Heat extrusion processing ANN optimization and microstructure of spray forming TiCP/ZA35 composites
The effects of heat extrusion processing of spray forming TiCp/ZA35 composites on extrusion ratio, extrusion specific pressure, extrusion temperature and extrusion rate had been studied by artificial neural network (ANN). The artificial neural network model was created for heat extrusion processing....
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Journal of Aeronautical Materials
2023-04-01
|
| Series: | Journal of Aeronautical Materials |
| Subjects: | |
| Online Access: | http://jam.biam.ac.cn/article/doi/10.11868/j.issn.1005-5053.2021.000200 |
| _version_ | 1797853385266823168 |
|---|---|
| author | LIU Jingfu YE Jianjun ZHOU Xiangchun ZHUANG Weibin WANG Yi |
| author_facet | LIU Jingfu YE Jianjun ZHOU Xiangchun ZHUANG Weibin WANG Yi |
| author_sort | LIU Jingfu |
| collection | DOAJ |
| description | The effects of heat extrusion processing of spray forming TiCp/ZA35 composites on extrusion ratio, extrusion specific pressure, extrusion temperature and extrusion rate had been studied by artificial neural network (ANN). The artificial neural network model was created for heat extrusion processing. The input parameters of the ANN model were extrusion ratio, extrusion specific pressure, extrusion temperature and extrusion rate. The output of the ANN model was ultimate tensile strength. The model can be used for the prediction of properties of spray forming TiCp/ZA35 composites as functions of processing parameters. It can also be used for the optimization of the processing parameters. The ANN results are in good agreement with experimental phenomena, the biggest relative error and coincidence rate is less than 1.8% and 0.986. The optimized heat extrusion ratio, extrusion specific pressure, extrusion temperature and extrusion rate are 22415 MPa, 315 ℃ and 8 mm·s−1 respectively, and the tensile strength of spray forming TiCp/ZA35 composites is 486.7 MPa. The reinforcement phase MnAl6 whisker or particle is precipitated in the grains due to the indirect aging treatment of composites by hot extrusion. Dispersion strengthen and dislocation strengthen contribute a combination factor to increase the room temperature mechanical properties of the hot extruded TiCp/ZA35 composites, which is 38.3% higher than that of TiCp/ZA35 composites without heat extrusion. |
| first_indexed | 2024-04-09T19:49:48Z |
| format | Article |
| id | doaj.art-6e0daa46a13642c1a77dd9f637075e11 |
| institution | Directory Open Access Journal |
| issn | 1005-5053 |
| language | zho |
| last_indexed | 2024-04-09T19:49:48Z |
| publishDate | 2023-04-01 |
| publisher | Journal of Aeronautical Materials |
| record_format | Article |
| series | Journal of Aeronautical Materials |
| spelling | doaj.art-6e0daa46a13642c1a77dd9f637075e112023-04-03T09:47:48ZzhoJournal of Aeronautical MaterialsJournal of Aeronautical Materials1005-50532023-04-01432596510.11868/j.issn.1005-5053.2021.0002002021-0200Heat extrusion processing ANN optimization and microstructure of spray forming TiCP/ZA35 compositesLIU Jingfu0YE Jianjun1ZHOU Xiangchun2ZHUANG Weibin3WANG Yi4College of Material Science and Engineering , Liaoning Technical University, Fuxin 123000，Liaoning，ChinaCollege of Material Science and Engineering , Liaoning Technical University, Fuxin 123000，Liaoning，ChinaCollege of Material Science and Engineering , Liaoning Technical University, Fuxin 123000，Liaoning，ChinaCollege of Material Science and Engineering , Liaoning Technical University, Fuxin 123000，Liaoning，ChinaCollege of Material Science and Engineering , Liaoning Technical University, Fuxin 123000，Liaoning，ChinaThe effects of heat extrusion processing of spray forming TiCp/ZA35 composites on extrusion ratio, extrusion specific pressure, extrusion temperature and extrusion rate had been studied by artificial neural network (ANN). The artificial neural network model was created for heat extrusion processing. The input parameters of the ANN model were extrusion ratio, extrusion specific pressure, extrusion temperature and extrusion rate. The output of the ANN model was ultimate tensile strength. The model can be used for the prediction of properties of spray forming TiCp/ZA35 composites as functions of processing parameters. It can also be used for the optimization of the processing parameters. The ANN results are in good agreement with experimental phenomena, the biggest relative error and coincidence rate is less than 1.8% and 0.986. The optimized heat extrusion ratio, extrusion specific pressure, extrusion temperature and extrusion rate are 22415 MPa, 315 ℃ and 8 mm·s−1 respectively, and the tensile strength of spray forming TiCp/ZA35 composites is 486.7 MPa. The reinforcement phase MnAl6 whisker or particle is precipitated in the grains due to the indirect aging treatment of composites by hot extrusion. Dispersion strengthen and dislocation strengthen contribute a combination factor to increase the room temperature mechanical properties of the hot extruded TiCp/ZA35 composites, which is 38.3% higher than that of TiCp/ZA35 composites without heat extrusion.http://jam.biam.ac.cn/article/doi/10.11868/j.issn.1005-5053.2021.000200spray forming ticp/za35 compositesheat extrusionartificial neural network(ann)optimizationstrengthen mechanism |
| spellingShingle | LIU Jingfu YE Jianjun ZHOU Xiangchun ZHUANG Weibin WANG Yi Heat extrusion processing ANN optimization and microstructure of spray forming TiCP/ZA35 composites Journal of Aeronautical Materials spray forming ticp/za35 composites heat extrusion artificial neural network(ann) optimization strengthen mechanism |
| title | Heat extrusion processing ANN optimization and microstructure of spray forming TiCP/ZA35 composites |
| title_full | Heat extrusion processing ANN optimization and microstructure of spray forming TiCP/ZA35 composites |
| title_fullStr | Heat extrusion processing ANN optimization and microstructure of spray forming TiCP/ZA35 composites |
| title_full_unstemmed | Heat extrusion processing ANN optimization and microstructure of spray forming TiCP/ZA35 composites |
| title_short | Heat extrusion processing ANN optimization and microstructure of spray forming TiCP/ZA35 composites |
| title_sort | heat extrusion processing ann optimization and microstructure of spray forming ticp za35 composites |
| topic | spray forming ticp/za35 composites heat extrusion artificial neural network(ann) optimization strengthen mechanism |
| url | http://jam.biam.ac.cn/article/doi/10.11868/j.issn.1005-5053.2021.000200 |
| work_keys_str_mv | AT liujingfu heatextrusionprocessingannoptimizationandmicrostructureofsprayformingticpza35composites AT yejianjun heatextrusionprocessingannoptimizationandmicrostructureofsprayformingticpza35composites AT zhouxiangchun heatextrusionprocessingannoptimizationandmicrostructureofsprayformingticpza35composites AT zhuangweibin heatextrusionprocessingannoptimizationandmicrostructureofsprayformingticpza35composites AT wangyi heatextrusionprocessingannoptimizationandmicrostructureofsprayformingticpza35composites |