A method and device for online detection of tensile strain hardening index of cold-rolled thin strip steel

Abstract

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

[0001] The present invention relates to the technical field of non-destructive testing, and more particularly to an online detection method and device for the tensile strain hardening index of cold-rolled thin strip steel. Background Art

[0002] The tensile strain hardening index, also known as the work hardening index or strain hardening index, is used to evaluate the stamping performance of thin sheet metal. The tensile strain hardening index is determined using a static axial tensile test. At room temperature, the slope of the curve is calculated on a logarithmic coordinate plane based on the stress-strain data within the uniform plastic deformation range after yield, or within a certain range within the uniform plastic deformation range. Testing standards are in accordance with GB / T 5028, JIS Z2253, ASTM E646, and other standards.

[0003] The N value represents an exponent on the stress-strain curve. Its physical meaning is the amount of deformation that occurs when necking occurs during uniaxial tension. A larger N value indicates a greater ability of the material to deform uniformly, thus reducing the likelihood of localized cracking. The N value is generally between 0.2 and 0.5, with austenitic steel having a higher N value, and austenitic stainless steel can reach over 0.5. Generally, a higher value is better, indicating a material with greater work hardening capacity. A larger N value indicates greater drawability and better stamping performance. Conversely, a smaller N value indicates poor stamping performance. Existing methods for measuring the tensile strain hardening index typically use off-line testing using cut specimens. This method offers the advantages of simplicity, direct results, and high accuracy. However, this method has the following drawbacks: 1. Significant data lag, making online control difficult. 2. Incomplete data, reflecting only the values ​​at the beginning and end of a coil. 3. Cutting waste. If a mill is shut down or operating at a reduced speed for some reason, a section of the strip that is suspected of failing must be removed to ensure that the beginning and end of the coil are acceptable, based on empirical evidence that the middle section is also acceptable. There is no standard for how much to cut, so you can only cut as much as possible, resulting in a lot of strip waste. 4. It requires someone to work at the machine 24 hours a day, which is labor-intensive and has high labor costs. Summary of the Invention

[0004] The purpose of the present invention is to provide an online detection method for the tensile strain hardening index of cold-rolled thin strip steel. By applying comprehensive electromagnetic detection to the running strip steel, multiple electromagnetic parameter groups are obtained in real time. At the same time, the electromagnetic parameter groups are expanded, and the spacing affecting the electromagnetic parameters is corrected and compensated. The influence of the strip steel thickness is taken into account, and a univariate linear regression statistical data model of the parameter cold-rolled thin strip steel is trained to achieve the purpose of online accurate measurement of the tensile strain hardening index of the strip steel.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] According to one aspect of the present invention, a method for online detection of the tensile strain hardening index of cold-rolled thin strip steel is provided, comprising the following steps:

[0007] S1. Establish a univariate linear regression statistical data model for the tensile strain hardening exponent of cold-rolled thin strip steel;

[0008] S2. train the applicability of the univariate linear regression statistical data model;

[0009] S3. Application of a univariate linear regression statistical data model to determine the tensile strain hardening exponent of cold-rolled thin strip steel.

[0010] According to the above aspect of the present invention, an online detection method for the tensile strain hardening index of cold-rolled thin strip steel is provided, wherein the tensile strain hardening index formula of the univariate linear regression statistical data model in S1 is as follows:

[0011]

[0012] Satisfy the conditions: 4≤G real ≤6

[0013] Where N is the tensile strain hardening index of the cold-rolled thin strip steel material, A n is a constant term, X i is the required electromagnetic parameter, C i is the corresponding regression coefficient, A n , C i All obtained through data experiments, G real is the actual fluctuation value of the distance between the strip and the sensor, B n is the compensation coefficient, G real is the actual fluctuation value G of the distance between the strip and the sensor.

