Using photodiodes and supervised machine learning for automatic classification of weld defects in laser welding of thin foils copper-to-steel battery tabs
نویسندگان
چکیده
This paper has been designed to study whether photodiodes and supervised machine learning (ML) algorithms are sufficient automatically classify weld defects caused by simultaneous variation of the part-to-part gap laser power during remote welding (RLW) thin foils, with applications in battery tabs. Photodiodes used as primary source data collected real-time RLW copper-to-steel foils lap joint. Experiments carried out nLight Compact 3 kW fiber integrated Scout-200 2D scanner. The reviews compares seven ML (namely, k-nearest neighbors, decision tree, random forest, Naïve–Bayes, support vector machine, discriminant analysis, discrete wavelet transform combined neural network) for automatic classification defects. Up 97% rate is obtained scenarios variations penetration depth gap. main causes misclassification imputed interaction between parameters (part-to-part power) process instability at high (high not captured photodiodes). Arising opportunities further development based on sensor fusion, integration multiphysical simulation, semi-supervised discussed throughout paper.
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ژورنال
عنوان ژورنال: Journal of Laser Applications
سال: 2022
ISSN: ['1042-346X', '1938-1387']
DOI: https://doi.org/10.2351/7.0000800