. For non-undercut features, the
sequence is reversed. When both features are present in a layer, a
mix of these strategies is followed.
Due to differential contraction and thermal gradients between layers,
internal residual stresses build up as each new layer is deposited.
To control the build up of stress in metallic structures, each layer
can be shot-peened. Small round metal spheres, called shot, are
projected at a high velocity against the surface. Peening imparts a
compressive load that counters the tensile load of the internal stress
field.
An automated testbed facility (See Section H.) has recently been
built, and a CAD-based process planner/controller has been developed
for investigating the Layered Shape Deposition manufacturing process.
This testbed configuration consists of four processing stations: CNC
milling, deposition, shot-peening and cleaning. The growing parts are
built on pallets that are transferred from station-to-station using a
robotic pallet system. Each station has a pallet receiver mechanism.
The part-transfer robot places the pallet on the receiver which
locates and clamps the pallet in place. A robotic deposition station
includes several thermal and non-thermal deposition sources. A novel
droplet-based deposition approach, called Microcasting, is also
incorporated into this system. Microcasting currently uses plasma to
produce super-heated molten droplets that remelt the underlying
substrate on impact, thus forming metallurgical bonds.
An artifact built with Layered Shape Deposition is shown in Figure
1.a. This part is a Micocast 308 stainless-steel artifact; the poor
surface appearance results, in part, from residual stress buildup due
to thermal gradients. While this phenomena may be corrected by
modified deposition and cutting strategies (at the expense of
increased building times), it would be advantageous to explore the use
of alloys with low coefficient of thermal expansion to minimize this
effect. For this purpose, we propose to explore the use of INVAR
nickel alloy. One problem with the shape deposition of INVAR is the
difficulty in milling this hard material. We will investigate the use
of CNC controlled electrical discharge machining to shape this
material.
a. 308 Stainless Steel Part
Figure 1:
b. Polyurethane Structure with
Embedded Electronic Circuits
Parts Created using Layered Shape Deposition
4.2.3. Rapid Design and Fabrication of Printed Circuit Boards
A significant portion of new products incorporate electronic control
and display circuitry. Therefore, rapid product development not only
requires rapid design and fabrication of mechanical parts, but also
rapid design and fabrication of PCBs. Both Stanford and Carnegie
Mellon have experience in mechatronics or smart product design. For
example, in the VuMan (Smailagic and Siewiorek, 1993) and Navigator
(Siewiorek et al., 1994) projects at Carnegie Mellon, we have used
several different rapid prototyping technologies, both virtual and
physical, including MICON (Birmingham et al., 1989a) and
stereolithography, to design and fabricate wearable computers.
MICON, developed at Carnegie Mellon under previous NSF funding, is a
design system for single board computers(The current research in
wearable computers grew out of an earlier NSF-sponsored project to
design and fabricate a single board computer in a day.). In addition
to this board-level design tool, we have access to many VLSI design
tools, through the CAD Center at Carnegie Mellon. Therefore, we will
not study electronic design, rather we will study the integration of
electronics with mechanical devices. For example, we are exploring
embedding 3-D circuitry into products being fabricated using Layered
Shape Deposition; that is, we are seeking to integrate packaging and
PCB assembly into a single process. We will also explore assembling
PCBs into products manufactured with the Precision Machining facility.
We have investigated extending the Layered Shape Deposition process to
building embedded conformal electronic structures directly. The goal
is to rapidly design and prototype electro-mechanical products such as
the wearable computers being developed at Carnegie Mellon. To create
conformal shapes, the circuit boards are fabricated concurrently,
while the Layered Shape Deposition process is used to form the
package. The circuit boards are embedded in the growing structure.
For example, the part in Figure 1.b is a polyurethane structure with
two layers of embedded electronic circuits. The biggest challenge to
automatic fabrication of more complex and dense embedded electronics
is the creation of three-dimensional interconnects between layers. We
propose to investigate the embedding of prefabricated printed circuit
boards connected by miniature mechanical interlocking pin
configurations.
Under separate funding, Carnegie Mellon will be installing a facility
for rapid prototyping of PCBs. This system will be based on
commercially available equipment that can build custom boards with up
to 12 circuit layers. A typical 4 layer board will take a student
about half a day to fabricate. The PCB fabrication process begins
with a CAD file in standard Gerber format which is converted into a
photo-plot format. This file drives a photo plotter/etcher that uses
hot wax ink to transfer the image of the PCB design onto a sheet of
flexible copper-clad polyimide film. The film is sprayed with sodium
persulfate, a mild salt solution, that etches all the unprotected
copper from the film leaving only the ink-covered circuit. The ink is
then removed and the result is a flexible PCB circuit. These flexible
circuits are protected against bridging and electrical shorts with a
non-lead-based solder masking process. The flexible layers are
aligned and laminated to form a rigid PCB. This board is then drilled
to form through holes for the interlayer connections.
This basic system will be available immediately for rapid PCB
prototyping. Once the consortium has established protocols and
accounting mechanisms, it will be made available as a service over the
in ernet for tud nts and pa ticipating co
5.tGovernmentsandeIndustrialrParticipationnsortium engineers.
Two classes of organizations will benefit from the proposed research:
rapid prototyping service providers and organizations that perform
design. Often a single organization falls into both classes. To
date, most of our industrial collaborators have been from the
mechanical and aerospace sector. Based on our strengths in wearable
computers and virtual prototyping, we will actively seek to enlist
more companies from the computer industry as collaborators. Each
physical prototyping technology has an established, but focused,
industrial consortium. For example, the Rapid Tooling Consortium at
Carnegie Mellon has about five companies using Layered Shape
Deposition technology to produce injection molding tools from CAD
models. We will work with representatives from all of the existing
consortia to articulate the core research issues. We have begun this
dialogue with with Boeing, United Technologies, ALCOA, and Sandia
National Laboratories (see the attached letters). Representatives
from the steering committee of each consortia will also meet to define
the steps in developing commercial services. We will work with each
consortium to provide at least one commercial service for that
consortium's technology.
Both ALCOA and Sandia already provide rapid prototyping services. We
will explore with them the issues of providing services on a network.
These issues include how to let designers inquire about the status of
the facility using on-line data, how to release data to those who need
it, and how to protect data from those who should not have it. Many
of these issues are being addressed as part of the ACORN project
(Coyne et al., 1994), as well as the I-MADE-IT project and ACAPS
project. Each of our industrial partners is working with us on at
least one of these projects. We also plan to involve Pittsburgh and
San Francisco Bay Area small manufacturers who have been informally
collaborating with us on several of our projects.
Organizations that perform design can use virtual and physical
prototyping to decrease time to market. It is difficult to change an
organizations design methodology in a short period of time. Thus at
least three different indirect approaches will be used:
- Education.
We will use rapid prototyping to design and
fabricate artifacts in multiple domains using
interdisciplinary teams of students. Design teams will
range in size from small (three students) to moderate
(twenty to fifty students). These teams will demonstrate
the feasibility and benefits of integrated virtual and
physical prototyping as well as train the next generation of
designers in the new methodology. More details can be found
in Section 2.2.
- Shadowing. A team of students will use rapid prototyping to
design a product that is already in process at an industrial
partner's site. Statistics on productivity, change orders,
and quality of artifact will be used to compare the two
design methodologies. By working concurrently with an
industrial partner, we anticipate being able to demonstrate
the effectiveness of the new tools within a familiar
context.
- Industrial Project. We will seek small design teams, at our
partner companies, willing to use the new technologies. We
will work closely with these teams to provide virtual
prototyping capabilities for their products. By working
with staff engineers, we will learn about the needs of the
eventual users of our research and we will begin to transfer
the rapid prototyping technologies into use in companies.
6. Research Plan
This section represents a brief summary of our annual research goals.
We will measure our success in terms of the capabilities of the rapid
prototyping infrastructure, the impact this technology has on our
industrial and government partners and on the design courses of our
universities, and finally the quality of the ideas we can contribute
to the engineering community.
Year One
- Infrastructure:
* standardize links to the Internet
* procure necessary software for coalition between
Berkeley, Stanford and Carnegie Mellon
- Virtual Prototyping:
* establish a preliminary part description language
suitable for simulation and incremental process
planning.
* implement a 3-D interactive simulator for rigid-body
kinematics and dynamics based on a polygonal
representation.
* develop collision detection and contact analysis
algorithms for trimmed NURBS surfaces.
- Physical Prototyping:
* manufacture parts of limited complexity for design
students
* establish a taxonomy of product families (group
technology) for Precision Machining and Layered Shape
Deposition.
* establish electrical and mechanical assembly
requirements for parts with embedded printed circuit
boards.
* Precision Machining
- integrate the open architecture control strategy
with the other prototyping technologies
- provide machining and sensing strategies for the
Layered Shape Deposition process based on
Precision Machining experience
* Layered Shape Deposition
- establish a taxonomy of primary and complementary
materials with equivalent or low coefficients of
thermal expansion suitable for selective
deposition and component embedding in Layered
Shape Deposition.
