Literature DB >> 31097908

A Review of Model Inaccuracy and Parameter Uncertainty in Laser Powder Bed Fusion Models and Simulations.

Tesfaye Moges1, Gaurav Ameta1, Paul Witherell1.   

Abstract

This paper presents a comprehensive review on the sources of model inaccuracy and parameter uncertainty in metal laser powder bed fusion (L-PBF) process. Metal additive manufacturing (AM) involves multiple physical phenomena and parameters that potentially affect the quality of the final part. To capture the dynamics and complexity of heat and phase transformations that exist in the metal L-PBF process, computational models and simulations ranging from low to high fidelity have been developed. Since it is difficult to incorporate all the physical phenomena encountered in the L-PBF process, computational models rely on assumptions that may neglect or simplify some physics of the process. Modeling assumptions and uncertainty play significant role in the predictive accuracy of such L-PBF models. In this study, sources of modeling inaccuracy at different stages of the process from powder bed formation to melting and solidification are reviewed. The sources of parameter uncertainty related to material properties and process parameters are also reviewed. The aim of this review is to support the development of an approach to quantify these sources of uncertainty in L-PBF models in the future. The quantification of uncertainty sources is necessary for understanding the tradeoffs in model fidelity and guiding the selection of a model suitable for its intended purpose.

Entities:  

Keywords:  additive manufacturing; model uncertainty; parameter uncertainty; powder bed fusion; uncertainty quantification

Year:  2019        PMID: 31097908      PMCID: PMC6513316          DOI: 10.1115/1.4042789

Source DB:  PubMed          Journal:  J Manuf Sci Eng        ISSN: 1087-1357            Impact factor:   3.033


  1 in total

1.  Data analytics approach for melt-pool geometries in metal additive manufacturing.

Authors:  Seulbi Lee; Jian Peng; Dongwon Shin; Yoon Suk Choi
Journal:  Sci Technol Adv Mater       Date:  2019-09-25       Impact factor: 8.090

  1 in total

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