Thick steel plate welding quality control method and system based on ultrasonic detection

Abstract

The embodiment of the invention provides a thick steel plate welding quality control method and system based on ultrasonic detection. The method is applied to the technical field of welding and comprises the steps that a welded thick steel plate finished product material is obtained and treated to obtain to-be-analyzed steel; an ultrasonic flaw detection method is adopted, and an SSGD matrix decomposition method based on automatic rank estimation is combined to conduct welding seam quality analysis on the to-be-analyzed steel welding position, and a welding seam quality result is obtained; and a welding process control model is built based on the welding seam quality result, welding parameters are adjusted based on the control model, and welding effect control is achieved. In this way, weld defect analysis is effectively achieved through an ultrasonic flaw detection method, on the basis, effective control over the welding speed, current and temperature in the welding process is achieved by building a welding process control model, and therefore the welding process is safe and reliable, and the welding quality is improved. And the welding efficiency is high, and the welding quality meets the industrial standard.

Description

Technical Field

[0001] The present disclosure relates to the field of welding technology, and in particular to a method and system for controlling the welding quality of thick steel plates based on ultrasonic detection. Background Art

[0002] As an important manufacturing process, welding is widely used in industrial production, especially in the automotive, shipbuilding, construction and aerospace industries.

[0003] The common practice of welding defect identification is to extract several defect features based on experience and then use machine learning to perform pattern recognition. With the development of the new energy industry, ultrasonic defect recognition methods are gradually being applied to various industries. Therefore, if there are defects or hidden dangers in the welding quality, there will inevitably be safety risks when the welded parts are used in products.

[0004] The ultrasonic welding detection process has nonlinear time-varying characteristics. Since the welding process is difficult to control accurately and is prone to problems such as cold welding, over-welding and weld cracks, the welding detection effect and quality are affected.

[0005] Therefore, there is an urgent need for a design solution that can effectively control the quality of the welding process. Summary of the invention

[0006] The present invention provides a method and system for controlling the welding quality of thick steel plates based on ultrasonic testing. The ultrasonic flaw detection method and system at least solve the technical problem of low efficiency of the existing ultrasonic testing methods.

[0007] According to a first aspect of the present disclosure, there is provided a method for controlling the welding quality of thick steel plates based on ultrasonic testing, comprising the following steps:

[0008] Obtaining the finished material of the thick steel plate after welding, and processing it to obtain the steel to be analyzed;

[0009] The ultrasonic flaw detection method is combined with the SSGD matrix decomposition method based on automatic rank estimation to analyze the weld quality of the steel welding parts to obtain the weld quality results;

[0010] A welding process control model is constructed based on the weld quality results, and welding parameters are adjusted based on the control model to achieve welding effect control.

[0011] According to the aspects and any possible implementation methods described above, an implementation method is further provided, in which the process of obtaining the finished material of the thick steel plate after welding and processing it to obtain the steel to be analyzed is: using an electric wire brush to remove welding spatter, rust, oxide scale and other impurities formed within 150 mm on both sides of the weld, and ensuring that the surface roughness of the steel to be analyzed does not exceed 6.3 μm, so as to obtain the steel to be analyzed.

[0012] According to the above aspects and any possible implementation, an implementation is further provided, wherein the process of using an ultrasonic flaw detection method in combination with an SSGD matrix decomposition method based on automatic rank estimation to perform weld quality analysis on the welded portion of the steel to be analyzed and obtaining the weld quality result is as follows:

[0013] Obtain the finished material of thick steel plate, and use ultrasonic flaw detection method to measure the maximum echo dB value of the thick steel plate and calculate the defect aperture;

[0014] The defect echo signal is calculated by using the SSGD matrix decomposition method based on automatic rank estimation, and the defect length is calculated based on the defect echo;

[0015] The defect equivalent is calculated based on the defect aperture and defect length, and the defect rate is calculated;

[0016] By plotting the defect rate, the relationship curve between defect rate and weld quality is obtained, and the weld quality result is obtained.

