Automatic inspection of surface defects in die castings after machining

• Autorzy: Świłło S. J. , Perzyk M.
• Języki publikacji: EN

Abstrakty

EN

A new camera based machine vision system for the automatic inspection of surface defects in aluminum die casting was developed by the authors. The problem of surface defects in aluminum die casting is widespread throughout the foundry industry and their detection is o f paramount importance in maintaining product quality. The casting surfaces are the most highly loaded regions of materials and components. Mechanical and thermal loads as well as corrosion or irradiation attacks are directed primarily at the surface of the castings. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks or tears, inclusions due to chemical reactions or foreign material in the molten metal, and pores that greatly influence the material ability to withs tand these loads. Surface defects may act as a stress concentrator initiating a fracture point. If a pressure is applied in this area, the casting can fracture. The human visual system is well adapted to perform in areas of variety and change; the visual inspection processes, on the other hand, require observing the same type of image repeatedly to detect anomalies. Slow, expensive, erratic inspection usually is the result. Computer based visual inspection provides a viable alternative to human inspectors. Developed by authors machine vision system uses an image processing algorithm based on modified Laplacian of Gaussian edge detection method to detect defects with different sizes and shapes. The defect inspection algorithm consists of three parameters. One is a parameter of defects sensitivity, the second parameter is a thres hold level and the third parameter is to identify the detected defects size and shape. The machine vision system has been successfully tested for the different types of defects on the surface of castings.

Słowa kluczowe

EN

nondestructive testing; casting defect; machined aluminum; die casting; image processing algorithms; vision system inspection

PL

badania nieniszczące; wada odlewu; odlewanie ciśnieniowe; algorytm przetwarzania obrazu; inspekcja wizyjna

• Wydawca: Komisja Odlewnictwa Polskiej Akademii Nauk Oddział w Katowicach
• Czasopismo: Archives of Foundry Engineering
• Rocznik: 2011
• Tom: Vol. 11, iss. 3 spec.
• Strony: 231--236
• Opis fizyczny: Bibliogr. 15 poz., rys.

Twórcy

autor

Świłło S. J.

• Institute of Manufacturing Technologies, Warsaw University of Technology, Narbutta 85, 02-524 Warsaw, Poland

autor

Perzyk M.

• Institute of Manufacturing Technologies, Warsaw University of Technology, Narbutta 85, 02-524 Warsaw, Poland

Bibliografia

• [1] Kupperman, D.S., Reimann, K.J., and Abrego-Lopez, “Ultrasonic NDE of Cast Stainless Steel”, NDT International, Vol. 20, No. 3, June 1987, pp. 145-152.
• [2] Nelligan, T.J., "Ultrasonic testing of nonferrous castings", Die Casting Engineer, Vol. 36, pp. 14-16, March 1992.
• [3] D. Mery, Th. Jaeger, and D. Filbert, “A review of methods for automated recognition of casting defects,” Insight, 44(7), 2002. pp. 428-436.
• [4] F. Herold, K. Bavendiek, and R. Grigat, “A third generation automatic defect recognition system,” Proc. 16th World Conference on Non Destructive Testing, Montreal, Canada, Aug. 30-Sep. 3, 2004.
• [5] Z. Xu, M. Pietikainen, and T. Ojala, “Defect classification by texture in steel surface inspection,” Proc. QCAV 97 International Conference on Quality Control by Artificial Vision, Le Creusot, Burgundy, France, pp. 179-184, May 1997, pp. 28-30.
• [6] J. Kyllonen, and M. Pietikainen, “Visual inspection of parquet slabs by combining color and texture,” Proc. IAPR Workshop on Machine Vision Applications (MVA’00), Tokyo, Japan, pp. 187-192, November 2000. pp. 28-30.
• [7] Z. Falęcki: Analiza wad odlewów. Wydawnictwa AGH, wydanie drugie, Kraków 1997.
• [8] Analysis of Casting Defects. Publ. American Foundrymen’s Society, 3d edition, Des Plaines, Illinois, USA.
• [9] M. Perzyk i inni: Odlewnictwo. WNT Warszawa 2003r.
• [10] Marr, D. and Hildreth, E., “Theory of edge detection,” Proceedings The Royal Society London, Vol. 207, 1980, pp. 187–217.
• [11] M. Perzyk, A. Kochański: Detection of causes of casting defects assisted by artificial neural networks. Journal of Engineering Manufacture, Proceedings of the Institution of Mechanical Engineers, Part B. Vol. 217, (2003). pp. 1279-1284.
• [12] S.J. Swillo, K.A. Iyer, S.J. Hu, “Optical system and method for measuring continuously distributed strain”, US Patent # 7,036,364 (Uniwersytet Michigan), 2005.
• [13] S. J. Swillo, G. Lin, S. J. Hu, K. Iyer, J. Yao, W. Cai, M. Koc: Detection and characterization of surface cracking in sheet metal hemming using optical method, Transactions of NAMRI/SME, Vol. 33, Aug. 2005.
• [14] S. J. Swillo, K. Iyer and S. J. Hu: Angled line method for spatially continuous strain distribution measurement in sheet bending, Journal of Manufacturing Science and Engineering, Vol. 128, Issue 3, Aug.2006, pp. 651-658.
• [15] S. Świłło, A. Kocańda, P. Czyżewski, P. Kowalczyk, Hemming process evaluation by using computer aided measurement system and numerical analysis, 10th ICTP, Aachen, Germany, September 25th-30th, 2011, accepted.