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首页> 外文会议>Materials science and technology VII >Optimization of induction brazing parameters for stellite strip of steam turbine blade using Taguchi-Neural Network
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Optimization of induction brazing parameters for stellite strip of steam turbine blade using Taguchi-Neural Network

机译：基于Taguchi神经网络的汽轮机叶片星形钢感应钎焊参数优化。

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To repair the damaged of steam turbine blades, the method of increasing wear resistance of the blade leading edge with brazing of stellite plates by induction heating has been addressed in this work. Induction brazing of a Cobalt-based alloy to SUS410Cb steam turbine blades in an electrical power plant has been carried out. Ag-Cu-Zn based material was chosen to be the filler metal sandwiched between the stellite strip and the turbine blade. In order to cope with the large size of blades, an automatic brazing process which used moving induction coils has been developed. In the proposed brazing process, there are three parameters that significantly affect the quality of the brazing joints including coil distance, moving coil speed, and power of induction heating. In this work, the optimum choice of brazing parameters is obtained by using a Taguchi-neural network technique. According to the results of our previous work, it is recommended to attain the temperature distribution at around 750℃ for maximum strength and good quality brazing in the brazed components. The objective for the evaluation of brazing quality is therefore a function of this temperature and defined as the temperature deviation from the best brazing temperature. The design is verified and confirmed by the manufacturing of brazed joint applying suggested parameters. The experimental results showed that the Taguchi-Neural network method resulted in a better brazed quality than when using only the Taguchi method.
机译：为了修复汽轮机叶片的损坏，这项工作已经解决了通过感应加热钎焊星状板来增加叶片前缘的耐磨性的方法。已经在发电厂中将钴基合金感应钎焊到SUS410Cb汽轮机叶片上。选择基于Ag-Cu-Zn的材料作为夹在司太立钢板和涡轮叶片之间的填充金属。为了应对大尺寸的叶片，已经开发了使用移动感应线圈的自动钎焊工艺。在建议的钎焊过程中，有三个参数会显着影响钎焊接头的质量，包括线圈距离，移动线圈速度和感应加热功率。在这项工作中，通过使用Taguchi神经网络技术获得了最佳的钎焊参数选择。根据我们先前工作的结果，建议在750℃左右获得温度分布，以使钎焊部件具有最大的强度和高质量的钎焊。因此，评估钎焊质量的目标是该温度的函数，并定义为与最佳钎焊温度的温度偏差。通过使用建议的参数制造钎焊接头，可以对设计进行验证和确认。实验结果表明，与仅使用Taguchi方法相比，Taguchi-神经网络方法可产生更好的钎焊质量。

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