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Prediction of Tensile Strength of 3D Printed Bronze PLA Part Using Response Surface Modelling

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 functional...

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Autores principales: Palanisamy, Chockalingam, Nagarajan, Sugendran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8815010/
https://www.ncbi.nlm.nih.gov/pubmed/35169462
http://dx.doi.org/10.12688/f1000research.70641.1
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author Palanisamy, Chockalingam
Nagarajan, Sugendran
author_facet Palanisamy, Chockalingam
Nagarajan, Sugendran
author_sort Palanisamy, Chockalingam
collection PubMed
description 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 a Makerbot 3D 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 and analyzing how 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.
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spelling pubmed-88150102022-02-14 Prediction of Tensile Strength of 3D Printed Bronze PLA Part Using Response Surface Modelling Palanisamy, Chockalingam Nagarajan, Sugendran F1000Res Research Article 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 a Makerbot 3D 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 and analyzing how 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. F1000 Research Limited 2021-10-11 /pmc/articles/PMC8815010/ /pubmed/35169462 http://dx.doi.org/10.12688/f1000research.70641.1 Text en Copyright: © 2021 Palanisamy C and Nagarajan S https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Palanisamy, Chockalingam
Nagarajan, Sugendran
Prediction of Tensile Strength of 3D Printed Bronze PLA Part Using Response Surface Modelling
title Prediction of Tensile Strength of 3D Printed Bronze PLA Part Using Response Surface Modelling
title_full Prediction of Tensile Strength of 3D Printed Bronze PLA Part Using Response Surface Modelling
title_fullStr Prediction of Tensile Strength of 3D Printed Bronze PLA Part Using Response Surface Modelling
title_full_unstemmed Prediction of Tensile Strength of 3D Printed Bronze PLA Part Using Response Surface Modelling
title_short Prediction of Tensile Strength of 3D Printed Bronze PLA Part Using Response Surface Modelling
title_sort prediction of tensile strength of 3d printed bronze pla part using response surface modelling
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8815010/
https://www.ncbi.nlm.nih.gov/pubmed/35169462
http://dx.doi.org/10.12688/f1000research.70641.1
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