The use of neural networks for the classification of casting defect Online publication date: Wed, 30-May-2007
by Leszek A. Dobrzanski, Mariusz Krupinski, Jerry Sokolowski, Piotr Zarychta
International Journal of Computational Materials Science and Surface Engineering (IJCMSSE), Vol. 1, No. 1, 2007
Abstract: Employment of the artificial intelligence tools for development of the methodology of assessing quality and structural defects in Al and Mg alloys and custom-made computer software will make it possible to determine the quality of manufactured element based on digital images registered in the X-ray flaw detection examinations. The possibility to correlate the frequency and morphology of defects with the technological process parameters will make it also possible to identify and classify these defects and control the process to minimise and eliminate them. The methodology is presented in this paper, making it possible to determine the types and classes of defects developed during casting elements from aluminium alloys, making use of photos obtained from the flaw detection method with X-ray radiation. [Received 10 November 2005; Accepted 9 January 2007]
Online publication date: Wed, 30-May-2007
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