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首页> 外文期刊>Journal of manufacturing science and engineering: Transactions of the ASME >Ultrasonic Welding of Magnesium-Titanium Dissimilar Metals: A Study on Influences of Welding Parameters on Mechanical Property by Experimentation and Artificial Neural Network
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Ultrasonic Welding of Magnesium-Titanium Dissimilar Metals: A Study on Influences of Welding Parameters on Mechanical Property by Experimentation and Artificial Neural Network

机译：镁钛不同金属超声波焊接：试验和人工神经网络焊接参数对力学性能影响的研究

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摘要

The advancement in the application of light alloys such as magnesium and titanium is closely related to the state of the art of joining them. As a new type of solid-phase welding, ultrasonic spot welding is an effective way to achieve joints of high strength. In this paper, ultrasonic welding was carried out on magnesium-titanium dissimilar alloys to investigate the influences of welding parameters on joint strength. The analysis of variance was adopted to study the weight of each welding parameter and their interactions. The artificial neural network (ANN) was used to predict joint strength. Results show that in ultrasonic welding of magnesium and titanium alloys, clamping force is the most significant factor, followed by vibration time and vibration amplitude; the interactions between vibration time and vibration amplitude, and between vibration amplitude and clamping force also significantly impact the strength. By using the artificial neural network, test data were trained to obtain a high precision network, which was used to predict the variations of joint strength under different parameters. The analytical model predicts that with the increase in vibration time, the increase in optimal joint strength is limited, but the range of welding parameters to obtain a higher joint strength increases significantly; the minimum joint strength increases as well; and the optimal vibration amplitude expands gradually and reaches the maximum when the vibration time is 1000 ms, then shifts toward the low end gradually.
机译：镁和钛等轻合金施加的进步与加入它们的领域的状态密切相关。作为一种新型的固相焊接，超声波点焊是实现高强度关节的有效方法。本文采用超声波焊接在镁 - 钛异种合金上进行，研究焊接参数对关节强度的影响。采用对方差分析来研究每种焊接参数的重量及其相互作用。人工神经网络（ANN）用于预测关节强度。结果表明，在镁和钛合金的超声波焊接中，夹紧力是最显着的因素，其次是振动时间和振动幅度;振动时间和振动幅度之间的相互作用，振动幅度和夹紧力也显着影响强度。通过使用人工神经网络，训练测试数据以获得高精度网络，用于预测不同参数下的关节强度的变化。分析模型预测，随着振动时间的增加，最佳关节强度的增加是有限的，但焊接参数范围以获得更高的关节强度显着增加;最小关节强度也增加;并且最佳振动幅度逐渐膨胀并且当振动时间为1000ms时达到最大值，然后逐渐向低端转移。

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