Segmentation of casting defect regions for the extraction of microstructural properties. (October 2019)

• Record Type: Journal Article
• Authors: Lin, Jinhua; Ma, Lin; Yao, Yu
• Is Part Of: Engineering applications of artificial intelligence. Volume 85(2019)
• Page Start: 150
• Page End: 163
• Publication Date: 2019-10
• Subjects: Microstructural properties -- Casting defect region -- Convolutional neural network; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2019.06.007
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Extracting microstructural properties of casting defect regions plays an important role in quality control efforts for casting production. However, it is not easy to extract microstructural properties via the existing extraction strategies because the microstructures of casting defect regions are extremely complex and irregular. In this paper, a 3D convolutional neural network, a nonlinear topological dimension parameter and an empirical model are proposed for extracting the microstructural properties of casting defect regions efficiently. First, taking the 3D region proposal network (RPN), the instance segmentation network (ISN) and the 3D RoIAlign layer as three subnetworks, a 3D convolutional neural network is constructed for the initial segmentation of casting defect regions, and the geometric features of casting defect regions are further characterized according to the nonlinear topological dimension parameter. In the end, based on the nonlinear topological dimension parameter, an empirical model is established for extracting four important microstructural properties of casting defect regions. The experimental results demonstrate that microstructural properties of casting defect regions can be extracted via this method.