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Lookup NU author(s): Lukman Abu, Dr Dehong Huo
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2026.Tool condition monitoring (TCM) is critical for micro-machining brittle materials to ensure precision, extend tool life, and maintain surface quality. This study investigated the integration of acoustic emission (AE) sensors and cutting force dynamometry for real-time monitoring of tool wear during micro-milling of glass and silicon substrates. A total of 150 slots were machined using 0.9 mm diamond-coated micro-end mills across progressive stages of tool wear, with separate tools used for glass and silicon substrates. Data were collected across three tool wear states: Initial Wear, Medium Wear, and Severe Wear, with approximately 25 slots machined at each wear stage per material. Tool wear progression was quantified using cutting-edge radius (CER) measurements obtained via SEM imaging, ranging from 7 μm (new tool) to 23 μm (severe wear) for glass and 7 μm to 21 μm for silicon. During each machining operation, signals were simultaneously acquired from acoustic emission (AE) sensors (100 kHz sampling rate) and a cutting force dynamometer (40 kHz sampling rate). Multi-domain signal processing, including time-, frequency-, and wavelet-domain analyses, was applied to extract diagnostic features. A Random Forest classifier trained with Leave-One-Out Cross-Validation (LOOCV) achieved 92% classification accuracy, demonstrating robust generalization across tools. Results revealed strong correlations between increased cutting forces, elevated AE spectral energy (4–6× baseline in high-frequency bands), and tool degradation. AE sensors demonstrated superior sensitivity for early-stage wear detection, while force signals provided reliable indicators during medium and severe wear stages. The integrated multi-sensor framework offers substantial improvements over single modality approaches and provides a foundation for real-time TCM implementation in precision micro-manufacturing environments.
Author(s): Abu L, Huo D, Liu Z
Publication type: Article
Publication status: Published
Journal: International Journal of Advanced Manufacturing Technology
Year: 2026
Pages: epub ahead of print
Online publication date: 31/03/2026
Acceptance date: 24/03/2026
Date deposited: 14/04/2026
ISSN (print): 0268-3768
ISSN (electronic): 1433-3015
Publisher: Springer Science and Business Media Deutschland GmbH
URL: https://doi.org/10.1007/s00170-026-17960-7
DOI: 10.1007/s00170-026-17960-7
Data Access Statement: The raw signal data (AE and force measurements) processed feature sets, and trained model parameters that support the findings of this study are available from the corresponding author upon reasonable request. Due to the large file sizes of raw signal data (> 60 GB), data sharing will be facilitated through institutional repository systems. Sample datasets and processing scripts are available at [repository URL to be provided upon acceptance].
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