Robustness-performance optimisation for automated composites manufacture

Lead Research Organisation: Cranfield University
Department Name: School of Water, Energy and Environment

Abstract

This project focuses on the development of a manufacturing route for composite materials capable of producing complex components in a single process chain based on advancements in the knowledge, measurement and prediction of uncertainty in processing. The methodology proposed uses measurements of the instantaneous state of a component during production, predictive modelling of associated variability and numerical optimisation. These three are integrated in a control loop that allows the process to adapt in real time in order to compensate for deviations from its nominal state due to variability. This manufacturing philosophy accepts the existence of variability in these highly heterogeneous and directional materials and uses it in order to improve the product as the process evolves.

The necessary developments comprise major manufacturing challenges, such as the real time measurement of fibre variability in robotic fibre placement and the processing of composite components involving areas of large thickness. These are accompanied by significant mathematical advancements, such as the numerical solution of coupled non-linear stochastic partial differential equations, the inverse estimation of composite properties and their probability distributions in different directions based on real time measurements and the formulation and solution of a stochastic model of the variability in fibre arrangements. The integration of these developments will be carried out on a single process chain of fibre placement, resin infusion and resin cure; however their applicability is generic in the context of manufacturing involving heterogeneous materials and variability.

The outcome of this work will enable a step change in the capabilities of composite manufacturing technologies to be made, overcoming limitations related to part thickness, component robustness and manufacturability as part of a single process chain, whilst yielding significant developments in mathematics with generic application in the fields of stochastic modelling and inverse problems.

Planned Impact

The main outcome of this project will be the capability to produce highly performing composite structures in a single process chain. The load bearing capabilities of composites will be increased whilst their cost will be reduced. Consequently, use of composites as a lighter, greener, less costly solution will result in significant environmental benefits, business opportunities and increased competitiveness. More generic application of the project outcomes will also enable a shift in how manufacturing addresses variability in general, with associated benefits in terms of production speeds and final product quality. This will lead to increased efficiency and result in a competitive advantage for UK manufacturing companies.

The impact of the work will be maximised through direct industrial collaboration in the context of the project. Furthermore, the opportunities for long term benefits will be enhanced by a communications strategy addressed to industry and the academic community thorough workshops, conference presentations and publications in scientific journals.

Publications

10 25 50
publication icon
Struzziero G (2019) Multi-objective optimization of Resin Infusion in Advanced Manufacturing: Polymer & Composites Science

publication icon
Elkington M (2015) Hand layup: understanding the manual process in Advanced Manufacturing: Polymer & Composites Science

publication icon
Struzziero G (2019) Multi-objective optimization of Resin Infusion in Advanced Manufacturing: Polymer & Composites Science ?

publication icon
Matveev M (2016) Understanding the buckling behaviour of steered tows in Automated Dry Fibre Placement (ADFP) in Composites Part A: Applied Science and Manufacturing

publication icon
Struzziero G (2017) Multi-objective optimisation of the cure of thick components in Composites Part A: Applied Science and Manufacturing

publication icon
Tifkitsis K (2019) A novel dielectric sensor for process monitoring of carbon fibre composites manufacture in Composites Part A: Applied Science and Manufacturing

publication icon
Struzziero G (2019) Numerical optimisation of thermoset composites manufacturing processes: A review in Composites Part A: Applied Science and Manufacturing

publication icon
Tifkitsis K (2018) Stochastic multi-objective optimisation of the cure process of thick laminates in Composites Part A: Applied Science and Manufacturing

publication icon
Elkington M. (2016) Automated layup of sheet prepregs on complex moulds in ECCM 2016 - Proceeding of the 17th European Conference on Composite Materials

publication icon
Matveev M.Y. (2016) Dual flow front measurements for improved permeability characterisation in ECCM 2016 - Proceeding of the 17th European Conference on Composite Materials

publication icon
Warburton J (2016) Digital Image Correlation Vibrometry with Low Speed Equipment in Experimental Mechanics

publication icon
Mahmood M (2019) Identification of conductivity in inhomogeneous orthotropic media in International Journal of Numerical Methods for Heat & Fluid Flow

publication icon
Elkington M. (2016) Automated Layup of sheet prepregs on complex moulds in International SAMPE Technical Conference

publication icon
Crowley D.M. (2016) Hand lay-up of complex geometries-prediction, capture and feedback in International SAMPE Technical Conference

publication icon
Iglesias M (2018) Bayesian inversion in resin transfer molding in Inverse Problems