[0017] According to the above aspects and any possible implementation, an implementation is further provided, wherein the process of calculating the defect echo signal using the SSGD matrix decomposition method based on automatic rank estimation is:

[0018] The time-frequency spectrum is obtained by processing the ultrasonic signal using the Hamming window. And take the modulus to get the amplitude spectrum A;

[0019] Compute the reconstruction error under low rank parameter ||N|| F , and record the first low-rank parameter m that satisfies the error change less than ξ;

[0020] Under the condition of rank m, the amplitude spectrum A is decomposed to obtain the noise part N, the low-rank part L and the sparse part S. The signal amplitude spectrum after removing the noise part N is recorded as Y LS ;

[0021] A masking value M is calculated based on the sparse part S and the low-rank part L, and a binary masking process is performed on the amplitude spectrum based on the masking value to obtain an amplitude spectrum of the defect signal;

[0022] Perform inverse STFT on the amplitude spectrum to obtain the defect echo signal after noise reduction.

[0023] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the calculation method of the masking value M is:

[0024]

[0025] Among them, l and n are the time-frequency point indices in the amplitude spectrum, S is the sparse part, and L is the low-rank part.

[0026] According to the above aspects and any possible implementation, an implementation is further provided, wherein the welding process control model is constructed based on the weld quality result, and the welding parameters are adjusted based on the control model to achieve the process of controlling the welding effect:

[0027] Construct the factors affecting weld forming quality and the influence model of weld forming quality;

[0028] Collecting welding parameters during the welding process, and constructing a heat conduction equation of welding current, welding speed and welding temperature based on the welding parameters;

[0029] Substituting the heat conduction equation into the influencing model to construct a welding process control model based on the influencing factors;

[0030] Based on the welding quality results, the control model is calculated by a numerical solution method to obtain the quality change during the welding process, and the welding current and welding speed are adjusted according to the quality change.

[0031] According to the above aspects and any possible implementation, an implementation is further provided, wherein the welding process control model is specifically:

[0032]

[0033] Among them, S(t) is the welding quality, I(t) is the welding current, v(t) is the welding speed, T(t) is the welding temperature, α, β, γ, δ, ε, are all parameters that affect the model, λ is the thermal conductivity, s is the heat transfer cross-sectional area, t is the heat transfer time, ρ is the material density, k is the thermal conductivity, μ is the heating effect coefficient related to the welding current, h is the convection heat transfer coefficient, T a is the ambient temperature.

[0034] According to the above aspects and any possible implementation, an implementation is further provided, wherein the method further comprises: using a correlation coefficient and a root mean square error as evaluation criteria to verify the accuracy of the defect echo signal, specifically:

[0035]

[0036] Where N is the signal sampling length, is the processed echo signal, x org The original signal.

[0037] According to a second aspect of the present disclosure, there is provided a thick steel plate welding quality control system based on ultrasonic detection, comprising: a welding steel acquisition module, a weld quality analysis module and a welding effect control module;

[0038] The welding steel material acquisition module is used to acquire the finished material of the thick steel plate after welding, and process it to obtain the steel material to be analyzed;

[0039] The weld quality analysis module is used to perform weld quality analysis on the welded parts of the steel to be analyzed by using an ultrasonic flaw detection method combined with an SSGD matrix decomposition method based on automatic rank estimation to obtain weld quality results;

[0040] The welding effect control module is used to construct a welding process control model based on the weld quality results, and adjust welding parameters based on the control model to achieve welding effect control.

[0041] Compared with the prior art, the present invention has the following technical effects:

[0042] The present invention adopts the SSGD matrix decomposition method based on automatic rank estimation to analyze the weld quality of the steel welding parts to be analyzed, thereby effectively realizing the analysis of weld defects using ultrasonic flaw detection methods. On this basis, by constructing a welding process control model, effective control of welding speed, current and temperature during the welding process is realized, thereby achieving the beneficial effects of safe and reliable welding process, high welding efficiency and welding quality that meets industry standards.

[0043] It should be understood that the contents described in the summary of the invention are not intended to limit the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0045] Figure 1 A schematic flow chart of a thick steel plate welding quality control method based on ultrasonic testing according to an embodiment of the present disclosure is shown;

[0046] Figure 2A schematic structural diagram of a thick steel plate welding quality control system based on ultrasonic detection according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0048] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0049] Reference Figure 1 As shown, this embodiment provides a thick steel plate welding quality control method based on ultrasonic detection, comprising the following steps:

[0050] S101. Obtain the finished material of the thick steel plate after welding, and process it to obtain the steel to be analyzed.

