航空航天用铝合金机器人焊接内部气孔缺陷在线检测  

On-Line Inner Porosity Defect Detection of Aluminum Alloy Robotic Welding for Aerospace

作　　者：张志芬 张林杰[2] 杨哲 任文静 温广瑞 ZHANG Zhifen;ZHANG Linjie;YANG Zhe;REN Wenjing;WEN Guangrui(Institute of Aero Engine,School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,China;State Key Laboratory of Metal Material Strength,School of Materials Science and Engineering,Xi’an Jiaotong University,Xi’an 710049,China)

机构地区：[1]西安交通大学机械工程学院航空发动机研究所,西安710049 [2]西安交通大学材料科学与工程学院金属材料强度国家重点实验室,西安710049

出　　处：《航空制造技术》2019年第23期14-24,共11页Aeronautical Manufacturing Technology

基　　金：国家自然科学青年基金(51605372);中国博士后科学基金面上资助项目(2016M602805);中国博士后科学基金特别资助项目(2018T111052);教育部新世纪优秀人才支持计划(NCET–13–0461)

摘　　要：铝合金机器人钨极氩弧焊(GTAW)是航空航天关键零部件的主要成形制造方法,内部气孔是其典型焊接缺陷。由于它与传感信息的弱相关性,实现准确的在线气孔检测仍极具挑战。利用SEM和EDS对气孔的宏观分布、成分含量及大小进行定性及定量表征,发现了两类内部气孔以及镁元素在气孔产生及长大过程中的重要性。利用主成分分析研究并建立了气孔与氢谱线(656.28nm)之间的强相关关系,提出了基于氢谱线主分量系数(PCoefHS)的气孔在线监测方法,最后通过集成PCA–Tsne技术对不同程度气孔缺陷进行可视化。验证结果表明所提方法可实现对严重链状气孔、中度气孔及无气孔缺陷焊缝的有效检测及可视化表征。Robotic argon tungsten arc welding(GTAW)of aluminum alloy is the main forming manufacturing method of key parts in aerospace,in which inner porosity is a typical defect.Due to its weak correlation to sensing data,accurate on-line porosity detection is still challenging.The porosity was qualitatively and quantitatively characterized in terms of distribution,composition and size using SEM and EDS.Two types of porosity were discovered as well as the importance of Mg during the process of porosity generation and growth.The strong correlation between porosity and hydrogen spectrum line(656.28nm)was investigated and established using principal component analysis.A new real-time porosity detection for Al alloy was proposed by means of PCoefHS(PCA Coefficient of HI Spectrum).Finally,different levels of porosity were visualized by combining PCA–Tsne.The verification shows that three types of welding seams with no porosity,moderate porosity and chain-type severe porosity can be effectively detected and visualized.

关 键 词：铝合金机器人焊接 内部气孔 在线检测 徵观表征 电弧光谱 数据挖掘 

分 类 号：TP2[自动化与计算机技术—检测技术与自动化装置]