Effect of Different Standard Geometry Shapes on the Tensile Properties of 3D-Printed Polymer

Faidallah, Rawabe Fatima; Hanon, Muammel M.; Vashist, Varun; Habib, A. Nour A.; Szakál, Zoltán; Oldal, István

doi:10.20944/preprints202306.0945.v1

2023

DOI: 10.20944/preprints202306.0945.v1

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Effect of Different Standard Geometry Shapes on the Tensile Properties of 3D-Printed Polymer

Rawabe Fatima Faidallah¹,

Muammel M. Hanon²,

Varun Vashist³

et al.

Abstract: The objective of this research is to conduct a comparative analysis between rectangular-shaped ASTM D3039 specimens with different angles (0°, 15°, and 90°) and various tensile test specimens based on ASTM and ISO standards, all 3D-printed using polyethylene terephthalate glycol (PETG) material through FDM. The study aimed to assesses the weak spot commonly found at the neck of the specimens. Two different printing orientations, namely flat and on-edge, were investigated, and a comprehensive examination and co… Show more

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2024

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Cited by 4 publications

References 42 publications

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An artificial neural network-based predictive model for tensile behavior estimation under uncertainty for fused deposition modeling

Obaidat,

Tamimi,

Mumani

et al. 2024

RPJ

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Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

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An artificial neural network-based predictive model for tensile behavior estimation under uncertainty for fused deposition modeling

Obaidat,

Tamimi,

Mumani

et al. 2024

RPJ

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Tensile Test Analysis of 3D Printed Specimens with Varying Print Orientation and Infill Density

Appalsamy,

Hamilton,

Kgaphola

2024

J. Compos. Sci.

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The research conducted aimed to investigate the effect of varying print orientation and infill density on the mechanical properties of different 3D printed polymer specimens by conducting tensile tests. The Stratasys Fortus 900mc Material Extrusion printer was used to produce multiple samples of different materials, namely, Acrylonitrile Styrene Acrylate (ASA), Nylon 12, Nylon 12 Carbon Fibre, ULTEM 1010, and ULTEM 9085 which were subjected to tensile tests according to the ASTM D638 standard. Samples were printed in flat, side, and upright orientations with both sparse (50%) and solid (100%) infill densities. The samples were then tensile tested to obtain the Young’s Modulus, ultimate tensile strength (UTS), yield strength, and strain at break. The results produced revealed that the solid infill specimens almost always outperformed the sparse infill specimens. In terms of print orientation, side-orientated specimens achieved higher values for the material properties, followed by the flat specimens, with the upright specimens producing the performance with the lowest values. There were, however, notable exceptions to the results trends mentioned above. These findings were analysed using fracture mechanics and composite theory to explain the unexpected behaviour.

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Determination of Residual Stresses in 3D-Printed Polymer Parts

Issametova,

Martyushev,

Zhastalap

et al. 2024

Polymers

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This paper presents the results of an investigation of the possibility of the reliable determination of the residual stress–strain state in polymers and composites using a combination of bridge curvature, optical scanning, and finite element methods. A three-factor experiment was conducted to determine the strength of printed PLA plastic products. The effect of the residual stresses on the strength of the printed products was evaluated. By comparing the values of the same strength stresses, a relationship between the nature of the stresses and the strength of the samples was found. A tendency of the negative influence of tensile stresses and the opposite strengthening effect of compressive stresses was obvious, so at the same values of tensile strength, the value of residual stress of 42.9 MPa is lower than that of the fibre compression at the value of 88.9 MPa. The proposed new methods of the residual stress determination allow obtaining a complete picture of the stressed state of the material in the investigated areas of the products. This may be necessary in confirming the calculated models of the residual stress–strain state, clarifying the strength criteria and assessing the quality of the selected technological modes of manufacturing the products.

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Effect of Different Standard Geometry Shapes on the Tensile Properties of 3D-Printed Polymer

Cited by 4 publications

References 42 publications

An artificial neural network-based predictive model for tensile behavior estimation under uncertainty for fused deposition modeling

An artificial neural network-based predictive model for tensile behavior estimation under uncertainty for fused deposition modeling

Tensile Test Analysis of 3D Printed Specimens with Varying Print Orientation and Infill Density

Determination of Residual Stresses in 3D-Printed Polymer Parts

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