Nano-size Al2O3–polyetheretherketone(PEEK) composite thick films have been prepared on stainless steel substrates from non-aqueous colloidal suspensions by electrophoretic deposition (EPD). The effects on the deposition efficiency of process parameters, such as the deposition time, the difference of potential applied and their interactions were studied using a neural network approach to develop a quantitative understanding of the system. Furthermore the use of the neural network was optimized in the number of epochs, hidden layers and artificial neurons in each hidden layer by a design of experiment (DOE) analysis, demonstrating that these two methods can work together improving the final results of the neural network approach. Afterwards, a MonteCarlo analysis based on a simulation of 100,000 virtual depositions has permitted to deeply investigate the effect of independent variables (e.g. deposition time and difference of potential applied) on the deposition yield (dependent variable).

Application of a neural network approach to the electrophoretic deposition of PEEK–alumina composite coatings / I., Corni; Cannio, Maria; Romagnoli, Marcello; A. R., Boccaccini. - In: MATERIALS RESEARCH BULLETIN. - ISSN 0025-5408. - STAMPA. - 44:7(2009), pp. 1494-1501. [10.1016/j.materresbull.2009.02.011]

Application of a neural network approach to the electrophoretic deposition of PEEK–alumina composite coatings

CANNIO, Maria;ROMAGNOLI, Marcello;
2009

Abstract

Nano-size Al2O3–polyetheretherketone(PEEK) composite thick films have been prepared on stainless steel substrates from non-aqueous colloidal suspensions by electrophoretic deposition (EPD). The effects on the deposition efficiency of process parameters, such as the deposition time, the difference of potential applied and their interactions were studied using a neural network approach to develop a quantitative understanding of the system. Furthermore the use of the neural network was optimized in the number of epochs, hidden layers and artificial neurons in each hidden layer by a design of experiment (DOE) analysis, demonstrating that these two methods can work together improving the final results of the neural network approach. Afterwards, a MonteCarlo analysis based on a simulation of 100,000 virtual depositions has permitted to deeply investigate the effect of independent variables (e.g. deposition time and difference of potential applied) on the deposition yield (dependent variable).
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1494
1501
Application of a neural network approach to the electrophoretic deposition of PEEK–alumina composite coatings / I., Corni; Cannio, Maria; Romagnoli, Marcello; A. R., Boccaccini. - In: MATERIALS RESEARCH BULLETIN. - ISSN 0025-5408. - STAMPA. - 44:7(2009), pp. 1494-1501. [10.1016/j.materresbull.2009.02.011]
I., Corni; Cannio, Maria; Romagnoli, Marcello; A. R., Boccaccini
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/598277
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