支持向量机
焊接
随机森林
激光束焊接
Boosting(机器学习)
决策树
材料科学
激光器
光谱学
特征提取
独立成分分析
激光诱导击穿光谱
光学
人工智能
计算机科学
物理
复合材料
量子力学
作者
Zhifen Zhang,Yiming Huang,Rui Qin,Zihao Lei,Guangrui Wen
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:70: 1-10
被引量:8
标识
DOI:10.1109/tim.2021.3062167
摘要
This article investigates a real-time nondestructive measurement method of the seam strength using the optical spectroscopy and ensemble learning in laser beam welding. First, a linear positive relationship between the seam strength and acquired optical spectrum signal was established. Then, a new feature reduction method was proposed to extract the independent feature set with low correlation and high diversity. Finally, a hybrid classification model is proposed based on fast independent component analysis (ICA) and extremely randomized trees (ET). The model was thoroughly verified with various welding experiments and careful comparison with decision tree (DT), extreme gradient boosting DT (XGBoost), and support vector machine (SVM). More importantly, driven by the monitoring data and random forest with fast independent component analysis (RFICA)-ET model, it was found that Al I at 669.8673 nm and Ar I at 610.5635 nm were the two key elements in the dynamic plasma for accurate seam strength measurement.
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