머신러닝을 이용한 레이저 용접부의 모델링 Part I: Al/Cu 이종재료 겹치기 레이저용접부의 용입깊이

초록

Thin sheets of Al/Cu dissimilar materials are overlap welded for the electrical connection of secondary battery electrodes by laser welding. The weld penetration depth is an important joint quality to ensure strength and electrical conductance. It is difficult to predict the penetration depth using analytical methods because of the high laser reflection and small thickness of the base materials. Several machine learning algorithms were investigated to develop regression models for the penetration depth. The models included linear regression, decision tree, supported vector regression, Gaussian process regression, and decision tree ensemble model groups. The regression models with high degrees of freedom showed excellent mean absolute percentage errors (MAPE) and coefficients of determination (R2 ). In particular, the Gaussian process regression model with exponential kernels had an MAPE of 0.2% and an R2 of unity.

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