Federated Learning for Surface Roughness - Kai-Lun Cheng; Yu-Hung Ting; Wen-Ren Jong; Shia-Chung Chen; Zhe-Wei Zhou (Applied Sciences)
This study proposes a federated learning-based real-time surface roughness prediction framework for WEDM to address issues of empirical parameter tuning and data privacy. By sharing only the model parameters, cross-machine training was enabled without exposing raw data. A custom data acquisition system collected discharge current and spindle current signals, which were solely used as input features to train the deep learning model. Data balancing techniques improved prediction accuracy, achieving performance comparable to centralized models. After optimizing the training dataset through balancing and augmentation, the federated model achieved a Root Mean Square Error (RMSE) of 0.076, which closely approaches the 0.074 RMSE obtained by the centralized model. The results show that federated learning enhances both data security and model generalization, offering an effective solution for smart manufacturing.