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首页> 外文期刊>Journal of Manufacturing Processes >Process optimization and weld forming control based on GA-BP algorithm for riveting-welding hybrid bonding between magnesium and CFRP
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Process optimization and weld forming control based on GA-BP algorithm for riveting-welding hybrid bonding between magnesium and CFRP

机译：基于GA-BP算法的方法优化和焊接成型控制磁化镁和CFRP之间的铆接杂交键合

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摘要

In the process of riveting-welding hybrid bonding for magnesium and the carbon fiber reinforced polymer (CFRP), the weld morphology including weld penetration and weld width makes an important influence on the property of welded joint. In order to understand the optimal weld morphology, the relationship between the morphology coefficient phi (the ratio of penetration depth to width) and the joint strength was established under a series groups of welding parameters. In addition, in order to obtain ideal appearance and optimized welding parameters, a BP neural network model optimized by genetic algorithm is established, in which the weld penetration and weld width are taken as the input units of neural network, and the welding process parameters such as welding speed, laser power, and laser defocused amount are regarded as the output units of the neural network. The results show that the mean absolute percentage error (MAPE) of each group of data did not exceed 3% while minimum mean square error (MSE) reached 0.0097, which can ensure the production of welded joints with ideal uniform appearance and mechanical properties. This research provides a new method for control of welding quality in the process of riveting-welding hybrid bonding for magnesium and CFRP.
机译：在用于镁和碳纤维增强聚合物（CFRP）的铆接焊接杂交键合的过程中，包括焊接渗透和焊接宽度的焊接形态对焊接接头的性能产生了重要影响。为了理解最佳焊接形态，在串联焊接参数组中建立了形态系数PHI（穿透深度与宽度的比率）与关节强度之间的关系。另外，为了获得理想的外观和优化的焊接参数，建立了通过遗传算法优化的BP神经网络模型，其中焊接穿透和焊接宽度被用作神经网络的输入单元，以及焊接工艺参数作为焊接速度，激光功率和激光散焦量被视为神经网络的输出单元。结果表明，每组数据的平均绝对百分比误差（MAPE）不超过3％，而最小均方误差（MSE）达到0.0097，可确保具有理想均匀外观和机械性能的焊接接头。本研究提供了一种新方法，用于控制摩擦锰杂交粘合过程中的焊接质量。

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