Thompson Martinez Rogfel, Alvarez Bestard Guillermo, Absi Alfaro Sadek C
Postgraduate Program in Mechatronic System (PPMEC), Student grant CAPES, University of Brasilia, Brazil.
Electronics Engineering, Campus Gama, University of Brasilia, Brazil.
Data Brief. 2021 Jan 29;35:106790. doi: 10.1016/j.dib.2021.106790. eCollection 2021 Apr.
The dataset was collected from experiments using the gas metal arc welding (GMAW) process. The experiments were planned with Central Composite Design to obtain a greater variety of data. This variability helps to develop a predictive model more generalistic with machine learning techniques. It was collected welding arc images and weld bead geometry images. Welding arc images were processed with a deep learning technique to detect drop detachment and short circuit transfer mode. These detections were useful to calc drop detachment frequency, short circuit frequency, and molten volume in every moment of GMAW process time. It was obtained the weld bead geometry parameters by process time too. All these data, joining input parameters were correlated, resulting in the datasets shown in this article.
该数据集是从使用气体金属电弧焊（GMAW）工艺的实验中收集的。实验采用中心复合设计进行规划，以获取更多种类的数据。这种变异性有助于利用机器学习技术开发更具通用性的预测模型。收集了焊接电弧图像和焊缝几何形状图像。利用深度学习技术对焊接电弧图像进行处理，以检测熔滴脱离和短路过渡模式。这些检测对于计算GMAW工艺时间每个时刻的熔滴脱离频率、短路频率和熔化体积很有用。通过工艺时间也获得了焊缝几何形状参数。所有这些数据与输入参数相关联，从而得到了本文所示的数据集。
