Frontiers of Engineering：Reports on Leading Edge Engineering from the 1996 NAE Symposium on Frontiers of Engineering（SECOND ANNUAL SYMPOSIUM ON FRONTIERS OF ENGINEERING）

ng: Reports on Leading Edge Engineering from the 1996 NAE Symposium on Frontiers of Enginee http://www.nap.edu/catalog/5576.html Information in the Design Proces ALICE M. AGOGINo Department of Mechanical Engineering University of california at Berkeley Intelligent Real Time Design(IRTD) is a framework that explicitly con- siders the cost of resources consumed during the design process(e. g, the value of the design teams time; the cost of further information gathering analyses, or experimentation; and"time-to-market"opportunity costs) and trades these off against the optimum of the design solution found. Such a process dynamically takes into account the"real-time"aspect of the design process. IRTD helps designers focus their information-gathering efforts on reducing uncertainty in areas of the design that have the greatest impact on the design objectives(Bradley, 1993; Bradley and Agogino, 1991, 1992, 1993, 1994) Uncertainty and ambiguity are greatest in the early conceptual stages of design, where the greatest potential for design improvement lies. Sources of uncertainty can be found in the design constraints, objectives, and parameters; evaluation models; and customer preferences. IRTD provides a prescriptive methodology for transforming the design space over the course of the design process. The results of an IRTD analysis are a set of expected costs associ- ated with the uncertainty in each design decision. The designer must decide whether the impact of reducing uncertainty--by updating preference func- tions, design models, or design parametersis worth the cost of information gathering(Wood, 1996; Wood and Agogino, 1996a) The fundamental theory behind IRTD is the decision-analytic notion of he expected value of information(EVI). a design decision must be formu lated as a nonlinear programming problem, with both objectives and con- straints, as well as probability measures on the uncertain variables or param eters. EVI is a relatively simple concept, representing the expectation of the Copyright National Academy of Sciences. All rights reserved

13 Intelligent Real Time Design (IRTD) is a framework that explicitly con￾siders the cost of resources consumed during the design process (e.g., the value of the design team’s time; the cost of further information gathering, analyses, or experimentation; and “time-to-market” opportunity costs) and trades these off against the optimum of the design solution found. Such a process dynamically takes into account the “real-time” aspect of the design process. IRTD helps designers focus their information-gathering efforts on reducing uncertainty in areas of the design that have the greatest impact on the design objectives (Bradley, 1993; Bradley and Agogino, 1991, 1992, 1993, 1994). Uncertainty and ambiguity are greatest in the early conceptual stages of design, where the greatest potential for design improvement lies. Sources of uncertainty can be found in the design constraints, objectives, and parameters; evaluation models; and customer preferences. IRTD provides a prescriptive methodology for transforming the design space over the course of the design process. The results of an IRTD analysis are a set of expected costs associ￾ated with the uncertainty in each design decision. The designer must decide whether the impact of reducing uncertainty—by updating preference func￾tions, design models, or design parameters—is worth the cost of information gathering (Wood, 1996; Wood and Agogino, 1996a). The fundamental theory behind IRTD is the decision-analytic notion of the expected value of information (EVI). A design decision must be formu￾lated as a nonlinear programming problem, with both objectives and con￾straints, as well as probability measures on the uncertain variables or param￾eters. EVI is a relatively simple concept, representing the expectation of the Information in the Design Process ALICE M. AGOGINO Department of Mechanical Engineering University of California at Berkeley Copyright © National Academy of Sciences. All rights reserved. Frontiers of Engineering: Reports on Leading Edge Engineering from the 1996 NAE Symposium on Frontiers of Engineering http://www.nap.edu/catalog/5576.html

