CN115688540B - Online detection method and device for tensile strain hardening index of cold-rolled thin strip steel - Google Patents
Online detection method and device for tensile strain hardening index of cold-rolled thin strip steelInfo
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Abstract
本发明公开了一种冷轧薄带钢拉伸应变硬化指数在线检测方法及其装置。该冷轧薄带钢拉伸应变硬化指数在线检测方法包括以下步骤：S1.建立冷轧薄带钢的一元线性回归统计数据模型；S2.对一元线性回归统计数据模型的适用性进行训练；S3.将一元线性回归统计数据模型模型应用于冷轧薄带钢拉伸应变硬化指数在线检测。该冷轧薄带钢拉伸应变硬化指数在线检测方法实用性能强，科学性能高，在线控制性能强，减少了切样离线试验法造成的在线控制性能差的技术问题，节约了大量带钢原料，人工成本低。
The present invention discloses a method and device for online detection of the tensile strain hardening index of cold-rolled thin strip steel. The method comprises the following steps: S1. establishing a univariate linear regression statistical data model for the cold-rolled thin strip steel; S2. training the applicability of the univariate linear regression statistical data model; and S3. applying the univariate linear regression statistical data model to online detection of the tensile strain hardening index of the cold-rolled thin strip steel. The method has strong practicality, high scientific performance, and strong online control performance, eliminating the technical problem of poor online control performance caused by offline cutting testing methods, saving a large amount of strip steel raw materials, and reducing labor costs.
Description
Technical Field
The invention relates to the technical field of nondestructive testing, in particular to an online testing method and device for tensile strain hardening index of cold-rolled thin strip steel.
Background
The tensile strain hardening index is also called as a work hardening index or a strain hardening index, and is used for evaluating the press forming property of the sheet. The method for detecting the tensile strain hardening index adopts a static axial tensile test method, and at room temperature, the slope of a relation curve is obtained on a double-logarithmic coordinate plane according to stress-strain data in a uniform plastic deformation range or stress-strain data in a section of a uniform plastic deformation range of a sample. The detection standards are in accordance with GB/T5028, JISZ2253, ASTM E646, etc.
The value of N represents an index on a stress-strain curve, the physical meaning is that the deformation amount is increased when unidirectional stretching occurs necking, the larger the value of N is, the stronger the uniform deformation capacity of the material is represented, and the possibility of local fracture is reduced. The N value is generally 0.2-0.5, the N value of the austenitic steel is larger, and the austenitic stainless steel can reach more than 0.5. Generally, the larger the value, the better, indicating that the material has a stronger work hardening capacity. The larger the N value is, the stronger the drawing capability is, and the punching performance is good. In contrast, when the N value is small, the punching capacity is poor. In the prior art, a sample cutting off-line test method is generally adopted for the detection method of the tensile strain hardening index, and the sample cutting off-line test method has the advantages of simplicity, direct result and high precision. But the method has the following defects that 1, the time lag of data is large, and the on-line control is difficult. 2. The data is incomplete, only the head and tail values of a roll of strip steel can be reflected, and 3, the shearing waste is generated. When the machine set is produced, the machine set is stopped for some reason or produced at a low speed, and in order to maintain the qualification of the head and the tail, the middle is also qualified for experience judgment, and a section of 'suspected unqualified' strip steel is usually cut off. The cutting amount is not judged standard, and the cutting amount can be only as much as possible, so that a large amount of strip steel is wasted. 4. The operation is carried out by a person beside the machine for 24 hours without interruption, the labor intensity is high, and the labor cost is high.
