Engineering Science and Technology, an International Journal (Aug 2024)
Optimizing mechanical properties of 3D-printed aramid fiber-reinforced polyethylene terephthalate glycol composite: A systematic approach using BPNN and ANOVA
Abstract
The advancement and broad application of 3D printing technology in industries such as aerospace, healthcare, and manufacturing highlight the critical need for optimizing printing processes to achieve superior mechanical properties and overall performance of printed objects. Despite the progress in 3D printing, achieving optimal mechanical properties remains challenging due to a lack of systematic parameter selection and understanding of parameter interactions. So, to overcome these drawbacks, this research focuses on the systematic optimization of mechanical properties through precise parameter selection and experimental analysis. Aramid fiber-reinforced Polyethylene Terephthalate Glycol (PETG-KF) is used as the printing material on an X1E Fused Filament Fabrication (FFF) 3D printer. Key printing parameters such as orientation, printing speed, layer height, and infill density are carefully chosen to explore their impact on mechanical properties. An L16-Orthogonal Array (L16-OA) is employed to systematically investigate various combinations of these parameters. The experiments are conducted using the FFF-3D printer, and the mechanical properties, including Ultimate Tensile Strength (UTS), hardness, Fatigue Resistance (FR), and Impact Strength (IS), are evaluated using a Universal Testing Machine (UTM). Data analysis incorporates Backpropagation Neural Network (BPNN) modeling for understanding non-linear relationships between input parameters and mechanical properties, alongside Analysis of Variance (ANOVA) to assess parameter significance. Further, a confirmation run, validates the optimized parameters, ensuring their practical applicability. This research offers a structured methodology to enhance the mechanical performance of 3D-printed objects, contributing valuable insights to the additive manufacturing field. In addition, the experimental UTS (E-UTS), experimental hardness (E-Hardness), experimental IS (E-IS), experimental FR (E-FR) are measured and compared with predicted UTS (P-UTS), predicted hardness (P-Hardness), predicted IS (P-IS), predicted FR (P-FR), which are estimated by BPNN model. Finally, the research concludes by comparing experimental and predicted mechanical properties and analyzing Relative Errors (RE) to identify the most effective parameter combinations for the L16-OA.
