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首页> 外文会议>IFAC Conference on Manufacturing Modelling, Management, and Control >Using Data Mining Techniques to Investigate the Correlation between Surface Cracks and Flange Lengths in Deep Drawn Sheet Metals
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Using Data Mining Techniques to Investigate the Correlation between Surface Cracks and Flange Lengths in Deep Drawn Sheet Metals

机译：使用数据挖掘技术来研究深拉纸金属表面裂缝与法兰长度之间的相关性

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摘要

A problem during manufacturing of body parts in the automobile industry is the frequent occurrence of surface cracks in sheet metal cold forming processes. In this paper, we compare different supervised data mining techniques to predict cracks in deep-drawn sheet metals using their flange lengths as correlating features. Inline images of sheet metals are taken during the deep drawing process through cameras that are installed in every stage of a six-stage press line. A commercial software is used to label the images as defective and non-defective. Additionally, flange lengths, which generally correlate with forces set at the machines, are measured along the periphery of the sheets. The results are promising, as the models achieved satisfactory accuracy rates, albeit with some margin for improvement. This paper aims to choose a binary classification algorithm that is best suitable for the given dataset. Furthermore, the results provide an insight on the correlation of flange lengths with the occurrence of cracks, which can be used for online parameter adjustments of the machines in the future.
机译：在汽车工业中制造身体部位期间的问题是金属板冷成型工艺中频繁发生的表面裂缝。在本文中，我们比较了不同的监督数据挖掘技术，以使用它们的法兰长度预测深拉纸金属中的裂缝作为相关性。通过安装在六级按线的每个阶段的摄像机期间拍摄纸张金属的内联图像。商业软件用于将图像标记为有缺陷和不缺陷。另外，通常与机器上设定的力相关的法兰长度沿着片材的周边测量。结果是有前途的，因为模型实现了令人满意的精度率，尽管有一些改进的余量。本文旨在选择最适合给定数据集的二进制分类算法。此外，结果提供了对凸缘长度随着裂缝的相关性的相关性，可用于将来的机器的在线参数调整。

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