Disclosure of Invention
The invention aims to provide an online detection method for tensile strain hardening indexes of cold-rolled thin strip steel, which is characterized in that a plurality of electromagnetic parameter groups are obtained in real time by applying comprehensive electromagnetic detection to running strip steel, meanwhile, the electromagnetic parameter groups are expanded, the space affecting the electromagnetic parameters is corrected and compensated, the influence of the thickness of the strip steel is considered, and a unitary linear regression statistical data model of the parameter cold-rolled thin strip steel is trained, so that the purpose of online accurate measurement of the tensile strain hardening indexes of the strip steel is realized.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
according to one aspect of the invention, there is provided an on-line detection method for tensile strain hardening index of cold-rolled thin strip steel, comprising the steps of:
s1, establishing a unitary linear regression statistical data model for a cold-rolled thin strip steel tensile strain hardening index;
s2, training the applicability of the unitary linear regression statistical data model;
S3, applying the unitary linear regression statistical data model to determine the tensile strain hardening index of the cold-rolled thin strip steel.
According to the online detection method for the tensile strain hardening index of the cold-rolled thin strip steel, which is disclosed by the invention, the tensile strain hardening index formula of the unitary linear regression statistical data model in S1 is as follows:
The conditions are satisfied: G real is more than or equal to 4 and less than or equal to 6
Wherein N is the tensile strain hardening index of the cold-rolled thin strip steel material, A n is a constant term, X i is a required electromagnetic parameter, C i is a corresponding regression coefficient, A n,Ci is obtained through a data test, G real is an actual fluctuation value of the distance between the strip steel and the sensor, B n is a compensation coefficient, and G real is an actual fluctuation value G of the distance between the strip steel and the sensor.
According to the online detection method for the tensile strain hardening index of the cold-rolled thin strip steel, S2 comprises the following steps:
S21, online detecting a group of electromagnetic parameters by using an online hardware detection system of strip steel, wherein the group of electromagnetic parameters comprises 41 electromagnetic response parameters which correspond to tangential magnetic field harmonic response parameters EM1-EM11, barkhausen noise detection response parameters EM12-EM18, incremental permeability electromagnetic detection response parameters EM19-EM25 and multifrequency eddy current electromagnetic detection response parameters EM26-EM41 respectively;
S22, detecting the actual fluctuation value G of the distance between the strip steel and the sensor and the thickness of the current strip steel on line by using an on-line hardware detection system of the strip steel;
S23, expanding the obtained electromagnetic parameter set according to rules;
s24, screening electromagnetic parameter sets suitable for the unitary linear regression statistical data model from the electromagnetic parameter sets and the extension items according to the data test and analysis;
S25, calculating the tensile strain hardening index of the strip steel.
According to the online detection method for the tensile strain hardening index of the cold-rolled thin strip steel, when G is more than or equal to 4mm and less than or equal to 6mm, the actual fluctuation value is introduced into a unitary linear regression statistical data model to carry out compensation operation, and when G is more than or equal to 6mm or G is less than or equal to 4mm, the electromagnetic parameter deviation is caused to be large, so that the cold-rolled thin strip steel detection system is in an abnormal state.
According to the online detection method for the tensile strain hardening index of the cold-rolled thin strip steel, the formula of the compensation operation is as follows:
B(G-4)
wherein B is a compensation coefficient, data are obtained through test, and G is an actual measurement value of the distance.
According to the online detection method for the tensile strain hardening index of the cold-rolled thin strip steel, the online hardware detection system in the S22 comprises carrier rollers, strip steel supported by the front carrier roller and the rear carrier roller, an electromagnetic detection unit which is positioned below the strip steel and is arranged between the carrier rollers, a probe lifting device which is arranged at the lower part of the electromagnetic detection unit, mechanical limiting devices which are arranged at two sides of the electromagnetic detection unit and a distance meter which is arranged at the side end of the electromagnetic detection unit.
According to the online detection method for the tensile strain hardening index of the cold-rolled thin strip steel, the rule in S23 is as follows:
wherein EM is the original detection electromagnetic signal, NM is the expansion electromagnetic signal.
