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

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.

Dimensional regulation in gigantic molybdenum blue wheels featuring{(W)Mo_(5)}motifs for enhanced proton conductivity

Dimensional regulation in polyoxometalates is an effective strategy during the design and synthesis of polyoxometalates-based high proton conductors,but it is not available to date.Herein,the precise regulation of dimensionality has been realized in an unprecedented gigantic molybdenum blue wheel family featuring pentagonal{(W)Mo5}motifs through optimizing the molar ratio of Mo/W,including[Gd_(2)Mo_(124)W_(14)O_(422)(H_(2)O)62]38-(0D-{Mo_(124)W_(14)},1),[Mo_(126)W_(14)O441(H_(2)O)51]^(70-)(1D-{Mo_(126)W_(14)}n,2),and[Mo_(124)W_(14)O_(430)(H_(2)O)50]60-(2D-{Mo_(124)W_(14)}n,3).Such important{(W)Mo5}structural motif brings new reactivity into gigantic Mo blue wheels.There are different numbers and sites of{Mo2}defects in each wheel-shaped monomer in 1-3,which leads to the monomers of 2 and 3 to form 1D and 2D architectures via Mo-O-Mo covalent bonds driven by{Mo2}-mediated H_(2)O ligands substitution process,respectively,thus achieving the controllable dimensional regulation.As expected,the proton conductivity of 3 is 10 times higher than that of 1 and 1.7 times higher than that of 2.The continuous proton hopping sites in 2D network are responsible for the enhanced proton conductivity with lower activation energy.This study highlights that this dimensional regulation approach remains great potential in preparing polyoxometalates-based high proton conductive materials.