Machine Vision and Metrology Systems: An Overview

Main Article Content

Desmond K. Moru
Darlington Agholor
Francis A. Imouokhome

Abstract

Metrology and machine vision are two fields that have been considered together frequently due to the versatility of artificial vision to solve industrial inspection problems. Metrology is one of the many applications of machine vision, which has the advantage that allows for the inspection of a total production batch that leaves an assembly line without creating a bottleneck in production. The aim of this paper is to present an overview of the current advancement in machine vision and metrology systems.  The paper exposes a wide range of machine vision software aimed at the inspection of application processes, systematically highlighting the relationship between machine vision and metrology systems. Some applications of machine vision and metrology for quality control inspections are also highlighted.

Article Details

How to Cite
[1]
D. K. Moru, D. Agholor, and F. A. Imouokhome, “Machine Vision and Metrology Systems: An Overview”, Int. J. Data. Science., vol. 2, no. 2, pp. 77-84, Dec. 2021.
Section
Articles

References

J. L. Sanz, Advances in machine vision. Springer Science & Business Media, 2012.

K. J. Dowling, G. G. Mueller, and I. A. Lys, “Systems and methods for providing illumination in machine vision systems,” 2006. US Patent 7,042,172.

T. S. Newman and A. K. Jain, “A system for 3d cad-based inspection using range images,” Pattern Recognition, vol. 28, no. 10, pp. 1555–1574, 1995.

B. G. Batchelor, “Coming to terms with machine vision and computer vision- they are not the same,” Advanced imaging, vol. 14, no. 1, p. 22,1999.

M. Z. Abidin and R. Pulungan, “A systematic review of machine- vision-based smart parking systems,” Sci. J. Informatics, vol. 7, no. 2, pp. 213–227, 2020.

C. Kavitha and S. D. Ashok, “A new approach to spindle radial error evaluation using a machine vision system,” Metrology and Measurement Systems, vol. 24, no. 1, 2017.

E. N. Malamas, E. G. Petrakis, M. Zervakis, L. Petit, and J.-D. Legat, “A survey on industrial vision systems, applications and tools,” Image and vision computing, vol. 21, no. 2, pp. 171–188, 2003.

A. D. Thomas, M. G. Rodd, J. D. Holt, and C. Neill, “Real-time in- dustrial visual inspection: A review,” Real-Time Imaging, vol. 1, no. 2, pp. 139–158, 1995.

V. Kakani, V. H. Nguyen, B. P. Kumar, H. Kim, and V. R. Pasupuleti, “A critical review on computer vision and artificial intelligence in food industry,” Journal of Agriculture and Food Research, vol. 2, p. 100033, 2020.

S. Yang, B. Li, Y.-P. Cao, H. Fu, Y.-K. Lai, L. Kobbelt, and S.-M. Hu, “Noise-resilient reconstruction of panoramas and 3d scenes using robot- mounted unsynchronized commodity rgb-d cameras,” ACM Transactions on Graphics (TOG), vol. 39, no. 5, pp. 1–15, 2020.

D. K. Moru and D. Borro, “A machine vision algorithm for quality control inspection of gears,” The International Journal of Advanced Manufacturing Technology, vol. 106, no. 1, pp. 105–123, 2020.

L. Mennel, J. Symonowicz, S. Wachter, D. K. Polyushkin, A. J. Molina- Mendoza, and T. Mueller, “Ultrafast machine vision with 2d material neural network image sensors,” Nature, vol. 579, no. 7797, pp. 62–66, 2020.

S. Manzeli, D. Ovchinnikov, D. Pasquier, O. V. Yazyev, and A. Kis, “2d transition metal dichalcogenides,” Nature Reviews Materials, vol. 2, no. 8, pp. 1–15, 2017.

T. Mueller and E. Malic, “Exciton physics and device application of two-dimensional transition metal dichalcogenide semiconductors,” npj 2D Materials and Applications, vol. 2, no. 1, pp. 1–12, 2018.

Y. Chai, “In-sensor computing for machine vision,” 2020.

S. Wu, Z. Wang, B. Shen, J.-H. Wang, and L. Dongdong, “Human- computer interaction based on machine vision of a smart assembly workbench,” Assembly Automation, 2020.

