UPSI Digital Repository (UDRep)
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UPSI Digital Repository (UDRep)
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| Abstract : Universiti Pendidikan Sultan Idris |
| In the context of Industry 4.0, manufacturing metrology is crucial for inspecting and measuring machines. The Internet of Things (IoT) technology enables seamless communication between advanced industrial devices through local and cloud computing servers. This study investigates the use of the MQTT protocol to enhance the performance of circularity measurement data transmission between cloud servers and round-hole data sources through Open CV. Accurate inspection of circular characteristics, particularly roundness errors, is vital for lubricant distribution, assemblies, and rotational force innovation. Circularity measurement techniques employ algorithms like the minimal zone circle tolerance algorithm. Vision inspection systems, utilizing image processing techniques, can promptly and accurately detect quality concerns by analyzing the models surface through circular dimension analysis. This involves sending the models image to a computer, which employs techniques such as Hough Transform, Edge Detection, and Contour Analysis to identify circular features and extract relevant parameters. This method is utilized in the camera industry and component assembly. To assess the performance, a comparative experiment was conducted between the non-contact-based 3SMVI system and the contact-based CMM system widely used in various industries for roundness evaluation. The CMM technique is known for its high precision but is time-consuming. Experimental results indicated a variation of 5 to 9.6 micrometers between the two methods. It is suggested that using a high-resolution camera and appropriate lighting conditions can further enhance result precision. 2023 Saif et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| References |
Berthold J, Imkamp D. Looking at the future of manufacturing metrology: Roadmap document of the German VDI/VDE Society for Measurement and Automatic Control. J Sensors Sens Syst. 2013; 2(1):1–7. Stojadinovic SM, Majstorovic VD, Durakbasa NM, Gaska A, Sładek J. Development of a coordinate measuring machine—based inspection planning system for industry 4.0. Appl Sci. 2021; 11(18). Lazzari A, Pou JM, Dubois C, Leblond L. Smart metrology: The importance of metrology of decisions in the big data era. IEEE Instrum Meas Mag. 2017; 20(6):22–9. Stojadinovic SM, Majstorovic VD, Durakbasa NM, Sibalija TV. Towards an intelligent approach for CMM inspection planning of prismatic parts. Meas J Int Meas Confed [Internet]. 2016; 92:326–39. Available from: https://doi.org/http%3A//dx.doi.org/10.1016/j.measurement.2016.06.037 Kasprzak B, Pękala J, Stępień AF, Świerczyński Z. Metrology and measurement systems. Architecture. 2010; XVII(4):537–47. Zhao Y, Xu X, Kramer T, Proctor F, Horst J. Dimensional metrology interoperability and standardization in manufacturing systems. Comput Stand Interfaces [Internet]. 2011; 33(6):541–55. Available from: https://doi.org/http%3A//dx.doi.org/10.1016/j.csi.2011.02.009 Saif Y, Yusof Y, Latif K, Kadir AZA, lliyas Ahmed M. Systematic review of STEP-NC-based inspection. Vol. 108, International Journal of Advanced Manufacturing Technology. Springer; 2020. p. 3619–44. Iliyas Ahmad M, Yusof Y, Daud ME, Latiff K, Abdul Kadir AZ, Saif Y. Machine monitoring system: a decade in review. Int J Adv Manuf Technol [Internet]. 2020; 108(11–12):3645–59. Available from: https://doi.org/10.1007/s00170-020-05620-3 Saif Y, Yusof Y, Latif K, Abdul Kadir AZ, Ahmad M binti I, Adam A, et al. Development of a smart system based on STEP-NC for machine vision inspection with IoT environmental. Int J Adv Manuf Technol. 2021;1–18. Batchelor BG. Machine vision handbook. Machine Vision Handbook. 2012. 1355–1560 p. 11. Sanz JLC, PetkoviC´ D. Machine Vision Algorithms for Automated Inspection of Thin-Film Disk Heads. IEEE Trans Pattern Anal Mach Intell [Internet]. 1988; 10(6):830–48. Available from: https://ieeexplore.ieee.org/document/9106 You BJ, Oh YS, Bien Z. A Vision System for an Automatic Assembly Machine of Electronic Components. IEEE Trans Ind Electron [Internet]. 