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Optimizing Steel Coil Production: An Enhanced Inspection System Based on Anomaly Detection Techniques

机译:优化钢卷生产:基于异常检测技术的增强检查系统

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Endless strip generation is the key to productivity and quality in several types of steel coil production lines. Coil-tocoil joining by means of welding machines provides such a strip. Since the joint is the weakest area of the strip, its quality must be assessed for the production line to accept it. Therefore, it is necessary to inspect the quality of the weld in the welding-cycle time. Based on our knowledge acquired in the previous development of quality assessment prototypes for steel strips, we present in this article an improved inspection system to detect defective resistance seam welds based on anomaly detection techniques. This system does not rely on the weld classifications done in production lines based on welding control programs. Therefore, it is immune to the influence of both incorrectly configured welding control programs and chemical composition variations from coil to coil of the same steel grade. Tuning the inspection system required a fully experimental design, which would have taken several months using a conventional computer. For this reason, the high-performance computing (HPC) facilities at the Edinburgh Parallel Computing Center were used to cut down the tuning time.
机译:在多种类型的钢卷生产线中,无休止的钢带生成是提高生产率和质量的关键。借助于焊接机的线圈-线圈连接提供了这种条带。由于接头是带材最薄弱的区域,因此必须评估其质量,生产线才能接受。因此,有必要在焊接周期内检查焊接质量。基于在以前的钢带质量评估原型开发中获得的知识,我们在本文中提出了一种改进的检测系统,用于基于异常检测技术来检测有缺陷的电阻缝焊缝。该系统不依赖基于焊接控制程序的生产线中的焊接分类。因此,它不受错误配置的焊接控制程序和相同钢种的不同卷材化学成分变化的影响。调整检查系统需要完全的实验设计,而使用传统计算机可能要花几个月的时间。因此,爱丁堡并行计算中心的高性能计算(HPC)设施被用于缩短调试时间。

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