Predictionof Tensile Strength of 3D PrintedBronze PLA Part Using Response Surface Modelling.

Chockalingam Palanisamy,Sugendran Nagarajan

doi:10.12688/f1000research.70641.1

Abstract

Background - 3D printing is a dynamic process with many process parameters influencing the product, including the type of the material; it is often difficult to understand the combined influence of these parameters. Purpose - The tensile strength of 3D printed parts is important for the functionality of components. The effects of process parameters on tensile strength must therefore be examined. The objective of this study is to develop a response surface model (RSM) to predict the final quality of a 3D printed bronze part from a different set of input parameters. Methods - The tensile test specimen was built in aMakerbot3D printer with bronze polylactic acid (PLA) material. The three controllable input parameters were; thickness of layers, number of shells, and infill density. The three levels of layer thickness were 0.1mm, 0.2mm and 0.3mm. The number of shells was 2, 3 and 4. The infill densities were 20%, 30% and 40%. A tensile experiment tested the strength of the specimens. RSM is a statistical approach for modelling andanalyzinghow different variables affect the response of interest, and for optimizing it. Results- The result obtained shows that the specimen with a high layer thickness of 0.3mm and infill density of 40% is the best among all the other parameters. Finally, the regression equation produced was used for random values of layer thickness, the number of shells, and infill density, to see whether the values obtained from the tests fall into the range of experimental data. Conclusion - Infill density and layer thickness are the two criteria that significantly influence the tensile property. The number of shells has the least influence on the tensile property. However, the best tensile strength is the part printed with higher infill density, a greater number of shells, and higher layer thickness.

Highlights

Competitiveness in the global marketplace is increasing and manufacturers must seek ways to increase production output and quality and reduce the costs of production. 3D printing, a very recent innovation and state-of-the-art technology, is a solution that could meet their requirements.[1]
A Pareto chart of the standardized effects was created to compare the relative magnitude and the statistical significance of main, square, and interaction effects of independent variables which contribute to the most variability to the dependent variable tensile strength, which plots a reference line to indicate which effects are statistically significant
Tensile testing was conducted on specimens that were printed according to the ASTM D256 specification, using the InstraonTensile tester, Instron 3360 Series dual-column table-frame equipment; the data was collected using

Summary

Introduction

Competitiveness in the global marketplace is increasing and manufacturers must seek ways to increase production output and quality and reduce the costs of production. 3D printing, a very recent innovation and state-of-the-art technology, is a solution that could meet their requirements.[1]. 3D printing, a very recent innovation and state-of-the-art technology, is a solution that could meet their requirements.[1] Bronze - polylactic acid (PLA) is the most widely used material in the 3D printing of most mechanical parts. 3D printed parts do not have high tensile properties. With a combination of input parameters, parts can be printed with enhanced tensile properties. A large number of parameters influence the properties of the product; it is often difficult to understand the combination of these parameters.[2] Among other considerations, printing parameters such as the thickness of each layer, number of shells and density of the infill have a major impact on the quality and performance of 3D printed components. Since tensile properties are important for the functioning of components, the effect of process parameters on mechanical properties will be studied. A response surface model (RSM) using Minitab 18 (Minitab, RRID:SCR_014483) software (alternatively, R Software) is developed

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Methods

Results

Conclusion