19 th International Conference on Production Research
A. Bargelis
Department of Manufacturing Technologies, Kaunas University of Technology
Kestucio 27, LT-44025 Kaunas, Lithuania
Abstract
The paper deals with the design for process capability and capacity (DFPC) method for product and process design at the early conception stage. DFPC method is developed on the theoretical and experimental process capability investigations and of acquired knowledge and good practice at the different organizations involved in new product and process development. It considers the contradictions among product performance parameters and functionality, against the process cost, machine tools capacity and process productivity. Developed method operates in Virtual Prototyping (VP) environment and tests each product alternative to process capability and capacity. Research investigates described problems in Order Handled
Manufacturing Systems (OHMS) for production of large variety product types and low production volumes.
The mathematical formalization of a developed DFPC method is provided and appropriate techniques are created. DFPC is used in industry and study process in university.
Keywords
:
Product, process, classification, DFX, capability, capacity, productivity.
1 INTRODUCTION
The new modern manufacturing environment is characterized by increased product diversity and greatly reduced product life cycle. Today’s market situation forces manufacturers to make products with high quality, low manufacturing cost and at the same time to produce broader variety of products. The competition in marketplaces requires quickly renew existing products and develop new ones. The large number of product variants and low manufacturing volumes makes it difficult to design product and process.
The key part of product development cycle is the conceptual design stage that greatly influences the resulting cost, quality, product manufacturability and its life cycle parameters [1] [2]. The traditional way for product and process development to achieve the required process capability and smallest possible manufacturing cost is by means of automation. In new manufacturing environment this approach is associated with serious production flexibility problems as well as heavy investments, which are difficult to justify financially. changing the product design and carry out it in Virtual
Prototyping (VP) environment. Engineering in a virtual environment helps to economize the cost and time of a product and process development. The key moment of engineering in virtual environment is the product Virtual
Prototyping (VP) using the 3D CAD systems for new product design [4] [5] and appropriate software for design of a production system [6]. VP can totally carry out all main functions of new product development in a virtual environment according to the individual customer requirements. VP early generates product characteristics that are possible to compare with customer requirements and manufacturing capabilities. The contradictions, unfortunately, among process capability and demand of its smallest manufacturing cost often appear when product and process design has not been sufficiently considered. A new intelligent support system is necessary for a successful solving of the problem mentioned above.
The emphasis on improved industrial productivity at reduced cost has appeared to be limited by the influence on the design, development, and implementation of manufacturing systems. It means no total stiff automation, but increase in the efficiency and effectiveness of a manufacturing enterprise through the integration and exploitation of available modern information and production technology, and human skills. This integration has to be guided by the changes in managing the way of thinking and the organizational structure. In most cases these requirements can satisfy Hybrid Manufacturing System (HMS) that consists of humans, machines and computers [3].
An increasing number of different product and process design methods based on the Concurrent Engineering
(CE) approach and Design for Excellence (DFX) methodology in order to improve the manufacturing situation without any additional investments have appeared in the past decades. Design for Manufacture (DFM),
Design for Assembly (DFA), Design for Quality (DFQ),
Design for Cost (DFC) and other methods have been proposed to improve the manufacturing situation by
The main objective of this research is to create and investigate the new methodology of Design for Process
Capability and Capacity (DFPC) for product and process design in virtual prototyping environment on the base of acquired knowledge at the different organizations involved in new product development. It considers the contradictions of product design procedure seeking its best performance and also among the principles of DFA and DFM when facilitating the product assembling process, the fabrication process of product parts and components gets more complicated; therefore, the problems related to the fabrication process capability can arise. The paper considers described problems in manufacturing of large number product types and low production volumes in Order
Handled Manufacturing Systems (OHMS). The findings and developments slightly can be used in mass production systems also. The mathematical formalization of a developed DFPC method is provided and appropriate techniques are created.
During past 20 years the methodology of Design for
Excellence (DFX) has achieved new advantages in the product design procedure [7]. The number of DFX methods

for assembling and manufacturing processes including
DFC, DFQ, and Design for Testing (DFT) was increased and they are going to increase in the future.
