Fig. 1: Data pipeline of the drive shaft digital material shadow. | npj Computational Materials

Fig. 1: Data pipeline of the drive shaft digital material shadow.

From: Digitalizing metallic materials from image segmentation to multiscale solutions via physics informed operator learning

Fig. 1

The production data and simulation results in each step of process chain is used in reduced models to predict the local microstructure and the material properties. The reduced models can be AI based or analytical, all linked by a data pipeline. By employing physics-informed operator learning, we aim to map the microstructure property to its mechanical deformation field. Consequently, this approach eliminates the necessity of data generation in forward problems.

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