Induction heating of carbon fibre reinforced polymer composites : Characterization and modelling

Sammanfattning: Carbon fibre reinforced polymers (CFRP) are lightweight materials with great potential due to their high strength and stiffness relative to their weight. This enables weight reduction in, for example, vehicles, which is important in reducing energy consumption. Their high strength and stiffness along the fibre direction also enable the development of new types of construction parts. The manufacturing of thermoset-based CFRP is often a time-consuming process with relatively low energy efficiency. Common manufacturing methods such as resin transfer moulding, compression moulding, and autoclaving use significantly more energy than is needed to cure the CFRP part. This is because the heat is transferred conductively via the part surface from a tool with a large mass. However, other potential heating methods are available. Due to the electrical conductivity of carbon fibres, it is possible to use induction heating. This means that the heat is generated directly within the CFRP part without the need to heat a tool with a large thermal mass. The idea of using this technique to heat CFRP is not new, but the anisotropy of the material means that it is associated with a higher level of complexity than the induction heating of metals.To make the heat and temperature distribution more predictable, there is a need for better models and knowledge of how the heat is generated and how the temperature is distributed within CFRP during induction heating. In this thesis, different CFRP configurations were characterized and modelled to provide knowledge and methods for predicting the induction heating behaviour of CFRP. The development of the models has resulted in temperature prediction tools, useful for a wide range of fibre volume fractions, and for both woven and cross-ply layups. Methods for characterization of thermal and electrical input parameters to the models were identified and developed. The temperature distributions predicted by the models were proven to be valid.