CN103223544A - Complex wavelet method for identifying welding torch posture welded with underwater wet method - Google Patents
Complex wavelet method for identifying welding torch posture welded with underwater wet method Download PDFInfo
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Abstract
The invention discloses complex wavelet method for identifying welding torch posture welded with an underwater wet method. The method is used for extracting and analyzing single features with the complex wavelet method in a process that a rotary arc sensor is studied and used as an automatic welded joint tracking sensor welded with the wet method. According to the method, a Morlet complex wavelet is used for analyzing a simulated welding joint arc length signal and an actual signal, the real part of a complex wavelet coefficient and deviation are in direct proportion, the imaginary part of the complex wavelet coefficient and a welding torch inclination angle are in direct proportion, accordingly, deviation and inclination angle information of a welding torch relative to a welding joint is obtained, and a welding torch posture is obtained. The method can achieve decoupling of the welding torch deviation and the welding torch inclination angle, and obtain posture information of the welding torch.
Description
Technical field
The present invention relates to a kind of Automation of Welding signal processing method, particularly to being used as underwater wet welding welding torch and the signal of sensor using rotating the arc formula sensor, the method that feature extraction is carried out using Phase information method, the feature extracted may be used as the identification of torch posture.
Background technology
One of forms of sensor that rotating the arc formula sensor is commonly used as soldering joint automatic tracking, sensor and welding arc unification, anti-arc light, high temperature and high-intensity magnetic field ability are strong, the difference without position and time.But its extraction to signal is influenceed by this poor weakness of anti-current interference performance, therefore has used integration differential method and characteristic harmonics method for its information extraction people research.Integration differential method carries out the current signal i (t) obtained when electric arc scans groove both sides the integration of short time, then welding and assembling height signal is used as using both sides integrated value sum, the deviation signal of welding torch disalignment is used as using the difference of integration, but it is influenceed by ssystem transfer function, it is actually used it is middle can be exceeded the time limit due to phase or delayed influence and produce error in judgement.Characteristic harmonics method utilizes the characteristic harmonics of groove scan input signal (characteristic harmonics are exactly the harmonic wave for reflecting groove bias state), it is indicated that the two characteristic components of the size and phase of characteristic harmonics component can react groove deviation.But this method is there is also certain defect, in the case where there is welding gun inclination angle, the information at weld seam deviation and welding gun inclination angle is all coupling in the size of characteristic harmonics component this value, so that it can not effectively recognize weld seam deviation.
The content of the invention
The content of the invention is keyed in here describes paragraph.
According to background technology, during soldering joint automatic tracking sensor of the research and utilization rotating the arc formula sensor as underwater wet welding, propose to have carried out the method that signal characteristic abstraction is analyzed using Phase information method.The weld seam arc length signal and actual signal of simulation are analyzed using Morlet Phase informations, the real part and deviation size of its Phase information coefficient are proportional, the imaginary part of Phase information coefficient and welding torch inclination angle are proportional, deviation and obliquity information of the welding torch relative to weld seam are obtained accordingly, obtain torch posture.
The beneficial effects of the invention are as follows the decoupling at welding gun deviation and welding gun inclination angle can be realized by carrying out signal transacting using Phase information.The real part summing value of the coefficient of 3 layers of wavelet decomposition of Phase information and the linear relation of weld seam deviation, the imaginary part summing value of the coefficient of 3 layers of wavelet decomposition and welding torch inclination angle are linear.2）The attitude information of welding gun can be obtained by carrying out signal transacting using Phase information.
Brief description of the drawings
The present invention is further described with reference to the accompanying drawings and examples
Fig. 1 is welding gun and weldment relative position figure
Fig. 2 is the relation of change of error and Phase information real part
Fig. 3 is the relation of change of pitch angle and Phase information imaginary part
The relation of change of error and Phase information real part when Fig. 4 is change of pitch angle
Embodiment
As shown in figure 1, since A points, the arc length h by the point where any time t welding wire apart from double V-groove is：
Wherein, electric arc radius of turn is R, and the angle of groove and horizontal H is α, and electric arc surfaces of revolution inclination angle is γ, and rotating the arc is turned clockwise by starting point A with angular velocity omega, and welding gun track is β with welding direction angle, and electric arc pivot is e with weld excursion amount.
When the dynamic quality that power supply has had, it is expressed as：
Therefore, there is linear relationship between I (s) and H (s).
