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运用铰链式六面顶高温高压装置制备TiC/NiTi复合材料,该制备方法能提高复合材料致密度,且能提高NiTi在形状记忆合金中的含量,以改善合金性能。分析了制备过程中烧结温度、烧结时间和原料粉体对NiTi马氏体相变潜热的影响,应用人工神经网络技术建立了参数预测模型,利用遗传算法的全局搜索能力,优化了BP网络权值,从而完善了基于BP网络的NiTi马氏体相变潜热预测模型。结果表明:该模型具有较高的精度,实现了预测的作用,为工艺参数选择提供理论依据。
The preparation of TiC / NiTi composite by means of a hinged six-top high temperature and high pressure device can improve the density of the composite and can improve the content of NiTi in the shape memory alloy to improve the alloy performance. The influence of sintering temperature, sintering time and raw material powder on NiTi martensite transformation latent heat was analyzed. Artificial neural network technology was used to establish the parameter prediction model. The global search ability of genetic algorithm was used to optimize the weight of BP network , Thus improving the prediction model of NiTi martensite phase transition latent heat based on BP network. The results show that the model has high accuracy and predictive effect, and provides a theoretical basis for the selection of process parameters.