Machine learning and CBR integrated mechanical product design approach

Abstract

Many works based on case-based reasoning (CBR) and rule-based reasoning (RBR) have been done to obtain a product concept intelligently, but researches on automatic and intelligent methods for the detailed design process are still lacking. To achieve the intelligent design of the whole process for mechanical product development, an approach based on cases and knowledge is proposed. First, considering that the actual product cases sample is small, correlations among product features and relationships between product features and requirements are evaluated based on design knowledge. According to these correlations and relationships, a design problem is decomposed into multiple parallel small-scale sub-problems not only to increase data samples but reduce data dimensions. Moreover, a hybrid method that combined Hamming distance with Euclidean distance is generated to retrieve similar cases, which can address both numerical and encoding factors. In addition, K-Nearest-Neighbor (KNN) algorithm and Linear regression are adopted to adapt the classification and numeric parameters, respectively, so that the detailed design process can be achieved without fixed knowledge template. Finally, a tool design software is developed based on Visual studio 2015 to verify the practicability of the proposed method.