I-DCGAN and TOPSIS-IFP:A simulation generation model for radiographic flaw detection images in light alloy castings and an algorithm for quality evaluation of generated images  

作　　者：Ming-jun Hou Hao Dong Xiao-yuan Ji Wen-bing Zou Xiang-sheng Xia Meng Li Ya-jun Yin Bao-hui Li Qiang Chen Jian-xin Zhou 

机构地区：[1]State Key Laboratory of Materials Processing and Die&Mould Technology,Huazhong University of Science and Technology,Wuhan 430074,China [2]Xinjiang Technology(Jiangsu)Co.,Ltd.,Nantong 226100,Jiangsu,China [3]Southwest Technique and Engineering Research Institute,Chongqing 400039,China [4]Shanghai Spaceflight Precision Machinery Institute,Shanghai 201600,China

出　　处：《China Foundry》2024年第3期239-247,共9页中国铸造（英文版）

基　　金：funded by the National Key R&D Program of China(2020YFB1710100);the National Natural Science Foundation of China(Nos.52275337,52090042,51905188).

摘　　要：The intelligent detection technology driven by X-ray images and deep learning represents the forefront of advanced techniques and development trends in flaw detection and automated evaluation of light alloy castings.However,the efficacy of deep learning models hinges upon a substantial abundance of flaw samples.The existing research on X-ray image augmentation for flaw detection suffers from shortcomings such as poor diversity of flaw samples and low reliability of quality evaluation.To this end,a novel approach was put forward,which involves the creation of the Interpolation-Deep Convolutional Generative Adversarial Network(I-DCGAN)for flaw detection image generation and a comprehensive evaluation algorithm named TOPSIS-IFP.I-DCGAN enables the generation of high-resolution,diverse simulated images with multiple appearances,achieving an improvement in sample diversity and quality while maintaining a relatively lower computational complexity.TOPSIS-IFP facilitates multi-dimensional quality evaluation,including aspects such as diversity,authenticity,image distribution difference,and image distortion degree.The results indicate that the X-ray radiographic images of magnesium and aluminum alloy castings achieve optimal performance when trained up to the 800th and 600th epochs,respectively.The TOPSIS-IFP value reaches 78.7%and 73.8%similarity to the ideal solution,respectively.Compared to single index evaluation,the TOPSIS-IFP algorithm achieves higher-quality simulated images at the optimal training epoch.This approach successfully mitigates the issue of unreliable quality associated with single index evaluation.The image generation and comprehensive quality evaluation method developed in this paper provides a novel approach for image augmentation in flaw recognition,holding significant importance for enhancing the robustness of subsequent flaw recognition networks.

关 键 词：light alloy casting flaw detection image generator DISCRIMINATOR comprehensive evaluation index 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]