The Measurement of different mechanical components using Machine Vision and digital image processing
Keywords:
Machine vision, Image acquisition, Image processing, Brinell Hardness, Single way tool postAbstract
Machine vision system is a technology that employs a computing device to inspect, evaluate, and identify still or moving parts. Post the acquisition of the image, it is processed, analysed and measured by various computer software in determining various characteristics. The role of machine vision in accurately measuring the dimensions of complex parts is inevitable. In this study an attempt has been made to accurately measure the dimensions of the boat shaped structure of the single way tool post which is used in machining process and in measuring the indentation diameter of the specimen used in Brinell Hardness. DMV 001 camera was used, and various lighting intensities and techniques have been employed during the image acquisition. A plethora of sensors and a myriad of lighting has been used to enhance the best possible way to acquire the image. Enumeration of the dimensions is made through MATLAB software. The dimensions of the boat structure were found to be 3.794 cm and of the indentation was found to be 1.8 mm. This study underscores the importance of continued research and development in machine vision systems to fully realize their potential in industrial applications.
References
. Javaid, M., Haleem, A., Singh, R.P., Rab, S. and Suman, R., 2022. Exploring impact and features of machine vision for progressive industry 4.0 culture. Sensors International, 3, p.100132.
. Smith, M.L., Smith, L.N. and Hansen, M.F., 2021. The quiet revolution in machine vision-a state-of-the-art survey paper, including historical review, perspectives, and future directions. Computers in Industry, 130, p.103472.
. Eshkevari, M., Rezaee, M.J., Zarinbal, M. and Izadbakhsh, H., 2021. Automatic dimensional defect detection for glass vials based on machine vision: A heuristic segmentation method. Journal of Manufacturing Processes, 68, pp.973-989.
. Che, J.K. and Ratnam, M.M., 2018. Real-time monitoring of workpiece diameter during turning by vision method. Measurement, 126, pp.369-377.
. Zhang, L., Yang, Q., Sun, Q., Feng, D. and Zhao, Y., 2019. Research on the size of mechanical parts based on image recognition. Journal of Visual Communication and Image Representation, 59, pp.425-432.
. Torabi, M., Mousavi, S.M. and Younesian, D., 2018. A high accuracy imaging and measurement system for wheel diameter inspection of railroad vehicles. IEEE Transactions on Industrial Electronics, 65(10), pp.8239-8249.
. Sathiyamoorthy, S., 2014. Industrial application of machine vision. International Journal of Research in Engineering and Technology (IJRET), 3(7), pp.678-682.
. Zohdy, B.S., Mahmood, M.A., Darwish, N.R. and Hefny, H.A., 2019. Machine vision application on science and industry: machine vision trends. In Optoelectronics in Machine Vision-Based Theories and Applications (pp. 233-254). IGI Global.
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Copyright (c) 2024 Ram Prakash T, M.Rishidev, N. Vignesh, S.A Vishnu Bahavath

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