DFM methods in general are classified into two streams – intuitive based and logic based. Many guidelines are proposed for product manufacturability, selection of materials and processes [8] [9]. The role and value of DFM tools and techniques in a team-based simultaneous engineering design process [10] is introduced. The author of this research describes the techniques of part count reduction, assembly cost reduction and product cost reduction. The second technique, quality function deployment, is emphasized as well and its advantages are presented. Traditionally, single interface concept of DFM to multiple “design for” elements has been discussed in the research [11]. Through an in-depth case study in an electronics plant and qualitative data analysis, it was found that the existence and functions of these “design for” elements are revealed on the levels of process engineering capability.
DFA has a major objective to reduce the number of parts in a product and to increase the ease of assembling the remaining parts. DFA methods can reduce up to 20-40% of a manufacturing cost and can increase up to 200% of an assembling productivity [12]. It is possible to obtain mentioned advantages when assembling process is followed by the developed guidelines. Design for
Manufacture and Assembly (DFMA) as one tool is used together with concurrent engineering product definition, digital design and determinant assembly techniques to improve product team performance and reduce costs [13].
Part count reduction and simplification also was accomplished using DFMA analyses, and all cost reduction techniques require integration with product design. It is important to note that the manufacturability of an industrial product has the influence to some factors but production cost, in reality, is only one of many evaluation aspects.
Quality, flexibility, lead time, efficiency and so on are other important areas of manufacturing performance getting the advantages and benefit.
Design for Quality (DFQ) approach introduces method and associated techniques to support the introduction of quality-competitive products and relationship between these techniques and other quality tools, including connections with DFM are also discussed in the paper [14].
Quality prediction of the new product at the early design stage and number of new product defects by the statistical data of assembling operations, assembling time and probability of product i th part defects has been defined.
Design for Testing (DFT) is a set of means for virtual product testing during the design procedure [15]. It helps to achieve better product quality, functionality, reliability and shorter lead time.
Process capability is estimated by well known indices C p and C pk
, which are the measure relating the actual performance of a process to its specified performance. In other words, process capability indices indicate the process variability to the product specification tolerance. Two types of tolerance are used in manufacturing: product tolerance and process tolerance. Product tolerances are determined by the designer and process tolerances are allocated by the process planner and are based on each operation characteristic and requirements [16]. Both types of tolerances have the influence to scrap, rework and manufacturing costs. When applying DFX methodology it is not always possible to achieve the required process capability and capacity, therefore problems arise in product and process development. This research is devoted to consider the problem mentioned above and to development of appropriate techniques including mathematical formalization means.
3.1 Estimation of process capability and capacity
New product design is the creative effort that seeks to turn customer’s wishes into an economically producible product which will lead to a useful life. In most design situations, compromises among performance, cost, capacity and delivery time cannot be avoided. Variation enters, therefore, into the design because different input data is available. Production processes sometimes do not make perfect products and introduce more variations and product defects. Machine tool capability
C pk
C p and process capability
are used to determine the efficiency. C p is used to determine the systems location in tolerance limits while C pk is used to determine the average so that the system will work better in the specification limits [17]. by the following equations [18]:
C p
and C pk
are the most popular process capability indices and are defined
C p
=
USL
−
6
σ
LSL
(1)
C pk
=
⎧ min
⎩
⎨
USL
3
σ
−
E
, or
E
−
LSL
3
σ
(2)
Then tolerance T or the target of part dimension is expressed as follows:
T
=
USL
−
LSL ,
(3) where USL is the upper specification limit of a part, mm;
LSL is the lower specification limit of a part, mm; σ is the process standard deviation or overall process variability, mm; E is the mean value of a whole process parameter, mm.
The process R of a part D is expressed as a set of operations:
R
= r j
U
=
1
O i
=
{
O
1
, O
2
,..., O r
}
.
(4)
The means of machine tool and process capability indices must be calculated for each operation O of C p
and follows:
C pk i
and hereby a lot
for whole process R could be expressed as
C p
=
{
C p
( ) ( )
,..., C p
( )
,
}
(5)
The critical operation in set R defined by expressions (4) and (5) is the one having the minimum value of C p index. On the other hand, the means of process capability indices with machine tool capacity or process productivity index K must be related: or C pk
⎪
⎪
⎧
0
<
K
⎨
⎪
⎪
⎩
C
C min p min pk
≤
≤
≤
K
C p max
C pk
≤
≤
C max p
C max pk
K
C
C p pk
→
→
→ max max max,
(6) where K max
is the biggest acceptable machine tool capacity or process productivity index;
C min p
=
C min pk
=
1 .
