The application of Resilient back propagation algorithm in evaluation ofprocess parameters and modeling of mechanical properties for ۳D printingtechniques

Among various additive manufacturing (AM) processes, fused filament fabrication (FFF) is knownas a popular technique for rapid prototyping and manufacturing of complex geometries. However,involvement of numerous process parameters and the need for the production of too many samplesto find the optimum parameters is the main drawback of this method. This study explores theeffects of three process parameters, including infill density, layer thickness, and printing speed, onthe tensile and flexural strength of polylactic acid (PLA) by the aid of a machine learningtechnique. Evaluation of statistical indices (correlation coefficient (R) and root mean squared error(RMSE)) indicates that the model predictions could facilitate the fabrication of ۳D printed partswith the desired mechanical characteristics and minimum number of experimental runs

Keywords:

Simulation and modeling of polymeric systems , Additive manufacturing , Artificialneural network , Mechanical properties , Polylactic acid.