Publisher
Springer Science and Business Media LLC
Reference26 articles.
1. Lim ML, Derani MN, Ratnam MM, Yusoff AR (2022) Tool wear prediction in turning using workpiece surface profile images and deep learning neural networks. Int J Adv Manuf Technol 120(11–12):8045–8062
2. Qiao H, Wang T, Wang P (2020) A tool wear monitoring and prediction system based on multiscale deep learning models and fog computing. Int J Adv Manuf Technol 108:2367–2384
3. Bazi R, Benkedjouh T, Habbouche H, Rechak S, Zerhouni N (2022) A hybrid CNN BiLSTM approach-based variational mode decomposition for tool wear monitoring. Int J Adv Manuf Technol 119(1):1–15. https://doi.org/10.1007/s00170-021-08448-7
4. Lee W, Abdullah M, Ong P, Abdullah H, Teo W (2021) Prediction of flank wear and surface roughness by recurrent neural network in turning process. J Adv Manuf Technol (JAMT) 15(1). Retrieved from https://jamt.utem.edu.my/jamt/article/view/6101
5. Marani M, Zeinali M, Kouam J, Songmene V, Mechefske CK (2020) Prediction of cutting tool wear during a turning process using artificial intelligence techniques. Int J Adv Manuf Technol 111:505–515