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 …
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 …
Material structure-property linkages using three-dimensional convolutional neural networks
The core materials knowledge needed in the accelerated design, development, and
deployment of new and improved materials is most accessible when cast in the form of …
deployment of new and improved materials is most accessible when cast in the form of …
[HTML][HTML] A computer vision approach for automated analysis and classification of microstructural image data
The 'bag of visual features' image representation was applied to create generic
microstructural signatures that can be used to automatically find relationships in large and …
microstructural signatures that can be used to automatically find relationships in large and …
Key computational modeling issues in integrated computational materials engineering
Designing materials for targeted performance requirements as required in Integrated
Computational Materials Engineering (ICME) demands a combined strategy of bottom–up …
Computational Materials Engineering (ICME) demands a combined strategy of bottom–up …
Modeling process–structure–property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches
Metal additive manufacturing (AM) presents advantages such as increased complexity for a
lower part cost and part consolidation compared to traditional manufacturing. The multiscale …
lower part cost and part consolidation compared to traditional manufacturing. The multiscale …
Materials data science: current status and future outlook
SR Kalidindi, M De Graef - Annual Review of Materials Research, 2015 - annualreviews.org
The field of materials science and engineering is on the cusp of a digital data revolution.
After reviewing the nature of data science and Big Data, we discuss the features of materials …
After reviewing the nature of data science and Big Data, we discuss the features of materials …
Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning
MV Pathan, SA Ponnusami, J Pathan… - Scientific reports, 2019 - nature.com
We present an application of data analytics and supervised machine learning to allow
accurate predictions of the macroscopic stiffness and yield strength of a unidirectional …
accurate predictions of the macroscopic stiffness and yield strength of a unidirectional …
Microstructure sensitive design for performance optimization
DT Fullwood, SR Niezgoda, BL Adams… - Progress in Materials …, 2010 - Elsevier
The accelerating rate at which new materials are appearing, and transforming the
engineering world, only serves to emphasize the vast potential for novel material structure …
engineering world, only serves to emphasize the vast potential for novel material structure …
Structure–property linkages using a data science approach: application to a non-metallic inclusion/steel composite system
Practical multiscale materials design is contingent on the availability of robust and reliable
reduced-order linkages (ie, surrogate models) between the material internal structure and its …
reduced-order linkages (ie, surrogate models) between the material internal structure and its …