Browsing by Author "Tifkitsis, Konstantinos"

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    Data for the paper "Stochastic multi-objective optimisation of the cure process of thick laminates"
    (Cranfield University, 2018-07-31 15:58) Tifkitsis, Konstantinos; Struzziero, Giacomo; Skordos, Alex
    Surrogate_model_validation.xlsx includes the data corresponding to the comparison between FE and surrogate model (responses surfaces, PDF of model error, stochastic simulation)-Uncertainty_quantification_results.xlsx includes the results of uncertainty quantification of convection coefficient and tool temperature.-Stochastic_optimisation_results.xlsx includes the stochastic and deterministic Pareto points of stochastic and deterministic optimisation respectively.-Sensitivity_analysis_results.xlsx includes the data corresponding to the sensitivity analysis of standard and optimal profiles.-flat_panel_15.6mm.dat : Marc input corresponding to Cure model of this study-usub.f: contains all the subroutines for material properties and boundary conditions
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    Data supporting "A novel dielectric sensor for process monitoring of carbon fibre composites manufacturing"
    (Cranfield University, 2019-05-28 11:53) Tifkitsis, Konstantinos; Skordos, Alexandros A.
    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.
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    Data supporting "Online optimisation and active control of the cure process of thick composite laminates"
    (Cranfield University, 2023-01-20 14:11) Tifkitsis, Konstantinos; Skordos, Alex
    The dataset comprises experimental data, and modelling and simulation results reported in the paper titled ‘Online optimisation and active control of the cure process of thick composite laminates’ published in Journal of Manufacturing Processes (2023). The dataset is organised into 5 files: - ‘Kinetics-DSC.xlsx’ contains the experimental DSC curves, after integration for the calculation of degree of cure evolution, and the corresponding cure kinetics modelling results. The file ia organised in two worksheets - ‘Dynamic’ reports the results of the one dynamic cure experiment and ‘Isothermal’ reports the results of the four isothermal experiments used in the analysis. - ‘Tg-MDSC.xlsx’ contains the MDSC specific heat capacity measurements and the corresponding model results for three isothermal experiments in worksheet ‘Specific heat capacity’ and the glass transition temperature values measured for partially cured samples alongside the corresponding glass transistor temperature evolution model in worksheet ‘Tg.’ - ‘FE model validation.xlsx’ contains the cure simulation validation trial results using a flat panel. The results are organised into two worksheets – ‘Temperature’ reports the experimental and simulation temperature as a function of time for 3 locations in the component and ‘Degree of cure’ the corresponding degree of cure results. - ‘Cable cure.xlsx’ reports the results of the active control trial for the cure of a cable. Worksheet ‘Active control trial’ reports the setpoint evolution and the surface and centre temperatures measured, alongside the corresponding degree of cure and glass transition evolution. Worksheet ‘Standard profile simulation’ reports the simulation results for the nominal cure profile. - Multi objective optimisation.xlsx presents the Pareto (temperature overshoot versus cure time) front obtained from offline optimisation for a two-cure dwell profile using an exhaustive search alongside the values of cure time and temperature overshoot for the nominal cure profile and the actively controlled process.
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    ItemOpen Access
    Integration of stochastic process simulation and real time process monitoring of LCM
    (SAMPE, 2018-09-13) Tifkitsis, Konstantinos; Skordos, Alexandros A.
    Liquid Composite Moulding (LCM) and its corresponding sub-processes of filling and curing involve several sources of uncertainty. The present study uses process conditions and material behaviour uncertainty measurements with Monte Carlo (MC) simulation to quantify the effect of variability on the final process outcome. Surrogate models of mould filling and curing based on Kriging have been constructed substituting Finite Elements (FE) solutions to achieve execution of the MC simulation with very small computational effort. Combination of stochastic simulation with on line monitoring results can narrow down gradually the envelope of possibilities predicted as the process progresses. This was carried out in this study using temperature and dielectric sensor signals and an inverse solution scheme based on Markov Chain Monte Carlo (MCMC). The integrated inverse solution is capable of predicting the process outcome with increased levels of certainty as the sensors uncover information gradually during the process. The use of surrogate models allows this solution to be carried out in real time in the manufacturing line.
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    ItemOpen Access
    A novel dielectric sensor for process monitoring of carbon fibre composites manufacturing
    (Elsevier, 2019-05-13) Tifkitsis, Konstantinos; Skordos, Alexandros A.
    A dielectric sensor appropriate for process monitoring of composites manufacture with carbon reinforcement has been developed in this study. The sensor concept overcomes problems of electrical sorting and interference with the electric field occurring when electrical/dielectric sensors are used with carbon reinforcement. The sensor comprises two uniformly twisted insulated copper wires. Two sensor designs based on the same concept have been implemented; a lineal sensor for flow front position tracking and a woven sensor used to monitor the cure. Resin Transfer Moulding (RTM) processing has been employed to evaluate the lineal sensor performance against visual monitoring of the flow front. Vacuum Assisted RTM (VARTM) has been carried out to validate the results of the woven cure sensor against calorimetric cure kinetics models. Both the lineal flow and woven cure sensors provide accurate monitoring signals.
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    ItemOpen Access
    A novel dielectric sensor for process monitoring of carbon fibre composites manufacturing (Conf paper)
    (Composites UK, 2018-12-31) Tifkitsis, Konstantinos; Skordos, Alexandros A.
