A Study on Tool Breakage Detection During Milling Process Using LSTM-Autoencoder and Gaussian Mixture Model
Author:
Funder
University of Seoul
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s12541-022-00647-w.pdf
Reference13 articles.
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2. Hesser, D. F., & Markert, B. (2019). Tool wear monitoring of a retrofitted CNC milling machine using artificial neural networks. Manufacturing Letters, 19, 1–4.
3. Neslušan, M., Mičieta, B., Mičietová, A., Čilliková, M., & Mrkvica, I. (2015). Detection of tool breakage during hard turning through acoustic emission at low removal rates. Measurement, 70, 1–13.
4. Sun, S., Hu, X., & Zhang, W. (2020). Detection of tool breakage during milling process through acoustic emission. The International Journal of Advanced Manufacturing Technology, 109(5), 1409–1418.
5. Li, W., & Liu, T. (2019). Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling. Mechanical Systems and Signal Processing, 131, 689–702.
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