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Recent Advances in CNC Technology: Toward Autonomous and Sustainable Manufacturing.

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Title: Recent Advances in CNC Technology: Toward Autonomous and Sustainable Manufacturing.
Authors: Lim, Jong-Min, Song, Wontaek, Lee, Joon-Soo, Park, Ji-Myeong, Shin, Hee-Min, Oh, In-Wook, Hwang, Soon-Hong, Jeong, Seungmin, Kang, Sangwon, Lee, Chan-Young, Min, Byung-Kwon
Source: International Journal of Precision Engineering & Manufacturing; Sep2025, Vol. 26 Issue 9, p2311-2344, 34p
Abstract: Computer Numerical Control (CNC) systems have evolved into indispensable platforms for modern manufacturing, enabling high-precision, multi-axis machining with programmable automation. This review provides a comprehensive overview of recent advancements in CNC technology, focusing on core system components such as interpolators and servo controllers. We examine high-order interpolation and advanced smoothing algorithms that enhance toolpath generation and surface quality, as well as servo control strategies that improve tracking performance and dynamic response. Error compensation techniques for geometric, thermal, and dynamic deviations are reviewed in the context of both modeling and real-time implementation. Sustainable machining is addressed through energy modeling, process optimization, and component-level consumption analysis. Furthermore, the paper explores emerging digital transformation technologies—including digital twins, cloud-based control, and robot integration—that enhance system intelligence and interoperability. Special attention is given to the integration of artificial intelligence and machine learning, which enables adaptive path planning, real-time optimization, and predictive maintenance. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Precision Engineering & Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: International Journal of Precision Engineering & Manufacturing; Sep2025, Vol. 26 Issue 9, p2311-2344, 34p
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  Data: Computer Numerical Control (CNC) systems have evolved into indispensable platforms for modern manufacturing, enabling high-precision, multi-axis machining with programmable automation. This review provides a comprehensive overview of recent advancements in CNC technology, focusing on core system components such as interpolators and servo controllers. We examine high-order interpolation and advanced smoothing algorithms that enhance toolpath generation and surface quality, as well as servo control strategies that improve tracking performance and dynamic response. Error compensation techniques for geometric, thermal, and dynamic deviations are reviewed in the context of both modeling and real-time implementation. Sustainable machining is addressed through energy modeling, process optimization, and component-level consumption analysis. Furthermore, the paper explores emerging digital transformation technologies—including digital twins, cloud-based control, and robot integration—that enhance system intelligence and interoperability. Special attention is given to the integration of artificial intelligence and machine learning, which enables adaptive path planning, real-time optimization, and predictive maintenance. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Precision Engineering & Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s12541-025-01334-2
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              M: 09
              Text: Sep2025
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              Y: 2025
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