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大功率光纤激光焊熔透状态模糊聚类识别方法

高向东, 梁剑斌, 刘桂谦, 张艳喜

高向东, 梁剑斌, 刘桂谦, 张艳喜. 大功率光纤激光焊熔透状态模糊聚类识别方法[J]. 焊接学报, 2017, 38(5): 22-25. DOI: 10.12073/j.hjxb.20170505
引用本文: 高向东, 梁剑斌, 刘桂谦, 张艳喜. 大功率光纤激光焊熔透状态模糊聚类识别方法[J]. 焊接学报, 2017, 38(5): 22-25. DOI: 10.12073/j.hjxb.20170505
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GAO Xiangdong, LIANG Jianbin, LIU Guiqian, ZHANG Yanxi. Identification of high-power fiber laser welding penetration based on fuzzy clustering algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2017, 38(5): 22-25. DOI: 10.12073/j.hjxb.20170505
Citation: GAO Xiangdong, LIANG Jianbin, LIU Guiqian, ZHANG Yanxi. Identification of high-power fiber laser welding penetration based on fuzzy clustering algorithm[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2017, 38(5): 22-25. DOI: 10.12073/j.hjxb.20170505
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大功率光纤激光焊熔透状态模糊聚类识别方法

  • 广东工业大学 机电工程学院, 广州 510006

基金项目: 国家自然科学基金资助项目（51675104）；广东省科技发展专项资金资助项目（2016A010102015）；广州市科技计划资助项目（201510010089）

Identification of high-power fiber laser welding penetration based on fuzzy clustering algorithm

  • School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China

摘要

    摘要
  • 摘要: 熔透是评价激光焊接质量的重要因素，但在焊接过程中难以直接检测熔透状态.以大功率光纤激光焊接304不锈钢紧密对接焊缝为试验对象，研究一种基于熔池红外热像的熔透状态识别方法.采用红外摄像机摄取焊接区域熔池动态图像，提取熔池特征量，应用模糊聚类算法分析熔池特征量与熔透状态之间的关系，使用模糊C-均值（FCM）和Gustafson-Kessel（GK）两种模糊聚类法建立熔透状态识别模型.激光焊接试验结果表明，熔池表征与熔透状态之间存在密切关联，通过GK模糊聚类算法建立的模型对熔透状态能达到78%以上的识别率，为大功率光纤激光焊接过程熔透状态的识别和控制提供试验依据.
    Abstract: Weld penetration is an important standard which evaluates the laser welding quality. However, it is difficult to directly measure the weld penetration during laser welding. Controlling the weld penetration can only be carried out through an indirect estimation of weld penetration by other sensing signals. A novel approach of weld penetration status identification based on molten pool infrared image processing is proposed during butt joint high-power fiber laser welding of type 304 austenitic stainless steel plates with laser power of 6 kW. An infrared high speed camera was used to capture the dynamic infrared images of molten pool. Characteristic parameters of a molten pool image were extracted by image processing, and fuzzy clustering algorithm was used to explore the relation between the molten pool parameters and the weld penetration status. An identification model of weld penetration status was established by using the fuzzy C-mean (FCM) and Gustafson-Kessel (GK) algorithm, respectively. Welding experiments confirmed that the molten pool surface characteristic had a close inherent relationship with the weld penetration, and the weld penetration status could be estimated by the proposed GK fuzzy clustering identification model, which could provide an experimental basis for detection and control of weld penetration.
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    • 参考文献(6)
  • [1] Gao Xiangdong, You Deyong, Katayama Seiji. Seam tracking monitoring based on adaptive kalman filter embedded elman neural network during high power fiber laser welding[J]. IEEE Transactions on Industrial Electronics, 2012, 59 (11):4315-4325.
    [2] 闫志鸿, 张广军, 高洪明, 等. P-GMAW正面熔透特征量及视觉传感方位[J]. 焊接学报, 2006, 27(8): 47-50. Yan Zhihong, Zhang Guangjun, Gao Hongming, et al. Visual sensing of P-GMAW penetration information from multiple view angles[J]. Transactions of the China Welding Institution, 2006, 27(8): 47-50.
    [3] Huang W, Kovacevic R. A neural network and multiple regression method for the characterization of the depth of weld penetration in laser welding based on acoustic signatures[J]. Journal of Intelligent Manufacturing, 2011, 22(2): 131-143.
    [4] Chen B, Wang J F, Chen S B. Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion[J]. International Journal of Advanced Manufacturing Technology, 2010, 48(1): 83-94.
    [5] Sun H J, Wang S R, Jiang Q S, et al. FCM-based model selection algorithms for determining the number of clusters[J]. Pattern Recognition, 2004, 37(10): 2027-2037.
    [6] Kim Y I, Kim D W, Lee D, et al. A cluster validation index for GK cluster analysis based on relative degree of sharing[J]. Inforamtion Sciences, 2004, 168(1-4): 225-242.
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  • 收稿日期:  2015-07-04

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