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This study aims to evaluate the low energy impact characteristics of 3D printed carbon fiber thermoplastic and thermoset polymer composite using the Izod impact test. The effects of infill density are examined on the Izod impact properties of 3D printed thermoset polymer and thermoplastic composite specimens. Furthermore, a thorough investigation is conducted into the effect of heat treatment using a hot-air oven on both types of 3D printed composite specimens. To characterize the impact characteristics of each specimen, the fracture surfaces caused by impact load are inspected, and the fracture mechanism is studied using scanning electron micrographs.

Izod Impact specimens of thermoset (epoxy resin) and thermoplastic carbon fiber of different infill density (70, 75, 80, 85, 90 and 100%) are fabricated using the different fiber impregnation 3D printing process. To carry out the heat treatment process, printing of composites is done for each infill design from both thermoset and thermoplastic composites and the impact characteristics of specimens are evaluated on a pendulum test-rig using the ASTM D-256 standard. Using a scanning electron microscope, each fracture zone underwent four separate scanning processes, ranging in size from 2 µm to 100 µm.

The impact resistance of the 3D printed thermoset and thermoplastic composite material is significantly influenced by the type of fiber placement and infill density in the matrix substrate. Because of the weak interfacial strength between the layers of fiber and polyamide 6, the specimen printed with continuous fiber implanted at the part exhibited reduced impact resistance. At 75% infill density, the impact specimen printed with coextruded fiber showed the highest impact resistance with a 367.02% greater magnitude than the continuous fiber specimen with the same infill density.

This work presents a novel approach to analyze the low energy impact characteristics and three-dimensional printing of carbon fiber reinforced thermoplastic and carbon fiber reinforced thermoset and thermoplastic composite material.

This paper aims to present a predictive model approach to estimate the tensile behavior of polylactic acid (PLA) under uncertainty using the fused deposition modeling (FDM) and American Society for Testing and Materials (ASTM) D638’s Types I and II test standards.

The prediction approach combines artificial neural network (ANN) and finite element analysis (FEA), Monte Carlo simulation (MCS) and experimental testing for estimating tensile behavior for FDM considering uncertainties of input parameters. FEA with variance-based sensitivity analysis is used to quantify the impacts of uncertain variables, resulting in determining the significant variables for use in the ANN model. ANN surrogates FEA models of ASTM D638’s Types I and II standards to assess their prediction capabilities using MCS. The developed model is applied for testing the tensile behavior of PLA given probabilistic variables of geometry and material properties.

The results demonstrate that Type I is more appropriate than Type II for predicting tensile behavior under uncertainty. With a training accuracy of 98% and proven presence of overfitting, the tensile behavior can be successfully modeled using predictive methods that consider the probabilistic nature of input parameters. The proposed approach is generic and can be used for other testing standards, input parameters, materials and response variables.

Using the proposed predictive approach, to the best of the authors’ knowledge, the tensile behavior of PLA is predicted for the first time considering uncertainties of input parameters. Also, incorporating global sensitivity analysis for determining the most contributing parameters influencing the tensile behavior has not yet been studied for FDM. The use of only significant variables for FEA, ANN and MCS minimizes the computational effort, allowing to simulate more runs with reduced number of variables within acceptable time.