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.

This paper has been designed to study whether photodiodes and supervised machine learning (ML) algorithms are sufficient to automatically classify weld defects caused by simultaneous variation of the part-to-part gap and laser power during remote laser welding (RLW) of thin foils, with applications in battery tabs. Photodiodes are used as the primary source of data and are collected in real-time during RLW of copper-to-steel thin foils in the lap joint. Experiments are carried out by the nLight Compact 3 kW fiber laser integrated with the Scout-200 2D scanner. The paper reviews and compares seven supervised ML algorithms (namely, k-nearest neighbors, decision tree, random forest, Naïve–Bayes, support vector machine, discriminant analysis, and discrete wavelet transform combined with the neural network) for automatic classification of weld defects. Up to 97% classification rate is obtained for scenarios with simultaneous variations of weld penetration depth and part-to-part gap. The main causes of misclassification are imputed to the interaction between welding parameters (part-to-part gap and laser power) and process instability at high part-to-part gap (high variation in the process not captured by the photodiodes). Arising opportunities for further development based on sensor fusion, integration with real-time multiphysical simulation, and semi-supervised ML are discussed throughout the paper.

In the attempt to produce lighter battery packs at a lower cost, replacing common copper parts with aluminum components has been a popular approach in recent years. With regard to joining technologies, there is a growing interest in applying laser beam welding in battery pack manufacturing due to several advantages such as single-sided and noncontact access while maintaining a narrow heat-affected zone. Motivated by the need to control and reduce weld porosity in AA1060 battery busbar welding with the ultimate goal to enhance durability and reduce electrical resistance, this paper has been developed with the aim to studying the effect of laser beam shaping on porosity formation and, hence, generate knowledge about the underlying physics of the welding process itself. First, a multiphysics computational fluid dynamics model has been developed and calibrated to experimental data; then, the model has been deployed to study the effect of both circular and tailing beam shapes on melt pool dynamics and the evolution of porosity due to the instability of the keyhole. The study elucidated the importance of the keyhole’s necking on porosity formation. Findings showed that the tail beam shapes, compared to the circular spot, have a pronounced effect on the reduction of the necking effect of the keyhole—this helps to reduce number of collapsing events of the keyhole itself, thereby leading to the reduction of porosity formation.