Prediction of martensite start temperature of steel combined with expert experience and machine learning
The martensite start temperature (MS) plays a pivotal role in formulating heat treatment regimes for steel. This paper, through the compilation of experimental data from literature and the incorporation of expert knowledge to construct features, employs machine learning algorithms to predict the MS...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Science and Technology of Advanced Materials |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/14686996.2024.2354655 |
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| _version_ | 1846111263614042112 |
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| author | Chengcheng Liu Hang Su |
| author_facet | Chengcheng Liu Hang Su |
| author_sort | Chengcheng Liu |
| collection | DOAJ |
| description | The martensite start temperature (MS) plays a pivotal role in formulating heat treatment regimes for steel. This paper, through the compilation of experimental data from literature and the incorporation of expert knowledge to construct features, employs machine learning algorithms to predict the MS of steel. The study highlights that the ETR algorithm attains optimal prediction accuracy, and the inclusion of atomic features enhances the model’s performance. Feature selection is accomplished by evaluating linear and nonlinear relationships between data using the Pearson correlation coefficient (PCC), variance inflation factor (VIF), and maximum information coefficient (MIC). Subsequently, the performance of machine learning models on unknown data is compared to validate the model’s generalization ability. The introduction of SHAP values for model interpretability analysis unveils the influencing mechanisms between features and the target variable. Finally, utilizing a specific steel type as an illustration, the paper underscores the practical value of the model. |
| format | Article |
| id | doaj-art-78ffdf1994d240408dbff53e69cdcd05 |
| institution | Kabale University |
| issn | 1468-6996 1878-5514 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Science and Technology of Advanced Materials |
| spelling | doaj-art-78ffdf1994d240408dbff53e69cdcd052024-12-23T08:54:39ZengTaylor & Francis GroupScience and Technology of Advanced Materials1468-69961878-55142024-12-0125110.1080/14686996.2024.2354655Prediction of martensite start temperature of steel combined with expert experience and machine learningChengcheng Liu0Hang Su1Institute of Structural Steel, Central Iron and Steel Research Institute, Beijing, ChinaMaterial Digital R&D Center, China Iron and Steel Research Institute Group, Beijing, ChinaThe martensite start temperature (MS) plays a pivotal role in formulating heat treatment regimes for steel. This paper, through the compilation of experimental data from literature and the incorporation of expert knowledge to construct features, employs machine learning algorithms to predict the MS of steel. The study highlights that the ETR algorithm attains optimal prediction accuracy, and the inclusion of atomic features enhances the model’s performance. Feature selection is accomplished by evaluating linear and nonlinear relationships between data using the Pearson correlation coefficient (PCC), variance inflation factor (VIF), and maximum information coefficient (MIC). Subsequently, the performance of machine learning models on unknown data is compared to validate the model’s generalization ability. The introduction of SHAP values for model interpretability analysis unveils the influencing mechanisms between features and the target variable. Finally, utilizing a specific steel type as an illustration, the paper underscores the practical value of the model.https://www.tandfonline.com/doi/10.1080/14686996.2024.2354655Martensite start temperatureexpert experiencemachine learningsteel |
| spellingShingle | Chengcheng Liu Hang Su Prediction of martensite start temperature of steel combined with expert experience and machine learning Science and Technology of Advanced Materials Martensite start temperature expert experience machine learning steel |
| title | Prediction of martensite start temperature of steel combined with expert experience and machine learning |
| title_full | Prediction of martensite start temperature of steel combined with expert experience and machine learning |
| title_fullStr | Prediction of martensite start temperature of steel combined with expert experience and machine learning |
| title_full_unstemmed | Prediction of martensite start temperature of steel combined with expert experience and machine learning |
| title_short | Prediction of martensite start temperature of steel combined with expert experience and machine learning |
| title_sort | prediction of martensite start temperature of steel combined with expert experience and machine learning |
| topic | Martensite start temperature expert experience machine learning steel |
| url | https://www.tandfonline.com/doi/10.1080/14686996.2024.2354655 |
| work_keys_str_mv | AT chengchengliu predictionofmartensitestarttemperatureofsteelcombinedwithexpertexperienceandmachinelearning AT hangsu predictionofmartensitestarttemperatureofsteelcombinedwithexpertexperienceandmachinelearning |