Advances in Computer Numerical Control Geometric Error Compensation: Integrating AI and On-Machine Technologies for Ultra-Precision Manufacturing

Geometric inaccuracies in machine configuration and part specifications are a major source of errors in CNC machining. These discrepancies have long affected the quality of manufactured components and continue to be a key research area in academia and industry. Over the years, significant efforts have been made to minimize these errors and enhance machining precision. Researchers have explored various methodologies to identify, measure, and compensate for spatial inaccuracies, improving accuracy in modern machining systems. This paper comprehensively reviews recent advancements in geometric error measurement and compensation techniques, particularly in five-axis machine tools. It examines the latest methods for detecting errors and explores volumetric error modeling approaches designed to enhance machining precision. This review highlights the growing role of emerging technologies, including on-machine measurement systems, machine learning algorithms, and digital twin frameworks, in improving real-time error detection and compensation strategies. Furthermore, advanced tools such as laser interferometry and hybrid software–hardware approaches are discussed for their potential to drive innovation in ultra-precision machining. This paper also addresses key challenges in achieving high volumetric accuracy and outlines future opportunities for improving CNC machining performance. Future research can enhance precision and reliability in modern manufacturing by integrating intelligent systems and advanced measurement techniques.

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Publication Details

Journal
Machines
Published
2025-02-12
DOI
https://doi.org/10.3390/machines13020140
Citations
12
Primary Topic
Advanced Numerical Analysis Techniques
Type
article
Field-Weighted Citation Impact
16.02
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Advances in Computer Numerical Control Geometric Error Compensation: Integrating AI and On-Machine Technologies for Ultra-Precision Manufacturing

Fred Lacerda Amorim, Yassmin Seid Ahmed
12 citations
Machines
Advanced Numerical Analysis Techniques
16.02
article

Advances in Computer Numerical Control Geometric Error Compensation: Integrating AI and On-Machine Technologies for Ultra-Precision Manufacturing

Fred Lacerda Amorim, Yassmin Seid Ahmed
article en
12 citations

Abstract

Geometric inaccuracies in machine configuration and part specifications are a major source of errors in CNC machining. These discrepancies have long affected the quality of manufactured components and continue to be a key research area in academia and industry. Over the years, significant efforts have been made to minimize these errors and enhance machining precision. Researchers have explored various methodologies to identify, measure, and compensate for spatial inaccuracies, improving accuracy in modern machining systems. This paper comprehensively reviews recent advancements in geometric error measurement and compensation techniques, particularly in five-axis machine tools. It examines the latest methods for detecting errors and explores volumetric error modeling approaches designed to enhance machining precision. This review highlights the growing role of emerging technologies, including on-machine measurement systems, machine learning algorithms, and digital twin frameworks, in improving real-time error detection and compensation strategies. Furthermore, advanced tools such as laser interferometry and hybrid software–hardware approaches are discussed for their potential to drive innovation in ultra-precision machining. This paper also addresses key challenges in achieving high volumetric accuracy and outlines future opportunities for improving CNC machining performance. Future research can enhance precision and reliability in modern manufacturing by integrating intelligent systems and advanced measurement techniques.

MachinesVol. 13(2)
King Fahd University of Petroleum and Minerals (SA), Pontifícia Universidade Católica do Paraná (BR)
Openalex Percentile: Top 0%
Advanced Numerical Analysis Techniques
16.02
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