Year Two
- Infrastructure:
* make the rapid prototyping tools available to a limited
number of design students and to participating
companies for experimental use.
- Virtual Prototyping:
* make the interactive simulator available to design
students for experimental use
* extend the simulator to use the shared part description
language
* investigate hierarchic partitioning and approximation
techniques for enhanced performance with simulation of
complex assemblies
- Physical Prototyping:
* Precision Machining
- complete open architecture controller for access
from Carnegie Mellon and Stanford
* Layered Shape Deposition
- design and test 3-D interconnects for
accommodating automatic assembly
- explore deposition and shaping strategies for
candidate metals through molten droplet deposition
and plastics through more traditional ultraviolet
light curing and two component hardening systems
Year Three
- Infrastructure:
* enable industrial partners and design groups at each
campus site to experiment with integrated virtual and
physical prototyping capabilities.
- Virtual Prototyping:
* extend the simulator to allow on-the-fly modification
of part geometry, subject to manufacturing constraints
- Physical Prototyping:
* Precision Machining
- increase tolerance capability through improved
sensing
- manufacture progressively more difficult
components
* Layered Shape Deposition
- generate process, materials, and performance maps
for layered manufacturing to establish the
relationships among these dimensions
- optimize electro-mechanical designs to maximize
achievable circuit density
7. Results of Prior Research
PI:
Mark Cutkosky
Title: Robotic Grasping and Manipulation
NSF Award Number: DMC-8552691
The work under this award resulted in new dynamic models, algorithms
and sensors for manipulation with robotic and tele-operated hands.
Modeling contributions included a novel approach for combining
measures of grasp compliance and friction to assess the stability of a
grasp and to compute sliding trajectories for objects manipulated with
friction. Other work resulted in the development of dynamic tactile
sensors with the ability to measure fine surface features and the
ability to detect the onset of slipping. The NSF project resulted in
three Ph.D. theses completed, Several of the main publications are:
(Cutkosky and Kao, 1989; Cutkosky, 1989; Howe and Cutkosky, 1992;
Kao and Cutkosky, 1992; Howe and Cutkosky, 1993; Kao and Cutkosky,
1993).
PI:
Paul Wright
NSF Award Number: DDM-881839
Title: Principles for Integrating Part Setup and Workholding in
Manufacturing
An automated machining environment, MOSAIC (Machine-Tool Open System
Advanced Intelligent Controller), was created for precision-engineered
mechanical parts with small batch sizes. The research goals were to
create methods for the automated planning of setups and fixtures and
to create a "machining research control platform" for precision
machining. MACHINIST contains over 450 rules on setup and clamping
procedures. In addition, a real-time-Unix controller was constructed
on a standard machine tool casting. Its open architecture allowed us
to demonstrate advanced mathematical algorithms such as full-partprofile quality control, probing for arbitrary part and clamp
positioning, and a hypothesis-and-test algorithm to adjust NC
programs. Publications from this project include: (Wright and
Greenfield, 1990; Hazen and Wright, 1990; Wright et al., 1990a;
Wright, 1990; Wright et al., 1990b; Wright et al., 1991).
PI:
James Rinderle and Susan Finger
NSF Award Number: DMC8814760
Title: Structured Synthesis Strategy for Mechanical Design
Under this research award, we developed an approach to transform
design specifications into a structural configuration. Behavioral and
physical requirements as well as behavioral and physical
characteristics of the mechanical components are represented using
grammars. We use bond graphs to represent the behaviors of systems
and components and use graph grammars to transform system behavior
into a set of components that fulfill that behavior. The
transformation takes advantage of the multiple behaviors of
components. The interactions of components are important, so the
representation of the behaviors of mechanical components is linked to
the representation of their physical characteristics. The results of
this work appear in a number of publications including: (Finger and
Rinderle, 1989; Finger and Rinderle, 1990; Hoover, 1991; Rinderle,
1987; Rinderle, 1990; Finger et al., 1992).
PI:
Daniel P. Siewiorek
NSF Award Number: MIP-8720086
Title: An Improved Knowledge-Based Integration Methodology
for Computer-Aided Design
The goal of this research was to develop environments for suites of
CAD tools which allowed simplified manipulation of individual tools,
ease of integration for new tools, and ease of interaction between
individual CAD tools. Four prototype environments are developed:
CADWELD/DFE encompassing over 30 VLSI circuits design tools, MICON/DFE
compassing approximately a dozen system-level synthesis tools,
MICON/MICON an alternative integration of the system-level synthesis
tools, and the SAW synthesis environment. The results of this work
appear in a number of publications including: (Birmingham and
Siewiorek, 1988; Birmingham et al., 1989a; Birmingham and Siewiorek,
1989a; Birmingham, 1989; Birmingham and Siewiorek, 1989b;
Birmingham et al., 1989b; Daniel, 1989; Vidovic et al. , 1989;
Akella et al., 1990; Birmingham et al., 1991; Birmingham et al.,
1992; Gupta et al., 1993; Vidovic and Siewiorek, 1990)
Post Script
This proposal was written over the Internet using Mosaic and
electronic mail. A few phone calls and faxes were necessary, but for
the most part, the collaboration occurred electronically.
D. Bibliography
Akella, J., Bhandalr, I. S. and Siewiorek, D. P., "On Automated
Generation of State Tables from Behavioral Descriptions of Bus
Interfaces," IEEE International Conference on Computer-Aided
Design, 1990.
Allen C., "Situated Design," Master's thesis, Design Studies,
Carnegie Mellon University, Pittsburgh, PA, 1989.
Ashley, S., "Prototyping with Advanced Tools," Mechanical
Engineering, Vol. 116, No. 6, 1994, pp. 48-55.
Baraff, D. and Witkin, A., "Dynamic Simulation of Non-penetrating
Flexible Bodies," Computer Graphics, Proceedings of ACM SIGGRAPH, July
1992, pp. 303-308.
Baudin, C., Kedar, S., Underwood, J. G. and Baya, V., "Question-based
Acquisition of Conceptual Indices for Multimedia Design
Documentation," Proceedings of the 11th National Conference on
Artificial Intelligence, AAAI-93, Washington D.C., July 1993, pp.
452-458.
Birmingham, W. P., Kapoor, A., Siewiorek, D. P. and Vidovic, N., "The
Design of an Integrated Environment for the Automated Synthesis of
Small Computer Systems," Proceedings of the Hawaii International
Conference on System Sciences-22, IEEE Computer Society, January 1989.
Birmingham, W. P., Gupta, A. P. and Siewiorek, D. P., "The MICON
System for Computer Design," IEEE Micro, Vol. 9, No. 5, 1989, pp.
61-68.
Birmingham, W. P., Gupta, A. P. and Siewiorek, D. P., "A General
Synthesis Engine Making MICON Domain-Independent," 26th IEEE/ACM
Design Automation Conference, 1989.
Birmingham, W. P., Gupta, A. P. and Siewiorek, D. P., "MICON:
Automated Design of Computer Systems," High-Level VLSI Synthesis,
Kluwer Academic Publishers, Hingham, MA, 1991.
Birmingham, W. P., Gupta, A. P. and Siewiorek, D. P., Automating the
Design of Computer Systems: The MICON Project,
Jones and Bartlett
Publishers, Inc., 1992.
Birmingham, W. P. and Siewiorek, D. P., "Single Board Computer
Synthesis," Systems for Engineering Design, Academic Press,1988.
Birmingham, W. P. and Siewiorek, D. P., "Automated Knowledge
Acquisition for a Computer Hardware Synthesis System," Knowledge
Acquisition Journal, Vol. 1, 1989, pp. 321-340.
Birmingham, W. P. and Siewiorek, D. P., "Capturing Designer Expertise:
The CGEN System," IEEE/ACM 26th Design Automation Conference, 1989.
Bourell, D. L., Beaman, J. J., Marcus, H. L. and Barlow, J. W., "Solid
Freeform Fabrication An Advanced Manufacturing Approach," Proceedings
of Solid Freeform Fabrication Symposium, The University of Texas at
Austin, Austin, Texas, August 1990, pp. 1-7.
Bradley, S. R. and Agogino, A., "Computer-Assisted Catalog Selection
with Multiple Objectives," Proceedings of the 5th International
Conference on Design Theory and Methodology, American Society of
Mechanical Engineers, Albuquerque, NM, September 1993, pp. 139-147.
Chace, M., "Methods and Experience in Computer Aided Design of
Large-Displacement Mechanical Systems," Computer Aided Analysis and
Optimization of Mechanical Systems Dynamics, Springer-Verlag,1984, pp.
233-259.
Chang, T.C., Expert Process Planning for Manufacturing,
Addison-Wesley, 1990.
