Deep learning; Directed energy deposition; Monte-Carlo method; Robust optimization; Uncertainty quantification; Conceptual frameworks; Directed energy; Energy depositions; Melt pool; MonteCarlo methods; Optimization under uncertainty; Uncertainty quantifications; Strategy and Management; Management Science and Operations Research; Industrial and Manufacturing Engineering
Abstract :
[en] This paper introduces a conceptual framework for the robust optimization under uncertainty in the directed energy deposition (DED) of M4 High-Speed Steel. The goal is to identify optimal process parameters for robust manufacturing of printed parts with a stationary melt pool depth and low consumed energy under uncertainty within the multiple layers of a bulk sample. To increase the computational efficiency, a deep learning-based surrogate model is built using the training data generated by a validated high-fidelity DED two-dimensional FE model. The robustness of the optimized result is verified using the Monte-Carlo method and compared with experiments and two other deterministic approaches. Furthermore, we conduct a global sensitivity analysis, which indicates that among six uncertain input variables, the thermal conductivity and the convection have the most significant impact on the melt pool depth variation. This study shows the promising possibilities of the presented framework in optimizing the DED process.
Disciplines :
Mechanical engineering
Author, co-author :
Pham Quy Duc, Thinh ; Université de Liège - ULiège > Aérospatiale et Mécanique (A&M)
Hoang, T.V.; Chair of Mathematics for Uncertainty Quantification, RWTH-Aachen University, Aachen, Germany
Tran, X.V.; Institute of Strategy Development, Thu Dau Mot University, Viet Nam
Fetni, Seifallah; University of Liège, Liège, Belgium
Duchene, Laurent ; Université de Liège - ULiège > Département ArGEnCo > Analyse multi-échelles dans le domaine des matériaux et structures du génie civil
Tran, Hoang Son ; Université de Liège - ULiège > Département ArGEnCo
Habraken, Anne ; Université de Liège - ULiège > Département ArGEnCo > Département Argenco : Secteur MS2F ; Fonds de la Recherche Scientifique de Belgique (F.R.S-FNRS), France
Language :
English
Title :
A framework for the robust optimization under uncertainty in additive manufacturing
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