[0014] According to the above aspect of the present invention, an online detection method for the tensile strain hardening index of cold-rolled thin strip steel is provided, wherein S2 comprises the following steps:

[0015] S21. A set of electromagnetic parameters is detected online using an online hardware detection system for the strip; the set of electromagnetic parameters includes 41 electromagnetic response parameters, corresponding 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 multi-frequency eddy current electromagnetic detection response parameters EM26-EM41;

[0016] S22. Using the strip online hardware detection system to detect the actual fluctuation value G of the distance between the strip and the sensor and the current strip thickness;

[0017] S23. Expanding the obtained electromagnetic parameter group according to the rules;

[0018] S24. Based on data testing and analysis, select an electromagnetic parameter group suitable for a univariate linear regression statistical data model from the electromagnetic parameter group and the extended items;

[0019] S25. Calculate the tensile strain hardening exponent of the strip steel.

[0020] According to the above-mentioned aspect of the present invention, in an online detection method for the tensile strain hardening index of cold-rolled thin strip steel, when 4mm≤G≤6mm, the actual fluctuation value is introduced into the univariate linear regression statistical data model to perform compensation calculation; when G>6mm or G<4mm, it leads to large deviation of the electromagnetic parameters, indicating that the cold-rolled thin strip steel detection system is in an abnormal state.

[0021] According to the above aspect of the present invention, in an online detection method for the tensile strain hardening index of cold-rolled thin strip steel, the compensation calculation formula is as follows:

[0022] B(G-4)

[0023] Among them, B is the compensation coefficient, which is obtained from data experiments, and G is the actual measured value of the spacing.

[0024] According to the above aspect of the present invention, an online detection method for the tensile strain hardening index of cold-rolled thin strip steel is provided, wherein the online hardware detection system in S22 includes rollers, a strip steel supported by two front and rear rollers, an electromagnetic detection unit located below the strip steel and arranged between the rollers, a probe lifting device arranged at the bottom of the electromagnetic detection unit, mechanical limit devices arranged on both sides of the electromagnetic detection unit, and a distance measuring instrument arranged at the side end of the electromagnetic detection unit.

[0025] According to the above aspect of the present invention, in an online detection method for the tensile strain hardening index of cold-rolled thin strip steel, the rule in S23 is as follows:

[0026]

[0027] Among them, EM is the original detection electromagnetic signal; NM is the expanded electromagnetic signal.

[0028] According to the above aspect of the present invention, in an online detection method for the tensile strain hardening index of cold-rolled thin strip steel, step S25 includes the following steps:

[0029] S251 obtains input parameters of cold-rolled thin strip;

[0030] S252. Substitute the input parameters into the univariate linear regression statistical data model;

[0031] S253. Obtain the nondestructive testing value of the tensile strain hardening exponent of cold-rolled thin strip steel based on a univariate linear regression statistical data model.

[0032] According to the above aspect of the present invention, an online detection method for the tensile strain hardening index of cold-rolled thin strip steel is provided, wherein the input parameters in S251 include digital steel coil, electromagnetic signal parameters, the distance between the probe and the strip steel, and a stepwise regression coefficient table for the tensile strain hardening index of the strip steel.

[0033] According to the above aspect of the present invention, an online detection method for the tensile strain hardening index of cold-rolled thin strip steel further includes the following steps:

[0034] The head and tail of the strip are sampled identically using an off-line tensile test method to obtain the value of the tensile strain hardening index of the strip;

[0035] Substitute the values ​​obtained in the above steps and the elongation at break at the corresponding position measured online into the values ​​of the univariate linear regression statistical data model for comparison to test whether the sample is qualified.

[0036] According to another aspect of the present invention, there is also provided an online detection system for the tensile strain hardening index of cold-rolled thin strip steel, comprising:

[0037] The electromagnetic detection unit is installed on the lifting device below the strip steel and performs electromagnetic detection on the strip steel to obtain multiple electromagnetic response signals;

[0038] a distance meter, provided on the electromagnetic detection unit, for obtaining a distance G between the lower surface of the steel strip and the electromagnetic detection unit;

[0039] A control computer is used to control the lifting and lateral movement of the lifting device, and to control the operation of the electromagnetic detection unit and the rangefinder,

[0040] The online detection system obtains the tensile strain hardening index of the cold-rolled thin strip steel by executing the online detection method for the tensile strain hardening index of the cold-rolled thin strip steel.

