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Keywords

Fused Filament Fabrication (FFF) PLA Tensile Strength, Compression strength Artificial Neural Network

Document Type

Research Paper

Abstract

Material Extrusion technology is one of the most widely used Additive Manufacturing processes due to its simplicity in use, affordable parts fabricating costs, product durability, and possibility for changing materials. Despite having many advantages, parts manufactured through this technique fall short in strength criteria. The present paper focuses on predicting and optimizing three critical printing parameters in additive manufacturing: printing temperature, extrusion width, and number of shells. A neural network model was built to predict the tensile and compressive strengths and optimize the process parameters for maximum strength. The full factorial design experiments found that higher strength is achieved at higher temperatures, extrusion width, and number of shells. Based on the Analysis of Variance (ANOVA), the most influential parameter on tensile strength was printing temperature with (44.2%). in the other hand, the extrusion width contributed more than others to compressive strength (51.3%). Comparisons between the experimental and the predicted values were illustrated.The mean error between the experimental and neural network models was (0.42%) for tensile strength and (0.45%) for compression strength, with a correlation coefficient equal to (0.996) and (0.992) for the two responses, respectively. The current proposed study demonstrates good agreements between the predicted model values and the experiment outcomes of tensile and compressive strengths.

References

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Highlights

Twenty-seven test samples were printed for evaluating tensile and compressive strength. A neural network model was developed to predict and optimize the process parameters. Temperature had the greatest effect on tensile strength, while extrusion width most impacted compressive strength.

DOI

10.30684/etj.2023.143270.1573

First Page

1539

Last Page

1551

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