[0051] In this embodiment, the structural steel parts used are H-shaped steel, the material of which is Q355B, and the connection form is mainly welding. The welding quality is the focus of control. First, the rib plate weld is welded, and then the connection welds between the bolt connection plate and the column and beam are welded. The welding rod model is E5015 and the brand is CHE507.

[0052] The specific treatment process is: in fact, at least all welding spatter, rust, oxide scale and other impurities within 150mm on both sides of the weld should be removed. Most of the inspection sites use electric wire brushes for grinding. For individual parts that are difficult to remove such as welding spatter, files or sandpaper can also be used for manual grinding. The surface roughness should not exceed 6.3μm. The weld and the inspection surface can be inspected only after they have passed the appearance inspection, and it is ensured that the probe can move freely in the moving area of ​​the inspection surface.

[0053] S102. An ultrasonic flaw detection method is used in combination with an SSGD matrix decomposition method based on automatic rank estimation to analyze the weld quality of the steel welding parts to obtain the weld quality results.

[0054] Weld defect detection of building steel structures can timely discover common defects such as slag inclusions, pores, and melt penetration. On this basis, the forming quality can be controlled based on the welds, thereby effectively improving the stability and safety of the building steel structures.

[0055] In this embodiment, a low-rank decomposition method is used to extract the defect echo signal from the original ultrasonic signal. The specific process is as follows:

[0056] The object of low-rank sparse decomposition method is a matrix with potential low-rank and sparse structural features. Its main purpose is to convert a data set containing multiple related components into a set of sparser and independent principal components. These principal components retain as much information as possible from the original data while reducing computational complexity and avoiding overfitting.

[0057] This embodiment uses short-time Fourier transform to construct a low-rank matrix, overlaps and segments the non-stationary original signal through a finite-length window moving weight to obtain a short-time stable data frame, and then performs fast Fourier transformation (FFT) on each frame of the preprocessed signal, specifically:

[0058]

[0059] in, is the time spectrum, n is the signal length, W is the frame length, h() is the normalized window function, t is the time, and y() is the Fourier function.

[0060] Then the obtained time-frequency spectrum is modulo-ed, and then the amplitude spectrum A is modulo-ed;

[0061] Compute the reconstruction error under low rank parameter ||N|| F , and record the first low-rank parameter m that satisfies the error change less than ξ;

[0062] Under the condition of rank m, the amplitude spectrum A is decomposed to obtain the noise part N, the low-rank part L and the sparse part S;

[0063] Specifically, in this embodiment, the low-rank part L and the sparse part S of the noise interference matrix Y are effectively estimated by using the GoDec method, and the solution is obtained by minimizing the solution error:

[0064]

[0065] Where N is the noise part, rank(L) is the rank of the matrix, and card(s) is the cardinality of the matrix S.

[0066] Then, in this embodiment, by solving equation (2), we can obtain:

[0067]

[0068] Among them, ||·|| is the norm.

[0069] By using the above formula, the amplitude spectrum A is decomposed under the condition of rank k to obtain the noise part N, the low-rank part L and the sparse part S. The signal amplitude spectrum after removing the noise part N is recorded as Y LS .

[0070] Subsequently, the masking value M is calculated based on the sparse part S and the low-rank part L, and the amplitude spectrum is subjected to binary masking based on the masking value to obtain the amplitude spectrum of the defect signal, which is specifically:

[0071]

[0072] Finally, the inverse STFT of the amplitude spectrum can be performed to obtain the noise-reduced defect echo signal.

[0073] In this embodiment, the weld seam during the welding process is detected by using an ultrasonic flaw detection method, and the specific process is as follows:

[0074] Obtain the finished material of thick steel plate, and use ultrasonic flaw detection method to measure the maximum echo dB value of the thick steel plate and calculate the defect aperture;

[0075] The defect echo is calculated by using the SSGD matrix decomposition method based on automatic rank estimation, and the defect length is calculated based on the defect echo. The specific process is: move the probe to the left and right for a distance so that the amplitude just drops to half the position, record the positions p1 and p2 at this time, measure the straight-line distance between p1 and p2, and obtain the defect length.

[0076] The defect equivalent is calculated based on the defect aperture and defect length, specifically:

[0077]

[0078] Where Q is the defect equivalent, is the defect aperture, and L is the defect length.

