Investigations on the warm spinning characteristics and processing parameters optimization of AA 7075 aluminum alloy conical parts with inner ribs
The warm spinning of AA7075 aluminum alloy conical parts with internal ribs involves complex deformation conditions including compression and shear plastic deformation. To mechanistically reproduce these deformation characteristics, the warm compression tests and notched specimen warm tensile tests...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785425019970 |
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| Summary: | The warm spinning of AA7075 aluminum alloy conical parts with internal ribs involves complex deformation conditions including compression and shear plastic deformation. To mechanistically reproduce these deformation characteristics, the warm compression tests and notched specimen warm tensile tests were implemented. The stress-strain responses and corresponding displacement-load curves of AA7075 aluminum alloy were systematically analyzed to develop a unified elastic-viscoplastic constitutive model. The developed model incorporates the interaction mechanism of the dislocation density and grain/subgrain size, along with a modified GTN constitutive model. Then, the model was implemented into ABAQUS software via user subroutines to reveal the influences of spinning parameters upon the Marco-forming characteristics including the rib height and conicality of AA7075 aluminum alloy conical parts. The SVM-XGBoost hybrid model was employed to optimize warm spinning parameters. The optimal processing window for the warm spinning of AA7075 aluminum alloy conical parts with internal ribs was determined as follows: forming temperature (200–230 °C), feed rate (0.8–1.2 mm·r−1), and thinning ratio (50–60 %). The engineering trial results demonstrate that components fabricated within the identified process window achieve satisfactory forming accuracy, with consistent mechanical properties and microstructural characteristics. This verifies the reliability of the SVM-XGBoost hybrid model for process parameter optimization. |
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| ISSN: | 2238-7854 |