Machine tool calibration: Measurement, modeling, and compensation of machine tool errors

• Published in: International journal of machine tools & manufacture 2023-04, Vol.187, p.104017, Article 104017
• Main Authors: Gao, Wei, Ibaraki, Soichi, Donmez, M. Alkan, Kono, Daisuke, Mayer, J.R.R., Chen, Yuan-Liu, Szipka, Károly, Archenti, Andreas, Linares, Jean-Marc, Suzuki, Norikazu
• Format: Article
• Language: English
• Subjects: Calibration; Computer Science; Engineering Sciences; Machine Learning; Machine tool; Measurement; Mechanical engineering; Mechanics; Self -calibration; Uncertainty
• ISSN: 0890-6955; 1879-2170; 1879-2170
• DOI: 10.1016/j.ijmachtools.2023.104017

Advanced technologies for the calibration of machine tools are presented. Kinematic errors independently of their causes are classified into errors within one-axis as intra-axis errors, errors between axes as inter-axis errors, and as volumetric errors. As the major technological elements of machine tool calibration, the measurement methods, modeling theories, and compensation strategies of the machine tool errors are addressed. The criteria for selecting a combination of the technological elements for machine tool calibration from the point of view of accuracy, complexity, and cost are provided. Recent applications of artificial intelligence and machine learning in machine tool calibration are introduced. Remarks are also made on future trends in machine tool calibration. [Display omitted] •Measurement, modeling, and compensation methods for machine tool errors are reviewed.•Measurement and modeling of quasi-static, dynamic, static load-induced and thermally-induced kinematic errors are reviewed.•Criteria for selecting technological elements for machine tool calibration are provided.•Artificial intelligence (AI) and machine learning (ML) tools for machine tool calibration are demonstrated.•Unsolved technical challenges and future research directions are presented.