Mining the relationship between the dynamic compression performance and basic mechanical properties of Ti20C based on machine learning methods
The dynamic compression performance of titanium alloys is important for material design under shock, but the intrinsic relationship between them and basic mechanical properties is still unclear. In this work, based on the mechanical-property data set of Ti20C sheet (4788 pieces of data), the correla...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-02-01
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| Series: | Materials & Design |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127523000485 |
| _version_ | 1811158451517128704 |
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| author | Haichao Gong Qunbo Fan Wenqiang Xie Hongmei Zhang Lin Yang Shun Xu Xingwang Cheng |
| author_facet | Haichao Gong Qunbo Fan Wenqiang Xie Hongmei Zhang Lin Yang Shun Xu Xingwang Cheng |
| author_sort | Haichao Gong |
| collection | DOAJ |
| description | The dynamic compression performance of titanium alloys is important for material design under shock, but the intrinsic relationship between them and basic mechanical properties is still unclear. In this work, based on the mechanical-property data set of Ti20C sheet (4788 pieces of data), the correlation between them was constructed through data-driven and machine-learning methods. Through the trained random-forest regression models, the Quantitative Maps were constructed, and the dynamic compression strength σD, critical fracture strain εf, as well as impact absorption energy ED, were effectively predicted, with the accuracy rates all over 86.11%. Accordingly, the zone of excellent dynamic performance and corresponding quasi-static tensile properties in Maps can be rapidly screened. Furthermore, combined with microstructure observation, it was found that nano-scale acicular secondary α-phase significantly increased σD, micro-scale secondary α-phase resulted in excellent dynamic strength and plasticity, while the equiaxed, bimodal and mixture microstructure without secondary α-phase corresponded to the high value of εf. Meanwhile, both nano-scale and micro-scale secondary α-phase contributed to the high value of ED. Finally, the generalization capabilities of models were validated. By comparing predicted values with experimental values, it was found the models realized the relatively accurate prediction of dynamic compression performance on other titanium alloys. |
| first_indexed | 2024-04-10T05:24:54Z |
| format | Article |
| id | doaj.art-82f05de47f9247b9952917fea04df9de |
| institution | Directory Open Access Journal |
| issn | 0264-1275 |
| language | English |
| last_indexed | 2024-04-10T05:24:54Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj.art-82f05de47f9247b9952917fea04df9de2023-03-08T04:13:36ZengElsevierMaterials & Design0264-12752023-02-01226111633Mining the relationship between the dynamic compression performance and basic mechanical properties of Ti20C based on machine learning methodsHaichao Gong0Qunbo Fan1Wenqiang Xie2Hongmei Zhang3Lin Yang4Shun Xu5Xingwang Cheng6School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing 100081, ChinaSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing 100081, China; Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401135, ChinaSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing 100081, ChinaSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing 100081, China; Corresponding author at: School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China.School of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing 100081, ChinaSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing 100081, ChinaSchool of Materials Science and Engineering, Beijing Institute of Technology, Beijing 100081, China; National Key Laboratory of Science and Technology on Materials Under Shock and Impact, Beijing 100081, ChinaThe dynamic compression performance of titanium alloys is important for material design under shock, but the intrinsic relationship between them and basic mechanical properties is still unclear. In this work, based on the mechanical-property data set of Ti20C sheet (4788 pieces of data), the correlation between them was constructed through data-driven and machine-learning methods. Through the trained random-forest regression models, the Quantitative Maps were constructed, and the dynamic compression strength σD, critical fracture strain εf, as well as impact absorption energy ED, were effectively predicted, with the accuracy rates all over 86.11%. Accordingly, the zone of excellent dynamic performance and corresponding quasi-static tensile properties in Maps can be rapidly screened. Furthermore, combined with microstructure observation, it was found that nano-scale acicular secondary α-phase significantly increased σD, micro-scale secondary α-phase resulted in excellent dynamic strength and plasticity, while the equiaxed, bimodal and mixture microstructure without secondary α-phase corresponded to the high value of εf. Meanwhile, both nano-scale and micro-scale secondary α-phase contributed to the high value of ED. Finally, the generalization capabilities of models were validated. By comparing predicted values with experimental values, it was found the models realized the relatively accurate prediction of dynamic compression performance on other titanium alloys.http://www.sciencedirect.com/science/article/pii/S0264127523000485Dual-phase titanium alloysDynamic compression performanceRandom forestData-driven |
| spellingShingle | Haichao Gong Qunbo Fan Wenqiang Xie Hongmei Zhang Lin Yang Shun Xu Xingwang Cheng Mining the relationship between the dynamic compression performance and basic mechanical properties of Ti20C based on machine learning methods Materials & Design Dual-phase titanium alloys Dynamic compression performance Random forest Data-driven |
| title | Mining the relationship between the dynamic compression performance and basic mechanical properties of Ti20C based on machine learning methods |
| title_full | Mining the relationship between the dynamic compression performance and basic mechanical properties of Ti20C based on machine learning methods |
| title_fullStr | Mining the relationship between the dynamic compression performance and basic mechanical properties of Ti20C based on machine learning methods |
| title_full_unstemmed | Mining the relationship between the dynamic compression performance and basic mechanical properties of Ti20C based on machine learning methods |
| title_short | Mining the relationship between the dynamic compression performance and basic mechanical properties of Ti20C based on machine learning methods |
| title_sort | mining the relationship between the dynamic compression performance and basic mechanical properties of ti20c based on machine learning methods |
| topic | Dual-phase titanium alloys Dynamic compression performance Random forest Data-driven |
| url | http://www.sciencedirect.com/science/article/pii/S0264127523000485 |
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