A method of case adaptation for variant design integrating data mining

Abstract

Case-based reasoning has proven to be a promising methodology for obtaining new mechanical products by adapting previous cases. However, case adaptation is still a bottleneck in case-based reasoning. The key issue in case adaptation is acquiring the adaptation knowledge. To realize the automation of case adaptation for variant design, a novel case adaptation method is proposed. The method consists of two parts. In the first part, a data mining technique is introduced to acquire the adaptation rules that reflect the relationship between the changes in design requirements and design results. In the second part, the most similar case is retrieved by first using the adaptation rules to weight the design requirements. Then, suitable adaptation rules are selected and used to realize the case adaptation. To validate the proposed method, two experiments are performed. The results show that our method outperforms other methods when the design requirements and design results have both numerical and categorical attributes.