Prediction of volume loss of reinforced polytetrafluoroethylene matrix composites using machine learning algorithms

• Main Authors: Ibrahim, M. A., Gidado, A. Y., Auwal, S. T., Kunya, B. I., Nura, M., Jacqueline, L.
• Format: Conference Proceeding
• Language: English
• Subjects: Algorithms; Machine learning; Matrices (mathematics); Particle swarm optimization; Particulate composites; Polymer matrix composites; Polytetrafluoroethylene; Predictions; Specific volume; Support vector machines
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0229592

Machine learning (ML) algorithms are getting unsurpassed exposure as a potential technique for solving and modelling the wear behaviour of polymer matrix composites (PMCs). This paper presents the application of ML algorithms in predicting volume loss of reinforced polytetrafluoroethylene (PTFE) matrix composites. Firstly, the Taguchi L27 was harnessed to generate data set in a regulated way. Then multi linear regression (MLR), support vector regression (SVR), particle swarm optimization (PSO) and Harris Hawk’s optimization (HHO) coupled with SVR ML algorithms were developed to accurately predict the volume loss of reinforced PTFE matrix composites. Based on the results achieved, it was found that SVR-HHO ML algorithm predicted the volume loss of reinforced PTFE matrix composites better than the other algorithms with determination coefficient (96 %) and root mean square error of 11 %. The ML algorithms could be used for prediction of volume loss of reinforced PTFE matrix composites and development of new PMCs with specific volume loss resistance.