publication icon
Iglesias M (2018) Bayesian inversion in resin transfer molding in Inverse Problems

publication icon
Tifkitsis K (2019) Real-time inverse solution of the composites' cure heat transfer problem under uncertainty in Inverse Problems in Science and Engineering

publication icon
Struzziero G (2019) Measurement of thermal conductivity of epoxy resins during cure in Journal of Applied Polymer Science

publication icon
Mesogitis T (2016) Stochastic heat transfer simulation of the cure of advanced composites in Journal of Composite Materials

publication icon
Park M (2017) Stochastic Resin Transfer Molding Process in SIAM/ASA Journal on Uncertainty Quantification

 
Description Evaluation of variability in the constituents of composite materials and how these propagate through the manufacturing process: The influence of fibre variability on the behaviour of the material during the process as well as the generation of defects, such as residual stress has been established. The effect of uncertainty in the initial state of the polymer matrix on the generation of temperature overshoot has been evaluated; whilst the role of variability in the process conditions imposed during the manufacturing of composite components (temperature of tooling, cooling through convection) has been established. The associated methods, comprising both experimental tests and simulation capabilities, have been developed. In the case of high performance aerospace grade composites the residual stress can vary by more than 10% due to uncertainty in fibre orientation, whilst the uncertainty in tool temperature alongside the initial chemical state of the thermosetting resin and the activation energy of its cross-linking reaction are the dominate variations in the final product. Estimation/approximation of the trade-off between process quality and cost through multi-objective optimisation: The manufacturing process has been investigated in terms of the interplay between process cost and the probability of deviations from nominal quality through unpredictable temperature deviations. A methodology based on Genetic Algorithms has been developed to approximate the corresponding trade-off surface and the problem of challenging curing of thick and ultra-thick components ha been addressed. It was shown that the methodology can lead to the adoption of process design that allow process robustness at similar levels with conventional/current conditions at a fraction of the cost associated with the duration of the process. Benefits in the range of 30-70% have been achieved. Efficient stochastic simulation methods for complex, high dimensionality variability: The complexity of the problems associated with composites manufacturing has been addressed with the development of tailored methodologies able to deal with the coupled physics associated with the process of cure (advection-reaction-diffusion), such as stochastic collocation and Wiener chaos constructed with a recursive multistage algorithm and the large number of random variables met in the flow through porous media problem of mould filling addressed using multilevel Monte Carlo and sampling on irregular grids. In addition a highly efficient method based on surrogate modelling using Kriging has been developed and implemented. Its efficiency has been tested in the manufacturing of fibrous composites using Resin Transfer Moulding and work is being carried for the adoption of this method to allow targeted non destructive evaluation of composite structures based on the outcome of process monitoring and stochastic simulation. Integration of inverse problem solution with on-line monitoring of the manufacturing process. The integration of flow monitoring based on dielectrics with inverse solutions of the corresponding flow and cure problem has been accomplished and demonstrated off-line. The capability of estimating unknown parameters , such as permeability, with a lower level of uncertainty has been demonstrated.
Exploitation Route Industrial benefits of the research outcomes of the work include: -Use of process optimisation for thermal conditions design with significant reduction of process duration and associated costs (30-70% in the case of thick/ultra-thick components). -Incorporation of explicit uncertainties in design resulting in less conservative solutions. Quantification of the potential benefit will be possible upon completion of the integration of optimisation/stochastic simulation tools. Research benefits are as follows: -Development of a set of methods able to cope with the complexity of spatial variability and coupled physics related to composites manufacturing. These address the combination of continuous fibre variability and scalar variations observed in matrix chemical state and thermal conditions with the free boundary flow problem and the reaction-diffusion associated with curing and can be expanded in a number of different research areas. -Development of efficient surrogate models that allow incorporation of non linear coupled manufacturing simulation within a stochastic optimisation procedure. -The quantification of actual input variability and its representation in stochastic objects provides the basis for variability analysis of current and future variations of process strategies used in composites manufacturing.
Sectors Aerospace, Defence and Marine,Energy,Transport

 
Description Feasibility study
Amount £50,000 (GBP)
Funding ID Active control of the RTM process under uncertainty using fast algorithms 
Organisation Engineering and Physical Sciences Research Council (EPSRC) 
Sector Public
Country United Kingdom
Start 02/2018 
End 07/2018
 
