电弧光谱深度挖掘下的铝合金焊接过程状态检测

Condition detection in Al alloy welding process based on deep mining of arc spectrum

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.