33
and
C max p
=
C max pk
≥
2
are the minimal and maximal value of the acceptable capability indices seeking the minimal process costs [18].
Machine tool capacity or process productivity is expressed by the index K [19]:

19 th International Conference on Production Research
K
= K r
=
1
V i l
S
=
1
L j
Dimensions and Tolerances
S pecifications where L is machine tool processing or whole process cost,
Eur; V is created value by machine tool or process, Eur. In case of order handled manufacturing system, created value V generally can be defined:
± b
± c
± d
V
=
L
+
P
(8) where P is the profit available from machine tool or process activity. Developed DFPC method is imposed to solve the contradictions among products performance, functionality, precision and manufacturing cost, machine tool and process capability as well as capacity and productivity.
3.2 Requirement of a good product design for process capability
The companies often operate in markets in which the price is the major decision for customers and drive for efficiency of high volume and repetitive production. It is available to achieve low cost efficiency and high standards of quality by designing and building quality rather than by inspection and reworking. Producers aspire to get the advances before their competitors using innovations in the design and manufacturing new products.
Requirements of a good product design are defined by its performance and manufacturability. A good design can only be made under consideration of the manufacturing processes. One point, which makes a lot of problems in practice, is tolerances definition and their allocation. In general, the designer is interested to have only small deviations from the dimensions because this eases his or her work and economizes time. But the small tolerances are expensive or often not possible to produce. So the designer is only able to define a suitable product if he or she is familiar with the process capabilities of producer production plants. Well designed tolerances enable to minimize manufacturing costs and hold place in the market. This tolerance optimization seams to be easy from the first view but in practice it is not. If there are, for instance, some parts in assembling unit, the design of separate parts should be made according worst case tolerances. In Figure 1 the dimension of the surrounding part has to be
A
+
+ b a mm, where a and b are more than 0.1-
0.2 mm otherwise the mounting is not possible without force. If the dimension of the outside part is fixed because it is a standard part, the other parts tolerances have to be optimized to meet requirements. To allocate the tolerances here is much complicated and consumes more time. This
Figure 1: Example for a tolerance optimization becomes more complex if some of the inner parts are standard parts with fixed tolerances. If there is a two or three-dimensional task, it is much more complicated, especially if it is necessary to fix the shape of parts. Then the time consumption will increase, the quantity of mistakes during allocation of tolerances will increase also and consequently will increase the manufacturing costs.
That means this situation needs improvement. There are some possibilities: first possibility is to use tolerances optimization by simulation tools such as Computer Aided
Tolerance (CAT); second possibility is when designer can re-allocate tolerances in a chain dimension combining the value of tolerances for standard and original parts. In this case designer should keep only in mind the standard parts’ tolerances and suggest which tolerances of original parts are fixed, and which have to be defined by any well known tolerances re-allocation method. It is necessary to take into account other restrictions like manufacturing costs, quality requirements, process capability and product delivery time to the customer. CAT is a tool that simulates the effects of dimensional variation of the manufacturing of original parts and their assembly as completed products. CAT models often consider the parts ideally rigid cinematically, with no elasticity or friction to compute the mutual position change between interacting parts and their clearances. The rigid and cinematic model is a strong idealization and more realistic models such as Multi-Body Systems simulations are necessary to use. Tolerance analysis consists of tolerance specification, variation modeling, and sensitivity analysis. The allowed variation on tolerance specification in shapes and configurations are defined for the parts of the products.
It is important for the designer to have an appropriate technique and software which could help him/her to vary the characteristics of product alternatives and their manufacturing costs. Various CAD systems widely used
Table 1: Products classification
Class Product type Production type
1
2
Machine tools and equipment
Ship building and repairing
3 Refrigerators
4 Molds and dies
5 Bicycles
6
7
8
Heating boilers
Trailers for trucks
Cisterns and tanks
Batch production
Job production
Batch production
Batch production
Job/Batch production
Job/Batch production
9 Solid metal and plastic parts production Job/Batch production
10 Sheet metal parts design and production Mass/Batch

Y
2.1
z x
α
α
Z X
α
1.1
x x z y y
2.2
z x
α
α
1.2
x x y y z
2.3
z x α α
α
1.3
x x z y y
2.4
z x
α
α x x
Figure 2: Rotational form design features for product design have no such technique. There are two problems: first related to the design of separate part finding best geometrical form and optimal tolerances, and second relating the design of assembling units and whole product with tolerances allocation. A lot of various models are created to solve tolerances allocation problem. These models are based on product requirements and manufacturing restrictions and are divided into three groups: worst case, statistical, and sampled. The product part tolerances could be allocated using three methods: 1) distributed equally among all parts; 2) allocated by proportional scaling; 3) allocated using weighted methods.
