Abstract
Laser beam welding is valued for the capability of contactless welding. Despite significant technological improvements to achieve stable processes and the required quality standards, quality determination is often done destructively and offline, with unavoidable expenses. Inline approaches address this problem by using and processing collected machine and sensor data to enable indirect conclusions about the welding quality in the process. This paper presents an inline approach that merges a set of complementary sensor signals to enable the prediction of weld quality by machine learning algorithms, paving the way for closed-loop quality control.
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