Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{171778,
        author = {BYSANI MITHIN RAGHAV and SIDDABATHUNI NAGA SRINIVAS and PATIL OMKAR and VVS HARNADH PRASAD},
        title = {Application of AI Based Techniques For CNC Machining (Milling)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {1362-1371},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=171778},
        abstract = {Tool life prediction is crucial in CNC milling to optimize machining operations and reduce costs. This study explores the application of artificial neural networks (ANNs) for predicting tool life based on machining parameters such as RPM, feed speed, axial depth of cut, and radial depth of cut. A dataset is utilized to train and evaluate the ANN model, which is optimized using standard scaling and Adam optimizer. The results demonstrate the efficacy of ANNs in accurately forecasting tool life, enabling proactive maintenance and enhancing machining efficiency and comparing with MATLAB.},
        keywords = {Artificial Neural Networks, CNC Milling, MATLAB},
        month = {January},
        }