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Tool condition monitoring in micro milling of brittle materials

Lookup NU author(s): Dr Dehong Huo

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Abstract

© 2024 The AuthorsThis paper introduces a novel indirect tool condition monitoring (TCM) system for micro-milling brittle materials (glass and silicon) based on acoustic emission (AE) and cutting force signals. The milling experiments are also applied to a ductile material (steel) for comparison. Tool wear calibration is conducted to determine the three tool wear stages. The collected signals are processed in time, frequency, and time-frequency domains. Specific frequency subrange combinations of cutting force signals in three directions after wavelet packet decomposition show strong correlation to the tool wear stages, whilst processed AE signals are the secondary feature to the tool wear. A tool wear prediction model for each material is built based on all the chosen features and back propagation (BP) neural network. The prediction model is simply structured and highly efficient with the highest prediction accuracy of more than 95%.

Publication metadata

Author(s): Gong Z, Huo D

Publication type: Article

Publication status: Published

Journal: Precision Engineering

Year: 2024

Volume: 87

Pages: 11-22

Print publication date: 01/05/2024

Online publication date: 19/01/2024

Acceptance date: 15/01/2024

Date deposited: 05/02/2024

ISSN (print): 0141-6359

ISSN (electronic): 1873-2372

Publisher: Elsevier Inc.

URL: https://doi.org/10.1016/j.precisioneng.2024.01.010

DOI: 10.1016/j.precisioneng.2024.01.010

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