According to the above aspect of the invention, the online detection method for the tensile strain hardening index of the cold-rolled thin strip steel comprises the following steps of:
S251, acquiring input parameters of the cold-rolled thin strip steel;
s252, substituting the input parameters into a unitary linear regression statistical data model;
S253, obtaining a nondestructive testing value of the tensile strain hardening index of the cold-rolled thin strip steel according to the unitary linear regression statistical data model.
According to the online detection method for the tensile strain hardening index of the cold-rolled thin strip steel, the input parameters in S251 comprise a digital steel coil, electromagnetic signal parameters, a distance between a probe and the strip steel and a gradual regression coefficient table of the tensile strain hardening index of the strip steel.
According to the aspect of the invention, the online detection method for the tensile strain hardening index of the cold-rolled thin strip steel further comprises the following steps:
sampling the head of the strip steel and the tail of the strip steel by adopting an off-line tensile test method to obtain the numerical value of the tensile strain hardening index of the strip steel;
And substituting the obtained value of the step and the elongation after breaking of the corresponding position of the online measurement into the value of the unitary linear regression statistical data model for comparison, and testing whether the sample is qualified.
According to another aspect of the present invention, there is also provided an on-line detection system for tensile strain hardening index of a cold-rolled thin strip steel, comprising:
The electromagnetic detection unit is arranged on the lifting device below the strip steel, and a plurality of electromagnetic response signals are obtained by carrying out electromagnetic detection on the strip steel;
The distance meter is arranged on the electromagnetic detection unit and used for acquiring the distance G between the lower surface of the strip steel and the electromagnetic detection unit;
A control computer for controlling the lifting and transverse movement of the lifting device and controlling the work of the electromagnetic detection unit and the range finder,
The online detection system obtains the tensile strain hardening index of the cold-rolled thin strip steel by executing the online detection method of the tensile strain hardening index of the cold-rolled thin strip steel.
By adopting the technical scheme, the invention has the following advantages:
The invention provides a cold-rolled thin strip steel tensile strain hardening index online detection method and a device thereof, which are characterized in that an electromagnetic parameter set at the corresponding position of the material is obtained by establishing a unitary linear regression statistical data model of the cold-rolled thin strip steel, the distance and the thickness of the strip steel are measured, then the data model is trained, and then the data model is used for online detection.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings.
FIG. 1 is a flow chart of the method for online detection of tensile strain hardening index of a strip steel according to the present invention;
FIG. 2 is a schematic diagram of a strip steel detection system according to the present invention;
FIG. 3 is a block diagram of an on-line hardware detection system for strip steel of the present invention;
FIG. 4 is a flow chart of the calculation of the tensile strain hardening index of the steel strip of the present invention;
FIG. 5 shows the tensile strain hardening index distribution in the full length direction of the steel strip according to the present invention.
Detailed Description
The technical proposal of the invention is specifically described below with reference to the accompanying drawings, the detailed features and advantages of the invention are described in detail in the detailed description, the matters are sufficient to enable any person skilled in the art to understand the technical matters of the present invention and to implement them, and those skilled in the art can easily understand the relevant objects and advantages of the present invention based on the description, claims and drawings disclosed in the present specification.
FIG. 1 shows a flow chart of the on-line detection method of the tensile strain hardening index of the strip steel;
The online detection method for the tensile strain hardening index of the cold-rolled thin strip steel is specifically shown in fig. 1, and comprises the following steps:
S1, establishing a unitary linear regression statistical data model of the cold-rolled thin strip steel;
the basic form of the unitary linear regression statistical data model in S1 is as follows:
The conditions are satisfied: G real is more than or equal to 4 and less than or equal to 6
N is tensile strain hardening index of the material, A is a constant term, X is a required electromagnetic parameter signal, C is a corresponding regression coefficient, A and C are obtained through a data test, and Greal is an actual fluctuation value of the distance between the strip steel and the sensor.
Fig. 2 shows a configuration diagram of the strip steel detection system of the present invention, and fig. 3 shows a structural diagram of the on-line hardware detection system of the strip steel of the present invention.
