Application of generative adversarial network and diverse feature extraction methods to enhance classification accuracy of tool-wear status

HIGHLIGHTS

• who: Bo-Xiang Chen and colleagues from the Department of Industrial Engineering and Management, National Yunlin University of Science and Technology have published the research work: Application of Generative Adversarial Network and Diverse Feature Extraction Methods to Enhance Classification Accuracy of Tool-Wear Status, in the Journal: Electronics 2022, 11, x FOR PEER REVIEW of /2022/
• what: In view of this the authors propose two improvements: using a generative adversarial network to generate realistic computer numerical control machine vibration data to overcome data imbalance and_(2) extracting features in the time domain the frequency domain . . .
  

   

  

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