Machine learning in additive manufacturing: State-of-the-art and perspectives
Additive manufacturing (AM) has emerged as a disruptive digital manufacturing technology.
However, its broad adoption in industry is still hindered by high entry barriers of design for …
However, its broad adoption in industry is still hindered by high entry barriers of design for …
Control of grain structure, phases, and defects in additive manufacturing of high-performance metallic components
T Mukherjee, JW Elmer, HL Wei, TJ Lienert… - Progress in Materials …, 2023 - Elsevier
The properties and serviceability of 3D-printed metal parts depend on a variety of attributes.
These include the chemical composition, phases, morphology, spatial distributions of grain …
These include the chemical composition, phases, morphology, spatial distributions of grain …
Invited review: Machine learning for materials developments in metals additive manufacturing
In metals additive manufacturing (AM), materials and components are concurrently made in
a single process as layers of metal are fabricated on top of each other in the near-final …
a single process as layers of metal are fabricated on top of each other in the near-final …
A review of the application of machine learning and data mining approaches in continuum materials mechanics
Machine learning tools represent key enablers for empowering material scientists and
engineers to accelerate the development of novel materials, processes and techniques. One …
engineers to accelerate the development of novel materials, processes and techniques. One …
Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering
The fields of machining learning and artificial intelligence are rapidly expanding, impacting
nearly every technological aspect of society. Many thousands of published manuscripts …
nearly every technological aspect of society. Many thousands of published manuscripts …
Recent advances and applications of machine learning in experimental solid mechanics: A review
For many decades, experimental solid mechanics has played a crucial role in characterizing
and understanding the mechanical properties of natural and novel artificial materials …
and understanding the mechanical properties of natural and novel artificial materials …
Three-dimensional convolutional neural network (3D-CNN) for heterogeneous material homogenization
Homogenization is a technique commonly used in multiscale computational science and
engineering for predicting collective response of heterogeneous materials and extracting …
engineering for predicting collective response of heterogeneous materials and extracting …
Machine learning in energy storage materials
With its extremely strong capability of data analysis, machine learning has shown versatile
potential in the revolution of the materials research paradigm. Here, taking dielectric …
potential in the revolution of the materials research paradigm. Here, taking dielectric …
A transfer learning approach for microstructure reconstruction and structure-property predictions
Stochastic microstructure reconstruction has become an indispensable part of computational
materials science, but ongoing developments are specific to particular material systems. In …
materials science, but ongoing developments are specific to particular material systems. In …
Overview: Computer vision and machine learning for microstructural characterization and analysis
Microstructural characterization and analysis is the foundation of microstructural science,
connecting materials structure to composition, process history, and properties …
connecting materials structure to composition, process history, and properties …