aiqbench analysis
This study validates federated learning as a viable solution for distributed CNC tool wear prediction, demonstrating accuracy comparable to centralized models while respecting data privacy constraints inherent in industrial settings. By proving that collaborative training justifies increased system complexity, the research offers a practical pathway for scaling machine learning across fragmented manufacturing networks without raw data exchange. The findings directly address the challenge of deploying advanced AI in environments where data silos traditionally limit model performance.
Federated Learning for Distributed CNC Tool Wear Prediction
arXiv:2608.11281v2 Announce Type: replace 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. However, it is open if federated learning can lead to accuracy gains in CNC tool wear prediction that justify the increased complexity of such a system. In this experimental study, real 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 and the increased complexity is justified.
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. However, it is open if federated learning can lead to accuracy gains in CNC tool wear prediction that justify the increased complexity of such a system. In this experimental study, real 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 and the increased complexity is justified.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2608.11281 [cs.LG]
(or arXiv:2608.11281v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.11281 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 268 words
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