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As newer high performance polymers in mechanical properties become available for material extrusion-based additive manufacturing, determining infill parameter settings becomes more important to achieve both operational and mechanical performance of printed outputs. For the material extrusion of carbon fiber reinforced poly-ether-ether-ketone (CFR-PEEK), this study aims not only to identify the effects of infill parameters on both operational and mechanical performance but also to derive appropriate infill settings through a multicriteria decision-making process considering the conflicting effects.

A full-factorial experimental design to investigate the effects of two major infill parameters (i.e. infill pattern and density) on each performance measure (i.e. printing time, sample mass, energy consumption and maximum tensile load) is separately performed to derive the best infill settings for each measure. Focusing on energy consumption for operational performance and maximum tensile load for mechanical performance, the technique for order preference by similarity to ideal solution is further used to identify the most appropriate infill settings given relative preferences on the conflicting performance measures.

The results show that the honeycomb pattern type with 25% density is consistently identified as the best for the operational performance measures, while the triangular pattern with 100% density is the best for the mechanical performance measure. Moreover, it is suggested that certain ranges of preference weights on operational and mechanical performance can guide the best parameter settings for the overall material extrusion performance of CFR-PEEK.

The findings from this study can help practitioners selectively decide on infill parameters by considering both operational and mechanical aspects and their possible trade-offs.

This study aims to model power demand and energy consumption of fused filament fabrication (FFF) for carbon fiber-reinforced polyether-ether-ketone (CFR-PEEK) based on a material addition rate (MAR), which is affected by process parameter changes in an FFF machine. Moreover, a virtual additive manufacturing (AM) plant handling multiple FFF machines and part designs is simulated to compare the energy and production dynamics of operational strategies that treat part orders differently based on their inherent MAR.

A full-factorial design of experiments considering major FFF parameters (i.e., layer thickness and printing speed) is planned to fabricate CFR-PEEK samples for each process parameter combination. Then, the MAR of each process parameter combination is calculated to derive regression models for average power demand and total energy consumption. Furthermore, a discrete-event simulation model for a virtual AM system of aircraft parts is built to analyze changes in power demand and energy consumption along with order lead time and production volume under three operational strategies (i.e., higher MAR first-out, first-in-first-out, and lower MAR first-out).

The MAR of FFF for CFR-PEEK plays a key role in energy dynamics in which a decrease in energy consumption dominates over an increase in power demand as the MAR increases. Furthermore, preferentially processing parts with a higher MAR in the AM system is the most beneficial strategy in both energy consumption and productivity.

The findings from this study show that the energy performance of CFR-PEEK applications in FFF should be understood with the MAR of an AM system because the impact of AM complexity on energy performance can be operationally controlled by managing the MAR of part orders for the entire AM system.