Multi-Objective Optimization for FDM Process Parameters with Evolutionary Algorithms

Abstract

Fused deposition modeling (FDM) based three-dimensional (3D) printing process has become a popular and economical alternative to produce complex 3D structures without the necessity of expensive tooling. There are a set of process parameters involved in the FDM process that should be appropriately set to ensure the desired 3D part properties are achieved. The relationships between these process parameters are often complicated, conflicting, and interdependent. The selection of optimum process parameters combination is essential. Many approaches can be taken to optimize FDM process parameters. Although experimental methods are exhaustive, they are often expensive and time-consuming. Alternatively, numerical methods have gained a lot of traction in the application of optimizing FDM process parameters. This chapter will introduce multi-objective optimization and review the state-of-the-art optimization methods based on evolutionary algorithms that can be employed in optimizing FDM process parameters. Multi-objective optimization involves optimizing more than one objective function simultaneously and often results in many optimal solutions known as Pareto-optimal solutions. This Pareto-optimal set of solutions can help decision-makers make a robust optimal selection for FDM process parameters.