The Manufacturing Advisory Service:

Web Based Process and Material Selection

Charles S. Smith, smythe@kingkong.me.berkeley.edu

5145 Etcheverry Hall, 510.642.9359, 510.643.6547 FAX

Paul K. Wright, pwright@robocop.berkeley.edu

5125 Etcheverry Hall, 510.642.2527, 510.643.5599 FAX

Carlo Séquin, sequin@cs.berkeley.edu

639 Soda Hall, 510.642.5103, 510.643.7352 FAX

Of

University of California at Berkeley

Berkeley, California 94720

USA

MAS Draft Page 2

1 Abstract

The Manufacturing Advisory Service (MAS) is a concept level manufacturing process and material

selection tool, geared toward educating novice designers in basic process capabilities and informing

experienced designers about new technologies. The designer enters a dialogue with the MAS, answering

questions about production quantity, typical tolerances, physical size, overall shape, and cost requirements.

At each step along the way, the user is presented with an updated, ranked list of manufacturing options.

This list can then be combined with material selection, resulting in an optimised matching of

process/material pairs to the total set of requirements. The MAS is a Java™ applet freely available via the

World Wide Web. It is interlinked with web pages of instructions, tutorials, and references explaining the

programme, fabrication processes and material choices. The MAS performs its calculations using capability

data from a remote database. The current version of the Manufacturing Advisory Service is located at:

http://cybercut.berkeley.edu/mas2/

KEYWORDS: Conceptual Design, Process Selection, Material Selection, Internet, Java

2 Introduction

What are the factors that determine the manufacturing cost of a consumer item? The costs of machinery,

raw materials, and human labour seem to be the key issues. In fact, a deeper analysis shows that at least

70% of these costs are ‘set in stone’ by decisions made during conceptual design (Giachetti & Jurrens

1997). In colloquial terms, the high level designer or team sets the stage for the vast majority of the final

‘manufactured cost’.

By far the most common method today for process selection is human experience. One of the biggest

drawbacks of this method is documented by (Boothrooyd, et. al., 1994). Their study shows that most

engineers are unfamiliar with more manufacturing processes than they are comfortable with. This is not a

problem for some designers -- the beverage industry will not switch to a wholly new process for making

soda cans. However, designers faced with new products will most likely be ignorant of many new,

MAS Draft Page 3

innovative fabrication methods. This is especially harmful in the field of rapid prototyping and small batch

manufacturing, where fabrication technology is constantly changing.

The MAS is designed to help communicate the abilities of these new processes to designers using the

Internet, as advocated by (Frost & Cutkosky 1996). New processes that have yet to achieve widespread

understanding in the engineering community can make their debut through services such as the MAS.

Two disparate groups exist that often have completely different views of the part to be fabricated: designers

and manufacturers. The designers have the functional requirements of the part in mind, along with

dimensions, quantities, and customer needs. On the other side are the manufacturing sites, staffed by

engineers and technicians who must build tooling and operate production equipment during fabrication.

However, constraint resolution can only take place when the language of the designer matches the language

of the manufacturing engineer. A shared view of the component must be established. The MAS provides

this common language, at the concept level, allowing a designer to describe a part so that an expert system

can decide which manufacturing processes can produce the desired part, in the desired time, with the

desired quality.

2.1 Review of Process Selection Software

2.1.1 Process vs. Material Selection

Software for process selection stems from the more widespread use of computer tools to assist with

material selection. An article by (AMPTIAC 1997) documents some of the more popular commercial tools

for material selection. The advent of the Web has also led many material suppliers to put database searches

on-line, allowing users to filter inventories based on user entered material property ranges.

Process selection software tools are more rare than material selection tools. Before describing existing

process selection software two definitions are needed. A requirement is something needed by the emerging

design. This may be a material requirement (i.e. hardness), or a process requirement (i.e. production rate,

producible geometries). An option is one of the possible processes (or materials) that the designer is trying

to select.

MAS Draft Page 4

2.1.2 Literature on Process Selection

(Bock 1991) describes the Computer Aided Material and Process Selector (CAMPS) as an upgrade of one

of the earliest conceptual process selectors, the Material and Process Selector (Dargie, Parmeshwar, &

Wilson 1982). In CAMPS, a data file is submitted to a series of modules: a fuzzy logic based material

exclusion module, and a Knowledge Acquisition and Consultation Module that ‘quizzes’ the designer about

the part. The user is then presented with two lists that fit all of the requirements: frequently and

infrequently used process/material pairs. The final module uses a spreadsheet from (Poggiali 1985) to

obtain generalised cost estimates for material, tooling, energy usage, and labour.

