Prediction of control parameters corresponding to in-flight particles in atmospheric plasma spray employing convolutional neural networks-Reference-Cited by-同舟云学术

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Prediction of control parameters corresponding to in-flight particles in atmospheric plasma spray employing convolutional neural networks

Published:2020-07 Issue: Volume:394 Page:125862
ISSN:0257-8972
Container-title:Surface and Coatings Technology
language:en
Short-container-title:Surface and Coatings Technology

Author:

Zhu Jinwei,Wang Xinzhi,Kou Luyao,Zheng Lili,Zhang Hui

Funder

National Key R&D Program of China

National Science Foundation of China

High-tech Discipline Construction Fundings for Universities in Beijing

Beijing Key Laboratory of City Integrated Emergency Response Science

Publisher

Elsevier BV

Subject

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

Reference65 articles.

1. Investigation of particle in-flight characteristics during atmospheric plasma spraying of yttria stabilized ZrO2: part 1. Experimental;Friis;J. Therm. Spray Technol.,2001

2. Influence of particle in-flight characteristics on the microstructure of atmospheric plasma sprayed yttria stabilized ZrO2;Friis;Surf. Coat. Technol.,2001

3. Developing empirical relationships to estimate porosity and microhardness of plasma sprayed ysz coatings;Karthikeyan;Ceram. Int.,2014

4. Developing empirical relationships to estimate porosity and Young’s modulus of plasma sprayed ysz coatings;Karthikeyan;Appl. Surf. Sci.,2014

5. Structure of plasma-sprayed zirconia coatings tailored by controlling the temperature and velocity of the sprayed particles;Prystay;J. Therm. Spray Technol.,2001

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