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<p>Incremental hot forging is a manufacturing process with a long history and continued prevalence today. This process involves repeated heating and compressing of a metal workpiece to deform the material to the desired geometry. The processing of metals in this way yields a final part crafted from a single piece of material that leads to desirable properties such as homogeneous microstructure and low porosity. The gold standard for predicting end-state geometry is finite element analysis (FEA). FEA is accurate as the physics of deformation are approximated, but available FEA tools require significant processing time. In this work, we detail the use of a graph neural network (GNN) to provide a method for rapid workpiece shape prediction based solely on surface geometry node positions and edge connectivity of the workpiece and press. The motivation for this work is to demonstrate a proof-of-concept method that can generate predicted shape information in a fraction of the time required for FEA. Our approach demonstrates learning toward the prediction of forged shape based on a limited set of training data from prior FEA simulations and proves to be effective for decreasing the time required for shape prediction.</p>

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Graph Neural Networks for Fast Prediction of Plastic Metal Deformation in Hot Forging

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Sam St. John,
  • Joshua Groves,
  • Ananya Kameswari,
  • Jordan Huff,
  • Tara Wagoner,
  • Barton Gattis,
  • Faezeh Bagheri,
  • Michael Groeber,
  • Steve Niezgoda,
  • Anahita Khojandi

摘要

Incremental hot forging is a manufacturing process with a long history and continued prevalence today. This process involves repeated heating and compressing of a metal workpiece to deform the material to the desired geometry. The processing of metals in this way yields a final part crafted from a single piece of material that leads to desirable properties such as homogeneous microstructure and low porosity. The gold standard for predicting end-state geometry is finite element analysis (FEA). FEA is accurate as the physics of deformation are approximated, but available FEA tools require significant processing time. In this work, we detail the use of a graph neural network (GNN) to provide a method for rapid workpiece shape prediction based solely on surface geometry node positions and edge connectivity of the workpiece and press. The motivation for this work is to demonstrate a proof-of-concept method that can generate predicted shape information in a fraction of the time required for FEA. Our approach demonstrates learning toward the prediction of forged shape based on a limited set of training data from prior FEA simulations and proves to be effective for decreasing the time required for shape prediction.