1. Experimental investigation and neural network development for modeling tensile properties of polymethyl methacrylate (PMMA) filament material.
- Author
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Kechagias, John D., Zaoutsos, Stephanos P., Fountas, Nikolaos A., and Vaxevanidis, Nikolaos M.
- Abstract
The present study focuses on an experimental investigation aiming at simultaneously optimizing tensile strength and elastic modulus of 3D-printed polymethyl methacrylate (PMMA) filament material while considering raster angle, printing speed, and layer thickness as the independent process-related control parameters. The Box-Behnken design of experiments (BBD) was applied to design the necessary number of experiments and establish the experimental design for fabricating experimental dog-bone samples of standard geometry. Further on, second-order regression models for tensile strength and elastic modulus were generated and employed to predict the three independent structural parameters and maximize tensile strength and elastic modulus. A neural network model was examined for its efficiency in terms of predicting the two responses. Results show a high correlation between inputs and outputs, enabling reliable modeling towards the objective of optimizing both the strength and elasticity of PMMA-fabricated parts. Regression models exhibited high correlation (R2) equal to 97.81% and 97.26% for tensile strength and elastic modulus, respectively. Simulation results referring to the neural network suggest a high correlation between outputs and targets during training (R = 0.9762), validation (R = 0.9974), and testing (R = 0.9808). [ABSTRACT FROM AUTHOR]
- Published
- 2024
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