Analysis of Dimensional Accuracy of Fused Filament Fabrication Parts Using Genetic Algorithm and Taguchi Analysis

The dimensional accuracy of parts created through Fused Filament Fabrication (FFF) process has been always a subject of investigation for researchers due to variation in results of different optimization algorithms. The situation becomes more complex when different response parameters with equal weightages are required to be optimized. The present investigation aims to solve the dimensional variations issues of FFF parts with different attributes such as length, width, diameter and thickness. Genetic Algorithm is implemented along with Taguchi optimization DOE technique for achieving the minimum dimensional variability in all the attributes. Total 27 experimental results were used for Taguchi and ANOVA analysis followed by Genetic Algorithm. The variable parameters of FFF process are layer thickness, orientation angle, raster angle, raster width and air gap. The results predicted by Genetic Algorithm were more accurate than Taguchi Analysis with higher prediction and validation accuracy. The recommended parametric settings were 0.1405, 0.0000, 0.0000, 0.5064, 0.0080 for layer thickness, orientation angle, raster angle, raster width and air gap respectively with objective function value of 0.5802.