Effect of Process Parameters on Tensile Strength of FDM Printed Carbon Fiber Reinforced Polyamide Parts

Abstract

Reinforcing the polymer with nanoparticles and fibers improves the mechanical, thermal and electrical properties. Owing to this, the functional parts produced by the FDM process of such materials can be used in industrial applications. However, optimal parameters’ selection is crucial to produce parts with optimal properties, such as mechanical strength. This paper focuses on the analysis of influential process parameters on the tensile strength of FDM printed parts. Two statistical methods, RSM and ANN, were applied to investigate the effect the layer thickness, printing speed, raster angle and wall thickness on the tensile strength of test specimens printed with a short carbon fiber reinforced polyamide composite. The reduced cubic model was developed by the RSM method, and the correlation between the input parameters and the output response was analyzed by ANOVA. The results show that the layer thickness and raster angle have the most significant influence on tensile strength. As for machine learning, among the nine different tested ANN topologies, the best configuration was found based on the lowest MAE and MSE test sample result. The results show that the proposed model could be a useful tool for predicting tensile strength. Its main advantage is the reduction in time needed for experiments with the LOSO (leave one subject out) k-fold cross validation scheme, offering better generalization ability, given the small set of learning examples.

A review on optimization of process parameters of fused deposition modeling

“…PCR-TOPSIS was used to find the optimal process parameters condition [37]. Muhamedagic et al [95] investigated the effect of layer height, print speed, raster angle, and wall thickness on the tensile strength of the specimen built using short carbon fiber reinforced polymide composite. The reduced cubic model was established using RSM and the correlation between selected process parameters and performance parameter was anlyzed by ANOVA.…”

“…The reduced cubic model was established using RSM and the correlation between selected process parameters and performance parameter was anlyzed by ANOVA. The layer thickness and raster angle were found most influential factors for tensile strength [95]. Ahmed et al [2] identified the influence of layer thickness, build orientation, infill density, and print speed on tensile strength, Young's modulus, and flexural strength.…”

Fused deposition modeling of carbon‐reinforced polymer matrix composites: A comprehensive review

“…The X and Y axes are considered parallel to the build platform, whereas the Z axis is along the part build direction. 207 Layer thickness refers to the thickness of the sedimentary layer.…”

“…206 Similarly, the optimization of process parameters can also be done using ANN artificial neural networks (Figure 8G). 207…”

“…(A) Schematic of FDM process parameters of CCF/PLA composites 181 ; (B) SEM micrographs of printed MWCNTs/ABS composite specimens at various layer thicknesses and raster angles 197 ; (C) Cross‐sectional scanning electrochemical characterization of printed GR/PLA electrodes with different nozzle temperature 203 ; (D) SEM micrographs of the inside structure of 3D‐printed components with varying parameter values 135 ; (E) Variation in composite FDM dimensional parameters and process‐induced inaccuracies 205 ; (F) Analysis of uncertainty in the multi‐level FDM process 205 ; (G) Experimental tensile strength in comparison to the expected tensile strength based on training data 207 …”