[HTML][HTML] Predicting stress, strain and deformation fields in materials and structures with graph neural networks
Developing accurate yet fast computational tools to simulate complex physical phenomena
is a long-standing problem. Recent advances in machine learning have revolutionized the …
is a long-standing problem. Recent advances in machine learning have revolutionized the …
[HTML][HTML] Machine learning unifies flexibility and efficiency of spinodal structure generation for stochastic biomaterial design
Porous biomaterials design for bone repair is still largely limited to regular structures (eg rod-
based lattices), due to their easy parameterization and high controllability. The capability of …
based lattices), due to their easy parameterization and high controllability. The capability of …
Time-dependent deep learning predictions of 3D electrode particle-resolved microstructure effect on voltage discharge curves
Battery electrode particle-resolved microstructure featured with a 3D active material (AM)
particle network bonded by carbon and binder (CBD) phase plays a significant role in …
particle network bonded by carbon and binder (CBD) phase plays a significant role in …
[HTML][HTML] Uncovering drone intentions using control physics informed machine learning
Abstract Unmanned Autonomous Vehicle (UAV) or drones are increasingly used across
diverse application areas. Uncooperative drones do not announce their identity/flight plans …
diverse application areas. Uncooperative drones do not announce their identity/flight plans …
Double generative network (DGNet) pipeline for structure-property relation of digital composites
Deep learning's fast and accurate inference between material configurations and properties
has been used to design digital composites with superior mechanical properties. However …
has been used to design digital composites with superior mechanical properties. However …
[HTML][HTML] Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks
M Rezasefat, JD Hogan - Machine Learning: Science and …, 2024 - iopscience.iop.org
This study investigates the application of machine learning models to predict time-evolving
stress fields in complex three-dimensional structures trained with full-scale finite element …
stress fields in complex three-dimensional structures trained with full-scale finite element …
Generative AI-enabled microstructure design of porous thermal interface materials with desired effective thermal conductivity
The conventional approach to achieve desired effective thermal conductivity (ETC) of porous
thermal interface materials (TIM) is processing-microstructure-properties forward analysis …
thermal interface materials (TIM) is processing-microstructure-properties forward analysis …
Phase-field modeling of aluminum foam based on molecular dynamics simulations
This paper presents a phase-field model that is consistent with the multiphase system of
aluminum foam to predict the microstructural evolution involved in the foaming process of …
aluminum foam to predict the microstructural evolution involved in the foaming process of …
Decentralized Condition Monitoring for Distributed Wind Systems: A Federated Learning-Based Approach to Enhance SCADA Data Privacy
The paper presents a new condition monitoring method for distributed wind systems (DWSs)
by combining federated learning with supervisory control and data acquisition (SCADA) …
by combining federated learning with supervisory control and data acquisition (SCADA) …
A universal convolutional neural network for the pixel-level detection and monitoring of weld beads.
Z Wang, M Kayitmazbatir… - Computer Methods in …, 2024 - search.ebscohost.com
In weld-based manufacturing processes such as welding and metal deposition additive
manufacturing (AM), the weld bead is a direct indicator of manufacturing quality. For …
manufacturing (AM), the weld bead is a direct indicator of manufacturing quality. For …