A Fully Automated Porosity Measure for Thermal Barrier Coating Images

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.

Application of Machine Learning to Solid Particle Erosion of APS-TBC and EB-PVD TBC at Elevated Temperatures

“…Some research also proved that CNNs are able to achieve satisfied accuracy results when deployed for microstructure quantification within thermal spray coatings. Chen et al [39] collected the ground truth of the porosity mask on two full-size images and then randomly selected pixels in the top coat layer (TCL) to form sub-images centred at the pixel and with different sizes to train convolutional neural networks (CNNs) with various architecture. Their proposed approach was evaluated on a dataset of 150 images of size 100 by 100 randomly selected from a set of 30 high-resolution thermal barrier coating images.…”