Abstract
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 resources, 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 unknown 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.
Highlights
Conducting experiments to better understand manufacturing processes is crucial, with real physical experiments being considered the gold standard
We present in the following the main related works to our research field organized in (1) Gaussian process regression (GP) regression and Finite Element Method (FEM) simulations, (2) GP regression trained with pure sensor data and (3) optimization with GP regression
The screwpress GP is trained with data that is generated by using inputs from Table 1 with (15) and tested on data generated by using inputs from Table 2 with (15)
Summary
Conducting experiments to better understand manufacturing processes is crucial, with real physical experiments being considered the gold standard. Conducting real physical experiments for each new experimental setting is impractical because of expensive materials, production stoppages and labor hours for monitoring and evaluation. One good alternative is conducting experiments via simulation, where numerical methods–such as Finite Element Method (FEM)–present a well-observed method in the field of structural analysis. In order to reduce the computational effort, surrogate modeling may offer a promising solution [1]. With a sufficient amount of training data with respect to the observed use case, a customized surrogate model is able to substitute for a FEM simulation up to a certain accuracy. When only specific dimensions with a controlled reduction in accuracy of a simulation result are desired, reduced-order surrogate modeling is an already known technique. Reduced-order surrogate modeling aims to substitute the high-resolution simulation domain with some carefully selected dimensions of importance, e.g., selected displacement measures of a deformed part can be predicted by a reduced-order surrogate modeling with low com-
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