Uncertainty Quantification in the Directed Energy Deposition Process Using Deep Learning-Based Probabilistic Approach

Thinh Quy Duc Pham; Truong Vinh Hoang; Xuan Van Tran; Seifallah Fetni; Laurent Duchêne; Hoang Son Tran; Anne M. Habraken

doi:10.4028/p-j9chvq

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HomeKey Engineering MaterialsKey Engineering Materials Vol. 926Uncertainty Quantification in the Directed Energy...

Uncertainty Quantification in the Directed Energy Deposition Process Using Deep Learning-Based Probabilistic Approach

Abstract:

This study quantifies the effects of uncertainty raised from process parameters, material properties, and boundary conditions in the directed energy deposition (DED) process of M4 High-Speed Steel using deep learning (DL)-based probabilistic approach. A DL-based surrogate model is first constructed using the data obtained from a finite element (FE) model, which was validated against experiment. Then, sources of uncertainty are characterized by the probabilistic method and are propagated by the Monte-Carlo (MC) method. Lastly, the sensitivity analysis (SA) using the variance-based method is performed to identify the parameters inducing the most uncertainty to the melting pool depth. Using the DL-based surrogate model instead of solely FE model significantly reduces the computational time in the MC simulation. The results indicate that all sources of uncertainty contribute to a substantial variation on the final printed product quality. Moreover, we find that the laser power, the convection, the scanning speed, and the thermal conductivity contribute the most uncertainties on the melting pool depth based on the SA results. These findings can be used as insights for the process parameter optimization of the DED process.

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Periodical:

Key Engineering Materials (Volume 926)

Pages:

323-330

DOI:

https://doi.org/10.4028/p-j9chvq

Citation:

Cite this paper

Online since:

July 2022

Authors:

Thinh Quy Duc Pham, Truong Vinh Hoang, Xuan Van Tran*, Seifallah Fetni, Laurent Duchêne, Hoang Son Tran, Anne M. Habraken

Keywords:

Deep Learning, Directed Energy Deposition Process, Melting Pool, Uncertainty Quantification

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Creative Commons CC BY 4.0

* - Corresponding Author

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