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盘型激光焊接状态多传感信息融合分析

高向东, 刘英英, 萧振林, 陈晓辉

高向东, 刘英英, 萧振林, 陈晓辉. 盘型激光焊接状态多传感信息融合分析[J]. 焊接学报, 2015, 36(12): 31-34,88.
引用本文: 高向东, 刘英英, 萧振林, 陈晓辉. 盘型激光焊接状态多传感信息融合分析[J]. 焊接学报, 2015, 36(12): 31-34,88.
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GAO Xiangdong, LIU Yingying, XIAO Zhenlin, CHEN Xiaohui. Analysis of high-power disk laser welding status based on multi-sensor information fusion[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(12): 31-34,88.
Citation: GAO Xiangdong, LIU Yingying, XIAO Zhenlin, CHEN Xiaohui. Analysis of high-power disk laser welding status based on multi-sensor information fusion[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(12): 31-34,88.
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盘型激光焊接状态多传感信息融合分析

  • 1.

    广东工业大学 机电工程学院, 广州 510006

  • 2.

    广州番禺高勋染整设备制造有限公司, 广州 511400

基金项目: 国家自然科学基金资助项目(51175095) ;广东省协同创新与平台环境建设专项资助项目(2015B090901013);广东省重大科技专项资助项目(2014B090921008);广州市科学研究专项资助项目(1563000554);佛山市科技创新专项资助项目(2014AG10015)

Analysis of high-power disk laser welding status based on multi-sensor information fusion

  • 1.

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

  • 2.

    Guangzhou Panyu Gofront Dyeing & Finishing Machinery Manufacturer Ltd., Guangzhou 511400, China

摘要

    摘要
  • 摘要: 针对大功率盘型激光焊接状态,研究一种基于支持向量机的多传感信息融合分析方法. 使用紫外、可视和红外波段的两个高速摄像机同时获取激光焊接过程中金属蒸气、飞溅和熔池动态图像. 通过模式识别技术提取焊接过程多传感信息特征及进行数据主成分特征分析,并以焊缝宽度变化作为衡量焊接状态稳定性的参数. 运用支持向量机融合各特征,通过网格搜索和粒子群算法优化支持向量机参数,建立基于支持向量机的多传感信息融合模型. 结果表明,支持向量机多传感信息融合方法能够有效预测焊缝宽度变化趋势,为大功率盘型激光焊接状态的实时监控提供试验依据.
    Abstract: A multi-sensor information fusion method based on support vector machine was studied to analyze the high-power disk laser welding status. During high-power disk laser welding, the metallic plume, spatters and molten pool are important phenomena which are related to the welding quality. An ultraviolet and visible sensitive video camera was used to capture the metallic plume and spatter dynamic images, and another infrared sensitive video camera was used to capture the molten pool images. The image processing and pattern recognition technologies were applied to extract the welding characteristics information and analyze the principal components. Weld bead width was used as a characteristic parameter that reflects the welding stability. After data normalization and characteristic analysis, the multi-sensor information was fused by the support vector machine, and the grid search method and particle swarm optimization were used to optimize the experimental parameters of support vector machine. Finally a fusion model based on support vector machine was established to estimate the weld bead width. Experimental results showed that the multi-sensor information fusion based on support vector machine could effectively predict the weld bead width, thus providing an experimental evidence for monitoring the high-power disk laser welding status.
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    • 参考文献(8)
  • [1] 高向东, 刘永华. 激光焊接不锈钢微间隙焊缝偏差小波变换检测法[J]. 焊接学报, 2015, 36(3): 1-4. Gao Xiangdong, Liu Yonghua. Detection of micro-gap seam offset based on wavelet transformation during high-power fiber laser welding[J]. Transactions of the China Welding Institution. 2015, 36(3): 1-4.[ZK)]
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    [4] 申俊琦, 胡绳荪, 冯胜强, 等. 基于支持向量机的焊缝尺寸预测[J]. 焊接学报, 2010, 31(2): 103-106. Shen Junqi, Hu Shengsun, Feng Shengqiang, et al. Bead geometry prediction based on SVM[J]. Transactions of the China Welding Institution, 2010, 31(2): 103-106.
    [5] Kim I S, Son J S, Park C E, et al. An investigation into an intelligent system for predicting bead geometry in GMA welding process[J]. Journal of Materials Processing Technology, 2005, 159(1): 113-118.
    [6] Zhou J, Li X B, Shi X Z, et al. Predicting pillar stability for underground mine using Fisher discriminant analysis and SVM methods[J]. Transactions of Nonferrous Metals Society of China, 2011, 21(12): 2734-2743.
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  • 收稿日期:  2013-07-08

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