A granulation analysis method for cutting tool material selection using granular computing

Reasonable selection of cutting tool materials has an important effect on machining efficiency, machining quality, cutting tools life, and production cost. There always exists a problem of correct matching between cutting tool materials and workpiece materials. The multi-criteria decision-making is currently the predominant method for cutting tool material selection, and its accuracy can be further improved based on full consideration of the workpiece materials and cutting parameters. For this reason, a granulation analysis method based on granular computing is presented. Firstly, according to the similarity of various cutting tool materials across different attributes represented in interval values including physical properties, mechanical properties, and cost, a fuzzy similarity matrix of all the cutting tool materials to be analyzed is established; and a series of material information granular layers with different granularity is constructed by using quotient space theory based on fuzzy tolerance relation. Afterwards, information entropy is applied to measure their granularity, and an optimal granular layer is determined based on a quantitative and objective standard. Finally, in the optimal granular layer, through determining the averages and ranks of all the material information granules under different attributes, the corresponding common characteristics of similar cutting tool materials in each material information granule are analyzed, and their matching workpiece materials and cutting parameters are summarized. The analysis and summary will provide effective guidance for subsequent multi-criteria decision-making of cutting tool materials. An application example proves the feasibility and validity of the proposed method.