Optimizing FFF Process Parameters to Enhance PLA Performance on Low-Cost 3D Printers
Keywords:
Fused Filament Fabrication process, optimization, ANN, PSO, DOEAbstract
PLA (Polylactic Acid) is well known for its biodegradable properties and ease of use; however, a careful tuning of parameters such as infill density, layer height, feed rate, build orientation, and nozzle temperature is crucial to achieve the optimum strength and durability of the sample produced. While it has its own benefits, a low-cost FDM 3D printer often comes with limitations that affect the mechanical performance of printed parts, including calibration issues such as improper bed levelling, misaligned axes, and inconsistent extrusion, decreased temperature stability, and poorer build quality. To overcome these factors, a Design of Experiments (DoE) was applied using Response Surface Methodology (RSM). A total of 32 experiments were conducted to evaluate the influence of these parameters on tensile and flexural strength. In addition, a hybrid optimization technique combining Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) was applied. ANN determined strength predictions, whereas PSO was employed to yield the best parameter setups. The maximum tensile strength and flexural strength achieved 34.48 MPa and 71.07 MPa, respectively, indicating considerable enhancements in the mechanical traits of PLA prints. This study shows that with proper process parameter optimization, the performance of PLA can be increased even using low-cost printers.
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