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.

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This paper addresses in-process monitoring of 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 largely implemented for structural welds due to its relatively low cost and ease of automation. However, the application of photodiode-based monitoring to RLW of thin foils of dissimilar metals for battery tab connectors remains an unexplored area of research and will be addressed in this paper. Motivated by the high variability during the welding process of thin foils of dissimilar metals, this paper aims to evaluate the photodiode-based signals to determine if variations in weld quality can be isolated and diagnosed. The main focus is in diagnosing defective weld conditions caused by part-to-part gap variations and/or excessive weld penetration depth. Photodiode-based signals have been collected during RLW of copper-to-steel thin foils lap joint (Ni-plated copper 300 μm to Ni-plated steel 300 μm). The 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 to 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 will be discussed throughout the paper.