SUMMARYA coupled finite element model is developed to simulate the metal casting process. We present a new method for capturing the solidification zone and obtaining the corresponding rate of phase change. The latent energy release is predicted by the heat conduction rate and introduced by following the enthalpy-temperature curve. The influence of mould-metal contact is considered in calculating the heat flux through the mould-metal interface. A viscoplastic model is employed to predict the gap opening and evolution of plastification.Four examples are presented to demonstrate certain numerical aspects and the capability of the model for industrial applications.

Printed circuit board (PCB) defect detection is critical for ensuring the safety of electronic devices, especially in the space industry. Non-reference-based methods, typically the deep learning methods, suffer from a large amount of annotated data requirements and poor interpretability. In contrast, conventional reference-based methods achieve higher detection accuracy by comparing with a template image but rely on precise image alignment and face the challenge of fine defects detection. To solve the problem, we propose a novel Edge-guided Energy-based PCB Defect Detection method (EEDD). We focus on the salient edge characteristic of PCB images and regard the functional defects as contour differences and then propose a novel energy measurement method for PCB contour anomaly detection. We introduce the energy transformation using the edge information of the template and test image, then Speeded-Up Robust Features method (SURF) is used for image alignment, and finally achieve defect detection by measuring the energy anomaly score pixel by pixel with the proposed energy-based defect localization and contour flood fill methods. Our method excels in detecting multi-scale defects, particularly tiny defects, and is robust against interferences such as non-finely aligned images and edge spurs. Experiments on the DeepPCB-A dataset and our high-resolution PCB dataset (HDPCB) show that the proposed method outperforms state-of-the-art methods in PCB defect-detection tasks.

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Blades are critical parts of aero-engine and nuclear turbine, and it is important to inspect blades by optical method accurately. In this paper, an optical scanning device is designed for inspecting blades. In general, the main external factor affecting the accuracy in the process is environmental vibration, the following parts studied the influence of environmental vibration on the device. First, the characteristics of the environmental vibration are analyzed to find out the frequency range affecting the inspection accuracy. Second, the intrinsic frequency of the device is obtained by modal analysis. Third, the response of the device under external vibration is obtained by simulation experiment. At last, experiments show that the inspection accuracy is 0.0277mm when the environmental vibration frequency is equal to the intrinsic frequency, which can meet the inspection requirements very well.

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