Machine learning for alloys

GLW Hart, T Mueller, C Toher, S Curtarolo - Nature Reviews Materials, 2021 - nature.com
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

A critical review of experimental results and constitutive descriptions for metals and alloys in hot working

YC Lin, XM Chen - Materials & Design, 2011 - Elsevier
In industrial forming processes, the metals and alloys are subject to complex strain, strain-
rate, and temperature histories. Understanding the flow behaviors of metals and alloys in hot …

Artificial intelligence-enabled smart mechanical metamaterials: advent and future trends

P Jiao, AH Alavi - International Materials Reviews, 2021 - journals.sagepub.com
Mechanical metamaterials have opened an exciting venue for control and manipulation of
architected structures in recent years. Research in the area of mechanical metamaterials …

[HTML][HTML] Mechanical and tribological behavior of particulate reinforced aluminum metal matrix composites–a review

GBV Kumar, CSP Rao, N Selvaraj - Journal of minerals and materials …, 2011 - scirp.org
Aluminum Metal Matrix Composites (MMCs) sought over other conventional materials in the
field of aerospace, automotive and marine applications owing to their excellent improved …

A modified Johnson–Cook model for tensile behaviors of typical high-strength alloy steel

YC Lin, XM Chen, G Liu - Materials Science and Engineering: A, 2010 - Elsevier
The uniaxial tensile tests were conducted with the initial strain rates range of (0.0001–0.01)
s− 1 and the temperature range of (1123–1373) K for typical high-strength alloy steel. Based …

Bayesian approach for inferrable machine learning models of process–structure–property linkages in complex concentrated alloys

GS Thoppil, JF Nie, A Alankar - Journal of Alloys and Compounds, 2023 - Elsevier
The difference in the mechanical behaviors of dilute solid solutions, complex solid solutions
and their corresponding strengthening mechanisms, is an evolving field of study. An …

Machine learning recommends affordable new Ti alloy with bone-like modulus

CT Wu, HT Chang, CY Wu, SW Chen, SY Huang… - Materials Today, 2020 - Elsevier
A neural-network machine called “βLow” enables a high-throughput recommendation for
new β titanium alloys with Young's moduli lower than 50 GPa. The machine was trained by …

[HTML][HTML] Optimization of flow behavior models by genetic algorithm: A case study of aluminum alloy

S Li, W Chen, S Jain, D Jung, J Lee - Journal of Materials Research and …, 2024 - Elsevier
Prediction of the flow stress of materials using a flow constitutive model provides strong
support for engineering practice and promotes the continuous development of aluminum …

Constitutive equations for elevated temperature flow stress of Ti–6Al–4V alloy considering the effect of strain

J Cai, F Li, T Liu, B Chen, M He - Materials & Design, 2011 - Elsevier
In order to study the workability of Ti–6Al–4V alloy, the experimental stress–strain data from
isothermal hot compression tests, in a wide range of temperatures (800–1050° C) and strain …

[HTML][HTML] Review on modeling and simulation of dynamic recrystallization of martensitic stainless steels during bulk hot deformation

HA Derazkola, E Garcia, A Murillo-Marrodán… - Journal of Materials …, 2022 - Elsevier
Bulk hot deformation is a relatively old manufacturing technique widely adopted in different
industry fields to form and shape different metallic alloys. Martensitic stainless steels (MSS) …