A methodology for modeling multiscale multiphysics nature that bridges basic science with sustainable manufacturing technologies using human and Artificial intelligence

Abstract

This dissertation deals with the modeling of multiscale multiphysics phenomena. These complex processes involve the interaction between physical occurrences of different nature, at different time and space scales, turning its description, prediction and control into a daunting task. Being pivotal technologies for the manufacturing of advanced materials, this work revolves around the complex technologies of Selective Laser Melting (SLM), electrospray, Ultrasonic Micro-Injection Molding (UMIM) and smart materials, i.e. Magneto-Rheological Elastomers (MRE). Modeling efforts are taken into action through classical yet powerful methodologies such as dimensional analysis and cutting-edge approaches such as fractal analysis and artificial intelligence, i.e., Artificial Neural Networks (ANNs) and Multiobjective Evolutionary Algorithms (MOEAs), with promising results that reflect on their ability to capture the intricate interplay of process parameters and material properties in these convoluted phenomena. Offering complementary benefits (attaining of meaningful physical insights and efficient handling computational processing operation and pattern identification in data, respectively) both approaches should be jointly exploited for handling multiscale multiphysics phenomena.