Machine learning for alloys
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 …
data-science-inspired work. The dawn of computational databases has made the integration …
A general perspective of Fe–Mn–Al–C steels
OA Zambrano - Journal of materials science, 2018 - Springer
During the last years, the scientific and industrial community has focused on the astonishing
properties of Fe–Mn–Al–C steels. These high advanced steels allow high-density reductions …
properties of Fe–Mn–Al–C steels. These high advanced steels allow high-density reductions …
Towards stacking fault energy engineering in FCC high entropy alloys
Abstract Stacking Fault Energy (SFE) is an intrinsic alloy property that governs much of the
plastic deformation mechanisms observed in fcc alloys. While SFE has been recognized for …
plastic deformation mechanisms observed in fcc alloys. While SFE has been recognized for …
Bayesian optimization with adaptive surrogate models for automated experimental design
Bayesian optimization (BO) is an indispensable tool to optimize objective functions that
either do not have known functional forms or are expensive to evaluate. Currently, optimal …
either do not have known functional forms or are expensive to evaluate. Currently, optimal …
Bayesian approach for inferrable machine learning models of process–structure–property linkages in complex concentrated alloys
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 …
and their corresponding strengthening mechanisms, is an evolving field of study. An …
[HTML][HTML] New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning
Abstract Peer-to-peer (P2P) Lending is a type of financial innovation that offers loans without
intermediaries to individuals and companies. In the P2P lending system, there is a risk of …
intermediaries to individuals and companies. In the P2P lending system, there is a risk of …
Advances in machine learning-and artificial intelligence-assisted material design of steels
G Pan, F Wang, C Shang, H Wu, G Wu, J Gao… - International Journal of …, 2023 - Springer
With the rapid development of artificial intelligence technology and increasing material data,
machine learning-and artificial intelligence-assisted design of high-performance steel …
machine learning-and artificial intelligence-assisted design of high-performance steel …
Stress or strain induced martensitic and bainitic transformations during ausforming processes
The so-called ausforming treatment consists in plastically deforming a fully austenitized steel
below the recrystallization stop temperature, prior to either a martensitic or a bainitic …
below the recrystallization stop temperature, prior to either a martensitic or a bainitic …
Cancer Classification with a Cost‐Sensitive Naive Bayes Stacking Ensemble
Y Xiong, M Ye, C Wu - Computational and Mathematical …, 2021 - Wiley Online Library
Ensemble learning combines multiple learners to perform combinatorial learning, which has
advantages of good flexibility and higher generalization performance. To achieve higher …
advantages of good flexibility and higher generalization performance. To achieve higher …
Stacking fault energy determination in Fe-Mn-Al-C austenitic steels by X-ray diffraction
JA Castañeda, OA Zambrano, GA Alcázar… - Metals, 2021 - mdpi.com
A critical assessment has been performed to determine the stacking fault energy (SFE) of the
austenite phase in high manganese steels using X-ray diffraction (XRD). It was found that …
austenite phase in high manganese steels using X-ray diffraction (XRD). It was found that …