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基于正交试验-BP神经网络的GH4169膜片微束TIG焊接工艺优化

余果,尹玉环,高嘉爽,郭立杰

余果,尹玉环,高嘉爽,郭立杰. 基于正交试验-BP神经网络的GH4169膜片微束TIG焊接工艺优化[J]. 焊接学报, 2018, 39(11): 119-123. DOI: 10.12073/j.hjxb.2018390285
引用本文: 余果,尹玉环,高嘉爽,郭立杰. 基于正交试验-BP神经网络的GH4169膜片微束TIG焊接工艺优化[J]. 焊接学报, 2018, 39(11): 119-123. DOI: 10.12073/j.hjxb.2018390285
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YU Guo, YIN Yuhuan, GAO Jiashuang, GUO Lijie. Orthogonal experiment method and BP neural networks in optimization of microbeam TIG welded GH4169[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2018, 39(11): 119-123. DOI: 10.12073/j.hjxb.2018390285
Citation: YU Guo, YIN Yuhuan, GAO Jiashuang, GUO Lijie. Orthogonal experiment method and BP neural networks in optimization of microbeam TIG welded GH4169[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2018, 39(11): 119-123. DOI: 10.12073/j.hjxb.2018390285
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基于正交试验-BP神经网络的GH4169膜片微束TIG焊接工艺优化

Orthogonal experiment method and BP neural networks in optimization of microbeam TIG welded GH4169

摘要

    摘要
  • 摘要: 将正交试验与BP神经网络结合用于0.2 mm厚GH4169膜片微束TIG焊接工艺参数优化，根据正交试验结果对神经网络模型进行训练，建立了峰值电流、基值电流、焊接速度、脉冲频率与接头直径、高度、抗拉力的BP神经网络模型. 结果表明，在BP神经网络模型预测的基础上，结合小步长搜索法获得的最佳工艺参数范围为峰值电流11.6 A±0.2 A、基值电流4.3 A±0.1 A、焊接速度4.14 mm/s±0.1 mm/s、脉冲频率52 Hz±2 Hz. 通过试验验证，4组试样各指标试验值均处于模型预测值范围内，抗拉力值均高于先期试验. 试验结果证明，该模型预测精度高，并且该工艺优化方法能有效提高实际工艺设计的效率.
    Abstract: Orthogonal experiment was combined with BP neural networks to optimize welding process parameters of 0.2 mm GH4169 diaphragms by using microbeam TIG welding echnique. The BP neural networks model was trained by the results of orthogonal experiment, and the BP neural networks model described the relationship between welding joints’ properties (diameter, height, and tensile resistance) and welding process parameters, including peak current, background current, welding velocity, and pulse frequency. The results shows that according to the orthogonal experiment and the prediction of BP neural networks model, then small-step method is used for further searching, the best parameters are found when peak current is 11.6 A±0.2 A, background current is 4.3 A±0.1 A, welding velocity is 4.14 mm/s±0.1mm/s, and pulse frequency is 52 Hz±2 Hz. The xperiment validated that the test values of four samples are within the prediction range, and the tensile force is higher than that of previous experiment. It proves that the model has a high accuracy of prediction and the method can increase the efficiency of process design availably.
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  • 收稿日期:  2017-07-30

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