Utilized a VAE (Variational Autoencoder) and CGAN (Conditional Generative Adversarial Network) models to generate synthetic chatter signals, addressing the challenge of imbalanced data in turning operations and compared performance of synthetic chatter signals. With the scope of this project , I employed the VAE model via PyTorch to produce synthetic chatter data, comparing the quality of synthetic chatter signals generated by CGAN and assessed the quality of synthetic chatter data produced by both the VAE and CGAN models by evaluating the performance of an SVM (Support Vector Machine) model designed for classifying chatter and stable signals.
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Utilized a VAE (Variational Autoencoder) and CGAN (Conditional Generative Adversarial Network) models to generate synthetic chatter signals, addressing the challenge of imbalanced data in turning operations. Compared othe performance of synthetic chatter signals.
ayseirmak/Chatter-Detection
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Utilized a VAE (Variational Autoencoder) and CGAN (Conditional Generative Adversarial Network) models to generate synthetic chatter signals, addressing the challenge of imbalanced data in turning operations. Compared othe performance of synthetic chatter signals.
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