Multi-Objective Optimization of Machining Parameters Based on Tool Wear Condition

Computer numerical control(CNC)milling is a machinery process, in which tool wear has a significant impact on the energy consumption of the machine tool spindle motor, which is directly related to the capacity of tool. Thus, it is necessary to adjust machining parameters in time, adapt to different wear conditions, and ensure a detailed multi-objective optimization approach. In response to this issue, based on tool wear conditions, optimization strategies of machining parameters have been used to adjust the machining parameters online in different wear periods. At first, energy consumption model considering tool wear was developed by the particle swarm optimization(PSO), which has a mean error of less than 5%. Based on the strong correlation with tool wear, machine tools' spindle power was used as a single indicator to evaluate tool wear conditions. Furthermore, to obtain dynamic indicators that affect costs, such as tool remaining useful life(RUL) and corresponding spindle power, the tool wear degradation was modeled using an artificial neural network(ANN), describing spindle power changes over time to indicate tool degradation process. The degree of model fit reaches 0.992 in this process. Finally, energy consumption, tool, and time cost have been weighed to design a multi-objective optimization function. Besides, the genetic algorithm(GA)was used to search optimal machining parameters, and obtain optimization strategies for different wear periods. Dynamic indicators, such as spindle power and tool RUL, have been incorporated into this multi-objective optimization of machining parameters. Tool wear conditions were identified online based on spindle power to determine wear periods, and then machining parameters were adjusted to the corresponding optimal level. The results show that this method incorporates dynamic indicators to adapt to tool wear conditions, and it decreases the total costs by an average of 24.258%, showing that the method is effective and practical. © 2022, Editorial Board of Journal of Tianjin University(Science and Technology). All right reserved.