Design of comprehensive mechanical properties by machine learning and high-throughput optimization algorithm in RAFM steels

Wang, Chenchong; Shen, Chunguang; Huo, Xiaojie; Zhang, Chi; Xu, Wei

doi:10.1016/j.net.2019.10.014

Cited by 18 publications

(11 citation statements)

References 38 publications

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“…For the design of materials, creep is considered an important material property. However, quite often such designs only focus on one objective (eg, creep) without considering the comprehensive design of multi‐property [164]. For the investigation of creep rupture life and rupture strength of austenitic stainless steels [146] once again ANNs are popular models.…”

Section: State Of the Artmentioning

confidence: 99%

“…Applications of traditional ML algorithms for the prediction of tensile properties were proposed by [137, 138, 151, 163, 164]. Shigemori et al [137, 138] reported about the successful application of locally weighted regression in predicting the tensile strength for a certain type of steel product which is produced by hot rolling.…”

Section: State Of the Artmentioning

confidence: 99%

“…The proposed least squares SVM model is capable of predicting reasonably accurately the elastic modulus and yield stress of materials based on the load‐indentation curves of dual conical indenters with different half‐angles. For material design, a RF model in combination with an optimization algorithm was found to relate yield strength, impact toughness and total elongation with material composition information and treatment parameters for the production of RAFM steels [163, 164]. For yield strength, highly correlated features were tempering temperature and C content; and tempering time and Cr content for elongation.…”

Section: State Of the Artmentioning

confidence: 99%

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Machine learning for material characterization with an application for predicting mechanical properties

Stoll

Benner

2021

GAMM-Mitteilungen

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Currently, the growth of material data from experiments and simulations is expanding beyond processable amounts. This makes the development of new data‐driven methods for the discovery of patterns among multiple lengthscales and time‐scales and structure‐property relationships essential. These data‐driven approaches show enormous promise within materials science. The following review covers machine learning (ML) applications for metallic material characterization. Many parameters associated with the processing and the structure of materials affect the properties and the performance of manufactured components. Thus, this study is an attempt to investigate the usefulness of ML methods for material property prediction. Material characteristics such as strength, toughness, hardness, brittleness, or ductility are relevant to categorize a material or component according to their quality. In industry, material tests like tensile tests, compression tests, or creep tests are often time consuming and expensive to perform. Therefore, the application of ML approaches is considered helpful for an easier generation of material property information. This study also gives an application of ML methods on small punch test (SPT) data for the determination of the property ultimate tensile strength for various materials. A strong correlation between SPT data and tensile test data was found which ultimately allows to replace more costly tests by simple and fast tests in combination with ML.

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Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

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Machine learning for material characterization with an application for predicting mechanical properties

Stoll

Benner

2021

GAMM-Mitteilungen

View full text Add to dashboard Cite

show abstract

“…However, quite often such designs only focus on one objective (e.g. creep) without considering the comprehensive design of multi-property [92] . For the investigation of creep rupture life and rupture strength of austenitic stainless steels [93] once again ANNs are popular models.…”

Section: Creepmentioning

confidence: 99%

“…Applications of traditional ML algorithms for the prediction of tensile properties were proposed by [92,103,[111][112][113] . Shigemori et al [111,112] reported about the successful application of Locally Weighted Regression (LWR) in predicting the tensile strength for a certain type of steel product which is produced by hot rolling.…”

Section: Tensile Propertiesmentioning

confidence: 99%

Machine Learning for Material Characterization with an Application for Predicting Mechanical Properties

Stoll¹,

Benner²

2020

Preprint

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Currently, the growth of material data from experiments and simulations is expanding beyond processable amounts. This makes the development of new data-driven methods for the discovery of patterns among multiple lengthscales and time-scales and structure-property relationships essential. These data-driven approaches show enormous promise within materials science. The following review covers machine learning applications for metallic material characterization. Many parameters associated with the processing and the structure of materials affect the properties and the performance of manufactured components. Thus, this study is an attempt to investigate the usefulness of machine learning methods for material property prediction.Material characteristics such as strength, toughness, hardness, brittleness or ductility are relevant to categorize a material or component according to their quality. In industry, material tests like tensile tests, compression tests or creep tests are often time consuming and expensive to perform. Therefore, the application of machine learning approaches is considered helpful for an easier generation of material property information. This study also gives an application of machine learning methods on small punch test data for the determination of the property ultimate tensile strength for various materials. A strong correlation between small punch test data and tensile test data was found which ultimately allows to replace more costly tests by simple and fast tests in combination with machine learning.

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Industrial Big Data‐Driven Modeling and Prediction for Hot‐Rolled Strip Crown with Multigrade and Multispecification Data

Xu,

Ding,

Liu

et al. 2024

steel research int.

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In the field of hot rolling big data, the presence of different steel types, specifications, and data heterogeneity poses significant challenges to the accuracy and stability of using single machine learning regression technology for prediction. Therefore, this study proposes a hot‐rolled strip crown prediction method that combines data clustering and fusion modeling. First, this article introduces a relevant mechanism for designing cluster strategies. The optimal clustering strategy is determined through comparative experiments using rolling process parameters, strip size, and main material components as the clustering features. Subsequently, the K‐Means++ algorithm is used to effectively cluster the training and testing datasets based on this strategy, generating multiple clusters for both datasets. Finally, this study establishes seven different training models to match the most suitable regression prediction model for each cluster, and matching between each cluster and the model is determined through rigorous testing. The evaluation of the fusion model shows an R2 value of 0.829 and a root mean square error value of 3.974. The experimental results show that the proposed method outperforms traditional methods in solving the challenges of multiclass classification and data heterogeneity, providing strong data support for the intelligent control of the hot‐rolled strip crown in the future.

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Design of comprehensive mechanical properties by machine learning and high-throughput optimization algorithm in RAFM steels

Cited by 18 publications

References 38 publications

Machine learning for material characterization with an application for predicting mechanical properties

Machine learning for material characterization with an application for predicting mechanical properties

Machine Learning for Material Characterization with an Application for Predicting Mechanical Properties

Industrial Big Data‐Driven Modeling and Prediction for Hot‐Rolled Strip Crown with Multigrade and Multispecification Data

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