Advanced machine learning and experimental studies of polypropylene based polyesters tribological composite systems for sustainable recycling automation and digitalization

Advanced machine learning and experimental studies of polypropylene based polyesters tribological composite systems for sustainable recycling automation and digitalization

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 polyes...

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Bibliographic Details
Main Authors: Abrar Hussain, Jakob Kübarsepp, Fjodor Sergejev, Dmitri Goljandin, Irina Hussainova, Vitali Podgursky, Kristo Karjust, Himanshu S. Maurya, Ramin Rahmani, Maris Sinka, Diāna Bajāre, Anatolijs Borodiņecs
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-03-01
Series:International Journal of Lightweight Materials and Manufacture
Subjects:
Materials computational analysis
Polymeric waste
Recycling
Sustainability
Fiber-reinforced composites
Green tribology
Online Access:http://www.sciencedirect.com/science/article/pii/S2588840424000970
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