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

Key engineering materials, vol. 926, pp. 323–330

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.

Authors 6

1. Thinh Quy Duc Pham

   Thu Dau Mot University

   Affiliation as printed

   Thu Dau Mot University

   Institute of Strategy Development, Thu Dau Mot University, 75100 Binh Duong Province, Vietnam

2. Truong‐Vinh Hoang Aachen Chair of Mathematics for Uncertainty Quantification

   RWTH Aachen University

   Affiliation as printed

   RWTH-Aachen University

   Chair of Mathematics for Uncertainty Quantification, RWTH-Aachen University, 52056 Aachen, Germany

3. Seifallah Fetni

   University of Liège

   Affiliation as printed

   University of Liège

4. Laurent Duchêne

   University of Liège

   Affiliation as printed

   University of Liège

5. Hoang Son Tran

   University of Liège

   Affiliation as printed

   University of Liège

6. Anne Marie Habraken

   University of Liège · Fund for Scientific Research

   Affiliation as printed

   University of Liège

   Fonds de la Recherche Scientifique de Belgique (F.R.S-FNRS)