Impact of Surface Roughness and Porosity on Lattice Structures Fabricated by Additive Manufacturing- A Computational Study

被引:13
|
作者
Jiang, Panwei [1 ]
Rifat, Mustafa [2 ]
Basu, Saurabh [2 ]
机构
[1] Penn State Univ, Dept Mat Sci & Engn, University Pk, PA 16802 USA
[2] Penna State Univ, Harold & Inge Marcus Dept Ind & Mfg Engn, University Pk, PA 16802 USA
来源
48TH SME NORTH AMERICAN MANUFACTURING RESEARCH CONFERENCE, NAMRC 48 | 2020年 / 48卷
基金
美国国家科学基金会;
关键词
additive manufacturing; lattice structure; surface roughness; porosity defects; finite element method; INCONEL; 718; CELLULAR STRUCTURES; MICROSTRUCTURE; SUPERALLOY; FAILURE;
D O I
10.1016/j.promfg.2020.05.114
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Research in this article presents a computational analysis of effects of defects in 2.5D lattice structures fabricated by Additive Manufacturing (AM). Components resulting from AM often suffer from rough surfaces and porosity defects. This complicates their response which must be understood for their service deployment. The core of the methodology used in this research is a workflow for generating various defects such as surface roughness and porosity analogous to those that naturally result from AM-fabricated lattices. Surface roughness is introduced by either discretizing a sinusoidal function or fitting experimental roughness data by Fourier Transforms. Porosity defects are implemented by drawing ellipses with assigned center position, radius and aspect ratio. A plane stress Finite Element Method (FEM) model is used under a uniform displacement boundary condition. Stress-strain and stiffness of the lattices are characterized as a function of the implanted defect. This methodology enables characterization of the effect of: (i) surface roughness, (ii) porosity defect density, (iii) porosity defect size, and (iv) algorithms with which random defects can be generated in simulated specimens. Effectiveness of this workflow also provides an efficient way to generate an adequate data pool for future machine learning and other data processing work. (c) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Scientific Committee of the NAMRI/SME.
引用
收藏
页码:781 / 789
页数:9
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