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.

中文翻译：

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使用光学相干断层扫描和机器学习实时预测激光束焊接质量特性

激光束焊接在灵活性和生产率方面明显优于传统的连接技术。该过程尤其受益于高度集中的能量，从而受益于明确定义的热输入。然而，高强度的明亮激光辐射会在相互作用区内引起非常动态的效应和复杂的过程。高过程动态需要一致和可靠的质量保证，以确保所需的焊接质量。基于光学相干断层扫描 (OCT) 的新型激光材料加工传感器概念用于测量深熔焊过程中小孔的毛细管深度。将 OCT 测量值与焊缝表面质量分析进行比较。可以利用机器学习方法来揭示焊缝深度信号与焊缝表面质量之间的相关性，强调 OCT 信号中包含的关于影响焊缝质量的特征过程现象的高级信息。对铝、铜和镀锌钢进行了基础研究，以分析 OCT 传感器记录的数据的结构。在此基础上，开发并验证了以质量特性为重点的评估策略，以实现对 OCT 信号的有效解释。焊缝的形貌用于对表面质量进行分类，并与 OCT 系统的焊缝深度信号相关联。为此，开发了 OCT 数据的预处理和地形信息的详细分析。处理后的数据使用人工神经网络进行关联。结果表明，通过使用适当的网络结构和训练方法，可以使用毛细管深度的在线过程数据来预测表面质量，并具有良好的预测精度。