Hybrid weighted mean for CBR adaptation in mechanical design by exploring effective, correlative and adaptative values

• Hybrid weighted mean method using various knowledge for CBR adaptation is present.
• We explore effective, correlative and adaptative values from similar case data.
• We integrate effective, correlative and adaptative values into weighted mean model.
• Using effective, correlative and adaptative values improve the adaptation accuracy.

The implementation of case based reasoning (CBR) adaptation in parametric mechanical design can generate the design solution to unknown design problem by adapting similar solutions from other problems already solved. Classical weighted mean (WM) method is a common statistic adaptation method because of its domain independent and easily to be implemented, but with lower adaptation accuracy. A new hybrid WM (HWM) method for CBR adaptation in mechanical design is proposed in this paper, and its contribution is taking advantage of various implicit knowledge hidden in similar case data to improve the performance of WM. To achieve this goal, multiple similarity analysis (MSA), grey relation analysis (GRA) and inductive adaptability analysis (IAA) are firstly used to systematically explore the effective value (EV) of similar case for new design problem, the correlative value (CV) between problem and solution features, and the adaptative value (AV) of similar case's solution element for new adaptation situation, respectively. Then CV, EV and AV compose the integrated weight value of each solution element of similar case in HWM, and the optimal proportion of EV, CV and AV on the integrated weight is also discussed. Based on the parametric transformer design cases, the comparisons of adaptation performances between HWM and other statistical and intelligent methods were carried out, and the empirical results show that HWM has the better adaptation performance than other comparative methods by comparing the adaptation accuracy.