45钢高速铣削表面粗糙度预测

摘要

为了提高高速铣削加工表面粗糙度预测的精确性以及模型的通用性,提出了一种基于粒子群最小二乘支持向量机( PSO-LSSVM)算法的高速铣削加工表面粗糙度预测方法. 以工件硬度以及铣削参数为影响因素,采用回归分析方法、最小二乘支持向量机( LSSVM)以及PSO-LSSVM方法,分别建立了45钢高速铣削加工表面粗糙度预测模型,并对模型的预测精度进行了试验验证和对比分析. 结果表明:相同样本条件下,回归分析方法的预测误差较大,PSO-LSSVM预测模型平均预测误差仅为LSSVM方法平均预测误差的50%. PSO-LSSVM预测模型具有较高的预测精度和泛化能力,能够准确地预测高速铣削不同硬度的工件表面粗糙度,同时为铣削参数的选择和表面质量的控制提供了依据.%In order to improve the accuracy and application scope of a surface roughness prediction model, such a model, for high speed milling, is proposed based on the particle swarm optimization-least square support vector ma-chine ( PSO-LSSVM) method. By regarding the hardness of workpieces and the milling parameters as the influence factors on the model, based on regression analysis, LSSVM and PSO-LSSVM, the prediction models of surface roughness in high speed milling of 45 steel were established, then the prediction accuracy of the models was com-pared and verified through experiments. The results show that under the same sample conditions, the mean predic-tion error of the PSO-LSSVM model is only 50% of the LSSVM model. Therefore, the prediction model established based on PSO-LSSVM has a high prediction accuracy and generalization ability. It can predict the surface roughness for workpieces with different hardnesses precisely and can provide the basis for proper selection of milling parame-ters and control of surface quality.