Description Flexible RTM tool with automated distortion correction - H2020, Clean Sky 2
Amount € 1,400,000 (EUR)
Funding ID 821488 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 09/2018 
End 08/2020
 
Description Simulation tool development for a composite manufacturing process default prediction integrated into a quality control system - H2020, Clean Sky 2
Amount € 695,000 (EUR)
Funding ID 686493 
Organisation European Commission 
Sector Public
Country European Union (EU)
Start 01/2016 
End 01/2019
 
Title A novel dielectric sensor for process monitoring of carbon fibre composites manufacture 
Description -Flow sensor validation: includes the lineal sensor results during RTM processing and comparison with visual monitoring data-Cure sensor: includes the cure sensors results of isothermal runs of neat resin and VART and the comparison with existing models. 
Type Of Material Database/Collection of data 
Year Produced 2019 
Provided To Others? Yes  
Impact New sensor able to to monitor continuously filling progress in liquid moulding of composites and the associated validation. 
URL https://cord.cranfield.ac.uk/articles/A_novel_dielectric_sensor_for_process_monitoring_of_carbon_fib...
 
Title Measurement of thermal conductivity of epoxy resins during cure 
Description - -Thermal_conductivity_glycerine: Experimental and FE data of the thermal conductivity test run on glycerine at 100 degC and 4 degC. Includes also the sensitivity analysis of the measurement on thermocouple misplacement. - -RTM6_cure_kinetics_results: Includes the isothermal and dynamic data for the RTM6 cure kinetics characterisation and the corresponding model. It also includes the sensitivity analysis on period and amplitude influence for RTM6 - - 890RTM_cure_kinetics_results: Includes the isothermal and dynamic data for the 890RTM cure kinetics characterisation and the corresponding model. It also includes the sensitivity analysis on period and amplitude influence for 890RTM - - XU3508_cure_kinetics_results: Includes the isothermal and dynamic data for the XU3508 cure kinetics characterisation and the corresponding model. - -Di Benedetto_XU3508: Includes the dataused to build the Di Benedetto equation for the XU3508 resin system and the corresponding model - - Cp_density_conductivity_data: Includes the Cp data and model, the density and the thermal conductivity data for the three resin systems under investigation: RTM6, 890RTM and XU3508 - -Thermal_conductivity_model: Includes the data from the thermal conductivity tests for the three resin system and the fitting of the experimental data with the analytical model. 
Type Of Material Database/Collection of data 
Year Produced 2018 
Provided To Others? Yes  
Impact Dataset of thermal conductivity development on three industrial epoxy resin systems as well as a new analysis technique for obtaining these. 
URL https://figshare.com/articles/Measurement_of_thermal_conductivity_of_epoxy_resins_during_cure/584124...
 
Title Multi-objective optimisation of Resin Infusion 
Description Dataset related to G Struzziero, AA Skordos. Multi-objective optimization of resin infusion. Advanced Manufacturing: Polymer & Composites Science. 2019;5:17-28 Exhaustive_analysis.xlsx: -'Exhaustive' sheet: All the data gathered during the exhaustive search are reported. Column 1 to 8 report respectively the temperature of first and second dwell, duration of first dwell and ramp, convection coefficient, gate location, filling time and maximum degree of cure. -'Gate-T1' sheet: Reports the data to plot the 3D landscape -'T1-T2' sheet: Reports the data to plot the 3D landscape Optimisation results.xlsx -'Results' sheet: Reports the 13 generations the GA requires to converge to a final optimal Pareto front. Column 1 to 9 report respectively the generation number, temperature of first and second dwell, duration of first dwell and ramp, gate location, convection coefficient, filling time and maximum degree of cure. -'Details of individuals' sheet: Reports the detailed analysis f the different strategies of solutions belonging to different region of the Pareto. Regions_analysis.xlsx -'Horizontal_flow_front_data' sheet: Reports the compact data for the Temperature, viscosity and degree of cure evolution for an horizontal region solution -'Viscosity_horizontal_region' sheet: Reports the viscosity evolution for different nodes in the model for an horizontal region solution -'Degree cure_horizontal_region' sheet: Reports the degree of cure envelope for an horizontal solution -'Temperature_horizontal region' sheet: Reports the temperature evolution for different nodes in the model for an horizontal region solution -'Model_verification' sheet: reports the analytical viscosity model implementation -'Vertical_flow front_data' sheet: Reports the compact data for the Temperature, viscosity and degree of cure evolution for a vertical region solution -'Degree_cure_vertical_region' sheet: Reports the degree of cure envelope for a vertical solution region -'Temperature_vertical_region' sheet: Reports the temperature evolution for different nodes in the model for a vertical region solution -'Viscosity_vertical_region' sheet: Reports the viscosity evolution for different nodes in the model for a vertical region solution -'Standard_flow_front_data' sheet: Reports the compact data for the Temperature, viscosity and degree of cure evolution for the standard solution -'Deg_cure_standard' sheet: Reports the degree of cure envelope for the standard solution '-Temperature_data_standard' sheet: Reports the temperature evolution for different nodes in the model for the standard solution -'Viscosity_standard' sheet: Reports the viscosity evolution for different nodes in the model for the standard solution -'Plots' sheet: plots summary 
Type Of Material Database/Collection of data 
Year Produced 2019 
Provided To Others? Yes  
Impact Development of new infusion strategy based on optimised thermal profile to achieve efficient processing. 
URL https://doi.org/10.17862/cranfield.rd.5505376
 
