Plastic Boss Design Using Knowledge Extraction Method

Complex product design often relies on design experience and numerous repeated equipment operations. In order to speed up the design process on a complicated product, a methodology of knowledge extraction (KE) is proposed. Moreover, a case study in designing a shaped boss using the KE technique in conjunction with a Back-Propagation Network (BPN) method as well as a genetic algorithm (GA) will be introduced. The results indicate that the prediction error between learned and examined data is found to be within 8%. Moreover, the error between the GA’s solution and the specific target is also found to be within 5%. Therefore, the bi-directional prediction scheme constructed in this project is deemed to be reliable. Consequently, knowledge extraction can provide a rapid and economical way to design and shape a complicated product.