AccScience Publishing / MSAM / Volume 1 / Issue 4 / DOI: 10.18063/msam.v1i4.23
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ORIGINAL RESEARCH ARTICLE

Process optimization and mechanical property investigation of Inconel 718 manufactured by selective electron beam melting 

Heng Dong1,2 Feng Liu1,2 Lin Ye1,2 Xiaoqiong Ouyang1,2 Qiangbing Wang3 Li Wang1,2 Lan Huang1,2* Liming Tan1,2* Xiaochao Jin4 Yong Liu1,2
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1 State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, China
2 Powder Metallurgy Research Institute, Central South University, Changsha 410083, China
3 Guangzhou Sailong Additive Manufacturing Co., Ltd., Guangzhou 510700, China
4 State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University, Xi’an 710049, China
Submitted: 31 October 2022 | Published: 23 November 2022
© 2022 by the Author(s). Licensee AccScience Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

To accelerate the optimization of selective electron-beam melting (SEBM) processing parameters, two machine learning models, Gaussian process regression, and support vector regression were applied in this work to predict the relative density of Inconel 718 from experimental data. The experimental validation indicated that the trained algorithms can precisely predict the relative density of SEBM samples. Moreover, the effects of different parameters on surface integrity, internal defects, and mechanical properties are discussed in this paper. The Inconel 718 samples with high density (>99.5%) prepared by the same SEBM energy density exhibit different mechanical properties, which are related to the existence of the unmelted powder, Laves phase, and grain structure. Finally, Inconel 718 sample with superior strength and plasticity was fabricated using the optimized processing parameters.

Keywords
Electron beam melting
Inconel 718
Machine learning
Parameter optimization
Defects
Tensile property
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Materials Science in Additive Manufacturing, Electronic ISSN: 2810-9635 Print ISSN: TBA, Published by AccScience Publishing