PSES, by (Farris & Knight 1991), is a methodical system for generating sequences of processes operating

on a single material. PSES first reduces material options through requirement specification and fuzzy logic

set matching.

(Kunchithapatham 1996) created the Design Advisor around separate qualitative material and process

searches. The possible material and process lists are reduced in a binary fashion: either they can or cannot

meet the requirements. Thus, the user has a list of materials and processes that are unranked -- each option

is equally valid as far as the Design Advisor is concerned.

MAMPS, by (Giachetti 1998), is another all-database solution for conceptual process selection. It uses

parallel material / process searches, followed by a combination step. All of the results in MAMPS are

ranked lists.

The Cambridge Engineering Selector (CES) is the only commercially available process selection software.

Discussed in (Esawi & Ashby 1998; Granta 2001), and contains information of more than 100 different

manufacturing processes and process variations. The CES and its process selection module retails for

hundreds of pounds. This fact makes the software somewhat inaccessible to most individual student

designers. Instead of going through the usual batch input, the user defines the part through a requirement by

requirement screening sequence. The user graphically defines the upper and lower bound for each

requirement - such as surface roughness - and processes whose lines extend above and below the user

bounds can meet the current requirement.

MAS Draft Page 5

After the user has stepped through all of the requirements, a final, unranked list of viable processes is

generated by taking the intersection of all of the result sets. The user may have many possible processes at

each screening step, only to discover that the intersection set has no members. In contrast, the MAS places

the final solution set on screen at all times, allowing the user to observe how a requirement change modifies

the final solution set. The CES process search has only a single material requirement: The user selects a

material from a list of material groups. For a multi-parameter search of materials, a separate material

database must be purchased. The results of the material seach can then be matched with the process search

to find valid combinations.

The Manufacturing Analysis Service, (Brown and Wright 1998) was conceived as a front end for the

CyberCut Machining Service (Wright, et al 1996). It was intended to be a proof-of-concept on-line process

selection tool, using criteria and experience from various sources such as (Kalpakjian 1997) and (Schey

1987). While it was the first to bring process selection on-line, it had a hard coded database, overly

simplified material selection, ambiguous requirements, and inconsistent process classification. A second

version was planned to fix the problems and extend the scope of the project.

2.2 Groundwork for MAS 2.0

The sequel to the Manufacturing Analysis Service (MAS 1.0) was renamed to the Manufacturing Advisory

Service (or MAS 2.0) to better convey the programme’s conceptual focus. Anywhere ‘MAS’ is used in rest

of this document, it refers to MAS 2.0. Initial work on the sequel determined that a similar searching

scheme - defining requirements and pruning a list of options - could be used to help a designer select

materials as well. (Giachetti 1998) outlined the key elements of a material/process selection tool as decision

and database support. Decision support concerns itself with comparisons of alternatives, while database

support communicates to the user an extensive, up-to-date set of alternatives.

The new MAS’s decision support consists of a multi-layered exploration. The user may enter a bare

minimum of data — just the batch size or the needed linear tolerance — and get initial feedback about the

appropriateness of various manufacturing options. However, should the designer wish to provide more

MAS Draft Page 6

information, they may fill in more requirements. Many of the requirements have an advanced mode to

allow the user to more explicitly define their requirements.

3 Using MAS 2

3.1 Initial Data Entry

The designer selects items from the requirement list, and enters its corresponding value into the data entry

panel, as shown in Figure 1. The coloured ranking bars for the options are updated continuously as the

designer changes the requirements. The history bars, as shown in Figure 2, show the option ranks prior to

the current requirement’s adjustment. While a requirement is adjusted, the current rank bar moves, but the

history bar remains fixed. It provides a reference to the last design state and visually alerts the designer if

the most recent specification makes the part difficult to manufacture.

Figure 1: MAS Process Search

Figure 2: Option Rank Feedback

After the first pass of entering data concerning geometric information, tolerances, and production

requirements, the designer may be disappointed to discover that very few or perhaps even no

manufacturing processes are good candidates to create the component. Clicking on any item in the option

list allows the user to determine why a manufacturing process received its rank.

