Multi-objective optimization of FDM process parameters for 3D-printed polycarbonate using Taguchi-based Gray Relational Analysis

Abstract This paper investigates the effects of 3D printing parameters on tensile strength (TS), material consumption (MC), and build time (BT) of polycarbonate (PC) specimens fabricated using fused deposition modelling (FDM) technology. The print parameters considered were layer thickness, print speed, print temperature, infill density, and line width. Taguchi and Analysis of Variance methods were applied separately to analyze the individual impact of the selected printing parameters on the evaluated properties. Furthermore, a Taguchi-based Gray Relational Analysis approach was utilized for simultaneous multi-objective optimization to maximize TS while minimizing MC and BT. The individual optimization results revealed that infill density and layer thickness were the most influential parameters to maximize TS, infill density to minimize MC, and layer thickness and line width to minimize BT. Through multi-objective optimization, a series of optimal printing parameters were identified including 0.3 mm for layer thickness, a 50 mm/min print speed, a print temperature of 270 °C, a 100% infill density, and line width of 0.5 mm. Printing with these optimized parameters led to negligible increases in MC and BT, while significantly enhancing TS with an improvement of approximately 141% compared to those manufactured using the initial parameter set. This improvement was achieved along with only a small improvement in the Gray Relational Grade of around 1%. The findings of this paper provide valuable insights for the multi-objective optimization of PC material in FDM-based additive manufacturing, supporting its applications across various emerging areas such as body armor and printed electronics applications.