CN114813921A - Cold-rolled steel sheet yield strength detection method based on multi-frequency eddy current technology - Google Patents
Cold-rolled steel sheet yield strength detection method based on multi-frequency eddy current technology Download PDFInfo
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- CN114813921A CN114813921A CN202210194288.5A CN202210194288A CN114813921A CN 114813921 A CN114813921 A CN 114813921A CN 202210194288 A CN202210194288 A CN 202210194288A CN 114813921 A CN114813921 A CN 114813921A
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Abstract
The invention provides a method for detecting the yield strength of a cold-rolled steel plate based on a multifrequency eddy current technology, wherein an eddy current probe is arranged on one side of a moving strip steel, a detection plate is used for exciting the eddy current probe at a plurality of groups of different frequencies, the detected change of characteristic values of a real part and an imaginary part of the eddy current probe is returned at regular time, and the characteristic values of the real part and the imaginary part of the eddy current probe are respectively recorded as electromagnetic excitation characteristic values; and taking a plurality of groups of electromagnetic excitation characteristic values detected on line as input values to be brought into the BP neural network model to obtain the yield strength of the current band steel. The invention considers the correction of the lift-off parameter influencing the eddy current detection signal, considers the influence of the strip steel thickness, does not depend on the process real-time parameter of a unit, and realizes the purpose of accurately measuring the yield elongation of the strip steel on line.
Description
Technical Field
The invention relates to the technical field of nondestructive testing, in particular to a method for testing yield strength of a cold-rolled steel plate based on a multi-frequency eddy current technology.
Background
The yield elongation of a strip is the percentage of the ratio of the extension of the extensometer gauge length to the extensometer gauge length between the onset of yielding and the onset of uniform work hardening of a metallic material exhibiting significant yield.
At present, the yield elongation (Ae) of cold-rolled thin strip steel is detected by domestic iron and steel enterprises mainly by a sample cutting off-line test method, which is widely adopted at present. The method is characterized in that a sample is cut at a certain part of a coil of strip steel, such as a head sample and a tail sample, and then the sample is sent to a laboratory for off-line testing, the yield elongation of the sample is obtained, and the yield elongation of the coil of strip steel is deduced.
The yield elongation test method of the sample off-line tensile test is shown in figure 1, and the test principle is as follows: for a discontinuously yielding material, the yield elongation Ae is obtained by subtracting the elongation corresponding to the upper yield strength ReL from the elongation at the uniform work hardening initiation point on the force-elongation diagram. The extension of the onset of uniform work hardening is determined by the intersection of a horizontal line through the last minimum of the discontinuous yield phase or a regression line through the yield range before uniform work hardening on the graph with the line of highest slope of the curve at the onset of uniform work hardening. The yield elongation is obtained by dividing the yield point elongation by the extensometer gauge length Le.
The off-line sample cutting test method has the advantages of simplicity, direct result and high precision. However, this method has the following disadvantages: first, data skew is large, help to the production process is limited, online control is more silent. And secondly, the data is incomplete, and only the values of the head and the tail of the coiled steel can be reflected. Thirdly, the shearing is wasted. When a unit is in production, the unit is shut down or is in low-speed production for some reason, and in order to maintain the experience judgment that the head and the tail are qualified and the middle is also qualified, a section of strip steel which is suspected to be unqualified is usually cut off at the moment. The cutting amount has no judgment standard, and can only be cut as much as possible, which obviously causes waste. Fourthly, people need to operate beside the aircraft all the time, the labor intensity is high, and the labor cost is high.
Disclosure of Invention
The invention provides a method for detecting the yield strength of a cold-rolled steel plate based on a multi-frequency eddy current technology, which aims to solve the problems in the prior art, takes the correction of a lift-off parameter influencing an eddy current detection signal into consideration, takes the influence of the thickness of the steel strip into consideration, does not depend on a process real-time parameter of a unit, and realizes the purpose of accurately measuring the yield elongation of the steel strip on line.