As an example, perhaps a designer expected stereolithography to be appropriate for a given set of

requirements, but it receives ‘0’ as a rank. Clicking on stereolithography in the option list reveals that

Surface Roughness has a requirement sub-rank, or R-Rank, of zero (See Figure 3). This means that

stereolithography cannot achieve the requested tolerance.

Figure 3: Stereolithography Requirement Ranks

If it is impossible for a process to meet any single requirement, it does not matter how well the rest are

satisfied, the part cannot be made. Thus, when one of the requirement ranks drops to zero, the entire option

rank must also drop to zero. (Otto & Antonsson 1994) refer to this as the ‘annihilation’ rule, and conclude

MAS Draft Page 7

that it is a necessary property of any weighting function, based on imprecise data, used to make engineering

decisions.

At the conclusion of this first scenario, the designer reviews of some of the MAS’s on-line literature

regarding common products and their associated tolerances to discover that the numbers entered into the

MAS were much tighter than needed for the application. This over-specification is a common problem in

design today -- especially among novice designers -- resulting in many expensive, difficult to manufacture

parts.

3.2 Refined Data Entry

Upon relaxing some of the design constraints, the designer is happy to discover that many processes are

suitable for the redesign. The designer switches over to the material search module. This mode is almost the

same as a process search, except that the options list now has materials, and the requirements, such as yield

strength and Young’s modulus, are material properties. After defining a set of desired material attributes,

the user is presented with a ranked list of materials that meet the requirements.

Switching to the Results Survey mode (See Figure 4), the MAS matches all of the candidate manufacturing

processes with compatible candidate materials, and presents the designer a final ranked list of

material/process combinations. The bottom list in Figure 4 shows all of the non-zero rank combinations

possible for a set of requirements. A complete usage scenario is presented in the Appendix.

Figure 4: Results of a Material & Process Search

In fifteen minutes, the designer has a set of feasible production options. The designer can now complete his

or her design using a traditional CAD tool or a process specific CAD package. Process specific software

allows the user to complete the part design in an environment with built-in manufacturing guidelines and

constraints. By guiding the user towards the creation of a manufacturable part, costly redesigns can be

avoided during the ramp up to production. Such CAD tools exist for a variety of processes, including

machining, sheet metal forming, and net shape processes (Smith & Wright 1996; Kim, et al 1999;

Mukherjee & Liu 1997; Chen & Wei 1997).

MAS Draft Page 8

Once the ‘Result Survey’ is complete, a user may follow-up with the MAS’s optional cost estimation or

process chain tools. Process chain generation allows the user to define a rough ‘global’ tolerance, and

match secondary operations to higher tolerance features. If a model exists for the selected process/material

combination, the user may click on the ‘Cost Estimate’ button. The user must then enter additional data for

the part that is specific to the manufacturing process in question. Currently the MAS only implements two

cost estimate modules (injection moulding and sand casting). The difficulty in implementing these

modules is discussed further in the Requirements section.

The MAS uses its process requirements to navigate through a ten dimensional design space, and map the

location to a set of viable processes. A similar space is defined by the seven material requirements for the

material search. Instead of searching through handbooks for meaningful data, the MAS enables a uniform

exploration to find a process that can meet a set of criteria. Moreover, the MAS can vastly improve trade-

off analysis, such as ‘How many more processes become plausible if I loosen dimensional tolerances by

0.020 inches?’ or ‘How much can the surface roughness be improved if productions starts in months

instead of weeks?’

4 MAS Process & Material Requirements

The requirements listed in the MAS share three properties.

• They have a concise, simple representation, such as a number or single selection from a list.

• They are universally applicable to all the manufacturing processes (or materials) in the MAS.

• A deterministic ranking method must exist to map from a user entered value to a numeric rank. Some

of the methods used in the MAS include table lookup and trapezoidal fuzzy numbers (Zadeh 1965).

In order to determine the candidate manufacturing processes, the user may define requirements for the part

and for the process. The part requirements are qualities that apply to the final component regardless of the

number made. The process requirements — batch size, production set-up cost, production set-up time, per

part cost, and production rate — all define business aspects of production: How many, how costly, and how

MAS Draft Page 9

soon? On-line help exists to give designers a quick reference for common material uses and noteworthy

properties.

Table 1 below lists all of the part/process related requirements currently included in the MAS, and Table 2

lists the material requirements.

