Neural lumped parameter differential equations with application in friction-stir processing
Author:
Funder
U.S. Department of Energy
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Industrial and Manufacturing Engineering,Software
Link
https://link.springer.com/content/pdf/10.1007/s10845-023-02271-5.pdf
Reference30 articles.
1. Ambrosio, D., Wagner, V., Dessein, G., Vivas, J., & Cahuc, O. (2023). Machine learning tools for flow-related defects detection in friction stir welding. Journal of Manufacturing Science and Engineering 1–15.
2. Balachandar, K., Arockiaraj, K. S., Sriraman, G., Jegadeeshwaran, R., Sakthivel, G., & Lakshmipathi, J. (2023). Development of a machine learning model to predict the friction stir welding tool condition. |textitMaterials Today: Proceedings.
3. Bastier, A., Maitournam, M. H., Van, K. D., & Roger, F. (2006). Steady state thermomechanical modelling of friction stir welding. Science and Technology of Welding and Joining, 11(3), 278–288.
4. Boukraa, M., Lebaal, N., Mataoui, A., Settar, A., Aissani, M., & Tala-Ighil, N. (2018). Friction stir welding process improvement through coupling an optimization procedure and three-dimensional transient heat transfer numerical analysis. Journal of Manufacturing Processes, 34, 566–578.
5. Bussetta, P., Dialami, N., Boman, R., Chiumenti, M., Agelet de Saracibar, C., Cervera, M., & Ponthot, J.-P. (2014). Comparison of a fluid and a solid approach for the numerical simulation of friction stir welding with a non-cylindrical pin. Steel Research International, 85(6), 968–979.
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