Open Access
Open access
volume 13 issue 2 pages 91

Non-Linear Hyperelastic Model Analysis and Numerical Validation of 3D Printed PLA+ Material Incorporating Various Infill Densities

Publication typeJournal Article
Publication date2025-01-24
scimago Q2
wos Q2
SJR0.570
CiteScore4.7
Impact factor2.5
ISSN20751702
Abstract

Additive manufacturing (AM) or 3D printing technology creates a tangible object by adding successive layers of materials. Nowadays, 3D printing is used for developing both metal and non-metal products. In the advancement of 3D printing technology, material specimen design, modification, and testing become very simple, especially for non-metal materials, such as hyperelastic, thermoplastic, or rubber-like materials. However, proper material modeling and validation are required for the analysis of these types of materials. In this study, 3D printed poly lactic acid (PLA+) material behavior is analyzed numerically for validation in the counterpart of experimental analysis to evaluate their behavior in both cases. The specimen was designed in SolidWorks by following ASTM D638 dimension standards with proper infill densities and raster angle or infill orientation angle. These infill layer densities and angles of orientation play an important role in the mechanical behavior of the specimen. This paper aims to present a numerical validation of five infill densities (20%, 40%, 60%, 80%, and 100%) for a ±45-degree infill angle orientation by incorporating a nonlinear hyperelastic model. Results indicate that infill densities affect the mechanical behavior of PLA+ material. The result also suggested that neo-Hookean and Mooney–Rivlin are the best-fitted hyperelastic material models for these five separate linear infill densities. However, neo-Hookean is easier to analyze, as it has only one parameter and a new equation is developed in this study for determining the parameter for different infill densities.

Found 
Found 

Top-30

Journals

1
Journal of Manufacturing and Materials Processing
1 publication, 33.33%
Designs
1 publication, 33.33%
Progress in Additive Manufacturing
1 publication, 33.33%
1

Publishers

1
2
MDPI
2 publications, 66.67%
Springer Nature
1 publication, 33.33%
1
2
  • We do not take into account publications without a DOI.
  • Statistics recalculated weekly.

Are you a researcher?

Create a profile to get free access to personal recommendations for colleagues and new articles.
Metrics
3
Share
Cite this
GOST |
Cite this
GOST Copy
Bhuiyan M. Z. H. et al. Non-Linear Hyperelastic Model Analysis and Numerical Validation of 3D Printed PLA+ Material Incorporating Various Infill Densities // Machines. 2025. Vol. 13. No. 2. p. 91.
GOST all authors (up to 50) Copy
Bhuiyan M. Z. H., Khanafer K., Rafi E. I., Shihab M. S. Non-Linear Hyperelastic Model Analysis and Numerical Validation of 3D Printed PLA+ Material Incorporating Various Infill Densities // Machines. 2025. Vol. 13. No. 2. p. 91.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.3390/machines13020091
UR - https://www.mdpi.com/2075-1702/13/2/91
TI - Non-Linear Hyperelastic Model Analysis and Numerical Validation of 3D Printed PLA+ Material Incorporating Various Infill Densities
T2 - Machines
AU - Bhuiyan, Md Zisanul Haque
AU - Khanafer, Khalil
AU - Rafi, Ehasanul Islam
AU - Shihab, Md Shadman
PY - 2025
DA - 2025/01/24
PB - MDPI
SP - 91
IS - 2
VL - 13
SN - 2075-1702
ER -
BibTex |
Cite this
BibTex (up to 50 authors) Copy
@article{2025_Bhuiyan,
author = {Md Zisanul Haque Bhuiyan and Khalil Khanafer and Ehasanul Islam Rafi and Md Shadman Shihab},
title = {Non-Linear Hyperelastic Model Analysis and Numerical Validation of 3D Printed PLA+ Material Incorporating Various Infill Densities},
journal = {Machines},
year = {2025},
volume = {13},
publisher = {MDPI},
month = {jan},
url = {https://www.mdpi.com/2075-1702/13/2/91},
number = {2},
pages = {91},
doi = {10.3390/machines13020091}
}
MLA
Cite this
MLA Copy
Bhuiyan, Md Zisanul Haque, et al. “Non-Linear Hyperelastic Model Analysis and Numerical Validation of 3D Printed PLA+ Material Incorporating Various Infill Densities.” Machines, vol. 13, no. 2, Jan. 2025, p. 91. https://www.mdpi.com/2075-1702/13/2/91.