httpverw.apedeecantaioge5676.mi rts on Leading Edge Engineering from the 1996 NAE Symposium on Frontiers of Enginee DESIGN RESEARCH objective function over the uncertain variables with the new information, mi nus the expected value of the objective with the current state of information. Definition: Expected Value of Information(EVi) is the expected value of the objective function with the new information expected value of the objective given the present state of information In equation form(Wood, 1996): it is where v, is an uncertain design variable obj is the value of the objective function dec* is the current(best) decision dec, is one of the set of possible decisions, c is the constraint vector. and P(v c) is the probability of the value of the design variable and con straint set Given the mathematical basis for the IRTD method, one might question its usefulness in conceptual design, where analytical models often are limited to those that can be written on the"back of an envelope. Although the envelope"in todays world of information technology is more likely to be an e-mail message, a document written on a word processor, a notation in a design database, a CAD drawing, or a sketch in a graphics program, the information in conceptual design is not likely to be in a form convenient for applying IRTD Research on case-based reasoning, " data mining, "and information re trieval(Dong and Agogino, 1996; Dong et al., 1995: Varma et al., 1996 Wood, 1996; Wood and Agogino, 1996a) is providing the tools needed for exploiting this structured and unstructured design information. These algo- rithms and techniques provide the theoretical engine for the UC Berkeley Concept Database Project (Bradley et al., 1994), aimed at supporting the conceptual design process by (1) identifying good prototype designs, (2)ob taining or deriving engineering models and uncertainty measures associated with the models, and (3)evaluating the value of reducing the uncertainty in the model(applying IRTD). The Concept Database methodology provides"smart navigation"through a hypermedia database of linked design concepts. Such a system expands the Copyright National Academy of Sciences. All rights reserved

14 DESIGN RESEARCH objective function over the uncertain variables with the new information, mi￾nus the expected value of the objective with the current state of information. Definition: Expected Value of Information (EVI) is the expected value of the objective function with the new information minus expected value of the objective given the present state of information In equation form (Wood, 1996): it is EVI ( ) max v | , , | *, , , i E obj dec v E obj dec v P v dv j ij j j j v j = ( ) [ ] − [ ]       ∫ c cc ( ) where vj is an uncertain design variable, obj is the value of the objective function, dec* is the current (best) decision, decj is one of the set of possible decisions, c is the constraint vector, and P(vj , c) is the probability of the value of the design variable and con￾straint set. Given the mathematical basis for the IRTD method, one might question its usefulness in conceptual design, where analytical models often are limited to those that can be written on the “back of an envelope.” Although the “envelope” in today’s world of information technology is more likely to be an e-mail message, a document written on a word processor, a notation in a design database, a CAD drawing, or a sketch in a graphics program, the information in conceptual design is not likely to be in a form convenient for applying IRTD. Research on case-based reasoning, “data mining,” and information re￾trieval (Dong and Agogino, 1996; Dong et al., 1995; Varma et al., 1996; Wood, 1996; Wood and Agogino, 1996a) is providing the tools needed for exploiting this structured and unstructured design information. These algo￾rithms and techniques provide the theoretical engine for the UC Berkeley Concept Database Project (Bradley et al., 1994), aimed at supporting the conceptual design process by (1) identifying good prototype designs, (2) ob￾taining or deriving engineering models and uncertainty measures associated with the models, and (3) evaluating the value of reducing the uncertainty in the model (applying IRTD). The Concept Database methodology provides “smart navigation” through a hypermedia database of linked design concepts. Such a system expands the Copyright © National Academy of Sciences. All rights reserved. Frontiers of Engineering: Reports on Leading Edge Engineering from the 1996 NAE Symposium on Frontiers of Engineering http://www.nap.edu/catalog/5576.html

ng: Reports on Leading Edge Engineering from the 1996 NAE Symposium on Frontiers of Enginee http://www.nap.edu/catalog/5576.html Information in the Design Process amount of information available to the designer and other members of the product development team in a manner that is selective, using search tech niques that are based on heuristic, deterministic, and decision-analytic meth ods. Prescriptive design methodologies are used to develop the structure of the search in order to guarantee that the full range of life-cycle issues is considered and to provide a framework for making design decisions that take into account multiple and often conflicting life-cycle objectives Mechatronic design, the domain of our first prototype system, is an area of key strategic importance, playing an important role in the design of con sumer products, computer peripherals, automation systems, and aerospace and defense systems. It is also an area where off-the-shelf component selection is essential and is one in which expertise crosses departmental boundaries: elec tronic design, mechanical design, interface design, supplier coordination, pack aging, logistics, and support all are involved in the realization of a good mechatronic system design. Research from the Concept Database Project has been integrated also into the curricular reform efforts of the NSF-funded Synthesis Engineering Educa- tion Coalition, providing tools for developing educational case studies of en gineering design and a database for archiving, searching, and retrieving engi- neering courseware(Agogino and Wood, 1994; Wood and Agogino, 1996b) A CKNOWLEDGMENTS I wish to acknowledge the team of students i have worked with over the past 5 years, who have contributed to various aspects of this research, as listed in the References below. Steve Bradley was the first to suggest that previous work in using information value theory in diagnostics and superv sory control might be modified effectively for use in real-time design environ- ments, and he developed the first IRTD framework. Bill Wood brought in the notions of case-based reasoning, data mining, and information retrieval. Andy Dong continued this work by developing text analysis algorithms for con structing design representations for"smart drawing"documentation and con cept retrieval. Anil Varma is extending this work to include neural network approaches to automated design classification, association, and retrieval REFERENCES Agogino, A. M. and w.H. Wood Ill. 1994. The Synthesis coalition: Information technolog tabling a paradigm shift in engineering education. (Keynote Address). Pp. 3-10 in Hyper-Media in Vaasa 94: Proceedings of the Conference on Computers and Hypermedia in Engineering Education. M. Linna and P. Ruotsala, eds. Vaasa, Finland: Vaasa Institute f Technology ) Bradley, S.R. 1993. Design optimization under resource constraints. Ph. D. dissertation. Uni versity of California at Berkeley. Copyright National Academy of Sciences. All rights reserved

