摘要
表面粗糙度是衡量微细加工零件表面质量的主要指标,为提高微铣削加工表面粗糙度预测的精准性,提出一种一维卷积-长短期记忆(1DCNN-LSTM)的深度神经网络预测模型。利用一维卷积网络高效的数据处理机制和长短期记忆网络精准的预测能力,有效解决了批量序列数据处理、样本关键特征学习以及小样本数据的表面粗糙度预测精确问题。以主轴转速、进给速度、铣削深度和微铣刀螺旋角作为控制变量,用实验数据对微铣削表面粗糙度预测模型进行训练并对该模型验证。结果表明:相比于传统机器学习模型,1DCNN-LSTM神经网络平均预测误差仅为5.9%,验证了该模型基于小样本数据的高精度预测性能,为微铣削表面粗糙度的预测研究提供了一种新的方法。
Surface roughness is the main index to measure the surface quality of micro machined parts.In order to improve the accuracy of surface roughness prediction in micro milling,a deep neural network prediction model based on one-dimensional convolution-long short-term memory(1DCNN-LSTM)is proposed.Using the efficient data processing mechanism of one-dimensional convolution network and the accurate prediction ability of long-term and short-term memory network,the problems of batch sequence data processing,sample key feature learning and small sample data surface roughness prediction are effectively solved.Taking spindle speed,feed speed,milling depth and micro milling cutter spiral angle as control variables,the prediction model of micro milling surface roughness is trained and verified by experimental data.The results show that compared with the traditional machine learning model,the average prediction error of 1DCNN-LSTM neural network is only 5.9%,which verifies the high-precision prediction performance of the model based on small sample data,and provides a new method for the prediction of micro-milling surface roughness.
作者
王志勇
马轩
杜金金
WANG Zhiyong;MA Xuan;DU Jinjin(School of Mechanical Engineering,Yanshan University,Qinhuangdao 066004,CHN;Hebei Heavy Intelligent Manufacturing Equipment Innovation Center,Qinhuangdao 066004,CHN)
出处
《制造技术与机床》
北大核心
2022年第5期128-133,共6页
Manufacturing Technology & Machine Tool
基金
河北省科学技术研究与发展计划科技支撑计划项目(21372004D)。
作者简介
第一作者/通信作者:王志勇,男,1969年生,博士,副研究员,研究方向为精密与超精密加工及高速加工,在国内外主要期刊已发表论文10余篇。E�mail:wang@ysu.edu.cn。
