A multi-criteria decision-making system for selecting cutting parameters in milling process

Abstract

The selection of cutting parameters is of great importance in machining, especially for machining aviation parts with high performance requirements, since cutting parameters directly determine the machining efficiency and final quality. Due to the ignorance of machining features and complex structures in real production, cutting parameter determined by existing works are often required to be further modified, which leads to low efficiency and unstable machining quality. Furthermore, the continuous accumulation of machining data in cutting experiments and real production will produce multiple available parameters for typical workpieces, and one optimal parameter should be selected from those alternatives considering different criteria in one machining step. Therefore, this paper proposes a novel multi-criteria decision-making system to decide the optimal cutting parameters from multiple alternatives for typical machining features. Technique for order preference by similarity to an ideal solution (TOPSIS) and adversarial interpretive structural modeling (AISM) are introduced and integrated to decide the optimal solutions. By applying the proposed TOPSIS-AISM method, optimal cutting parameters can be decided from multiple alternatives with criteria values of cutting efficiency, surface roughness and cutting force. Decision-making processes of cutting parameters for freeform surface and rib feature using proposed method were illustrated as case studies, and decision results were verified in the milling tests. The largest and the second largest relative error of original alternative results compared with experiment results are 13.64 % and 10.97 % respectively, and other relative errors are all lower than 5 %, which proves the reliability of the alternatives and decision results. Experiment results are further used to update the data of alternatives to carry out a new decision-making process for the second verification. The new decision-making processes output the same decision results with the first-time decision, which demonstrates the reliability and stability of the proposed decision-making system.