S2, training the applicability of the unitary linear regression statistical data model;
The complete strip steel detection system of the invention is shown in figure 1, and comprises an online hardware detection system, a matched software system, a mathematical model, a corresponding data interface, a computer network and the like. The invention detects a group of input parameters manually by an on-line hardware detection system of strip steel, and the on-line hardware detection system of strip steel comprises carrier rollers 2, strip steel 1 supported by front and rear carrier rollers, an electromagnetic detection unit 3 positioned below the strip steel 1 and arranged between the carrier rollers, a probe lifting device 5 arranged at the lower part of the electromagnetic detection unit, mechanical limiting devices 6 arranged at two sides of the electromagnetic detection unit 3 and a distance meter 4 arranged at the side end of the electromagnetic detection unit 3.
The working principle of the on-line hardware detection system is that the hardware detection system is shown in fig. 3, the strip steel 1 usually runs at a speed of 0-300m/min, the strip steel 1 realizes the stable running track line of the strip steel 1 through two carrier rollers 2 which are arranged back and forth, an electromagnetic detection unit 3 which can be lifted and transversely moved is arranged between the two carrier rollers 2, the electromagnetic detection unit 3 is arranged below the running strip steel, and the lifting and the transverse movement of the electromagnetic detection unit 3 are realized by a control system. The hardware detection system also comprises a distance meter 4, and the distance meter 4 is used for measuring the distance between the electromagnetic detection unit 3 and the lower surface of the strip steel 1 in real time and sending the distance to the control computer. The probe lifting device 5 realizes the up-and-down motion of the electromagnetic detection unit 3, and the mechanical limiting device 6 ensures the safety distance between the electromagnetic detection unit 3 and the strip steel 1.
Wherein S2 comprises the steps of:
s21, manually measuring a group of input parameters on line through an on-line hardware detection system of the strip steel;
the input parameters in S21 include the electromagnetic parameter set, the actual fluctuation value G of the distance between the strip and the sensor, and the current strip thickness.
In particular, the distance 7 between the lower surface of the strip and the probe lifting device 5 is a key parameter, and the distance between the strips slightly fluctuates due to the influence of external factors such as jitter during the running of the strip 1 and inherent fluctuation of the flatness of the thin strip, and the distance between the strips is measured in real time by the distance meter 4, the target value is 5mm, the allowable error is ±1mm, and the parameter is called G, and is used as one input parameter for detecting a mathematical model. When G is more than or equal to 4mm and less than or equal to 6mm, the actual fluctuation value is introduced into a unitary linear regression statistical data model to carry out compensation operation, and when G is more than 6mm or G is less than 4mm, the electromagnetic parameter deviation is caused to be large, so that the cold-rolled thin strip steel detection system is in an abnormal state.
The formula of the compensation calculation is B (G-4), wherein B is a compensation coefficient, B is obtained through a data test, and G is an actually measured distance value between the strip steel and the sensor.
S22, an online hardware detection system of the strip steel is utilized, and a plurality of detection methods are comprehensively applied to obtain electromagnetic parameter groups of the cold-rolled thin strip steel;
The on-line hardware detection system of the strip steel is a physical basis for detection. The on-line hardware detection system of the strip steel in the technical scheme comprehensively uses four methods of tangential magnetic field harmonic analysis, barkhausen noise, incremental magnetic permeability and multi-frequency eddy current to comprehensively measure, and each electromagnetic measurement method outputs a curve signal. For ease of application, the resulting curves of the four electromagnetic detections described above are characterized by defining a transformation into a number of quantization parameters. The details are shown in tables 1-4 below:
Table 1 excitation field tangential magnetic field harmonic response parameters (11 entries, EMi, i=1.,. The.
