2017
DOI: 10.3390/app7090884
|View full text |Cite
|
Sign up to set email alerts
|

Real-time Monitoring for Disk Laser Welding Based on Feature Selection and SVM

Abstract: Abstract:In order to automatically evaluate the welding quality during high-power disk laser welding, a real-time monitoring system was developed. The images of laser-induced metal vapor during welding were captured and fifteen features were extracted. A feature selection method based on a sequential forward floating selection algorithm was employed to identify the optimal feature subset, and a support vector machine (SVM) classifier was built to recognize the welding quality. The experiment results demonstrat… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
10
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
8
1

Relationship

0
9

Authors

Journals

citations
Cited by 16 publications
(10 citation statements)
references
References 23 publications
0
10
0
Order By: Relevance
“…The main methods involved in the feature selection stage are principal component analysis (Z. Y. He et al 2019), independent principal component analysis (Ahmad et al 2019), and sequential forward floating selection (Wang et al 2017). The main methods involved in the feature recognition stage are perceptron (Zeng, Dai, and Mu 2007), support vector machine (Xiao et al 2020), decision tree (Z. F. , and clustering (Wang et al 2015).…”
Section: Current Development Of Iiprmentioning
confidence: 99%
“…The main methods involved in the feature selection stage are principal component analysis (Z. Y. He et al 2019), independent principal component analysis (Ahmad et al 2019), and sequential forward floating selection (Wang et al 2017). The main methods involved in the feature recognition stage are perceptron (Zeng, Dai, and Mu 2007), support vector machine (Xiao et al 2020), decision tree (Z. F. , and clustering (Wang et al 2015).…”
Section: Current Development Of Iiprmentioning
confidence: 99%
“…Therefore, researchers frequently used vision sensors to capture images of the welding process and monitored the welding state [ 6 ]. Hand-crafted image processing algorithms were used to extract features from images and machine learning methods like support vector machine [ 7 , 8 , 9 , 10 ], random forest [ 5 ], and k-nearest neighbors [ 11 ] were applied to predict the welding state. However, image data captured by vision sensors can be complex and high-dimensional.…”
Section: Introductionmentioning
confidence: 99%
“…Conventional ML methods include decision tree (random forest) [23], support vector machine (SVM) [24], Naive Bayes [25]. In general, these methods heavily rely on heuristic handcrafted data fusion and feature extraction involving extensive domain knowledge [26].…”
Section: Introductionmentioning
confidence: 99%