Community Research and Development Information Service - CORDIS

H2020

SimCoDeQ Report Summary

Project ID: 686493
Funded under: H2020-EU.3.4.5.4.

Periodic Reporting for period 1 - SimCoDeQ (Simulation tool development for a composite manufacturing process default prediction integrated into a quality control system)

Reporting period: 2016-01-01 to 2017-06-30

Summary of the context and overall objectives of the project

Resin Transfer Moulding (RTM) involves moderate pressure resin injection of a dry preform placed in sealed rigid tooling. Fast and effective processing requires correct placement of the reinforcement to avoid defects and potential race tracking, appropriate selection of inlet and outlet locations, and careful control of flow speeds to minimise porosity and dry regions; furthermore, suitable cure conditions are needed to avoid under-cure, or exothermic effects that generate excessive residual stresses and final part distortions. Today, finite element simulation is regularly used to design injection processes and cure. However, purely predictive simulation suffers from issues related to uncertainty and variability in material state and numerous process variables. Online monitoring of resin flow in tests and stochastic simulations to understand effects of material and model variability on flow processes are two methods that can enhance fidelity of numerical simulation models.

SimCoDeq integrates three approaches to provide a unified integrated simulation tool combining predictive modelling, variability propagation and process monitoring. Input utilises material data and models to be developed with physical sensor results, from which process outcomes, conditional on material and process variables, are determined. The proposed work develops this concept for the three stages of RTM processing; namely, preforming, injection and cure. The overall concept will be implemented on a pilot RTM line and then transferred to the Topic Manager’s manufacturing site, where it will be used for trials.

SimCoDeq combines two universities with specialist knowledge in fabric mechanical and permeability modelling, resin test and modelling and numerical simulation of RTM processes and final part distortion. One industrial partner collaborates on industrial RTM and flow monitoring.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

1. IFB
• Application and validation of all Project CAE tools for preforming and infusion
• Design and manufacture of tooling for the first RTM pilot plant
• Permeability characterisation of fabrics (undeformed, sheared and compacted) for the Airbus fabrics
• Porosity studies: Experimental and numerical studies to predict porosity distribution
• Application of Image Analysis techniques to compute fibre directions and identify defects

2. TWI Ltd
• Development and manufacture of tooling for flow monitoring.
• Development of flow monitoring sensor and measurement system is ongoing.
• DSC, DMA and TMA at different degree of cure is ongoing.
• Pressure measurements are on-going.

3. Cranfield University
• Process defects definition and characterisation (cure defects)
• Materials and process parameters characterisation (resin, shrinkage, thermal conductivity)
• Resin RTM6: Viscosity, heat capacity, kinetics, Tg developments
• Thermal boundary conditions variability (heat transfer coefficient, tool temperature and stochastic object for thermal BC variability
• Process modelling with a deterministic cure model implemented in MSC.MARC
• Variability propagation and simulation
• Surrogate cure model (using kriging and stochastic inversion using MCMC)

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The project advances the state of the in a number of areas:
• Stochastic simulation of the full RTM process chain (placement, impregnation, cure) will be carried out for the first time. Successful simulation of variability propagation through the individual stages of composites processing has been reported in the literature. However, the integration of the variability propagation through the whole chain will allow the interdependencies between variables acting across the different stages to be resolved leading to process designs that can be robust and less conservative at the same time. Stochastic simulation involves numerous physics (mechanics, thermal, flow, chemical), non-linearity and strong coupling that will advance applications of these techniques.
• The new framework developed will translate the outcome of stochastic simulation to quality control metrics, which will enable individualised NDT of components to be carried out. Non-destructive inspection and testing are currently performed under a set of circumstances defined a priori. Similarly, the initial parameters of the inspection are generic and apply to all components of a manufacturing line. The developments in SimcoDeQ will result in an individualised NDT specification for each part produced in the line. Thus, success of the methodology will significantly improve efficiency and accuracy of quality control.
• Inverse scheme for linking modelling to monitoring will be applied to and demonstrated on an industrial scale. Inverse methods have found applications in the context of polymer composites processing, here they will be applied and demonstrated for the first time in the field of composites manufacturing.
• Hardware and software interfacing of process modelling, monitoring and quality control in a seamless operation will carried out for the first time. Currently, links between composites processing, modelling, NDT are indirect; whilst online monitoring and processing have been integrated successfully, and the fibre placement process as well as NDT are driven by CAD models of the tooling or product. The interfaces developed in the project will constitute a significant advancement in this area to link simulation, processing and NDT.
• Influence of discrete local imperfections on permeability will be quantified and incorporated in macro-scale models. The effects of fabric variability and its influence on permeability together with local imperfections and their effect on permeability will be investigated. This will progress investigations of the effects of fabric imperfections on defect generation during impregnation, as flow models will incorporate the correct permeability variations around the potential defect site.
• Compaction effects on fabric architecture incorporating imperfections will be modelled and linked to the fabric permeability. The work within SimCoDeQ will address this aspect at a local level both with experiments and 3-D simulations, evaluating how the presence of an imperfection influences the local fibre architecture upon compression. This will then be translated to modified permeabilities and incorporated in infusion simulations.
• New materials testing (fabric permeability, resin viscosity, cure kinetics) will be undertaken to develop constitutive models for analysis. Material characterisation and constitutive model development will be carried fully for all properties and parameters controlling material behaviour during the three stages of the process will be identified. Furthermore, the corresponding material models will be available as implementations within commercial finite element packages.
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