Machine learning investigation of polylactic acid bead foam extrusion

Abstract

Abstract This study employs machine learning algorithms to analyze the bead foam extrusion process and to assess the impact of processing parameters, specifically focusing on their effects on bead foam density and melt pressure in under water granulation (UWG) for polylactic acid (PLA). These interrelated parameters, influenced by processing parameters such as temperature, screw speed, and blowing agent, possess challenges for traditional empirical methods to capture. The key factors that significantly impact the prediction of melt pressure in UWG are blowing agent, injector pressure, temperature in B‐extruder and die size. Likewise, essential parameters for predicting bead foam density comprise blowing agent, injector pressure, temperature in B‐extruder, die plate temperature, melt temperature in B‐extruder, and melt pressure in B‐extruder. Machine learning (ML) models were employed to forecast bead foam density and melt pressure in UWG using various processing parameters in PLA bead foam extrusion. The random forest model achieved a high coefficient of determination score of 0.96 for predicting melt pressure in UWG. Additionally, the decision tree model demonstrated effective predictions for bead density, with the score: 0.81. These ML models can be applied to diverse materials, leading to more sustainable, efficient processes for bead foam extrusion.