H (t) passes through complex wavelet transform, equivalent to the bandpass filtering for the frequency content that rotating the arc has been carried out to H (t), its harmonic amplitude obtained after transmission function G (s) by arc sensor is proportional to H (s) harmonic amplitude.Therefore the coefficient and deviation, the relation at inclination angle after research H (t) complex wavelet transform just can be derived that electric current and and deviation, the relation at inclination angle.
Because Morlet Phase informations frequency domain energy is concentrated, passband is narrower, thus frequency alias influence is small, carries out signal characteristic abstraction using Morlet Phase informations here.Morlet Phase informations are the complex-exponential functions under a kind of Gaussian envelope, and in a frequency domain, it is expressed as：
As can be seen from the above equation, it is also Gaussian function.Its centre frequency is in Fc, and it is a narrow-band bandpass function, with a width of
, by changing bandwidth factor
It can just change
Bandwidth,
Bigger, B is narrower.Therefore, as long as changing can just characterize
Local property.In terms of frequency domain angle, wavelet transformation is made of different scale and is roughly equivalent to handle signal with one group of bandpass filter.Given small echo is corresponding with the sampling period, use following relation：
In formula：α --- yardstick;Δ --- the sampling period;The centre frequency of+--small echo, unit Hz
When using this arc length model, it is assumed that when sensor rotation radius is 2, β=0：
(1) change e value sizes ,+3, interval 0.5 are transitioned into by -3, the large deviations of corresponding diagram 2 change 1 to 11 times, bandpass filtering is carried out with Morlet Phase informations, weld seam deviation is characterized with the real part summing value of the coefficient of 3 layers of wavelet decomposition, acquired results are as shown in Figure 2.As can be seen from the figure the real part summing value and weld seam deviation of the coefficient of 3 layers of wavelet decomposition are linear.
(2) inclination angle of the electric arc surfaces of revolution is changed
The tilt angle gamma for changing the electric arc surfaces of revolution is transitioned into π/4 by π/4, change of pitch angle 1 to 17 times, bandpass filtering is carried out with Morlet Phase informations in change interval π/32, corresponding diagram 3, welding torch inclination angle is characterized with the imaginary part summing value of the coefficient of 3 layers of wavelet decomposition, acquired results are as shown in Figure 3.As can be seen from the figure the imaginary part summing value of the coefficient of 3 layers of wavelet decomposition and welding torch inclination angle are linear.
Still use this arc length model, it is assumed that when sensor rotation radius is that 2, β is not 0（β=pi/6）Time, as a result as shown in Figure 4, it is seen that its 3 layers of wavelet coefficient real part summing values are different but overall still linear with inclination angle difference.
Claims (1)
1. a kind of underwater wet welding rotary arc sensor torch posture Phase information recognition methods, it is characterized in that:By the use of rotating the arc formula sensor as during the welding torch of underwater wet welding and soldering joint automatic tracking sensor, signature analysis is carried out to rotating the arc formula sensor welding current under water using Phase information, the real part and deviation size of its Phase information coefficient are proportional, the imaginary part of Phase information coefficient and welding torch inclination angle are proportional, deviation and obliquity information of the welding torch relative to weld seam are obtained accordingly, obtain torch posture.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105328303A (en) * | 2015-11-18 | 2016-02-17 | 湘潭大学 | Pose identification method of arc sensing welding gun |
| CN105458463A (en) * | 2016-01-07 | 2016-04-06 | 湘潭大学 | Real-time welding seam tracking method of intelligent welding robot based on rotating arc sensing |
| CN106964875A (en) * | 2017-04-18 | 2017-07-21 | 湘潭大学 | A kind of gun welder space gesture recognition method based on arc sensor |
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| JP2002248566A (en) * | 2001-02-26 | 2002-09-03 | National Institute Of Advanced Industrial & Technology | Method and device for underwater welding |
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| CN101774065A (en) * | 2010-03-17 | 2010-07-14 | 昆山工研院工业机器人研究所有限公司 | Robot welding line tracking deviation compensation method |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105328303A (en) * | 2015-11-18 | 2016-02-17 | 湘潭大学 | Pose identification method of arc sensing welding gun |
| CN105458463A (en) * | 2016-01-07 | 2016-04-06 | 湘潭大学 | Real-time welding seam tracking method of intelligent welding robot based on rotating arc sensing |
| CN106964875A (en) * | 2017-04-18 | 2017-07-21 | 湘潭大学 | A kind of gun welder space gesture recognition method based on arc sensor |
| CN106964875B (en) * | 2017-04-18 | 2020-02-07 | 湘潭大学 | Welding gun space attitude identification method based on arc sensor |
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Application publication date: 20130731 |