Knowledge based engineering: Between AI and CAD. Review of a language based technology to support engineering design |
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| Affiliation: | 1. UC Berkeley, College of Environmental Design, Center for the Built Environment, USA;2. UC Berkeley, College of Environmental Design, Department of Architecture, USA;1. University of Vaasa, Networked Value Systems, Box 700, FI-65101 Vaasa, Finland;2. Georgia Institute of Technology, The Woodruff School of Mechanical Engineering, 813 Ferst Drive, NW, Atlanta, GA 30332-0405, USA;1. School of Computer Science and Technology, Hangzhou Dianzi University, China;2. State Key Laboratory of CAD&CG, Zhejiang University, China;3. School of Computer Science and Engineering, Beifang University of Nationalities, China;1. Airbus Group Innovations Technical Capability Center 5 (Systems Engineering, Information Technologies and Applied Mathematics), Golf Course Lane, Filton, Bristol BS997AR, United Kingdom;2. Manufacturing and Materials Department, School of Applied Sciences, Cranfield University, Cranfield, United Kingdom;1. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China;2. Zijin College, Nanjing University of Science and Technology, Nanjing 210046, China |
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| Abstract: | Knowledge based engineering (KBE) is a relatively young technology with an enormous potential for engineering design applications. Unfortunately the amount of dedicated literature available to date is quite low and dispersed. This has not promoted the diffusion of KBE in the world of industry and academia, neither has it contributed to enhancing the level of understanding of its technological fundamentals. The scope of this paper is to offer a broad technological review of KBE in the attempt to fill the current information gap. The artificial intelligence roots of KBE are briefly discussed and the main differences and similarities with respect to classical knowledge based systems and modern general purpose CAD systems highlighted. The programming approach, which is a distinctive aspect of state-of-the-art KBE systems, is discussed in detail, to illustrate its effectiveness in capturing and re-using engineering knowledge to automate large portions of the design process. The evolution and trends of KBE systems are investigated and, to conclude, a list of recommendations and expectations for the KBE systems of the future is provided. |
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