pps proceeding - Abstract Preview
pps proceeding
Symposium: S12 - Process modeling and simulations
Oral Presentation
 
 

PROCESS SIMULATION OF PEI-CF/PEEK HYBRID THERMOPLASTIC COMPOSITE MANUFACTURING SETUP

BAHO OMAR (1)*, Ausias Gilles (1), Férec Julien (1)

(1) Univ. Bretagne Sud, UMR CNRS 6027, IRDL, F-56100 - Lorient - France

Key Words: Thermoplastic composite material, Automated Fibre Placement (AFP), Thermal degradation, simulation tool. Manufacturing time and strong demand for light structures have paved the way for technological progress in the field of thermoplastic composite materials. These constraints lead the growing interest of reinforced thermoplastic composite materials. The among reasons that make these materials are gaining this importance are their recyclability, their potential for fast forming and allowing fabrication of complex composite parts. It is rather a challenge to choose the right materials to better meet current requirements. In this work, an innovative hybrid thermoplastic manufacturing process has been investigated. The new hybrid thermoplastic material consists of carbon fibre (CF) reinforced poly (ether-ether-ketone) (PEEK) prepreg sandwiched between amorphous PEI films. By using the laser automated fibre placement (AFP), the laminate is produced by the placement of hybrid prepreg tapes, layer by layer without using autoclave technology. There are numerous process parameters that should be considered in order to ensure good quality of parts such as processing temperature, winding speed compaction pressure. In order to establish the heating law during the AFP process, a numerical simulation tool has been developed using the MATLAB platform and commercial finite element software. Furthermore, with this simulation tool, the influence of process parameters, deformation of the roller, the temperature distribution in the tape and through the thickness of the composite and degradation of material can be generated and analysed. ACKNOWLEDGEMENTS These activities were carried out in the frame of the NHYTE project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 723309.