An Investigation of Cause-and-Effect Relationships Within a 3D-Printing System and the Applicability of Optimum Printing Parameters from Experimental Models to Different Printing Jobs

Abstract

Abstract

The goal of this research was to determine the cause-and-effect relationships within a 3D printer and the applicability of experimental process models on generating optimum printing parameters, regardless of the printed 3D object. Four target quality parameters and seven factors were chosen to be examined experimentally. A set of hypotheses for cause-and-effect relationships was derived from the evaluation of the 3D-printing system, prior to the experiments being executed. A model was determined from the significant correlations to generate optimum sets of parameters. Five samples of two different 3D objects each were printed for two sets of optimization plots to validate the optimized parameter settings. The accuracy of the model predictions was evaluated in regard to the general applicability of the process model toward finding optimum process parameters. The results indicate that predictions from experimental process models of 3D printers remain mostly valid if they are used to predict target values for different 3D objects. A deviation of 7–9% was observed in the prediction of the surface quality. It can be concluded that a combination of the experimental model with existing expert knowledge and physical correlations in an advanced gray-box process model could enhance the accuracy and applicability of the experimental results.