Simultaneous Optimization of Manufacturing Variables for 3D-Printed PETG Prototypes Leveraging Taguchi-Coupled Grey Relational Approach
Abstract
This study investigates the optimization of Fused Deposition Modeling (FDM) parameters for Polyethylene Terephthalate Glycol (PETG) a material widely utilized in clinical and biomedical applications due to its favorable combination of lightweight properties, durability, and versatility. By applying a Grey-Taguchi methodology, the research systematically addresses how manufacturing variables influence mechanical performance, specifically focusing on tensile and compressive strength. Through rigorous signal-to-noise (S/N) ratio evaluations, the investigation identified the optimal parameter settings for individual mechanical properties. For tensile strength, a peak performance of 48.264MPa was achieved using level 1 extrusion temperature, a 55% infill density, and a printing speed of 20mm/s. Conversely, maximizing compressive strength to 47.762MPa required level 1 extrusion temperature, a higher infill density of 60%, and an increased printing speed of 30mm/s. Analysis of Variance (ANOVA) further confirmed that extrusion temperature, infill density, and printing speed all exert a statistically significant influence on these mechanical outcomes. Because biomedical and clinical components often require a balance of multiple mechanical traits, the study utilized Grey Relational Analysis (GRA) to establish a multi-response setting configuration of 1-3-3. This configuration yielded a peak grey relational grade of 1, representing the ideal compromise and overall optimal condition for dual-performance requirements. To ensure reliability, experimental validation tests were conducted. The results showed error margins of 4.01% for tensile strength and 6.14% for compressive strength. Because both error values remain well within acceptable engineering thresholds, the findings successfully verify the efficacy and precision of this optimization framework for additive manufacturing applications.