Digital Twin Based Machining Condition Optimization for CNC Machining Center

Abstract

Optimization of machining conditions is important in determining productivity and machining quality. This study proposes a method for optimizing the machining conditions of a machine tool using a digital twin of a commercial machine tool comprising physical models of a controller, feed drive systems, and cutting load. The digital twin is constructed and evaluated based on machining experiments, and a genetic algorithm is adopted to determine the machining conditions to minimize the machining time and production cost. The optimal feed rate and spindle speed are obtained for each line of the part program when the cutting force is limited. The machining results demonstrate the effectiveness of the proposed method. After optimization for maximizing the machining speed, the machining time decreased by 16.9%. Similarly, after optimization for minimizing the production cost, the production cost reduced by 36.4%.