Quality Monitoring of Complex Manufacturing Systems on the basis of Model Driven Approach

Castaño Romero, Fernando and Haber Guerra, Rodolfo E. and Mohammed, Wael M. and Nejman, Miroslaw and Villalonga Jaén, Alberto and Martínez Lastra, José Luis (2020). Quality Monitoring of Complex Manufacturing Systems on the basis of Model Driven Approach. "Smart Structures and Systems", v. 26 (n. 4); pp. 495-506. ISSN http://www.techno-press.org/content/?page=article&journal=sss&volume=26&num=4&ordernum=7. https://doi.org/10.12989/sss.2020.26.4.495.

Description

Title: Quality Monitoring of Complex Manufacturing Systems on the basis of Model Driven Approach
Author/s:
  • Castaño Romero, Fernando
  • Haber Guerra, Rodolfo E.
  • Mohammed, Wael M.
  • Nejman, Miroslaw
  • Villalonga Jaén, Alberto
  • Martínez Lastra, José Luis
Item Type: Article
Título de Revista/Publicación: Smart Structures and Systems
Date: October 2020
ISSN: http://www.techno-press.org/content/?page=article&journal=sss&volume=26&num=4&ordernum=7
Volume: 26
Subjects:
Freetext Keywords: quality monitoring; model-driven; Artificial Intelligence-based models; surface roughness, fuzzy clustering; manufacturing; embedded systems; hybrid incremental model
Faculty: Centro de Automática y Robótica (CAR) UPM-CSIC
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - Non commercial

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Abstract

Monitoring of complex processes faces several challenges mainly due to the lack of relevant sensory information or insufficient elaborated decision-making strategies. These challenges motivate researchers to adopt complex data processing and analysis in order to improve the process representation. This paper presents the development and implementation of quality monitoring framework based on a model-driven approach using embedded artificial intelligence strategies. In this work, the strategies are applied to the supervision of a microfabrication process aiming at showing the great performance of the framework in a very complex system in the manufacturing sector. The procedure involves two methods for modelling a representative quality variable, such as surface roughness. Firstly, the Hybrid Incremental Modelling strategy is applied. Secondly, a Generalized Fuzzy Clustering C-Means method is developed. Finally, a comparative study of the behavior of the two models for predicting a quality indicator, represented by surface roughness of manufactured components, is presented for specific manufacturing process. The manufactured part used in this study is a critical structural aerospace component. In addition, the validation and testing is performed at laboratory and industrial levels, demonstrating proper real-time operation for non-linear processes with relatively fast dynamics. The results of this study are very promising in terms of computational efficiency and transfer of knowledge to manufacturing industry.

More information

Item ID: 64980
DC Identifier: http://oa.upm.es/64980/
OAI Identifier: oai:oa.upm.es:64980
DOI: 10.12989/sss.2020.26.4.495
Deposited by: Dr. Rodolfo Haber
Deposited on: 26 Oct 2020 06:03
Last Modified: 26 Oct 2020 06:03
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