Federated Learning for Distributed CNC Tool Wear Prediction

• URL: http://arxiv.org/abs/2608.11281v1
• Date: Tue, 11 Aug 2026 11:24:22 GMT
• Title: Federated Learning for Distributed CNC Tool Wear Prediction
• Abstract summary: Federated learning can support collaborative tool wear prediction in distributed CNC manufacturing environments.<n>Tool trajectories are distributed across simulated clients to represent a federated learning scenario.<n>Results show that federated learning achieves performance close to centralized learning and improves significantly over local client models.
• Score: 0.7340017786387767
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tool wear prediction is an important task in CNC machining, where accurate monitoring of tool condition supports product quality and process reliability. Machine learning methods have shown potential for this task, but their use in industrial environments is limited by the distributed nature of machining data and by restrictions on data sharing between machines, sites, or organizations. Federated learning offers a suitable framework for this setting by enabling collaborative model training without transferring raw operational data. This paper investigates federated learning for CNC tool wear prediction. Tool trajectories are distributed across simulated clients to represent a federated learning scenario. The federated models are compared against centralized references and local client baselines. Results show that federated learning achieves performance close to centralized learning and improves significantly over local client models. These findings indicate that federated learning can support collaborative tool wear prediction in distributed CNC manufacturing environments.

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