Information in the Design Process 15 amount of information available to the designer and other members of the product development team in a manner that is selective, using search tech￾niques that are based on heuristic, deterministic, and decision-analytic meth￾ods. Prescriptive design methodologies are used to develop the structure of the search in order to guarantee that the full range of life-cycle issues is considered and to provide a framework for making design decisions that take into account multiple and often conflicting life-cycle objectives. Mechatronic design, the domain of our first prototype system, is an area of key strategic importance, playing an important role in the design of con￾sumer products, computer peripherals, automation systems, and aerospace and defense systems. It is also an area where off-the-shelf component selection is essential and is one in which expertise crosses departmental boundaries: elec￾tronic design, mechanical design, interface design, supplier coordination, pack￾aging, logistics, and support all are involved in the realization of a good mechatronic system design. Research from the Concept Database Project has been integrated also into the curricular reform efforts of the NSF-funded Synthesis Engineering Educa￾tion Coalition, providing tools for developing educational case studies of en￾gineering design and a database for archiving, searching, and retrieving engi￾neering courseware (Agogino and Wood, 1994; Wood and Agogino,1996b). ACKNOWLEDGMENTS I wish to acknowledge the team of students I have worked with over the past 5 years, who have contributed to various aspects of this research, as listed in the References below. Steve Bradley was the first to suggest that my previous work in using information value theory in diagnostics and supervi￾sory control might be modified effectively for use in real-time design environ￾ments, and he developed the first IRTD framework. Bill Wood brought in the notions of case-based reasoning, data mining, and information retrieval. Andy Dong continued this work by developing text analysis algorithms for con￾structing design representations for “smart drawing” documentation and con￾cept retrieval. Anil Varma is extending this work to include neural network approaches to automated design classification, association, and retrieval. REFERENCES Agogino, A. M., and W. H. Wood III. 1994. The Synthesis coalition: Information technologies enabling a paradigm shift in engineering education. (Keynote Address). Pp. 3–10 in Hyper-Media in Vaasa ’94: Proceedings of the Conference on Computers and Hypermedia in Engineering Education, M. Linna and P. Ruotsala, eds. Vaasa, Finland: Vaasa Institute of Technology). Bradley, S. R. 1993. Design optimization under resource constraints. Ph.D. dissertation. Uni￾versity of California at Berkeley. Copyright © National Academy of Sciences. All rights reserved. Frontiers of Engineering: Reports on Leading Edge Engineering from the 1996 NAE Symposium on Frontiers of Engineering http://www.nap.edu/catalog/5576.html

