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作 者:黄宏星 吴頔 曾达 彭彪 孙涛 张培磊[1,2] 史海川 HUANG Hongxing;WU Di;ZENG Da;PENG Biao;SUN Tao;ZHANG Peilei;SHI Haichuan(School of Materials Science and Engineering,Shanghai University of Engineering Science,Shanghai,201620,China;Shanghai Collaborative Innovation Center of Laser Advanced Manufacturing Technology,Shanghai,201620,China;Taier Intelligence(Shanghai)Laser Technology Co.,Ltd.,Shanghai,201100,China;Key&Core Technology Innovation Institute of the Greater Bay Area,Guangzhou,510535,China)
机构地区:[1]上海工程技术大学,材料科学与工程学院,上海201620 [2]上海市激光先进制造技术协同创新中心,上海201620 [3]泰尔智慧(上海)激光科技有限公司,上海201100 [4]广东粤港澳大湾区硬科技创新研究院,广州510535
出 处:《焊接学报》2024年第11期128-132,共5页Transactions of The China Welding Institution
基 金:国家自然科学基金资助项目(52075317);上海市Ⅲ类高峰学科-材料科学与工程(高能束智能加工与绿色制造);2024年上海市大零号湾关键核心技术攻关“揭榜挂帅”项目。
摘 要:为了快速准确定量评价金属飞溅以优化工艺和保证焊接质量,采用1060铝合金可调环模(variable beam profile,VBP)激光焊为研究对象,搭建了基于光学相干层析成像(opticalc coherence tomography,OCT)的激光焊匙孔深度原位测量系统,并提出了一种1DCNN-BiLSTM复合深度学习模型,该模型利用两种网络单元的特性对匙孔深度信息进行局部和全局时序特征挖掘,实现了飞溅状态的定量评价.结果表明,该模型的飞溅识别准确率达到99.69%,为VBP激光焊工艺优化和质量控制提供了指导依据和闭环反馈.In order to quickly and accurately quantitatively evaluate the metal spatter to optimize the process and ensure the welding quality.This study focuses on the variable beam profile(VBP)laser welding process of 1060 aluminum alloy and develops an insitu keyhole depth measurement system based on optical coherence tomography(OCT).An innovative 1DCNN-BiLSTM deep learning composite model is proposed,leveraging the distinct characteristics of the two network units to perform local-global temporal feature extraction,achieving quantitative evaluation of spatter status.Results indicate that the constructed model achieves 99.69%accuracy in identifying spatter status,providing guidance and closed-loop feedback for optimizing the VBP laser welding process and quality control.
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