A Digital Twin Approach for the Prediction of the Geometry of Single Tracks Produced by Laser Metal Deposition

Hermann, Florian; Chen, Bowen; Ghasemi, Golsa; Stegmaier, Valentin; Ackermann, Thomas; Reimann, Peter; Vogt, Sabrina; Graf, Thomas; Weyrich, Michael

doi:10.1016/j.procir.2022.04.014

Cited by 9 publications

(1 citation statement)

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“…However, deterministic models cannot provide uncertainty quantification (UQ), which is crucial for reliable additive manufacturing due to the various sources of uncertainties in additive manufacturing [30][31][32][33]. Probabilistic machine learning models such as Gaussian Process Regression (GPR) [34,35] can account for this UQ and have been applied in laser powder bed fusion (LPBF) to predict the melt pool geometry [36][37][38][39][40][41] or in DED to predict the mechanical properties [42], the component height [43], the geometry of single tracks [44,45], or melt pool geometry [46,47] based on the process parameters. The inverse problem, i.e., the determination of a suitable process to produce the desired track geometry, can principally be solved by combining the regression model with an optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Prediction and Uncertainty Quantification of Process Parameters for Directed Energy Deposition

Hermann,

Michalowski,

Brünnette

et al. 2023

Materials

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Laser-based directed energy deposition using metal powder (DED-LB/M) offers great potential for a flexible production mainly defined by software. To exploit this potential, knowledge of the process parameters required to achieve a specific track geometry is essential. Existing analytical, numerical, and machine-learning approaches, however, are not yet able to predict the process parameters in a satisfactory way. A trial-&-error approach is therefore usually applied to find the best process parameters. This paper presents a novel user-centric decision-making workflow, in which several combinations of process parameters that are most likely to yield the desired track geometry are proposed to the user. For this purpose, a Gaussian Process Regression (GPR) model, which has the advantage of including uncertainty quantification (UQ), was trained with experimental data to predict the geometry of single DED tracks based on the process parameters. The inherent UQ of the GPR together with the expert knowledge of the user can subsequently be leveraged for the inverse question of finding the best sets of process parameters by minimizing the expected squared deviation between target and actual track geometry. The GPR was trained and validated with a total of 379 cross sections of single tracks and the benefit of the workflow is demonstrated by two exemplary use cases.

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