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IJAT Vol.18 No.4 pp. 545-580
doi: 10.20965/ijat.2024.p0545
(2024)

Review:

Advanced Sensing and Machine Learning Technologies for Intelligent Measurement in Smart and Precision Manufacturing

Ryo Sato*,† ORCID Icon, Kuangyi Li* ORCID Icon, Masaki Michihata** ORCID Icon, Satoru Takahashi** ORCID Icon, and Wei Gao* ORCID Icon

*Department of Finemechanics, Tohoku University
6-6-01 Aramaki Aza-Aoba, Aoba-ku, Sendai, Miyagi 980-8579, Japan

Corresponding author

**Department of Precision Engineering, The University of Tokyo
Tokyo, Japan

Received:
January 16, 2024
Accepted:
April 12, 2024
Published:
July 5, 2024
Keywords:
intelligent measurement, sensing, machine learning, dimensional metrology, process monitoring
Abstract

This paper provides an overview of state-of-the-art sensing and machine learning technologies for intelligent measurement in smart and precision manufacturing. Length, angle, and force are identified as the fundamental quantities for production quality management based on process monitoring as well as geometrical metrology in optical lithography and mechanical machining. Advancements in length-based measurement technologies such as laser interferometers and optical encoders, as well as advancements regarding depth and thickness measurements, are presented. Various types of optical microscopes, such as evanescent field microscopes, structured illumination microscopes, and confocal microscopes, are also described. For angle-based measurement technologies, in addition to the conventional continuous-wave laser autocollimators, the newly developed Fabry–Pérot angle sensor and nonlinear optics angle sensor using an ultrashort pulse laser are presented. Finally, on-machine and in-process force sensing and machining learning techniques for dimensional and machining process monitoring are reviewed.

Cite this article as:
R. Sato, K. Li, M. Michihata, S. Takahashi, and W. Gao, “Advanced Sensing and Machine Learning Technologies for Intelligent Measurement in Smart and Precision Manufacturing,” Int. J. Automation Technol., Vol.18 No.4, pp. 545-580, 2024.
Data files:
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