摘要
机械加工质量预测是智能制造的重要组成内容,也是实现质量闭环控制的前提条件,对推动智能制造系统真正落地应用具有极其重要的作用.在对机械加工质量预测的历史进行简要回顾时发现,学者多将研究重点放在机床某一关键部件对加工质量影响的机理研究,却鲜见部件耦合影响的关联性研究.基于上述难题,本文首先剖析影响机械加工质量的7类要素,包括刀具几何参数、切削参数、切削液类型、热误差与热变形、数控机床零部件性能退化、切削颤振以及系统特性;随后,根据各要素数据种类和测量方式的不同,将机械加工质量监测与预测方法划分为4大类,包括机器视觉测量、功率测量、振动测量以及其他测量方法,并对各方法的技术特点、局限性和发展动态进行了阐述;最后,考虑各机械加工质量监测与预测方法的不足,指出材料切削机制研究、数据质量评估方法、面向工业现场数据库构建的标准以及质量预测信息的智能表征与可视化等方面可能是未来的发展趋势.
The prediction of machining quality is a vital component of intelligent manufacturing and a prerequisite for achieving quality closed-loop control,playing an extremely important role in promoting the practical application of intelligent manufacturing systems.A brief review of the history of machining quality prediction reveals that scholars have mostly focused on the mechanism of the influence of a key component of the machine tool on machining quality,while research on the correlation between the coupling effects of machine components is rare.Based on the aforementioned challenges,firstly,seven types of factors that affect machining quality are analyzed,including tool geometry parameters,cutting parameters,cutting fluid type,thermal errors and deformations,degradation of CNC machine tool components,cutting chatter,and system characteristics.Subsequently,according to the different types of data and measurement methods for each factor,the monitoring and prediction methods of machining quality are divided into four categories,including machine vision measurement,power measurement,vibration measurement,and other measurement methods.The technical characteristics,limitations,and development trends of each method are then expounded.Finally,considering the deficiencies of various machining quality monitoring and prediction methods,this paper points out that research on material cutting mechanisms,data quality assessment methods,standards for constructing industry site databases,and intelligent representation and visualization of quality prediction information may be future development trends.
作者
高宏力
孙弋
郭亮
由智超
刘岳开
李世超
雷云聪
GAO Hongli;SUN Yi;GUO Liang;YOU Zhichao;LIU Yuekai;LI Shichao;LEI Yuncong(Engineering Research Center of the Advanced Driving Energy Saving Technology,Ministry of Education,Southwest Jiaotong University,Chengdu 610031,China;School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China)
出处
《西南交通大学学报》
EI
CSCD
北大核心
2024年第1期121-141,共21页
Journal of Southwest Jiaotong University
基金
国家自然科学基金(51775452)
中央引导地方科技发展专项资金(2020ZYD012)。
关键词
加工质量预测
切削力
振动
功率与电流信号
机器视觉
工业大数据
machining quality prediction
cutting force
vibration
power and current signals
machine vision
industrial big data era
作者简介
第一作者:高宏力(1971-),男,教授,研究方向为设备智能化状态监测与故障诊断技术,E-mail:hongligao@swjtu.edu.cn。
