Estimating the behavior of particles sprayed by a single-cathode plasma torch based on a nonlinear autoregressive exogenous model

Liu, Taikai; Deng, Sihao; Planche, Marie-Pierre; Montavon, Ghislain

doi:10.1016/j.surfcoat.2014.10.040

Cited by 7 publications

(4 citation statements)

References 28 publications

(32 reference statements)

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“…Only a few articles consider such aspect. For instance, Liu et al examined a time-series-sensitive nonlinear autoregressive exogenous (NLARX) model combined with the wavelet network to predict the in-flight particle characteristics of a mono-cathode plasma spray torch using a system identification approach (Ref 8 ). It was mentioned that such approach can better capture the dynamic characteristics, comparing with the normal neural network.…”

Section: Discussionmentioning

confidence: 99%

“…The scheme simplified the model structure and improved the generalization of the model overall. Liu et al employed a nonlinear autoregressive exogenous (NLARX) model combined with the wavelet network to predict the in-flight particle characteristics of a mono-cathode plasma spray torch using a system identification approach (Ref 8 ). Compared with normal neural network, such approach could be more suitable for dynamical conditions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble Methods for APS In-Flight Particle Temperature and Velocity Prediction Considering Torch Electrodes Ageing

Cojocaru

Ilinca

et al. 2023

J Therm Spray Tech

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The nonlinear relationship between the input process parameters and in-flight particle characteristics of the atmospheric plasma spray (APS) is of paramount importance for coating properties design and quality. It is also known that the ageing of torch electrodes affects this relationship. In recent years, machine learning algorithms have proven to be able to take into account such complex nonlinear interactions. This work illustrates the application of ensemble methods to predict the in-flight particle temperature and velocity during an APS process considering torch electrodes ageing. Experiments were performed to record simultaneously the input process parameters, the in-flight powder particle characteristics and the electrodes usage time. Random Forest (RF) and Gradient Boosting (GB) were used to rank and select the features for the APS process data recorded as the electrodes aged and the corresponding predictive models were compared. The time series aspect of the multivariate APS in-flight particle characteristics data is explored. Two strategies of time series embedding are considered. The first one simply embeds the attributes and the targets from the previous $$n$$ n time segments considered without any modification; whereas the second strategy first performs differencing to make the time series stationary before embedding. For the present application, RF is found to be more suitable than GB since RF can predict both the in-flight particle velocity and temperature simultaneously, properly considering the interactions between the two targets. On the other hand, GB can only predict these two targets one at a time. The superior performance of both embedded predictive models and the feature rankings of them suggest that it is better to consider the APS data as time series for the in-flight particle characteristic prediction. In particular, it is demonstrated that it is advantageous to first make the time series stationary using the traditional differencing technique, even when modeling using RF.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble Methods for APS In-Flight Particle Temperature and Velocity Prediction Considering Torch Electrodes Ageing

Cojocaru

Ilinca

et al. 2023

J Therm Spray Tech

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“…In the thermal plasma process, considerable attempts have been made to improve the process quality using modelbased control systems [12][13][14][15][16]. To obtain satisfactory performances in this approach, explicit models, which can accurately describe the dynamic behaviour of the plasma employed within a reasonable time frame, are needed.…”

Section: Introductionmentioning

confidence: 99%

Control-oriented dynamic model of an inductively coupled plasma torch by artificial intelligence methodology

2019

Plasma Phys. Control. Fusion

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Inductively coupled plasma (ICP) torches have been widely used in various materials processing. To improve the control of their processes, there has been a growing demand for simplified numerical models which can rapidly predict the dynamics of plasma jets subject to time-varying inputs or external disturbances. In this paper, control-oriented dynamic models of an ICP torch are developed as an alternative to the complex, high-level 2D time-dependent numerical model (i.e. a model based on magneto-hydrodynamic equations). Prior to model development, the detailed dynamic and nonlinear nature of the ICP torch to its time-varying operation conditions was numerically investigated using a 2D numerical model to gain insight into choosing appropriate dynamic model structures. Linear ARX (AutoRegressive with eXogenous input) and ARX-type neural network models were selected in the model identification, and their parameters or weightings were determined using the input/output data obtained from the 2D time-dependent numerical model. The dynamic behaviours of the ICP torch predicted from the developed models were in good agreement with the data from the 2D time-dependent numerical model. Using the developed models, a simple control system for the plasma temperature and axial velocity regulations was also designed and tested. The feedback control simulations demonstrated good set point tracking and disturbance rejection performances, indicating that the developed approach can be directly applied to the model-based control system design of an ICP torch as well as other thermal plasma torches.

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“…A plasma sprayed coating is the result of the impact of melted and partially melted particles, their flattening and the corresponding splats layering on the substrate surface. Therefore, coating properties depend primarily on the particles behaviors prior to substrate, including mainly velocities, temperatures and sizes of particles in-flight [1].The higher is the velocity of the particles, the larger is the impulse force during deformation, hence the particles strike the substrate much more heavily, the deformation of particles is more sufficient, and with higher temperature, particles can be melted sufficiently, which is useful in increasing the area of contact region and contributes to improving the bonding strength of the coating. Therefore, a better understanding of the particle in-flight characteristics, such as velocity, temperature, and melting status, as well as the distribution of the plasma jet, will contribute to coating properties and optimizations of operation parameters [2].…”

Section: Introductionmentioning

confidence: 99%

Numerical Simulation and Experimental Investigation of Multi-function Micro-plasma Jet and Alumina Particle Behaviour

Wang

et al. 2016

MATEC Web Conf.

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Abstract. Turbulent flow in multi-function micro-plasma spray, as well as the trajectories and state-changing course of alumina particles in the plasma jet were simulated. The distribution of temperature and velocity of the plasma jet and in-flight alumina particles is discussed. Calculations show that particles are heated and accelerated sufficiently by the plasma flame due to a longer travel time than that of external injection system, therefore, possess higher temperature and velocity. Alumina particles temperature and velocity increase rapidly along the jet axis at the initial stage, but then decrease gradually. The velocity and surface temperature of in-flight alumina particles are measured by Spray Watch-2i system. The velocity and surface temperature of alumina particles measured agree well with the simulation results, confirming that the simulation model is suitable for the prediction of the turbulent flow and the particle characteristics, which also reveals the superiority of the plasma spray gun in this multi-function micro-plasma spraying system.

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Estimating the behavior of particles sprayed by a single-cathode plasma torch based on a nonlinear autoregressive exogenous model

Cited by 7 publications

References 28 publications

Ensemble Methods for APS In-Flight Particle Temperature and Velocity Prediction Considering Torch Electrodes Ageing

Ensemble Methods for APS In-Flight Particle Temperature and Velocity Prediction Considering Torch Electrodes Ageing

Control-oriented dynamic model of an inductively coupled plasma torch by artificial intelligence methodology

Numerical Simulation and Experimental Investigation of Multi-function Micro-plasma Jet and Alumina Particle Behaviour

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