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

Wang, Teng; Chen, Juequan; Gao, Xiangdong; Qin, Yuxin

doi:10.3390/app7090884

Cited by 15 publications

(10 citation statements)

References 23 publications

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“…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%

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Liu

Bao

Wang

et al. 2021

International Journal of Computer Integrated Manufacturing

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Deep learning (DL) is an important enabling technology for intelligent manufacturing. The DLbased industrial image pattern recognition (DLBIIPR) plays a vital role in the improvement of product quality and production efficiency. Although DL technology has been widely used in the field of natural image, industrial image often has some mixed characteristics, such as small sample, imbalance, small target, strong interference, fine-grained, temporality and semantical, which reduce the feasibility and generalization of DLBIIPR. To solve this problem, this paper provides an overview of approaches commonly used in industry by enriching the sample space and limiting the hypothesis space. In order to improve the confidence of front-line workers in using DL models, the explainable deep learning (XDL) methods are reviewed, and a case study is used to verify the effectiveness of XDL.

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Section: Current Development Of Iiprmentioning

confidence: 99%

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Liu

Bao

Wang

et al. 2021

International Journal of Computer Integrated Manufacturing

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“…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%

Multi-Output Monitoring of High-Speed Laser Welding State Based on Deep Learning

Xue

Chang

2021

Sensors

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In order to ensure the production quality of high-speed laser welding, it is necessary to simultaneously monitor multiple state properties. Monitoring methods combining vision sensing and deep learning models are popular but most models used can only make predictions on single welding state property. In this contribution, we propose a multi-output model based on a lightweight convolutional neural network (CNN) architecture and introduce the particle swarm optimization (PSO) technique to optimize the loss function of the model, to simultaneously monitor multiple state properties of high-speed laser welding of AISI 304 austenitic stainless steel. High-speed imaging is performed to capture images of the melt pool and the dataset is built. Test results of different models show that the proposed model can achieve monitoring of multiple welding state properties accurately and efficiently. In addition, we make an interpretation and discussion on the prediction of the model through a visualization method, which can help to deepen our understanding of the relationship between the melt pool appearance and welding state. The proposed method can not only be applied to the monitoring of high-speed laser welding but also has the potential to be used in other procedures of welding state monitoring.

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“…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%

A Multi-Sensor Data Fusion System for Laser Welding Process Monitoring

Deng

Yong-shen

et al. 2020

IEEE Access

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Most existing laser welding process monitoring (LWPM) technologies focus on detecting postprocess defects. However, in sheet metal laser welding applications such as welding of electronic consumer products during mass production, in-process defect detection is more important. In this paper, a compact LWPM system using multi-sensor data fusion to detect in-process defects has been built. This system can collect the time series of plasma intensity, light intensity and temperature data for feature analysis. To verify the system's effectiveness, a plasma-light-temperature dataset has been compiled, which consists of 5,836 samples of nine classes, including one positive class and eight negative classes of typical in-process defects. A multi-sensor data fusion network based on a convolution neural network for in-process defect detection, called IDDNet, has also been proposed. Experimental results have demonstrated that IDDNet can achieve better multi-classification results than the support vector machine, with an overall accuracy of 97.57%. In particular, considering this monitoring process as a binary classification problem, IDDNet can achieve a 99.42% accuracy. Moreover, IDDNet can reach an average speed of 0.79ms per sample on a single GTX 1080ti graphics card, which meets the real-time requirement for industrial production. The proposed LWPM system has been successfully verified in real applications of sheet metal laser welding. INDEX TERMS laser welding process monitoring, in-process defect detection, multi-sensor data fusion, convolution neural network

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

Cited by 15 publications

References 23 publications

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Deep learning for industrial image: challenges, methods for enriching the sample space and restricting the hypothesis space, and possible issue

Multi-Output Monitoring of High-Speed Laser Welding State Based on Deep Learning

A Multi-Sensor Data Fusion System for Laser Welding Process Monitoring

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