基于熔池视觉特征的铝合金双丝焊熔透识别  

Welding penetration recognition in aluminum alloy tandem arc welding based on visual characters of weld pool

作　　者：杨嘉佳[1] 王克鸿[1] 吴统立[1] 周晓晓[1] 

机构地区：[1]南京理工大学材料科学与工程学院,南京210094

出　　处：《焊接学报》2017年第3期49-52,共4页Transactions of The China Welding Institution

基　　金：国家部委资助项目(5131850117;A2620110005)

摘　　要：熔透是焊接质量的重要评价指标之一,铝合金对焊接工艺敏感性较高,容易出现熔透不均匀情况.试验利用近红外视觉传感方法获取了铝合金单面焊双面成形焊接过程中未熔透、熔透和过熔透三种情况下的清晰熔池图像,通过图像处理获得了准确的熔池轮廓,定义并提取了熔宽、半长、面积、周长和抛物线系数等能反映熔透状态的熔池特征参数,建立了基于BP神经网络的铝合金双丝焊熔透识别模型.结果表明,5-13-3结构的BP神经网络对熔透状态识别的正确率最高,达到89.05%.Penetration is one of the most important index in the welding quality evaluation.Nonuniform penetration is easily happened in aluminum alloy welding process because of the high sensitivity of aluminum to welding parameters.In the single-side welding and double-side molding experiment clear weld pool images of three kinds of penetration status-incomplete,complete and over penetration have been obtained by near-infrared visual sensing method.The characters of weld pool image such as weld width,weld half-length,molten pool area,perimeter and parabolic coefficient which is associated to weld penetration can be extracted by a special image processing algorithm.The penetration recognition model of aluminum alloy in tandem arc welding based on BP neural network was established and the result showed that the 5-13-3 structured BP neural network model has the highest recognition accuracy which is 89.05%.

关 键 词：铝合金 熔透识别 熔池视觉特征 神经网络 

分 类 号：TG409[金属学及工艺—焊接]