[0079] After the defect equivalent is calculated, the defect rate of the weld is calculated based on the defect equivalent, specifically:

[0080]

[0081] Among them, P is the defect rate and M is the maximum cross-sectional area of ​​the weld at the defect.

[0082] Subsequently, this embodiment constructs a defect rate-weld quality relationship curve based on the defect rate, thereby obtaining the weld quality result.

[0083] In order to verify the accuracy of the result of echo signal calculation, this embodiment uses the correlation coefficient (COR) and the root mean square error (RMSE) as evaluation criteria, specifically:

[0084]

[0085] Where N is the signal sampling length, is the processed echo signal, x org The original signal.

[0086] S103. Construct a welding process control model based on the weld quality results, adjust welding parameters based on the control model, and complete welding effect control.

[0087] In this embodiment, the welding quality of thick steel plates is defined by the weld forming quality S(t), wherein the factors affecting the weld forming quality specifically include welding current I, welding speed v and welding temperature T;

[0088] Since the influencing factors directly affect the quality of welding during the welding process, it is obtained that:

[0089] S(t)=f(I,v,T) (9)

[0090] Subsequently, a model based on the influence of weld forming quality factors and weld forming quality was constructed, specifically:

[0091]

[0092] Among them, α, β, γ, δ, ε, These are parameters that affect the model.

[0093] At the same time, this embodiment describes the heat conduction equation of the welding process temperature and the welding current and the heat conduction equation of the welding temperature and the welding speed, respectively:

[0094]

[0095] Where λ is the thermal conductivity, s is the heat transfer cross-sectional area, t is the heat transfer time, ρ is the material density, k is the thermal conductivity, μ is the heating effect coefficient related to the welding current, h is the convection heat transfer coefficient, T a is the ambient temperature.

[0096] Substitute the obtained heat conduction equation into the influence model to obtain the welding process control model, which is:

[0097]

[0098] By numerically solving the above equations, the mass change during the welding process is obtained, and then the welding current and welding speed are optimized to ensure the stability and consistency of the welding process.

[0099] like Figure 2As shown, this embodiment also provides a thick steel plate welding quality control system based on ultrasonic detection, including: a welding steel acquisition module 1, a weld quality analysis module 2 and a welding effect control module 3;

[0100] The welding steel material acquisition module 1 is used to obtain the finished material of the thick steel plate after welding, and process it to obtain the steel material to be analyzed;

[0101] The weld quality analysis module 2 is used to analyze the weld quality of the welded parts of the steel to be analyzed by using the ultrasonic flaw detection method combined with the SSGD matrix decomposition method based on automatic rank estimation to obtain the weld quality results;

[0102] The welding effect control module 3 is used to construct a welding process control model based on the weld quality results, and adjust the welding parameters based on the control model to achieve welding effect control.

[0103] The present invention adopts the SSGD matrix decomposition method based on automatic rank estimation to analyze the weld quality of the steel welding parts to be analyzed, thereby effectively realizing the analysis of weld defects using ultrasonic flaw detection methods. On this basis, by constructing a welding process control model, effective control of welding speed, current and temperature during the welding process is realized, thereby achieving the beneficial effects of safe and reliable welding process, high welding efficiency and welding quality that meets industry standards.

[0104] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present disclosure is not limited by the order of the actions described, because according to the present disclosure, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

[0105] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0106] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for controlling the welding quality of thick steel plates based on ultrasonic testing, characterized in that: The following steps are involved: Obtaining the finished material of the thick steel plate after welding, and processing it to obtain the steel to be analyzed; The ultrasonic flaw detection method is combined with the SSGD matrix decomposition method based on automatic rank estimation to analyze the weld quality of the steel welding parts to obtain the weld quality results; A welding process control model is constructed based on the weld quality results, and welding parameters are adjusted based on the control model to achieve welding effect control.

2. The method for controlling the welding quality of thick steel plates based on ultrasonic testing according to claim 1, characterized in that: The process of obtaining the finished thick steel plate material after welding and processing it to obtain the steel to be analyzed is: using an electric wire brush to remove welding spatter, rust, oxide scale and other impurities formed within 150 mm on both sides of the weld, and ensuring that the surface roughness of the steel to be analyzed does not exceed 6.3 μm, so as to obtain the steel to be analyzed.

