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April 30, 2026Journal of the Korea Academia-Industrial cooperation SocietyOpen Access

A Transfer Learning-Based Weld Tensile Strength Prediction System for Battery Pack Manufacturing

Seungmin Lee Heesung Lee Beomseong Kim

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Key Points

The aim is to develop a model to predict the tensile strength of welded joints using data from plasma sensors during laser welding processes.
Used a deep learning model that combines a pre-trained autoencoder for feature extraction with a 1D CNN regression network.
Performed 10-fold cross validation on a dataset with normal and five defect conditions.
Analyzed performance improvements against a baseline model.
Achieved improved predictive performance compared to baseline models.
Demonstrated excellent prediction accuracy under consistent tensile strength conditions.
Showed enhanced performance even under high variability conditions through transfer learning.

Implication

Study predicts weld tensile strength in battery pack manufacturing, suggesting potential for quality control.

Abstract

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Cite This Study

Lee et al. (2026) studied this question.

synapsesocial.com/papers/69fed0abb9154b0b82877b82 https://doi.org/10.5762/kais.2026.27.4.735

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