基于BP神经网络的微铣削切削比能预测

Prediction of specific cutting energy in micro-milling based on BP neural network

针对微铣削加工过程中功率和加工能耗变化问题,对微铣削机床主轴系统加工功率进行了采集。建立了主轴转速、每齿进给量和切削深度3个重要切削参数影响切削比能的BP神经网络预测模型。通过45#钢子午线轮胎模具微铣削试验,获得试验数据样本来训练和检测BP神经网络,实现了不同切削参数组合下切削比能的预测,并利用遗传算法对切削参数进行寻优。预测和优化结果表明,最小切削比能可在最大切削参数组合下取得。因此在不考虑表面粗糙度和刀具磨损的情况下,高水平的切削参数组合可获得大的材料去除率和相对较小的切削比能,提高加工效率并降低加工能耗。

Aiming at the change of power and energy consumption in micro-milling process,the processing power of the spindle system of micro-milling machine tool was collected.A BP neural network prediction model was established to predict the effect of three important cutting parameters,spindle speed,feed per to-oth and cutting depth,on the specific cutting energy(SCE).Through the 45#steel radial tire die micro-milling test,the test data samples were obtained totrain and detect the BP neural network,and the prediction of SCE under the combination of different cutting parameters was realized,and genetic algorithm(GA)was used to optimize the cutting parameters.The prediction and optimization results show that the minimum specific cutting energy can be obtained under thecombination of the maximum cutting parameters.Therefore,without considering the surface roughness and tool wear,a high level cutting parameters combinationcan obtain large material removal rate and relatively small specific cutting energy to improve processing efficiency and reduce processing energy consumption.