High stability multi-objective decision-making approach of dry hobbing parameters

High-speed dry hobbing is an advanced and green manufacturing technology. The use of intelligent algorithm for process parameter decision-making is receiving more and more attention in dry hobbing. The process parameter results of each operation of these methods are inconsistent, that is, the numerical fluctuation of results. To resolve this problem, a multi-objective decision-making approach of process parameters is proposed for highspeed dry hobbing based on K-means clustering, multi-objective Runge Kutta optimizer (MORUN) and Analytic Hierarchy Process (AHP). First, based on the past process cases of dry hobbing, K-means clustering is used to determine the cluster centers and case clusters. For the problem of dry hobbing process parameters to be decided, the distance from each cluster center is calculated and the similar case clusters are found. The numerical range of process parameters is determined. Then, based on the well-established machining parameters model of dry hobbing with the optimization objectives of machining energy consumption, quality and time, MORUN is developed and used to find the optimized process parameters. Finally, regarding the users' emphasis on objectives, AHP is used to sort all the optimized process parameters obtained, and the first ranked process parameter is used for machining. The feasibility of the proposed approach is verified by the actual hobbing. Compared with other well-established methods, the proposed approach can effectively solve the problem of numerical fluctuation of optimization results, has high stability, and select the optimal term from several optimization process parameters.