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This paper addresses sensor characterization to detect variations in part-to-part gap and weld penetration depth using photodiode-based signals during remote laser welding (RLW) of battery tab connectors. Photodiode-based monitoring has been implemented largely for structural welds due to its relatively low cost and ease of automation. However, research in sensor characterization, monitoring, and diagnosis of weld defects during joining of battery tab connectors is at an infancy and results are inconclusive. Motivated by the high variability during the welding process of dissimilar metallic thin foils, this paper aims to characterize the signals generated by a photodiode-based sensor to determine whether variations in weld quality can be isolated and diagnosed. Photodiode-based signals were collected during RLW of copper-to-steel thin-foil lap joint (Ni-plated copper 300 µm to Ni-plated steel 300 µm). The presented methodology is based on the evaluation of the energy intensity and scatter level of the signals. The energy intensity gives information about the amount of radiation emitted during the welding process, and the scatter level is associated with the accumulated and un-controlled variations. Findings indicated that part-to-part gap variations can be diagnosed by observing the step-change in the plasma signal, with no significant contribution given by the back-reflection. Results further suggested that over-penetration corresponds to significant increment of the scatter level in the sensor signals. Opportunities for automatic isolation and diagnosis of defective welds based on supervised machine learning are discussed.

journal of manufacturing science and engineering

Characterization of Photodiodes for Detection of Variations in Part-to-Part Gap and Weld Penetration Depth During Remote Laser Welding of Copper-to-Steel Battery Tab Connectors