Additively manufactured composite lattices: A state-of-the-art review on fabrications, architectures, constituent materials, mechanical properties, and future directions
Finding ideal materials remains a crucial challenge in the aerospace, automotive,
construction, and biomedical industries. Moreover, a growing concern about environmental …
construction, and biomedical industries. Moreover, a growing concern about environmental …
[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 …
Prediction of maximum tensile stress in plain-weave composite laminates with interacting holes via stacked machine learning algorithms: A comparative study
Plain weave composite is a long-lasting type of fabric composite that is stable enough when
being handled. Open-hole composites have been widely used in industry, though they have …
being handled. Open-hole composites have been widely used in industry, though they have …
Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations
M Duquesnoy, C Liu, DZ Dominguez, V Kumar… - Energy Storage …, 2023 - Elsevier
The optimization of the electrodes manufacturing process constitutes a critical step to ensure
high-quality Lithium-Ion Battery (LIB) cells, in particular for automotive applications. Because …
high-quality Lithium-Ion Battery (LIB) cells, in particular for automotive applications. Because …
Random forest-based surrogates for transforming the behavioral predictions of laminated composite plates and shells from FSDT to Elasticity solutions
In the present work, a surrogate model based on the Random Forest (RF) machine learning
is employed for transforming the First-order Shear Deformation Theory (FSDT) based …
is employed for transforming the First-order Shear Deformation Theory (FSDT) based …
Machine learning for accelerating the design process of double-double composite structures
Current composite design processes go through expensive numerical simulations that can
quantitatively describe the detailed complex stress state embedded in the laminate structure …
quantitatively describe the detailed complex stress state embedded in the laminate structure …
[HTML][HTML] A multiscale deep learning model for elastic properties of woven composites
Time-consuming and costly computational analysis expresses the need for new methods for
generalizing multiscale analysis of composite materials. Combining neural networks and …
generalizing multiscale analysis of composite materials. Combining neural networks and …
[HTML][HTML] Machine learning of evolving physics-based material models for multiscale solid mechanics
In this work we present a hybrid physics-based and data-driven learning approach to
construct surrogate models for concurrent multiscale simulations of complex material …
construct surrogate models for concurrent multiscale simulations of complex material …
Progresses and challenges of composite laminates in thin-walled structures: a systematic review
Most engineering technologies, gadgets, and systems have been developed around the use
of sophisticated materials. Composite laminates have found widespread application in …
of sophisticated materials. Composite laminates have found widespread application in …
Finite element modelling, predictive modelling and optimization of metal inert gas, tungsten inert gas and friction stir welding processes: a comprehensive review
Welding is an essential fabrication process in any of the construction or manufacturing
industries. Over the years, numerous welding techniques have been developed to fulfil the …
industries. Over the years, numerous welding techniques have been developed to fulfil the …