Random forest-based real-time defect detection of Al alloy in robotic arc welding using optical spectrum
Random forest-based real-time defect detection of Al alloy in robotic arc welding using optical spectrum
复制标题
基于随机森林的机器人电弧焊铝合金光谱实时缺陷检测
DOI：
10.1016/j.jmapro.2019.04.023
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发表时间：
2019-06
影响因子：
6.2
通讯作者：
Wen Guangrui
中科院分区：
文献类型：
--
作者：
Zhang Zhifen;Yang Zhe;Ren Wenjing;Wen Guangrui
铝合金弧焊是航空航天、核电、船舶等关键零部件制造的主要技术。由于焊接缺陷的复杂性和多样性，焊接缺陷的实时检测仍然具有挑战性。弧光发射是弧焊过程中产生的关键信息。然而，如何从高维的电弧谱中提取有效的光谱特征，是提高缺陷识别准确率的关键。提出了一种基于随机森林和电弧谱的机器人弧焊铝合金缺陷在线检测方法。首先对电弧谱进行预处理，然后提取50个特征。在此基础上，提出了基于平均降低准确率和平均降低基尼系数的特征重要度量化指标，以减少特征冗余度。选取了6个光谱特征，并根据结构模式进行了分析。在此基础上，建立了基于随机森林和最优特征子集的缺陷识别模型。与径向基函数神经网络和BP神经网络模型相比，该模型能更好地识别未穿透、穿透和孔洞三种典型缺陷。本文对光学信息数据挖掘和智能制造具有一定的指导意义。
Aluminum alloy of arc welding is the main technology for the key components manufacturing in aerospace, nuclear power, ship and so on. Real-time weld defects detection is still challenging due to the complexity and diversity of weld defects. Arc optical Spectroscopy emission is the key information generated during arc welding process. However, how to select the effective spectrum feature from high dimension of arc spectrum is crucial for improving the accuracy of defects recognition. This paper proposed an on-line defects detection method for aluminum alloy in robotic arc welding based on random forest and arc spectrum. Firstly, preprocessing of arc spectrum was carried out before 50 features were extracted. Then, a quantitative index of feature importance is proposed based on mean decrease accuracy and mean decrease Gini to reduce the feature redundancy. Six spectral features were selected and analyzed in terms of the construction pattern. Furthermore, the defect identification model was established based on random forest and the optimal feature subset. Comparing with RBF and BP models, it can achieve better performance in identifying three typical defects, including incomplete penetration, burn-through and porosity. This paper can provide some guidance for data mining of optical information and intelligent manufacturing.
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影响因子：
6.2
作者：
Yukang Liu;Yuming Zhang
通讯作者：
Yukang Liu;Yuming Zhang
DOI：
10.1007/978-1-4419-9326-7_5
发表时间：
2012-01-01
期刊：
ENSEMBLE MACHINE LEARNING: METHODS AND APPLICATIONS
影响因子：
--
作者：
Cutler, Adele;Cutler, D. Richard;Stevens, John R.
通讯作者：
Stevens, John R.
DOI：
10.1109/tmech.2014.2363050
发表时间：
2015-06
期刊：
IEEE/ASME Transactions on Mechatronics
影响因子：
--
作者：
Yukang Liu;Yuming Zhang
通讯作者：
Yukang Liu;Yuming Zhang
DOI：
10.1007/978-0-387-77501-2_5
发表时间：
2020
期刊：
Statistical Learning from a Regression Perspective
影响因子：
--
作者：
Richard A. Berk
通讯作者：
Richard A. Berk
影响因子：
4.6
作者：
Harooni, Masoud;Carlson, Blair;Kovacevic, Radovan
通讯作者：
Kovacevic, Radovan