Optimization framework of laser oscillation welding based on a deep predictive reward reinforcement learning net
Author:
Funder
National Natural Science Foundation of China
Equipment pre-research areospace science and Technology application innovation project
Young top-notch talent cultivation program of Hubei province
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10845-024-02465-5.pdf
Reference38 articles.
1. Acherjee, B., Kuar, A. S., Mitra, S., & Misra, D. (2015). Empirical modeling and Multi-response Optimization of Laser Transmission Welding of Polycarbonate to ABS. Lasers in Manufacturing and Materials Processing, 2(3), 103–123. https://doi.org/10.1007/s40516-015-0009-0.
2. Ai, Y., Shao, X., Jiang, P., Li, P., Liu, Y., & Yue, C. (2015). Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials. Applied Physics A, 121(2), 555–569. https://doi.org/10.1007/s00339-015-9408-5.
3. Bagherzadeh, S. A., Shamsipour, M., Kholoud, M. J., & Dehkordi, M. H. R. (2021). ANN modeling and multiobjective genetic algorithm optimization of pulsed laser welding of Ti6Al4V alloy sheets with various thicknesses. Journal of Laser Applications, 33(1), 012056. https://doi.org/10.2351/7.0000356.
4. Casalino, G., Facchini, F., Mortello, M., & Mummolo, G. (2016). ANN modelling to optimize manufacturing processes: The case of laser welding. IFAC-PapersOnLine, 49(12), 378–383. https://doi.org/10.1016/j.ifacol.2016.07.634.
5. El-Batahgy, A., & Kutsuna, M. (2009). Laser Beam Welding of AA5052, AA5083, and AA6061 aluminum alloys. Advances in Materials Science and Engineering, 2009, 974182. https://doi.org/10.1155/2009/974182.
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