Table 2 barkhausen noise detection response parameters (total 7 entries, EMi, i=12..the use of 18)
Table 3 delta permeability electromagnetic test response parameters (total 7 entries, EMi, i=19..the., 25)
Table 4 multifrequency eddy current electromagnetic test response parameters (16 items total, EMi, i=20.,. The.,. 41.)
On-line hardware detection system of strip steel outputs 41 electromagnetic parameters at most
S23, expanding the obtained electromagnetic parameter set according to rules;
Expanding the 41 electromagnetic parameters according to the rule
Wherein EM is the original detection electromagnetic signal, and NM is the extension electromagnetic signal.
The tensile strain hardening index of the steel strip is represented by "N90" below.
S24, according to data test and analysis, an electromagnetic parameter set suitable for the unitary linear regression statistical data model is screened from the electromagnetic parameter set and the extension items, and in a specific embodiment, the following 25 electromagnetic parameters are obtained from 41 electromagnetic parameters and the extension items thereof, and can be used for calculating the N90 value of the strip steel as shown in the following table 5:
TABLE 5 electromagnetic parameter sets selected
| X | EM equipment numbering | EMi |
| X1 | EM1 | A3 |
| X2 | EM3 | A7 |
| X3 | EM9 | Hco |
| X4 | EM10 | Hro |
| X5 | EM11 | Vmag |
| X6 | EM13 | MMEAN |
| X7 | EM20 | UMEAN |
| X8 | EM23 | DH25U |
| X9 | EM36 | Mag3 |
| X10 | EM41 | Ph4 |
| X11 | EM1’ | A3 |
| X12 | EM2’ | A5 |
| X13 | EM10’ | Hro |
| X14 | EM22’ | HCU |
| X15 | EM27’ | Re2 |
| X16 | EM29’ | Re4 |
| X17 | EM36’ | Mag3 |
| X18 | EM41’ | Ph4 |
FIG. 4 shows a flow chart for calculating the tensile strain hardening index of the steel strip of the present invention.
S25, calculating the tensile strain hardening index of the strip steel.
Step S25 includes the following specific steps as shown in fig. 4:
S251, acquiring input signal indexes of the cold-rolled thin strip steel;
S252, substituting the input signal index into a unitary linear regression statistical data model frame;
S253, performing signal processing on the input signal index to obtain a nondestructive testing value of the tensile strain hardening index of the cold-rolled thin strip steel.
The input signal index comprises a digital steel coil, electromagnetic signal parameters, a distance between a probe and the strip steel and a gradual regression coefficient table of the strip steel tensile strain hardening index.
In a specific embodiment, a mathematical model of N90 is calculated:
The conditions are satisfied: G real is more than or equal to 4 and less than or equal to 6
X and G real are electromagnetic parameter variables and interval variables, and are obtained through online measurement, and An90, C and Bn90 are obtained through a certain-scale data test as follows:
the C coefficients corresponding to a n90＝-241.6679,Bn90 = 0.05689 and electromagnetic parameter set X are detailed in table 6 below:
TABLE 6 electromagnetic parameter set and coefficient values
S3, applying the unitary linear regression statistical data model to online detection of the tensile strain hardening index of the cold-rolled thin strip steel.
S3 comprises the following specific steps:
S31, sampling the head of the strip steel and the tail of the strip steel by adopting an off-line tensile test method to obtain the numerical value of the tensile strain hardening index of the strip steel;
s32, substituting the obtained value of the S31 and the elongation after break of the corresponding position of the online measurement into the value of the unitary linear regression statistical data model for comparison, and testing whether the sample is qualified.
In a specific embodiment, the technical scheme is applied to online detection of a roll of strip steel on a production line, the thickness of the strip steel is 0.655mm, the width of the strip steel is 1565mm, the total length of the strip steel is 3309m, and an online hardware detection system has 2900 outputs, namely an average of 1.14 m of measurement results.
