Tensile performance data of 3D printed photopolymer gyroid lattices

被引:0
|
作者
Peloquin, Jacob [1 ]
Kirillova, Alina [2 ]
Mathey, Elizabeth [3 ]
Rudin, Cynthia [4 ]
Brinson, L. Catherine [1 ]
Gall, Ken [1 ]
机构
[1] Duke Univ, Dept Mech Engn & Mat Sci, Durham, NC 27708 USA
[2] Iowa State Univ, Dept Mat Sci & Engn, Ames, IA 50011 USA
[3] Univ Colorado, Dept Mech Engn, Denver, CO 80204 USA
[4] Duke Univ, Dept Comp Sci, Durham, NC 27708 USA
来源
DATA IN BRIEF | 2023年 / 49卷
基金
美国国家科学基金会;
关键词
Additive manufacturing; Lattice structures; Machine learning; Mechanics; Porosity; Photopolymer;
D O I
10.1016/j.dib.2023.109396
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Additive manufacturing has provided the ability to manufacture complex structures using a wide variety of materials and geometries. Structures such as triply periodic minimal surface (TPMS) lattices have been incorporated into products across many fields due to their unique combinations of mechanical, geometric, and physical properties. Yet, the near limitless possibility of combining geometry and material into these lattices leaves much to be discovered. This article provides a dataset of experimentally gathered tensile stress-strain curves and measured porosity values for 389 unique gyroid lattice structures manufactured using vat photopolymerization 3D printing. The lattice samples were printed from one of twenty different photopolymer materials available from either Formlabs, LOCTITE AM, or ETEC that range from strong and brittle to elastic and ductile and were printed on commercially available 3D printers, specifically the Formlabs Form2, Prusa SL1, and ETEC Envision One cDLM Mechanical. The stress-strain curves were recorded with an MTS Criterion C43.504 mechanical testing apparatus and following ASTM standards, and the void fraction or "porosity" of each lattice was measured using a calibrated scale. This data serves as a valuable resource for use in the development of novel printing materials and lattice geometries and provides insight into the influence of photopolymer material properties on the printability, geometric accuracy, and mechanical performance of 3D printed lattice structures. The data described in this article was used to train a machine learning model capable of predicting mechanical properties of 3D printed gyroid lattices based on the base mechanical properties of the printing material and porosity of the lattice in the research article [1]. (c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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页数:9
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