We have proposed a SRTM-Twin Injection Method in which resin is injected into a mold cavity from multiple gates. The void flow behavior in sequential injection was investigated by measuring the void distribution in short-shot moldings. Flow analysis using finite element method (FEM) was performed and the flow velocity and pressure distributions were related to void distribution. For the sequential injection, the resin was first injected through a pin gate, then followed by injection through a fan gate. Resin injected through the fan gate flowed outside the region filled by the resin flowing through the pin gate. Both quantity and size of the voids changed in the region where the flows through the pin and fan gates met as a result of the convergence flow from fan gate injection. A large number of voids were found in the region from the fan gate injection just behind that from the pin gate injection. According to FEM analysis, a dead water region with pressures less than zero was formed in this region. It was apparent that a high void region was formed because the movement of voids was restricted in this region.

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Prognostics and health management (PHM) has an important part in aerospace systems. Information sensing and testing are the bases of PHM, and design for testability (DFT) developed concurrently with system design is considered a fundamental way to improve PHM performance. The traditional DFT, which is only based on the requirements of fault detection and isolation, is not suitable for sensor design and optimization for PHM. Aiming to solve this problem, the intrinsic requirements of PHM for testability are firstly analyzed qualitatively and the corresponding testability indexes are defined quantitatively. Then, a sensor selection/optimization process for PHM is presented. Fault detection uncertainty is also analyzed systematically from the view of fault attributes, sensor attributes and fault-sensor matching attributes, respectively. Based on the requirements and process, the object and constraint models of sensor optimization selection problem are studied in great detail. For aerospace system health management, a sensor optimization selection model is constructed that treats sensor total cost as the objective function and the proposed testability indexes under uncertainty test as constraint conditions. Due to the NP-hard property of the model, a generic algorithm (GA) is introduced to obtain the optimal solution. The application examples show that the proposed model and algorithm are effective and feasible.

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