Breakout detection for fast EDM drilling by classification of machining state graphs
Breakout detection for fast EDM drilling by classification of machining state graphs
复制标题
通过加工状态图分类进行快速电火花钻孔的断线检测
DOI：
10.1007/s00170-019-04530-3
复制
发表时间：
2019-12
影响因子：
3.4
通讯作者：
Zhao Wansheng
中科院分区：
文献类型：
--
作者：
Xia Weiwen;Li Zilun;Zhang Yaou;Zhao Wansheng
由于其加工难切削材料的能力以及与常规电火花加工（EDM）工艺相比的高加工效率，快速电火花钻孔（快速EDM钻孔）被广泛应用于模具和航空航天部件制造等行业。漏失探测是判断井眼完整性和防止井眼返冲的重要技术。本文提出了一种新的方法，称为分类的加工状态图（CMSG），在线检测漏钢事件。加工状态图（MSG）是由特征信号的近期变化模式组成的，当漏钢发生时，这些特征信号会发生突变。然后，通过对实时MSG进行分类来解决检测问题。本文选取工具电极的正常放电率、短路率和伺服进给率作为特征信号。为了提高检测精度和减少判决延迟，对信号进行了预处理。建立了味精的分类模型。为了简化建模过程，提高检测器的泛化能力，采用模式识别算法作为分类的核心算法。通过离线训练得到检测器的分类模型，并加载到控制系统的启动中进行在线检测。实验结果证明了该方法的有效性。
Due to its capability of machining hard-to-cut materials as well as its high machining efficiency as compared with conventional electrical discharge machining (EDM) processes, fast electrical discharge drilling (fast EDM drilling) is widely applied in industries such as mold and die as well as aerospace component manufacturing. The breakout detection is an essential technique for hole completion judgment and back-strike prevention. This paper presents a novel method, called classification of machining state graphs (CMSG), for online detection of breakout events. A machining state graph (MSG) is formed by the recent changing patterns of feature signals, which would change abruptly when breakout happens. Then, the detection problem is solved by classification of real-time MSGs. In this paper, the feature signals were selected to be normal discharge ratio, short circuit ratio, and servo feedrate of the tool electrode. The signals were preprocessed in order to improve the detection accuracy and reduce the decision lag. A classification model was built to classify MSGs. To simplify the modeling process and improve the generalization ability of the detector, a pattern recognition (PR) algorithm was adopted as the core algorithm for classification. The classification model of the detector was acquired through offline training and loaded on the start-up of the control system for online detection. Performance judgment criteria were proposed and experimental results proved the high performance of the proposed method.
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DOI：
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发表时间：
1973
期刊：
--
影响因子：
--
作者：
K. Lu
通讯作者：
K. Lu
DOI：
10.1016/j.isprsjprs.2011.11.002
发表时间：
2012-01-01
影响因子：
12.7
作者：
Rodriguez-Galiano, V. F.;Ghimire, B.;Rigol-Sanchez, J. P.
通讯作者：
Rigol-Sanchez, J. P.
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：
10.1007/978-0-387-77501-2_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
DOI：
10.1007/s00170-011-3493-1
发表时间：
2011-07
期刊：
The International Journal of Advanced Manufacturing Technology
影响因子：
--
作者：
R. Ji;Yonghong Liu;Yanzhen Zhang;Bao-ping Cai;J. Ma;Xiaopeng Li
通讯作者：
R. Ji;Yonghong Liu;Yanzhen Zhang;Bao-ping Cai;J. Ma;Xiaopeng Li