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811.
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针对某航天电子管壳焊接组件冷却过程中的热力耦合影响问题,建立了焊接组件的有限元热分析模型,研究了在快速冷却过程中梯度材料分布对低温共烧陶瓷(low temperature co-fired ceramic, LTCC)基板、梯度管壳的残余应力和变形的影响。以不超过基板断裂强度为前提条件,以降低管壳整体的残余应力与变形为优化目标,采用了多因素变换优选法,确定了管壳材料的最优梯度分布方案,即合金管壳自上而下的梯度分布为Al-35Si、Al-42Si、Al-50Si、Al-60Si、Al-70Si。其中,Al-35Si厚度为2.5mm, Al-42Si与Al-60Si的厚度均为1.6mm, Al-50Si厚度为0.8mm, Al-70Si厚度为2mm。在该方案下,LTCC基板冷却至室温时的最大变形量为4.86μm,最大第一主应力为6.761MPa,远小于LTCC材料的断裂强度320MPa;管壳冷却至室温时的最大变形量为18.291μm,最大残余应力值为20.46MPa,远小于管壳材料的屈服强度100MPa。管壳各层之间的应力集中现象不明显,管壳的整体焊接质量得到提升。 相似文献
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Lithium-ion batteries have become the third-generation space batteries and are widely utilized in a series of spacecraft. Remaining Useful Life (RUL) estimation is essential to a spacecraft as the battery is a critical part and determines the lifetime and reliability. The Relevance Vector Machine (RVM) is a data-driven algorithm used to estimate a battery’s RUL due to its sparse feature and uncertainty management capability. Especially, some of the regressive cases indicate that the RVM can obtain a better short-term prediction performance rather than long-term prediction. As a nonlinear kernel learning algorithm, the coefficient matrix and relevance vectors are fixed once the RVM training is conducted. Moreover, the RVM can be simply influenced by the noise with the training data. Thus, this work proposes an iterative updated approach to improve the long-term prediction performance for a battery’s RUL prediction. Firstly, when a new estimator is output by the RVM, the Kalman filter is applied to optimize this estimator with a physical degradation model. Then, this optimized estimator is added into the training set as an on-line sample, the RVM model is re-trained, and the coefficient matrix and relevance vectors can be dynamically adjusted to make next iterative prediction. Experimental results with a commercial battery test data set and a satellite battery data set both indicate that the proposed method can achieve a better performance for RUL estimation. 相似文献
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816.
首先通过实验分析不同工艺参数下对去除函数稳定性的影响,并进行了仿真分析,确定了机床的敏感方向;其次,对磁流变机床运动轴误差进行了测量,分析这些误差引起法线方向误差的大小;最后,实验验证采用较大的压深和补偿法线方向误差可以有效提高机床的加工精度. 相似文献
817.
Additive Manufacturing (AM) processing has attracted wide attention due to its advantages, e.g., rapid prototyping and free design. In aeronautic and astronautic engineering, it is especially attractive to apply AM processing, especially Selective Laser Melting (SLM), into primary structures that always occupy most of the whole mass of aircraft or spacecraft. In this paper, an innovative lattice sandwich structure that is suitable to be manufactured with SLM is adopted to design the typical cylinder under axial compression. Firstly, the mechanical properties of pyramidal lattice are investigated, and the bearing capacity of the lattice sandwich cylinder is given analytically for each failure mode. Secondly, a composite cylinder is re-designed in the form of lattice sandwich structure, which sizes are ultimately obtained through optimization. Finally, finite element model of the innovative cylinder is established and several kinds of imperfection are introduced to simulate the defects caused by SLM. Numerical result shows that the lattice sandwich design can obviously enhance the bearing capacity of cylindrical shell even though the original one is designed with composite material. In other words, SLM has great potential in improving the primary structure of spacecraft in the future. 相似文献
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