By M J Hague, A Taleb-Bendiab, M J Brandish (auth.), John E. E. Sharpe (eds.)
It is recognized that a few eighty five% of the assets essential to layout and convey to marketplace a product are dedicated by means of judgements taken within the first 10% of the layout task. This including the desire to decrease extra the time-to-market of top quality leading edge items has elevated the necessity for laptop help on the conceptual layout level of the engineering layout technique way of life. This complaints firmly makes a speciality of the continued study into new makes use of of synthetic Intelligence (AI) through the conceptual layout strategy. It exhibits how novel purposes of AI can be built-in with elements of sturdy modelling, simulation optimisation and a number of criterion determination- making. a specific emphasis is put on using AI equipment within the total layout of goods from significant Civil Engineering constructions to shopper Electronics.
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Extra info for AI System Support for Conceptual Design: Proceedings of the 1995 Lancaster International Workshop on Engineering Design, 27–29 March 1995
It also simplifies the construction of the fitness function. The result should be the production of a number of evaluation criteria. Although these criteria could be combined in a weighted function, eg cost-benefit analysis and the concept ranked on the basis of this score, human designers appear to evaluate the pros & cons across criteria. The approach adopted here is to follow this process by using Pareto Optimisation techniques. This avoids the problem of having to decide the relative impOitance of criteria.
Pareto Optimisation techniques allow the 32 designer to select designs with desired characteristics, however like animal husbandry it might be important to also provide the designer with information about the pedigree of the design. (vi) Genetic Engineeling The cUiTent prototypes provide the designer with basic facilities for alteIing the genotype of designs. Genetic engineering offers the prospect of rapid improvements to designs, but also Iisks seIiously damaging the gene pool. The nature of the information required by the designer to effectively manipulate genotypes, and through these individuals the gene pool is currently being investigated.
The second prototype, the grammar based GA, supports the functional decomposition process. Design grammars have been successfully applied to many problems, eg Coyne (1988)2. One of their major advantages is the criteria of completeness. Theoretically, a design grammar is complete - it will generate all possible designs composable under the grammar. This is an important characteristic. Given that human recognition is better than recall, a system which is designed to enhance a human designer can use the completeness of the grammar to prompt the human designer with design ideas that he would otherwise not have remembered.