Wannes Van Ransbeeck, Dick Botteldooren, Sarah Verhulst, Marc Leman
abstract
Within the metaverse a tendency exists towards a seamless and intuitive interaction in the virtual world by an immersive and interactive environment. Capturing user sensation and experience during the interaction can help to build and maintain this immersion in any interaction, including music and rhythm. Here, besides physical user inputs, brain monitoring and bio-synchronization, can be a relevant tool for user analysis of the experience.
For example, neural entrainment, being the unidirecti
This poster presents a novel hybrid model for the optimization and prediction of surface waviness of components produced by wire and arc additive manufacturing. It consists of an artificial neural network optimized by the rank-Gaussian particle swarm optimization (PSO) and combined with an RGPSO algorithm. The novelty is that RGPSO is not only used to optimize the hyperparameters of the ANN model improving its prediction performance, but also to solve the problem of optimizing surface waviness.
Lightweight fiber-reinforced polymer composites offer a promising alternative to metal-based engineering solutions. However, understanding and predicting their complex nonlinear mechanical behavior poses challenges due to intricate microstructures and experimental limitations. Developing constitutive models for accurate Finite Element (FE) simulations demands significant expertise and time investment.
This research proposes the integration of Artificial Intelligence (AI) into constitutive mater
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Design of High-Performance Composites via "Self-constructible Finite Element Material Library" Driven by Reinforcement-Based Machine Learning
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Title
Design of High-Performance Composites via "Self-constructible Finite Element Material Library" Driven by Reinforcement-Based Machine Learning
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Poster ID
2
Session
2
authors
Ninghan Tang, Pei Hao, Francisco A. Gilabert
abstract
Lightweight fiber-reinforced polymer composites offer a promising alternative to metal-based engineering solutions. However, understanding and predicting their complex nonlinear mechanical behavior poses challenges due to intricate microstructures and experimental limitations. Developing constitutive models for accurate Finite Element (FE) simulations demands significant expertise and time investment.
This research proposes the integration of Artificial Intelligence (AI) into constitutive material modeling. Firstly, we establish a comprehensive database that combines fundamental experiments with FE-based data, enabling the categorization of elementary nonlinear thermo-mechanical features. Secondly, we develop a Neural Network (NN)-based architecture to identify nonlinear features in stress-strain responses. Lastly, we construct a self-consistent AI-based framework to determine the appropriate combination of physics-based rheological analogs needed to replicate the observed mechanical response of the material under various loading scenarios.
This innovative approach harnesses existing experimental and simulation data, employing advanced AI algorithms to overcome traditional modeling challenges associated with composite materials. By digging into the existing experimental and simulated data, new material models are autonomously constructed. Furthermore, the prediction of mechanical behavior for materials under diversity of loading and environmental conditions are accomplished through this advanced method. The resulting framework serves as a valuable tool for guiding composite design and facilitating their integration across diverse engineering fields.