Volume 29, Issue 4 (IJIEPR 2018)                   IJIEPR 2018, 29(4): 461-469 | Back to browse issues page


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Dehdar M M, Jahangoshai Rezaee M, zarinbal M, Izadbakhsh H. Integrating Fuzzy Inference System, Image Processing and Quality Control to Detect Defects and Classify Quality Level of Copper Rods. IJIEPR. 2018; 29 (4) :461-469
URL: http://ijiepr.iust.ac.ir/article-1-820-en.html
1- Faculty of Industrial Engineering, Urmia University of Technology
2- Faculty of Industrial Engineering, Urmia University of Technology , m.jahangoshai@uut.ac.ir
3- Iranian Research Institute for Information Science and Technology, Tehran
4- Faculty of Engineering, Department of Industrial Engineering, Kharazmi University
Abstract:   (646 Views)
Human-based quality control reduces the accuracy of this process. Also, the speed of decision making in some industries is very important. For removing these limitations in human-based quality control, in this paper, the design of an expert system for automatic and intelligent quality control is investigated. In fact, using an intelligent system, the accuracy in quality control is increased. It requires the knowledge of experts in quality control and design of expert systems based on the knowledge and information provided by human and equipment. For this purpose, Fuzzy Inference System (FIS) and Image Processing approach are integrated. In this expert system, the input information is the images of the products and the results of processing on images for quality control are as output. At first, they may be noisy images; the pre-processing is done and then a fuzzy system is used to be processed. In this fuzzy system, according to the images, the rules are designed to extract the specific features that are required. At second, after the required attributes are extracted, the control chart is used in terms of quality. Furthermore, the empirical case study of copper rods industry is presented to show the abilities of the proposed approach.
 
Full-Text [PDF 1343 kb]   (153 Downloads)    
Type of Study: Research | Subject: Intelligent Systems
Received: 2018/04/16 | Accepted: 2018/12/9 | Published: 2018/12/19

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