Periodic Reporting for period 2 - SimCoDeQ (Simulation tool development for a composite manufacturing process default prediction integrated into a quality control system)
Période du rapport: 2017-07-01 au 2019-04-30
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.
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.
1. IFB
• Application and validation of all project CAE tools to be used for preforming and infusion was completed and training provided to Airbus.
• Permeability characterisation of fabrics (undeformed, sheared and compacted) for the Airbus fabrics was completed and data transfered to Airbus.
• Porosity investigations covered experimental and numerical studies to predict porosity distribution.
• A testing programe for the flat plates was completed and conducted for porosity studies. This work will be continued at IFB and published.
• Porosity characterization for Airbus fabrics and RTM6 was completed and published in 2 MSc theses.
• Permeability characterization was extended in order to account for aging and activation of binder with data transferred to Airbus.
• Design and manufacture of tooling for the first and second RTM pilot plants were completed. These will be used in future IFB research projects.
• The demonstrator moulds where manufactured and sensors integrated.
• Filling simulation, including porosity model and previously developed reinforcement deformation models, were performed according to a testing plan agreed with Airbus.
• Simulation results were compared to online monitoring results. In particular sensors flow estimation was compared with simulation results for all the cases studied.
• A training course was delivered at Airbus on permeability testing, porosity modelling and infusion analysis.
2. TWI Ltd
• Development and manufacture of tooling for flow monitoring. This will be published in a PhD thesis.
• Development of flow monitoring sensor and measurement system completed.
• DSC, DMA and TMA at different degree of cure has been completed and results published in deliverables.
• Experiments on different sensor configurations were conducted. Sensor manufacturing process and testing/calibration method were finalised.
• The effect of reinforcement with different fibre types were accessed including effect of reinforcement conductivity and penetration depth.
• Sensor distortion (flat and curved/bent sensors) was assessed using a series of flat and L-shape composite infusion trials.
• In order to establish the ability to detect race-tracking, sensor at different orientations were investigated.
• The sensor system was installed in the pilot line at IFB and three sets of pilot plant trials were successfully performed.
• A training course was delivered at Airbus on the sensor development, manufacturing, calibration, installation, measurements and data interpretation.
3. Cranfield University
• Material properties characterisation and constitutive models development was completed covering cure kinetics, specific heat capacity, thermal conductivity, viscosity and resin shrinkage. These results were published.
• 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), and published in a PhD thesis
• Uncertainty quantification of material properties was completed covering viscosity variations and boundary conditions (tool temperature, heat transfer coefficient).
• Stochastic objects development representing properties (cure kinetics, viscosity) and process (tool temperature, heat transfer coefficient) was finished and published.
• Process modelling of flow and cure process using FE software (PAM-RTM, MSC.MARC).
• Stochastic simulation of flow and cure process using Monte Carlo scheme was validated.
• Surrogate model development based on Kriging method replacing Finite Element model reducing computational effort.
• Validation of inversion procedure for the pilot line during filling and curing stages.
• A training course was delivered at Airbus on resin characterisation, cure kinetics and the surrogate analysis methods.
• Application and validation of all project CAE tools to be used for preforming and infusion was completed and training provided to Airbus.
• Permeability characterisation of fabrics (undeformed, sheared and compacted) for the Airbus fabrics was completed and data transfered to Airbus.
• Porosity investigations covered experimental and numerical studies to predict porosity distribution.
• A testing programe for the flat plates was completed and conducted for porosity studies. This work will be continued at IFB and published.
• Porosity characterization for Airbus fabrics and RTM6 was completed and published in 2 MSc theses.
• Permeability characterization was extended in order to account for aging and activation of binder with data transferred to Airbus.
• Design and manufacture of tooling for the first and second RTM pilot plants were completed. These will be used in future IFB research projects.
• The demonstrator moulds where manufactured and sensors integrated.
• Filling simulation, including porosity model and previously developed reinforcement deformation models, were performed according to a testing plan agreed with Airbus.
• Simulation results were compared to online monitoring results. In particular sensors flow estimation was compared with simulation results for all the cases studied.
• A training course was delivered at Airbus on permeability testing, porosity modelling and infusion analysis.
2. TWI Ltd
• Development and manufacture of tooling for flow monitoring. This will be published in a PhD thesis.
• Development of flow monitoring sensor and measurement system completed.
• DSC, DMA and TMA at different degree of cure has been completed and results published in deliverables.
• Experiments on different sensor configurations were conducted. Sensor manufacturing process and testing/calibration method were finalised.
• The effect of reinforcement with different fibre types were accessed including effect of reinforcement conductivity and penetration depth.
• Sensor distortion (flat and curved/bent sensors) was assessed using a series of flat and L-shape composite infusion trials.
• In order to establish the ability to detect race-tracking, sensor at different orientations were investigated.
• The sensor system was installed in the pilot line at IFB and three sets of pilot plant trials were successfully performed.
• A training course was delivered at Airbus on the sensor development, manufacturing, calibration, installation, measurements and data interpretation.
3. Cranfield University
• Material properties characterisation and constitutive models development was completed covering cure kinetics, specific heat capacity, thermal conductivity, viscosity and resin shrinkage. These results were published.
• 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), and published in a PhD thesis
• Uncertainty quantification of material properties was completed covering viscosity variations and boundary conditions (tool temperature, heat transfer coefficient).
• Stochastic objects development representing properties (cure kinetics, viscosity) and process (tool temperature, heat transfer coefficient) was finished and published.
• Process modelling of flow and cure process using FE software (PAM-RTM, MSC.MARC).
• Stochastic simulation of flow and cure process using Monte Carlo scheme was validated.
• Surrogate model development based on Kriging method replacing Finite Element model reducing computational effort.
• Validation of inversion procedure for the pilot line during filling and curing stages.
• A training course was delivered at Airbus on resin characterisation, cure kinetics and the surrogate analysis methods.
Overview of main results
1. A taxonomy of defects relevant to aerospace RTM components has been identified with specifications for their detection by Non Destructive Testing (NDT).
2. Characterisation of material properties for the two airbus fabrics and chosen resin (RTM6)
3. Characterisation of variabilities for process, fabrics and resin.
4. Development of constitutive models for RTM processes taking account of variability.
5. Implementation of process models into simulation of the draping/compaction, injection and curing/cooling stage of RTM.
6. Stochastic simulation to characterise process and material variability for infusion analysis.
7. Development of monitoring systems for all stages of the process.
8. Specification, development and implementation of software and hardware interfaces linking process simulation to RTM equipment.
9. Development and implementation of a pilot RTM line that integrates simulation, monitoring systems and relevant interfaces.
10. Validation of the integrated tools using trials on a pilot RTM line and NDT of the produced components.
1. A taxonomy of defects relevant to aerospace RTM components has been identified with specifications for their detection by Non Destructive Testing (NDT).
2. Characterisation of material properties for the two airbus fabrics and chosen resin (RTM6)
3. Characterisation of variabilities for process, fabrics and resin.
4. Development of constitutive models for RTM processes taking account of variability.
5. Implementation of process models into simulation of the draping/compaction, injection and curing/cooling stage of RTM.
6. Stochastic simulation to characterise process and material variability for infusion analysis.
7. Development of monitoring systems for all stages of the process.
8. Specification, development and implementation of software and hardware interfaces linking process simulation to RTM equipment.
9. Development and implementation of a pilot RTM line that integrates simulation, monitoring systems and relevant interfaces.
10. Validation of the integrated tools using trials on a pilot RTM line and NDT of the produced components.