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[HTML][HTML] Enhancing property prediction and process optimization in building materials through machine learning: A review
Abstract Analysis and design, as the most critical components in material science, require a
highly rigorous approach to assure long-term success. Due to a recent increase in the …
highly rigorous approach to assure long-term success. Due to a recent increase in the …
Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design
The superior multi-functional properties of polymer composites have made them an ideal
choice for aerospace, automobile, marine, civil, and many other technologically demanding …
choice for aerospace, automobile, marine, civil, and many other technologically demanding …
[HTML][HTML] Stress field prediction in fiber-reinforced composite materials using a deep learning approach
Stress analysis is an important step in the design of material systems, and finite element
methods (FEM) are a standard approach of performing computational analysis of stresses in …
methods (FEM) are a standard approach of performing computational analysis of stresses in …
[HTML][HTML] Artificial intelligence in predicting mechanical properties of composite materials
The determination of mechanical properties plays a crucial role in utilizing composite
materials across multiple engineering disciplines. Recently, there has been substantial …
materials across multiple engineering disciplines. Recently, there has been substantial …
Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review
In the last few decades, the influence of machine learning has permeated many areas of
science and technology, including the field of materials science. This toolkit of data driven …
science and technology, including the field of materials science. This toolkit of data driven …
[HTML][HTML] Advances in machine learning-aided design of reinforced polymer composite and hybrid material systems
Reinforced composite is a preferred choice of material for the design of industrial lightweight
structures. As of late, composite materials analysis and development utilizing machine …
structures. As of late, composite materials analysis and development utilizing machine …
Artificial neural networks for inverse design of a semi-auxetic metamaterial
This study introduces an artificial neural network approach for the inverse design of a novel
semi-auxetic mechanical metamaterial to achieve a specified stress-strain curve and/or …
semi-auxetic mechanical metamaterial to achieve a specified stress-strain curve and/or …
[HTML][HTML] Performance prediction and Bayesian optimization of screw compressors using Gaussian Process Regression
Optimizing the performance of screw compressors is critical for achieving high efficiency and
reducing costs in various industrial and engineering applications. Often, the design and …
reducing costs in various industrial and engineering applications. Often, the design and …
Buckling response of CNT based hybrid FG plates using finite element method and machine learning method
In this study, a C 0 finite element model (FEM) based on modified third-order shear
deformation (MTSDT) theory in conjunction with a deep neural network (DNN), extreme …
deformation (MTSDT) theory in conjunction with a deep neural network (DNN), extreme …
Machine learning-based accelerated property prediction of two-phase materials using microstructural descriptors and finite element analysis
E Ford, K Maneparambil, S Rajan… - Computational Materials …, 2021 - Elsevier
This study explores the use of supervised machine learning (ML) to predict the mechanical
properties of a family of two-phase materials using their microstructural images. Random two …
properties of a family of two-phase materials using their microstructural images. Random two …