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<p>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.</p>

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Recent Advances in CNC Technology: Toward Autonomous and Sustainable Manufacturing

  • 期刊论文

  • 发表日期：

    2025年9月9日

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  • Jong-Min Lim,
  • Wontaek Song,
  • Joon-Soo Lee,
  • Ji-Myeong Park,
  • Hee-Min Shin,
  • In-Wook Oh,
  • Soon-Hong Hwang,
  • Seungmin Jeong,
  • Sangwon Kang,
  • Chan-Young Lee,
  • Byung-Kwon Min

摘要

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.