摘要
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.
作者
张杰翔
李志永
郝文华
刘俨后
赵玉刚
ZHANG Jiexiang;LI Zhiyong;HAO Wenhua;LIU Yanhou;ZHAO Yugang(School of Mechanical Engineering,Shandong University of Technology,Zibo 255049,CHN;Himile Mechanical Science and Technology(Shandong)Co.,Ltd.,Gaomi 261500,CHN)
出处
《制造技术与机床》
北大核心
2020年第10期119-123,共5页
Manufacturing Technology & Machine Tool
基金
山东省重点研发计划(重大科技创新工程)项目(2018CXGC0602)。
关键词
微铣削
切削比能
BP神经网络
预测
遗传算法
micro-milling
specific cutting energy
BP neural network
prediction
genetic algorithm