Table 1: Part/Process Selection Requirements

Table 2: Material Selection Requirements

Note that the requirements listed with an ‘*’ have a special property: They are redundant if the designer

intends on using all three of modules: Process Search, Material Search, and the Results Survey. In this case,

it is not necessary to specify a material in the process search if the designer is also going to be searching the

possible material options as well. Thus, the ‘*’ requirements should only be used in the case where the

designer is using only one of the search modules.

The user selects from one of five cost categories for each of the production cost requirements, from ‘Very

Low’ to ‘Very High’. In order to get a high tolerance part fast, you have to be willing to pay a premium

price. These qualitative groups establish relative costs between the processes. It should be noted that at this

level of abstraction, the MAS de-couples cost from the other requirements.

The cost classification for the processes is based on ‘standard’ parts for the process. In reality, cost is

coupled to every other requirement: faster production costs more, looser tolerances are cheaper, complex

geometries are more expensive, and physically bigger parts require more material. Some processes have

costs driven by properties that other processes can completely ignore. Injection moulding cost, for example,

is strongly affected by undercuts, while stereolithography effectively ignores them. At the conceptual level,

it is unrealistic to expect to capture all of these relationships for all processes. Even if every process

included had a concept level £-value cost estimation scheme, they would be driven by different

requirements. Thus, as the user enters data, some of the processes would suddenly acquire a cost estimate.

This conflicts with the universally applicable criteria described at the beginning of this section. Therefore,

£-value cost estimates were not included as a process selection requirement.

MAS Draft Page 10

4.1 Calculating Option Rankings

The ranking scheme begins with the user-entered requirements. Each possible process (or material) can

then be assigned a requirement rank based upon the requirement’s value. To obtain the ranking for a

process, each of its R-Ranks contributes to a weighting function. The output of this weighting function, the

figure of merit, is used as the final option rank. The MAS repeats this, for every option, each time any

requirement value is changed.

In order to comply with the annihilation rule, a simple geometric mean (Equation 1) is the normal

weighting function used to combine the requirement sub-ranks into the final option rank.

)/1(

121 ),..,(

nn

iiOPTION fR

== ∏

=

υυυ (EQ 1)

Where υi is the ith requirement rank of the n currently defined requirements.

The geometric mean assumes all of the requirements are equally important. However, it is reasonable to

assume that in some design scenarios one aspect of the part will outweigh others in terms of overall

importance. Thus, the MAS provides a mechanism for the designer to selectively rate each of the

requirements with regards to their importance. (Yagers 1978) recommends adjusting the importance of

each term by raising it to the power of a normalised rank. The normalised ranks are found using Equation 2,

where wi is a number (1-5) corresponding to how important the designer holds the ith requirement, with 5

being the most important.

=

∑=

n

ii

ii

w

wr

1

(EQ 2)

Equation 3 is the new weighting function, where υi is the R-Rank for the ith requirement, and ri is the

normalised rank from Equation 2. In the event that all of the requirement weights are equal, Equation 3

simplifies to the geometric mean.

= ∏

=

n

i

riOPTION

iR1

υ (EQ 3)

MAS Draft Page 11

In Results Survey mode, the list of viable pairs is generated by combining the separate material and process

ranked option lists using Equation 4 to give each process/material combination a rank.

3MPMPF CRRR →= (EQ 4)

Where Rp is the option rank of a process, RM is the option rank of a material, RF is the final rank of a

process/material pair, and CP>M is a number between zero and 100 that indicates how well the process can

use the material. For example, the following materials are in order of increasing machinability: titanium,

steel, and aluminium, and their corresponding CP>M would be 50, 80, 100. Some combinations, such as

stainless steel with die casting, have a CP>M of zero. This means it is possible to have viable processes and

viable materials, but no valid pairings. In other words, it is not sufficient to choose a material that meets the

design requirements independently from choosing a process, the process must be able to utilise the

material.

5 Process Options

The MAS supports many common manufacturing process, such as: plastic injection moulding, forging,

sand casting, sheet metal forming, extrusion, milling, die casting, shell mould casting, investment casting,

and EDM. However, since one of the goals of the MAS is to educate a user about new or novel

manufacturing methods, it includes the capabilities of several new or less well-known processes:

• CyberCut Milling Service - CyberCut uses a Java-based 3D programme, WebCAD, to guide the design

process to ensure that parts are manufacturable. Completed geometry is sent to a process planner that

creates a manufacturing plan, which is then executed on a remote milling machine at U.C. Berkeley.

