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Disturbances generated during arc welding processes can be detrimental to the quality of welded structures, and existing automated disturbance-detection methods lack the capability for real-time deployment. This paper proposes an AI-based disturbance-detection framework for gas metal arc welding using microphone-recorded acoustic signals. Representative disturbances—shielding-gas interruption, tip wear, and cutting-oil contamination—were experimentally reproduced and acoustically recorded under three welding conditions. Acoustic features were then extracted from the Mel-spectrograms of the recorded welding sounds and combined with welding parameters (current, voltage, and travel speed) to train a multilayer perceptron classifier capable of identifying both the occurrence and type of disturbance. The trained model achieved an overall accuracy of 81.5% and a macro-F1 score of 82.9%, demonstrating reliable generalization performance. Time-series evaluation indicated that the model could maintain a stable classification performance from the early stage of welding and immediately after the onset of disturbance. Furthermore, a SHAP (Shapley Additive exPlanations) analysis revealed that the decision criteria of the model were physically interpretable: high-frequency attenuation was dominant in shielding-gas interruption, while low-frequency vibration components were characteristic of tip wear. Both the spectral intensity and its variance were identified as key features for accurate disturbance classification. The proposed approach provides a low-cost, noncontact, and real-time monitoring solution that can be easily integrated into robotic welding systems and adapted to various industrial environments, thereby contributing to the realization of autonomous and explainable in-process quality assurance in smart manufacturing.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Acoustic sensing, in-process monitoring, gas metal arc welding, disturbance detection, machine learning, explainable AI, smart manufacturing

Date published: 2026-12-31

Publisher: Informa UK Limited

Journal:

• Science and Technology of Advanced Materials-Methods (ISSN: 27660400) vol. 6 issue. 1 2688746

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Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1080/27660400.2026.2688746

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Updated at: 2026-06-25 09:51:40 +0900

Published on MDR: 2026-06-25 12:26:53 +0900