Multi-objective optimization of material extrusion additively manufactured parts from PETG feedstock material using grey wolf algorithm

The present study examines the multi-objective optimization of material extrusion additively manufactured (MEAM) parts utilizing the Grey Wolf Algorithm (GWO) with a focus on polyethylene terephthalate glycol (PETG) material. The process parameters are involved like nozzle temperature, layer thickness, and part orientation, while the response parameters have effects on tensile strength, flexural strength, and surface roughness. The primary aim of this optimization aspires to maximize tensile strength and flexural strength, and second, to minimize surface roughness. For achieving the optimization process, a central composite design (FCCCD) methodology linked with analysis of variance (ANOVA) is used in the present study. The experimental results establish the connection between process parameters and response characteristics. The present study findings underline the importance of certain settings i.e., higher nozzle temperature and layer thickness, and lower part orientation. Improving the mechanical properties such as tensile strength and flexural strength is the most important for ensuring the structural integrity and functional performance of MEAM parts. The optimization framework is guided by the GWO algorithm, strategically adjusts the process parameters to achieve these objectives. As an initial objective, experimental outcomes show a notable improvement in mechanical properties, particularly when operating at higher nozzle temperatures. This suggests that thermal energy plays an important role in enhancing the bonding strength of PETG-based MEAM parts. In addition to that, the second objective focuses on reducing issues related to surface roughness which is critical for achieving high-quality surface finishes. The optimization process shows that reducing part orientation, with adjustments in nozzle temperature and layer thickness, contributes significantly to minimizing surface roughness. This underscores the importance of present multi-objective optimizing process. Furthermore, the FCCCD methodology, augmented by ANOVA, facilitates a comprehensive analysis of the optimization process by discerning the significant effects of individual and interaction terms. Through systematic experimentation and statistical analysis, this study unveils the intricate relationships between process parameters and response characteristics, thereby providing valuable insights for process optimization in MEAM. The results show that the utilization of the GWO for multi-objective optimization in MEAM, particularly with PETG material, holds promise for enhancing the mechanical properties and surface quality of fabricated parts. Utilizing the cooperative interactions among process parameters, this methodology facilitates the achievement of enhanced performance attributes, thus driving forward the effectiveness and versatility of MEAM technology across various industrial sectors.