High Throughput Screening of Process Parameters for Quality Control of Additive Manufacturing by Fused Filament Fabrication

A digital model-based correlation between component design, machine programming, and the physically realized component geometry was developed for analysis of critical process parameters in additive manufacturing by Fused Filament Fabrication (FFF). Aiming for a correlation at high throughput rates, a method for specifying the machine control protocol (G-code) was developed, which enables the automated adaptation of wall thicknesses, layer thickness, and support structure geometry. Although this method was applied to planar surfaces in this study, it is suitable for mapping any curved geometry, provided that the cross-section of the printed filament remains unchanged by the printing speed. Thus, a representative pre-manufacturing model is automatically derived from the specific G-code, which is suitable for predicting the structure-property relationships of additively manufactured components. This representative pre-manufacturing model is compared with a second automatically generated post-manufacturing model from physical prints. Thus, the modelled pre-manufacturing structure-property relationships can be compared with the generatively manufactured component structures. Detected deviations between the modelled and manufactured components are suitable for quality management and the optimization of process parameters. In result, a linear statistical model was developed to quantify the relationship between the mechanical component properties and specific G-codes for an FFF component. Additionally, an inverse model was developed to facilitate the selection of process parameters that lead to the desired mechanical properties of components manufactured in FFF.