3D printing food flow in different extruders based on crazy and adaptive salp swarm algorithm-deep extreme learning machine improved-lattice Boltzmann method

Abstract

Extrusion is significant in achieving 3D printing emulsion. The piston and screw extruders are the common structures to achieve the extrusion. The chocolate emulsion is taken for example, two extruders are numerically investigated and compared based on the fluid dynamic analysis. To conduct the simulations, the crazy and adaptive salp swarm algorithm-deep extreme learning machine (CASSA-DELM) is proposed to predict the viscosity of the chocolate emulsion, which is used to replace the traditional fitted model. The built model can avoid non-consistency in the whole shearing rate range. Then, an improved lattice Boltzmann method (I-LBM) is introduced to process the non-Newtonian behavior of the emulsion. In the simulation, the CASSA-DELM model provides the viscosities for each iteration in I-LBM based on the obtained shearing rates. Because the attachment(s) may be generated on the walls, the no-attachment and with-attachment(s) cases are explored, and the necessary results are obtained, which indicate that the piston extruder is more suitable for extruding the single component of food fluid. The screw extruder is recommended for the multiple components of food fluid because the vortex in the X-Y cross-section contributes to further mixing action for the emulsion containing different materials, such as the investigated chocolate emulsion. The indirect experiments are conducted to validate the above conclusions. The current work can contribute to improving the extruding theory of material extrusion technologies.