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For semi-solid metalMetals forming, metallic billets are usually heated inductively into the two-phase region between solidus and liquidus temperature. A homogeneous distributionDistribution of the liquid and solid phase in the billets is thereby crucial for the forming process respectively the manufacturing of high-quality components. Nevertheless, as the skin effect during induction heating leads to a thermal gradient, temperatures need to be determined both in the core and near the outer diameter of the cylindric raw material to achieve optimum heat-up control. Currently, the billet temperatures are measured via thermocouples, which need to be cleaned or even replaced after each heating process due to the high adhesion of the semi-solid metalMetals material. For this reason, an image-based model is presented in this paper, which was first trained with data from various heating trials and, based on this, provides a high prediction quality regarding the liquid phase distributionDistribution already during billet heating.

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Applying Object Detection for In-Situ Prediction of the Internal Temperature Distribution of Inductively Heated Semi-Solid Metal Billets

  • 会议论文

  • 发表日期：

    2025年

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  • Marco Speth,
  • Mathias Liewald,
  • Kim Rouven Riedmüller

摘要

For semi-solid metalMetals forming, metallic billets are usually heated inductively into the two-phase region between solidus and liquidus temperature. A homogeneous distributionDistribution of the liquid and solid phase in the billets is thereby crucial for the forming process respectively the manufacturing of high-quality components. Nevertheless, as the skin effect during induction heating leads to a thermal gradient, temperatures need to be determined both in the core and near the outer diameter of the cylindric raw material to achieve optimum heat-up control. Currently, the billet temperatures are measured via thermocouples, which need to be cleaned or even replaced after each heating process due to the high adhesion of the semi-solid metalMetals material. For this reason, an image-based model is presented in this paper, which was first trained with data from various heating trials and, based on this, provides a high prediction quality regarding the liquid phase distributionDistribution already during billet heating.