Gaussian Process Surrogates for Modeling Uncertainties in a Use Case of Forging Superalloys

Johannes Hoffer*, Bernhard Geiger, Roman Kern*

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikelBegutachtung


The avoidance of scrap and the adherence to tolerances is an important goal in manufacturing. This requires a good engineering understanding of the underlying process. To achieve this, real physical experiments can be conducted. However, they are expensive in time and re-sources, and can slow down production. A promising way to overcome these drawbacks is process exploration through simulation, where the finite element method (FEM) is a well-established and robust simulation method. While FEM simulation can provide high-resolution results, it requires extensive computing resources to do so. In addition, the simulation design often depends on un-known process properties. To circumvent these drawbacks, we present a Gaussian Process surrogate model approach that accounts for real physical manufacturing process uncertainties and acts as a substitute for expensive FEM simulation, resulting in a fast and robust method that adequately depicts reality. We demonstrate that active learning can be easily applied with our surrogate model to improve computational resources. On top of that, we present a novel optimization method that treats aleatoric and epistemic uncertainties separately, allowing for greater flexibility in solving inverse problems. We evaluate our model using a typical manufacturing use case, the preforming of an Inconel 625 superalloy billet on a forging press.

FachzeitschriftApplied Sciences
PublikationsstatusVeröffentlicht - 1 Feb. 2022

ASJC Scopus subject areas

  • Ingenieurwesen (insg.)
  • Instrumentierung
  • Werkstoffwissenschaften (insg.)
  • Fließ- und Transferprozesse von Flüssigkeiten
  • Prozesschemie und -technologie
  • Angewandte Informatik


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