CD Laboratory for Superimposed Mechanical-Environmental Ageing of Polymeric Hybrid Laminates

Hybrid laminates with a layered sequence of different materials are used, for example, in photovoltaic modules or wind power generators. Under unfavourable load conditions, long-term failure can occur due to delamination. This CD Laboratory researches characterisation and testing methods to understand delamination mechanisms between materials under complex loading conditions.

Polymer hybrid laminates are materials in which different materials are combined with plastics in layers to achieve a customised property profile. Under simultaneous and superimposed mechanical stress and environmental influences (e.g. temperature, humid gases, UV radiation, media), these layers can separate from each other. This is known as delamination. In order to gain a fundamental understanding of these processes, so-called model laminates are produced and analysed using innovative fracture mechanics methods. The structure of the polymer materials (resin, crosslinker, catalyst and additive) and the process conditions during laminate production are systematically varied in order to derive the interaction between material structure, production process, the properties obtained from the model laminates and their performance on the basis of the results. Using high-resolution, surface-sensitive analysis methods, chemical and physical ageing and failure mechanisms are clarified and differentiated at molecular and supra-molecular level.

Once these model laminates are fully understood, the knowledge can be transferred to more complex, industrially relevant hybrid laminates and these can be analysed using the methods to be implemented. The mix of methods developed in this CD Laboratory and the established structure/property correlations provide a scientific basis for the future design of interfaces, the highly efficient testing of hybrid laminates for demanding applications with superimposed loading conditions and the derivation of improved, science-based service life prediction models.