Multi-Objective Parametric Optimization of Fused Deposition Additive Manufacturing (FDAM) for Prosthetic Part

Fused deposition additive manufacturing (FDAM) is a widely used technology in the field of additive manufacturing, offering a variety of applications. This process involves the extrusion of melted plastic filament through a heated nozzle to create physical models layer by layer. A study conducted on the impact of the FDAM process on the manufacturing time, weight, and compressive strength of a phalange for prosthetic hands found that the process can be optimized using RSM, NSGA-II, and TOPSIS. Factors such as build orientation, layer height, printing temperature, and raster pattern were identified and modeled using RSM to reduce printing time and weight while increasing compressive strength. The optimized results showed a 5.14% reduction in manufacturing time, a 0.014% decrease in model weight, and a 4.14% increase in compressive strength compared to the initial results. These findings are essential for researchers and prosthetic manufacturers aiming to create durable and lightweight prosthetics. Integrating RSM, NSGA-II, and TOPSIS is an effective way to optimize the FDAM process and improve outcomes. Highlights This work presents a novel strategy for improving the efficiency of prosthetic manufacturing by integrating the Non-dominated Sorting Genetic Algorithm II (NSGA-II), Response Surface Methodology (RSM), and Multi-Criteria Decision Making (MCDM) approaches to optimize the FDAM process parameters. In comparison to initial results, the optimized results show a 5.14% reduction in manufacturing time, a 0 0.04% decrease in model weight, and a 4.14% increase in compressive strength, demonstrating the observable improvements made possible by the suggested methodology. The understanding of process parameter effects on key performance metrics is advanced in this study, which makes a significant contribution to the field and lays the groundwork for future innovation in prosthetic manufacturing. The prosthetics industry can benefit from proposed novel methods that ensure better and more balanced prosthetic part quality.