基于EfficientNetV2的车刀磨损检测方法

doi:10.16039/j.cnki.cn22-1249.2024.03.004

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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，延长车刀加工的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。

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	陈娜
	孔繁星
	王彦旭
	何腾飞
	李胜男

关键词: 卷积神经网络 EfficientNetV2 车刀磨损磨损分类

Abstract: 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 is 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.

Key words: convolutional neural networks EfficientNetV2 turning tool wear wear classification

出版日期: 2024-03-25 发布日期: 2024-03-25 整期出版日期: 2024-03-25

ZTFLH:	TP183
	TP39

引用本文:

陈娜, 孔繁星, 王彦旭, 何腾飞, 李胜男. 基于EfficientNetV2的车刀磨损检测方法[J]. 吉林化工学院学报, 2024, 41(3): 21-24.
CHEN Na, KONG Fanxing, WANG Yanxu, HE Tengfei, LI Shengnan. Turning Tool Wear Detection Method Based on EfficientNetV2. Journal of Jilin Institute of Chemical Technology, 2024, 41(3): 21-24.

链接本文:

http://xuebao.jlict.edu.cn/CN/10.16039/j.cnki.cn22-1249.2024.03.004 或 http://xuebao.jlict.edu.cn/CN/Y2024/V41/I3/21

[1]	陈娜, 孔繁星, 王彦旭, 何腾飞, 李胜男. 基于卷积神经网络的车刀磨损研究 [J]. 吉林化工学院学报, 2023, 40(9): 43-47.
[2]	辛瑞昊 , 王甜甜 , 苗冯博 , 董哲原 , 马占森 , 冯欣. 基于深度学习的机械轴承故障智能诊断 [J]. 吉林化工学院学报, 2022, 39(11): 25-29.
[3]	吴青云, 邹亚囡, 史雪莹. 基于卷积神经网络的电子鼻分类识别 [J]. 吉林化工学院学报, 2022, 39(11): 38-41.
[4]	钟楚轶, 朱建军. 人体活动识别数据集的数据处理方法 [J]. 吉林化工学院学报, 2020, 37(3): 81-84.

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