Machine vision system for automatic inspection of surface defects in aluminium die casting Frayman, Yakov, Zheng, Hong and Nahavandi, Saeid 2004, Machine vision system for automatic inspection of surface defects in aluminium die casting, in InTech'04 : Proceedings of the 5th International Conference on Intelligent Technologies, University of Houston-Downtown, Houston, Tex., pp. 1-5. Attached Files Name Description MIMEType Size Downloads Title Machine vision system for automatic inspection of surface defects in aluminium die casting Author(s) Frayman, Yakov Zheng, Hong Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Conference name International Conference on Intelligent Technologies (5th : 2004 : Houston, Texas) Conference location Houston, Texas Conference dates 2-4 December 2004 Title of proceedings InTech'04 : Proceedings of the 5th International Conference on Intelligent Technologies Editor(s) Alo, Richard Publication date 2004 Start page 1 End page 5 Publisher University of Houston-Downtown Place of publication Houston, Tex. Keyword(s) aluminium die casting automatic vision inspection genetic algorithms surface defect recognition Summary A machine vision system is presented for the automatic inspection of surface defects in aluminium die casting. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production. Language eng Field of Research 090699 Electrical and Electronic Engineering not elsewhere classified HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©Reproduced with the specific permission of the copyright owner. Persistent URL http://hdl.handle.net/10536/DRO/DU:30005553 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Engineering and Technology   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 28 Nov 2008, 11:15:56 EST Tue, 31 Mar 2009, 09:29:33 EST Tue, 31 Mar 2009, 09:34:42 EST Tue, 31 Mar 2009, 09:36:15 EST Tue, 31 Mar 2009, 09:46:18 EST Wed, 01 Apr 2009, 13:52:42 EST Wed, 01 Apr 2009, 13:55:43 EST Wed, 01 Apr 2009, 14:46:43 EST Wed, 01 Apr 2009, 14:47:36 EST Wed, 01 Apr 2009, 14:53:18 EST Thu, 02 Apr 2009, 08:34:17 EST Wed, 06 May 2009, 12:02:31 EST Fri, 17 Oct 2014, 08:42:16 EST Tue, 06 Sep 2016, 10:06:38 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 1991 Abstract Views, 175 File Downloads  -  Detailed Statistics Created: Mon, 07 Jul 2008, 09:51:09 EST

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