Multi-objective optimization of directed energy deposition process by using Taguchi-Grey relational analysis

In this study, a multi-objective optimization of directed energy deposition (DED) process was conducted with Taguchi-Grey relational analysis. The used part was designed as a flat rectangle which would be deposited by a single-layer and multi-track DED process. Firstly, after finishing Taguchi experiments, the effects of five control factors (laser power, overlap ratio, powder feed rate, scanning speed and laser defocus distance) on three DED product qualities (cladding efficiency, surface roughness and porosity) were, respectively, analyzed. Then, through Grey relational analysis (GRA), an optimal factor setting which can take all qualities into account was found and had better deposition results compared with previous setting. Furthermore, ANOVAs were conducted to find out significant factors of each qualities. By using the significant factors as variations, three second-order polynomial regression predictive models for qualities were created. Based on the GRA and ANOVAs results, additional one-factor-at-a-time (OFAT) experiments which used the optimal setting as the center point were performed. The qualities variation resulting from adjusting overlap ratio and laser defocus distance of optimal setting were investigated, and the results were also used as additional data to verified the accuracies of three regression models.