A Fully Automated Porosity Measure for Thermal Barrier Coating Images

Wei-Bang Chen, Benjamin N. Standfield, Song Gao, Yongjin Lu, Xiaoliang Wang and Ben Zimmerman
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Wei-Bang Chen: Department of Engineering and Computer Science, Virginia State University, Petersburg, USA
Benjamin N. Standfield: Department of Engineering and Computer Science, Virginia State University, Petersburg, USA
Song Gao: Google Inc., Mountain View, USA
Yongjin Lu: Department of Mathematics and Economics, Virginia State University, Petersburg, USA
Xiaoliang Wang: Department of Technology, Virginia State University, Petersburg, USA
Ben Zimmerman: Commonwealth Center for Advanced Manufacturing, USA

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2018, vol. 9, issue 4, 40-58

Abstract: Thermal barrier coating (TBC), a widely used advanced manufacturing technique in various industries, provides thermal insulation and surface protection to a substrate by spraying melted coating materials on to the surface of the substrate. This article is an extended version of a previously published work. To quantify microstructures in the TBC, the authors introduce a fully automated image analysis-based TBC porosity measure (TBCPM) framework which includes 1) top coat layer (TCL) detection module, and 2) microstructure recognition and porosity measure module. The first module is designed to automatically identify the TCL in a TBC image using a histogram-based approach. The second module recognizes the microstructures in the TCL using a local thresholding-based method. This article extends the previous work by introducing convolutional neural networks (CNNs) to enhance the performance of the second module. The experimental results show that the CNN-based methods outperform local thresholding-based methods, and results of the proposed porosity measure are comparable to that of the domain experts.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jmdem0:v:9:y:2018:i:4:p:40-58

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