    Process monitoring techniques have been developed to monitor critical parameters of manufacturing such as flow front position and cure reaction progress and to identify potential defects. The main technologies proposed include dielectric spectroscopy [1], fibre optics [2], time domain reflectometry [3], and pressure transducers [4]. Monitoring based on impedance/dielectric spectroscopy is considered advantageous due to the sensitivity of response to filling state and cure progress, robustness and relatively low cost of the sensors and measuring setup and capability for incorporation on tooling surfaces. Lineal dielectric flow sensors are appropriate for use with non-conductive reinforcement [1] as presence of carbon would disturb the electric field, whilst setups making use of the conductive carbon reinforcement as one of the electrodes of the sensing system [5] involve significant practical complexity as their operation requires electrical insulation of the reinforcement from the tooling assembly. The solution adopted in cure applications for carbon composites is to cover the sensor with a permeable non-conductive material such as glass cloth or a polymer weave [6-8]. This type of solution increases the intrusiveness of the sensing system and generates some differences between the material monitored and the material of the composite. The present study reports the development of a novel non-intrusive dielectric sensor overcoming the difficulties arising by the conductive properties of carbon reinforcement. The concept is used for the design of two sensor types; a lineal flow front position sensor applied to resin transfer moulding (RTM) and a woven arrangement used to monitor the cure and of a carbon fibre/epoxy composite.
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    ItemOpen Access
    Online optimisation and active control of the cure process of thick composite laminates
    (Elsevier, 2023-02-03) Tifkitsis, Konstantinos; Winistoerfer, A.; Skordos, Alexandros A.
    This paper addresses the development of an active control tool for the in-situ optimisation of the cure process of thick composite parts. The methodology integrates a cure process model with real time monitoring data exploring potential temperature dwells that accelerate the curing while avoiding undesirable thermal gradients. The cure process simulation is based on a Finite Element (FE) model solution validated in the curing of a thick composite flat panel with an average absolute error of about 1.8 °C. The active control tool interacts PID controller of an oven setting a new cure temperature periodically. The implementation of active control tool during the curing of a thick carbon fibre/epoxy cable has the capability to determine in real time optimal solutions minimising process duration whilst also satisfying constraints in temperature overshoot. A process duration reduction of about 70 % can be achieved compared to a nominal cure cycle with a maximum temperature overshoot lower than 9 °C. The active control tool can replace off-line optimisation tools exploiting in situ with real time monitoring to drive the process and minimise the effects of uncertainty.
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    ItemOpen Access
    Real time inverse solution of the composites cure heat transfer problem under uncertainty - Dataset
    (Cranfield University, 2019-12-10 12:12) Skordos, Alex; Tifkitsis, Konstantinos
    - 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.
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    ItemOpen Access
    Real time inverse solution of the composites' cure heat transfer problem under uncertainty
    (Taylor & Francis, 2019-12-09) Tifkitsis, Konstantinos; Skordos, Alexandros A.
    This paper addresses the development of an inversion scheme based on Markov Chain Monte Carlo integrating process modelling with monitoring data for the real-time probabilistic estimation of unknown stochastic input parameters such as heat transfer coefficient and resin thermal conductivity and process outcomes during the manufacture of fibrous composites materials. Kriging was utilized to build an efficient surrogate model of the composite curing process based on finite element modelling. The utilization of an inverse scheme with real-time temperature monitoring driving the estimation of process parameters during manufacture results in real-time probabilistic prediction of process outcomes.
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    ItemOpen Access
    Real time uncertainty estimation in filling stage of resin transfer molding process
    (Wiley, 2020-09-24) Tifkitsis, Konstantinos; Skordos, Alexandros A.
    This paper addresses the development of a digital twin, based on an inversion procedure, integrating process monitoring with simulation of composites manufacturing to provide a real time probabilistic estimation of process outcomes. A computationally efficient surrogate model was developed based on Kriging. The surrogate model reduces the computational time allowing inversion in real time. The tool was implemented in the filling stage of an resin transfer molding processing of a carbon fiber reinforced part resulting in the probabilistic prediction of unknown parameters. Flow monitoring data were acquired using dielectric sensors. The inverse scheme based on Markov Chain Monte Carlo uses input parameters, such as permeability and viscosity, as unknown stochastic variables. The scheme enhances the model by reducing model parameter uncertainty yielding an accurate on line estimation of process outcomes and critical events such as racetracking. The inverse scheme provides a prediction of filling duration with an error of about 5% using information obtained within the first 30% of the process
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    ItemOpen Access
    Real time uncertainty estimation in filling stage of RTM process - Dataset
    (Cranfield University, 2020-09-24 16:50) Tifkitsis, Konstantinos; Skordos, Alex
    - Surrogate models validation: includes comparison between FE model and surrogate model - Sensors data: includes the response of the three lineal sensors - Real time uncertainty estimation: includes the results of the inversion procedure - Prior model: confidence intervals using prior knowledge - Post model: confidence intervals using inversion solution - CDF Filling time estimation: Cumulative density function of filling time estimation at different times during filling process
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    ItemOpen Access
    Stochastic multi-objective optimisation of the cure process of thick laminates
    (Elsevier, 2018-06-11) Tifkitsis, Konstantinos; Mesogitis, Tassos S.; Struzziero, Giacomo; Skordos, Alexandros A.
    A stochastic multi-objective cure optimisation methodology is developed in this work and applied to the case of thick epoxy/carbon fibre laminates. The methodology takes into account the uncertainty in process parameters and boundary conditions and minimises the mean values and standard deviations of cure time and temperature overshoot. Kriging is utilised to construct a surrogate model of the cure substituting Finite Element (FE) simulation for computational efficiency reasons. The surrogate model is coupled with Monte Carlo and integrated into a stochastic multi-objective optimisation framework based on Genetic Algorithms. The results show a significant reduction of about 40% in temperature overshoot and cure time compared to standard cure profiles. This reduction is accompanied by a reduction in variability by about 20% for both objectives. This highlights the opportunity of replacing conventional cure schedules with optimised profiles achieving significant improvement in both process efficiency and robustness.