Quality monitoring of complex manufacturing systems on the basis of model driven approach

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