Application of probabilistic multi-objective optimization in material milling

In this paper, it takes the material milling as an example to illuminate the application of probabilistic multi-objective optimization (PMOO) in material processing. The multi-objective optimization problem is seen as the integral optimization of a system. It adopts the method of probability theory to deal with the issue of multi-objective optimization problem in a system, a new concept of “preferable probability” is introduced, and the objectives (attributes) of candidate scheme in the optimization are preliminarily divided into two basic types, i.e., beneficial attribute and unbeneficial attribute, and a quantitative evaluation method of partial preferable probability corresponding to the type of attribute whether beneficial or unbeneficial is established. Moreover, the total preferable probability of each scheme is the product of entire partial preferable probabilities of all possible attributes of the candidate objective in the spirit of probability theory. The total preferable probability of each scheme is the unique and decisive indicator of the candidate scheme to win the competition in this optimization. As an application in material processing, the material milling is taken as example, orthogonal (Taguchi) design with twenty-seven experiments is employed, in which the number of insert, cutting piece materials and tool tip radius, cutting speed, feed speed and cutting depth are taken as input variables; Minimizing surface roughness (SR) and maximizing material removal rate (MRR) of the workpiece are taken as the target responses simultaneously. Finally, the optimum status corresponding to the highest total preferable probability is obtained in viewpoint of system theory.