A novel approach to CNC machining center processing parameters optimization considering energy-saving and low-cost. (April 2021) - Xiao, Yongmao; Jiang, Zhigang; Gu, Quan; Yan, Wei; Wang, Ruping
Highlights: Analyzed the the energy flow characteristics of machining system of machining center. The concept of workstep element was proposed. A multi-objective optimization model of the processing parameters was built. An intelligent combinatorial algorithm combining APSO and NGSA-II was proposed. The weighted sum method is introduced to transform the nonlinear mathematical model. Abstract: NC machining is currently a machining method widely used in mechanical manufacturing systems. Reasonable selection of process parameters can significantly reduce the processing cost and energy consumption. In order to realize the energy-saving and low-cost of CNC machining, the cutting parameters are optimized from the aspects of energy-saving and low-cost, and a process parameter optimization method of CNC machining center that takes into account both energy-saving and low -cost is proposed. The energy flow characteristics of the machining center processing system are analyzed, considering the actual constraints of machine tool performance and tool life in the machining process, a multi-objective optimization model with milling speed, feed per tooth and spindle speed as optimization variables is established, and a weight coefficient is introduced to facilitate the solution to convert it into a single objective optimization model. In order to ensure the accuracy of the model solution, a combinatorial optimization algorithm based on particle swarm optimization and NSGA-II is proposed to solve the model. Finally, take plane milling as an example to verify the feasibility of this method. The experimental results show that the multi-objective optimization model is feasible and effective, and it can effectively help operators to balance the energy consumption and processing cost at the same time, so as to achieve the goal of energy conservation and low-cost. In addition, the combinatorial optimization algorithm is compared with the NSGA-II, the results show that the combinatorial optimization algorithm has better performance in solving speed and optimization accuracy.