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Modeling of Weld Lap-shear strength for Laser Transmission Welding of Thermoplastic using Artificial Neural Network

机译:使用人工神经网络对热塑性塑料激光传输焊接焊接膝剪力强度的建模

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Laser welding of plastic materials has a wide range of applications in the packaging, medical, electronics and automobile industries provided it can predict high quality welds compared with other joining methods. Laser welding process parameters can affect the quality of welds. In this paper, Artificial Neural Network (ANN) is used to model the effects of laser power, welding speed, clamp pressure and stand-off distance on weld lap-shear strength in laser transmission welding (LTW) of acrylic (polymathy methacrylate). A set of experimental data on diode laser weld lapshear strengths was used to train and test the ANN from which the neurons relations were gradually extracted to develop a model. The developed ANN model can be used for the analysis and prediction of the complex relationships between the above mentioned process parameters and weld lap-shear strength. The results indicated that increase in laser power and clamp pressure increases the weld lap-shear strength whereas welding speed and stand -off distance had a decreasing affect on shear strength at high value.
机译:塑料材料的激光焊接在包装,医疗,电子和汽车行业中具有广泛的应用,提供了与其他连接方法相比它可以预测高质量的焊缝。激光焊接工艺参数会影响焊缝的质量。本文中,人工神经网络(ANN)用于模拟激光功率,焊接速度,钳位压力和脱扣距离丙烯酸(多种多数甲基丙烯酸酯)激光透射焊接(LTW)中焊接圈剪切强度的影响。关于二极管激光焊接Lapshear强度的一组实验数据用于培训和测试神经元关系逐渐提取的ANN以开发模型。开发的ANN模型可用于分析和预测上述工艺参数和焊接圈剪强之间的复杂关系。结果表明,激光功率和钳位压力的增加会增加焊接搭接剪切强度,而焊接速度和支架-OFF距离在高值下对剪切强度的影响降低。

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