This work presents a thermal–elastic–plastic constitutive equation based on the radial basis function (RBF) artificial neural network and application with the finite element (FE) analysis. In order to capture the stress data in the coupled temperature-strain doamin, a constitutive equation was defined based on the RBF model, and the trained model was validated by test data that were not used in the training. The RBF based constitutive model was then combined with the stress integration and tangent modulus formulation of FE analysis to apply the new model to a warm V-bending process that includes elastic–plastic deformation and elastic recovery under non-isothermal conditions. The heating method was the infrared (IR) local heating method. The results show that the RBF constitutive model can provide good agreement with the experimental data for V-bending in the non-isothermal conditions. The effects of the parameters of the basis function are also discussed in this work.

这项工作提出了一个基于径向基函数（RBF）人工神经网络的热弹塑性本构方程，并结合了有限元（FE）分析的应用。为了捕获耦合的温度-应变doamin中的应力数据，基于RBF模型定义了一个本构方程，并通过训练中未使用的测试数据验证了训练后的模型。然后将基于RBF的本构模型与有限元分析的应力积分和切线模量公式相结合，以将新模型应用于温暖的V型弯曲过程，包括非等温条件下的弹塑性变形和弹性恢复。加热方法是红外（IR）局部加热方法。结果表明，RBF本构模型可以与非等温条件下V弯曲的实验数据很好地吻合。在这项工作中还讨论了基本函数的参数的影响。