Modeling, Controlling and Analysis of Polymeric Composites Materials

Polymeric composites have shown great potential in recent years for their superior multi-functional properties, and have become an ideal choice for various industries including aerospace, civil engineering, composites, etc. The diversified applications of these composites demand an extensive investigation of their physical, mechanical, interfacial and structural behavior under elevated conditions. Therefore, developing, modelling, and controlling of polymeric composites products is a challenging task. Nowadays, machine learning has gained immense importance in the enhancement of productivity. Polymeric composites and their properties often confront with uncertainties due to the variations in reinforced materials and fillers properties, inaccurate geometrical features and measurement uncertainty. Therefore, the aim of this project is to develop new solutions based on different machine learning algorithms for various applications including prediction, features identification, optimization, uncertainty quantification and reliability within the framework of polymeric composites.