N90 mathematical model
Wherein, the values of An90, C coefficient and Bn90 are brought in, and the input parameters X and G obtained by real-time detection are obtained, and the following calculation results are shown in the following table 7:
TABLE 7 actual values, spacing values and calculated values of N90 electromagnetic parameters
FIG. 5 shows the tensile strain hardening index distribution of the full length direction of a strip steel according to the present invention, and the results of the measurement of the tensile strain hardening index (N90) of the full length direction of a strip steel using a unitary linear regression statistical data model for real-time measurement of a roll of the strip steel are shown in FIG. 5 below, wherein the abscissa in FIG. 5 is the strip steel length (m) and the ordinate is the tensile strain hardening index (N90) of the strip steel. Compared with the prior art, the method can only cut the sample for testing, and the data volume and the real-time performance are greatly improved.
The online detection method of the tensile strain hardening index of the cold rolled thin strip steel is applied to online measurement of the plastic strain ratio of 100 rolls of SEDDQ strip steel in a certain production line, and the off-line tensile test method is adopted to obtain the elongation after break as well as 2000 groups, the obtained result is compared with the corresponding position value of the online measurement, the reliability is 94%, and the qualification rate of the sample is more than 90% within the relative error precision range of 10%.
The invention also provides an online detection system of the tensile strain hardening index of the cold-rolled thin strip steel, which comprises the following steps:
The electromagnetic detection unit is arranged on the lifting device below the strip steel, and a plurality of electromagnetic response signals are obtained by carrying out electromagnetic detection on the strip steel;
The distance meter is arranged on the electromagnetic detection unit and used for acquiring the distance G between the lower surface of the strip steel and the electromagnetic detection unit;
A control computer for controlling the lifting and transverse movement of the lifting device and controlling the work of the electromagnetic detection unit and the range finder,
The online detection system obtains the tensile strain hardening index of the cold-rolled thin strip steel by executing the online detection method of the tensile strain hardening index of the cold-rolled thin strip steel.
Finally, it is pointed out that while the invention has been described with reference to a specific embodiment thereof, it will be understood by those skilled in the art that the above embodiments are provided for illustration only and not as a definition of the limits of the invention, and various equivalent changes or substitutions may be made without departing from the spirit of the invention, therefore, all changes and modifications to the above embodiments shall fall within the scope of the appended claims.
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| CN107520257A (en) * | 2016-06-22 | 2017-12-29 | 上海宝钢工业技术服务有限公司 | Strip mechanical performance on-line detecting system |
| CN111241750A (en) * | 2020-01-14 | 2020-06-05 | 邯郸钢铁集团有限责任公司 | BP network cold-rolled strip steel mechanical property prediction method combining genetic algorithm |
| CN112231368A (en) * | 2020-09-18 | 2021-01-15 | 邯郸钢铁集团有限责任公司 | Unary linear regression analysis method based on steel production big data |
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| JP2006523143A (en) * | 2003-03-28 | 2006-10-12 | ターター スチール リミテッド | Online property prediction system and method for hot rolled coils in hot strip mills |
| CN102886383B (en) * | 2011-07-22 | 2015-03-04 | 宝山钢铁股份有限公司 | Method for controlling mechanical properties of cold-rolling strip steel in online manner |
| CN108280272B (en) * | 2018-01-05 | 2020-07-31 | 北京科技大学 | A Prediction Method for Surface Roughness of Textured Work Rolls During Cold Rolling |
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| CN107520257A (en) * | 2016-06-22 | 2017-12-29 | 上海宝钢工业技术服务有限公司 | Strip mechanical performance on-line detecting system |
| CN111241750A (en) * | 2020-01-14 | 2020-06-05 | 邯郸钢铁集团有限责任公司 | BP network cold-rolled strip steel mechanical property prediction method combining genetic algorithm |
| CN112231368A (en) * | 2020-09-18 | 2021-01-15 | 邯郸钢铁集团有限责任公司 | Unary linear regression analysis method based on steel production big data |
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