Multi-objective Optimization of FDM Using Hybrid Genetic Algorithm-Based Multi-criteria Decision-Making (MCDM) Techniques

Fused Deposition Modelling (FDM) is the most widely used to produce complex and intricate shape parts and prototypes in aerospace and biomedical industries. This work aims to study the effect of process parameters on the mechanical properties of FDM printed part. Experiments are designed using a central rotatable composite design. Specimens for tensile, impact, flexural, and surface roughness tests are prepared considering ASTM standards. The process parameters are optimized to obtain a lower surface roughness and maximum strength (Impact, tensile, and flexural strengths) for the printed part. The optimization techniques such as a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the desirability function approach-based response surface methodology (RSM), the Non-dominated Sorting Genetic Algorithm (NSGA-II), and Grey Relational Analysis (GRA) are used to find the best optimal FDM process parameters. This study finds better prediction accuracy with the hybrid optimization approach, i.e. a genetic algorithm with RSM. This study observes that the lowest surface roughness and maximum strengths for an FDM part could be obtained using an infill density of 61.02%, the layer thickness of 0.26 mm, print speed of 37.77 mm/s, and extrusion temperature should be 191.1 °C.