Advanced Machine Learning and Experimental Studies of Polypropylene Based Polyesters Tribological Composite Systems for Sustainable Recycling Automation and Digitalization

International Journal of Lightweight Materials and Manufacture 2025
Abrar Hussain, Jakob Kübarsepp, Fjodor Sergejev, Dmitri Goljandin, Irina Hussainova, Vitali Podgursky, Kristo Karjust, Himanshu S. Maurya, Ramin Rahmani, Māris Šinka, Diāna Bajāre, Anatolijs Borodiņecs

Digitalization and automation are emerging solutions to the complex problems of recycling. In this research work, the experimental and Python based Archard deep learning wear rate models are introduced regarding recycling automation and composite tribological systems optimization. The optimum polyester fibers (PESF) of length of 3–3.5 mm were used for fabrication of polypropylene (PP)-PESF composite systems. The deformation, high texture, asperities, and micro-cracks were observed during scanning electron microscope and machine-learning studies.

Keywords
Materials computational analysis | Polymeric waste | Recycling | Sustainability | Fiber-reinforced composites | Green tribology
DOI
10.1016/j.ijlmm.2024.11.001
Hyperlink
https://www.sciencedirect.com/science/article/pii/S2588840424000970

Hussain, A., Kübarsepp, J., Sergejev, F., Goljandin, D., Hussainova, I., Podgursky, V., Karjust, K., Maurya, H., Rahmani, R., Šinka, M., Bajāre, D., Borodiņecs, A. Advanced Machine Learning and Experimental Studies of Polypropylene Based Polyesters Tribological Composite Systems for Sustainable Recycling Automation and Digitalization. International Journal of Lightweight Materials and Manufacture, 2025, Vol. 8, No. 2, pp. 252-263. ISSN 2589-7225. e-ISSN 2588-8404. Available from: doi:10.1016/j.ijlmm.2024.11.001

Publication language
English (en)