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

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.

Automated summary

Reinforcing polymers with nanoparticles and fibers improves their mechanical, thermal, and electrical properties, making them suitable for industrial applications in functional parts produced by the FDM process. This paper focuses on analyzing the influential process parameters on tensile strength and applies RSM and ANN statistical methods to investigate the effects of layer thickness, printing speed, raster angle, and wall thickness on the tensile strength of short carbon fiber reinforced polyamide composite test specimens. The results show that layer thickness and raster angle have the most significant influence on tensile strength. Machine learning techniques are also employed, and the best configuration is determined based on the lowest MAE and MSE test sample result, demonstrating the usefulness of the proposed model in predicting tensile strength.