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Found 4 papers, 1 papers with code
Image-based Artificial Intelligence empowered surrogate model and shape morpher for real-time blank shape optimisation in the hot stamping process
The IAISM, which is based on a Mask-Res-SE-U-Net architecture, is trained to predict the full thinning field of the as-formed component given an arbitrary blank shape.
On the feasibility of small-data learning in simulation-driven engineering tasks with known mechanisms and effective data representations
The application of machine learning (ML) in scientific tasks is increasing, especially ML in simulation-driven engineering tasks.
SuperMeshing: A New Deep Learning Architecture for Increasing the Mesh Density of Metal Forming Stress Field with Attention Mechanism and Perceptual Features
In stress field analysis, the finite element analysis is a crucial approach, in which the mesh-density has a significant impact on the results.
A study on using image based machine learning methods to develop the surrogate models of stamp forming simulations
It is demonstrated that the IBMLM model is advantageous over the MLP SBMLM model in accuracy, generalizability, robustness, and informativeness.
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