(http://cad.berkeley.edu)

• ME3 Die Casting - A specialised plaster mould process designed by Metalcast Engineering

(www.metalcast.com) that produces parts having die cast material properties, with much lower tooling

cost and lead times. The drawback is lower feasible batch sizes.

• Injection Moulding of Ceramics & Metals - Similar to plastic injection moulding, a molten polymer

and ceramic powder (or metal powder) paste is forced into a mould. The part is removed when the

MAS Draft Page 12

polymer cools, and then can be heated to fuse the ceramic or metal and melt out the polymer. The size

of the part is restricted to small components, and material properties such as yield strength are lowered.

• Slip or Drain Casting - A porous mould is filled with a slip. Slip is a liquid containing a ceramic

material in a water suspension. The slip loses water to the mould and solidifies, conforming to the

mould geometry. The fluid centre is then drained. It’s used to generate common, thin-walled ceramic

parts, such as vases, sinks, bathtubs, and toilets.

6 Supporting the MAS

Samples from the extensive on-line help ‘manual’ are shown in Figure 5. A step by step tutorial provides

MAS instructions to users who are not familiar with manufacturing terms. Descriptions and sample values

are given for each of the process and material requirements - allowing users to compare their tolerancing

values with common products. Each of the processes included in the MAS has a set of descriptive web

pages. The information includes production numbers, shape capabilities, design rules, sample parts,

material usage notes, pros/cons, related processes, and links to equipment suppliers and fabrication sites.

All of the documentation is linked through the applet itself. The designer may select any process, material,

or requirement, and click the ‘Get Info’ button to call up an informational web page.

Figure 5: Supporting Documentation

One of the criteria used for the development of MAS 2.0 was to create a repository of manufacturing data

separate from the code for the main programme. This remote process capability database contains

information about the MAS’s component processes, materials, and vendors. Microsoft Access was selected

to develop the relational database. Opening the database on the server brings up a menu of tools:

• Vendor Editor - Provides the account management for companies that have processes in the MAS.

• Material Editor - Edit the properties of the generic raw materials

• File Exporter - Generates MAS data files and human readable reports

• Process Editor - Specifies the performance of all processes, compatibilities with materials, and

locations of on-line resources.

MAS Draft Page 13

These tools can add a new process or material to the MAS without any changes to the compiled code.

Active Server Pages were chosen as the simplest method for implementing remote vendor access to content

in the MAS database.

7 Conclusions

The MAS is currently used in product development and manufacturing courses at Berkeley, and the

University of Illinois at Urbana-Champaign. It uses the Internet to bring together engineering reference

material and an informative educational tool.

The Java-based MAS provides a dynamic environment for educating designers about the trade-offs

available in different manufacturing processes. The tool can be used for simple single-parameter searches

to answer questions such as ‘Which processes can produce a 4 microinch surface finish?’ It can also be

used for process/material combined searches with secondary processes mapped to high-tolerance features.

The rankings change in real time with user input — without the need to query a database for results. The

sub-ranks for each material and process option are easily accessible, allowing the user to quickly determine

which requirements in an emerging design are resulting in unexpected rankings. Colour coded graphic

feedback provides the user with instant visual cues regarding overspecification.

The underlying MAS database is extensible through a set of administrator tools, or via the Web — which

gives commercial manufacturing facilities the ability to update their own processes. The current version of

the Manufacturing Advisory Service can be found at:

http://CyberCut.berkeley.edu/mas2/

8 Acknowledgments

This article concludes the work initially described in (Brown and Wright 1998). Thanks are extended to the

CyberCut project team, and the National Science Foundation for funding the research. Simon Brown and

the students from numerous classes at U.C. Berkeley are also acknowledged.

MAS Draft Page 14

9 Appendix: MAS Trash Can

This example presents the use of the MAS to explore the possibilities for making a production run garbage

can. The following specifications were made in the process:

• Shape = Thinwall (many consumer items fall into this category)

• Dimensional Tolerance = 75 mils

• Surface Roughness = 250 microinches

• Set-up Cost = High

• Set-up Time = Months

• Bounding Box = 2 ft. x 2 ft. x 4 ft. is approximately 27000 cubic inches. That specification eliminated

many processes.