3. The method for controlling the welding quality of thick steel plates based on ultrasonic testing according to claim 1, characterized in that: The process of using the ultrasonic flaw detection method combined with the SSGD matrix decomposition method based on automatic rank estimation to analyze the weld quality of the steel welding part to be analyzed and obtain the weld quality result is as follows: Obtain the finished material of thick steel plate, and use ultrasonic flaw detection method to measure the maximum echo dB value of the thick steel plate and calculate the defect aperture; The defect echo signal is calculated by using the SSGD matrix decomposition method based on automatic rank estimation, and the defect length is calculated based on the defect echo; The defect equivalent is calculated based on the defect aperture and defect length, and the defect rate is calculated; The defect rate-weld quality relationship curve is obtained by plotting the defect rate, and the weld quality result is obtained.

4. The method for controlling the quality of thick steel plate welding based on ultrasonic testing according to claim 3 is characterized in that: The process of calculating the defect echo signal using the SSGD matrix decomposition method based on automatic rank estimation is as follows: The time-frequency spectrum is obtained by processing the ultrasonic signal using the Hamming window. And take the modulus to get the amplitude spectrum A; Compute the reconstruction error under low rank parameter ||N|| F , and record the first low-rank parameter m that satisfies the error change less than ξ; Under the condition of rank m, the amplitude spectrum A is decomposed to obtain the noise part N, the low-rank part L and the sparse part S. The signal amplitude spectrum after removing the noise part N is recorded as Y LS ; A masking value M is calculated based on the sparse part S and the low-rank part L, and a binary masking process is performed on the amplitude spectrum based on the masking value to obtain an amplitude spectrum of the defect signal; Perform inverse STFT on the amplitude spectrum to obtain the defect echo signal after noise reduction.

5. The method for controlling the welding quality of thick steel plates based on ultrasonic testing according to claim 4 is characterized in that: The calculation method of the masking value M is: Among them, l and n are the time-frequency point indices in the amplitude spectrum, S is the sparse part, and L is the low-rank part.

6. The method for controlling the welding quality of thick steel plates based on ultrasonic testing according to claim 1, characterized in that: The process of building a welding process control model based on the weld quality result and adjusting welding parameters based on the control model to achieve welding effect control is as follows: Construct the factors affecting weld forming quality and the influence model of weld forming quality; Collecting welding parameters during the welding process, and constructing a heat conduction equation of welding current, welding speed and welding temperature based on the welding parameters; Substituting the heat conduction equation into the influencing model to construct a welding process control model based on the influencing factors; Based on the welding quality results, the control model is calculated by a numerical solution method to obtain the quality change during the welding process, and the welding current and welding speed are adjusted according to the quality change.

7. The method for controlling the quality of thick steel plate welding based on ultrasonic testing according to claim 6, characterized in that: The welding process control model is specifically: Among them, S(t) is the welding quality, I(t) is the welding current, v(t) is the welding speed, T(t) is the welding temperature, α, β, γ, δ, ε, are all parameters that affect the model, λ is the thermal conductivity, s is the heat transfer cross-sectional area, t is the heat transfer time, ρ is the material density, k is the thermal conductivity, μ is the heating effect coefficient related to the welding current, h is the convection heat transfer coefficient, T a is the ambient temperature.

8. The method for controlling the welding quality of thick steel plates based on ultrasonic testing according to claim 3, characterized in that: The method further includes: using correlation coefficient and root mean square error as evaluation criteria to verify the accuracy of the defect echo signal, specifically: Where N is the signal sampling length, is the processed echo signal, x org The original signal.

9. A thick steel plate welding quality control system based on ultrasonic detection, implemented by the thick steel plate welding quality control method based on ultrasonic detection as claimed in any one of claims 1 to 8, characterized in that: include: Welding steel material acquisition module (1), weld quality analysis module (2) and welding effect control module (3); The welding steel material acquisition module (1) is used to acquire the finished material of the thick steel plate after welding, and process it to obtain the steel material to be analyzed; The weld quality analysis module (2) is used to perform weld quality analysis on the welded parts of the steel to be analyzed by using an ultrasonic flaw detection method combined with an SSGD matrix decomposition method based on automatic rank estimation, and obtain weld quality results; The welding effect control module (3) is used to construct a welding process control model based on the weld quality result, and to adjust welding parameters based on the control model to achieve welding effect control.

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