电弧光谱深度挖掘下的铝合金焊接过程状态检测
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摘要
铝合金焊接过程状态检测对确保航空航天构件的质量稳定性,推动机器人焊接智能制造具有重要意义.由于缺少有效的电弧光谱知识挖掘方法,光谱与缺陷相关性模糊,故展开以下研究.经试验确定了光谱探头位置敏感区间,以确保采集光谱的可靠度;利用主成分分析法选择了AlI,MgI及FeI金属谱线,定量评价了各谱线的状态变化敏感程度;基于金属谱线主分量系数特征,分析了不同金属元素的动态响应规律,挖掘出了FeI谱线与送丝状态的强相关性.基于所提出的FeI谱线特征参数进行了送丝状态检测.通过不同焊接状态试验的重复验证,结果表明,该方法具有较高稳定性,抗干扰能力强.
Condition detection during the welding process of aluminum alloy is of great significance both for guaranteeing the quality stability of aerospace structure and promoting the robotic intelligent welding manufacturing(IWM).The following research was carried out considering being lack of the effective knowledge mining method of arc spectrum and the unclear correlation between arc spectrum and weld defects.The sensitivity position interval of spectrum probe was experimentally determined to ensure the reliability of the collected spectrum information.By means of principle component analysis of metal spectrum,FeI(407.84 nm),MgI(383.83 nm) and AlI(369.15 nm) were selected,and their correlation to wire feeding state was qualitatively and quantitatively evaluated.Subsequently,based on the feature of principle component coefficients of the metal spectral line,the dynamic response rules of different metal elements were analyzed,and then,the strong correlation was found between FeI spectral line and the wire feeding state.Status detection of wire feeding was performed based on the proposed feature of FeI line spectrum.After repeat verification through different welding tests,the results showed that the method had high stability and strong anti-interference ability.
Condition detection during the welding process of aluminum alloy is of great significance both for guaranteeing the quality stability of aerospace structure and promoting the robotic intelligent welding manufacturing(IWM).The following research was carried out considering being lack of the effective knowledge mining method of arc spectrum and the unclear correlation between arc spectrum and weld defects.The sensitivity position interval of spectrum probe was experimentally determined to ensure the reliability of the collected spectrum information.By means of principle component analysis of metal spectrum,FeI(407.84 nm),MgI(383.83 nm) and AlI(369.15 nm) were selected,and their correlation to wire feeding state was qualitatively and quantitatively evaluated.Subsequently,based on the feature of principle component coefficients of the metal spectral line,the dynamic response rules of different metal elements were analyzed,and then,the strong correlation was found between FeI spectral line and the wire feeding state.Status detection of wire feeding was performed based on the proposed feature of FeI line spectrum.After repeat verification through different welding tests,the results showed that the method had high stability and strong anti-interference ability.
引文
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[2]Xu Y,Yu H,Zhong J,et al.Real-time seam tracking control technology during welding robot GTAW process based on passive vision sensor[J].Journal of Materials Processing Technology,2012,212(8):1654-1662.
[3]Zhang Y M.Modeling of human welder response to 3D weld pool surface[J].Welding Journal,2012,91(11):s310-s318.
[4]LüN,Xu Y,Li S,et al.Automated control of welding penetration based on audio sensing technology[J].Journal of Materials Processing Technology,2017,250(12):81-98.
[5]马跃洲,陈剑虹,梁卫东.GMAW电弧声的参数化模型及应用[J].机械工程学报,2005,41(11):109-114.Ma Yuezhou,Chen Jianhong,Liang Weidong.Parametric modeling of the arc sound in GMAW for on-line quality monitoring[J].Journal of Mechanical Engineering,2005,41(11):109-114.
[6]高向东,吴嘉杰.微间隙焊缝磁光成像检测及跟踪方法[J].机械工程学报,2015,51(4):71-77.Gao Xiangdong,Wu Jiajie.Approach of detecting and tracking micro weld joint based on magneto optiacl imaging[J].Journal of Mechanical Engineering,2015,51(4):71-77.
[7]Pan Q,Mizutani M,Kawahito Y,et al.High power disk lasermetal active gas arc hybrid welding of thick high tensile strength steel plates[J].Journal of Laser Applications,2016,28(1):012004.
[8]Zhao C,Fezzaa K,Cunningham R W,et al.Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction[J].Scientific Reports,2017,7(1):3602.
[9]Mirapeix J,Vila E,Valdiande J J,et al.Real-time detection of the aluminium contribution during laser welding of Usibor1500 tailorwelded blanks[J].Journal of Materials Processing Teachnology,2016,235:106-113.
[10]李俊岳,宋永伦,李桓,等.焊接电弧光谱信息的基本理论和基本方法[J].焊接学报,2002,23(6):5-8.Li Junyue,Song Yonglun,Li Huan,et al.Basic Theory and Method of Welding Arc Spectrum Information[J].Transactions of the China Welding Institution,2002,23(6):5-8.
[11]陈波.脉冲GTAW过程多传感器信息融合处理方法研究[D].上海:上海交通大学,2010.
[12]Zhang Z,Chen H,Xu Y,et al.Multisensor-based real-time quality monitoring by means of feature extraction,selection and modeling for Al alloy in arc welding[J].Mechanical Systems&Signal Processing,2015,60-61:151-165.
[13]宋永伦.焊接电弧等离子体的光谱诊断法及其应用的研究[D].天津:天津大学,1990.
[14]肖笑.双组分气体TIG焊电弧物理特性的动态光谱诊断方法研究[D].上海:上海交通大学,2015.
[15]余焕伟.基于电弧光谱信息的铝合金脉冲GTAW焊接动态过程及缺陷特征研究[D].上海:上海交通大学,2013.
[16]Tanaka M,Tsujimura M Y,Yamazaki M K.Dynamic behaviour of metal vapour in ARC plasma during TIG welding[J].Welding in the World,2012,56(1-2):30-36.
[17]Mazumder J,Song L.Smart additive manufacturing system(SAMS),USA,9981341[P].2018-05-29.
[18]Song L,Huang W,Han X,et al.Real-time composition monitoring using support vector regression of laser-induced plasma for laser additive manufacturing[J].IEEE Transactions on Industrial Electronics,2016,64(1):633-642.
[19]Zhang Z,Yu H,LüN,et al.Real-time defect detection in pulsed GTAW of Al alloys through on-line spectroscopy[J].Journal of Materials Processing Technology,2013,213(7):1146-1156.
[20]Huang Y,Wu D,Zhang Z,et al.EMD-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM[J].Journal of Materials Processing Technology,2017,239:92-102.