The Study of Machine Learning Assisted the Design of Selected Composites Properties

Abstract

One of the basic points of Industry 5.0 is to make the industry sustainable. There is a need to develop circular processes that reuse, repurpose, and recycle natural resources, and thus, reduce waste. This part can also include composite materials, which were used for some time in many areas. An essential feature of their applicability is the properties of these materials. The ratio of the individual components determines the properties of composite materials, and artificial intelligence machine learning (ML) techniques are already used to determine the optimal ratio. ML can be briefly described as computer science that uses existing data to predict future data. This approach is made possible by the current possibilities of collecting and analysing a large amount of data. It improves the chance of finding more variable influences (predictors) in the processes. These factors can be quantified more objectively; their mutual interactions can be identified, and, thanks to longer-term sampling, their future development behavior can be predictively modelled. The present article deals with the possibility of applying machine learning in predicting the absorption properties of composite material, which consists of a thermoplastic and matrix recycled polyvinyl butyral (PVB), obtained after recycling car glass windshields.