Adding an STFT-based spectral loss to a conditional GAN improves high-frequency fidelity of generated machining force signals and, used as training augmentation, cuts surface roughness prediction MAPE from 31.4% to 8.8%.
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Hybrid Adversarial Spectral Loss Conditional Generative Adversarial Networks for Signal Data Augmentation in Ultra-precision Machining Surface Roughness Prediction
Adding an STFT-based spectral loss to a conditional GAN improves high-frequency fidelity of generated machining force signals and, used as training augmentation, cuts surface roughness prediction MAPE from 31.4% to 8.8%.