Chryssolouris, G. and Domroese, M, "Experimental Study of Strategies
for Integrating Sensor Information in Machining," Annals CIRP, Vol.
38, No. 1, 1987, pp. 425-428.
Coyne, R., Finger, S., Konda, S., Prinz, F. B., Siewiorek, D. P.,
Subrahmanian, E., Tenenbaum, M. J., Weber, J., Cutkosky, M., Leifer,
L., Bajcsy, R., Koivunen, V. and Birmingham, W., "Creating an Advanced
Collaborative Open Resource Network," To appear in the Proceedings of
the Sixth International ASME Conference on Design Theory and
Methodology, American Society of Mechanical Engineers, Minneapolis,
MN, September 1994.
Cremer, J.F. and Steward, A.J., "The Architecture of Newton, a
General-Purpose Dynamics Simulator," IEEE International Conference on
Robotics and Automation, 1989, pp. 1806-1811.
Cutkosky, M. R. , "On Grasp Choice, Grasp Models, and the Design of
Hands for Manufacturing Tasks," IEEE Transactions on Robotics and
Automation, Vol. 5, No. 3, 1989, pp. 269-279.
Cutkosky, M. R. and Tenenbaum, J. M., "Providing Computational Support
for Concurrent Engineering," International Journal of Systems
Automation: Research and Applications, Vol. 1, No. 3, 1991, pp.
239-261.
Cutkosky, M. R. and Kao, I., "Computing and Controlling the Compliance
of a Robotic Grasp," IEEE Transactions on Robotics and
Automation, Vol. 5, No. 2, 1989, pp. 151-165.
Cutkosky, M. R. and Tenenbaum, J. M., "CAD/CAM Integration Through
Concurrent Process and Product Design," Intelligent and Integrated
Manufacturing Analysis and Synthesis, American Society of Mechanical
Engineers, New York, December 1987, pp. 1-10.
Cutkosky, M. R. and Tenenbaum, J. M., "A Methodology and Computational
Framework for Concurrent Product and Process Design," Mechanism and
Machine Theory, Vol. 25, No. 3, April 1990, pp. 365-381.
Daniel, J. and Director, S.W., "An Object Oriented Approach to CAD
Tool Control within a Design Framework," Proceedings of the 26th
Design Automation Conference, IEEE Computer Society, ed., 1989.
Dornfeld, D.A , "Monitoring of Machining Processes- Literature
Review,"
presented to CIRP STC "C" meeting, 1992 .
Dym, C. L. and Levitt, R. E.,
McGraw-Hill, Inc., 1991.
Knowledge-Based Systems in Engineering,
Finger, S., Fox, M. S., Prinz, F. B. and Rinderle, J. R., "Concurrent
Design," Applied Artificial Intelligence, Vol. 6, 1992, pp. 257-283.
Finger, S. and Rinderle, J. R., "A Transformational Approach to
Mechanical Design Using a Bond Graph Grammar," Proceedings of the
First ASME Design Theory and Methodology Conference, American Society
of Mechanical Engineers, Montreal, Quebec, September 1989.
Finger, S. and Rinderle, J. R., "Transforming Behavioral and Physical
Representations of Mechanical Designs," Proceedings of the First
International Workshop on Formal Methods in Engineering Design,
Manufacturing, and Assembly, Colorado State University, January 15-17
1990, pp. 133-151.
Finger, S. and Safier, S. A., "Representing and Recognizing Features
in Mechanical Designs," Second International Conference on Design
Theory and Methodology, DTM'90, American Society of Mechanical
Engineers, Chicago, September, 1990, pp. 19-25.
Gruber, T. R., Tenenbaum, J. M. and Weber, J. C. , "Toward a Knowledge
Medium for Collaborative Product Development," Artificial Intelligence
in Design'92, Kluwer Academic Publishers, Boston, MA, 1992, pp.
413-432.
Gupta, A. P., Birmingham, W. P. and Siewiorek, D. P., "Automating the
Design of Computer Systems," IEEE Transactions on Computer-Aided
Design of Integrated Circuits and Systems, Vol. 12, No. 4, 1993, pp.
473-487.
Gursoz, E. L., Choi, Y. and Prinz, F., "Vertex-based Representation of
Non-manifold Boundaries," Geometric Modeling for Product Engineering,
North-Holland, New York, 1990, pp. 107-130.
Hansen, F., Pavlakos, E., Hoffman, E., Kanade, T., Reddy, R. and
Wright, P. K., "PARES: A Prototyping and Reverse Engineering System
for Mechanical Parts On Demand on the National Network," Journal of
Manufacturing Systems, Vol. 12, No. 4, 1993, pp. 269-281.
Hartmann, K., Krishnan, R., Merz, R., Neplotnik, G., Prinz, F. B.,
Schultz, L., Terk, M. and Weiss, L. E., "Robotic-Assisted Shape
Deposition Manufacturing," Proceedings of the 1994 IEEE International
Conference on Robotics and Automation, San Diego, CA, May 1994, pp.
2890-2895.
Hayes, C. C. and Wright, P. K., "Automating Process Planning: Using
Feature Interactions to Guide Search ," The Journal of Manufacturing
Systems, Vol. 8, No. 1, January 1989, pp. 1-15.
Hazen, F. B and Wright, P. K., "Workholding Automation: Innovations in
Planning, Analysis and Design," Manufacturing Review, Vol. 3, No.
4, 1990, pp. 224-237.
Hoover, S. P., Rinderle, J. R. and Finger, S., "Models and
Abstractions in Design," Design Studies, Vol. 12, No. 4, 1991, pp.
237-245.
Howe, R. D. and Cutkosky, M. R., "Touch Sensing for Robotic
Manipulation and Recognition," The Robotics Review 2, M.I.T.
Press, Cambridge, MA, 1992, pp. 55-112.
Howe, R. D. and Cutkosky, M. R., "Dynamic Tactile Sensing: Perception
of Fine Surface Features with Stress Rate Sensing," IEEE Transactions
on Robotics and Automation, Vol. 9, No. 2, 1993, pp. 140-151.
Kambhampati, S., Cutkosky, M. R., Tenenbaum, J. M. and Lee, S. H.,
"Integrating General Purpose Planners and Specialized Reasoners: Case
Study of a Hybrid Planning Architecture," IEEE Transactions on
Systems, Man, and Cybernetics, Vol. 23, No. 6, November/December 1993,
pp. 1503-1518.
Kao, I. and Cutkosky, M. R., "Quasistatic Manipulation with Compliance
and Sliding," The International Journal of Robotics Research, Vol.
11, No. 4, 1992, pp. 20-24.
Kao, I. and Cutkosky, M. R., "Comparison of Theoretical and
Experimental Force/Motion Trajectories for Dextrous Manipulation with
Sliding," The International Journal of Robotics Research, Vol. 12, No.
6, 1993, pp. 529-534.
Konda, S., Monarch, I., Sargent, P. and Subrahmanian, E., "Shared
Memory in Design: A Unifying Theme for Research and
Practice," Research in Engineering Design, Vol. 4, No. 1, 1992, pp.
23-42.
Marcus H. L. and Bourell, D. L., "Solid Freeform
Fabrication," Advanced Materials & Processes, Vol. 144, No. 3, 1993,
pp. 28-35.
Palmer, R. S. and Cremer, J. F., "SIMLAB: Automatically Creating
Physical Systems Simulators," Technical Report TR 91-1246, Department
of Computer Science, Cornell University, 1991.
Rinderle, J. R. and Watton, J. D., "Automatic Identification of
Critical Design Relationships," Proceedings of the 1987 International
Conference on Engineering Design, ICED 87, Eder, W. E., ed., American
Society of Mechanical Engineers, Cambridge, MA, August 1987.
Rinderle, J. R. and Finger, S., "A Transformational Approach to
Mechanical Design Synthesis," Proceedings of the NSF Engineering
Design Research Conference, Arizona State University, January, 8-12
1990.
Sacks, E. and Joskowicz, L., "Automated Modeling and Interpretation in
Mechanism Analysis," Computer-Aided Design, Vol. 25, No.
2, February 1993, pp. 106-118.
Sarma, S. S., MacFarlane, J., and Wright, P. K., "Reducing Global
Feature Interactions: A New Paradigm for Simplifying Concurrent
Process Planning," Proceedings of the ASME Winter Annual Meeting:
Manufacturing Science and Engineering, PED-Vol. 64, American Society
of Mechanical Engineers, 1993, pp. 281-290.
Shu, C. and Haug, E., "A Hybrid Variational Method for Multibody
Dynamics," International Journal for Numerical Methods in
Engineering, Vol. 36, No. 1, 1993, pp. 87-109.
Siewiorek, D. P., Smailagic, A., Lee, J. C.Y. and Tabatabai,
A. R. A. , "An Interdisciplinary Concurrent Design Methodology Applied
to the Navigator Wearable Computer System," Journal of Computer and
Software Engineering, Vol. 2, No. 2, 1994.