• Batch size = 100000

• Advanced Wall Thickness = max/min wall of 0.15 inches, web span of 24’ (the diameter of the trash

can)

• Per Part Cost = Very Low. The search was for the lowest possible cost over a long production cycle.

At the end of the process search, Injections Moulding (rank 100) was ahead of the only other possibility:

Sheet Metal Forming (rank 92). After clicking on ‘Material Search’ the following requirement values were

entered:

• Yield Strength = 10 KSI. The trash cans were not intended for industrial applications.

Stereolithography photopolymers were the only material affected by this choice.

• Density to 210 lbm / ft.3 - We want the cans to be relatively light.

• Cost Per Pound = £ 2.75 / lb.

• Elastic Modulus = 0.5 x 106 psi

MAS Draft Page 15

This resulted in five viable materials, with aluminium ranking the highest at 100. The ‘'Results Survey’

button was clicked to combine the results of both searches to find the best material/process combination.

The two boxes at the top of Figure 6 were a summary of the viable materials and processes. The final box

(‘Ranked Combos’) listed all of the feasible combinations, taking into account a compatibility factor

between each process and material. Sheet metal forming with aluminium was the best choice, with injection

moulded thermoplastic second.

Figure 6: Final Trash Can Results

10 References

(AMPTIAC 1997) The AMPTIAC Newsletter, MaterialEASE insert, Advanced Materials and Processes

Technology, - a DoD Information Analysis Center, Volume 1, Number 3, 3rd Quarter 1997.

(Bock 1991) Bock, L. ‘Material Process Selection Methodology: Design for Manufacturing and Cost Using

Logic Programming,’ Cost Engineering, Vol. 33, Num. 5, May 1991, pp. 9-14.

(Boothrooyd, et al 1994) Boothroyd, G., Dewhurst, P, Knight, W., Product Design for Manufacture and

Assembly, Marcel Dekker, Inc., New York, 1994.

(Brown & Wright 1998) Brown, S.M., Wright, P.K., ‘A Progress Report on the Manufacturing Analysis

Service,’ Journal of Manufacturing Systems, Volume 17, Number 5, pp. 389-398.

(Chen & Wei 1997) Chen, Y., Wei, C., ‘Computer-Aided Feature-Based Design for net Shape

Manufacturing,’ Computer Integrated Manufacturing Systems, Volume 10, Number 2, pp. 147-164, 1997.

(Esawi & Ashby 1998) Esawi, A.M.K., Ashby, M.F., ‘The Development and Use of a Software Tool for

Selecting Manufacturing Processes at the Early Stages of Design,’ Proceedings of the Third Biennial World

Conference on Integrated Design and Process Technology, Berlin, Germany, July 1998, volume 3, pp. 210-

217.

(Farris & Knight 1991) Farris, J., Knight, W.A., ‘Selecting Sequences of Process and Material

Combinations for Part Manufacture,’ Proceedings of the International Forum of Design for Manufacture

and Assembly, Newport, R.I., June 10-11, 1991.

MAS Draft Page 16

(Frost & Cutkosky 1996) Frost, H.R., Cutkosky, M., ‘Design for Manufacturability via Agent Interaction,’

1996 ASME Design for Manufacturing Conference, Irvine, CA August 18-26.

(Giachetti 1998) Giachetti, R.E., ‘A Decision Support System for Material and Manufacturing Process

Selection,’ Journal of Intelligent Manufacturing, Volume 9, Issue 3, 1998, pp. 265-276.

(Giachetti & Jurrens 1997) Giachetti, R.E., Jurrens, K.K., ‘Manufacturing Evaluation of Designs: A

Knowledge-Based Approach,’ Proceedings of the Third Joint Conference on Information Sciences (JCIS),

Research Triangle Park, NC March 1-5 1997, vol. 1, p194-197.

(Granta 2001) Distributors of Cambridge Engineering Selector: Granta Design Limited, Rustat House, 62

Clifton Road, Cambridge CB1 7EGUK, http://www.granta.co.uk/

(Kalpakjian 1991) Kalpakjian, S., Manufacturing Processes for Engineering Materials - 2nd Edition,

Addison-Wesley, 1997.

(Kim , et al 1999) Kim, J.H., Wang, F., Sequin, C.H., and Wright, P.K., ‘Design for Machining over the

Internet,’ submitted to the 1999 ASME Design Engineering Technical Conference, Las Vegas, NV.

