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@article{189155,
        author = {Urmila Nagargoje and Kharde prafulla and Kshirsagar Deesha and Khedkar maharudra and Kotkar Payal},
        title = {The Integration of Artificial Intelligence in CNC Machining: A Comprehensive Review of Innovations, Applications, and Future Prospects},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {6154-6158},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189155},
        abstract = {This paper presents a comprehensive review of the application of Artificial Intelligence (AI) techniques in Computer Numerical Control (CNC) machining systems. It examines the integration of classical machine learning, deep learning, and reinforcement learning methods for improving machining intelligence, autonomy, and efficiency. The review covers modern AI-enabled system architectures, including edge-AI frameworks for real-time decision-making and digital twin models for virtual process monitoring, optimization, and predictive analysis. Key industrial applications such as predictive maintenance of machine tools, in-process adaptive control of cutting parameters, automated surface quality and defect inspection, tool wear prediction, and AI-driven generative design are discussed in detail.
To provide a strong technical foundation, the paper outlines basic mathematical formulations used in AI-based machining models, including regression, classification, and optimization frameworks, along with commonly adopted performance evaluation metrics such as accuracy, root mean square error, mean absolute error, and computational latency. Additionally, critical considerations related to cybersecurity, data integrity, model robustness, and ethical issues such as transparency and trustworthiness in AI-assisted manufacturing are addressed. Finally, the paper highlights emerging trends and identifies open research challenges, including data scarcity, model generalization across machining conditions, real-time implementation constraints, and the integration of human machine collaboration in AI-enabled CNC machining environments.},
        keywords = {},
        month = {December},
        }