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1 Introduction The selection of the proper materials for a structural component is critical to engineering design. Existing design procedures may currently be sufficient, especially where experience exists, but fierce industrial competition is spurring the search for improved methods and tools. The main drivers are quality, life-cycle cost, and time-to-market. Improved design efficiency and accuracy may have an enormous impact on the economic viability of the final product. Materials selection is governed by many factors, some of which are often in opposition. The principal selection factors include the service requirements and design life of the product; the availability of candidate materials and the appropriate data on application-specific properties for them; the company's make or buy decision for the system components; the customer preferences; and most importantly, the total life-cycle cost. BENEFITS Many designs initially fail because of a lack of relevant experience or because the design team did not include appropriate experts who "could have told us so." In the end, the rework associated with design requalification significantly increases cost and time-to-market. Thus, the use of computer-aided systems that assist design teams could potentially reduce product cost and time-to-market. Computer-aided systems for materials selection could assist concurrent engineering activities by helping to resolve many of the materials dilemmas presented during the initial design phase and by helping to guide the selection process based on the data and experience compiled from previous product development. Advanced computer technologies would also make it feasible to archive design experience as cases in a corporate knowledge base for subsequent re-use, tailoring, and evolution. The development of a computer-aided system to support materials selection could also accelerate the general acceptance of new materials and processing technologies. The quality and efficiency of the materials selection process would be enhanced by increasing access to knowledge of factors such as materials options and life-cycle costs. For instance, designers could be provided with a range of possible materials and manufacturing methods for a proposed part, based on a given set of characteristics and cost-performance criteria. Thus, the members of the design team would not be totally reliant on their own personal experience and limited design-handbook information during the materials selection process but would have access to information on promising new materials and processing technologies that could be exploited. Computing technology is no longer a barrier to the development of computer-aided systems for materials selection. Advances in reduced instruction set computer (RISC) chip technology already allow high-performance, inexpensive workstations to perform design layout, structural analysis, and materials processing simulation. It was generally believed in the early 1980s that the use of advanced modeling techniques, such as three-dimensional modeling, was not practical because of the large amount of computer time required for analytical simulations. Since then, computer speeds have dramatically increased. Accurate modeling and simulation of a unit process is currently becoming the norm. A range of new computer products are now available that enable the development of computer-aided systems for materials selection: high-performance microcomputers; high-performance workstations (minicomputers); workstation clusters; RISC parallel systems (e.g., 16 computer processing units); mainframe-workstation networks; vector supercomputing; and massively parallel computing. The wide range of hardware capabilities will soon bring the cost of implementing computer-aided system logic and process simulation within affordable limits. The
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continued evolution of cost-effective, high-performance computing in conjunction with a national information superhighway infrastructure will further assist the nation's manufacturing sector in becoming more competitive in the international marketplace. DEFINITIONS Computer-aided systems are broadly interpreted in this report as advanced computing technologies that access various modules to provide specific information when requested by user input. A computer-aided system has three primary elements: (1) an interface with the user, (2) a reasoning element that triggers system action, and (3) a knowledge element in the form of databases, knowledge bases, and modeling modules that provide the information and analyses to be applied. Computer-aided systems for materials selection in design will contain only a subset of the total product knowledge applied during design. Knowledge representation is the form in which facts and relationships are encoded and stored in the knowledge component of a computer-aided system. Knowledge representation serves five distinct roles. First, knowledge representation is a surrogate for knowledge. Second, it serves a set of ontological commitments or terms with which a computer-aided system can reason. Third, it is a partial theory of intelligence that expresses the fundamental concept of intelligence, the inferences that are possible, and the inferences that are made. Fourth, it is a medium for pragmatic computations. Fifth, it is a medium for human expression (Davis et al., 1993). Concurrent engineering is "a systematic approach to the integrated, concurrent design of products and their related processes, including manufacture and support, [that] is intended to cause the developers, from the outset, to consider all elements of the product life-cycle from conception through disposal, including quality, cost, schedule, and user requirements" (Winner et al., 1988). The process of conducting design tradeoffs can be done sequentially or in parallel. In this report, the committee focuses on computer-aided systems that can support a design process in which design decisions are made in parallel, or concurrently, by several members of a design team. When the design team contains members that have access to all knowledge pertinent to the creation of the product, its use, and its ultimate retirement, that team is called an "integrated product-development team" (IPDT). To practice concurrent engineering effectively, all knowledge related to the manufacture of a component and its maintenance in a delivered system must be available to the IPDT. Life-cycle data and experience knowledge is thus an important prerequisite for the full application of computer-aided systems in which design choices are evaluated. Because of their importance, the establishment of life-cycle databases is now required by the major Department of Defense initiative on Computer-Aided Acquisition and Logistics Support.1 STUDY OBJECTIVES AND SCOPE This study concentrates on the materials-specific knowledge elements of a computer-aided system. The Committee on the Application of Expert Systems to Materials Selection during Structural design determined that the development of generic computer-aided systems is already receiving a great deal of attention within the computer science community. The basic information requirements for a computer-aided system for materials selection are receiving little attention within the materials community, however. Thus, the committee assessed that this study would have the greatest impact if it (1) detailed the capabilities required for computer-aided systems to be of value to the materials selection process during concurrent engineering, (2) identified the issues inhibiting the development of such a system, and (3) recommended materials-specific applications and developments in database, knowledge base, and materials modeling that would aid the production of a knowledge element appropriate for computer-aided systems for materials selection during design. During the study, the committee examined engineering-related design decisions involving geometry or spatial relationships to determine how design rules incorporated materials data and how assessments of performance, 1 Computer-Aided Acquisition and Logistics Support is a three-phase program that requires: (1) the adherence by contractors to data exchange standards; (2) the linking of contractor and government agency systems databases with strong emphasis on demonstration of concurrent engineering during system design; and (3) the development and automation of design knowledge bases (DOD, 1986).
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processing, manufacture, and support (e.g., reliability and maintainability) were made. The state-of-the-art computer-aided systems that assist the materials optimization or design tradeoff processes were reviewed. The study also considered the use of materials modeling since it can potentially reduce the quantity of data required by the system. The committee used case studies to provide instances of design decisions involving geometry or spatial relations; design rules associated with performance, processing, manufacture, and supportability; and advanced computer technologies and concepts that aid in the optimization or design tradeoff process. The committee recommended the research and development (R&D) that is required in materials-specific databases, knowledge bases, and modeling to facilitate concurrent engineering design. The committee composed this report for the study's sponsors: the Department of Defense and National Aeronautics and Space Administration. However, the committee also recognized that the report's audience included: structural engineers, materials technologists, and computer technologists in the sponsoring government R&D agencies who fund research, reduce technological barriers to agency projects and missions, and enable the transition of technology into products and processes; structural, materials, and computer scientists and engineers engaged in university and industrial R&D who strive to innovate in order to overcome technological barriers, demonstrate technological advancements, and enable the transition of technology into products and processes; and product design teams who aspire to maximize the quality of the design process and the resultant value of the product to the ultimate customer: the product's user.
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