Hoyos, E., Hoyos, M., Serna, M.C., Lochmuller, C., Montoya, Y., & Cordoba, J. (2024, May). A data-driven approach to predicting joint efficiency in FSW of aluminium alloys. 13th International Symposium.
Hoyos E, Hoyos M, Serna M C, et al. "A data-driven approach to predicting joint efficiency in FSW of aluminium alloys". 13th International Symposium (May.2024).
Hoyos, E, Hoyos, M, Serna, M C, et al. "A data-driven approach to predicting joint efficiency in FSW of aluminium alloys". 13th International Symposium (May.2024).
Hoyos E., Hoyos M., Serna M.C., Lochmuller C., Montoya Y. and Cordoba J.A data-driven approach to predicting joint efficiency in FSW of aluminium alloys. 13th International Symposium. 2024 May; .
Hoyos, et al 2024, 'A data-driven approach to predicting joint efficiency in FSW of aluminium alloys', 13th International Symposium. Available from: https://www.twi-global.com/technical-knowledge/fsw-symposium-papers/FSWSymposia-202405-10Paper02.pdf.
Hoyos E, Hoyos M, Serna MC, Lochmuller C, Montoya Y, Cordoba J. A data-driven approach to predicting joint efficiency in FSW of aluminium alloys. 13th International Symposium. 2024;. https://www.twi-global.com/technical-knowledge/fsw-symposium-papers/FSWSymposia-202405-10Paper02.pdf.
Hoyos EE, Hoyos MM, Serna MC , Lochmuller CC, Montoya YY, Cordoba JJ. A data-driven approach to predicting joint efficiency in FSW of aluminium alloys. 13th International Symposium. 2024 May;. https://www.twi-global.com/technical-knowledge/fsw-symposium-papers/FSWSymposia-202405-10Paper02.pdf.
A data-driven approach to predicting joint efficiency in FSW of aluminium alloys
- Hoyos E. ,
- Hoyos M. ,
- Serna M.C. ,
- et al
- Hoyos E. ,
- Hoyos M. ,
- Serna M.C. ,
- Lochmuller C. ,
- Montoya Y. and
- Cordoba J.
May 2024
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Description
This study addresses the challenges of predicting joint efficiency for friction stir welding (FSW). It adopts a data driven approach, leveraging data analytics to develop accurate predictive models. A database of 1780 records from diverse aluminium series (2XXX, 5XXX, 6XXX and 7XXX), parameter combinations, and tool designs were compiled and augmented through synthetic data generation.
13th International Symposium, 21-23 May 2024, Session 10: Modelling, Paper 02
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