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基于机器学习的CF/PEEK复合材料和7075铝合金电阻植入焊接参数优化

Parameter optimization of CF/PEEK composites and 7075 aluminum alloy by resistance implant welding based on machine learning

  • 摘要: 文中以碳纤维/聚醚醚酮 (CF/PEEK) 复合材料和7075铝合金为研究对象,进行电阻植入焊接工艺参数研究. 首先采用正交试验设计进行了电阻植入焊接,确定了影响焊接接头单搭接拉伸剪切强度的4个主要工艺参数:焊接电流、焊接时间、焊接压力和夹持距离;然后采用 BP 神经网络和遗传算法相结合,优化焊接工艺参数组合.结果表明,BP 神经网络模型能够描述焊接接头单搭接拉伸剪切强度与焊接工艺参数之间复杂映射关系,预测平均相对误差为3.62%. 焊接电流15.05 A、焊接时间155 s、焊接压力0.99 MPa、夹持距离2.34 mm为最优电阻植入焊接工艺参数,最大焊接接头单搭接拉伸剪切强度为19.26 MPa,较优化前提高了31.2%.

     

    Abstract: In this paper, the carbon fiber/polyetheretherketone (CF/PEEK) composites and 7075 aluminum alloy were taken as research objects, and the resistance implant welding process parameters were investigated. Firstly, the resistance implant welding was carried out by using an orthogonal design of experiments. The welding current, welding time, welding pressure, and clamping distance were identified as four main process parameters affecting the single lap shear strength of welded joints. Then, a BP neural network and genetic algorithm were employed to optimize the welding process parameter combinations. The results indicate that the BP neural network can describe the complex mapping relationship between the single lap shear strength of welded joints and welding process parameters, and the predicted mean relative error is 3.62%. The welding current of 15.05 A, welding time of 155 s, welding pressure of 0.99 MPa, and clamping distance of 2.34 mm are the optimal resistance implant welding parameters, and the maximum single lap shear strength of the welded joint is 19.26 MPa, which is 31.2% higher than that before optimization.

     

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