Investigating slicing parameters in FFF for time and mass estimation: a statistical approach

Fused filament fabrication (FFF) is one of the additive manufacturing methods used to transform digital models cost-effectively into prototypes, mockups, and functional parts for industrial customized applications, mainly aerospace, automotive, and biomedicine. In an industrial standard design-to-manufacturing workflow, the slicing software is responsible for translating the digital model of the object into a set of instructions for the FFF machine. However, setting printing profiles for FFF machines is a painstaking process in the operative environment due to the long time needed to carry out the required tests and tuning phases. Moreover, the scientific literature needs to include the influence of digital model topologies on the more influencing manufacturing parameters. Thus, this paper proposes a reproducible methodology to understand how the choice of the manufacturing parameters affects the time estimation and mass of the production process. Through a half-factorial Design of Experiment approach, the manufacturing parameters that most significantly affect the time required are identified; furthermore, the methodology aims to suggest adjustments to enhance the accuracy of build time predictions in commercial slicing software. Several case studies in the paper provide empirical support for the findings, highlighting that proper configuration of commercial slicing software can substantially enhance manufacturing process accuracy. In particular, the results show that the best configuration cannot be chosen a priori since the topology of the component affects the optimal choice of parameters. Moreover, a rigorous statistical approach allows for producing functional components with excellent printing times and optimal material consumption, compared to a more random approach that may lead to non-functional components. The methodology suits the industrial environment where processes must be set up quickly with satisfying results.