[0041] By adopting the above technical solution, the present invention has the following advantages:

[0042] The present invention provides an online detection method and device for the tensile strain hardening index of cold-rolled thin strip steel. By establishing a univariate linear regression statistical data model for the cold-rolled thin strip steel, an electromagnetic parameter set at corresponding positions of the material is obtained, the spacing and the thickness of the strip steel are measured, and then the data model is trained. The data model is then used for online detection. The online detection method for the tensile strain hardening index of cold-rolled thin strip steel has strong practical performance, high scientific performance, and strong online control performance. It reduces the technical problem of poor online control performance caused by the offline cutting test method, saves a large amount of strip steel raw materials, and reduces labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Other features, objects and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0044] Figure 1 This is a flow chart of the on-line detection method for the tensile strain hardening index of steel strip according to the present invention;

[0045] Figure 2 It is a configuration diagram of the strip steel detection system of the present invention;

[0046] Figure 3 It is a structural diagram of the online hardware detection system for steel strip of the present invention;

[0047] Figure 4 is a flow chart for calculating the tensile strain hardening index of the strip steel of the present invention;

[0048] Figure 5 It is the tensile strain hardening index distribution along the entire length of the steel strip of the present invention. DETAILED DESCRIPTION

[0049] The technical solution of the present invention is described in detail below in conjunction with the drawings of the specification. The detailed features and advantages of the present invention are described in detail in the specific implementation mode. The content is sufficient to enable any technical personnel in this field to understand the technical content of the present invention and implement it accordingly. According to the description, claims and drawings disclosed in this specification, those skilled in the art can easily understand the relevant purposes and advantages of the present invention.

[0050] Figure 1 A flow chart showing the method for online detection of the tensile strain hardening index of a steel strip according to the present invention;

[0051] A method for online detection of tensile strain hardening index of cold-rolled thin strip steel is as follows: Figure 1 As shown, the following steps are included:

[0052] S1. Establish a linear regression statistical data model for cold-rolled thin strip steel;

[0053] The basic form of the univariate linear regression statistical data model in S1 is as follows:

[0054]

[0055] Satisfy the conditions: 4≤G real ≤6

[0056] Among them, N is the tensile strain hardening index of the material, A is the constant term, X is the required electromagnetic parameter signal, C is the corresponding regression coefficient, A and C are obtained through data experiments, and Greal is the actual fluctuation value of the distance between the strip and the sensor.

[0057] Figure 2Shows the configuration diagram of the strip steel detection system of the detection system of the present invention; Figure 3 The structural diagram of the online hardware detection system for strip steel of the present invention is shown.

[0058] S2. train the applicability of the univariate linear regression statistical data model;

[0059] The complete strip steel detection system of the present invention is as follows Figure 1 As shown, the system includes an online hardware detection system, a supporting software system, a mathematical model, and corresponding data interfaces and computer networks. The present invention manually detects a set of input parameters through the online hardware detection system for the steel strip. The online hardware detection system for the steel strip includes rollers 2, a steel strip 1 supported by two front and rear rollers, an electromagnetic detection unit 3 located below the steel strip 1 and disposed between the rollers, a probe lifting device 5 disposed below the electromagnetic detection unit, mechanical limit devices 6 disposed on both sides of the electromagnetic detection unit 3, and a distance meter 4 disposed on the side ends of the electromagnetic detection unit 3.

[0060] The working principle of the online hardware detection system is as follows: Figure 3 As shown, the steel strip 1 typically runs at a speed of 0-300 m / min. It passes between two rollers 2 arranged one behind the other, ensuring a stable running trajectory. An electromagnetic detection unit 3, capable of both raising and lowering and lateral movement, is positioned between the rollers 2. This electromagnetic detection unit 3 is positioned beneath the running strip and is controlled by a control system. The hardware detection system also includes a distance meter 4, which measures the distance between the electromagnetic detection unit 3 and the bottom surface of the steel strip 1 in real time and transmits this information to the control computer. A probe lifting mechanism 5 enables the electromagnetic detection unit 3 to move up and down, while a mechanical limiter 6 ensures a safe distance between the electromagnetic detection unit 3 and the steel strip 1.

[0061] Among them, S2 includes the following steps:

[0062] S21. A set of input parameters are manually measured online by the strip online hardware detection system;

[0063] The input parameters in S21 include the electromagnetic parameter group, the actual fluctuation value G of the distance between the strip and the sensor, and the current strip thickness.

