Optimizing multiple process parameters in fused deposition modeling with particle swarm optimization

Fused deposition modeling (FDM) is one of the most widely used additive manufacturing processes to produce prototypes as well as functional parts from thermoplastics. However, the applications of the FDM process are still limited due to several drawbacks, such as poor surface quality, poor mechanical properties, or high built time. It has been deemed that the part characteristics and build time can be improved by determining an optimum combination of process parameters. To optimize multiple process parameters in FDM, this paper employs a multi-objective particle swarm optimization based on the data collected from the experimental study. Four (04) process parameters, namely layer thickness, build orientation, infill density, and extrusion temperature are optimized to achieve higher compressive strength and lower build time. The optimization results provide information on the combined impacts of the four process parameters on compressive strength and build time. This information can aid decision makers with better judgment when dealing with multiple conflicting objectives.