Intelligent defect management system for porcelain industry through cyber-physical systems

Manuella Kadar, Ricardo Jardim-Goncalves, Cristian Covaciu, Santiago Bullon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents an intelligent defect management system based on cyber-physical systems that orchestrate computational resources with physical systems in the specific environment of porcelain manufacturing. Sensors and computer vision techniques detect defects and faults, an intelligent information processing system based on ontologies and deep learning methods quantify different types of defects in porcelain products and provide information to the decision making system and actuators in the workflow. The system is developed within a project in collaboration with one of the biggest porcelain ware producer in Europe. It focuses on defect causation analysis and provide an advanced management system aiming to facilitate defect measures and rectifications by: (1) collecting and classifying defect data; (2) identifying causations of defect and analyzing its impact; (3) searching and managing defect information by means of knowledge management (KM) techniques, namely ontologies; and (4) developing agile defect control of porcelain ware. The final goal and result of the project is to achieve an intelligent and agile manufacturing system.

Original languageEnglish
Title of host publication2017 International Conference on Engineering, Technology and Innovation: Engineering, Technology and Innovation Management Beyond 2020: New Challenges, New Approaches, ICE/ITMC 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1338-1343
Number of pages6
Volume2018-January
ISBN (Electronic)9781538607749
DOIs
Publication statusPublished - 2 Feb 2018
Event23rd International Conference on Engineering, Technology and Innovation, ICE/ITMC 2017 - Madeira Island, Portugal
Duration: 27 Jun 201729 Jun 2017

Conference

Conference23rd International Conference on Engineering, Technology and Innovation, ICE/ITMC 2017
CountryPortugal
CityMadeira Island
Period27/06/1729/06/17

Keywords

  • computer vision
  • Cyber-physical system
  • deep learning
  • defect detection
  • ontology

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