Authors:
Perfecto Mariño Espiñeira
;
Vicente Pastoriza Santos
;
Miguel Santamaría Sánchez
and
Emilio Martínez Expósito
Affiliation:
Universidad de Vigo, Spain
Keyword(s):
Image processing, fuzzy control, machine vision system, intelligent fault detection.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Feature Extraction
;
Features Extraction
;
Fuzzy Control
;
Fuzzy Systems
;
Image and Video Analysis
;
Image Processing
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Intelligent Fault Detection and Identification
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
Abstract:
The authors have been involved in developing an automated inspection system, based on machine vision, to improve the repair coating quality control (RCQ control) in can ends of metal containers for fish food. The RCQ of each end is assesed estimating its average repair coating quality (ARCQ). In this work we present a fuzzy model building to make the acceptance/rejection decision for each can end from the information obtained by the vision system. In addition it is interesting to note that such model could be interpreted and supplemented by process operators. In order to achieve such aims, we use a fuzzy model due to its ability to favour the interpretability for many applications. Firstly, the easy open can end manufacturing process, and the current, conventional method for quality control of easy open can end repair coating, are described. Then, we show the machine vision system operations. After that, the fuzzy modeling, results obtained and their discussion are presented. Finally
, concluding remarks are stated.
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