Unfortunately, reviewed methods cannot relate tolerances, process capability indices and manufacturing costs.
This research considers the tolerancing of separate product parts seeking sufficient process capability and minimal manufacturing cost. When designer has selected the best product’s functionality alternative at the early product development stage with minimal manufacturing costs, this decision is risky for the producer. It is necessary to verify a process capability achieving the high quality of product or part with less manufacturing costs. The modeling of process capability and capacity for costs minimization is a technique of the task mentioned above.
The classification of variety mechanical and electronical products and their design features were made for facilitating solving of a modeling problem.
1.4
z y y
Figure 3: Prismatic form design features
When product consists only of one part D , then S =0 and
G = D , and it is the simplest case without necessity of tolerances allocation. Each part D consists of the design features (DF) with various qualitative-quantitative parameters. The classification of DF is made seeking to relate its manufacturing cost, machine-tool and the whole process capability that are needed for DFPC realization.
DF is classified into two classes – rotational geometrical form (Figure 2) and prismatic form (Figure3). Each class is classified deeper into subclasses, as 1.1; 1.2 and 2.1; 2.2 and so on, which totally characterize the part geometrical form. The qualitative-quantitative parameters (QQP) of DF as dimensions and accuracy, surfaces roughness and DF number are additional data which estimate whole part manufacturing cost; the same data and also statistical facts of a real process of various product classes for evaluation of machine tool and the whole process capability have been used. For this aim the prediction of indices C p
and C pk at the early stage of new product and process development stage has been applied. The statistical average of process standard deviation σ a in real manufacturing system has been defined (Figure 4). Then C p applying the substitution of
(2). If predicted indices C p
σ to σ a
and C pk
and C pk
are predicted in expressions (1) and
are not sufficient then necessary corrections of product and process design according to the developed DFPC method are made.
3.3 Classification of products, processes and design features
Implementation of a DFX methodology is more effective when applying it not for all available products but for their separate classes. In this case at the product conception stage general methods, guidelines and good practice are used while deeper knowledge of various DFX peculiarities could be specified to separate product classes and their appropriate processes. Table 1 shows classification of mechanical and electronical products that are produced and developed in Lithuania. Product G is expressed as an original parts D and standard component S set:
Product
Class 1
Product
Class 2
DF 1.1
DF 2.3
DF 1.5
DF 1.6
QQP1
QQP2
QQP3
QQP4
QQP5
σ
σ
σ
σ
σ
⎧
⎩
0
0
<
<
⎝
⎜⎜
⎛ n
U i
=
1
D i
D
≤
⎠
⎟⎟
⎞
U
⎝
⎜⎜
⎛ m j
U
=
1
S
……………
DF 2.1
DF 1.3
QQP6
σ
G
=
S
≤ n m
,
, j
⎠
⎟⎟
⎞ where n is the quantity of original parts; m is the quantity of standard components.
DFPC approach requires the following conditions: n
→ m
→
Class 10 DF 2.2
QQP7
QQP8
QQP9
QQP10
σ
σ
σ
σ
Figure 4: Interaction among product class, DF and QQP for definition of the real process standard deviation min, max .
(10)

19 th International Conference on Production Research
Standard components
Blank work-pieces
Manufacturing engineering
DFM DFA
Process plan
Conventional
Manufacturing
3D
Enterprise resources planning
CNC
Manufacturing
CAD
ERP
FMS HMS
BOM DFC Process capability
Production volume
DFPC
Capacity
Manufacturing cost
DFPC has a task to operate in the three distinct functions of the product realization chain – product design, process design and process execution. The structure of DFPC method is presented in the Figure 5. It consists of three blocks with the above-mentioned functions. Each block with other one by interface I1 and interface I2 is connected.
