This paper presents an innovative investigation on prototyping a digital twin (DT) as the platform for human-robot interactive welding and welder behavior analysis. This human-robot interaction (HRI) working style helps to enhance human users' operational productivity and comfort; while data-driven welder behavior analysis benefits to further novice welder training. This HRI system includes three modules: 1) a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles; 2) a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite; 3) a DT system that is developed based on virtual reality (VR) as a digital replica of the physical human-robot interactive welding environment. The DT system bridges a human user and robot through a bi-directional information flow: a) transmitting demonstrated welding operations in VR to the robot in the physical environment; b) displaying the physical welding scenes to human users in VR. Compared to existing DT systems reported in the literatures, the developed one provides better capability in engaging human users in interacting with welding scenes, through an augmented VR. To verify the effectiveness, six welders, skilled with certain manual welding training and unskilled without any training, tested the system by completing the same welding job; three skilled welders produce satisfied welded workpieces, while the other three unskilled do not. A data-driven approach as a combination of fast Fourier transform (FFT), principal component analysis (PCA), and support vector machine (SVM) is developed to analyze their behaviors. Given an operation sequence, i.e., motion speed sequence of the welding torch, frequency features are firstly extracted by FFT and then reduced in dimension through PCA, which are finally routed into SVM for classification. The trained model demonstrates a 94.44% classification accuracy in the testing dataset. The successful pattern recognition in skilled welder operations should benefit to accelerate novice welder training.

中文翻译：

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数字双胞胎用于人机交互焊接和焊工行为分析

本文提出了一种创新的研究，该研究涉及将数字孪生（DT）原型制作为人机交互交互式焊接和焊工行为分析的平台。这种人机交互（HRI）的工作方式有助于提高人类用户的操作效率和舒适度；数据驱动的焊工行为分析有助于进一步的新手焊工培训。该HRI系统包括三个模块：1）人类用户，其通过运动跟踪的手柄记录的操作在现场演示了焊接操作；2）机器人执行演示的焊接操作以现场完成物理焊接任务；3）基于虚拟现实（VR）开发的DT系统，作为物理人机交互交互式焊接环境的数字副本。DT系统通过双向信息流桥接人类用户和机器人：a）将VR中演示的焊接操作传输到物理环境中的机器人；b）在VR中向人类用户显示物理焊接场景。与文献中报道的现有DT系统相比，开发的系统通过增强的VR提供了更好的功能，可以吸引人类用户与焊接场景进行交互。为了验证有效性，六名焊工，他们受过一定的手工焊接培训，并且没有任何培训，没有任何技能，他们通过完成相同的焊接工作对系统进行了测试；三名熟练的焊工生产出满意的焊接工件，而其他三名不熟练的焊工则没有。一种数据驱动的方法，结合了快速傅立叶变换（FFT），主成分分析（PCA），并开发了支持向量机（SVM）来分析其行为。在给定操作序列（即焊枪的运动速度序列）的情况下，首先通过FFT提取频率特征，然后通过PCA缩小维数，最后将其路由到SVM中进行分类。经过训练的模型在测试数据集中显示出94.44％的分类准确性。在熟练的焊工操作中成功识别出模式应有助于加速新手焊工培训。