Federated Learning for Surface Roughness
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Privacy-Preserving Technologies in Data · Mobile Crowdsensing and Crowdsourcing · Adversarial Robustness in Machine Learning
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Mechanical Engineering, Chung Yuan Christian University, Taoyuan 320314, Taiwan
RD Center for Smart Manufacturing, Chung Yuan Christian University, Taoyuan 320314, Taiwan
RD Center for Semiconductor Carrie, Chung Yuan Christian University, Taoyuan 320314, Taiwan
Moldintel Co., Ltd., Taoyuan 32023, Taiwan
References (25)
Show source-provided references
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