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Fusion zone microstructure image dataset of the flux-cored and shielded metal arc welding processes

机译:熔接区和屏蔽金属电弧焊接工艺的融合区微结构图像数据集

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摘要

This paper presents high quality (2048 × 1532 pixels) Light Microscope steel images sampled from the welding fusion zone. The microstructure images were acquired from the Design of Experiments (22 full factorial design) planned to compare two different arc welding processes at two different arc welding energies [1]. The 400 raw images appear as they were captured by the microscope and they are categorized into four groups: that acquired from the Flux Cored Arc Welding process and that acquired from the Shielded Metal Arc Welding process; both of them run for high and low levels of arc energy. For the Flux Cored Arc Welding process, ASME SFA 5.20 E71T-5C(M) tubular wire was used, with a nominal diameter of 1.2 mm. For the Shielded Metal Arc Welding process, AWS E7018 coated electrodes were used, with nominal diameters of 3.25 mm (for the low energy level) and 5.00 mm (for the high energy level). The deposition of the beads was run on AISI 1010 steel plates in the flat position (bead-on-plate). Different proportions of primary grain boundary ferrite; polygonal ferrite; acicular ferrite; nonaligned side-plate ferrite and aligned side-plate ferrite can be observed in each image. This image dataset is ready to visual and automatic microstructure recognition and quantification. It can be a useful resource for computational intelligence research teams, e.g. [2], by offering images for handling as filtering, feature extraction, training, validation and testing in pattern recognition and machine learning techniques.
机译:本文介绍了从焊接融合区采样的高质量(2048×1532像素)光学显微镜钢图像。从计划的设计(22个完整因子设计)中获取微结构图像,以比较两个不同的电弧焊接能量的两个不同的电弧焊接过程[1]。将通过显微镜捕获400个原始图像,并将其分为四组:从磁通芯弧焊过程中获取的,从屏蔽金属电弧焊接过程中获取。它们都运行了高低水平的电弧能量。对于磁通芯弧焊工艺,使用ASME SFA 5.20 E71T-5C(M)管状线,标称直径为1.2mm。对于屏蔽金属电弧焊接工艺,使用AWS E7018涂层电极,标称直径为3.25mm(用于低能平)和5.00mm(用于高能平)。在平坦位置(凸缘板)的AISI 1010钢板上运行珠子的沉积。不同比例的原发性晶界铁氧体;多晶铁氧体;针状铁氧体;在每个图像中可以观察到非基准侧板铁氧体和对准的侧板铁氧体。此图像数据集已准备好可视化和自动组织识别和量化。它可以是计算智能研究团队的有用资源,例如, [2],通过为模式识别和机器学习技术提供作为过滤,特征提取,培训,验证和测试的图像来处理图像。

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