Siewiorek, D. P., Giuse, D., Birmingham, W. P., Hirsh, M., Rao, V. and
York, G., "DEMETER Project: Phase 1 (1984)," Technical Report
CMUCAD-84-35, Carnegie Mellon University, July 1984.
Smailagic, A. and Siewiorek, D. P., "A Case Study in Embedded-System
Design: The VuMan 2 Wearable Computer," IEEE Design and Test of
Computers, September 1993, pp. 56-67.
Tlusty, J. and Andrews, G.C, "A Critical Review of Sensors for
Unmanned Machining," Annals of the CIRP, Vol. 32, No. 2, 1983, pp.
563-572.
Tonshoff, H. K. et al., "Developments and Trends in Monitoring and
Control of Machining Processes," Annals of the CIRP, Vol. 37, No.
2, 1988, pp. 611-621.
Toye, G., Cutkosky, M. R., Leifer, L. J., Tenenbaum, J. M. and
Glicksman, J., "SHARE: A Methodology and Environment for Collaborative
Product Development," to appear in The International Journal of
Intelligent and Cooperative Information Systems, 1994.
Vidovic, N., Siewiorek, D. P., Vrsalovic, D. and Segall, Z., "A
Framework for Distributed Problem-Solving Environments Providing a
Consistent View of the Design Tools and Process," Proceedings of the
Hawaii International Conference on System Sciences-22, IEEE Computer
Society, ed., January 1989.
Vidovic, N. and Siewiorek, D. P., "Integration Infrastructure,"
Proceedings of the IEEE International Conference on Systems
Engineering, Pittsburgh, PA, August 1990.
Wang, H. P. and Li, J. L., Computer-Aided Process Planning,
Elsevier, Amsterdam,
1991.
Weiss, L. E., Prinz, F. B., Adams D. A. and Siewiorek, D. P., "Thermal
Spray Shape Deposition," Journal of Thermal Spray Technology, Vol.
1, No. 3, 1992, pp. 231-237.
Wright, P. K., "Rapid Prototyping in an Open Architecture
Manufacturing Manufacturing System," Proceedings of Artificial
Intelligence in Engineering Vol. II, Manufacture and
Planning, Boston, MA, 1990, pp. 3-28.
Wright, P. K., Greenfeld, I. and Hayes, C. C., "A Prototype of a Next
Generation Control Environment," Transactions of the 18th North
American Manufacturing Research Institute, 1990, pp. 322-328.
Wright, P. K., Hansen F. and Pavlakos, L., "Tool Wear and Failure
Monitoring on an Open-Architecture Machine Tool," ASME Winter Annual
Meeting, Fundamental Issues in Machining, American Society of
Mechanical Engineers, Dallas, TX, 1990, pp. 211-228.
Wright, P. K., Hansen F. and Pavlakos, L., "Controlling the Physics of
Machining on a New Architecture Manufacturing System," ASME Winter
Annual Meeting, Control of Flexible Manufacturing Cells, American
Society of Mechanical Engineers, Atlanta, GA, 1991.
Wright, P. K. and Greenfeld, I., "Open-Architecture Manufacturing: The
Impact of Open-System Computers on Self-Sustaining Machinery and the
Machine Tool Industry," Proceedings of Manufacturing
International'90, Atlanta, GA, March 1990, pp. 41-47.
E. Biographical Sketches
DAVID BARAFF
Robotics Institute
Carnegie Mellon University
Pittsburgh, Pennsylvania 15213
phone: (412) 268-6795
e-mail: baraff@cs.cmu.edu
Education
1987
BS.E., Computer Science, University of Pennsylvania,
Philadelphia, PA.
1992
Ph.D., Computer Science, Cornell University, Ithaca, NY.
Professional Experience
1980-1983 Computer graphics programming, part time, Computer
Technology Research Division, Bell Laboratories, Murray
Hill, NJ.
1984-1987 Computer graphics research, summer only, Computer Technology
Research Division, AT&T Bell Laboratories, Murray Hill, NJ.
1992-present
Assistant Professor, Robotics Institute and School of
Computer Science, Carnegie Mellon University, Pittsburgh,
PA.
Publications Related to Proposed Project
1. D. Baraff, "Issues in Computing Contact Forces for
Non-Penetrating Rigid Bodies," Algorithmica, 10, 1993, pp.
292-352.
2. D. Baraff and A. Witkin, "Dynamic Simulation of
Non-Penetrating Flexible Bodies, Computer Graphics,
Proceedings of ACM SIGGRAPH, Vol. 26, 1992, pp. 303-308.
3. D. Baraff, "Coping with Friction for Non-Penetrating Rigid
Body Simulation," Computer Graphics, Proceedings of ACM
SIGGRAPH, Vol. 25, 1991, pp. 31-40.
4. D. Baraff, "Curved Surfaces and Coherence for Non-
Penetrating Rigid Body Simulation," Computer Graphics,
Proceedings of ACM SIGGRAPH, Vol. 24, 1990, pp. 19-28.
5. D. Baraff, "Analytical Methods for Dynamic Simulation Of
Non-Penetrating Rigid Bodies, Computer Graphics,
Proceedings of ACM SIGGRAPH, Vol. 23, 1989, pp. 223-232.
Other Significant Publications
1. R. Mattikalli, D. Baraff and P. Khosla, "Finding All
Gravitationally Stable Orientations of Assemblies. IEEE
International Conference on Robotics and Automation, 1994.
2. R. Mattikalli, D. Baraff, P. Khosla and B. Repetto,
"Gravitational Stability of Assemblies," conditionally
accepted in IEEE Journal of Robotics and Automation,
Outside Collaborators (not included as co-authors in publications)
None.
Graduate Advisor
Donald Greenberg, Computer Science, Cornell, Ithaca, NY.
PhD Advisees and Post-Docs
None.
MARK R. CUTKOSKY
Department of Mechanical Engineering
Stanford University
Stanford, CA 94305-4021
phone: (415) 725-1588
e-mail: cutkosky@sunrise.stanford.edu
Education
1978
B.S., Mechanical Engineering, University of Rochester,
Rochester, NY.
1982
M.S., Mechanical Engineering, Carnegie-Mellon University,
Pittsburgh, PA.
1985
Ph.D., Mechanical Engineering, Carnegie-Mellon University,
Pittsburgh, PA.
Professional Experience
1978-1980 Machine Design Engineer, ALCOA, Pittsburgh, PA.
1985
Lecturer/Research Associate, Department of Mechanical
Engineering, Carnegie-Mellon University, Pittsburgh, PA.
1985-1991
Assistant Professor of Mechanical Engineering, Stanford
University, Stanford, CA.
1991-present
Associate Professor of Mechanical Engineering, Stanford
University, Stanford, CA.
1993-present
Associate Chair for Design and Manufacturing, Mechanical
Engineering Department, Stanford University, Stanford, CA.
Awards
1981-1984 Phillip M. McKenna Foundation Fellowship
1986
National Science Foundation Presidential Young Investigator
Award
1989
Appointed Anderson Faculty Scholar at Stanford
Publications Related to Proposed Project
1. M. Cutkosky, R. Engelmore, R. Fikes, T. Gruber,
M. Genesereth, W. Mark, J. Tenenbaum and W. Weber, "PACT:
An Experiment in Integrating Concurrent Engineering
Systems," IEEE Computer, special issue on Computer Support
for Concurrent Engineering, January 1993, pp. 28-37.
2. M. R. Cutkosky, D. Brown and J. M. Tenenbaum, "Working With
Multiple Representations in a Concurrent Design System,"
ASME Journal of Mechanical Design, Vol. 114, No. 3, 1992,
pp. 515-524.
3. M. R. Cutkosky and J.
Computational Support
International Journal
Applications, Vol. 1,
M. Tenenbaum, "Providing
for Concurrent Engineering," The
of Systems Automation: Research and
No. 3, pp. 239-261, 1991.
4. S-H. Lee and M. R. Cutkosky, "Fixture Planning with
Friction," ASME Journal of Engineering for Industry, Vol.
113, No. 3, 1991, pp. 320-327.
5. M. R. Cutkosky and J. M. Tenenbaum, "A Methodology and
Computational Framework for Concurrent Product and Process
Design," Mechanism and Machine Theory, Vol. 25, No. 3,
April,1990, pp. 365-381.
Other Significant Publications
1. H. Park, M. R. Cutkosky, A. B. Conru and S-H. Lee, "An
Agent-Based Approach to Concurrent Cable Harness Design,"
Artificial Intelligence for Engineering Design, Analysis
and Manufacturing, Vol. 8, 1994, pp. 45-61.
2. G. Toye, M. R. Cutkosky, L. J. Leifer, J. M. Tenenbaum and
J. Glicksman, "SHARE: A Methodology and Environment for
Collaborative Product Development," to appear in The
International Journal of Intelligent and Cooperative
Information Systems, 1994.
