Comparative study of evolutionary machine learning approaches to simulate the rheological characteristics of polybutylene succinate (PBS) utilized for fused deposition modeling (FDM)

Abstract

Abstract Polymer filament and its printability, which is strongly influenced by the rheological behavior, can represent a significant hurdle in translating fused deposition modeling (FDM) from the lab to the industrial or clinical settings. The aim of this study is to demonstrate the potential of machine learning (ML) approaches to speed up the development of polymer filaments for FDM. Four types of ML methods; artificial neural network, support vector regression, polynomial chaos expansion (PCE), and response surface model were used to predict the rheological behaivior of polybutylene succinate. In general, all four approaches presented significantly high correlation values with respect to the training and testing data stages. Remarkably, the PCE algorithm repeatedly provided the highest correlation for each response variable in both the training and testing stages. Noteworthy, variation differs between response variables rather than between algorithms. Taken together, these modeling approaches could be used to optimize filament extrusion processes.