Specifications extraction and synthesis: Their correlations with preliminary design.

Abstract

This report addresses the research applied towards the automation of the engineering design process, in particular the structural design process. The three important stages of the structural design process are: the specifications, preliminary design and the detailed design. An iterative redesign architecture of the structural design process lends itself to automation. The automation of the structural design can improve both the cost and the reliability, and enhance the productivity of the human designers. To the extent that the assumptions involved in the design process are explicitly represented and automatically inforced, the design errors resulting from the violated assumptions can be avoided. Artificial Intelligence (AI) addresses the automation of complex and knowledge-intensive tasks such as the structural design process. It involves the development of the Knowledge Based Expert System (KBES). There are several tools, also known as expert shells, and languages available for the development of knowledge-based expert systems. A general purpose language, called LISP, is very popular among researchers in AI and is used as an environmental tool for the development of the KBES for the structural design process. The resulting system, called Expert-SEISD, is very generic in nature. The Expert-SEISD is composed of the user interface, inference engine, domain specific knowledge and data bases and the knowledge acquisition. The present domain of the Expert-SEISD encompasses the design of structural components such as beams and plates. The knowledge acquisition module is developed to facilitate the incorporation of new capabilities (knowledge or data) for beams, plates and for new structural components. The decision making is an integral part of any design process. A decision-making model suitable for the specifications extraction and the preliminary design phases of the structural design process is proposed and developed based on the theory of fuzzy sets. The methods developed here are evaluated and compared with similar methods available in the literature. The new method, based on the union of fuzzy sets and contrast intensification, was found suitable for the proposed model. It was implemented as a separate module in the Expert-SEISD. A session with the Expert-SEISD is presented to demonstrate its capabilities of beam and plate designs and knowledge acquisition.