Integrating object detection and image segmentation for detecting the tool wear area on stitched image.
Integrating object detection and image segmentation for detecting the tool wear area on stitched image.
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整合目标检测和图像分割以检测拼接图像上的刀具磨损区域
DOI：
10.1038/s41598-021-97610-y
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发表时间：
2021-10-07
影响因子：
4.6
通讯作者：
Young HT
中科院分区：
文献类型：
--
作者：
Lin WJ;Chen JW;Jhuang JP;Tsai MS;Hung CL;Li KM;Young HT
侧面磨损是端铣加工过程中最常见的磨损形式。然而，检测侧面磨损的过程很繁琐。为了实现对螺旋端铣刀侧面磨损区域的全面自动化检测，本研究提出了一种将模板匹配和深度学习技术相结合的新型侧面磨损检测方法，将曲面图像扩展为全景图像，这样在不选择刀具图像特定位置的情况下更便于检测侧面磨损区域。采用You Only Look Once v4模型自动检测刀尖范围。然后，使用流行的分割模型，即U - Net、Segnet和自动编码器来提取刀具侧面磨损区域。为了评估这些模型之间的分割性能，U - Net模型获得了最佳的最大骰子系数得分，为0.93。此外，U - Net模型预测的磨损区域在趋势图中呈现，可根据刀具磨损曲线确定刀具更换次数。总体而言，实验表明所提出的方法能够有效提取螺旋刀具的刀具磨损区域。通过开发的系统，用户可以在刀具严重磨损之前获取有关刀具的详细信息，以便提前更换刀具。
Flank wear is the most common wear that happens in the end milling process. However, the process of detecting the flank wear is cumbersome. To achieve comprehensively automatic detecting the flank wear area of the spiral end milling cutter, this study proposed a novel flank wear detection method of combining the template matching and deep learning techniques to expand the curved surface images into panorama images, which is more available to detect the flank wear areas without choosing a specific position of cutting tool image. You Only Look Once v4 model was employed to automatically detect the range of cutting tips. Then, popular segmentation models, namely, U-Net, Segnet and Autoencoder were used to extract the areas of the tool flank wear. To evaluate the segmenting performance among these models, U-Net model obtained the best maximum dice coefficient score with 0.93. Moreover, the predicting wear areas of the U-Net model is presented in the trend figure, which can determine the times of the tool change depend on the curve of the tool wear. Overall, the experiments have shown that the proposed methods can effectively extract the tool wear regions of the spiral cutting tool. With the developed system, users can obtain detailed information about the cutting tool before being worn severely to change the cutting tools in advance.
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基于APSO-LS-SVM方法的钻井过程刀具磨损监测研究
DOI：
10.1007/s00170-020-05549-7
发表时间：
2020-06-04
影响因子：
3.4
作者：
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通讯作者：
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Moon, Hyeonjoon