Tool wear prediction method based on bidirectional long short-term memory neural network of single crystal silicon micro-grinding
Author:
Publisher
Springer Science and Business Media LLC
Subject
Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering
Link
https://link.springer.com/content/pdf/10.1007/s00170-023-12070-0.pdf
Reference35 articles.
1. Li W, Ren YH, Li CF, Li ZP, Li MJ (2020) Investigation of machining and wear performance of various diamond micro-grinding tools. Int J Adv Manuf Technol 106(3-4):921–935. https://doi.org/10.1007/s00170-019-04610-4
2. An Q, Tao Z, Xu X, El Mansori M, Chen M (2020) A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network. Measurement 154:107461. https://doi.org/10.1016/j.measurement.2019.107461
3. Ren Y, Li C, Li W, Li M, Liu H (2019) Study on micro-grinding quality in micro-grinding tool for single crystal silicon. J Manuf Process 42:246–256. https://doi.org/10.1016/j.jmapro.2019.04.030
4. Mohanraj T, Shankar S, Rajasekar R, Sakthivel NR, Pramanik A (2020) Tool condition monitoring techniques in milling process - a review. J Mater Res Technol-Jmr&T 9(1):1032–1042. https://doi.org/10.1016/j.jmrt.2019.10.031
5. Wu SH, Li Y, Li WG, Zhao XZ, Luo CL, Yu QL, Lin SJ (2023) A hybrid network capturing multisource feature correlations for tool remaining useful life prediction. Intal J Adv Manuf Technol 125(5-6):2815–2831. https://doi.org/10.1007/s00170-023-10837-z
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