ANALYSIS OF ARTIFICIAL INTELLIGENCE MODELS FOR PREDICTING VICAT TEMPERATURE OF VARIOUS COMPOUNDS BASED ON BIOHDPE COMPARING DIFFERENT MACHINE LEARNING TECHNIQUES

Materials science is an interdisciplinary field that studies the structure, properties, behavior, and performance of materials. This work focuses on polyethylene polymers, the most widely used in the industry, with a particular emphasis on bio-polyethylenes, which align with sustainability goals by being derived from renewable materials such as biomass and agricultural crops, reducing dependence on fossil resources and minimizing the carbon footprint. The VICAT temperature is a key measure for evaluating the thermal resistance of thermoplastic materials, indicating the temperature at which they begin to soften under load. A higher VICAT temperature suggests better resistance to thermal deformation. A BioHDPE matrix, known for its high impact and tensile strength, suitable for demanding applications, was used. To improve mechanical and thermal properties, hemp fibers, almond powder, argan powder, and Halloysite nanotubes (HNT) were incorporated into the matrix, using organic waste to reduce costs. These components, available in the Mediterranean region, affect the VICAT temperature differently: hemp fiber and HNT enhance mechanical strength, while almond and argan powders may reduce it due to their tendency to decompose. Machine learning (ML) techniques were applied to predict the VICAT temperature, aiming to reduce time and costs in the development of new materials. Regression models, including Multiple Linear Regression (MLR), Neural Networks (NN), Decision Trees (DT), and k-Nearest Neighbors (kNN), were compared, evaluated using the Mean Absolute Percentage Error (MAPE) and the coefficient of determination (R²). The best ML model was identified by a low MAPE and high R², ensuring high prediction accuracy. This study demonstrates the potential of ML in predicting material properties, contributing to the efficient and sustainable design of new polymers. Keywords: BioHDPE, Machine Learning, VICAT Temperature, Neural Networks, Prediction