发表机构
Maastricht University（马斯特里赫特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对分布式CNC加工中数据分布与共享限制的问题，采用联邦学习框架训练刀具磨损预测模型，其性能接近集中式学习且显著优于本地模型，为分布式CNC制造提供了协作式预测方案。
AI 中文摘要
刀具磨损预测是数控（CNC）加工中的一项重要任务，对刀具状态的精准监测可保障产品质量与工艺可靠性。机器学习方法在该任务中展现出潜力，但其在工业环境中的应用受限于加工数据的分布式特性，以及不同机床、站点或组织间的数据共享限制。联邦学习为此场景提供了合适的框架，可在不传输原始操作数据的情况下实现协作式模型训练。本文研究面向CNC刀具磨损预测的联邦学习，将刀具轨迹分布于模拟客户端以模拟联邦学习场景，对比联邦模型与集中式基准及本地客户端基线。结果显示，联邦学习的性能接近集中式学习，且显著优于本地客户端模型。这些发现表明，联邦学习可支持分布式CNC制造环境中的协作式刀具磨损预测。
英文摘要
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.
