用联邦学习实现跨设备刀具磨损预测，不传数据也能协同建模。
Federated Learning for Distributed CNC Tool Wear Prediction
- 在分布式机床间用联邦学习联合训练预测模型
- 性能接近集中式训练，显著优于本地单独建模
- 适合数据分散、禁止共享的工业制造场景
刀具磨损预测是数控加工中的关键任务，准确监控刀具状态可保障产品质量与工艺可靠性。机器学习方法在此任务中展现潜力，但工业环境中数据分布于多台设备、站点或组织之间，且存在数据共享限制，制约了其应用。联邦学习提供合适框架，可在不传输原始操作数据的前提下实现协同模型训练。本文研究了面向数控机床刀具磨损预测的联邦学习方法。将刀具轨迹分布于模拟客户端以构建联邦学习场景，并对比联邦模型与集中式基准及本地客户端基线的表现。结果表明，联邦学习性能接近集中式学习，显著优于本地模型。这些发现表明，联邦学习可有效支持分布式数控制造环境下的协同刀具磨损预测。
原文摘要 · Abstract (English)
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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