Affiliations: [a] School of Computer Application Technology, Changchun University of Technology, Changchun, Jilin, China | [b] Machinery and Electronics Engineering, University of Chinese Academy of Sciences, Beijing, China | [c] FAW Foundry Co., Ltd, Changchun, Jilin, China
Correspondence:
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Corresponding author: Jinhua Lin, School of Computer Application Technology, Changchun University of Technology, Yan’an Street No. 2055, Changchun, Jilin, China. E-mail: linjinhua@ccut.edu.cn.
Abstract: Accurate segmentation of casting defects plays a positive role in the quality control of casting products, and is of great significance for accurate extraction of the mechanical properties of defects in the casting solidification process. However, as the shape of casting defects is complex and irregular, it is challenging to segment casting defects by existing segmentation methods. To address this, a spectrum domain instance segmentation model (SISN) is proposed for segmenting five types of casting defects with complex shapes accurately. The five defects are inclusion, shrinkage, hot tearing, cold tearing and micro pore. The proposed model consists of three sub-models: the spectrum domain region proposal model (SRPN), spectrum domain region of interest alignment model (SRoIAlign) and spectrum domain instance generation model (SIGN). SRPN uses a multi-scale anchoring mechanism to detect defects of various sizes, where the SSReLU and SCPool functions are used to solve the spectrum domain gradient explosion problem and the spectrum domain over-fitting problem. SRoIAlign uses the floating-point quantization operation and the tri-linear interpolation method to quantize the 3D proposals to the feature values in an accurate manner. SIGN is a full-spectrum domain neural network applied to 3D proposals, generating a segmentation instance of defects in a point-wise manner. In the experiments, we test the effectiveness of the proposed model from three aspects: segmentation accuracy, time performance and mechanical property extraction accuracy.
