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Smart Structures and Systems
  Volume 26, Number 4, October 2020, pages 495-506
DOI: http://dx.doi.org/10.12989/sss.2020.26.4.495
 


Quality monitoring of complex manufacturing systems on the basis of model driven approach
Fernando Castaño, Rodolfo E. Haber, Wael M. Mohammed, Miroslaw Nejman, Alberto Villalonga and Jose L. Martinez Lastra

 
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 are 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.
 
Key Words
    quality monitoring; model-driven; artificial intelligence-based models; surface roughness; fuzzy clustering; manufacturing; embedded systems; hybrid incremental model
 
Address
(1) Fernando Castaño, Rodolfo E. Haber, Alberto Villalonga:
Spanish National Research Council-Technical University of Madrid, Centre for Automation and Robotics,
Ctra. Campo Real km. 0.200, 28500, Arganda del Rey, Spain
(2) Wael M. Mohammed, Jose L. Martinez Lastra:
Tampere University, Faculty of Engineering and Natural Sciences; FAST-Lab, PO Box 600, 33101 Tampere, Finland
(3) Miroslaw Nejman:
Warsaw University of Technology, Faculty of Production Engineering, Narbutta 85, 02-524 Warsaw, Poland
 

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