Objective
Polyurethanes form a particular versatile class of adhesive polymers, and their usage will continue to increase. Polyurethane (PU) adhesives include both one and two component systems, with and without solvents, heat-activated thermoplastic adhesives, cross-linkable liquid Polyurethanes and various aqueous dispersions. The type and chemical characteristics of a PU adhesive and its relationship to the formulation constituents, processing and application parameters plays a fundamental part in the performance and cost effectiveness of a PU bonded composite structure. The challenge for technical, commercial and environmental advancement, such as the need for optimum bond strengths and faster production targets, in addition to the requisite for adhesives to support more environmental safe product offerings, is placing great demands not only on the PU adhesives, but on those involved in their development and use. The research will be focused on developing a predictive modelling tool to take the guess work out of the process of selecting the optimum PU adhesive for a particular process and application, and replace it with sound scientific and technical predictions. Once the model is formulated it will be validated by SME's by a comparison between predicted and experimental data. Significant benefits to be gained from this research are the opportunities to develop innovative light weight PU bonded composites for structural and non-structural applications, improvements in processing techniques, in addition to determining new and improved PU adhesives. The consortium seeks to be as broad as possible to ensure a wide range of PU adhesives, processes and applications, as well as to SME cultures, are covered in order to give a widely acceptable methodology.
Fields of science
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
Call for proposal
Data not availableFunding Scheme
EAW - Exploratory awardsCoordinator
NN17 4AP CORBY
United Kingdom