Deep learning-based autonomous weld quality inspection in battery pack manufacturing using a two-stage model

Laser welding plays a critical role in advanced manufacturing, particularly in battery pack assembly for electric vehicles, where weld quality directly impacts performance and safety. However, current inspection methods rely on manual visual, electrical, and physical checks, resulting in inconsistent outcomes and limited detection of latent defects undetectable by traditional techniques. This study proposed a two-stage deep learning-based quality inspection framework designed to remain effective even when defect data were scarce. In the first stage, a convolutional neural network-based autoencoder was trained exclusively on normal data and augmented with a novel loss function that captured distributional features. In the second stage, multi-class defect classification was performed using the encoder and bottleneck layers of the first stage as a shared backbone, enabling robust performance in data-constrained environments. Data augmentation techniques were further applied to improve the generalization capability. A high-quality dataset collected from an actual industrial setting was used to validate the proposed approach. Experimental results showed that the proposed two-stage framework achieved 100% accuracy and an F1-score of 1.0 in defect detection and classification, outperforming the conventional rule-based system (99.31% accuracy and 0.995 F1-score) and demonstrating perfect consistency across all test samples. The proposed framework offers a scalable and intelligent solution for automated inspection in industrial laser welding applications.