{"title":"利用传感器预测铝合金激光焊接重叠接头配置的界面宽度","authors":"Yoo-Eun Lee, Woo-In Choo, Sungbin Im, Seung Hwan Lee, Dong Hyuck Kam","doi":"10.2351/7.0001367","DOIUrl":null,"url":null,"abstract":"We present a method that can predict the interface width in an overlapping joint configuration for laser welding of Al alloys using sensors and a convolutional neural network (CNN)-based deep-learning model. The inputs for multi-input CNN-based deep-learning prediction models are spectral signals, represented by the light intensity measured by a spectrometer and dynamic images of the molten pool filmed by a charge-coupled device (CCD) camera. The interface width, used as learning data for modeling, was constructed as a database along with the process signal by cross-sectional analysis. In this study, we present results showing high accuracy in predicting the interface width in the overlap joint configuration for Al alloy laser welding. For predicting the interface width, five models are created and compared: a single CCD and spectrometer sensor algorithm, a multi-sensor algorithm with two input variables (CCD, spectrometer), a multi-sensor algorithm excluding the processing beam in the spectrometer data on the combination of Al 6014-T4 (top)/Al 6014-T4 (bottom), and a multi-sensor algorithm applied to the combination of Al 6014-T4 (top)/Al 5052-H32 (bottom). The multi-sensor algorithm with two input variables (CCD and spectrometer) on the same material combination showed the highest accuracy among the models.","PeriodicalId":508142,"journal":{"name":"Journal of Laser Applications","volume":"6 6","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of interface width in overlap joint configuration for laser welding of aluminum alloy using sensors\",\"authors\":\"Yoo-Eun Lee, Woo-In Choo, Sungbin Im, Seung Hwan Lee, Dong Hyuck Kam\",\"doi\":\"10.2351/7.0001367\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a method that can predict the interface width in an overlapping joint configuration for laser welding of Al alloys using sensors and a convolutional neural network (CNN)-based deep-learning model. The inputs for multi-input CNN-based deep-learning prediction models are spectral signals, represented by the light intensity measured by a spectrometer and dynamic images of the molten pool filmed by a charge-coupled device (CCD) camera. The interface width, used as learning data for modeling, was constructed as a database along with the process signal by cross-sectional analysis. In this study, we present results showing high accuracy in predicting the interface width in the overlap joint configuration for Al alloy laser welding. For predicting the interface width, five models are created and compared: a single CCD and spectrometer sensor algorithm, a multi-sensor algorithm with two input variables (CCD, spectrometer), a multi-sensor algorithm excluding the processing beam in the spectrometer data on the combination of Al 6014-T4 (top)/Al 6014-T4 (bottom), and a multi-sensor algorithm applied to the combination of Al 6014-T4 (top)/Al 5052-H32 (bottom). The multi-sensor algorithm with two input variables (CCD and spectrometer) on the same material combination showed the highest accuracy among the models.\",\"PeriodicalId\":508142,\"journal\":{\"name\":\"Journal of Laser Applications\",\"volume\":\"6 6\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Laser Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2351/7.0001367\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Laser Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2351/7.0001367","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
我们介绍了一种利用传感器和基于卷积神经网络(CNN)的深度学习模型预测铝合金激光焊接重叠接头配置中界面宽度的方法。基于 CNN 的多输入深度学习预测模型的输入是光谱信号(由光谱仪测量的光强度和电荷耦合器件 (CCD) 摄像机拍摄的熔池动态图像表示)。作为建模学习数据的界面宽度是通过横截面分析与过程信号一起构建的数据库。在本研究中,我们展示了高精度预测铝合金激光焊接重叠接头配置界面宽度的结果。为了预测界面宽度,我们创建了五个模型并进行了比较:单一 CCD 和光谱仪传感器算法、具有两个输入变量(CCD、光谱仪)的多传感器算法、在 Al 6014-T4(上)/Al 6014-T4(下)组合的光谱仪数据中排除加工光束的多传感器算法,以及应用于 Al 6014-T4(上)/Al 5052-H32(下)组合的多传感器算法。在相同的材料组合中,具有两个输入变量(CCD 和光谱仪)的多传感器算法显示出最高的精确度。
Prediction of interface width in overlap joint configuration for laser welding of aluminum alloy using sensors
We present a method that can predict the interface width in an overlapping joint configuration for laser welding of Al alloys using sensors and a convolutional neural network (CNN)-based deep-learning model. The inputs for multi-input CNN-based deep-learning prediction models are spectral signals, represented by the light intensity measured by a spectrometer and dynamic images of the molten pool filmed by a charge-coupled device (CCD) camera. The interface width, used as learning data for modeling, was constructed as a database along with the process signal by cross-sectional analysis. In this study, we present results showing high accuracy in predicting the interface width in the overlap joint configuration for Al alloy laser welding. For predicting the interface width, five models are created and compared: a single CCD and spectrometer sensor algorithm, a multi-sensor algorithm with two input variables (CCD, spectrometer), a multi-sensor algorithm excluding the processing beam in the spectrometer data on the combination of Al 6014-T4 (top)/Al 6014-T4 (bottom), and a multi-sensor algorithm applied to the combination of Al 6014-T4 (top)/Al 5052-H32 (bottom). The multi-sensor algorithm with two input variables (CCD and spectrometer) on the same material combination showed the highest accuracy among the models.