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The purpose of this paper is to develop a new technology for controlling the quenching deformation of light cast aluminum alloy wheels. First, based on the existing wheel heat treatment process, a gas–liquid–solid multi-phase flow coupling model was established through the ANSYS Workbench platform to analyze the gas–liquid phase change, heat exchange on wheel surface and quenching deformation characteristics during the process of wheel immersion into the water. The results show that heat exchange characteristics of the wheel surface are comprehensively affected by wheel structure, quenching fluid flow field and gas–liquid phase transition. There are a lot of non-uniform heat exchange areas in the outer rim, spoke area and center area, which affect the overall deformation characteristics. Affected by spoke structure, the maximum deformation occurs at the outer and inner rim end faces farthest away from the wheel. Based on the above research, this paper independently develops a new deformation control strategy of spray and water immersion composite step process. Through spraying, the influence of spoke structural stiffness on the overall deformation characteristics of the wheel is effectively reduced, and the wheel deformation control is realized by meeting the mechanical properties of the wheel, with the maximum deformation reduction of 39.2%. This study provides a new option for the integrated control of deformation and mechanical properties of aluminum alloy wheels.

In low-pressure casting, aluminum alloy wheels are prone to internal defects such as gas holes and shrinkage cavities, which call for X-ray inspection to ensure quality. Automatic defect segmentation of X-ray images is an important task in X-ray inspection of wheels. For this, a solution is proposed here that combines adaptive threshold segmentation algorithm and mathematical morphology reconstruction. First, the X-ray image of the wheel is smoothed, and then the smoothed image is subtracted from the original image, and the resulting difference image is binarized; the binary image resulting from the low threshold is taken as the marker image, and that from the high threshold is taken as mask image, and mathematical morphology reconstruction is performed on the two images, with the resulting image being the preliminary result of the wheel defect segmentation. Finally, with area and diameter parameters as the conditions, the preliminary segmentation result is analyzed, and the defect regions satisfying the conditions are taken as the ultimate result of the whole solution. Experiments proved the feasibility of the above solution, which is found capable of extracting different types of wheel defects satisfactorily.