Power-based Model for Temperature Prediction in FSW - Archive ouverte HAL
Article Dans Une Revue Journal of Physics: Conference Series Année : 2022

Power-based Model for Temperature Prediction in FSW

Danilo Ambrosio
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  • PersonId : 1277175
  • IdRef : 269993975
Vincent Wagner
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Gilles Dessein
Amèvi Tongne
Marina Fazzini
Christian Garnier
Olivier Cahuc
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  • PersonId : 1106667

Résumé

This paper describes a thermal numerical model accessible to all users for predicting temperature in friction stir welding from the power, material thermal properties, process parameters, tool, and plate dimensions. Starting with the information obtained from the machine, power or torque, the heat flux is modeled as a circular moving source with a diameter equal to that of the shoulder. The model calibrated in a specific setup (CNC machine) successfully predicted without recalibration the weld temperature field in another one (robot). The simple thermal model was applied without recalibration to data available in the literature to test its effectiveness. The results obtained with this model are promising, although more tests are needed to cover all possible varieties of tool geometries and material thickness. If extended over a broader range of configurations (i.e., process parameters and tool-workpiece geometries), it could be a handy tool for all FSW users. The tool may help study the thermal cycles in the heat affected zone that influence final mechanical properties and make it easier to identify optimal parameters if the desired optimal peak temperatures are determined.
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hal-03776039 , version 1 (13-09-2022)

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Danilo Ambrosio, Vincent Wagner, Gilles Dessein, Amèvi Tongne, Marina Fazzini, et al.. Power-based Model for Temperature Prediction in FSW. Journal of Physics: Conference Series, 2022, 2022 12th International Conference on Applied Physics and Mathematics (ICAPM 2022) 18/02/2022 - 20/02/2022 Singapore, Singapore, 2287 (1), pp.012025. ⟨10.1088/1742-6596/2287/1/012025⟩. ⟨hal-03776039⟩
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