1.Jiangsu Key Laboratory of Advanced Numerical Control Technology,Nanjing institute of Technology;2.Nanjing Gongda CNC Technology CoLtd
The National Natural Science Foundation of China (51405220), the University-level Research Fund of Nanjing Institute of Technology (JCYJ201843), the Jiangsu Graduate Practical Innovation Project (SJCX23_1182).
According to the energy consumption and workpiece surface roughness information of gear milling machine, a predictive optimization method combining multivariate nonlinear fitting and particle swarm optimization algorithm is proposed to provide the optimal process parameters for gear milling. Based on the dynamic structure of NC gear milling machine, the energy consumption model is established, and the concept of cutting specific energy of gear milling is put forward. Orthogonal and full factorial experiments were carried out to monitor the power and surface roughness data of multi-operating gear milling machine. The prediction model of machine tool specific energy and workpiece roughness is established by multivariate nonlinear fitting function. The fitting objective function group was substituted into particle swarm optimization algorithm to optimize the process parameters. The experimental results show that the goodness of fit of the prediction model based on multivariate nonlinear fitting is more than 0.99, and the eight solution sets obtained by particle swarm optimization algorithm take into account the objective needs of machine tool energy saving and efficiency improvement.