数据驱动的车削和钻削加工能耗预测  

Data-driven methodology for energy consumption prediction of turning and drilling processes

作　　者：吕景祥 唐任仲[2] 郑军[3] LYU Jingxiang;TANG Renzhong;ZHENG Jun(Key Laboratory of Road Construction Technology and Equipment,Ministry of Education,Chang an University,Xi an 710064,China;Industrial Engineering Center,College of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China;School of Mechanical and Automotive Engineering,Zhejiang University of Science and Technology,Hangzhou 310023,China)

机构地区：[1]长安大学道路施工技术与装备教育部重点实验室,陕西西安710064 [2]浙江大学工业与系统工程系,浙江杭州310027 [3]浙江科技学院机械与汽车工程学院,浙江杭州310023

出　　处：《计算机集成制造系统》2020年第8期2073-2082,共10页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(51705428);陕西省自然科学基础研究计划资助项目(2020JQ-380)。

摘　　要：为准确快捷地预测零件车削和钻削加工工艺过程的电能消耗,提出一种基于数据驱动的能耗预测方法,包括能耗数据采集和预处理、特征属性预处理、特征选择算法和能耗预测算法4个关键技术。将样本分类和RReliefF算法结合进行特征选择,采用神经网络、支持向量回归、随机森林3种算法预测能耗,并通过对算法进行调参提高预测精度。在此基础上进行了实验研究,应用所提方法预测零件外圆车削和钻削加工能耗,并同实测能耗进行比较。案例分析结果表明,所提方法能够分析得出零件加工能耗的主要影响因素,3种算法的平均预测误差在4.94%~9.94%之间,误差随着训练样本的增加逐步下降,其中神经网络算法的预测误差最小,低于现有方法,具有很大的应用潜力。To accurately and quickly predict the energy consumption of turning and drilling processes,a data-driven methodology to predict energy consumption of machining a part was proposed,which included four key technologies that were manufacturing data acquisition and preprocessing,preprocessing of feature attribute,algorithm for feature selection and energy consumption prediction.The feature selection was achieved by combining sample classification and RReliefF algorithm.The energy consumption was predicted using three algorithms that were neural network,support vector regression and random forest,and the prediction accuracy was improved by adjusting the parameters of the algorithms.Experiments were conducted to validate the proposed method.The energy consumption of cylindrical turning and drilling processes of parts was predicted using the proposed methodology and compared with the measured energy.Results showed that the proposed method could be used to identify the main factors influencing the machining energy consumption.The average prediction errors range from 4.94%to 9.94%and decreased as the number of training samples increasing.The neural network algorithm could achieve the lowest prediction error,which was lower than those obtained using existing method.The proposed methodology had a big potential for industrial applications.

关 键 词：数据驱动 车削 钻削 特征选择 机械加工 能耗预测 

分 类 号：TG501[金属学及工艺—金属切削加工及机床]