3. C. Petrie, M. Cutkosky and H. Park, "Design Space
Navigation," to appear in the Proceedings of the Third
International Conference on AI in Design, August, 1994,
Lausanne, Switzerland.
4. S. Kambhampati, M. R. Cutkosky and J. M. Tenenbaum,
"Integrating General Purpose Planners and Specialized
Reasoners," IEEE Transactions on Systems, Man, and
Cybernetics, Vol. 23, No. 6, November/December, 1993, pp.
1503-1518.
5. J-C. Tsai, R. Konkar and M. R. Cutkosky, "Issues in
Incremental Analysis of Assemblies for Concurrent Design,"
in J. Gero (ed.) Artificial Intelligence in Design '92,
Kluwer Academic Publishers, 1992, pp.617-635.
Outside Collaborators (not included as co-authors in publications)
Paul Wright, University of California - Berkeley, Berkeley, CA.
Graduate Advisor
Paul K. Wright, University of California - Berkeley, Berkeley, CA.
PhD Advisees and Post-Docs
Prasad Akella, Visiting Fellow, Mechanical Engineering Laboratory,
MITI, Japan
Robert Howe, Associate Professor, Division of Applied Sciences, Harvard
Dr. Imin Kao, Assistant Professor, Mech. Eng. Dept., San Jose State
Ranjit Konkar, Technical Staff, TATA Research Laboratories, Madras, India
Soo-Hong Lee, Assistant Professor, Mech. Eng. Dept., Yonsei University,
Seoul, Korea
Karthik Ramani, Assistant Professor, Mech. Eng. Dept., Purdue University
J-C Tsai, Assistant Professor, Mech. Eng. Dept., National Chung-Hsing
University, Taiwan
Charles Vann, Technical Staff, Laser Laboratory, Livermore National Lab
SUSAN FINGER
Civil Engineering
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213
phone: (412) 268-8828
e-mail: Susan.Finger@cmu.edu
Education
1972
B.A., magna cum laude with honors in Astronomy, minor in
Medieval Language and Literature, University of
Pennsylvania, Philadelphia, PA.
1974
M.A., Operations Research, University of Pennsylvania,
Philadelphia, PA.
1981
Ph.D., interdepartmental degree in Electric Power Systems
through Civil Engineering Massachusetts Institute of
Technology, Cambridge, MA.
Professional Experience
1980-1981
Associate Scientist, Energy Systems Research Group, Boston,
MA.
1981-1984
Assistant Professor of Manufacturing Engineering, College of
Engineering, Boston University, Boston, MA.
1984-1985
Visiting Assistant Professor, Laboratory for Manufacturing
and Productivity in Mechanical Engineering, Massachusetts
Institute of Technology, Cambridge, MA.
1985-1987
Program Director, Design Theory and Methodology Program,
Design, Manufacturing, and Computer-Integrated Engineering
Division, National Science Foundation, Washington, DC.
1986-1987
Acting Deputy Division Director Design, Manufacturing, and
Computer-Integrated Engineering, National Science
Foundation, Washington, DC.
1987-1988
Research Associate, Intelligent Systems Laboratory, Robotics
Institute, Carnegie Mellon University, Pittsburgh, PA.
1989-1991 Research Scientist and Head, Concurrent Engineering
Laboratory, Center for Integrated Manufacturing Decision
Systems, Robotics Institute, Carnegie Mellon University,
Pittsburgh, PA; Adjunct Faculty in Mechanical and Civil
Engineering.
1991-present
Assistant Professor, Civil Engineering, Carnegie Mellon
University, Pittsburgh, PA; Courtesy appointments in the
Robotics Institute and Mechanical Engineering
Publications Related to Proposed Project
1. D. W. Rosen, J. R. Dixon and S. Finger, "Conversions of
Feature-Based Design Representations using Graph Grammar
Parsing," accepted for publication in ASME Journal of
Mechanical Design.
2. R. Coyne, S. Finger, S. Konda, I. Monarch, F. B. Prinz,
D. P. Siewiorek, E. Subrahmanian, J. M. Tenenbaum,
J. Weber, M. Cutkosky, L. Leifer, R. Bajcsy, V. Koivunen
and W. Birmingham, "Creating an Advanced Collaborative Open
Resource Network," to appear in Proceedings of the Sixth
International ASME Conference on Design Theory and
Methodology, Minneapolis, MN, September 11-14, 1994.
3. S. Finger, J. Stivoric, C. Amon, E. L. Gursoz, F. B. Prinz,
D. P. Siewiorek, A. Smailagic and L. E. Weiss, "Reflections
on a Concurrent Design Methodology: A Case Study in
Wearable Computer Design," submitted to Computer-Aided
Design, special issue on Concurrent Design, 1994.
4. S. Finger, M. Fox, F. B. Prinz, and J. Rinderle,
"Concurrent Design," Applied Artificial Intelligence, Vol
6, 1992, pp. 257-283.
5. M. S. Fox, S. Finger, E. Gardner, D. Navin chandra,
S. A. Safier and M. Shaw, "Design Fusion: An Architecture
for Concurrent Design," Knowledge Aided Design, Chapter 6,
Academic Press, New York, 1992, pp. 157-195.
Other Significant Publications
1. R. Ganeshan, J. Garrett, and S. Finger, "A Framework for
Representing Design Intent," Design Studies, 15(1), January
1994, pp 59-84.
2. S. P. Hoover, J. R. Rinderle, and S. Finger, "Models and
Abstractions in Design," Design Studies, Vol 12, number 4,
1991, pp. 237-245.
3. S. Finger and S. A. Safier, "Representing and Recognizing
Features in Mechanical Designs," Proceedings of the Second
International ASME Design Theory and Methodology
Conference, American Society of Mechanical Engineers,
Chicago, Illinois, September 1990, pp. 19-25, (best paper
award).
4. S. Finger and J. Rinderle, "A Transformational Approach to
Mechanical Design Using a Bond Graph Grammar," Proceedings
of the First ASME Design Theory and Methodology Conference,
American Society of Mechanical Engineers, Montreal, Quebec,
September 1989.
5. S. Finger and J. Dixon, "A Review of Research in Mechanical
Engineering Design, Part I and II," Research in Engineering
Design, Vol 1, number 1, 1989, pp. 51-67 and Vol 1, number
2, 1989, pp. 121-137.
Outside Collaborators (not included as co-authors in publications)
Robert Woodbury, University of Adelaide, Australia
Graduate Advisors
Fred C. Schweppe (deceased), Electrical Engineering, MIT, Cambridge, MA.
David H. Marks, Civil Engineering, MIT, Cambridge, MA.
PhD Advisees and Post-Docs
Yi-Shin Ding, Industrial Design, National Chang Kung University, Taiwan
Rajaram Ganeshan, Construction Engineering Research Laboratory,
Champaign, IL
FRIEDRICH (FRITZ) B. PRINZ
Professor of Mechanical Engineering and
Materials Science & Engineering
Stanford University
Stanford, CA 94305-4021
phone: (415) 723-0084
email: fbp@cdr.stanford.edu
Education
1975
Ph.D., Physics, University of Vienna, Vienna, Austria.
Professional Experience
1976-1977 Assistant Professor, Department of Solid State Physics,
University of Vienna, Vienna, Austria.
1977
Visiting Assistant Professor, Department of Mechanical
Engineering, Massachusetts Institute of Technology,
Cambridge, MA.
1977-1979 Assistant Professor, Department of Solid State Physics,
University of Vienna.
1979-1980 Visiting Associate Professor, Department of Mechanical
Engineering, Massachusetts Institute of Technology,
Cambridge, MA.
1980-1983 Assistant Professor, Department of Mechanical Engineering,
Carnegie Mellon.
1980-present
Member, Robotics Institute, School of Computer Science,
Carnegie Mellon.
1983-1987 Associate Professor, Department of Mechanical Engineering,
Carnegie Mellon.
1987-present
Professor, Department of Mechanical Engineering, Carnegie
Mellon University.
1989-1994 Director, Engineering Design Research Center, Carnegie
Mellon University.
1994-present
Rodney H. Adams Professor of Engineering, Stanford
University.
Honors and Awards
1977
1982
1983
1991
Fulbright-Hays Fellowship
Ladd Award, Carnegie Mellon
Teare Teaching Award, Carnegie Mellon
Sir Christopher Hinton Lecture, Royal Academy of Engineering
1991-1992 Engineer-of-the-Year, American Society of Mechanical Engineers
Publications Related to Proposed Project
1. R. Coyne, S. Finger, S. Konda, I. Monarch, F. B. Prinz,
D. P. Siewiorek, E. Subrahmanian, J. M. Tenenbaum,
J. Weber, M. Cutkosky, L. Leifer, R. Bajcsy, V. Koivunen
and W. Birmingham, "Creating an Advanced Collaborative Open
Resource Network," to appear in Proceedings of the Sixth
International ASME Conference on Design Theory and
Methodology, Minneapolis, MN, September 11-14, 1994.
