焊接
材料科学
激光器
光学相干层析成像
锁孔
信号(编程语言)
激光束焊接
信号处理
声学
机械工程
计算机科学
光学
电子工程
工程类
冶金
数字信号处理
程序设计语言
物理
作者
Christian Stadter,Maximilian Schmoeller,Lara von Rhein,Michael F. Zaeh
出处
期刊:Journal of Laser Applications
[Laser Institute of America]
日期:2020-05-01
卷期号:32 (2)
被引量:45
摘要
Laser beam welding significantly outperforms conventional joining techniques in terms of flexibility and productivity. The process benefits, in particular, from the highly focused energy and thus from a well-defined heat input. The high intensities of brilliant laser radiation, however, induce very dynamic effects and complex processes within the interaction zone. The high process dynamics require a consistent and reliable quality assurance to ensure the required weld quality. A novel sensor concept for laser material processing based on optical coherence tomography (OCT) was used to measure the capillary depth of the keyhole during deep penetration welding. The OCT measurements were compared with analyses of the surface quality of the weld seams. A machine learning approach could be utilized to reveal correlations between the weld depth signal and the weld seam surface quality, underlining the high level of information contained in the OCT signal about characteristic process phenomena that affect the weld seam quality. Fundamental investigations on aluminum, copper, and galvanized steel were carried out to analyze the structure of the data recorded by the OCT sensor. Based on that, evaluation strategies focusing on quality characteristics were developed and validated to enable a valid interpretation of the OCT signal. The topography of the weld seams was used to classify the surface quality and correlated with the weld depth signal of the OCT system. For this purpose, a preprocessing of the OCT data and a detailed analysis of the topographic information were developed. The processed data were correlated using artificial neural networks. It was shown that by using adequate network structures and training methods, the inline process data of the capillary depth can be used to predict the surface quality with decent prediction accuracy.
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