Hongxia Wang1,2, Zhiqiang Duan2, Qingwei Guo2, Yongmei Zhang1,2,*, Yuhong Zhao2,3,4,*
CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1393-1409, 2023, DOI:10.32604/cmc.2023.045871
- 29 November 2023
Abstract Due to the large unexplored compositional space, long development cycle, and high cost of traditional trial-anderror experiments, designing high strength aluminum-lithium alloys is a great challenge. This work establishes
a performance-oriented machine learning design strategy for aluminum-lithium alloys to simplify and shorten
the development cycle. The calculation results indicate that radial basis function (RBF) neural networks exhibit
better predictive ability than back propagation (BP) neural networks. The RBF neural network predicted tensile
and yield strengths with determination coefficients of 0.90 and 0.96, root mean square errors of 30.68 and 25.30,
and mean absolute errors of More >
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