Acoustic Emission Metrology for Real-Time TIG Welding Monitoring: Spectral Characterization and Interpretability via Explainable AI

Tungsten inert gas (TIG) welding of AA5083 aluminum alloy is sensitive to heat input and shielding conditions, which can lead to defects such as porosity and burn-through that compromise weld quality. Conventional non-destructive testing methods are generally performed after welding and therefore provide limited capability for real-time process monitoring. This study presents an acoustic emission (AE)-based frequency-domain framework for real-time classification of weld conditions during TIG welding of AA5083. Acoustic signals were acquired at a sampling rate of 10 kHz and transformed into frequency-domain representations using the Fast Fourier Transform (FFT). Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), and ensemble-learning classifiers were evaluated using the resulting spectral features. To address the high dimensionality of the FFT representation, Principal Component Analysis (PCA) was incorporated into the modeling workflow, with the retained components determined from the model-development data. Five-fold cross-validation and grid-search-based hyperparameter tuning were used during model development, while an independent test set was reserved for final evaluation. The best-performing classifiers achieved a classification accuracy of approximately 0.99 for distinguishing good-weld, porosity, and burn-through conditions. Weld-condition labels were independently validated using visual inspection and radiographic testing. FFT and Short-Time Fourier Transform (STFT) analyses were used to characterize global and time-dependent spectral behavior, respectively, while SHapley Additive exPlanations (SHAP) were employed to identify frequency regions contributing to model predictions. A comparative analysis with time-domain statistical features further demonstrated the stronger discriminative capability of the FFT-derived representation under the investigated conditions. The proposed framework combines high classification performance with interpretable frequency-domain information and provides a basis for in-process weld-condition monitoring and quality control of TIG-welded AA5083.