Scientific reports
Rapid thickness distribution prediction of superplastic formed parts based on geometry adapted PSO-BP neural network surrogate model.
Muqi Sun, Chengyue Xiong, Yuwei Zhou, Xicheng Zhang, Aoming Yuan, Yan Sun, Yongbing Li
Published: 202510.1038/s41598-025-33493-7
Abstract
Rapid and accurate prediction of thickness distribution remains a critical challenge in achieving real-time process optimization for superplastic forming (SPF) operations. Conventional prediction method based on finite element analysis (FEA) faces co…
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