Taguchi gray relational analysis for multi-objective optimization of carbon fiber reinforced PLA

Abstract

The most widely used additive manufacturing method is FDM. This technique depends on printing parameters. Most studies optimize a single mechanical property of a part. However, parts are often subjected to multiple loadings. This study, which focuses on Carbon Fiber reinforced PLA was multi-objectively optimized using Taguchi (L9) Gray Relational Analysis, the GRA method and the Taguchi method were used in combination to limit the number of experiments, considering printing speed, printing temperature, and layer height. GRA shows that optimal parameters set for both tensile and compressive strength are 100 mm s ⁻¹ printing speed, 220 °C printing temperature, and 0.1 mm layer height. This parameter combination is responded to slowest printing speed, lowest printing temperature, and thinnest layer height. Results show that material is suitable for high-speed manufacturing, as the printing speed change test results do not change significantly. Furthermore, increasing the layer height was observed to decrease both tensile and compressive strength, while analysis-of-variance indicated that it was an effective parameter for compressive strength. Finally, preliminary studies have shown that the most significant limitation and challenging aspect of Carbon Fiber reinforced PLA filament printing using the FDM method is the selection of layer height and printing temperature for high-speed prints.