Penetration state recognition based on the double-sound-sources characteristic of VPPAW and hidden Markov Model

Song, Shuo; Chen, Huabin; Lin, Tao; Wu, Di; Chen, Shanben

doi:10.1016/j.jmatprotec.2016.03.002

Cited by 48 publications

(13 citation statements)

References 8 publications

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“…The SVM model finds a hyperplane that has the highest margin of class separation. The model itself has already proved as efficient in classifying welded joints, as confirmed by the authors [7,13,14,16].…”

Section: Figure 1 Block Diagram Of the Processing Arc Sound Signalsmentioning

confidence: 65%

“…In order to perform high quality analysis of the obtained parameters, it is necessary to record and collect appropriate amount of data. The authors [1,16] used sampling frequency of 20 kHz, while the referential literature [9,8,10,14,17] states 40 kHz sampling frequency. There is also 48 sampling frequency applied [11,18], however, some authors used also 25,6 kSamples/s [4].…”

Section: Figure 1 Block Diagram Of the Processing Arc Sound Signalsmentioning

confidence: 99%

“…The use of the SVM classification method has been shown to be of high quality for classifying multi welding defects, such as porosity, incomplete penetration, so the highest precision of the quality assurance of the welded compound can be obtained with satisfactory precision of the model [14].…”

Section: Introductionmentioning

confidence: 99%

“…The influence of welding sound and penetration in welding processes has been widely investigated. Also SVM classification model based on acoustics signal has proved to be good for the classification wire extension welding process [14].…”

Section: Introductionmentioning

confidence: 99%

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Application of SVM Models for Classification of Welded Joints

et al. 2019

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Classification algorithm based on the support vector method (SVM) was used in this paper to classify welded joints in two categories, one being good (+1) and the other bad (−1) welded joints. The main aim was to classify welded joints by using recorded sound signals obtained within the MAG welding process, to apply appropriate preprocessing methods (filtering, processing) and then to analyze them by the SVM. This paper proves that machine learning, in this specific case of the support vector methods (SVM) with appropriate input conditions, can be efficiently applied in assessment, i.e. in classification of welded joints, as in this case, in two categories. The basic mathematical structure of the machine learning algorithm is presented by means of the support vector method.

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Section: Figure 1 Block Diagram Of the Processing Arc Sound Signalsmentioning

confidence: 65%

Section: Figure 1 Block Diagram Of the Processing Arc Sound Signalsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of SVM Models for Classification of Welded Joints

et al. 2019

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“…Due to the complexity of welding process, the system model changes with the operating point of the welding process. Therefore, it is difficult to achieve accurate and stable control for VPPAW system with strongly nonlinear by using conventional linear control method [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Research on Adaptive Controller for Variable Polarity Plasma Arc Welding Power Supply with High-Frequency

Zhou

Zheng

2019

MATEC Web Conf.

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Aiming at the problem of output current waveform distortion caused by superimposed high frequency components in the output current of varying polarity of VPPAW power supply. A novel adaptive PID controller with automatic switching is introduced in this paper, and the controller is established based on the evolution path of the operating point of the VPPAW power supply. Compared with the traditional linear PID controller, the adaptive controller proposed in this paper is very suitable for the control of arc load with strong dynamic characteristics and can effectively improve output waveform of the welding system.

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