This paper deals with fluidized bed coating of metal substrates with high performance thermoplastic powders (polyftalamide, PPA). Two different experimental scenarios were investigated: the conventional hot dipping fluidized bed (CHDFB) process and the electrostatic fluidized bed (EFB) coating process. The preliminary experimental plan was scheduled employing design of experiment (DOE) technique. Three experimental factors and operative ranges large enough for practical purposes were considered in both of the examined scenarios. In particular, coating time and airflow rate were chosen as experimental factors in both CHDFB and EFB. The third factor was the preheating temperature of metal substrates in CHDFB and the applied voltage in EFB. A general linear model based upon analysis of variance (ANOVA) was used to evaluate the significance on coating processes of each experimental factor. Main effect plots (MEPs) and interaction plots (IPs) of coating thickness were drawn. Trends consistent with the settings of the operative parameters were displayed. The experimental trends were first modelled by numerical regression of the experimental data and, subsequently, by using artificial neural network. The reliability of the neural network solution and of the built ad hoc regression models was comparatively evaluated. Multi-layer perceptron (MLP) neural network trained with back propagation (BP) algorithm was found to be the most valuable in fitting the coating thicknesses trends for both the coating processes. Examining the developed models outside the operative ranges they were designed for, the good generalization capability and high flexibility of the neural network solution was definitely stated. © 2008 Elsevier Ltd. All rights reserved.

Barletta, M., Gisario, A., Guarino, S., & Tagliaferri, V. (2008). Fluidized bed coating of metal substrates by using high performance thermoplastic powders: Statistical approach and neural network modelling. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 21(8), 1130-1143 [10.1016/j.engappai.2008.01.004].

Fluidized bed coating of metal substrates by using high performance thermoplastic powders: Statistical approach and neural network modelling

BARLETTA, MASSIMILIANO;
2008

Abstract

This paper deals with fluidized bed coating of metal substrates with high performance thermoplastic powders (polyftalamide, PPA). Two different experimental scenarios were investigated: the conventional hot dipping fluidized bed (CHDFB) process and the electrostatic fluidized bed (EFB) coating process. The preliminary experimental plan was scheduled employing design of experiment (DOE) technique. Three experimental factors and operative ranges large enough for practical purposes were considered in both of the examined scenarios. In particular, coating time and airflow rate were chosen as experimental factors in both CHDFB and EFB. The third factor was the preheating temperature of metal substrates in CHDFB and the applied voltage in EFB. A general linear model based upon analysis of variance (ANOVA) was used to evaluate the significance on coating processes of each experimental factor. Main effect plots (MEPs) and interaction plots (IPs) of coating thickness were drawn. Trends consistent with the settings of the operative parameters were displayed. The experimental trends were first modelled by numerical regression of the experimental data and, subsequently, by using artificial neural network. The reliability of the neural network solution and of the built ad hoc regression models was comparatively evaluated. Multi-layer perceptron (MLP) neural network trained with back propagation (BP) algorithm was found to be the most valuable in fitting the coating thicknesses trends for both the coating processes. Examining the developed models outside the operative ranges they were designed for, the good generalization capability and high flexibility of the neural network solution was definitely stated. © 2008 Elsevier Ltd. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/316509
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