Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach
A machine learning-based approach is presented for predicting the mechanical properties of Cu-Ti alloys utilizing a dataset of various features, including compositional elements and processing parameters. The features encompass chemical composition elements such as Cu, Al, Ce, Cr, Fe, Mg, Ti, and Zr...
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MDPI AG
2024-07-01
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| Series: | Modelling |
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| author | Mihail Kolev |
| author_facet | Mihail Kolev |
| author_sort | Mihail Kolev |
| collection | DOAJ |
| description | A machine learning-based approach is presented for predicting the mechanical properties of Cu-Ti alloys utilizing a dataset of various features, including compositional elements and processing parameters. The features encompass chemical composition elements such as Cu, Al, Ce, Cr, Fe, Mg, Ti, and Zr, as well as various thermo-mechanical processing parameters. This dataset, comprising more than 1000 data points, was selected from a larger collection of various Cu-based alloys. The dataset was divided into training, validation, and test sets, with a Random Forest Regressor model being trained and optimized using GridSearchCV. The model’s performance was evaluated based on the R<sup>2</sup> score. The results demonstrate high predictive accuracy, with R<sup>2</sup> scores of 0.9929, 0.9851, and 0.9937 for the training, validation, and testing sets, respectively. The Random Forest model was compared with other machine learning models and showed better results in terms of predictive accuracy. A feature importance analysis of the mechanical characteristics was conducted, further clarifying the influence of each feature. The correlation heatmap further elucidates the relationships among the features, offering insights into the effects of alloy composition and processing on mechanical properties. This study underscores the potential of machine learning in advancing the development and optimization of Cu-Ti alloys, providing a valuable tool for materials scientists and engineers. |
| format | Article |
| id | doaj-art-5e03611bcdca462885b05cf26fb43388 |
| institution | OA Journals |
| issn | 2673-3951 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | MDPI AG |
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| series | Modelling |
| spelling | doaj-art-5e03611bcdca462885b05cf26fb433882025-08-20T01:55:42ZengMDPI AGModelling2673-39512024-07-015390191010.3390/modelling5030047Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning ApproachMihail Kolev0Institute of Metal Science, Equipment and Technologies with Center for Hydro- and Aerodynamics “Acad. A. Balevski”, Bulgarian Academy of Sciences, 1574 Sofia, BulgariaA machine learning-based approach is presented for predicting the mechanical properties of Cu-Ti alloys utilizing a dataset of various features, including compositional elements and processing parameters. The features encompass chemical composition elements such as Cu, Al, Ce, Cr, Fe, Mg, Ti, and Zr, as well as various thermo-mechanical processing parameters. This dataset, comprising more than 1000 data points, was selected from a larger collection of various Cu-based alloys. The dataset was divided into training, validation, and test sets, with a Random Forest Regressor model being trained and optimized using GridSearchCV. The model’s performance was evaluated based on the R<sup>2</sup> score. The results demonstrate high predictive accuracy, with R<sup>2</sup> scores of 0.9929, 0.9851, and 0.9937 for the training, validation, and testing sets, respectively. The Random Forest model was compared with other machine learning models and showed better results in terms of predictive accuracy. A feature importance analysis of the mechanical characteristics was conducted, further clarifying the influence of each feature. The correlation heatmap further elucidates the relationships among the features, offering insights into the effects of alloy composition and processing on mechanical properties. This study underscores the potential of machine learning in advancing the development and optimization of Cu-Ti alloys, providing a valuable tool for materials scientists and engineers.https://www.mdpi.com/2673-3951/5/3/47Cu-based alloysCu-Ti alloysmechanical propertieshardnessyield strengthultimate tensile strength |
| spellingShingle | Mihail Kolev Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach Modelling Cu-based alloys Cu-Ti alloys mechanical properties hardness yield strength ultimate tensile strength |
| title | Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach |
| title_full | Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach |
| title_fullStr | Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach |
| title_full_unstemmed | Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach |
| title_short | Predictive Analysis of Mechanical Properties in Cu-Ti Alloys: A Comprehensive Machine Learning Approach |
| title_sort | predictive analysis of mechanical properties in cu ti alloys a comprehensive machine learning approach |
| topic | Cu-based alloys Cu-Ti alloys mechanical properties hardness yield strength ultimate tensile strength |
| url | https://www.mdpi.com/2673-3951/5/3/47 |
| work_keys_str_mv | AT mihailkolev predictiveanalysisofmechanicalpropertiesincutialloysacomprehensivemachinelearningapproach |