基于ResNet的锁眼TIG缺陷检测与分类  

Keyhole TIG Defect Detection and Classification Based on ResNet

作　　者：章轩 马晨晨 王明娣[1] Zhang Xuan;Ma Chenchen;Wang Mingdi(School of Mechanical and Electric Engineering,Soochow University,Suzhou 215131,Jiangsu,China;School of Textile and Clothing,Nantong University,Nantong 226019,Jiangsu,China)

机构地区：[1]苏州大学机电工程学院,江苏苏州215131 [2]南通大学纺织服装学院,江苏南通226019

出　　处：《光学学报》2024年第21期183-193,共11页Acta Optica Sinica

基　　金：国家自然科学基金(52375459);国家自然科学基金(52305399);江苏省重点研发计划(产业前瞻与关键核心技术)(BE2022066-3);苏州市重点产业技术创新-关键核心技术研发(SGC2021010)。

摘　　要：作为一种先进而高效的焊接方法,小孔钨极惰性气体保护焊(TIG)在制造业中受到了广泛关注。为了提升小孔TIG的制造质量和自动化水平,开发一套用于在线监测焊接过程的系统是必不可少的。本研究利用专用的焊接摄像机开发了一套视觉监控系统,以监测TIG在焊接过程中的熔池和小孔。设计了一种先进的ResNet-18深度学习架构,以识别各种焊接状态,包括良好、烧穿、污染、缺乏熔合、错位、未穿透等情况。为增加训练数据集的多样性,采用图像增强技术进行处理。此外,在优化训练过程中引入了结合中心损失的度量学习策略。通过应用引导梯度类激活映射和特征映射等可视化方法,能够理解和解释深度学习过程的有效性。为小孔TIG在线监测系统的开发提供方案。Objective The primary objective of our study is to enhance the quality and automation of keyhole Tungsten inert gas(TIG)welding by developing a real-time visual monitoring system using deep learning techniques,specifically convolutional neural networks(CNNs).Keyhole TIG welding is a widely recognized advanced welding method known for its efficiency and precision.Despite its advantages,traditional methods of monitoring and quality control are limited by their reliance on manual feature selection and subjective judgment,resulting in inconsistent results and increased labor costs.We aim to deal with these limitations by leveraging the capabilities of deep learning to automatically and accurately identify various welding states,thus improving the overall welding process.The importance and necessity of our study are underscored by the growing demand for high-quality welds in various industries,such as aerospace,automotive,and construction fields,where precision and reliability are paramount.Traditional welding quality control methods often fall short in meeting these rigorous standards due to their dependence on human operators,who may have differences in skill and consistency.By developing an automated system that employs state-of-the-art deep learning algorithms,we aim to provide a more reliable and efficient solution,ultimately leading to improved production outcomes and reduced operational costs.Methods The experimental setup integrates a specialized welding camera into a robotic system equipped with welding torches,deployed at a 45°angle to the welding path to comprehensively capture the weld pool and the area near the welding arc(Fig.1).The camera system is designed to filter out the intense arc light and enhance the details within the weld pool.Meanwhile,the experiments are conducted under a range of parameters such as gas flow rate,traveling speed,voltage,and currents to simulate different welding conditions and induce various types of defects like burn-through,contamination,lack of fusion,misalignment,and in

关 键 词：激光光学 焊接 深度学习 视觉监控 激光工程 

分 类 号：O436[机械工程—光学工程]