A machine learning investigation of low-density polylactide batch foams

Developing novel foams with tailored properties is a challenge. If properly addressed, efficient screening can potentially accelerate material discovery and reduce material waste, improving sustainability and efficiency in the development phase. In this work, we address this problem using a hybrid experimental and theoretical approach. Machine learning (ML) models were trained to predict the density of polylactide (PLA) foams based on their processing parameters. The final ML ensemble model was a linear combination of gradient boosting, random forest, kernel ridge, and support vector regression models. Comparison of the actual and predicted densities of PLA systems resulted in a mean absolute error of 30 kg center dot m(-3) and a coefficient of determination (R (2)) of 0.94. The final ensemble model was then used to explore the ranges of predicted density in the space of processing parameters (temperature, pressure, and time) and to suggest some parameter sets that could lead to low-density PLA foams. The new PLA foams were produced and showed experimental densities in the range of 36-48 kg center dot m(-3), which agreed well with the corresponding predicted values, which ranged between 38 and 54 kg center dot m(-3). The experimental-theoretical procedure described here could be applied to other materials and pave the way to more sustainable and efficient foam development processes.

Automated summary

This research uses a hybrid experimental and theoretical approach to develop novel foams with tailored properties. Machine learning models are trained to predict the density of polylactide (PLA) foams, and the predicted values are validated through experiments. This experimental-theoretical procedure can be applied to other materials and provides insights for sustainable and efficient foam development.