2. C. H. Amon, R. Merz, F. B. Prinz and K. S. Schmaltz,
"Thermal Modeling and Experimental Testing of MD* Spray
Shape Deposition Processes," Thermal Aspects in
Manufacturing, Proceedings of The International Heat
Transfer Conference, Brighton, England, August, 1994.
3. K. Hartmann, R. Krishnan, R. Merz, G. Neplotnik,
F. B. Prinz, L. Schultz, M. Terk and L. E. Weiss,
"Robotic-Assisted Shape Deposition Manufacturing,"
Proceedings of the 1994 IEEE International Conference on
Robotics and Automation, San Diego, May, 1994.
4. C. H. Amon, J. Beuth, H. Kirchner, R. Merz, F. B. Prinz,
K. S. Schmaltz and L. E. Weiss, "Material Issues in Layered
Forming," Proceedings of the Solid Freeform Fabrication
Symposium, The University of Texas at Austin, August, 1993.
5. L. Weiss, F. B. Prinz and D. A. Adams, "Solid Freeform
Fabrication by Thermal Spray Shape Deposition," 1992
International Thermal Spray Conference, ASM International,
Vol. 1, No. 3, 1992.
Other Significant Publications
1. J-M. Chen, E. L. Gursoz and F. B. Prinz, "Integration of
Parametric Geometry and Non-Manifold Topology in Geometric
Modeling," Proceedings of the Second ACM/IEEE Symposium on
Solid Modeling and Applications, Montreal, Canada, 1993,
pp.53-64.
2. A. Sudhalkar, E. L. Gursoz and F. B. Prinz, "Continuous
Skeletons of Discrete Objects," Proceedings of the Second
ACM/IEEE Symposium on Solid Modeling and Applications,
Montreal, Canada, 1993, pp. 85-94.
3. D. Dutta, N. Kikuchi, P. Papalambros, F. B. Prinz and
L. E. Weiss, "Project MAXWELL: A Methodology for Material
Composition and Fabrication of Structural Components,"
Proceedings Solid Freeform Fabrication Symposium, Austin,
Texas, 1992, pp. 54-62.
4. R. Gadh and F. B. Prinz, "Shape Feature Abstraction in
Knowledge-based Analysis of Manufactured Products,"
Proceedings Seventh IEEE Conference on Artificial
Intelligence Applications, Miami Beach, Florida, 1991, pp.
198-204.
5. L. E. Weiss, E. L. Gursoz, F. B. Prinz, P. Fussell,
S. Mahalingham and E. Patrick, "A Rapid Tool Manufacturing
System Based On Stereolithography and Thermal Spraying",
ASME Manufacturing Review, Vol. 3, No. 1, March, 1990, pp.
40-48.
Outside Collaborators (not included as co-authors in publications)
Richard Riesenfeld, University of Utah
Daniel Whitney, Massachusetts Institute of Technology
Graduate Advisor
Gunther Schoeck, University of Vienna, Vienna, Austria.
PhD Advisees and Post-Docs
Jyun-Ming Chen, faculty member, Taiwan.
Young Choi, Korea Institute of Technology, Seoul, Korea.
Paul S. Fussell, ALCOA, Pittsburgh, PA.
E. Levent Gursoz, Engineering Design Research Center, Carnegie Mellon
Kristjan T. Gunnarsson, Union Switch & Signal, Pittsburgh, PA.
Balan Gurumoorthy, Indian Institute of Technology.
Mark A. Hall, NEC Corporation, Princeton, NJ.
James S. Hemmerle, Robotics Institute, Carnegie Mellon University.
Rajit Gadh, University of Wisconsin, Madison.
Robert Merz, Stanford University.
Anthony D. Rosato, New Jersey Institute of Technology.
Atul Sudhalkar, Aeroquip Corporation, Ann Arbor, MI.
Yu Wang, University of Maryland.
DANIEL P. SIEWIOREK
Electrical and Computer Engineering & Computer Science
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213
phone: (412) 268-2570
e-mail: dps+@cs.cmu.edu
Education
1968
B.S., Electrical Engineering (summa cum laude), University
of Michigan, Dearborn, MI.
1969
M.S., Electrical Engineering, Stanford University, Palo
Alto, CA.
1972
PhD, Electrical Engineering (minor in Computer Science),
Stanford University, Palo Alto, CA.
Professional Experience
1972-1976 Assistant Professor, Computer Science and Electrical and
Computer Engineering, Carnegie Mellon University,
Pittsburgh, PA.
1976-1979 Associate Professor, Computer Science and Electrical and
Computer Engineering, Carnegie Mellon University,
Pittsburgh, PA.
1979-present
Full Professor, Computer Science and Electrical and Computer
Engineering, Carnegie Mellon University, Pittsburgh, PA.
1987-1989 Founding Director, Center for Dependable Systems, Carnegie
Mellon.
1989-1994 Director, Design for Manufacturing Laboratory, Engineering
Design Research Center, Carnegie Mellon University,
Pittsburgh, PA.
1994-present
Director, Engineering Design Research Center, Carnegie
Mellon.
Publications Related to Proposed Project
1. D. P. Siewiorek, A. Smailagic, J. C.Y. Lee and
A. R. Adl-Tabatabai, "An Interdisciplinary Concurrent
Design Methodology as Applied to The Navigator Wearable
Computer System," Journal of Computer and Software
Engineering, Special Issue on Hardware/Software Co-Design,
Vol. 2, No. 2, May, 1994.
2. D. P. Siewiorek and A. Smailagic, "A Case Study in
Embedded-System Design:The VuMan 2 Wearable Computer,"
Special Co-Design issue of IEEE Design & Test of Computers,
Vol. 10, No. 3, September, 1993, pp 56-67.
3. U. Flemming, S. Finger, J. Adams, C. Carlson, R. Coyne,
S. J. Fenves, R. Ganeshan, J. Garrett, A. Gupta, Y. Reich,
D. P. Siewiorek, R. Sturges, D. Thomas, R. Woodbury,
"Form-Function Synthesis in Engineering Design", AAAI Fall
Symposium Series, Design from Physical Principles,
Cambridge, MA, October 1992.
4. L. E. Weiss, F. B. Prinz, D. A. Adams, and D. P. Siewiorek,
"Thermal Spray Shape Deposition," Journal of Thermal Spray
Technology, June, 1992, pp. 231-237.
5. W. P. Birmingham, A. P. Gupta and D. P. Siewiorek,
Automating the Design of Computer Systems - The MICON
Project, Jones and Bartlett Publishing, Inc. Boston, MA,
1992. 278 pages.
Other Significant Publications
1. J. Hudak, B.-H. Suh, D. P. Siewiorek, Z. Segall,
"Evaluation & Comparison of Fault-Tolerant Software
Techniques," IEEE Transactions on Reliability, Special
Issue: Fault-Tolerant Software, Vol. 42, No. 2, June 1993,
pp 190-204.
2. D. P. Siewiorek, J. Hudak, B.-H. Suh,
Z. Segall,"Development of a Benchmark to Measure System
Robustness," Proceedings of the 23rd Annual International
Symposium on Fault-Tolerant Computing, Toulouse, France,
June 22-24, 1993.
3. J. C. Willis and D. P. Siewiorek, "Optimizing VHDL
Compilation for Parallel Simulation," IEEE Design & Test of
Computers, September 1992, pp. 42-53.
4. D. P. Siewiorek, D. Ciplickas, J. Willis, A. Gupta, and
J. Quinlan, "Laboratory Experiences with Verilog Simulation
in an Undergraduate Computer Architecture Course,"
Proceedings of Open Verilog International User Group
Meeting, March 24-25, 1992.
5. D. P. Siewiorek and R. S. Swarz, Reliable Computer Systems:
Design and Evaluation, Digital Press, Burlington, MA 1992,
908 pages.
Outside Collaborators (not included as co-authors in publications)
Jacob Abraham, University of Texas, Austin, TX.
Ravi Iyer, University of Illinois, Urbana, IL.
Graduate Advisor
E. J. McCluskey, Stanford University, Palo Alto, CA.
PhD Advisees and Post-Docs
Janaki Akella, Hewlett-Packard, Cupertino. CA.
Inderpal Bhandari, IBM - Yorktown Heights, NY.
William P. Birmingham, Univ. of Michigan Ann Arbor
Michael Buckley, IBM T.J. Watson Research Center
Edward W. Czeck, Northeastern University
Alfred Dunlop, Bell Labs
Frank Feather, H-P, Cupertino, CA.
Anurag Gupta, H-P, Cupertino, CA.
Jeffrey Hansen, Toshiba ULSI Research Center, Japan.
Mark Hirsch, Mentor Graphics1989
Mark Holland, finished May 1994
Philip John Koopman, Jr.,United Technology Research Center,
Davis Chenhsiang Lee. IBM - Austin, TX.
