| Summary: | Numerical methods based on finite element (FE) have proven their efficiency for many years in the thermomechanical simulation of forming processes. Nevertheless, the application of these methods to new materials requires the identification and implementation of constitutive and flow laws within FE codes, which sometimes pose problems, particularly because of the strongly non-linear character of the behavior of these materials. Computational techniques based on machine learning and artificial neural networks are becoming more and more important in the development of these models and help the FE codes to integrate more complex behavior. In this paper, we present the development, implementation and use of an artificial neural network (ANN) based flow law for a GrC15 alloy under high temperature thermomechanical solicitations. The flow law modeling by ANN shows a significant superiority in terms of model prediction quality compared to classical approaches based on widely used Johnson–Cook or Arrhenius models. Once the ANN parameters have been identified on the base of experiments, the implementation of this flow law in a finite element code shows promising results in terms of solution quality and respect of the material behavior.
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