基于EfficientNetV2的车刀磨损检测方法  

Turning Tool Wear Detection Method based on EfficientNetV2

作　　者：陈娜 孔繁星[2] 王彦旭 何腾飞 李胜男 CHEN Na;KONG Fanxing;WANG Yanxu;HE Tengfei;LI Shengnan(School of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin City 132022,China;School of Mechanical and Electrical Engineering,Jilin Institute of Chemical Technology,Jilin City 132022,China;Department of Advanced Manufacturing Technology,Jilin Institute of Chemical Technology,Jilin City 132022,China)

机构地区：[1]吉林化工学院信息与控制工程学院,吉林吉林132022 [2]吉林化工学院机电工程学院,吉林吉林132022 [3]吉林化工学院先进制造技术部,吉林吉林132022

出　　处：《吉林化工学院学报》2024年第3期21-24,共4页Journal of Jilin Institute of Chemical Technology

基　　金：吉林化工学院博士启动基金项目(吉化院博金合字2021第031号)。

摘　　要：刀具磨损会对工业生产造成不良影响,在智能制造带动工业加工的发展态势下,自动化刀具磨损智能识别系统的研究逐渐出现,旨在提高加工效率,延长车刀使用寿命以降低成本。利用一种基于EfficientNetV2网络的数控机床车削刀具磨损分类方法,解决当前磨损信息识别不准确、模型参数多计算量大、准确率不高的问题。EfficientNetV2网络能自动选取特征,这种方法更加直观和准确,实现较高的分类准确率,从而判别车削刀具的磨损情况。Tool wear will cause adverse effects on industrial production.With the development of industrial processing driven by intelligent manufacturing,research on automated tool wear intelligent recognition system has gradually emerged,aiming to improve processing efficiency and prolong the service life of turning tool processing to reduce costs.In this paper,a tool wear classification method for CNC machine turning based on EfficientNetV2 network was used to solve the problems of inaccurate wear information recognition,large amount of calculation and low accuracy of the current model parameters.The EfficientNetV2 network can automatically select features,which is more intuitive and accurate,and achieves a high classification accuracy,so as to distinguish the wear of the turning tool.

关 键 词：卷积神经网络 EfficientNetV2 车刀磨损 磨损分类 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP39[自动化与计算机技术—控制科学与工程]