The eddy current probe is arranged on one side of moving strip steel, a detection plate is utilized to excite the eddy current probe at a plurality of groups of different frequencies, detected characteristic values of a real part and an imaginary part of the eddy current probe are returned at regular time, and the characteristic values of the real part and the imaginary part of the eddy current probe are respectively recorded as electromagnetic excitation characteristic values; and taking a plurality of groups of electromagnetic excitation characteristic values detected on line as input values to be brought into the BP neural network model to obtain the yield strength of the current band steel.
In the BP neural network model, the yield elongation of the cold-rolled thin strip steel corresponds to eddy current detection characteristic values of a plurality of detection frequencies, the variable is controlled by inputting the thickness parameter of the current strip steel, and the BP neural network mathematical model is established.
In a further improvement, the electromagnetic excitation eigenvalue selects 4 frequencies as detection frequencies of the multifrequency eddy currents, 4 real part eigenvalues and 4 imaginary part eigenvalues detected at four frequency points are used as the electromagnetic excitation eigenvalues, each eigenvalue output of each frequency is a curve signal, and each curve signal is converted into a eigenvalue parameter by definition.
In a further refinement, the electromagnetic excitation eigenvalues include real and imaginary eddy current eigenvalues measured at an excitation frequency of 15Khz, real and imaginary eddy current eigenvalues measured at a frequency of 30Khz, real and imaginary eddy current eigenvalues measured at a frequency of 60Khz, and real and imaginary eddy current eigenvalues measured at a frequency of 95 Khz.
The method is further improved, in the process of detecting the yield strength of the strip steel, the distance between the eddy current probe and the strip steel, namely the lift-off, changes randomly along with time, the current lift-off value is predicted through the characteristic value which is insensitive to the yield strength and sensitive to the lift-off among a plurality of characteristic values, and the current lift-off value is fed back to a system to compensate the prediction result.
The invention has the beneficial effects that:
1. by carrying out multifrequency eddy current nondestructive testing on running strip steel, the characteristic values of eddy current testing under multiple frequencies are obtained in real time, eddy current testing signals are expanded, the lifting-off parameters influencing the eddy current testing signals are considered for correction, the influence of the thickness of the strip steel is considered, the developed method does not depend on the real-time technological parameters of a unit, and the purpose of accurately measuring the yield elongation of the strip steel on line is achieved.
2. Within the relative error precision range of 10%, the sample qualification rate is more than 90%.
3. The method is applied to an online detection system for the mechanical property quality of the cold-rolled strip steel, carries out real-time online detection on the indexes such as the yield elongation and the like of the cold-rolled strip steel, realizes the continuous detection, classification and recording of the production quality of the steel plate, and plays a very positive role in improving the production efficiency, the product quality and the product competitiveness.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of elongation at yield test.
FIG. 2 is a schematic diagram of eddy current testing.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The eddy current probe is arranged on one side of moving strip steel, a detection plate is utilized to excite the eddy current probe at a plurality of groups of different frequencies, detected characteristic values of a real part and an imaginary part of the eddy current probe are returned at regular time, and the characteristic values of the real part and the imaginary part of the eddy current probe are respectively recorded as electromagnetic excitation characteristic values; and taking a plurality of groups of electromagnetic excitation characteristic values detected on line as input values to be brought into the BP neural network model to obtain the yield strength of the current band steel.
In the BP neural network model, the yield elongation of the cold-rolled thin strip steel corresponds to eddy current detection characteristic values of a plurality of detection frequencies, the variable is controlled by inputting the thickness parameter of the current strip steel, and the BP neural network mathematical model is established.
In a further improvement, the electromagnetic excitation eigenvalue selects 4 frequencies as detection frequencies of the multifrequency eddy currents, 4 real part eigenvalues and 4 imaginary part eigenvalues detected at four frequency points are used as the electromagnetic excitation eigenvalues, each eigenvalue output of each frequency is a curve signal, and each curve signal is converted into a eigenvalue parameter by definition.
In a further refinement, the electromagnetic excitation eigenvalues include real and imaginary eddy current eigenvalues measured at an excitation frequency of 15Khz, real and imaginary eddy current eigenvalues measured at a frequency of 30Khz, real and imaginary eddy current eigenvalues measured at a frequency of 60Khz, and real and imaginary eddy current eigenvalues measured at a frequency of 95 Khz.
