Modular implementation of artificial neural network in predicting in-flight particle characteristics of an atmospheric plasma spray process
- Authors: Choudhury, Tanveer , Berndt, Christopher , Man, Zhihong
- Date: 2015
- Type: Text , Journal article
- Relation: Engineering Applications of Artificial Intelligence Vol. 45, no. (2015), p. 57-70
- Full Text: false
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- Description: This paper presents a modular implementation of an artificial neural network to model the atmospheric plasma spray process in predicting the in-flight particle characteristics from the input processing parameters. The in-flight particle characteristics influence the structure and properties of the thermal spray coating and, thus, are considered important parameters to comprehend, simulate and predict the manufacturing process. The modular implementation allows simplification of the optimized model structure with enhanced ability to generalise the network. As well, the underlying relationship between each of the output in-flight characteristics with respect to the input processing parameters is explored. Smaller networks are constructed that achieves better, or in some cases, similar results. The training process is found to be more robust and stable along with fewer fluctuations in the values of the network parameters. The networks also respond to the variations of the number of hidden layer neurons with some definite trend. The predictable trend enhances reliability of the application of the artificial neural network in modelling the atmospheric plasma spray process and overcomes the variability and non-linearity associated with the process. © 2015 Elsevier Ltd. All rights reserved.
An extreme learning machine algorithm to predict the in-flight particle characteristics of an atmospheric plasma spray process
- Authors: Choudhury, Tanveer , Berndt, Christopher , Man, Zhihong
- Date: 2013
- Type: Text , Journal article
- Relation: Plasma Chemistry and Plasma Processing Vol. 33, no. 5 (2013), p. 993-1023
- Full Text: false
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- Description: A robust single hidden layer feed forward neural network (SLFN) is used in this study to model the in-flight particle characteristics of the atmospheric plasma spray (APS) process with regard to the input processing parameters. The in-flight particle characteristics influence the structure and properties of the APS coating and, thus, are considered important parameters to comprehend the manufacturing process. The training times of traditional back propagation algorithms, mostly used to model such processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. Performance comparisons of the networks trained with ELM algorithm and standard error back propagation algorithms are presented. It is found that networks trained with ELM have good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The trends represent robustness of the trained networks and enhance reliability of the application of the artificial neural network in modelling APS processes. © 2013 Springer Science+Business Media New York.
Artificial Neural Network application for predicting in-flight particle characteristics of an atmospheric plasma spray process
- Authors: Choudhury, Tanveer , Hosseinzadeh, Nasser , Berndt, Christopher
- Date: 2011
- Type: Text , Journal article
- Relation: Surface and Coatings Technology Vol. 205, no. 21-22 (2011), p. 4886-4895
- Full Text: false
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- Description: Thermal spray consists of a group of coating processes that are used to apply metal or non-metallic coatings to protect a functional surface or to improve its performance. There are some 40 processing parameters that define the overall coating quality and these must be selected in an optimized fashion to manufacture a coating that exhibits desirable properties. The proper combination of processing variables is critical since these influence the cost as well as the coating characteristics.Because of this high number of processing parameters, a major challenge is to have full control over the system and to understand parameter interdependencies, correlations and their individual effects on the in-flight particle characteristics, which have significant influence on the in service coating properties. This paper proposes an approach, based on the Artificial Neural Network (ANN) method, to play this role and illustrates the model's design, network optimization procedures, the database handling and expansion steps, and analysis of the predicted values, with respect to the experimental ones, in order to evaluate the network's performance. © 2011 Elsevier B.V.