TY - GEN
T1 - Using artificial neural network to predict the particle characteristics of an atmospheric plasma spray process
AU - Choudhury, T. A.
AU - Hosseinzadeh, N.
AU - Berndt, C. C.
PY - 2010
Y1 - 2010
N2 - Thermal Spray is a general term for a group of coating processes used for metallic or non-metallic coatings to protect a functional surface or improve its performance. There are several processing parameters defining the coating quality and they must be combined and planned in an optimised way in order to have the selected coating exhibit the desired properties. To have the proper combination is critical as it influences both the cost and coating characteristics. The plasma spray process combines the highest number of processing parameters and to have full control over the system, one of the major challenges is to understand the parameters interdependencies, correlations and their individual effects on coating properties and characteristics. A robust methodology is thus required to study these interrelated effects. This paper proposes a new approach based on Artificial Neural Network (ANN) to play this role. The obtained database of the input processing parameters and the output particle characteristics is used to train, validate and optimise the neural network. The optimisation steps are discussed and the predicted outputs are compared with the experimental ones.
AB - Thermal Spray is a general term for a group of coating processes used for metallic or non-metallic coatings to protect a functional surface or improve its performance. There are several processing parameters defining the coating quality and they must be combined and planned in an optimised way in order to have the selected coating exhibit the desired properties. To have the proper combination is critical as it influences both the cost and coating characteristics. The plasma spray process combines the highest number of processing parameters and to have full control over the system, one of the major challenges is to understand the parameters interdependencies, correlations and their individual effects on coating properties and characteristics. A robust methodology is thus required to study these interrelated effects. This paper proposes a new approach based on Artificial Neural Network (ANN) to play this role. The obtained database of the input processing parameters and the output particle characteristics is used to train, validate and optimise the neural network. The optimisation steps are discussed and the predicted outputs are compared with the experimental ones.
KW - Artificial neural network
KW - Atmospheric plasma spray
KW - In-flight particle characteristics
KW - Intelligent multivariable control
KW - Process control
KW - Processing parameters
KW - Real-time monitoring
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U2 - 10.1109/ICELCE.2010.5700795
DO - 10.1109/ICELCE.2010.5700795
M3 - Conference contribution
AN - SCOPUS:79951785722
SN - 9781424462797
T3 - ICECE 2010 - 6th International Conference on Electrical and Computer Engineering
SP - 726
EP - 729
BT - ICECE 2010 - 6th International Conference on Electrical and Computer Engineering
T2 - 6th International Conference on Electrical and Computer Engineering, ICECE 2010
Y2 - 18 December 2010 through 20 December 2010
ER -