Title: Identification of tangential and normal forces in micro end milling through machine learning analysis of force signals

Authors: Yiğit Karpat

Addresses: Department of Industrial Engineering, Bilkent University, Ankara, 06800, Turkey; Department of Mechanical Engineering, Bilkent University, Ankara, 06800, Turkey; UNAM – Institute of Materials Science and Nanotechnology, 06800, Turkey

Abstract: Developing digital twins of manufacturing processes, like computer numerical control (CNC) machining, is vital due to their importance for creating high value-added parts. Tool condition monitoring has been an important research topic within this context where a major focus is on analysing machining force signals. Micro-milling is a complex process due to contributing factors like tool runout, deflection, edge radius, elastic recovery of materials, microstructure effects, and machining dynamics. This paper focuses on machine learning analysis of force signals to identify normal and tangential forces acting on the micro end mill. A machine learning algorithm based on Gaussian Process Regression (GPR) has been used to identify normal and tangential forces as a function of uncut chip thickness. The novelty of this approach is that identified normal force variation as a function of uncut chip thickness reveals information on minimum uncut chip thickness and edge radius. Monitoring the variation of these characteristic points on the force curves can be used to identify tool wear and predict remaining useful tool life.

Keywords: micro-milling; mechanistic modelling; machine learning; GPR; Gaussian process regression.

DOI: 10.1504/IJMMS.2025.150076

International Journal of Mechatronics and Manufacturing Systems, 2025 Vol.18 No.2, pp.121 - 133

Received: 08 Sep 2025
Accepted: 18 Sep 2025
Published online: 28 Nov 2025 *

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