Tensile property prediction by feature engineering guided machine learning in reduced activation ferritic/martensitic steels

Wang, Chenchong; Shen, Chunguang; Cui, Qing; Zhang, Chi; Xu, Wei

doi:10.1016/j.jnucmat.2019.151823

Cited by 30 publications

(8 citation statements)

References 40 publications

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“…This process finds features that correlate with the problem statement which enhances the results of the learning model. In a study [43], the authors showed that the performance of feature engineering guided random forest surpassed the performance of traditional models. There are several strategies for feature engineering of textual data.…”

Section: Feature Engineeringmentioning

confidence: 99%

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

et al. 2021

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Sentiment analysis is the extraction and categorization of sentiments that have been expressed in text data using text analysis techniques. Manifested by earlier studies, sentiment analysis of drug reviews has a large potential for providing valuable insights to assist health-care professionals and companies for evaluating the safety of drugs after it has been marketed. Such insights help safeguard patients and increase their trust in medical companies. The existing systems either follow a lexicon-based approach or a learningbased approach for sentiment analysis in the medical domain. Learning-based techniques require annotated data while lexicon-based techniques tend to be domain-specific which restricts their wide use. This research embarks on a hybrid technique that utilizes both learning-based and lexicon-based approaches to achieve better results. General-purpose sentiment lexicons, such as AFFIN, TextBlob, and VADER, are used for annotating the reviews. Furthermore, several feature engineering techniques, such as term frequency (TF), term frequency-inverse document frequency (TF-IDF), and union of TF and TF-IDF (TF U TF-IDF) have been incorporated for the extraction of useful features. Finally, the learning models including logistic regression (LR), AdaBoost classifier (AB), random forest (RF), extra tree classifier (ETC), and multilayer perceptron (MLP) are used to classify sentiments of the reviews. The performance of the proposed hybrid approach is evaluated using accuracy, precision, recall, and F1-score. Experimental results indicate that the combination of learning-based and lexicon-based approaches provide improved results than their individual use. Moreover, TextBlob has shown promising results giving an accuracy of 96% with MLP when used with TF-IDF and with LR when used with TF U TF-IDF.

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Section: Feature Engineeringmentioning

confidence: 99%

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

et al. 2021

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“…Determining the tensile properties is crucial because it provides information about the modulus of elasticity, elastic limit, elongation, proportional limit, reduction in area, tensile strength, yield point, yield strength, and other tensile properties [123] which then define the state of the material, its longevity, or its ability to perform in an application. Thus, the accurate prediction of tensile properties has great importance for the service life assessment of structural materials [163].…”

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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“…On the other hand, many researchers have studied SS mechanical properties, residual stress, high-temperature properties, tensile properties, and so on [14][15][16][17][18][19][20][21][22][23]. In the uniaxial tensile test at room temperature, SS does not have a well-defined yield point and shows great strain hardening behavior.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Theoretical Studies on Flexural Performance of Stainless Steel Reinforced Concrete Beams

Guo

Liu

et al. 2020

Advances in Civil Engineering

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In this paper, the flexural characteristics of stainless steel (SS) reinforced concrete beams are studied and analyzed. We mainly focus on their crack mode, failure mode, load-deflection curve, and bearing capacity. Six beams with test parameters, including the diameter of reinforcement, the type of the reinforcement, and the stirrup spacing, were tested in 4-point bending. The test results indicate that the failure mode of SS reinforced concrete beam can be divided into three stages: elastic stage, cracking stage, and failure stage. The midspan section deformation of SS reinforced concrete beam conforms to the assumption of plane section. Under the same reinforcement condition, the normal section and the oblique section bearing capacities of the SS reinforced concrete beams are significantly higher than those of the ordinary reinforced concrete beams. In addition, the prediction of cracking moment and bearing capacity calculated by ACI 318-14 and GB 50010-2010 was also evaluated. The calculation results of the two codes were safe and conservative, and GB 50010-2010 provided more accurate prediction of cracking moments. Furthermore, to verify the reliability of the test results, finite element models were established and the analytical results corroborated well with the test results.

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Tensile property prediction by feature engineering guided machine learning in reduced activation ferritic/martensitic steels

Cited by 30 publications

References 40 publications

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

Determining the Efficiency of Drugs Under Special Conditions From Users’ Reviews on Healthcare Web Forums

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

Experimental and Theoretical Studies on Flexural Performance of Stainless Steel Reinforced Concrete Beams

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