Thermal barrier coating, 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. As the melted coating materials solidify, it creates microstructures that affect the coating quality. An important coating quality assessment metric that determines its effectiveness is porosity, the quantity of microstructures within the coating. In this article, we aim to build a novel algorithm to determine the microstructures in a thermal barrier coating, which is used to calculate porosity. The hybrid approach combines the efficiency of thresholding-based techniques and the accuracy of convolutional neural network (CNN) based techniques to perform a binary semantic segmentation. We evaluate the performance of the proposed hybrid approach on coating images generated from two different types of coating powders. These images exhibit various texture features. The experimental results show that the proposed hybrid approach outperforms the thresholding-based approach and the CNN-based approach in terms of accuracy on both types of images. In addition, the time complexity of the hybrid approach is also greatly optimized compared to the CNN-based approach.
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A Hybrid Image Segmentation Approach for Thermal Barrier Coating Quality Assessments
Semantic Scholar · Materials Science · 2021
Abstract
Thermal barrier coating, 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. As the melted coating materials solidify, it creates microstructures that affect the coating quality. An important coating quality assessment metric that determines its effectiveness is porosity, the quantity of microstructures within the coating. In this article, we aim to build a novel algorithm to determine the microstructures in a thermal barrier coating, which is used to calculate porosity. The hybrid approach combines the efficiency of thresholding-based techniques and the accuracy of convolutional neural network (CNN) based techniques to perform a binary semantic segmentation. We evaluate the performance of the proposed hybrid approach on coating images generated from two different types of coating powders. These images exhibit various texture features. The experimental results show that the proposed hybrid approach outperforms the thresholding-based approach and the CNN-based approach in terms of accuracy on both types of images. In addition, the time complexity of the hybrid approach is also greatly optimized compared to the CNN-based approach.