Intelligent tool wear monitoring using XGBoost, SVR, and DNN models in NMQL environment.
- DOI
- 10.1038/s41598-026-40968-8
- Published
- 2026 Feb 20
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-40968-8,
title = {Intelligent tool wear monitoring using XGBoost, SVR, and DNN models in NMQL environment.},
author = {Almomani O and Venkatesh B and Chaudhary SP and Mishra A and Sujai S and Juneja S and Pradhan P and Venkatesan SP and Bhowmik A and Tamene Y},
year = {2026},
journal = {Scientific reports},
doi = {10.1038/s41598-026-40968-8},
url = {https://doi.org/10.1038/s41598-026-40968-8}
}RIS
TY - JOUR TI - Intelligent tool wear monitoring using XGBoost, SVR, and DNN models in NMQL environment. AU - Almomani O AU - Venkatesh B AU - Chaudhary SP AU - Mishra A AU - Sujai S AU - Juneja S AU - Pradhan P AU - Venkatesan SP AU - Bhowmik A AU - Tamene Y PY - 2026 JO - Scientific reports DO - 10.1038/s41598-026-40968-8 UR - https://doi.org/10.1038/s41598-026-40968-8 ER -
APA
O, A., B, V., SP, C., A, M., S, S., S, J., P, P., SP, V., A, B., & Y, T. (2026). Intelligent tool wear monitoring using XGBoost, SVR, and DNN models in NMQL environment.. Scientific reports. https://doi.org/10.1038/s41598-026-40968-8
Source records
- pubmed · retrieved 2026-09-25T12:54:28.188Z