Evolution analysis of γ' precipitate coarsening in Co-based superalloys using kinetic theory and machine learning

Liu, Pei; Huang, Haiyou; Jiang, Xue; Zhang, Yan; Omori, Toshihiro; Lookman, Turab; Su, Yanjing

doi:10.1016/j.actamat.2022.118101

Cited by 24 publications

(4 citation statements)

References 65 publications

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“…As such, Zhang Yan can be considered the most influential author in the studied subjects. In his latest research, he has used machine learning for material design and microstructure evolution prediction [36,37]. It is worth noting that images from Figure 2 are not tailored to the data of Table 1 since these networks' charts are focused on searching for collaborations-total link strength-and they depend on the minimum number of articles per author and on the decision of presenting the interconnection of nodes.…”

Section: Most Cited Authors and Their Collaborationsmentioning

confidence: 99%

Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021

et al. 2023

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Material science is a broad discipline focused on subjects such as metals, ceramics, polymers, electronics, and composite materials. Each of these fields covers areas associated with designing, synthesizing, and manufacturing, materials. These are tasks in which the use of technology may constitute paramount importance, reducing cost and time to develop new materials and substituting try-and-error standard procedures. This study aimed to analyze, quantify and map the scientific production of research on the fourth industrial revolution linked to material science studies in Scopus and Web of Science databases from 2017 to 2021. For this bibliometric analysis, the Biblioshiny software from RStudio was employed to categorize and evaluate the contribution of authors, countries, institutions, and journals. VOSviewer was used to visualize their collaboration networks. As a result, we found that artificial intelligence represents a hotspot technology used in material science, which has become usual in molecular simulations and manufacturing industries. Recent studies aim to provide possible avenues in the discovery and design of new high-entropy alloys as well as to detect and classify corrosion in the industrial sector. This bibliometric analysis releases an updated perspective on the implementations of technologies in material science as a possible guideline for future worldwide research.

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Section: Most Cited Authors and Their Collaborationsmentioning

confidence: 99%

Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021

et al. 2023

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“…Recently, the segmentation for precipitate analysis using the machine learning tool has been attracting increasing attention. Liu et al 23 proposed a CNN-based model to identify materials descriptors describing γ ′ precipitate coarsening in Co-based superalloys. Wang et al 24 adopted the U-Net segmentation model and a regression model to predict the morphological parameters of the microstructure.…”

Section: Introductionmentioning

confidence: 99%

Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

Xia

Cheng

et al. 2023

Phys. Chem. Chem. Phys.

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“…Artificial intelligence (AI) and machine learning (ML) can capture complex relationships between multi-dimension factors and targets; thus, promoting new materials and insight discovery [10][11][12][13][14][15] . Since the 1980s, researchers have applied artificial neural networks, random forests, and other machine learning algorithms to predict the uniform corrosion of materials and design new corrosion-resistant materials, making notable progress in solving the multi-factor coupling corrosion problem [16][17][18][19][20][21] .…”

Section: Introductionmentioning

confidence: 99%

Data-driven pitting evolution prediction for corrosion-resistant alloys by time-series analysis

Jiang

2022

npj Mater Degrad

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Corrosion initiation and propagation are a time-series problem, evolving continuously with corrosion time, and future pitting behavior depends closely on the past. Predicting localized corrosion for corrosion-resistant alloys remains a great challenge, as macroscopic experiments and microscopic theoretical simulations cannot couple internal and external factors to describe the pitting evolution from a time dimension. In this work, a data-driven method based on time-series analysis was explored. Taking cobalt-based alloys and duplex stainless steels as the case scenario, a corrosion propagation model was built to predict the free corrosion potential (Ecorr) using a long short-term memory neural network (LSTM) based on 150 days of immersion testing in saline solution. Compared to traditional machine learning methods, the time-series analysis method was more consistent with the evolution of ground truth in the Ecorr prediction of the subsequent 70 days’ immersion, illustrating that time-series dependency of pitting propagation could be captured and utilized.

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Evolution analysis of γ' precipitate coarsening in Co-based superalloys using kinetic theory and machine learning

Cited by 24 publications

References 65 publications

Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021

Bibliometric Analysis of Fourth Industrial Revolution Applied to Material Sciences Based on Web of Science and Scopus Databases from 2017 to 2021

Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

Data-driven pitting evolution prediction for corrosion-resistant alloys by time-series analysis

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