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Automatic optimization of focal point position in CO{sub}2 laser welding with neural network in a focus control system

机译:焦点控制系统中神经网络激光焊接焦点位置的自动优化

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CO{sub}2 lasers are increasingly being utilized for quality welding in production. Considering the high cost of equipment, the start-up time and the set-up time should be minimized. Ideally the parameters should be set up and optimized more orless automatically.In this paper a control system is designed and built to automatically optimize the focal point position, one of the most important parameters in CO{sub}2 laser welding, in order to perform a desired deep/full penetration welding. The control system mainly consists of a multi-axis motion controller -PMAC, a light sensor -Photo Diode, a data acquisition card -DAQCard-700, and a self-learning mechanism -Neural Network.The optimization procedure starts with the welding process being carried out by continuously moving the focal point position from above a welding plate to below the plate, thus the process is ensured to be shifted from initially surface welding todeep/full penetration welding and back to surface welding again. A clear change on plasma brightness from the process is monitored by the photo diode on the front side of the plate with a viewing angle of 450 The photo diode signal is acquired with theA/D converter card and installed in a computer hard disk for later data processing. Thereafter the optimum focal point position (OFPP) is output by the self-learning mechanism - the neural network. The optimization procedure is completed with the weldingprocess being carried out by adjusting the focus of the laser beam to the OFPP.A self-learning mechanism - neural network as the essence of the control system is trained with the photo diode signals extracted from various welding processes with the changes on the laser power, translation speed, material and thickness of the plate,shielding gas type and flow rate, and welding configuration. The results of the self-learning focus control system show that the neural network is capable of optimizing the focal point position with good accuracy in CW CO{sub}2 laser welding.
机译:CO {SUB} 2激光仪越来越多地用于生产中的质量焊接。考虑到设备的高成本,应最小化启动时间和设置时间。理想情况下,应将参数设置和优化自动优化。本文设计并构建了控制系统,以自动优化焦点位置,是CO {Sub} 2激光焊接中最重要的参数之一,以便执行所需的深/完全穿透焊接。控制系统主要由多轴运动控制器-PMAC,光传感器 - 光电二极管,数据采集卡-DAQCard-700和自学机制 - 神经网络组成。优化过程从焊接过程开始通过将焦点位置从上方从焊接板上连续移动到板下方,因此确保该过程从最初的表面焊接/完全穿透焊接偏移并再次返回表面焊接。通过具有450的视角的板的前侧的光电二极管监测来自该过程的等离子体亮度的清晰改变,具有450的视角,使用TheA / D转换器卡获取光电二极管信号,并安装在计算机硬盘中以供以后的数据加工。此后,通过自学习机制 - 神经网络输出最佳焦点位置(OFPP)。通过将激光束的焦点调整到OFPP.A自学习机制 - 神经网络作为控制系统的本质,利用各种焊接过程提取的光电二极管信号训练,通过将激光束的焦点进行了焊接过程来完成焊接过程。随着激光功率,平移速度,材料和厚度的变化,屏蔽气体型和流速,以及焊接配置。自学习聚焦控制系统的结果表明,神经网络能够在CW CO {SUB} 2激光焊接中以良好的精度优化焦点位置。

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