摘要
采用正交试验法研究干磨和湿磨方式下砂轮线速度、工件转速、磨削深度对合金钢40CrNiMoA高速外圆磨削比能的影响,并建立基于BP神经网络的磨削比能预测模型,最后对预测结果进行验证。结果表明:合金钢40CrNiMoA高速外圆磨削比能随砂轮线速度增大呈现先增加后减小的趋势,随工件转速、磨削深度的增大而减小,工件转速对磨削比能的影响程度最大;湿磨方式的磨削比能比干磨方式的小。在试验工艺参数范围内,当砂轮线速度为60 m/s、工件转速为125 r/min、磨削深度为40μm时,磨削比能最小;预测模型的预测值与试验结果的绝对误差小于10%,表明BP神经网络预测模型是有效的。
According to the orthogonal experiment in the high speed cylindrical grinding under dry and wet modes,the influence of the grinding wheel linear speed,workpiece rotate speed and grinding depth on the specific energy of 40CrNiMoA was studied.The model for predicting the specific energy of grinding was established based on the BP neural network,and the prediction results were validated.The results show that the specific energy of grinding increases first and then decreases with the increasing of grinding wheel linear speed,and it decreases with the increasing of workpiece rotate speed and the grinding depth.The workpiece rotate speed has the greatest influence on the specific energy of grinding.The specific energy of grinding in wet mode is smaller than that in dry mode.For the range of process parameters in the experiment,when the grinding wheel linear speed,the workpiece rotate speed and the grinding depth are 60 m/s,125 r/min and 40μm,separately,the specific energy of grinding is the smallest.The absolute errors between the predicted value and the experimental are less than 10%,which shows that the prediction model based on the BP neural network is effective.
作者
刘伟
石新宇
李希晨
唐都波
刘顺
LIU Wei;SHI Xinyu;LI Xichen;TANG Dubo;LIU Shun(Hunan Provincial Key Laboratory of High Efficiency and Precision Machining of Difficult-to-cut Material, Hunan University of Science and Technology, Xiangtan 411201, Hu′nan, China)
出处
《机械科学与技术》
CSCD
北大核心
2022年第1期98-103,共6页
Mechanical Science and Technology for Aerospace Engineering
基金
湖南省自然科学基金项目(2020JJ5178)
湖南省教育厅科学研究一般项目(15C0544)
湖南省电磁装备设计与制造重点实验室开放基金项目(DC201901)。
关键词
40CrNiMoA
高速外圆磨削
正交试验
比能
预测
40CrNiMoA
high speed cylindrical grinding
orthogonal experiment
specific energy
prediction
