On-line defect detection of aluminum coating using fiber optic sensor
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Aluminum metallization using the sprayed coating for exhaust mild steel (MS) pipes of tractors is a standard practice for avoiding rusting. Patches of thin metal coats are prone to rusting and are thus considered as defects in the surface coating. This paper reports a novel configuration of the fiber optic sensor for on-line checking the aluminum metallization uniformity and hence for defect detection. An optimally chosen high bright 440 nm BLUE LED (light-emitting diode) launches light into a transmitting fiber inclined at the angle of 60° to the surface under inspection placed adequately. The reflected light is transported by a receiving fiber to a blue enhanced photo detector. The metallization thickness on the coated surface results in visually observable variation in the gray shades. The coated pipe is spirally inspected by a combination of linear and rotary motions. The sensor output is the signal conditioned and monitored with RISHUBH DAS. Experimental results show the good repeatability in the defect detection and coating non-uniformity measurement.
KeywordsFiber optic sensors on-line defect detection aluminum coating corrosion resistance color detection exhaust pipes of vehicles
- E. Caner, R. Farnood, and N. Yan, “Relationship between gloss and surface texture of coated papers,” Tappi Journal, 2008, 7(4), 19–26.Google Scholar
- J. Zheng, X. Zhao, and L. Zhou, “Non-contact surface roughness measurement by using laser,” Laser and Infrared, 2005, 35(3): 148–150.Google Scholar
- K. Meng and D. Wang, “Online optical measurement of surface roughness,” Journal of Harbin Engineering University, 2003, 24(5): 560–562.Google Scholar
- K. J. Oh, C. S. Lim, and K. Daiwoo, “Development of on-line measurement system of surface roughness for cold-rolled steel sheet,” in Instrumentation and Measurement Technology Conference, Brussels, pp. 335–337, 1996.Google Scholar
- K. Zhang, C. Butler, Q. Yang, and Y. Lu, “A fiber optic sensor for the measurement of surface roughness and displacement using artificial neural networks,” in Instrumentation and Measurement Technology Conference, Brussels, pp. 917–920, 1996.Google Scholar
- Tâmara C. Do Nascimento and Rafael Galli, “An equipment to measure whiteness and transparency of rice,” Revista Ciências Exatas — Universidade de taubaté (UNITAU) — Brasil, 2008, 2(1): 1–7.Google Scholar
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