On-line defect detection of aluminum coating using fiber optic sensor

Abstract

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.

Keywords

Fiber optic sensors / on-line defect detection / aluminum coating / corrosion resistance / color detection / exhaust pipes of vehicles

References

[1] Caner E, Farnood R, Yan N. Relationship between gloss and surface texture of coated papers. Tappi Journal, 2008, 7(4): 19-26.

[2] Wang W, Wong P L, Luo J B, Zhang Z. A new optical technique for roughness measurement on moving surface. Tribology International, 1998, 31(5): 281-287.

[3] Zheng J, Zhao X, Zhou L. Non-contact surface roughness measurement by using laser. Laser and Infrared, 2005, 35(3): 148-150.

[4] Meng K, Wang D. Online optical measurement of surface roughness. Journal of Harbin Engineering University, 2003, 24(5): 560-562..

[5] Oh K J, Lim C S, Daiwoo K. Development of on-line measurement system of surface roughness for cold-rolled steel sheet. Instrumentation and Measurement Technology Conference, 1996 335-337.

[6] Zhang K, Butler C, Yang Q, Lu Y. A fiber optic sensor for the measurement of surface roughness and displacement using artificial neural networks. Instrumentation and Measurement Technology Conference, 1996 917-920.

[7] Toh S L, Quan C, Woo K C, Tay C J, Shang H M. Whole field surface roughness measurement by laser speckle correlation technique. Optics and Laser Technology, 2001, 33(6): 427-434.

[8] Toh S L, Shang H M, Tay C J. Surface-roughness study using laser speckle method. Optics and Lasers in Engineering, 1998, 29(3): 217-2256.

[9] Wong P L, Li K Y. In-process roughness measurement on moving surfaces. Optics and Laser Technology, 1999, 31(8): 543-548.

[10] Persson U. In-process measurement of surface roughness using light scattering. Wear, 1998, 215(1–2): 54-58.

[11] Chen J C, Savage M. A fuzzy-net based multilevel in process surface roughness recognition system in milling operations. The International Journal of Advanced Manufacturing Technology, 2001, 17(9): 670-676.

[12] Nascimento T C D, Galli R. An equipment to measure whiteness and transparency of rice. Revista Ciências Exatas — Universidade de taubaté (UNITAU) — Brasil, 2008, 2(1): 1-7.

[13] Faria J. A theoretical analysis of the bifurcated fiber bundle displacement sensor. IEEE Transactions on Instrumentation and Measurement, 2002, 47(3): 742-748.