基于光电同轴传感的极耳激光焊虚焊实时检测

TG456.7; 针对多层铝箔极耳和铝片的搭接形式,首先搭建了基于多波段光电同轴传感的激光焊过程实时监测系统,开展了不同激光功率和离焦量的激光焊试验,实时采集不同激光能量下的多波段光电信号;其次,利用小波散射网络从原始信号中提取出多尺度高维特征,并结合长短期记忆网络实现时间动态建模,最终达到实时检测虚焊缺陷的目标.结果表明,在小样本规模下,构建的WSN-LSTM模型准确率达到 99.6%,其分类性能优于其他循环神经网络和轻量化卷积神经网络模型.同时,WSN-LSTM模型轻量化使其在训练时间最短,且平均单个样本处理时间仅为 0.15 ms,有利于在动力电池产线快速部署,并实现虚焊缺陷的实时检测....

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Published in焊接学报 Vol. 45; no. 11; pp. 110 - 114
Main Authors 曾达, 吴頔, 彭彪, 杜辉, 魏于桐, 张培磊, 占小红
Format Journal Article
LanguageChinese
Published 上海工程技术大学,材料科学与工程学院,上海,201620 01.11.2024
上海市激光先进制造技术协同创新中心,上海,201620%泰尔智慧(上海)激光科技有限公司,上海,201100%必能信超声(上海)有限公司,上海,201620%南京航空航天大学,材料科学与技术学院,南京,211106
Subjects
小波散射网络
虚焊检测
wavelet scattering network
pseudo-defect detection
在线监测
photoelectric sensing
激光焊
laser welding
光电传感
online monitoring
Online AccessGet full text
ISSN0253-360X
DOI10.12073/j.hjxb.20240711001

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Abstract TG456.7; 针对多层铝箔极耳和铝片的搭接形式,首先搭建了基于多波段光电同轴传感的激光焊过程实时监测系统,开展了不同激光功率和离焦量的激光焊试验,实时采集不同激光能量下的多波段光电信号;其次,利用小波散射网络从原始信号中提取出多尺度高维特征,并结合长短期记忆网络实现时间动态建模,最终达到实时检测虚焊缺陷的目标.结果表明,在小样本规模下,构建的WSN-LSTM模型准确率达到 99.6%,其分类性能优于其他循环神经网络和轻量化卷积神经网络模型.同时,WSN-LSTM模型轻量化使其在训练时间最短,且平均单个样本处理时间仅为 0.15 ms,有利于在动力电池产线快速部署,并实现虚焊缺陷的实时检测.
AbstractList TG456.7; 针对多层铝箔极耳和铝片的搭接形式,首先搭建了基于多波段光电同轴传感的激光焊过程实时监测系统,开展了不同激光功率和离焦量的激光焊试验,实时采集不同激光能量下的多波段光电信号;其次,利用小波散射网络从原始信号中提取出多尺度高维特征,并结合长短期记忆网络实现时间动态建模,最终达到实时检测虚焊缺陷的目标.结果表明,在小样本规模下,构建的WSN-LSTM模型准确率达到 99.6%,其分类性能优于其他循环神经网络和轻量化卷积神经网络模型.同时,WSN-LSTM模型轻量化使其在训练时间最短,且平均单个样本处理时间仅为 0.15 ms,有利于在动力电池产线快速部署,并实现虚焊缺陷的实时检测.
Abstract_FL Targeting the lap joint of multilayer aluminum tabs and an aluminum sheet,a real-time monitoring system for the laser welding process based on multi-band photoelectric coaxial sensing was established.Experiments on laser welding processes with different laser powers and defocusing conditions were conducted,and multi-band photoelectric signals under different laser energies were collected in real-time.Secondly,a wavelet scattering network(WSN)was used to extract multi-scale high-dimensional features from the raw signals.Combined with a long short-term memory(LSTM)network for temporal dynamic modeling,this approach ultimately achieves the goal of real-time detection of pseudo welding defects.The results indicate that,with a small sample size,the constructed WSN-LSTM model achieves an accuracy of 99.6%,and its classification performance surpasses that of other recurrent neural networks and lightweight convolutional neural network models.Additionally,the lightweight characteristic of the WSN-LSTM model results in the shortest training time,with an average processing time per sample of only 0.15 ms,making it advantageous for rapid deployment on power battery production lines and real-time detection of pseudo welding defects.
Author 杜辉
魏于桐
张培磊
彭彪
占小红
吴頔
曾达
AuthorAffiliation 上海工程技术大学,材料科学与工程学院,上海,201620;上海市激光先进制造技术协同创新中心,上海,201620%泰尔智慧(上海)激光科技有限公司,上海,201100%必能信超声(上海)有限公司,上海,201620%南京航空航天大学,材料科学与技术学院,南京,211106
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Author_FL ZENG Da
PENG Biao
ZHANG Peilei
WU Di
DU Hui
ZHAN Xiaohong
WEI Yutong
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DocumentTitle_FL Real-time detection of pseudo-defect in laser welding of power battery tabs based on photoelectric coaxial sensing
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Keywords 小波散射网络
虚焊检测
wavelet scattering network
pseudo-defect detection
在线监测
photoelectric sensing
激光焊
laser welding
光电传感
online monitoring
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PublicationTitle_FL Transactions of the China Welding Institution
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上海市激光先进制造技术协同创新中心,上海,201620%泰尔智慧(上海)激光科技有限公司,上海,201100%必能信超声(上海)有限公司,上海,201620%南京航空航天大学,材料科学与技术学院,南京,211106
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Title 基于光电同轴传感的极耳激光焊虚焊实时检测
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