This paper addresses in-process monitoring of weld penetration depth (WPD) during remote laser welding of battery tab connectors using optical coherence tomography (OCT). The research aims at studying the impact of welding process parameters on the accuracy of WPD measurements. In general, the highest measurement accuracy is achievable by positioning the OCT measuring beam toward the bottom of the keyhole. However, finding and maintaining the alignment between the OCT measuring beam and the bottom of the keyhole is a challenging task because of the dynamic changes in the size and shape of the keyhole itself. The paper addresses the above challenge by (1) developing welding process parameters for the Al-Cu thin foil lap joint (Al 1050 foil 450 μm and Ni-plated Cu foil 300 μm) using a novel adjustable ring mode (ARM) laser and (2) integrating OCT technology with two beams: one targeting the bottom of the keyhole and another as a reference to the part surface (TwinTec technology). The methodology is underpinned by the “Keyhole Mapping” approach, which helps one to identify the optimal placement of the OCT measuring beam with considerations to both measurement accuracy and stability of the keyhole. Findings indicated that welding with the ARM laser results in a more stable process, reduces fluctuations of the keyhole opening, and, therefore, helps one to improve the measurement accuracy by a factor of 50% (from the average error of 0.22 mm to 0.11 mm). Results further identified that the feasible operating window of the OCT measuring beam, corresponding to the highest measurement accuracy, is below 20 μm in length.

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.

Remote laser welding (RLW) combines the positive features of tactile laser welding with additional benefits such as increased processing speed, reduced operational cost and service, and higher process flexibility. A leading challenge preventing the full uptake of RLW technology in industry is the lack of efficient closed loop in-process (CLIP) monitoring and weld quality control solutions. This underpins the need to fuse multiple sensor technologies and data analytics with predictive engineering simulations. Although the development and integration of a variety of sensors covers the radiation spectrum from ultraviolet to far-infrared, the flawless deployment of CLIP solutions is still challenged by the need for the following: signal denoising in the case of process instability; real-time data analytics; and adaptive control engineering architecture to cope with process variations induced by manufacturing tolerances. This paper focuses on the aspect of weld penetration depth control using optical coherence tomography (OCT) as a necessary step to enable adaptive penetration depth control during RLW of aluminum components in the fillet lap joint configuration with consideration to part-to-part gap variation. The approach entails decoupling the welding process parameters in two subsets: (1) in-plane control of the heat input on the upper part to facilitate the droplet formation; and (2) out-of-plane heat management to achieve the desired level of penetration control in the keyhole mode. This paper presents the results of finding the optimal placement of the OCT beam with variable part-to-part gap conditions. Results have shown that statistical signal processing of the raw OCT signal gives insight not only into the depth of the keyhole but can infer the shape of the keyhole itself. Current limitations and next phases of research and development are highlighted based on the experimental study.

Advances in laser beam shaping technologies are being studied and are considered beneficial in many aspects of dissimilar metal joining, which include reducing intermetallic compounds (IMCs), optimizing weld pool profiles, and controlling porosity and spatters. This paper utilizes a coaxial ring and core dual beam laser and aims to study the impact of the power ratios between core and ring beams on the weldability of 1100 aluminum alloy to hilumin (steel). High-resolution electron microscopy was performed in the cross sections of the weld pools to quantify the melt pool composition and subsequent IMC formation and weld defects (cracking and cavitation). Lap-shear mechanical testing and electrical resistivity testing were also carried out. Results showed that the optimal power ratio for lap-shear strength was 0.4 (intermediate core and ring) due to the reduction in the Fe-rich liquid into the upper weld region. As a result, this produced a smaller interface between the Fe-rich region and Al, thus reducing the formation of the most detrimental IMC (e.g., Fe2Al5). Conversely, a power ratio of 0.2 (core-dominant) was found beneficial for reducing electrical resistance due to a reduced total IMC volume.