The method is further improved, in the process of detecting the yield strength of the strip steel, the distance between the eddy current probe and the strip steel, namely the lift-off, changes randomly along with time, the current lift-off value is predicted through the characteristic value which is insensitive to the yield strength and sensitive to the lift-off among a plurality of characteristic values, and the current lift-off value is fed back to a system to compensate the prediction result. The eddy current probe is arranged on one side of moving strip steel, a detection plate is utilized to excite the eddy current probe at a plurality of groups of different frequencies, detected characteristic values of a real part and an imaginary part of the eddy current probe are returned at regular time, and the characteristic values of the real part and the imaginary part of the eddy current probe are respectively recorded as electromagnetic excitation characteristic values; and taking a plurality of groups of electromagnetic excitation characteristic values detected on line as input values to be brought into the BP neural network model to obtain the yield strength of the current band steel.
In the BP neural network model, the yield elongation of the cold-rolled thin strip steel corresponds to eddy current detection characteristic values of a plurality of detection frequencies, the variable is controlled by inputting the thickness parameter of the current strip steel, and the BP neural network mathematical model is established.
In a further improvement, the electromagnetic excitation eigenvalue selects 4 frequencies as detection frequencies of the multifrequency eddy currents, 4 real part eigenvalues and 4 imaginary part eigenvalues detected at four frequency points are used as the electromagnetic excitation eigenvalues, each eigenvalue output of each frequency is a curve signal, and each curve signal is converted into a eigenvalue parameter by definition.
In a further refinement, the electromagnetic excitation eigenvalues include real and imaginary eddy current eigenvalues measured at an excitation frequency of 15Khz, real and imaginary eddy current eigenvalues measured at a frequency of 30Khz, real and imaginary eddy current eigenvalues measured at a frequency of 60Khz, and real and imaginary eddy current eigenvalues measured at a frequency of 95 Khz.
The method is further improved, in the process of detecting the yield strength of the strip steel, the distance between the eddy current probe and the strip steel, namely the lift-off, changes randomly along with time, the current lift-off value is predicted through the characteristic value which is insensitive to the yield strength and sensitive to the lift-off among a plurality of characteristic values, and the current lift-off value is fed back to a system to compensate the prediction result.
Principle of operation
The eddy current detection principle is shown in fig. 2. The changing magnetic field induces an electromotive force and a current in the conductor, i.e., an electromagnetic induction phenomenon. It is known from lenz's law that when the magnetic flux passing through the coil changes, the induced current in the coil always attempts to cause the magnetic field generated by itself to block the change in the magnetic flux of the coil. The magnitude of the electromotive force epsilon induced in the coil is proportional to the rate of change of the magnetic flux phi through the coil with time t, as expressed by:
wherein epsilon is induced electromotive force and has a unit of V; n is the number of turns of the coil; Φ is the magnetic flux through the coil, in Wb; t is time in units of s.
The eddy current test (EC) is based on the electromagnetic induction theory and is mainly suitable for testing various metal materials. As shown in fig. 2, when an excitation coil through which an alternating current is passed is close to a measured object, an eddy current is induced in a measured metal test piece by excitation of an external magnetic field. The amplitude, phase, etc. of the eddy currents are influenced by the object to be measured, and the magnetic field generated by the eddy currents in the object to be measured will also induce a voltage in the excitation coil. Therefore, by observing the change of the induced voltage on the detection coil, the related state information on the object to be detected is obtained.
In the process of eddy current detection, a coil is used as a probe, under the condition of dividing voltage with a resistor, information in a detected object can be reflected through output voltage changes of coils with different sizes, and the output voltage value, the excitation size, the excitation frequency, coil parameters, the lift-off value, state information of the detected object and other factors are related
The conventional eddy current test uses an excitation signal of a single frequency to excite an eddy current probe for testing, and due to the fact that only one excitation frequency exists, the conventional eddy current test is often interfered by some external interference signals which are related to frequency parameters of the conventional eddy current probe and are difficult to eliminate. The multi-frequency eddy current detection is to excite the same or different coils by using two or more excitation frequencies, and eliminate some interference signals which only affect a single frequency by adjusting the amplitude, phase and waveform of the signals, so that the detection result is more accurate.
