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Top1. Introduction
Thermal spray is an important surface technique which has been widely used in various manufacturing industries ranging from aerospace to biomedical engineering. This technique is used to create a Thermal Barrier Coating (TBC) by spraying melted coating materials, such as metals, alloys, ceramics, etc., onto the surface of a substrate. The thermal barrier coating layer not only provides thermal insulation, but also protects the surface from wear, erosion, oxidation, sulfidation, and hot corrosion (Portinha et al., 2005; Ctibor, Sampath, & Wang, 2006).
During the thermal barrier coating process, the deposition of the melted coating material naturally forms microstructures with pores and cracks in the thermal barrier coating layers. Globular microstructures (pores) provide thermal insulation while interlamellar microstructures (cracks) accommodate the stress. These microstructures have great impacts on the quality, reliability and durability of the coatings (Chi, Sampath, & Wang, 2008). For this reason, microstructural characterization plays a crucial role to ensure coating quality. In order to assist the quantification of these microstructures, confocal light microscope is used to capture digital images from the thermal barrier coatings. As shown in Figure 1, two coating layers, i.e., bond coat layer (BCL) and top coat layer (TCL), adhere to the top of a substrate, which protect the substrate.
Figure 1. Thermal barrier coating (TBC) image
The traditional approach for quantification of microstructures is known as total porosity measure, which is an essential performance indicator commonly used in coating quality assessment in industry. Porosity, also known as void fraction, is a measure of the void space in a material. This measure is characterized as a percentage of the volume of voids within the total volume of the material (Brunhouse, Foy, & Moody, 2012).
The conventional way of analyzing thermal barrier coating images to measure total porosity is a laborious and subjective work. The domain experts have to manually select the region of interest (ROI) such as top coat layer in a captured TBC image, and then subjectively determine a global threshold value to differentiate foreground (microstructures) from background (coating layer). Determining a global threshold value is a challenging step due to uneven background in the TBC images. For reasons aforementioned, the results are less accurate and inconsistent due to the subjective measurement of domain experts.
In order to objectively characterize coating quality, various tools were developed to detect and measure microstructures using image analysis (Ctibor et al., 2006; Mailot, Gitzhofer, & Boulos, 1998; Lavigne et al., 1999; Deshpande, Kulkarni, Sampath, & Herman, 2004). However, these approaches rely on high-resolution scanning electron microscope images captured by a well-trained operator, and therefore, could not be generalized to images captured by light microscope. Some commercial software, e.g., Olympus Stream, introduce image analysis techniques to measure porosity. However, human operators are required to manually select a region of interest (ROI) such as top coat layer in a captured image, and then, apply a global threshold value to segment the selected area into foreground (microstructures), and background (coating layer). This approach leads to two major problems that will potentially affect the accuracy. First, ROI selection is an inevitable trade- off between accuracy and efficiency due to the fact that microstructure distribution is rather not uniform and that the selection of irregularly shaped coating layer boundaries is a tedious step. To the best of our knowledge, none of the existing tools or software offers the ability to detect the top coat layer in a fully automatic manner. Second, TBC images captured from a light microscope always have uneven background. Tweaking the global threshold value is a subjective process, therefore has the potential to reduce the accuracy of porosity measurement. Shi, Zhao, Long, and Wang (2018) proposed a detection method for porosity of gas turbine blade coating based on Gray Gradient Space Histogram Entropy (GGSHE) combined with Sparse Representation-based Classifier (SRC).