Enhancing mechanical properties of 3D-printed PLAs via optimization process and statistical modeling

Shahrjerdi, A., Karamimoghadam, M. and Bodaghi, M. ORCID: 0000-0002-0707-944X, 2023. Enhancing mechanical properties of 3D-printed PLAs via optimization process and statistical modeling. Journal of Composites Science, 7 (4): 151. ISSN 2504-477X

1751320_Bodaghi.pdf - Published version

Download (5MB) | Preview


This paper investigates the optimization of 3D printing by 1.75 mm filaments of poly-lactic acid (PLA) materials. The samples are printed separately and glued together to join the tensile device for the failure load and checking the surface roughness. The printing method in this research is Fused Deposition Modeling (FDM), in which the parameters of Infill Percentage (IP), Extruder Temperature (ET), and Layer Thickness (LT) are considered variable parameters for the 3D printer, and according to the Design of Experiments (DOE), a total of 20 experiments are designed. The parametric range is considered to be 15–55% for IP, 190–250 °C for ET, and 0.15–0.35 mm for LT. The optimization model is conducted according to the Response Surface Method (RSM), in which the ANOVA and plot tables are examined. Moreover, the samples’ maximum failure load, weight, fabrication time, and surface roughness are considered output responses. Statistical modeling shows that by increasing the IP and setting the ET at 220 °C, the failure load of the samples increases, and the maximum failure load reaches 1218 N. The weight and fabrication time of the specimen are optimized at the same time to achieve maximum failure load with less surface roughness. By comparing the predicted and actual output for the optimum samples, the percentage error for all results is less than 5%. The developed optimization method is revealed to be accurate and reliable for FDM 3D printing of PLAs.

Item Type: Journal article
Publication Title: Journal of Composites Science
Creators: Shahrjerdi, A., Karamimoghadam, M. and Bodaghi, M.
Publisher: MDPI AG
Date: 2023
Volume: 7
Number: 4
ISSN: 2504-477X
Rights: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions: Schools > School of Science and Technology
Record created by: Laura Ward
Date Added: 18 Apr 2023 08:17
Last Modified: 18 Apr 2023 08:17
URI: https://irep.ntu.ac.uk/id/eprint/48760

Actions (login required)

Edit View Edit View


Views per month over past year


Downloads per month over past year