Integrated Design and Manufacturing in Mechanical Engineering

Abstract

Automated concept generation is non-trivial task. The complexity of this problem is mainly due to lack of formal representation frameworks that lend themselves easily to a computational approach. Generative grammar has emerged as a potential solution to this problem and presents a number of different possibilities for conceptual design automation. A novel search method is presented: it has been developed specifically for search trees defined by a special class of generative grammar in which rules of the grammar have parameters associated with them. A novel feature of the proposed search is Human in the loop approach in which learning about the search space is achieved by querying the user. The user fatigue restricts the maximum number of comparisons of candidate solutions (30–50). Prom the data gathered from the comparisons, a stochastic decision making process proposed in this paper quickly converges to a region of design space which best meet the user's preference. The method is implemented and applied to a grammar for shampoo bottle concept generation. It is shown through multiple user-guided and automated experiments that the method has ability to learn and adopt through human computer interaction process.