Carlos Liceago, NASA Langley
Ting-Ting Y. Lin, UC - San Diego
Ann Marie Grizzaffi Maynard, BM, Austin, TX.
James Quinlan, Intel, Hillsboro, OR
Bart Vashaw, IBM, Research Triangle Park, NC.
John Willis, IBM, Rochester, MN.
Gary York, Cadence Design Systems, Inc
Chuck Yount, Inter-National Research Institute
LEE E. WEISS
Robotics Institute
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, PA 15213
phone: (412) 268-7657
e-mail: lew@cmu.edu
Education
1972
Bachelor of Science, Electrical Engineering University of
Pittsburgh, Pittsburgh, PA.
1976
Master of Science, Biomedical Engineering, Carnegie-Mellon
University, Pittsburgh, PA.
1984
Ph.D. in Electrical and Computer Engineering, CarnegieMellon University, Pittsburgh, PA. Dissertation Title:
"Dynamic Visual Servo Control of Robots: An Adaptive
Image-Based Approach"
Professional Experience:
1973-1974 Electrical Estimator, Fischback and Moore, Greentree PA.
1974-1976 Research Assistant, Bioengineering Department, Carnegie
Mellon University, Pittsburgh, PA.
1976-1979 Biomedical Engineer, ARCO Medical Products Co., (Atlantic
Richfield Subsidiary), Vandergrift, PA.
1979-1983 Research Assistant, The Robotics Institute, Carnegie Mellon,
Pittsburgh, PA.
1983-1985 Research Associate, The Robotics Institute, Carnegie Mellon,
Pittsburgh, PA.
1985-1990 Research Scientist, The Robotics Institute, Carnegie Mellon,
Pittsburgh, PA.
1990-present
Senior Research Scientist, The Robotics Institute and The
Engineering Design Research Center of Carnegie Mellon,
Pittsburgh, PA.
Publications Related to Proposed Project
1. K. Hartmann, R. Krishnan, R. Merz, G. Neplotnik,
F. B. Prinz, L. Schultz, M. Terk and L. E. Weiss,
"Robotic-Assisted Shape Deposition Manufacturing,"
Proceedings of the 1994 IEEE International Conference on
Robotics and Automation, San Diego, May, 1994.
2. C. H. Amon, J. Beuth, H. Kirchner, R. Merz, F. B. Prinz,
K. Schmaltz and L. E. Weiss, "Material Issues in Layered
Forming," Proceedings of the Solid Freeform Fabrication
Symposium, The University of Texas at Austin, August, 1993.
3. L. E. Weiss, F. B. Prinz, D. A. Adams and D. S. Siewiorek,
"Thermal Spray Shape Deposition," ASM Journal of Thermal
Spray Technology, Vol. 1, No. 3, 1992.
4. H. Han, M. L. Reed and L. E. Weiss, "Micromechanical
Velcro," IEEE Journal of Micromechanical Systems, January
1992, pp. 37-43.
5. L. E. Weiss, E. L. Gursoz, F. B. Prinz, P. Fussell,
S. Mahalingham and E. Patrick, "A Rapid Tool Manufacturing
System Based On Stereolithography and Thermal Spraying",
ASME Manufacturing Review, Vol. 3, No. 1, March, 1990, pp.
40-48.
Other Significant Publications
1. T. Kanade, M. L. Reed and L. E. Weiss, "Robotics: New
Technologies and Applications," Communications of the ACM,
Vol. 37, No. 3, March 1994.
2. A. Kutay and
Operations:
Robotics and
4, 1992, pp.
L. E. Weiss, "Economic Analysis of Robotic
A Case Study of a Thermal Spraying Robot,"
Computer Integrated Manufacturing, Vol. 9, No.
279-287.
3. S. D. Rapoport, M. L. Reed and L. E. Weiss, "Fabrication
and Testing of a Microdynamic Rotor for Blood Flow
Measurements," Journal of Micromechanical Microengineering
No. 1, 1991.
4. S. K. Nayar, A. C. Sanderson, L. E. Weiss and D. A. Simon,,
"Structured Highlight Inspection Using Gaussian Images,"
IEEE Journal of Robotics and Automation, (6):2, April,
1990, pp. 208-218.
5. L. E. Weiss, A. C. Sanderson and C. P. Neuman, "Dynamic
Sensor-Based Control of Robots with Visual Feedback," IEEE
Journal on Robotics and Automation, Vol. RA-3, No. 5,
October 1987, pp. 404-417.
Outside Collaborators (not included as co-authors in publications)
None.
Graduate Advisor
Arthur Sanderson, Electrical Engineering, Rensselaer Polytechnic
Institute, Troy. NY.
PhD Advisees and Post-Docs
Dr. David Simon, Carnegie Mellon University, Pittsburgh, PA.
Rober Merz, Stanford University, Palo Alto, CA.
ANDREW WITKIN
Department of Computer Science and Robotics Institute
Carnegie Mellon University
5000 Forbes Avenue
Pittsburgh, Pennsylvania 15213
phone: (412) 268-6244
e-mail: aw+@cmu.edu
Education
1975
BA, Psychology, Columbia College, New York NY.
1980
Ph.D., Psychology, Massachusetts Institute of Technology,
Cambridge, MA.
Professional Experience
1980-1981
Computer Scientist, Artificial Intelligence Center, SRI
International, Menlo Park, CA.
1981-1983
Computer Scientist, Schlumberger Palo Alto Research
(formerly Fairchild AI LAB), Palo Alto, CA.
1983-1988
Program Manager, Vision and Modeling, Schlumberger Palo Alto
Research, Palo Alto, CA.
1988-1992
Associate Professor, Department of Computer Science,
Carnegie Mellon University, Pittsburgh, PA.
1992-present
Professor, Department of Computer Science, Carnegie Mellon
University, Pittsburgh, PA.
Publications Related to Proposed Project
1. M. Gleicher and A. Witkin, "Drawing with Constraints," To
appear in The Visual Computer, 1994.
2. D. Baraff and A. Witkin, "Dynamic Simulation of
Non-Penetrating Flexible Bodies, Computer Graphics,
Proceedings of ACM SIGGRAPH, Vol. 26, pp. 303-308, 1992.
3. M. Gleicher and A. Witkin, "Through-the-lens Camera
Control," Computer Graphics, Proceedings of ACM SIGGRAPH,
Vol. 26, 1992.
4. M. Gleicher and A. Witkin, "Snap Together Mathematics,"
Proceedings of the 1990 Eurographics Workshop on Object
Oriented Graphics, Springer-Verlag, 1991.
5. D. Terzopoulos and A. Witkin, "Deformable Models," IEEE
Computer Graphics and Applications, Vol. 8, No. 6,
November, 1988, pp. 41-51
Other Significant Publications
1. W. Welch and A. Witkin, "Free Form Shape Design using
Triangulated Surfaces," Computer Graphics, Proceedings of
ACM SIGGRAPH, Vol. 28, 1994.
2. A. Witkin and P. Heckbert, "Using Particles to Sample and
Control Implicit Surfaces," Computer Graphics, Proceedings
of ACM SIGGRAPH, Vol. 28, 1994.
3. W. Welch and A. Witkin, "Variational Surface Modeling,"
Computer Graphics, Proceedings of ACM SIGGRAPH, Vol. 26,
1992.
4. A. Witkin and M. Kass, "Reaction-diffusion Textures,"
Computer Graphics, Proceedings of ACM SIGGRAPH, Vol. 25,
1991.
5. A. Witkin and M. Kass, "Spacetime Constraints," Computer
Graphics, Proceedings of ACM SIGGRAPH, Vol. 22, 1988, pp
159-168.
Outside Collaborators (not included as co-authors in publications)
None.
Graduate Advisor
Whitman Richards, Massachusetts Institute of Technology, Cambridge,
MA.
PhD Advisees and Post-Docs
Dr. Michael Gleicher, will complete July 1994.
Dr. William Welch, will complete July, 1994.
PAUL KENNETH WRIGHT
Professor of Mechanical Engineering
College of Engineering
University of California, Berkeley
Berkeley, California 94720
phone: (510) 642-2527
e-mail: pwright@robocop.berkeley.edu
Education
1968
B.Sc. Industrial Metallurgy, University of Birmingham,
England.
1971
Ph.D. Industrial Metallurgy, University of Birmingham,
England.
Professional Experience
1972-1974
Consulting Engineer, Department of Scientific and Industrial
Research, New Zealand Government, Auckland, New Zealand.
1975-1979
Senior Lecturer, Department of Mechanical Engineering, the
University of Auckland, New Zealand.
1978-1979
Research Associate in Physics, Cavendish Laboratory, the
University of Cambridge, England (concurrent position for
one year).
1979-1987
Professor of Mechanical Engineering and The Robotics
Institute, Carnegie-Mellon University, Pittsburgh, PA 15213.
