In the first model, failed printings were included in the dataframe, and n was the most relevant variable for predicting ∆W . In the second model, tanδ and printing temperature became more prominent. The inherent nature of the flow index, which characterizes the behavior of the steady flow curve and likely exerts a greater influence on the outcome of unsuccessful printing attempts, explains this finding. The tanδ characterizes the material's viscoelastic behavior and is the ratio of storage modulus to loss modulus. Along with the printing temperature, it turned out to be more effective in predicting successful printings. The importance of forecasting successful printings (filtered ∆W ) highlights the impact of viscoelastic behavior on printing stability. Also, Pearson correlation coefficients did not show any direct links between ∆W and printing parameters. However, the random forest model did show that printing parameters and material properties had a big impact on the quality of the predictions.