1990; 37(5):349–57. Available from: https://ieeexplore.ieee.org/document/103429 Elmasry G, Cubero S, Molto´ E, Blasco J. In-line sorting of irregular potatoes by using automated computer-based machine vision system. J Food Eng [Internet]. 2012; 112(1–2):60–8. Available from: https://doi.org/http%3A//dx.doi.org/10.1016/j.jfoodeng.2012.03.027 Sofu MM, Er O, Kayacan MC, Cetişli B. Design of an automatic apple sorting system using machine vision. Comput Electron Agric. 2016; 127:395–405. Davies ER. Computer vision for automatic sorting in the food industry. In: Computer Vision Technology in the Food and Beverage Industries [Internet]. University of London, UK: Woodhead Publishing Limited; 2012. p. 150–80. http://dx.doi.org/10.1533/9780857095770.2.150 Nerakae P, Uangpairoj P, Chamniprasart K. Using Machine Vision for Flexible Automatic Assembly System. Procedia Comput Sci [Internet]. 2016; 96(September):428–35. Available from: https://doi.org/http%3A//dx.doi.org/10.1016/j.procs.2016.08.090 Wu Wen-Yen, Mao-Jim J. Wang C-ML. Automated inspection of printed circuit boards. Intell Syst Technol Appl Six Vol Set [Internet]. 1996; 28:103–11. Available from: https://www.sciencedirect.com/science/article/abs/pii/0166361595000631 Golnabi H, Asadpour A. Design and application of industrial machine vision systems. Robot Comput Integr Manuf [Internet]. 2007; 23(6):630–7. Available from: https://www.sciencedirect.com/science/article/abs/pii/S0736584507000233 Wang T, Chen Y, Qiao M, Snoussi H. A fast and robust convolutional neural network-based defect detection model in product quality control. Int J Adv Manuf Technol. 2018; 94(9–12):3465–71. Liao Z, Abdelhafeez A, Li H, Yang Y, Diaz OG, Axinte D. State-of-the-art of surface integrity in machining of metal matrix composites. Int J Mach Tools Manuf. 2019; 143:63–91. Kim DH, Kim TJ, Wang X, Kim M, Quan YJ, Oh JW, et al. Smart Machining Process Using Machine Learning: A Review and Perspective on Machining Industry. Int J Precis Eng Manuf—Green Technol. 2018; 5(4):555–68. Bulnes FG, Usamentiaga R, Garcia DF, Molleda J. An efficient method for defect detection during the manufacturing of web materials. J Intell Manuf [Internet]. 2016; 27(2):431–45. Available from: https://doi.org/http%3A//dx.doi.org/10.1007/s10845-014-0876-9 Song K, Yan Y. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl Surf Sci [Internet]. 2013; 285:858–64. Available from: https://doi.org/http% 3A//dx.doi.org/10.1016/j.apsusc.2013.09.002 Zhang Huang, Shen Xingquan, Bo Arixin, Li Yaoming, Zhan Haifei, & Gu Y. Amultiscale evaluation of the surface integrity in boring trepanning association deep hole drilling. Int J Mach Tools Manuf. 2017;123:48–56. Rao X, Zhang F, Lu Y, Luo X, Chen F. Surface and subsurface damage of reaction-bonded silicon carbide induced by electrical discharge diamond grinding. Int J Mach Tools Manuf. 2020; 154. Huang SH, Pan YC. Automated visual inspection in the semiconductor industry: A survey. Comput Ind [Internet]. 2015; 66:1–10. Available from: https://doi.org/http%3A//dx.doi.org/10.1016/j.compind.2014.10.006 Ravimal D, Kim H, Koh D, Hong JH, Lee SK. Image-Based Inspection Technique of a Machined Metal Surface for an Unmanned Lapping Process. Int J Precis Eng Manuf—Green Technol [Internet]. 2020; 7 (3):547–57. Available from: https://doi.org/10.1007/s40684-019-00181-7 Ren Z, Fang F, Yan N, Wu Y. State of the Art in Defect Detection Based on Machine Vision. Int J Precis Eng Manuf—Green Technol [Internet]. 2021; https://doi.org/10.1007/s40684-021-00343-6 Penumuru DP, Muthuswamy S, Karumbu P. Identification and classification of materials using machine vision and machine learning in the context of industry 4.0. J Intell Manuf [Internet]. 2020; 31(5):1229–41. Available from: https://doi.org/10.1007/s10845-019-01508-6 Ali MAH, Lun AK. A cascading fuzzy logic with image processing algorithm–based defect detection for automatic visual inspection of industrial cylindrical object’s surface. Int J Adv Manuf Technol. 2019; 102 (1–4):81–94. |
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