Self-Learning approach to support lifecycle optimization of Manufacturing processes

Modern manufacturing companies are betting on the application of intelligent and more integrated monitoring and control solutions to reduce maintenance problems, production line downtimes and reduction of manufacturing operational costs while guarantying a more efficient management of the resources and an improved quality of products. The shoe industry provides a fertile ground in this direction since traditionally the production and manufacturing of shoes involves a wide variety of materials and a large number of both operations and machines characterized by a huge number of parameters as well. Thereby, the optimization of manufacturing process parameters during production activities is recognized as one of the most important task. As a matter of fact, the selection of the best set of manufacturing process parameters can improve final product quality, cost effectiveness while reducing anomalous situations that potentially may cause a line stopping. The present paper describes the research background that has driven the design and development of the Self-Learning methodology and reference architecture as the foundation for a new generation of monitoring and control solutions. Furthermore, a real application scenario from the shoe industry is also described to demonstrate the applicability of the proposed solution.