I. Leite and A. Cabral, “An optical metrology system for the measurement of the refractive index of glass,” in EPJ Web of Conferences, vol. 238, p. 06013, EDP Sciences, 2020.

H. Choi, M. A. Esparza, A. Lamdan, Y.-T. Feng, T. Milster, D. Apai, and D. W. Kim, “In-process metrology for segmented optics uv curing control,” in Optical Manufacturing and Testing XIII, vol. 11487, p. 114870M, International Society for Optics and Photonics, 2020.

X. Zhang, Y. Zheng, V. Suresh, S. Wang, Q. Li, B. Li, and H. Qin, “Correlation approach for quality assurance of additive manufactured parts based on optical metrology,” Journal of Manufacturing Processes, vol. 53, pp. 310–317, 2020.

I. Gibson, D. Rosen, B. Stucker, and M. Khorasani, “Materials for additive manufacturing,” in Additive Manufacturing Technologies, pp. 379–428, Springer, 2021.

C. Chen, X. Wang, Y. Wang, D. Yang, F. Yao, W. Zhang, B. Wang, G. A. Sewvandi, D. Yang, and D. Hu, “Additive manufacturing of piezoelectric materials,” Advanced Functional Materials, vol. 30, no. 52, p. 2005141, 2020.

A. Majeed, Y. Zhang, S. Ren, J. Lv, T. Peng, S. Waqar, and E. Yin, “A big data-driven framework for sustainable and smart additive manufacturing,” Robotics and Computer-Integrated Manufacturing, vol. 67, p. 102026, 2021.

F. Pedreschi, J. Le´on, D. Mery, and P. Moyano, “Development of a computer vision system to measure the color of potato chips,” Food Research International, vol. 39, no. 10, pp. 1092–1098, 2006.

G. Peng, Z. Zhang, and W. Li, “Computer vision algorithm for mea- surement and inspection of o-rings,” Measurement, vol. 94, pp. 828–836, 2016.

D. K. Moru, “Improving the pipeline of an optical metrology system.,” 2020.

B. G. Batchelor, Machine Vision Handbook. Springer, 2012.

S. Andonov and M. Cundeva-Blajer, “Calibration for industry 4.0 metrology: touchless calibration,” in Journal of Physics: Conference Series, vol. 1065, p. 072019, IOP Publishing, 2018.

Q. Wang, Y. Peng, A.-K. Wiemann, F. Balzer, M. Stein, N. Steffens, and G. Goch, “Improved gear metrology based on the calibration and compensation of rotary table error motions,” CIRP Annals, vol. 68, no. 1, pp. 511–514, 2019.

M. Baghery, S. Yousefi, and M. J. Rezaee, “Risk measurement and prioritization of auto parts manufacturing processes based on process failure analysis, interval data envelopment analysis and grey relational analysis,” Journal of Intelligent Manufacturing, vol. 29, no. 8, pp. 1803–1825, 2018.

Q. Hou, J. Sun, and P. Huang, “A novel algorithm for tool wear on- line inspection based on machine vision,” The International Journal of Advanced Manufacturing Technology, vol. 101, no. 9, pp. 2415–2423, 2019.

J. Schlett, “Machine vision helps adhesive trend stick in auto industry,” Photonics Media, 2016.

M. T. Habib, A. Majumder, A. Jakaria, M. Akter, M. S. Uddin, and F. Ahmed, “Machine vision based papaya disease recognition,” Journal of King Saud University-Computer and Information Sciences, vol. 32, no. 3, pp. 300–309, 2020.

D. K. Moru and D. Borro, “Improving optical pipeline through better alignment and calibration process,” The International Journal of Advanced Manufacturing Technology, vol. 114, no. 3, pp. 797–809, 2021.

A. A. Robie, K. M. Seagraves, S. R. Egnor, and K. Branson, “Machine vision methods for analyzing social interactions,” Journal of Experimental Biology, vol. 220, no. 1, pp. 25–34, 2017.

C. Steger, M. Ulrich, and C. Wiedemann, Machine vision algorithms and applications. John Wiley & Sons, 2018.