Both interfaces secure the information sharing among members of the product realization chain.
The interface I1 operates with design and process plan parameters of the product, such as material type, mass, volume, number of parts, overall dimensions, tolerances, machine tools, operations sequence, capability and
2.
Table 2: Fragment of DFPC guidelines
Figure 5: DFPC structure
Number Guideline manufacturing cost. It is able to carry out knowledge sharing and support of a collaborative product and process design. The main task of this interface is to verify the C p index and product manufacturing cost at the early design stage applying the intelligent model for cost prediction [20] in virtual prototyping environment.
The interface I2 operates with parameters of the process plan and manufacturing system, in particular, variety of products, production volume, bottlenecks, quality control, re-works and so on. Sometimes the re-design of a product and process is carried out or lead time is changed if the capability of processes is insufficient.
The Knowledge Base (KB) for DFPC has been developed.
It consists of two types of knowledge – guidelines and machine tool capability rules. Guidelines (Table 2) present
DFPC methodology while machine tool capability rules are logical expressions that may be used for tolerable process capability. They are classified into separate groups according to product and DF class and manufacturing system type (Figure 6).
3.
4.
5.
6.
Analyze part DF tolerances and part producers when C p
<1.33
Analyze various manufacturing systems for better σ a
Make part design corrections according to C p index
Verify product and part manufacturing cost at the early design stage
Optimize product part number, geometrical form, dimensions and tolerances seeking
C p
1.33 and K max
For part DF selection
For C p
index prediction
For the best manufacturing
Machine tool capability rules system selection
For DF dimensions and tolerancing
For capacity index
K prediction
For product and process design correction
Figure 6: Classification of the machine tool capability rules
4 CASE
There is shown a se quence of DFPC method work aiming the optimal process capability index C p
at the early product and process design stage. The applicability of a developed research in simplistic and comple x production examples is considered. Simplistic production examples as machining of separate parts of mechanical products were considered.
The simplicity of production is expressed as a total part machining applying only one machine tool. A typical sheet metal design part (product class 3, 7, 10) with design features (DF) Ø10
+0.05
and Ø14.6
+0.1
(subclass 2.4) and iron caste part (product classes 1, 2, 9) with DF Ø60
+
+
0
0
.
.
08
02 and various qualitative-quantitative parameters (QQP) has been taken. Firstly, parts’ fabrication statistical average of process standard deviation σ a of separate DF for each part in partners manufacturing companies is defined. The second step of DFPC methodology is the prediction of C
LC machine has given better C p index for above-mentioned DF and parts (Table 3). CNC p
results comparing to CNC
TP machine, however, machining cost applying CNC LC is approximately twice more than CNC TP [20]. It is preferred for sheet part machining to use CNC TP machine, because of less cost. Analogical procedures for caste part were made and manufacturers A and B have been considered.
Manufacturer A fits to random customers while B – to the settled customers in accordance of the predicted C p mean.

Table 3: Predicted C p
me ans of various DF
Part DF D σ a
T C p
Machine tool
Sheet metal part 2.4 Ø10
1 st Simplistic example Ø14.6
0.021 0.05 2.38 CNC turret punching (CNC TP)
0.048 0.1 2.08 CNC turret punching (CNC TP)
Ø10 CNC laser cutting (CNC LC)
C aste part 2.4
Ø14.6
Ø60
+
+
0
.
08
0 .
02
0.0098
0.06
1.02 Manufacturer
2 Simplistic example 2.4 +
+
0
0
.
.
08
02
Complex part 2.1
Ø18
+
0 .
03
−
0 .
01
0.0051
T he complex production example when part’s manufacturing cycle consists of more than 3-5 operations
(Figure 7) or product has more than 2 assembling units.
The process R (equation 4) of a complex part has 6 operations as preparation, turning, milling, drilling, treatment and grinding. The critical operation in set R is the grinding (Table 3).
[6]
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5.47 1.33 CNC grinding machine development, Proc. of Nord Design Conf. on Product
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Watson B., Radcliffe D., Structuring Design for X Tool
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5
Developed DFPC meth od helps to minimize the engineers work scope and product manufacturing cost by rational matching of product and process design applying predicted process capability index C p
at the early product design stage in the order handled manufacturing system of large number product types and low production volumes.
6
Figure 7: Complex mechanical part
CONCLUSIONS
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