[0064] In particular, the distance 7 between the lower surface of the strip and the probe lifting device 5 is a key parameter. Due to external factors such as the vibration of the strip 1 during operation and the inherent fluctuations in the flatness of thin strip steel, the distance between the strips fluctuates slightly. This distance is measured in real time by a distance meter 4, with a target value of 5mm and an allowable error of ±1mm. This parameter, called G, serves as an input parameter of the detection mathematical model. When 4mm≤G≤6mm, the actual fluctuation value is introduced into the univariate linear regression statistical data model for compensation. When G>6mm or G<4mm, the electromagnetic parameter deviation is large, indicating that the cold-rolled thin strip detection system is in an abnormal state.

[0065] The compensation calculation formula is: B(G-4), where: B is the compensation coefficient, B is obtained through data experiments, and G is the actual measured distance value between the strip and the sensor.

[0066] S22. Using an online hardware detection system for steel strip, a combination of several detection methods is used to obtain an electromagnetic parameter set for cold-rolled thin steel strip;

[0067] The strip steel online hardware detection system is the physical basis of detection. In this technical solution, the strip steel online hardware detection system integrates four measurement methods: tangential magnetic field harmonic analysis, Barkhausen noise, incremental magnetic permeability, and multi-frequency eddy current. Since each electromagnetic measurement method outputs a curve signal, for ease of application, the result curves of the above four electromagnetic tests are defined and converted into several quantitative parameters to represent them. The details are shown in Tables 1-4 below:

[0068] Table 1 Excitation magnetic field tangential magnetic field harmonic response parameters (11 items in total, EMi, i = 1, ..., 11)

[0069]

[0070]

[0071] Table 2 Barkhausen noise detection response parameters (7 items in total, EMi, i = 12, ..., 18)

[0072]

[0073] Table 3 Incremental permeability electromagnetic detection response parameters (7 items in total, EMi, i = 19, ..., 25)

[0074]

[0075] Table 4 Multi-frequency eddy current electromagnetic testing response parameters (16 items in total, EMi, i = 20, ..., 41)

[0076]

[0077] The online hardware detection system for strip steel can output up to 41 electromagnetic parameters

[0078] S23. Expanding the obtained electromagnetic parameter group according to the rules;

[0079] Expand the 41 electromagnetic parameters according to this rule

[0080]

[0081] Wherein: EM is the original detection electromagnetic signal; NM is the expanded electromagnetic signal.

[0082] In the following, "N90" is used to represent the tensile strain hardening index of the strip steel.

[0083] S24. Based on data testing and analysis, an electromagnetic parameter group suitable for a univariate linear regression statistical data model is selected from the electromagnetic parameter group and its extensions. In a specific embodiment, the following 25 electromagnetic parameters are obtained from the 41 electromagnetic parameters and their extensions, as shown in Table 5 below, and can be used to calculate the N90 value of the steel strip:

[0084] Table 5. Screened electromagnetic parameter groups

[0085] X EM equipment number EMi X1 EM1 A3 X2 EM3 A7 X3 EM9 <![CDATA[H co ]]> X4 EM10 <![CDATA[H ro ]]> X5 EM11 <![CDATA[V mag ]]> X6 EM13 <![CDATA[M MEAN ]]> X7 EM20 <![CDATA[U MEAN ]]> X8 EM23 <![CDATA[DH25 U ]]> X9 EM36 Mag3 X10 EM41 Ph4 X11 EM1' A3 X12 EM2' A5 X13 EM10' <![CDATA[H ro ]]> X14 EM22' <![CDATA[H CU ]]> X15 EM27' Re2 X16 EM29' Re4 X17 EM36' Mag3 X18 EM41' Ph4

[0086] Figure 4 The flowchart of calculating the tensile strain hardening index of the steel strip according to the present invention is shown.

[0087] S25. Calculate the tensile strain hardening exponent of the strip steel.

[0088] Step S25 includes the following specific steps: Figure 4 As shown:

[0089] S251 obtains the input signal index of the cold-rolled thin strip;

[0090] S252. Substitute the input signal indicator into the univariate linear regression statistical data model framework;

[0091] S253. Perform signal processing on the input signal index to obtain a non-destructive testing value of the tensile strain hardening index of the cold-rolled thin strip steel.

