Automatic Vision System for Wheel Surface Inspection and Monitoring

In order to stand out from the competition, the only alternative for automotive industries, including wheels manufacturing industries, is to grant lot of importance to their products quality. In recent years, most of the customer returns of defective products are due to appearance defects, which are associated with the wheel aesthetics. These defects are located on the outside of the wheel and are mainly related to the quality of the painting. In general, the defect detection process is a manual process conducted by operators, which is subjective and difficult due to the complicated wheels surface. This paper proposes to design a fully automatic computer vision system to inspect the whole surface of the wheel. It is proposed to use four cameras placed over the production line. A diffused lighting system is considered in order to illuminate homogeneously the whole surface of the wheel. Each wheel is designed with specific parameters that define its form and geometry. For defect detection, an original adaptive linear parametric model is proposed. This model is sufficiently general to describe any type of wheels. The adaptivity of the proposed model makes it sufficiently accurate to describe the inspected wheel surface while detecting small defects by using an optimal statistical hypothesis test. In addition, the computer vision system monitors in real time the wheel coating intensity. Using a parametric sequential method, this monitoring allows the detection of any abrupt changes, i.e. small decreasing amount of paint, that would reveal a sudden problem in the painting process.

References

(1) Holweg M., “The Evolution of Competition in the Automotive Industry,” pages 13-34. Springer, 2008.

(2) Road vehicles -- Passenger car wheels for road use -- Test methods, Standard, International Organization for Standardization, June 2015.

(3) Ramamurty Raju P. & al., “Evaluation of fatigue life of aluminium alloy wheels under bending loads,” Fatigue and Fracture of Engineering Materials and Structures, 32(2):119-126, 2009.

(4) Wang L. & al., “Fatigue life analysis of aluminum wheels by simulation of rotary fatigue test,” Strojniki vestnik - Journal of Mechanical Engineering, 57(1), 2011.

(5) Guerra A. S., M. Pillet, and J.L. Maire, “Control of variability for man measurement,” 12th IMEKO TC1-TC7 joint Symposium on Man, Science and Measurement, Annecy, France, September 2008.

(6) Maire J.L., M. Pillet, and N. Baudet, “Gage r2&e2: an effective tool to improve the visual control of  products,” International Journal of Quality & Reliability Management, 30(2):161-176, 2013.

(7) Kopardekar P., A. Mital, and S. Anand, “Manual, hybrid and automated inspection literature and current research,” Integrated Manufacturing Systems, 4(1):18-29, 1993.

(8) Pizarro L. & al., “Robust automated multiple view inspection,” Pattern Analysis and App., 11(1):21-32, 2008.

(9) Carrasco M. and D Mery, “Automatic multiple view inspection using geometrical tracking and feature analysis in aluminum wheels,” Machine Vision and Applications, 22(1):157-170, Jan 2011.

(10) Theis G. and T. Kahrs, “Fully automatic x-ray inspection of aluminium wheels,” In European Conference on Non-Destructive Testing, 2002.

(11) Herold F., “Automatic wheel inspection using building blocks,” In European Conference on NDT, 2006.

(12) Hillebrand M., N. Stevanovic, B. J. Hosticka, J. E. Santos Conde, A. Teuner, and M. Schwarz, “High speed camera system using a cmos image sensor,” In Intelligent Vehicles Symposium, pp. 656-661, 2000.

(13) Ngan H. & al., “Automated fabric defect detection a review,” Image and Vision Comp., 29(7):442-458, 2011.

(14) Yang Y. & al., “On-line conveyor belts inspection based on machine vision,” Optik-International Journal for Light and Electron Optics, 125(19):5803-5807, 2014.

(15) Neogi N., D.K. Mohanta, and P.K. Dutta, “Review of vision based steel surface inspection systems,” EURASIP Journal on Image and Video Processing, 2014(1):50, 2014.

(16) Batchelor B.G., “Selecting Cameras for Machine Vision,” pages 477-506, Springer London, London, 2012.

(17) Narendran N. and Y. Gu, “Life of LED-based white light sources,” Journal of Display Technology, vol. 1, no.  1, pp. 167-171, 2005.

(18) Tout K., R. Cogranne, and F. Retraint, “Fully automatic detection of anomalies on wheels surface using an adaptive accurate model and hypothesis testing theory,” European Signal Processing Conference, pp. 508-512, Budapest, 2016.

(19) Tout K., R. Cogranne, and F. Retraint, “Fully automatic detection of anomalies using an adaptive statistical model and testing theory: Application to wheel surface inspection,” (submitted), 2017.

(20) Page E. S, “Continuous inspection schemes,” Biometrika, vol. 41, no. 1/2, pp. 100-115, 1954.

(21) Tout K., F. Retraint, and R. Cogranne, “Wheels Coating Process Monitoring in the Presence of Nuisance Parameters Using Sequential Change-Point Detection Method,” European Signal Processing Conference, 2017.

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