A modularized case adaptation method of case-based reasoning in parametric machinery design

Abstract

Case adaptation is fundamentally to successfully applying case-based reasoning (CBR) in parametric machinery design, and support vector machine (SVM)-based adaptation is a promising method for CBR adaptation. But the standard formulation of SVM can only be used as a univariate modeling technique due to its inherent single-output structure, which result in the construction of different SVM-based adaptation engine for each solution element adaptation, and such engines could ignore the effects of the mutual parameter relationships for the adaptation results. This paper focuses on the multivariable adaptation problem in CBR adaptation, and proposes a modularized adaptation method by integrating with multiply relational analysis, case parameter clustering and adaptation engine construction. Firstly, the hidden parameter relationships between problem and solution (P–S), problem and problem (P–P), and solution and solution (S–S) parameters are extracted from old cases, then these parameters are clustered into several parameter clustering (PC) modules in terms of their internal relationships. Finally, multi-output SVM (MSVM) is used to build the adaptation engine for each PC module. This method not only improves the performance of SVM-based adaptation by utilizing the mutual parameter relationships, but also reduces the computational expense of MSVM-based adaptation by partitioning the only one adaptation engine into several sub-engines. Actual design examples are introduced to illustrate the process of modularized adaptation, and the empirical experiments in the different examples are carried out to validate the superiority of our proposed method. Through comparing the adaptation accuracies with those provided by other classical neuro-adaptation methods, the modularized adaptation is proved to be a feasible method for case adaptation.