[0092] Among them: the input signal indicators include digital steel coil, electromagnetic signal parameters, the distance between the probe and the strip steel, and the stepwise regression coefficient table of the strip steel tensile strain hardening index.

[0093] In a specific embodiment, the mathematical model of N90 is calculated as follows:

[0094]

[0095] Satisfy the conditions: 4≤G real ≤6

[0096] X and G real are electromagnetic parameter variables and spacing variables, which are obtained through online measurement. Through a certain scale of data experiments, An90, C, and Bn90 are obtained as follows:

[0097] A n90 =-241.6679, B n90 = 0.05689 and the C coefficient corresponding to the electromagnetic parameter set X are shown in Table 6 below:

[0098] Table 6: Electromagnetic parameter sets and coefficient values

[0099]

[0100]

[0101] S3. Applying the univariate linear regression statistical data model to the online detection of tensile strain hardening index of cold-rolled thin strip steel.

[0102] S3 includes the following specific steps:

[0103] S31. Using an offline tensile test method, the strip head and the strip tail are sampled identically to obtain the value of the tensile strain hardening index of the strip;

[0104] S32. Substitute the value obtained in S31 and the elongation at break at the corresponding position measured online into the value of the univariate linear regression statistical data model for comparison to test whether the sample is qualified.

[0105] In a specific embodiment, the above technical solution was applied on a production line for online detection of a roll of steel strip with a thickness of 0.655 mm, a width of 1565 mm, and a total length of 3309 m. The online hardware detection system had 2900 outputs, which means an average of one measurement result every 1.14 m.

[0106] N90 mathematical model

[0107]

[0108] Where: Substituting An90, C coefficient, Bn90 value, and input parameters X and G obtained by real-time detection, the following calculation results are obtained as shown in Table 7:

[0109] Table 7 Actual values, spacing values ​​and calculated values ​​of N90 electromagnetic parameters

[0110]

[0111] Figure 5The tensile strain hardening index distribution of the strip steel in the full length direction of the present invention is as follows: Figure 5 As shown, Figure 5 The horizontal axis represents the strip length (m), and the vertical axis represents the strip's tensile strain hardening index (N90). Compared to existing technologies that rely solely on shearing specimens for testing, this method significantly increases both data volume and real-time performance.

[0112] An online detection method for the tensile strain hardening index of cold-rolled thin strip steel was applied to the online measurement of the plastic strain ratio of 100 rolls of SEDDQ strip steel on a certain production line. The same samples were taken at the head and tail, and the elongation after fracture was obtained by offline tensile testing. A total of 2000 groups of results were compared with the corresponding position values ​​measured online, with a reliability of 94%. Within the relative error accuracy range of 10%, the qualified rate of the samples was above 90%.

[0113] The present invention also provides an online detection system for the tensile strain hardening index of cold-rolled thin strip steel, comprising:

[0114] The electromagnetic detection unit is installed on the lifting device below the strip steel and performs electromagnetic detection on the strip steel to obtain multiple electromagnetic response signals;

[0115] a distance meter, provided on the electromagnetic detection unit, for obtaining a distance G between the lower surface of the steel strip and the electromagnetic detection unit;

[0116] A control computer is used to control the lifting and lateral movement of the lifting device, and to control the operation of the electromagnetic detection unit and the rangefinder,

[0117] The online detection system obtains the tensile strain hardening index of the cold-rolled thin strip steel by executing the online detection method for the tensile strain hardening index of the cold-rolled thin strip steel.

[0118] Finally, it should be pointed out that although the present invention has been described with reference to the current specific embodiments, ordinary technicians in this technical field should realize that the above embodiments are only used to illustrate the present invention and are not used to limit the present invention. Various equivalent changes or substitutions can be made without departing from the concept of the present invention. Therefore, as long as the changes and modifications to the above embodiments are within the scope of the essential spirit of the present invention, they will fall within the scope of the claims of the present invention.

