A comprehensive study of interfacial debonding in lightweight composites and their computational modelling

Understanding fracture behavior of composite materials (CM) is important for engineering applications. The interfacial debonding plays a pivotal role elucidating fracture mechanism in lightweight composites. The study of interfacial debonding with interface traction-separation law will be our primary aim. CM often confront uncertainties due to the chemistry of different matrices; the variations in reinforced materials; and fillers. Machine learning (ML) is an excellent tool in terms of productivity, process improvisation and forecasting. ML works for data-driven modeling and gives an unprecedented insight to explore the properties. A reduction in uncertainty (fracture), accurate prediction of interfacial bonding, and simultaneous increment in adhesion are challenges, the project will solve. This project will provide a theoretical framework (novel optimized solution) based on ML for fracture behavior. This project will combine and highlight several ML algorithms by investigating new trends to improve the characteristics of existing materials with enhanced properties.