(Kunchithapatham 1996) Kunchithapatham, A., A Manufacturing Process and Materials Design Advisor,

M. S. Thesis.

(Mukherjee & Liu 1997) Mukherjee, A., Liu, C.R., ‘Conceptual Design, Manufacturability Evaluation and

Preliminary Process Planning Using Function-Form Relationships in Stamped Metal Parts,’ Robotics &

Computer manufacturing, Vol. 13, No. 3., pp. 253-270, 1997.

(Schey 1987) Schey, J.A., Introduction to Manufacturing Processes, Second Edition, McGraw-Hill, Inc.,

New York, 1987.

(Smith & Wright 1996) Smith, C.S., Wright, P.K., ‘CyberCut: A World Wide Web Based Design-to-

Fabrication Tool,’ Journal of Manufacturing Systems, Volume 15, Number 6, 1996, pp. 432-442.

(Wright, et al 1996) Wright, P.K. Sheng, P., Pister, K.J., Goldberg, K.Y., Sastry, S.S., ‘CyberCut: A

Networked Manufacturing Service,’ NSF Award #9617995, 1996-2000.

MAS Draft Page 17

(Yagers 1978) Yagers R.R., ‘Fuzzy Decision Making Including Unequal Objectives,’ Fuzzy Sets and

Systems, Volume 1, 1978, pp. 87-95.

(Zadeh 1965) Zadeh, L.A., ‘Fuzzy Sets,’ Information and Control, volume 8, 1965, pp. 338-353.

11 Author’s Biographies

Charles S. Smith, a National Science Foundation Fellowship recipient, is currently a lecturer at the

University of California at Berkeley, where he received his Ph.D. in mechanical engineering. He has

worked for Lawrence Berkeley Labs on the open architecture machine tool controllers, and researched

distributed manufacturing, CAD/CAPP/CAM integration, and Web based engineering tools for the

Integrated Manufacturing Lab (http://kingkong.me.berkeley.edu). Current interests include design, product

development, and manufacturing applications of the Internet.

Paul Kenneth Wright's research work and teaching have focussed on manufacturing processing operations,

robotics, expert systems, open-architecture control, rapid prototyping, Internet-based CAD/CAM, the

management of technology and distance learning. He has written three books: 21st Century Manufacturing,

Metal Cutting (with Edward Trent), and Manufacturing Intelligence (with David Bourne). He was born in

London, educated at Birmingham University and has held positions at Cambridge University, Auckland

University in New Zealand, Carnegie Mellon University in Pittsburgh, and the Courant Institute of

Mathematical Sciences in New York. He is presently the A. Martin Berlin Professor of Mechanical

Engineering, the Co-Chair of the Management of Technology Program, and the Associate Dean of Distance

Learning at The University of California, Berkeley.

Calro H. Séquin is a professor of Computer Science at the University of California, Berkeley. He received

his Ph.D degree in experimental physics from the University of Basel, Switzerland in 1969. In 1977 he

joined the faculty in the EECS Department at Berkeley. In the early 1980's, jointly with D. Patterson he

introduced the `RISC' concept to the world of microcomputers. He was head of the Computer Science

Division from 1980 till 1983. Since then he has concentrated on computer graphics, geometric modelling,

and on the development of computer aided design (CAD) tools for circuit designers, architects, and

MAS Draft Page 18

mechanical engineers. Dr. Séquin is a Fellow of the ACM, a Fellow of the IEEE, and has been elected to

the Swiss Academy of Engineering Sciences.

MAS Draft Page 19

TABLE 1: Part/Process Selection Requirements

Typical Batch Size Cost: Set-upDimensional Tolerances Cost: Per PartSurface Roughness Time: Set-upGeometry Shape Time: Per PartBounding Box Volume Wall ThicknessMaterial Compatibility*

MAS Draft Page 20

TABLE 2: Material Selection Requirements

Yield Strength Cost Per PoundDensity Elastic ModulusThermal Expansion HardnessProcess Compatibility*

MAS Draft Page 21

Figure 1: MAS Process Search

Figure 2: Option Rank Feedback

Figure 3: Stereolithography Requirement Ranks

Figure 4: Results of a Material & Process Search

Figure 5: Supporting Documentation

Figure 6: Final Trash Can Results