Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel

Chen, Shang; Wang, Chuanjun; Wu, Hong‐Hui; Liu, Wenyue; Chen, Yimian; Pan, Guangfei; Wang, Shuize; Wu, Guilin; Gao, Junheng; Zhao, Haitao; Zhang, Chaolei; Mao, Xinping

doi:10.1007/s11431-023-2372-x

Cited by 11 publications

(7 citation statements)

References 53 publications

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“…Shang et al [59,60] . proposed an ML model to predict drop weight tear testing shear area and Charpy impact toughness (CIT) of pipeline steel based on the production line datasets provided by steel mills and experimental data collected from the literature.…”

Section: Ai Technology In Steel Materials Design and Discoverymentioning

confidence: 99%

“…Consequently, data mining using an industrial manufacturing dataset with high dimensions and small fluctuation of change presents a significant challenge [55] . To enhance the accuracy of modeling with industrial manufacturing datasets, three effective strategies have been developed: combining the ML model with multiscale calculation, [56] integrating the ML model with PM variables, [57,58] and expanding industrial data with literature data [59–61] …”

Section: Ai Technology In Steel Materials Design and Discoverymentioning

confidence: 99%

“…(b) Prediction framework employing a classification and regression model in conjunction with PM variables [57] . (c) Flowchart of ML strategies for predicting CIT [60] . (d) Predictive performance of the ML model built on the production line datasets provided by steel mills and experimental data collected from the literature [60] .…”

Section: Ai Technology In Steel Materials Design and Discoverymentioning

confidence: 99%

“…(c) Flowchart of ML strategies for predicting CIT [60] . (d) Predictive performance of the ML model built on the production line datasets provided by steel mills and experimental data collected from the literature [60] . CIT, Charpy impact toughness; ML, machine learning; PM, physical metallurgy.…”

Section: Ai Technology In Steel Materials Design and Discoverymentioning

confidence: 99%

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Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

Geng,

Wang,

et al. 2023

Materials Genome Engineering Advances

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With the development of new information technology, big data technology and artificial intelligence (AI) have accelerated material research and development and industrial manufacturing, which have become the key technology driving a new wave of global technological revolution and industrial transformation. This review introduces the data resources and databases related to steel materials. It then examines the fundamental strategies and applications of machine learning (ML) in the design and discovery of steel materials, including ML models based on experimental data, industrial manufacturing data, and simulation data, respectively. Given the advancements in big data, AI/ML, and new communication technologies, an intelligent manufacturing mode featuring digital twins is deemed critical in guiding the next industrial revolution. Consequently, the application of intelligence manufacturing with digital twins in the iron and steel industry is reviewed and discussed. Furthermore, the applications of ML in service performance prediction of steel products are addressed. Finally, the future development trends for data‐driven and AI approaches throughout the entire life cycle of steel materials are prospected. Overall, this work presents an in‐depth examination of the integration of data‐driven and AI technologies in the steel industry, highlighting their potential and future directions.

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Section: Ai Technology In Steel Materials Design and Discoverymentioning

confidence: 99%

Section: Ai Technology In Steel Materials Design and Discoverymentioning

confidence: 99%

Section: Ai Technology In Steel Materials Design and Discoverymentioning

confidence: 99%

Section: Ai Technology In Steel Materials Design and Discoverymentioning

confidence: 99%

See 2 more Smart Citations

Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

Geng,

Wang,

et al. 2023

Materials Genome Engineering Advances

Self Cite

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“…In this work, we use SISSO (Sure Independence Screening and Sparsifying Operator), 43 one of the interpretable ML or feature engineering algorithms, 44–47 to construct new descriptors for phase prediction of HEAs. The learned new descriptors combining traditionally used empirical descriptors with arithmetic operations, show high accuracy and are physically interpretable.…”

Section: Introductionmentioning

confidence: 99%

Descriptors for phase prediction of high entropy alloys using interpretable machine learning

Zhao,

Yuan,

Liao

et al. 2024

J. Mater. Chem. A

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Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte

Li,

Zhou,

et al. 2024

Advanced Energy Materials

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Machine learning (ML) exhibits substantial potential for predicting the properties of solid‐state electrolytes (SSEs). By integrating experimental or/and simulation data within ML frameworks, the discovery and development of advanced SSEs can be accelerated, ultimately facilitating their application in high‐end energy storage systems. This review commences with an introduction to the background of SSEs, including their explicit definition, comprehensive classification, intrinsic physical/chemical properties, underlying mechanisms governing their conductivity, challenges, and future developments. An in‐depth explanation of the ML methodology is also elucidated. Subsequently, the key factors that influence the performance of SSEs are summarized, including thermal expansion, modulus, diffusivity, ionic conductivity, reaction energy, migration barrier, band gap, and activation energy. Finally, it is offered perspectives on the design prerequisites for upcoming generations of SSEs, focusing on real‐time property prediction, multi‐property optimization, multiscale modeling, transfer learning, automation and high‐throughput experimentation, and synergistic optimization of full battery, all of which are crucial for accelerating the progress in SSEs. This review aims to guide the design and optimization of novel SSE materials for the practical realization of efficient and reliable SSEs in energy storage technologies.

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Improved data-driven performance of Charpy impact toughness via literature-assisted production data in pipeline steel

Cited by 11 publications

References 53 publications

Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

Descriptors for phase prediction of high entropy alloys using interpretable machine learning

Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte

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