期刊
MATERIALS & DESIGN
卷 219, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.matdes.2022.110814
关键词
3D printing; Composite extrusion modeling; Printing parameters optimization; Green density; Surface roughness
资金
- Federal Ministry for Economic Affairs and Energy [KK5037202WO0]
This paper presents a method for 3D printing of low alloy-steel using Composite Extrusion Modeling (CEM) and explores the influence of printing parameters on green density and surface roughness. By optimizing the parameters, high density green parts with low surface roughness were achieved.
Composite Extrusion Modeling (CEM) is an advanced material extrusion additive manufacturing technique for low-cost rapid production of complex parts. In this work, a conventional Metal Injection Moulding (MIM) feedstock is used for 3D printing of low alloy-steel AISI 8740 via CEM. This steel is widely used in aircraft, aerospace, and MIM industries. However, it has, so far, not been processed using CEM-based 3D printing. The influence of four printing parameters, extrusion multiplier, extrusion temperature, nozzle velocity, and layer thickness on green density and surface roughness was explored following the feedstock's investigation. Full-factorial and face-centered response designs were utilized to study the influence of printing parameters and their optimization to achieve maximum green density and minimum surface roughness. The optimized parameters were found to be an extrusion multiplier of 107.6 %, extrusion temperature of 180 degrees C, nozzle velocity of 20 mm/s and layer thickness of 0.050 mm through a multiple response optimization process. The dense green part with relative densities of >= 98 % was achieved with minimum surface roughness of Ra = (2.3 +/- 0.1) mu m and Rz = (16.1 +/- 1.1) mu m. Moreover, a scanning electron microscope was utilized to study the surfaces of green parts. The parts printed with optimized printing parameters showed the best quality with minimized printing voids and smooth extrusion. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据