After a plurality of experiments, the 4 frequencies are selected as the detection frequencies of the multifrequency eddy current, and the yield strength of the strip steel is predicted by using 4 real part characteristic values and 4 imaginary part characteristic values detected under four frequency points.
After experimental data are accumulated in multiple experiments, a BP neural network is used for modeling. And (3) modeling the accumulated experimental data by using a neural network tool box in matlab and the BP neural network in the matlab to obtain a neural network model for predicting the yield strength.
The technology is applied to the on-line detection of a coil of strip steel on one production line. The thickness is 0.7mm, the width is 1600mm, and the total length of the strip steel is 942 m; the detection system has 2453 outputs, i.e. an average of 0.38 meters per measurement. When the detection effect satisfies above-mentioned detection requirement, compared with prior art can only cut the sample and test, data volume and real-time all promote greatly.
The method is used for the on-line measurement of the yield elongation of the strip steel of a certain production line 10, the sampling is the same from head to tail, the numerical value is obtained by adopting an off-line tensile test method, and the obtained result is compared with the elongation value after fracture at the corresponding position of the on-line measurement. Within the relative error precision range of 10%, the sample qualification rate is more than 90%.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus embodiment, the above is only a preferred embodiment of the present invention, and since it is basically similar to the method embodiment, it is described simply, and the relevant points can be referred to the partial description of the method embodiment. The above description is only for the specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the protection scope of the present invention should be covered by the principle of the present invention without departing from the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (5)
1. A cold-rolled steel sheet yield strength detection method based on a multi-frequency eddy current technology is characterized in that: installing an eddy current probe at one side of the moving strip steel, exciting the eddy current probe by using a detection plate at a plurality of groups of different frequencies, periodically returning the detected characteristic values of the real part and the imaginary part of the eddy current probe, and respectively recording the characteristic values of the real part and the imaginary part of the eddy current probe as electromagnetic excitation characteristic values; and taking a plurality of groups of electromagnetic excitation characteristic values detected on line as input values to be brought into the BP neural network model to obtain the yield strength of the current band steel.
2. The method for detecting the yield strength of a cold-rolled steel sheet based on the multifrequency eddy current technology as set forth in claim 1, wherein: the yield elongation of the cold-rolled thin strip steel corresponds to eddy current detection characteristic values of a plurality of detection frequencies by the BP neural network model, and the variable is controlled by inputting the thickness parameter of the current strip steel to establish the BP neural network mathematical model.
3. The method for detecting the yield strength of a cold-rolled steel sheet based on the multifrequency eddy current technology as set forth in claim 1, wherein: the electromagnetic excitation characteristic value selects 4 frequencies as the detection frequency of the multifrequency eddy current, 4 real part characteristic values and 4 imaginary part characteristic values detected at four frequency points are used as the electromagnetic excitation characteristic value, each characteristic value of each frequency is output as a curve signal, and each curve signal is converted into a characteristic value parameter by definition.
4. The method for detecting the yield strength of a cold-rolled steel sheet based on the multifrequency eddy current technology as set forth in claim 3, wherein: the electromagnetic excitation characteristic values comprise eddy current real part and imaginary part characteristic values measured at 15Khz excitation frequency, eddy current real part and imaginary part characteristic values measured at 30Khz excitation frequency, eddy current real part and imaginary part characteristic values measured at 60Khz excitation frequency and eddy current real part and imaginary part characteristic values measured at 95Khz excitation frequency.
5. The method for detecting the yield strength of a cold-rolled steel sheet based on the multifrequency eddy current technology as set forth in claim 1, wherein: in the process of detecting the yield strength of the strip steel, the distance between the eddy current probe and the strip steel is lifted away and changes randomly along with time, the current lifted-away value is predicted through the characteristic value which is insensitive to the yield strength and sensitive to the lifting-away in a plurality of characteristic values, and the predicted result is compensated by feeding back the predicted value to a BP neural network.
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