Estimation of coating thickness in electrostatic spray deposition by machine learning and response surface methodology-Reference-Cited by-同舟云学术

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Estimation of coating thickness in electrostatic spray deposition by machine learning and response surface methodology

Published:2021-09 Issue: Volume:422 Page:127559
ISSN:0257-8972
Container-title:Surface and Coatings Technology
language:en
Short-container-title:Surface and Coatings Technology

Author:

Paturi Uma Maheshwera Reddy,Reddy N.S.,Cheruku Suryapavan,Narala Suresh Kumar Reddy,Cho Kwon Koo,Reddy M.M.

Publisher

Elsevier BV

Subject

Materials Chemistry,Surfaces, Coatings and Films,Surfaces and Interfaces,Condensed Matter Physics,General Chemistry

Reference34 articles.

1. Modelling of electrostatic fluidized bed (EFB) coating process using artificial neural networks;Barletta;Eng. Appl. Artif. Intell.,2007

2. Optimization of spray parameters in the fabrication of SnO2 layer using electrostatic assisted deposition technique;Ghanshyam;J. Electrost.,2013

3. Box-Behnken technique based multi-parametric optimization of electrostatic spray coating in the manufacturing of thermoplastic composites;Goud;Mater. Manuf. Process.,2019

4. Response surface modeling and optimization of single axis automatic application of automotive polyurethane coatings on plastic components;Arunkumar;Int. J. Adv. Manuf. Technol.,2012

5. Electrostatic solid lubricant coatings: optimization of process parameters and performance in tribological tests;Paturi;Solid State Phenom.,2017

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