Title Multi-objective optimisation of composites manufacturing under uncertainty 
Description Computational methodology for incorporation of variability in the objectives of process optimisation for filling and curing in composites manufacturing. The methodology involves and integration of a genetic algorithm, with a Kriging surrogate model of the process and Monte Carlo simulation. 
Type Of Material Computer model/algorithm 
Year Produced 2017 
Provided To Others? Yes  
Impact Incorporation of development in Clean Sky (H20202) developments through project SimCoDeQ. 
 
Title Real time inverse solution of the composites cure heat transfer problem under uncertainty 
Description -panel_glass.f : subroutines representing material properties and boundary conditions.-panel_glass_3.3mm.dat: Marc input corresponding to the cure model of this paper.-Process monitoring results.xlsx: Experimental data acquired by thermocouples.-Surrogate model validation.xlsx: Response surfaces of surrogate and FE model.-MCMC results: Statistical properties estimation of unknown parameters with inversion procedure.-Real time probability estimation: Minimum final degree of cure and minimum glass transition temperature probability estimation during manufacturing process. 
Type Of Material Database/Collection of data 
Year Produced 2019 
Provided To Others? Yes  
Impact Validation of on line uncertainty estimation method applied to composite cure using temperature sensors. 
URL https://cord.cranfield.ac.uk/articles/Real_time_inverse_solution_of_the_composites_cure_heat_transfe...
 
Title Real time uncertainty estimation in filling stage of RTM process 
Description Surrogate models validation: includes comparison between FE model and surrogate modelSensors data: includes the response of the three lineal sensorsReal time uncertainty estimation: includes the results of the inversion procedurePrior model: confidence intervals using prior knowledgePost model: confidence intervals using inversion solutionCDF Filling time estimation: Cumulative density function of filling time estimation at different times during filling process 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
Impact First application of on line inverse solution based flow monitoring data for estimation of process uncertainty. 
URL https://cord.cranfield.ac.uk/articles/dataset/Real_time_uncertainty_estimation_in_filling_stage_of_R...
 
Title Surrogate process model 
Description Computationally efficient of composite manufacturing based on Kriging. This allows an off line execution of computationally intensive non linear process simulation producing results that are subsequently captured in the Kriging model. The later can be executed in real time as part of process control in a stochastic simulation/optimisation loop accompanied by on line process monitoring. The method has been demonstrated successfully for the curing stage of composites manufacturing, which constitutes the most computationally demanding simuation step. 
Type Of Material Computer model/algorithm 
Year Produced 2017 
Provided To Others? Yes  
Impact The method is a core element of a Clean Sky project SimCoDeq (Simulation tool development for a composite manufacturing process default prediction integrated into a quality control system) aiming at reducing variability in the manufacture of composite spars (Airbus Spain). 
 
Title Block Circulant Embedding Method 
Description The block circulant embedding method (BCEM) is a method for sampling from stationary Gaussian random field on a grid which is not regular but has a regular block structure which is often the case in applications. It was introduced in a paper titled "A Block Circulant Embedding Method for Simulation of Stationary Gaussian Random Fields on Block-regular Grids" by M. Park and M.V. Tretyakov. 
Type Of Technology Software 
Year Produced 2015 
Open Source License? Yes  
Impact The introduced block-circulant embedding method (BCEM) can outperform the classical circulant embedding method (CEM), which requires a regularization of the irregular grid before its application. 
URL https://github.com/parkmh/bcempaper