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

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