Integrating multiple adaptation results by utilization of support vector regression in case-based mechanical product design

Abstract

Case adaptation is crucial for a good and reasonable case-based design which is a common method in computer-aided design, because the solution of old case is not always the exact answer for the encountered new designing problem. Recently, a statistical feature-oriented adaptation method in the principle of k-nearest neighbors has been employed widely in case-based design because of its easily understandable mechanism for designers, but its adaptation accuracy is relatively low compared with knowledge-intensive method. This article presents a new method by integrating with multiple adaptation values from statistical feature-oriented adaptation methods to improve the adaptation accuracy. First, two simple statistical feature-oriented adaptation methods including mean and median approaches and other four statistical feature-oriented adaptation methods based on Euclidean distance, Manhattan distance, Gaussian transformation, and gray coefficient are used as individual statistical feature-oriented adaptation mode in this article, and each statistical feature-oriented adaptation method generates pre-adapting value under k-nearest neighbor. Then, support vector regression is utilized to make a combined adaptation which takes pre-adapting values as its inputs, and the new multiple statistical feature-oriented adaptation method integration based on support vector regression is named as MSFA-SVR. Furthermore, to validate the feasibility and superiority of MSFA-SVR, it was applied to the power transformer design and was compared with the classical statistical feature-oriented adaptation methods. Empirical comparison results indicated that MSFA-SVR achieves the better adaptation performance under k-nearest neighbor than other statistical feature-oriented adaptation methods in terms of adaptation accuracy.