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.H...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.展开更多
This study aims to investigate the mechanical response and acoustic emission(AE)characteristic of pre-flawed sandstone under both monotonic and multilevel constant-amplitude cyclic loads.Specifically,we explored how c...This study aims to investigate the mechanical response and acoustic emission(AE)characteristic of pre-flawed sandstone under both monotonic and multilevel constant-amplitude cyclic loads.Specifically,we explored how coplanar flaw angle and load type impact the strength and deformation behavior and microscopic damage mechanism.Results indicated that being fluctuated before rising with increasing fissure angle under monotonic loading,the peak strength of the specimen first increased slowly and then steeply under cyclic loading.The effect of multilevel cyclic loading on the mechanical parameters was more significant.For a single fatigue stage,the specimen underwent greater deformation in early cycles,which subsequently stabilized.Similar variation pattern was also reflected by AE count/energy/b-value.Crack behaviors were dominated by the fissure angle and load type and medium-scale crack accounted for 74.83%–86.44%of total crack.Compared with monotonic loading,crack distribution of specimen under cyclic loading was more complicated.Meanwhile,a simple model was proposed to describe the damage evolution of sandstone under cyclic loading.Finally,SEM images revealed that the microstructures at the fracture were mainly composed of intergranular fracture,and percentage of transgranular fracture jumped under cyclic loading due to the rapid release of elastic energy caused by high loading rate.展开更多
The R F first order second moment method will produce more error for calculating the reliability of welded engineering pipe structures when the failure function is seriously nonlinear and the random variables don...The R F first order second moment method will produce more error for calculating the reliability of welded engineering pipe structures when the failure function is seriously nonlinear and the random variables don′t serve as normal distribution. In order to increase the computing accuracy of reliability, an improved FOSM method is used for calculating the failure probability of welded pipes with flaws in this paper. Because of solving the problems of the linear expansion of failure function at the failure point and constructing equivalent normal variables, the new algorithm can greatly improve the calculating accuracy of probability of the welded pipes with cracks. The examples show that this method is simple, efficient and accurate for reliability safety assessment of the welded pipes with cracks. It can save more time than the Monte Carlo method does, so that the improved FOSM method is recommended for engineering reliability safety assessment of the welded pipes with flaws.展开更多
基金funded by the National Key R&D Program of China(2020YFB1710100)the National Natural Science Foundation of China(Nos.52275337,52090042,51905188).
文摘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.
基金This work was financially supported by the National Natural Science Foundation of China(Nos.42077231 and 51574156).
文摘This study aims to investigate the mechanical response and acoustic emission(AE)characteristic of pre-flawed sandstone under both monotonic and multilevel constant-amplitude cyclic loads.Specifically,we explored how coplanar flaw angle and load type impact the strength and deformation behavior and microscopic damage mechanism.Results indicated that being fluctuated before rising with increasing fissure angle under monotonic loading,the peak strength of the specimen first increased slowly and then steeply under cyclic loading.The effect of multilevel cyclic loading on the mechanical parameters was more significant.For a single fatigue stage,the specimen underwent greater deformation in early cycles,which subsequently stabilized.Similar variation pattern was also reflected by AE count/energy/b-value.Crack behaviors were dominated by the fissure angle and load type and medium-scale crack accounted for 74.83%–86.44%of total crack.Compared with monotonic loading,crack distribution of specimen under cyclic loading was more complicated.Meanwhile,a simple model was proposed to describe the damage evolution of sandstone under cyclic loading.Finally,SEM images revealed that the microstructures at the fracture were mainly composed of intergranular fracture,and percentage of transgranular fracture jumped under cyclic loading due to the rapid release of elastic energy caused by high loading rate.
文摘The R F first order second moment method will produce more error for calculating the reliability of welded engineering pipe structures when the failure function is seriously nonlinear and the random variables don′t serve as normal distribution. In order to increase the computing accuracy of reliability, an improved FOSM method is used for calculating the failure probability of welded pipes with flaws in this paper. Because of solving the problems of the linear expansion of failure function at the failure point and constructing equivalent normal variables, the new algorithm can greatly improve the calculating accuracy of probability of the welded pipes with cracks. The examples show that this method is simple, efficient and accurate for reliability safety assessment of the welded pipes with cracks. It can save more time than the Monte Carlo method does, so that the improved FOSM method is recommended for engineering reliability safety assessment of the welded pipes with flaws.
基金supported by the National Natural Science Foundation of China(Nos.51927808,51904335,52174098)the Fundamental Research Funds for the Central Universities of Central South University,China(No.2020zzts199)。
