A cascading fuzzy logic with image processing algorithm–based defect detection for automatic visual inspection of industrial cylindrical object’s surface
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This paper proposes a cascading fuzzy logic algorithm with image processing technique for defect detection and classification on the lateral surface of industrial cylindrical object using a camera and multiple flat mirrors. The finishing surface of industrial parts such as shafts, bearings, pistons, rings, and pins should be smooth within permissible limits before installation process, as the defects in these parts may damage or reduce the life of the whole machine. The optical surface inspection of cylindrical products and highly curved surfaces is quite challenging in the industrial automation, due to that it needs to be acquired with several views and subsequently combined in one view. Thus, a time–cost-effective visual inspection method with fuzzy logic–based decision making approach is developed to investigate the optical defects on the lateral surfaces of the cylindrical products. The image processing algorithm has been developed to extract the main features of the tested objects such as defects, borders, and noise. A cascading fuzzy logic algorithm with two stages has been implemented to eliminate the effect of the noise in the captured images and thereafter classify the objects into defective and non-defective objects. The 1st stage of fuzzy logic algorithm is used to eliminate the low noise from the captured images; however, the 2nd stage is used to differentiate between the big noise and defects on the objects. Results show that the defects can be detected if the ratio of detection is higher than 0.1 and the accuracy of defectiveness levels is 80%.
KeywordsDefect detection Multiple flat mirror system Fuzzy logic Non-destructive testing Cylindrical object
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The authors would like to thank Universiti Malaysia Pahang (UMP) and Ministry of High Education (MOHE) for providing the research grant and facilities.
This research is supported using UMP-Research University grant, RDU160131.
- 1.Ali M, Mailah M, Kazi S, Hing T (2011) Defects detection of cylindrical object’s surface using vision system. In: Proceedings of The 10th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics (CIMMACS '11), Jakarta, 1–3 December. online: http://www.wseas.us/e-library/conferences/2011/Jakarta/CIMISP/CIMISP-36.pdf Accessed 14 Dec 2018
- 2.Ali M, Mailah M, Hing T (2012) Visual inspection of cylindrical Product's lateral surface using camera and image processing. Int J Math Model meth Appl Sci 6(2):340-348Google Scholar
- 3.Weyrich M, Klein P, Laurowski M, Wang Y (2011) Vision based defect detection on 3D objects and path planning for processing. Proceedings of the 9th WSEAS International Conference on ROCOMGoogle Scholar
- 4.Weyrich M, Klein P, Laurowski M, Wang Y (2011) A real-time and vision-based methodology for processing 3D objects on a conveyor belt. WSEAS WSEAS Int J Sys Appl, Eng Devel 5(4):561-569Google Scholar
- 5.Yuxiang Y, Zheng J, Mingyu G, Zhiwei H (2015) A robust vision inspection system for detecting surface defects of film capacitors. In press. Elsavier Signal ProcessingGoogle Scholar
- 6.Yang Y, Gao M, Yin K, Wu Z, Li Y (2015.B) An automatic visual inspection system for cone surface defects. J Comp Meth Sci Eng 15(2):269–276Google Scholar
- 8.Dhou S, Mutai Y (2015) Dynamic 3D surface reconstruction and motion modeling from a pan–tilt–zoom camera. Comput Ind 70(3):183–193 Elsevier. online: https://www.sciencedirect.com/science/article/pii/S0166361515000391 Accessed 14 Dec 2018
- 9.Vilas D, Kiran H (2013) Quality inspection and grading of mangoes by computer vision and image analysis. J Eng Res Appl 3(5):1208–1212 online: http://www.ijera.com/papers/Vol3_issue5/GW3512081212.pdf Accessed 14 Dec 2018
- 10.Mendoza F, Kelly J, Cichy K (2016) Automated prediction of sensory scores for color and appearance in canned black beans (Phaseolus vulgaris L.) using machine vision. Int J Food Prop. https://doi.org/10.1080/10942912.2015.1136939
- 11.Anvarkhah S, Panah A, Anvarkhah A (2016) The influence of color features on seed identification using machine vision. Not Sci Biol 8(1):93-97Google Scholar
- 12.Santos J, Leta F (2012) Applications of computer vision techniques in the agriculture and food industry: a review. Eur Food Res Technol 235(6):989-1000Google Scholar
- 13.Ali M (2014) Autonomous mobile robot navigation and control in the road following and roundabout environments incorporating laser range finder and vision system. Ph.D thesis UTMGoogle Scholar
- 14.Deepika B, Shanu S (2014) A survey of machine vision techniques for fruit sorting and grading. International Journal of Engineering Research & Technology 3(7):1187-1193Google Scholar
- 15.Teledyne DALSA line scan camera. online https://www.teledynedalsa.com/en/learn/knowledge-center/color-line-scan-imaging Accessed 14 Dec 2018
- 16.Hyperfine spectrometer. online : https://lightmachinery.com/spectrometers/hyperfine-spectrometer/?gclid=EAIaIQobChMIg4vZ57r_3gIVioqPCh0FQwwHEAAYASAAEgICcPD_BwE Accessed 14 Dec 2018