Effectiveness of invertible neural network in variable material 3D printing: Application to screw-based material extrusion

Variable material screw-based material extrusion (S-MEX) 3D printing technology provides a novel approach for fabricating composites with continuous material gradients. Nevertheless, achieving precise alignment between the process parameters and material compositions is challenging because of fluctuations in the melt rheological state caused by material variations. In this study, an invertible extrusion prediction model for 0–40 wt% short carbon fiber reinforced polyether-ether-ketone (SCF/PEEK) in the S-MEX process was established using an invertible neural network (INN) that demonstrated the capabilities of forward flow rate prediction and inverse process optimization with accuracies of 0.852 and 0.877, respectively. Moreover, a strategy for adjusting the screw speeds using process parameters obtained from the INN was developed to maintain a consistent flow rate during the variable material printing process. Benefiting from uniform flow, the linewidth accuracy was improved by 77 %, and the surface roughness was reduced by 51 %. Adjusting the process parameters by using an INN offers significant potential for flow rate control and the enhancement of the overall performance of variable material 3D printing.