Real-time Material Measurements and Process Control in Automated Fibre Placement Composites Manufacture

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Advanced composite materials and structures are an important technology for future UK economic success. The Composites Leadership forum predicts the UK has the potential to grow its current £2.3bn annual market to £12bn by 2030. Processes that manufacture parts right-every-time at high-rate are needed to take advantage of the potential growth in this sector. The aim of this project is to connect material data to process models in real-time, in order to deliver highly automated and robust composites manufacturing processes. In the same way that autopilots keep aircraft flying through turbulence, this grant proposes to develop an equivalent 'autopilot' for advanced composite manufacturing process to detect material disturbances and respond before defects occur. This idea will be delivered by designing and building a new, well-controlled testbench, and using it to demonstrate active process control software. Sensors will be selected and installed on the testbench to measure key material and process variables before, during, and after the material is deposited. Variables that will be monitored include composite material thickness and width, process speed, temperature, and pressure. The sensor data will be analysed in real-time by mathematical models to determine whether the incoming material will make the structures that was designed. New software will be written and tested to interface with the testbench hardware, overcoming current sensor integration barriers encountered with proprietary industrial equipment. If the mathematical simulation deviates from the intended design, the process will be adjusted to overcome any unwanted deficiencies. The sensing developments, process models, and control software will be validated by manufacturing trials using ideal and imperfect materials. The outputs of the project will be a highly-instrumented, finely controlled testbench that exceeds current industrial composite deposition machines. The high-resolution data collected in this grant will support validation of existing numerical models or identify new requirements for process models. The process models and control algorithms will be written in non-proprietary programming languages, enabling other researchers to build-upon the work done in this grant. The process models and control codes will be managed on Github (a web-based software development platform), and made open source once reaching the appropriate maturity. Future research will investigate the processing characteristics of new materials, add new algorithms for other processes, or apply machine learning approaches to the digital platform created in this project. Industrial project partners Coriolis Composites, Hexcel Composites, the National Composites Centre, and Rolls-Royce will bring practical manufacturing interests and benefit from an accelerated design-make-test cycle for composite structures. An academic collaboration with the Technical University of Munich will strengthen experimental characterisation and testing, and numerical modelling of composite manufacturing processes.