ng: Reports on Leading Edge Engineering from the 1996 NAE Symposium on Frontiers of Enginee http://www.nap.edu/catalog/5576.html 16 DESIGN RESEARCH Bradley, S.R., and A M. Agogino. 199 ligent real time Application to prototype selection. Pp. 815-837 in Artificial Intelligence in Desi J. S. Gero. ed. Oxford England: Butterworth-Heinemann Publishers Bradley, S.R., and A M. Agogino. 1992. Optimal design as a real time Al problem. Pp 638 in System Modeling and Optimization, P. Kall, ed. Lecture 22 Information Sciences 180. New York: Springer-Verlag Bradley, S.R., and A. M. Agogino. 1993. Computer-assisted catalog selection with multiple objectives. Pp. 139-147 in Proceedings of the ASME 1993 Design Theory and Methods Conference Bradley, S.R., and A. M. Agogino. 1994. An intelligent real time design methodology for catalog selection. ASME Journal of Mechanical Design 116: 980-988 Bradley, S.R., A. M. Agogino, and w.H. Wood Ill. 1994. Intelligent engineering componen catalogs. Pp. 641-658 in Al in Design 94, J. S. Gero and F. Sudweeks, eds. Norwell, Mass.:KluwerAcademicPublishing.Url:http://hart.Me.Berkeley.Edu/-best/papers/ AlD94_p AID94. html Dong, A, and A M. Agogino. 1996. Text analysis for constructing design representations. Pp 21-38 in Artificial Intelligence in Design 96. J.S. Gero and F. Sudweeks, eds. Norwell. Mass. Kluwer Academic Publishers Dong, A. F. Moore, C. Woods and A. M. Agogino. 1995. Managing design knowledge in nterprise-wide CAD. Pp 329-347 in Advances in Formal Design Methods for CAD, J S Gero and F. Sudweeks, eds. Preprints of the IFIP WG 5.2 Workshop on Formal Design Methods for CAD. Sydney: Key Centre of Design Computing, University of Sydney. Varma, A.,A. M. Agogino, and w. H. Wood Ill. 1996. A machine learning approach to automated design classification, association and retrieval. Pp. 429-445 in Artificial Intelli ence in Design 96, J S Gero and F. Sudweeks, eds. Norwell, Mass.: Kluwer Academic Publishers Wood Ill, .H. 1996. Supplying concurrent engineering information to the designer: The conceptual design information server. Ph. D. dissertation. University of California at Berke- Wood Ill, w.H., and A M. Agogino. 1996a. A case-based conceptual design information server forconcurrentengineeringComputer-aidedDesign(cad)28(5):361-369.Url:http:// art. ME. Berkeley. EDU/-best/papers/CAD_paper/CAD. html Wood Ill, W.H., and A M. Agogino. 1996b. Engineering courseware content and delivery: The NEEDS infrastructure for distance-independent education. Journal of the American Society for Information Science 47(11): 863-869 Copyright National Academy of Sciences. All rights reserved

16 DESIGN RESEARCH Bradley, S. R., and A. M. Agogino. 1991. Intelligent real time design: Application to prototype selection. Pp. 815–837 in Artificial Intelligence in Design ’91, J. S. Gero, ed. Oxford, England: Butterworth-Heinemann Publishers. Bradley, S. R., and A. M. Agogino. 1992. Optimal design as a real time AI problem. Pp. 629– 638 in System Modeling and Optimization, P. Kall, ed. Lecture Notes in Control and Information Sciences 180. New York: Springer-Verlag. Bradley, S. R., and A. M. Agogino. 1993. Computer-assisted catalog selection with multiple objectives. Pp. 139–147 in Proceedings of the ASME 1993 Design Theory and Methods Conference. Bradley, S. R., and A. M. Agogino. 1994. An intelligent real time design methodology for catalog selection. ASME Journal of Mechanical Design 116:980–988. Bradley, S. R., A. M. Agogino, and W. H. Wood III. 1994. Intelligent engineering component catalogs. Pp. 641–658 in AI in Design ’94, J. S. Gero and F. Sudweeks, eds. Norwell, Mass.: Kluwer Academic Publishing. URL: http://hart.ME.Berkeley.EDU/~best/papers/ AID94_paper/AID94.html Dong, A., and A. M. Agogino. 1996. Text analysis for constructing design representations. Pp. 21–38 in Artificial Intelligence in Design ’96, J. S. Gero and F. Sudweeks, eds. Norwell, Mass.: Kluwer Academic Publishers. Dong, A., F. Moore, C. Woods, and A. M. Agogino. 1995. Managing design knowledge in enterprise-wide CAD. Pp. 329–347 in Advances in Formal Design Methods for CAD, J. S. Gero and F. Sudweeks, eds. Preprints of the IFIP WG 5.2 Workshop on Formal Design Methods for CAD. Sydney: Key Centre of Design Computing, University of Sydney. Varma, A., A. M. Agogino, and W. H. Wood III. 1996. A machine learning approach to automated design classification, association and retrieval. Pp. 429–445 in Artificial Intelli￾gence in Design ’96, J. S. Gero and F. Sudweeks, eds. Norwell, Mass.: Kluwer Academic Publishers. Wood III, W. H. 1996. Supplying concurrent engineering information to the designer: The conceptual design information server. Ph.D. dissertation. University of California at Berke￾ley. Wood III, W. H., and A. M. Agogino. 1996a. A case-based conceptual design information server for concurrent engineering. Computer-Aided Design (CAD) 28(5):361–369. URL: http:// hart.ME.Berkeley.EDU/~best/papers/CAD_paper/CAD.html Wood III, W. H., and A. M. Agogino. 1996b. Engineering courseware content and delivery: The NEEDS infrastructure for distance-independent education. Journal of the American Society for Information Science 47(11):863–869. Copyright © National Academy of Sciences. All rights reserved. Frontiers of Engineering: Reports on Leading Edge Engineering from the 1996 NAE Symposium on Frontiers of Engineering http://www.nap.edu/catalog/5576.html

