Modeling and analysis of design parameters and interactions in 3D-printed components using response surface methodology

This study focuses on optimizing key design parameters in the Fused Deposition Modeling (FDM) process, a widely used method in 3D printing. Using response surface methodology (RSM), a regression model was developed to analyze the effects of six critical variables: temperature, nozzle movement speed, layer thickness, extrusion width, test tube positioning, and internal infill angle. Each variable was investigated at two levels to evaluate its influence on the mechanical properties of 3D-printed materials. A comprehensive set of 64 experimental tests was conducted to examine three key objective functions: Young's modulus, which measures material stiffness; breakage tension, indicative of the material's tensile strength; and breakage deformation, representing its flexibility under stress. The findings revealed that nozzle movement speed, temperature, and positioning were primary contributors to variations in Young’s modulus. For breakage tension, speed, layer thickness, and positioning emerged as significant factors. Similarly, nozzle speed, extrusion width, and positioning were found to strongly influence breakage deformation. Statistical analysis highlighted the significance of the process for optimizing Young’s modulus and breakage tension, with a p-value < 0.05 indicating strong evidence against the null hypothesis. However, the process's impact on breakage deformation was not statistically significant, suggesting the need for further investigation or potential inclusion of additional variables. These insights underline the criticality of parameter optimization in enhancing the structural integrity and mechanical performance of 3D-printed components. The study demonstrates the effectiveness of RSM in systematically identifying and quantifying interactions between variables, providing a pathway for improving FDM outputs. By fine-tuning the process parameters, manufacturers can achieve desired mechanical properties tailored to specific applications, advancing the potential of FDM in diverse industries.

Publication Details

Journal
ARPN Journal of Engineering and Applied Sciences
Published
2025-06-27
DOI
https://doi.org/10.59018/042557
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling and analysis of design parameters and interactions in 3D-printed components using response surface methodology

ARPN Journal of Engineering and Applied Sciences
Manufacturing Process and Optimization
article

Modeling and analysis of design parameters and interactions in 3D-printed components using response surface methodology

article en

Abstract

This study focuses on optimizing key design parameters in the Fused Deposition Modeling (FDM) process, a widely used method in 3D printing. Using response surface methodology (RSM), a regression model was developed to analyze the effects of six critical variables: temperature, nozzle movement speed, layer thickness, extrusion width, test tube positioning, and internal infill angle. Each variable was investigated at two levels to evaluate its influence on the mechanical properties of 3D-printed materials. A comprehensive set of 64 experimental tests was conducted to examine three key objective functions: Young's modulus, which measures material stiffness; breakage tension, indicative of the material's tensile strength; and breakage deformation, representing its flexibility under stress. The findings revealed that nozzle movement speed, temperature, and positioning were primary contributors to variations in Young’s modulus. For breakage tension, speed, layer thickness, and positioning emerged as significant factors. Similarly, nozzle speed, extrusion width, and positioning were found to strongly influence breakage deformation. Statistical analysis highlighted the significance of the process for optimizing Young’s modulus and breakage tension, with a p-value < 0.05 indicating strong evidence against the null hypothesis. However, the process's impact on breakage deformation was not statistically significant, suggesting the need for further investigation or potential inclusion of additional variables. These insights underline the criticality of parameter optimization in enhancing the structural integrity and mechanical performance of 3D-printed components. The study demonstrates the effectiveness of RSM in systematically identifying and quantifying interactions between variables, providing a pathway for improving FDM outputs. By fine-tuning the process parameters, manufacturers can achieve desired mechanical properties tailored to specific applications, advancing the potential of FDM in diverse industries.

ARPN Journal of Engineering and Applied Sciences
Openalex Percentile: Top 79%
Manufacturing Process and Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.