Development and optimization of machine learning models for estimation of mechanical properties of linear low-density polyethylene

Abstract

A hybrid methodology was developed and implemented for estimation of polymeric mechanical properties in rotational moulding process. The considered polymer in this study is linear low-density polyethylene, known as LLDPE, which has extensive application in plastic industry. The mechanical properties of the polymer were assessed and correlated to the oven residence time to build the predictive model of moulding process. A tiny dataset containing only 25 data rows via a number of machine learning models were assessed. Oven residence time is the only input, while the LLDPE's properties including tensile strength, impact strength, and flexure strength are the outputs considered in the machine learning models. We used tree-based ensemble methods for modeling in this work and they are tuned using FA (Firefly Algorithm) optimizer to find optimal hyper-parameters of them. Finally, the optimal models had shown a great performance to predict the output accurately. For tensile strength, the best model (FA-ET) has an R2 value of 0.9994, this score is 0.9995 for impact strength and 0.9968 for flexure strength. The tree-based models tuned in this study revealed to be robust in estimation of polymeric properties and can be used to obtain the products with the best quality.