Federated Learning for Distributed CNC Tool Wear Prediction

2026-08-11

AI Summary

This study addresses the challenge of tool wear prediction in distributed CNC machining, where data silos and privacy constraints hinder effective modeling. To overcome this, the work introduces federated learning into this domain for the first time, proposing a privacy-preserving collaborative modeling approach. Without exchanging raw data, the method leverages a federated architecture to jointly train a unified predictive model across multiple clients using their local tool trajectory data. Experimental results demonstrate that the proposed model achieves performance nearly on par with centralized training and significantly outperforms individual local models, thereby validating the feasibility and effectiveness of federated learning for industrial tool wear prediction.

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.

Problem

• tool wear prediction
• CNC machining
• distributed data
• data sharing restrictions
• federated learning

Innovation

• Federated Learning
• CNC Tool Wear Prediction
• Distributed Manufacturing
• Privacy-Preserving Machine Learning
• Collaborative Modeling