High temperature performance of additively manufactured Al 2024 alloy: Constitutive modelling, dynamic recrystallization evolution and kinetics
The current study investigates the high-temperature deformation behaviour, and microstructural evolution of the laser powder bed fused, Ti modified Al 2024 alloy. The high-temperature performance was evaluated using a hot compression test performed in the temperature range of 200–350 °C and strain r...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Journal of Materials Research and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785423013509 |
| _version_ | 1797745189823971328 |
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| author | Saurabh Gairola Gaurav Singh R. Jayaganthan Joe Ajay |
| author_facet | Saurabh Gairola Gaurav Singh R. Jayaganthan Joe Ajay |
| author_sort | Saurabh Gairola |
| collection | DOAJ |
| description | The current study investigates the high-temperature deformation behaviour, and microstructural evolution of the laser powder bed fused, Ti modified Al 2024 alloy. The high-temperature performance was evaluated using a hot compression test performed in the temperature range of 200–350 °C and strain rate range of 0.1–10 s−1. The flow behaviour at elevated temperatures at different strain rates can provide us insight into the high temperature application and can also be utilized for optimization of deformation-based post processing technique. The deformation-based post processing method utilizes work hardening to improve the mechanical properties and to reduce the inherent defect of additively manufactured parts, such as pores and lack of fusion. The optimal deformation conditions for these processes can be obtained from the processing map. The flow stress during different deformation conditions (strain, strain rate and temperature) was predicted using different phenomenological models such as Johnson-Cook (JC) model, strain compensated Arrhenius equation, and artificial neural network (ANN). The JC model was observed to be the least suited method in the current investigation, whereas the ANN method was observed to be best suited for predicting flow stress with an average absolute relative error of 0.5% and a correlation coefficient of 0.9998. Different deformation mechanisms such as dynamic recovery (DRV) and dynamic recrystallization (DRX) were investigated for different deformation conditions using empirical models, finite element analysis (FEA) and microstructural characterization using TEM, and EBSD. The primary DRX mechanism in the current study was observed to be continuous dynamic recrystallization or CDRX mechanism. |
| first_indexed | 2024-03-12T15:20:45Z |
| format | Article |
| id | doaj.art-59ce4af544e14831b71842bd313608a2 |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-03-12T15:20:45Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-59ce4af544e14831b71842bd313608a22023-08-11T05:33:34ZengElsevierJournal of Materials Research and Technology2238-78542023-07-012534253443High temperature performance of additively manufactured Al 2024 alloy: Constitutive modelling, dynamic recrystallization evolution and kineticsSaurabh Gairola0Gaurav Singh1R. Jayaganthan2Joe Ajay3Department of Engineering Design, Additive Manufacturing Group, Center of Excellence for Materials and Manufacturing for Futuristic Mobility, Indian Institute of Technology Madras, Chennai, 600036, IndiaDepartment of Engineering Design, Additive Manufacturing Group, Center of Excellence for Materials and Manufacturing for Futuristic Mobility, Indian Institute of Technology Madras, Chennai, 600036, IndiaDepartment of Engineering Design, Additive Manufacturing Group, Center of Excellence for Materials and Manufacturing for Futuristic Mobility, Indian Institute of Technology Madras, Chennai, 600036, India; Corresponding author.EOS India Branch Office, Sivananda Nagar Kolathur No.36, Chennai, 600099, IndiaThe current study investigates the high-temperature deformation behaviour, and microstructural evolution of the laser powder bed fused, Ti modified Al 2024 alloy. The high-temperature performance was evaluated using a hot compression test performed in the temperature range of 200–350 °C and strain rate range of 0.1–10 s−1. The flow behaviour at elevated temperatures at different strain rates can provide us insight into the high temperature application and can also be utilized for optimization of deformation-based post processing technique. The deformation-based post processing method utilizes work hardening to improve the mechanical properties and to reduce the inherent defect of additively manufactured parts, such as pores and lack of fusion. The optimal deformation conditions for these processes can be obtained from the processing map. The flow stress during different deformation conditions (strain, strain rate and temperature) was predicted using different phenomenological models such as Johnson-Cook (JC) model, strain compensated Arrhenius equation, and artificial neural network (ANN). The JC model was observed to be the least suited method in the current investigation, whereas the ANN method was observed to be best suited for predicting flow stress with an average absolute relative error of 0.5% and a correlation coefficient of 0.9998. Different deformation mechanisms such as dynamic recovery (DRV) and dynamic recrystallization (DRX) were investigated for different deformation conditions using empirical models, finite element analysis (FEA) and microstructural characterization using TEM, and EBSD. The primary DRX mechanism in the current study was observed to be continuous dynamic recrystallization or CDRX mechanism.http://www.sciencedirect.com/science/article/pii/S2238785423013509Hot deformationAdditive manufacturingAl 2024Flow stressArtificial neural networkMicrostructural characterization |
| spellingShingle | Saurabh Gairola Gaurav Singh R. Jayaganthan Joe Ajay High temperature performance of additively manufactured Al 2024 alloy: Constitutive modelling, dynamic recrystallization evolution and kinetics Journal of Materials Research and Technology Hot deformation Additive manufacturing Al 2024 Flow stress Artificial neural network Microstructural characterization |
| title | High temperature performance of additively manufactured Al 2024 alloy: Constitutive modelling, dynamic recrystallization evolution and kinetics |
| title_full | High temperature performance of additively manufactured Al 2024 alloy: Constitutive modelling, dynamic recrystallization evolution and kinetics |
| title_fullStr | High temperature performance of additively manufactured Al 2024 alloy: Constitutive modelling, dynamic recrystallization evolution and kinetics |
| title_full_unstemmed | High temperature performance of additively manufactured Al 2024 alloy: Constitutive modelling, dynamic recrystallization evolution and kinetics |
| title_short | High temperature performance of additively manufactured Al 2024 alloy: Constitutive modelling, dynamic recrystallization evolution and kinetics |
| title_sort | high temperature performance of additively manufactured al 2024 alloy constitutive modelling dynamic recrystallization evolution and kinetics |
| topic | Hot deformation Additive manufacturing Al 2024 Flow stress Artificial neural network Microstructural characterization |
| url | http://www.sciencedirect.com/science/article/pii/S2238785423013509 |
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