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Digitalized Evaluation of Welder Skill by using Cyclogram Characteristics

机译:利用心动图特征对焊工技能进行数字化评估

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摘要

This paper proposes a new evaluation method for welder skill in Gas Metal Arc Welding (GMAW) process in term of studying the natural hand-movement that affect the signal processing. Weld quality of GMAW generally depends on welder skill to maintain the uniform of hand movement. Therefore, the welder skill is considered as the critical point to maintain the weld quality. Hence, welding current and voltage signal could be an alternative way for monitoring and assessing the skill of welder based on the signal variation of the welding process. This research defines in two stages, first is the physical-simulation using robot welding Fanuc Arc Mate 100iB and monitoring the signal using Cyclogram technique. Second is comparing the Cyclogram characteristic of robot welding and manual welder. By using the data acquired, the characteristic of Cyclogram was analyzed by varying Torch angle change (W1) and Torch-height change (W2) to investigate the signal processing. Furthermore, the data of current and voltage were generated as a quantitative method to determine the size of Cyclogram. The results show that the method capable of differentiating the two beginner welders compare to the robot welding performance based area of Cyclogram characteristic. Finally, the Cyclogram could be a novel tool for monitoring and evaluating the welder skill with high sensitivity to detect hand motion.
机译:本文通过研究影响信号处理的自然手部运动,提出了一种新的气体金属电弧焊(GMAW)工艺焊工技能评估方法。 GMAW的焊接质量通常取决于焊工技能,以保持手部动作均匀。因此,焊工的技能被视为维持焊接质量的关键点。因此,基于焊接过程的信号变化,焊接电流和电压信号可能是监测和评估焊工技能的另一种方法。这项研究分为两个阶段,首先是使用机器人焊接Fanuc Arc Mate 100iB进行物理模拟,然后使用Cyclogram技术监测信号。其次是比较机器人焊接和手动焊机的循环图特性。通过使用获得的数据,通过改变割炬角度变化(W1)和割炬高度变化(W2)来分析Cyclogram的特性,以研究信号处理。此外,生成电流和电压数据作为定量方法来确定循环图的大小。结果表明,该方法能够区分两个初学者,与基于循环图特征的机器人焊接性能区域相比。最后,Cyclogram可能是一种监测和评估焊工技能的新颖工具,具有很高的检测手部动作的灵敏度。

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