Claims

1. A method for online detection of tensile strain hardening index of cold-rolled thin strip steel, characterized in that: The following steps are involved: S1. Establish a univariate linear regression statistical data model for the tensile strain hardening exponent of cold-rolled thin strip steel; S2. Train the applicability of the univariate linear regression statistical data model, The S2 comprises the following steps: S21. Using the online hardware detection system for the strip to detect a set of electromagnetic parameters online; The set of electromagnetic parameters includes 41 electromagnetic response parameters, which respectively correspond to tangential magnetic field harmonic response parameters EM1-EM11, Barkhausen noise detection response parameters EM12-EM18, incremental magnetic permeability electromagnetic detection response parameters EM19-EM25 and multi-frequency eddy current electromagnetic detection response parameters EM26-EM41; S22. Using the strip online hardware detection system to detect the actual fluctuation value G of the distance between the strip and the sensor and the current strip thickness; S23. Expanding the obtained electromagnetic parameter group according to the rules; S24. Based on data testing and analysis, select an electromagnetic parameter group suitable for a univariate linear regression statistical data model from the electromagnetic parameter group and the extended items; S25. Calculate the tensile strain hardening exponent of the strip; S3. Applying a linear regression statistical data model to determine the tensile strain hardening exponent of cold-rolled thin strip steel, The tensile strain hardening exponent formula of the univariate linear regression statistical data model is as follows: Satisfy the conditions: 4≤G real ≤6 Where N is the tensile strain hardening index of the cold-rolled thin strip steel material, A n is a constant term, X i is the electromagnetic parameter, C i is the corresponding regression coefficient, A n , C i All obtained through experiments, G real is the actual fluctuation value of the distance between the strip and the sensor, B n is the compensation coefficient, G real is the actual fluctuation value G of the distance between the strip and the sensor.

2. The method for online detection of tensile strain hardening index of cold-rolled thin strip steel according to claim 1, characterized in that: When 4mm≤G≤6mm, the actual fluctuation value is introduced into the univariate linear regression statistical data model for compensation calculation; when G>6mm or G<4mm, the electromagnetic parameter deviation is large, indicating that the cold-rolled thin strip steel detection system is in an abnormal state.

3. The online detection method for tensile strain hardening index of cold-rolled thin strip steel according to claim 1, characterized in that: The rule in S23 is as follows: Among them, EM is the original detection electromagnetic signal, and NM is the expanded electromagnetic signal.

4. The online detection method for tensile strain hardening index of cold-rolled thin strip steel according to claim 1, characterized in that: The step S25 includes the following steps: S251 obtains input parameters of cold-rolled thin strip; S252. Substitute the input parameters into the univariate linear regression statistical data model; S253. Obtain the nondestructive testing value of the tensile strain hardening exponent of cold-rolled thin strip steel based on a univariate linear regression statistical data model.

5. The method for online detection of tensile strain hardening index of cold-rolled thin strip steel according to claim 4, characterized in that: The input parameters in S251 include the digital steel coil, electromagnetic signal parameters, the distance between the probe and the strip, and the stepwise regression coefficient table of the strip tensile strain hardening exponent.

6. The online detection method for tensile strain hardening index of cold-rolled thin strip steel according to claim 1, characterized in that: The following steps are also included: The head and tail of the strip are sampled identically using an off-line tensile test method to obtain the value of the tensile strain hardening index of the strip; Substitute the values ​​obtained in the above steps and the elongation at break at the corresponding position measured online into the values ​​of the univariate linear regression statistical data model for comparison to test whether the sample is qualified.

7. An online detection system for tensile strain hardening index of cold-rolled thin strip steel, characterized in that include: The electromagnetic detection unit is installed on the lifting device below the strip steel and performs electromagnetic detection on the strip steel to obtain multiple electromagnetic response signals; a distance meter, provided on the electromagnetic detection unit, for obtaining a distance G between the lower surface of the steel strip and the electromagnetic detection unit; A control computer is used to control the lifting and lateral movement of the lifting device, and to control the operation of the electromagnetic detection unit and the rangefinder, The online detection system obtains the tensile strain hardening index of the cold-rolled thin strip steel by executing the online detection method for the tensile strain hardening index of the cold-rolled thin strip steel according to any one of claims 1 to 6.

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