Sensitivity Analysis Of Directed Energy Deposition Simulation Results To Aluminum Material Properties
Abstract
Due to the high cost of each experimental run in additive manufacturing (AM), there has been a drive to develop simulations that can find the optimal processing parameters. The accuracy of these simulations is dependent on the accuracy of the material parameters recorded in literature. These reported parameters can vary widely resulting in differing simulation results. For that reason, it is necessary to determine which parameters are the driving parameters. This will allow for only critical parameters to be experimentally found resulting in more accurate simulations faster. This article uses a Plackett-Burman design of experiment to screen for the material properties with the greatest effect on the results of a thermal mathematical model of a laser-based directed energy deposition (DED) AM process. It was found that variances in the absorption of the laser at 880 and 922 K along with variances in the thermal conductivity at 922 K have the largest effect on the range of the response variables that were used to characterize the melt pool. Having a smaller impact on the results are the thermal conductivity at 1491 K and the specific heat at 733 K, and the remainder of the factors have a negligible effect on the melt pool characteristics within the simulation.
Recommended Citation
A. Flood and F. W. Liou, "Sensitivity Analysis Of Directed Energy Deposition Simulation Results To Aluminum Material Properties," 3D Printing and Additive Manufacturing, Mary Ann Liebert, Jan 2023.
The definitive version is available at https://doi.org/10.1089/3dp.2023.0054
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
3D printing; additive manufacturing; additive manufacturing processes; software
International Standard Serial Number (ISSN)
2329-7670; 2329-7662
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Mary Ann Liebert, All rights reserved.
Publication Date
01 Jan 2023
Comments
National Science Foundation, Grant CMMI 1625736