Diagnostics of Product by Vision System

Article Preview

Abstract:

This article describes the vision system, which is designed for diagnostics of defects in casted products. In the first part an overview about image processing, edge and pattern recognition algorithms and current status in available free and commercial vision libraries is found. For the described task we selected open source Aforge .NET library. The next part describes common defects in casted products. Modular education system MPS 500 from Festo with conveyor and palette with plastic parts is used for simulation of production system. This system contains an industrial robot which can be used for sorting defective parts. The selected vision library is used for two level diagnostics of algorithm implementation. The first level algorithm detects position of part, its dimensions and edge disturbances. The second algorithm detects any defects inside of a part. The basic algorithm is presented only for circular shape with red color texture, but can be easily extended to other basic shapes by shape detector.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

33-38

Citation:

Online since:

February 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] M. Magee, S. Seida, An Industrial Model-Based Computer Vision System, Journal of Manufacturing Systems, vol. 14, (1995) pp.169-186.

DOI: 10.1016/0278-6125(95)98885-a

Google Scholar

[2] T. Lindeberg, Edge detection, in Hazewinkel, Michiel, Encyclopedia of Mathematics, Springer, (2001), pp.30-36.

Google Scholar

[3] H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, Speeded-Up Robust Features (SURF), In: ETH Zurich, (2008), pp.1-14.

DOI: 10.1016/j.cviu.2007.09.014

Google Scholar

[4] OpenCv Library, information on http://opencv.willowgarage.com/wiki.

Google Scholar

[5] EmguCv Library, information on http://www.emgu.com/wiki/index.php/Main_Page.

Google Scholar

[6] AForge.Net Library, information on http://www.aforgenet.com/framework.

Google Scholar

[7] National Instruments, Machine Vision Software, http://www.ni.com/vision/software.

Google Scholar

[8] Mathworks, Matlab, Computer Vision System Toolbox, information on http://www.mathworks.com/products/computer-vision/description5.html.

Google Scholar

[9] Module-I of Manufacturing Science-I, Casting defects, information on http://www.scribd.com/chinmaydas/d/19162058-Casting-Defects.

Google Scholar

[10] J. Svetlík, P. Demeč, Virtual machining and its experimental verification, In: Acta Mechanica Slovaca, Roč. 13, No. 4 (2009), pp.68-73.

DOI: 10.2478/v10147-010-0039-8

Google Scholar

[11] J. Boržíková, A. Hošovský, J. Piteľ, Modeling of heat transfer through the wall for simulation of heating process control, In: Innovacia, ekologia i resursosberegajusčie technologii na predprijatiach mašinostrojenia, aviastrojenia, transporta i seľskovo chozjajstva: (2010), Rostov na Done, pp.298-301.

Google Scholar

[12] M. Balara, The parametric invariants control system, In: 20. DIDMATTECH (2007), Olomouc: Votobia, pp.109-114.

Google Scholar

[13] F. Duchoň, Marian K., L. Jurišica, adislav: Reactive Navigation of Mobile Robot with Visual System. In: Acta Mechanica Slovaca, 13, č. 2-A (2009), pp.47-52.

Google Scholar