PCM mechanical properties

Polymer composite materials (PCMs) are multi-component materials whose reinforcing and binding fillers possess significantly different physical and chemical properties. Mechanical properties are one of the most important indicators that can affect the entire life cycle of PCMs. Experimental measurements are the most common and classical approach to studying the mechanical properties of PCMs; however, they require significant time and money. Considering the resource intensity and duration of the empirical measurement processes of PCM properties, as well as the necessity to determine such properties to meet the high requirements of PCM production, the aim of this work was to create a method for predicting the main properties of PCMs based on machine learning algorithms.
During the study, a dataset was obtained, including information about empirically measured mechanical properties of PCMs, manufacturing technology, binding properties, main fabric properties, main thread properties (warp), and main thread properties (weft). Using the obtained dataset, a machine learning model based on the LASSO algorithm was trained to predict the mechanical properties of PCM using information about manufacturing technology, binding properties, main fabric properties, main thread properties (warp), and main thread properties (weft). The LASSO algorithm demonstrated a high R2 value, reaching 96%. These results indicate the potential application of this algorithm in further research and its important practical significance for improving the processes and quality of manufacturing new PCMs.