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The counter-gravity casting methods have been developed to remove the casting defects of Mg based alloys. However, the effects of different counter-gravity casting methods on the microstructure and mechanical properties have not been studied in detail. ZM5 alloys were prepared by gravity casting, low-pressure casting and counter-pressure casting, respectively. The mechanical properties, microstructure and fracture morphologies were examined and compared by means of optical microscopy, scanning electron microscopy methods and tensile testing. Results show that casting defects such as gas pore, shrinkage porosity and cavity can be eliminated by counter-pressure casting. The grain size of α-Mg is decreased signifi cantly by counterpressure casting. Moreover, the precipitated particles are more uniform and fi ner in the counter-pressure casting sample. As a result, the mechanical properties of the alloys are greatly improved. The tensile strength and elongation of the samples by counter-pressure casting are 285 MPa and 13.9%, respectively, which are much higher than those of the low pressure casting and gravity casting.

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. [ABSTRACT FROM AUTHOR]