Milling ITwin: An Intelligent Digital Twin of Milling Dynamics for Process Monitoring and Chatter Suppression

Abstract

The tool-workpiece engagement dynamics in milling involve abrupt interaction forces that can degrade manufacturing performance. This paper presents a machine learning-based real-time digital twin, iTwin, for end-mill deformation estimation and chatter suppression. By integrating cutting force sensing, milling dynamics, and a learning-based surrogate model, the proposed framework estimates the realtime end-mill deformation response for process inspection and adaptive control. Offline validation at representative-point and whole-dynamics levels shows agreement with laser-measured maximum displacement and finite element analysis (FEA)-predicted nodal displacement fields. Online experiments on different aluminum materials and toolpath geometries verify that the iTwin captures the transition from chatter to stable cutting after control and consistently improves surface roughness. These results demonstrate the effectiveness of the proposed iTwin for real-time tool-state estimation and chatteraware machining-quality improvement.