Towards optimizing the thermal processes in aluminum alloys using a full-field CA based approach for static recrystallization modeling
A combination of thermal and mechanical processing is used to produce flat rolled aluminum products. Typically, hot rolled sheets undergo significant time at elevated temperatures during coil cooling. This results in static recrystallization. It is important to understand the linkage between the ann...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Journal of Materials Research and Technology |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785425002200 |
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| _version_ | 1832087451295809536 |
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| author | Abhijit Brahme Chal-Lan Park Jeffrey Tschirhart Aaditya Lakshmanan Sazol Das Kaan Inal |
| author_facet | Abhijit Brahme Chal-Lan Park Jeffrey Tschirhart Aaditya Lakshmanan Sazol Das Kaan Inal |
| author_sort | Abhijit Brahme |
| collection | DOAJ |
| description | A combination of thermal and mechanical processing is used to produce flat rolled aluminum products. Typically, hot rolled sheets undergo significant time at elevated temperatures during coil cooling. This results in static recrystallization. It is important to understand the linkage between the annealing schedule and the microstructure development to design robust manufacturing process that maximizes product performance and minimizes material loss in the subsequent product manufacturing. To achieve this, accurate process-microstructure linkage models are needed. This work proposes a framework capable of handling complex annealing schedules and can be used to predict microstructure evolution and the kinetics of recrystallization. The framework uses measured data like the electron backscatter diffraction maps and the annealing schedule as inputs. It uses the measured data to calculate internal variables like the stored energy and predict the evolved microstructure. The results are validated with measured data. The proposed model can further be utilized to optimize the manufacturing process while minimizing expensive plant trials. |
| format | Article |
| id | doaj-art-0770b13aa4d94589b9247c48b771cb9f |
| institution | Kabale University |
| issn | 2238-7854 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj-art-0770b13aa4d94589b9247c48b771cb9f2025-02-06T05:11:55ZengElsevierJournal of Materials Research and Technology2238-78542025-03-013529462954Towards optimizing the thermal processes in aluminum alloys using a full-field CA based approach for static recrystallization modelingAbhijit Brahme0Chal-Lan Park1Jeffrey Tschirhart2Aaditya Lakshmanan3Sazol Das4Kaan Inal5Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, CanadaNovelis Inc., Kennesaw, GA, 30144, United StatesNovelis Inc., Kennesaw, GA, 30144, United StatesNovelis Inc., Kennesaw, GA, 30144, United StatesNovelis Inc., Kennesaw, GA, 30144, United StatesDepartment of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada; Corresponding author.A combination of thermal and mechanical processing is used to produce flat rolled aluminum products. Typically, hot rolled sheets undergo significant time at elevated temperatures during coil cooling. This results in static recrystallization. It is important to understand the linkage between the annealing schedule and the microstructure development to design robust manufacturing process that maximizes product performance and minimizes material loss in the subsequent product manufacturing. To achieve this, accurate process-microstructure linkage models are needed. This work proposes a framework capable of handling complex annealing schedules and can be used to predict microstructure evolution and the kinetics of recrystallization. The framework uses measured data like the electron backscatter diffraction maps and the annealing schedule as inputs. It uses the measured data to calculate internal variables like the stored energy and predict the evolved microstructure. The results are validated with measured data. The proposed model can further be utilized to optimize the manufacturing process while minimizing expensive plant trials.http://www.sciencedirect.com/science/article/pii/S2238785425002200 |
| spellingShingle | Abhijit Brahme Chal-Lan Park Jeffrey Tschirhart Aaditya Lakshmanan Sazol Das Kaan Inal Towards optimizing the thermal processes in aluminum alloys using a full-field CA based approach for static recrystallization modeling Journal of Materials Research and Technology |
| title | Towards optimizing the thermal processes in aluminum alloys using a full-field CA based approach for static recrystallization modeling |
| title_full | Towards optimizing the thermal processes in aluminum alloys using a full-field CA based approach for static recrystallization modeling |
| title_fullStr | Towards optimizing the thermal processes in aluminum alloys using a full-field CA based approach for static recrystallization modeling |
| title_full_unstemmed | Towards optimizing the thermal processes in aluminum alloys using a full-field CA based approach for static recrystallization modeling |
| title_short | Towards optimizing the thermal processes in aluminum alloys using a full-field CA based approach for static recrystallization modeling |
| title_sort | towards optimizing the thermal processes in aluminum alloys using a full field ca based approach for static recrystallization modeling |
| url | http://www.sciencedirect.com/science/article/pii/S2238785425002200 |
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