摘要
针对ER316L不锈钢激光填丝焊过程中因送丝不稳定导致的焊缝质量问题,提出了基于光致等离子体发射光谱诊断的在线监测方法,构建了焊缝质量预测模型,对实现焊接过程自适应控制和激光焊接智能化有重要意义。为深入研究激光焊中激光与焊材的相互作用机制,进行了激光自熔焊、激光填丝焊试验,同步采集了光致等离子体的光谱信息,并与TIG焊工艺下的电弧光谱进行了对比分析。结果表明激光自熔焊时光谱由连续谱和强度较弱的FeⅠ636.44 nm、CrⅠ427.48 nm线谱组成;激光填丝焊时辐射光强显著增加,并产生大量CrⅠ谱线;电弧光谱包含大量的ArⅠ、ArⅡ谱线及少量的FeⅠ谱线。根据Boltzmann作图法和Stark展宽法,求得激光填丝焊时光致等离子体电子温度为5024.9 K,电子密度为2.375×10^(16)cm^(-3),满足局部热力学平衡状态。在此基础上,深入探究了激光焊接质量与光谱特征参量的内在联系。结果表明,谱线强度和电子温度与焊缝质量有很强的相关性。当成形良好时,CrⅠ谱线强度数值较高,FeⅠ谱线强度较低,电子温度在小范围内稳态波动;当产生偏丝缺陷时,CrⅠ谱线强度较低,而FeⅠ谱线强度较高,电子温度急剧变化。以平滑去噪处理后的CrⅠ529.83 nm谱线强度、FeⅠ636.44 nm谱线强度和电子温度为输入,构建单隐含层神经网络焊缝质量分类模型,识别成形良好和偏丝缺陷两种状态,测试10次的平均准确率为88%。采用t分布随机邻域嵌入算法对光谱数据进行维数约简,以得到的3维嵌入向量为输入特征,采用同样的神经网络结构进行焊缝质量模式识别,平均准确率为97%。结果表明,对光谱数据进行降维处理得到的特征包含了线谱和连续谱信息,比人为选取的特征线谱更能准确表征焊缝质量。
In view of the weld quality problems caused by unstable wire feeding in laser welding of ER316L stainless steels,this paper proposes an on-line monitoring method based on plasma emission spectrum diagnosis and builds a weld quality prediction model,which is of great significance to realize the adaptive control of welding process and intelligent laser welding.In order to further study the interaction mechanism between laser and welding material in laser welding,experiments of laser welding and laser wire filling welding were carried out.The laser-induced plasma’s spectral information was collected synchronously and compared with the arc spectrum of TIG welding process.The results showed that the spectrum during laser welding consisted of continuous spectrum and FeⅠ636.44 nm and CrⅠ427.48 nm line spectrum.During laser wire filling welding,the radiation intensity increased significantly,and many CrⅠlines were generated.The arc spectrum contained a large number of ArⅠand ArⅡlines and a small number of FeⅠlines.According to Boltzmann plotting and Stark broadening methods,the plasma electron temperature and electron density during laser wire filling welding were calculated.They were 5024.9 K and 2.375×10^(16) cm^(-3),respectively,satisfying the local thermodynamic equilibrium state.The intrinsic relationship between laser welding quality and spectral features was explored on this basis.The results showed that the spectral line intensity and electron temperature strongly correlated with the weld quality.When the forming was good,the intensity of the CrⅠspectral line was higher than that of the FeⅠspectral line,and the electron temperature fluctuated steadily in a small range.The intensity of the CrⅠline was lower than that of the FeⅠline,and the electron temperature changed sharply when the bias defect occurred.Using the CrⅠ529.83 nm spectral line intensity,FeⅠ636.44 nm spectral line intensity and electron temperature as inputs,the weld quality classification model of a single hidden layer neu
作者
冯英超
黄一鸣
刘金平
贾晨鹏
陈鹏
武少杰
任绪凯
余焕伟
FENG Ying-chao;HUANG Yi-ming;LIU Jin-ping;JIA Chen-peng;CHEN Peng;WU Shao-jie;REN Xu-kai;YU Huan-wei(China Nuclear Industry 23 Construction Co.,Ltd.,Nuclear Industry Research and Engineering Co.,Ltd.,Key Laboratory for Highly Efficient and Intelligent Welding,Beijing 101300,China;Tianjin Key Laboratory of Advanced Joining Technology,School of Materials Science and Engineering,Tianjin University,Tianjin 300350,China;Shaoxing Special Equipment Testing Institute,Shaoxing Key Laboratory of Special Equipment Intelligent Testing and Evaluation,Shaoxing 312071,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2023年第6期1927-1935,共9页
Spectroscopy and Spectral Analysis
基金
中国博士后科学基金项目(2020M670651)
国家自然科学基金项目(52005366)资助。
关键词
激光填丝焊
光谱诊断
特征提取
机器学习
预测模型
Laser wire filling welding
Spectral diagnosis
Feature extraction
Machine learning
Prediction model
