Machine vision system for automatic inspection of surface defects in aluminum die casting Frayman, Yakov, Zheng, Hong and Nahavandi, Saeid 2006, Machine vision system for automatic inspection of surface defects in aluminum die casting, Journal of advanced computational intelligence and intelligent informatics, vol. 10, no. 3, pp. 281-286. Attached Files Name Description MIMEType Size Downloads Title Machine vision system for automatic inspection of surface defects in aluminum die casting Author(s) Frayman, Yakov Zheng, Hong Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name Journal of advanced computational intelligence and intelligent informatics Volume number 10 Issue number 3 Start page 281 End page 286 Publisher Fuji Technology Press Ltd Place of publication Tokyo, Japan Publication date 2006 ISSN 1343-0130 Keyword(s) aluminum die casting automatic vision inspection genetic algorithms surface defect recognition Summary A camera based machine vision system for the automatic inspection of surface defects in aluminum die casting is presented. 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 Indigenous content off Field of Research 080104 Computer Vision Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30004038 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Engineering and Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Mon, 01 Dec 2008, 14:42:35 EST Tue, 31 Aug 2010, 12:10:23 EST Thu, 16 Feb 2012, 13:09:09 EST Fri, 17 Oct 2014, 08:29:41 EST Fri, 31 Jan 2020, 11:26:46 EST Filtered Full Citation counts:   Cited 9 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 1662 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Mon, 07 Jul 2008, 09:10:19 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.