Influence of Process Parameters on Rapid Prototype Part Using Response Surface Methodology

Chockalingam, Palanisamy; Chinnasamy, Natarajan; Karthikeyan, M.

doi:10.33093/jetap.2019.1.1.20

Cited by 3 publications

(3 citation statements)

References 6 publications

(7 reference statements)

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“…The specimen's tensile strength increases as the infill density increases. 5 An ANN model was created as shown in Figure 3, by entering input such as layer thickness, number of shells and infill density and tensile strength data as a target data.…”

Section: Resultsmentioning

confidence: 99%

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Experimental study and modeling the tensile strength of 3D‐printed aluminium polylactic acid (PLA) parts using artificial neural networks

Chockalingam

Kiat

2021

F1000Res

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Background: High quality 3D printed products are in high demand, resulting in an increase in the production of 3D printed parts with precise tolerances, improved surface roughness, and overall durability. The processing parameters of 3D printers have a significant impact on the quality of 3D printed parts. Three-dimensionally printed parts must be durable, especially in terms of tensile strength, and its impact on the printer's process parameters must be investigated. Methods: Tensile test specimens were printed in the Makerbot 3D printer with aluminium polylactic acid (PLA) material. The three controllable input parameters taken into consideration were layer thickness, infill density and number of shells. The three levels for each of the respective parameters were 0.1mm, 0.2mm and 0.3mm for layer thickness; 2,3 and 4 for number of shells; 20% 40% and 60% for Infill density. Tensile testing was carried out on the specimens and data was tabulated. Using these data, an artificial neural network model was created using Matlab R2021b software’s neural network toolbox (alternatively Scilab can be used). Results: A high layer thickness (0.3mm) and a 40% infill density were found to be the most effective among all other parameters. The specimen with the lowest layer thickness of 0.1mm, four shells, and a 20% infill density had the highest tensile strength. With the tensile test data, a Matlab ANN model was developed. Validation was done by comparing the values obtained from the model with the experimental data by using random layer thickness, infill density, and number of shells. Conclusions: In conclusion, higher layer thickness has lower tensile strengths. However, as the number of shells and infill density increases, the tensile strength increases. In summary an ANN model was successfully developed and validated to predict 3D printed aluminium parts.

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Section: Resultsmentioning

confidence: 99%

“…There are eight corners, six face centers, and one center, for a total of 15 specimen points. 4,5 Table 1 shows the three levels of parameters. Table 2 displays the experiment plan as well as the test results.…”

Section: Methodsmentioning

confidence: 99%

Experimental study and modeling the tensile strength of 3D‐printed aluminium polylactic acid (PLA) parts using artificial neural networks

Chockalingam

Kiat

2021

F1000Res

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“…The equation was developed using the coefficient of the terms of the response surface model. 6 Tensile Test = 0.6648 + 0.0795 A + 0.0540 B + 0.0582 C À 0.0960 AA + 0.0693 BB + 0.0455 CC À 0.0818 AB + 0.0686 AC À 0.0763 BC…”

Section: Discussionmentioning

confidence: 99%

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

Chockalingam

Nagarajan

2021

F1000Res

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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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Experimental classification and response surface modelling of compression property of 3D printed polylactic acid parts

Chockalingam¹,

Krishnan²

2021

AIP Conference Proceedings

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Influence of Process Parameters on Rapid Prototype Part Using Response Surface Methodology

Cited by 3 publications

References 6 publications

Experimental study and modeling the tensile strength of 3D‐printed aluminium polylactic acid (PLA) parts using artificial neural networks

Experimental study and modeling the tensile strength of 3D‐printed aluminium polylactic acid (PLA) parts using artificial neural networks

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

Experimental classification and response surface modelling of compression property of 3D printed polylactic acid parts

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