A STFT-based ML framework defines Arc Stability Index, spectral entropy, and harmonic distortion features to classify welding arc stability, with SVM achieving 85-94% accuracy depending on validation method.
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A Hidden Markov Model on STFT-derived spectral features from welding current signals identifies three temporally coherent arc regimes: transient, stable, and extinction.
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A Hybrid STFT-Based Machine Learning Framework for Physically Interpretable Arc Stability Classification in Electric Arc Welding Systems
A STFT-based ML framework defines Arc Stability Index, spectral entropy, and harmonic distortion features to classify welding arc stability, with SVM achieving 85-94% accuracy depending on validation method.
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A Hidden Markov Framework for Physically Interpretable Arc Stability Dynamics in Welding Systems
A Hidden Markov Model on STFT-derived spectral features from welding current signals identifies three temporally coherent arc regimes: transient, stable, and extinction.