A novel hybrid multi-criteria optimization of 3D printing process using grey relational analysis (GRA) coupled with principal component analysis (PCA)

Abstract

Abstract Fused deposition modeling (FDM) is renowned as a prominent approach in the realm of 3D printing, where objects are built layer by layer using a heated nozzle to extrude melted materials. This research was conducted to identify the most effective FDM process variables to enhance tensile strength while simultaneously reducing surface roughness. Polylactic Acid (PLA) was chosen to fabricate test samples, showcasing the applications of 3D printing. In the course of this research, we conducted a series of 27 experiments to investigate the fundamental relationship between the parameters and the corresponding responses. The central aim of this study lies in optimizing the input variables viz. printing speed, layer thickness, and carbon deposition (C-deposition) for the technological manufacturing process of embossing parts in the context of Industry 4.0. To enhance both tensile strength and surface roughness simultaneously, a new hybrid method has been suggested. This approach integrates grey relational analysis (GRA) with principal component analysis (PCA) to determine the optimal combination of process parameters in the 3D printing process. Notably, the experiment trial exhibited the highest grey relational grade (GRG), indicating optimal process parameter settings at a printing speed of 100 mm s−1, layer thickness of 0.1 mm, and C-deposition of 15 mg respectively. Additionally, mathematical models are created through response surface methodology to explore the impact of FDM parameters on the grey relational grade. The findings from this study can be utilized in various industries and applications where FDM 3D printing is employed.