数控铣齿比能耗与工件粗糙度预测优化

Numerical Control Milling Gear Specific Energy Consumption and Workpiece Roughness Prediction Optimization

根据铣齿机床能耗与工件表面粗糙度信息,提出一种结合多元非线性拟合和粒子群算法的预测优化方法,旨在为齿轮铣削提供优选工艺参数。基于数控铣齿机床动力结构建立能耗模型,进而提出铣齿切削比能的概念;开展正交和全析因试验对多工况铣齿机床的功率和表面粗糙度数据进行监测;通过多元非线性拟合函数建立机床切削比能和工件粗糙度的预测模型;将拟合目标函数组代入粒子群算法进行工艺参数的优化。试验结果表明,基于多元非线性拟合的预测模型拟合优度均超过了0.99,采用粒子群算法求解获得的8组解集考虑到了机床节能增效的客观需要。

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 was proposed to provide the optimal process parameters for gear milling.Based on the dynamic structure of NC gear milling machine,the energy consumption model was established,and the concept of cutting specific energy of gear milling was 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 was 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 fit goodness 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.