An advanced technology developed by TWI can significantly reduce the substantial financial and environmental costs associated with autoclaving composite materials
OPTO-CLAVE is an automatic learning software solution that uses precise modelling information to create an accurate simulation of the curing process inside an autoclave. The technology is able to provide information about the material state of the composite and proposes optimised cure cycles based on the materials being processed and the specification of the autoclave itself.
This information enables more efficient and effective autoclave use, reducing the time and energy required to manufacture parts using the technique.
Why was this project needed?
Autoclave processing is used by the aerospace industry to produce high-performance composite structures of large size and complex shape. Composite parts manufactured in autoclave are widely used and serve as a benchmark for the quality of composite parts produced using other manufacturing processes such as resin transfer moulding and resin infusion.
Temperature control in autoclaves is generally poor with temperature overshoots of more than 10◦C being typical. The reason for this lack of accurate control is the convective heat transfer mechanism and the need to heat a large volume of air in order to cure the part. Autoclave is an expensive manufacturing process in terms of both capital investment and energy usage.
OPTO-CLAVE was developed as part of, and largely funded by, a European project called Clean Sky: a major aeronautical research programme seeking to lessen the environmental impact of air transport.
The Clean Sky project is divided up into six 'Integrated Technology Demonstrators' (ITDs), each focusing on improving the environmental efficiency of a distinct aspect of aeronautics. The ITD for which OPTO-CLAVE has been developed is 'Eco-Design', the element of the project focusing on reducing the ecological impact of aircraft design, production, maintenance, withdrawal and recycling.
How was the project work structured?
The OPTO-CLAVE project was divided into three phases.
Phase one assessed the material and equipment to be used for a specific component manufacture chosen as a case study. Engineers studied the resin matrix and fibres and developed appropriate models for the material cure kinetics, the flow of the resin into the fibres and the temperature distribution within the autoclave.
Using the information established in phase one, phase two focused on the development of an optimisation algorithm for the case study. The algorithm took into account equipment constraints (eg the maximum heating rate that can be achieved), material state properties, part geometry and customer specifications (eg a pre-defined time the part needs to stay at a specific temperature).
OPTO-CLAVE algorithms are auto-learning. Process information (temperature data at specific locations and dielectric sensors embedded in sacrificial areas in the part) can be added to the algorithm at all times. The more data the algorithm receives from the process, the more accurate the final output becomes.
Finally, in phase three, standalone software was developed that can easily be used by the operative. The software incorporated all the results from phases one and two.
How significant were the efficiency gains?
In the case study, the optimised cure cycle achieved savings of about 13 per cent in terms of time and about 16 per cent in terms of energy usage (see illustration).
The input of the operative in the design of the graphical user interface ensured that the software can be operated with minimum training. It has the flexibility to be applied to different autoclave systems and materials, provided that the appropriate models are available.
OPTO-CLAVE brings considerable efficiency gains and could lead to major savings for autoclave-using industry.
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