通过金属棒的温度曲线分析进行生产质量控制:钢铁部件使用案例

Paolo Catti , Michalis Ntoulmperis , Vittoria Medici , Milena Martarelli , Nicola Paone , Wilhelm van de Kamp , Nikolaos Nikolakis , Kosmas Alexopoulos
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引用次数: 0

摘要

金属棒热成型过程中的不均匀加热可能会影响其直线度。本研究提出了一种红外无损检测系统,用于在运行时获取钢材温度曲线,该曲线应与直线度偏差相关联。此外,机器学习算法可检测异常值,识别金属上的氧化物,进而与工艺参数相关联。这样就可以根据历史曲线主动调整温度,降低风险。所提出的方法已在钢铁行业的一个使用案例中进行了测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quality control in manufacturing through temperature profile analysis of metal bars: A steel parts use case
Non-uniform heating during metal bar hot forming may impact its straightness. In this study, an infrared non-destructive inspection system is proposed to acquire steel temperature profiles in runtime which should correlate to straightness deviations. Additionally, a machine learning algorithm detects outliers to identify oxides on the metal, which in turn is correlated to process parameters. This allows for proactive temperature adjustment to mitigate the risk based on historical profiles. The proposed approach has been tested in a use case coming from the steel industry.
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CiteScore
3.80
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