ng: Reports on Leading Edge Engineering from the 1996 NAE Symposium on Frontiers of Enginee http://www.nap.edu/catalog/5576.html Product modularity: a Key Concept in Life-Cycle design KOSUKE ISHI Department of Mechanical Engineering Stanford University Stanford, California The goal of life-cycle design is to maximize the values of the acturer's line of products while containing the products'costs to the manu facturer, the user, and society(Figure 1). Engineers must consider perfor- mance,cost, and any environmental impact of their designs. Our research develops systematic methodologies that apply to the early stages of product development in integrating life-cycle quality (Ishii, 1995). We address not simply one product but entire product families and changes over product generations I focus here on the concept of product modularity: a key concept in achieving life-cycle quality. Modularity is particularly important for electro- mechanical products, such as computers, telecommunication devices, and pe ripherals. The short technology life-cycle of many of the functions in these products, combined with customer demand for a wide variety of features, necessitates that designers optimize the modularity of components and subas semblies for manufacturability and serviceability. More recently, recyclability of durable products has also become an important consideration. For the past several years we at Stanford University have been developing evaluation metrics of modular designs from different perspectives. Such metrics should lead to design charts that enable engineers to achieve modules with an optimum life- cycle balance. Our current research focuses on three perspectives that drive decisions on modularity. The first perspective is manufacturability. Engineers must ad dress not only one product but the entire product family. They must design he product line in such a way that it has maximum market coverage; at the same time its modularity minimizes the cost of providing variety. This is Copyright National Academy of Sciences. All rights reserved

17 The goal of life-cycle design is to maximize the values of the manu￾facturer’s line of products while containing the products’ costs to the manu￾facturer, the user, and society (Figure 1). Engineers must consider perfor￾mance, cost, and any environmental impact of their designs. Our research develops systematic methodologies that apply to the early stages of product development in integrating life-cycle quality (Ishii, 1995). We address not simply one product but entire product families and changes over product generations. I focus here on the concept of product modularity: a key concept in achieving life-cycle quality. Modularity is particularly important for electro￾mechanical products, such as computers, telecommunication devices, and pe￾ripherals. The short technology life-cycle of many of the functions in these products, combined with customer demand for a wide variety of features, necessitates that designers optimize the modularity of components and subas￾semblies for manufacturability and serviceability. More recently, recyclability of durable products has also become an important consideration. For the past several years we at Stanford University have been developing evaluation metrics of modular designs from different perspectives. Such metrics should lead to design charts that enable engineers to achieve modules with an optimum life￾cycle balance. Our current research focuses on three perspectives that drive decisions on modularity. The first perspective is manufacturability. Engineers must ad￾dress not only one product but the entire product family. They must design the product line in such a way that it has maximum market coverage; at the same time its modularity minimizes the cost of providing variety. This is Product Modularity: A Key Concept in Life-Cycle Design KOSUKE ISHII Department of Mechanical Engineering Stanford University Stanford, California Copyright © National Academy of Sciences. All rights reserved. Frontiers of Engineering: Reports on Leading Edge Engineering from the 1996 NAE Symposium on Frontiers of Engineering http://www.nap.edu/catalog/5576.html