1987-1991
Professor of Computer Science, and Director of the Robotics
and Manufacturing Research Laboratory The Courant Institute
of Mathematical Sciences New York University, New York, NY
10003.
1991-present
Professor of Mechanical Engineering, School of Engineering,
University of California at Berkeley, Berkeley, CA 94720.
Awards
1980
1980
1981
1985
George Tallman Ladd Award for Young Faculty, Carnegie-Mellon
Outstanding Young Manufacturing Engineering, Society of
Manufacturing Engineers
Ralph J. Teetor Award, Society of Automotive Engineers.
Blackall Award, American Society of Mechanical Engineers.
Publications Related to Proposed Project
1. P. K. Wright, "Rapid Prototyping in an Open Architecture
Manufacturing Manufacturing System," Proceedings of
Artificial Intelligence in Engineering Vol. II, Manufacture
and Planning, Boston, MA, 1990, pp 3-28.
2. P. K. Wright and I. Greenfeld, "Open-Architecture
Manufacturing: The Impact of Open-System Computers on
Self-Sustaining Machinery and the Machine Tool Industry,"
Manufacturing International, Vol. II, Atlanta, GA, March,
1990, pp. 41-47.
3. C. C. Hayes and P. K. Wright, "Automating Process Planning:
Using Feature Interactions to Guide Search," Journal of
Manufacturing Systems, Vol. 8, No. 1, 1989, pp. 1-15.
4. P. K. Wright and D. A. Bourne, Manufacturing Intelligence,
Addison-Wesley, Boston, MA, 1988.
5. P. K. Wright and D. W. Yen, "Adaptive Control in Machining:
A New Approach Based on the Physical Constraints of Tool
Wear Mechanisms," Transactions of ASME, Journal of
Engineering for Industry, Vol. 105, No. 1, 1983, pp. 31-38.
Other Significant Publications
1. F. B Hazen and P. K. Wright, "Workholding Automation:
Innovations in Planning, Analysis and Design,"
Manufacturing Review, American Society of Mechanical
Engineers, Vol. 3, No. 4, 1990, pp. 224-237.
2. P. K. Wright, F. Hansen and L. Pavlakos, "Tool Wear and
Failure Monitoring on an Open-Architecture Machine Tool,"
American Society of Mechanical Engineers, Winter Annual
Meeting, Fundamental Issues in Machining, PED Vol. 43,
Dallas, 1990, pp. 211-228.
3. P. K. Wright, "Knowledge Engineering for Small Batch
Manufacturing Systems," Journal of Manufacturing Systems,
Vol. 8, No. 4, 1989, pp. 245-256.
4. A. Thangaraj and P. K. Wright, "Drill Wear Sensing and
Failure Prediction for Unattended Machining," International
Journal of Robotics and Computer Integrated Manufacturing,
1988, Vol. 4, No. 3/4, pp. 429-435.
5. P. K. Wright and J. G. Chow, "On-Line Estimation of
Tool/Chip Interface Temperatures for Turning," Transactions
of ASME Journal of Engineering for Industry, 1988, Vol.
110, No. 1, pp. 56-64.
Outside Collaborators (not included as co-authors in publications)
J. T. Schwartz, J. W. Demmel, K. Perlin, Courant Institute, New York
University
R. Reddy, R. Sturges, Carnegie Mellon University.
J. Hong, CIMPLUS, New York, NY.
S. C. Y. Lu, Mechanical Engineering, University of Illinois, UrbanaChapaign
X. Tan, CIMPLUS, New York, NY.
M. S. Fox, Industrial Engineering, University of Toronto.
Mark R. Cutkosky, J. M. Jourdain, Stanford University.
P. J. Englert, Bell Labs, Whippany, NJ.
Graduate Advisor
Dr. Edward M. Trent, University of Birmingham, England.
PhD Advisees and Post-Docs
Amit Bagchi, Clemson University.
Joseph Chow, Florida Atlantic University.
David Yen, General Motors Tech Center, Warren, MI.
Mark Cutkosky, Stanford University.
Raj Thangaraj, Michigan Technological University.
Robert Sturges, Carnegie Mellon University.
Paul Englert, AT&T Bell Laboratories, Whippany, NJ.
Morris Goldstein, Carnegie Group Inc., Pittsburgh, PA.
Caroline Hayes, University of Illinois - Urbana-Champaign.
H. Facilities, Equipment and other Resources
Center for Design Research (Stanford)
The Center for Design Research (CDR) is a 10,000 square foot research
institute within the Department of Mechanical Engineering at Stanford
University. Its goals are to contribute to the development of
cost-effective, quality products, to develop new enterprise R&D tools,
and to develop a deeper understanding of the technical culture that
produces new products. CDR is used to design state-of-the-art
prototypes that range from mobile robots to gravity probe satellites.
User-transparent computer instrumentation allows study of how people
use design tools, access design data bases, and formulate thoughts for
communication within the computer environment. There is a strong
emphasis on human factors, product simulation, rapid prototyping, and
manufacturing for the full product life-cycle. The approach is
integrative, with attention to visualization (human and machine),
modeling (analytic and physical), and communication among design,
manufacturing, and management processes.
The Center houses 16 high-performance computer workstations as well as
specialized systems for graphics, computer-controlled machine tools
and robotics, digital controls and virtual-reality user interfaces.
All of the above systems are connected to SUNet, the campus network,
which provides access to the InterNet. The Center also houses a
multimedia authoring system. A wide array of design and program
development software is used, including SDRC IDEAS, Wisdom Systems
Concept Modeler, PATRAN and ANSYS.
The Integrated Manufacturing Laboratory (Berkeley)
The rapid prototyping facility at Berkeley incorporates design,
process planning, and fabrication. The projects in the Lab cover
several areas: a Constrained Destructive Solid Geometry (CDSG) design
center, a Knowledge Based Process Planning system (KBPP), and the
Machine-tool Open System Advanced Intelligent Controller for Precision
Machining (MOSAIC-PM). All three activities function in an integrated
software and hardware environment, enabling rapid-prototyping.
The Integrated Manufacturing Laboratory (IML) at Berkeley contains a
Haas 3-axis CNC machine tool that has been equipped with an
Open-Architecture control for the work described in this proposal.
This means that design files, together with appropriate processplanning files can be automatically sent to the machine for
rapid-prototyping by precision machining. This machining facility is
already a node on the Internet and has supported remote manufacturing
for Sandia National Laboratories from both their California and
Albuquerque locations. In addition, it is supported by a coaxial spine
to the new 100 Megabit fiber optic data links at Berkeley.
The other Laboratories for Automation (Rooms 1172 and 1115 in
Etcheverry) remain as backrooms for trying out devices, sensor
development, and general preparations. Room 1172 also contains the
Tree Turning Center that continues to support some experiments on AE
applied to turning, under the direction of Professor Dornfeld. The
room also houses the Matsuura Milling Center which is in high use by
Professor Tomizuka's students who are developing precision control
algorithms for accurate profile generation in high speed milling.
These laboratories are supported by a wide array of fast computational
facilities and the extensive Mechanical and Electronic Shops that are
a natural part of the educational infrastructure at Berkeley.
Shape Deposition Laboratory (Carnegie Mellon)
The Shape Deposition Lab at Carnegie Mellon consists of four
processing stations: milling, thermal deposition, shot-peening and
cleaning. The parts are built on pallets which are transferred from
station-to-station using a robotic pallet system. Each station has a
pallet receiver mechanism. The part transfer robot places the pallet
on the receiver which locates and clamps the pallet in place. A
photograph of the lab is shown in Figure 2.
The thermal deposition station incorporates several deposition
methods. The station consists of an acoustic chamber (for noise
abatement and dust containment), an air handling system (for dust
filtration and collection), and a robotic deposition system. The
deposition robot is equipped with a tool changing wrist and can select
one of several deposition torches which are mounted on a docking
mechanism. The current deposition methods include arc and plasma
spraying, TIG and MIG welding, and micro-casting. To deposit
material, the robot picks the appropriate torch and traverses the part
while depositing material on the growing layer. The feedstock
mechanisms and power supplies for the deposition systems are located
on the mezzanine above the acoustic chamber; the pallet is moved into
the chamber through a trap door.
The shaping station is a 5-axis, high-speed CNC machine with an
21-head tool changer mechanism (i.e., it stores 21 different cutting
tools which can be automatically loaded into the mill spindle). The
hydraulically-actuated receiver used in this station is able to locate
the pallet repeatedly within approximately .0002 inches. When cutting
fluids are used during milling, the pallet is transferred to a
cleaning station to remove residuals. The cleaning station is a
high-pressure wash, rinse, and drying unit. The shot peening station,
which uses a conventional pressurized media delivery system, also
incorporates grit-blasting capabilities for surface preparation prior
to conventional spraying operations.
Figure 2:
The Shape Deposition Laboratory