Compensation method of high-strength cold-rolled strip steel deformation resistance model

Abstract

The invention discloses a compensation method for a high-strength cold-rolled strip steel deformation resistance model, and belongs to the field of cold-rolled strip steel production. According to the method, regression fitting is carried out on different deformation rates and corresponding yield strengths to obtain a basic deformation resistance model, an artificial neural network is trained in combination with data of strip steel not lower than 450 coils, an artificial neural network model is established to obtain a deformation resistance curve, and back calculation is carried out on multiple deformation rates corresponding to a platform period and a rapid rising period of the deformation resistance curve to obtain a deformation resistance curve. The deformation resistance Ka of the strip steel is calculated, reduction rate correction factors Ff corresponding to different true strains are calculated, a deformation resistance correction factor C is obtained, and therefore a corrected deformation resistance model K is obtained. According to the method, through a'physical model + data driving 'compensation or optimization method combining field data with laboratory data, a deformation resistance model with clear physical significance and high precision is obtained. Therefore, the purposes of improving the precision of the deformation resistance model, further improving the setting precision of the rolling force and the roll bending force and improving the thickness fluctuation and the plate shape defect of the strip steel are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of cold-rolled strip steel production, and in particular to a compensation method for a deformation resistance model of high-strength cold-rolled strip steel. Background Art

[0002] Deformation resistance is the force that resists plastic deformation when metal undergoes plastic deformation. In the cold rolling process, it is often accompanied by a strong work hardening phenomenon, so the deformation resistance increases with the increase of deformation degree. The deformation resistance model obtained by regression of experimental data can be used to predict the deformation resistance of the cold rolling process, and then the process parameters such as rolling force and bending roll force can be set to improve the quality of cold rolled products. However, high-strength cold-rolled strip steel is a kind of cold-rolled strip steel with a tensile strength of ≥600MPa. The strain hardening characteristics of high-strength cold-rolled strip steel are different from those of ordinary cold-rolled strip steel. Under certain strains, its deformation resistance does not increase with the increase of strain, and a so-called plateau period occurs (such as Figure 1 At the same time, many factors such as material organization uniformity, rolling mill equipment and lubrication conditions will affect the deformation resistance, and the accuracy of the theoretical model obtained in the laboratory needs to be improved.

[0003] The Chinese invention patent with application number 202210152425.9 discloses "a method and optimization method for predicting the full-length deformation resistance of cold-rolled strip steel", which predicts the deformation resistance of the entire coil of strip steel through hot rolling production data and cold rolling historical production data. This method does not conduct laboratory experiments, relies too much on actual data, and does not have a good grasp of the deformation characteristics of the material itself. When there is less accumulation of actual production data, the model accuracy is low.

[0004] The Chinese invention patent with application number 201610461127.2 discloses "a method for quickly correcting the deformation resistance coefficient of a new cold-rolled steel grade", which calculates and corrects the deformation resistance model by solving the rolling force accuracy coefficient of each stand. This method also does not conduct laboratory experiments, ignores the true characteristics of the material itself, and the physical meaning of the established deformation resistance model is unclear, making it difficult to promote and apply it in other production lines.

[0005] The Chinese invention patents with application numbers 202010208339.6, "A method for predicting cold rolling deformation resistance based on chemical composition of steel plates" and 202010209360.8, "A method for predicting cold rolling deformation resistance based on hot rolling process parameters of hot rolled original plates", both use the relationship between process parameters or chemical composition and rolling force to determine the deformation resistance through reverse calculation. However, the process of reverse calculation of deformation resistance requires the determination of process parameters that cannot be measured on site, such as the friction coefficient and the roller flattening radius, which makes it difficult to accurately determine the deformation resistance of the material.

[0006] It can be seen from the above literature that most optimization methods for deformation resistance are based on inverse calculation of field data. The amount of data is limited, and field data will bring more experimental products, wasting raw materials. The obtained deformation resistance model cannot be applied to high-strength cold-rolled strip steel. Summary of the invention

[0007] Based on the problems existing in the prior art, the present invention provides a compensation method for the deformation resistance model of high-strength cold-rolled strip steel, which uses a "physical model + data-driven" compensation or optimization method that combines field data with laboratory data to obtain a high-strength cold-rolled strip steel deformation resistance model with clear physical meaning and high precision. This improves the accuracy of the deformation resistance model, and further improves the setting accuracy of the rolling force and bending roll force, so as to improve the thickness fluctuation and plate shape defects of the strip steel.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0009] A method for compensating a deformation resistance model of a high-strength cold-rolled steel strip according to the present invention comprises the following steps:

[0010] Step 1: Conduct rolling experiments on the impurity-removed high-strength hot-rolled steel plates at different deformation rates to obtain high-strength cold-rolled steel plates with different deformation rates; the deformation rate varies in the range of 10 to 85%; and the different deformation rates vary in increments of 2 to 5%;

[0011] In the step 1, the high-strength hot-rolled steel plate after impurities removal is a high-strength hot-rolled steel plate with a tensile strength of ≥600MPa after pickling.

[0012] In the step 1, the deformation rate is calculated as follows: deformation rate = ((entrance thickness - exit thickness) / (entrance thickness)) × 100%.

[0013] Step 2: Stretching the high-strength cold-rolled steel plates with different deformation rates to obtain the yield strength of the high-strength cold-rolled steel plates with each deformation rate;

[0014] In the step 2, at least three tensile specimens are prepared for each deformation rate of the high-strength cold-rolled steel sheets with different deformation rates; the yield strength corresponding to the high-strength cold-rolled steel sheets with each deformation rate is the average value of the three tensile specimens.

[0015] Step 3: Perform regression fitting on each deformation rate and the corresponding yield strength according to the deformation resistance model of formula (1) to obtain the model parameters a, b, c in formula (1), thereby determining the basic deformation resistance model of the high-strength hot-rolled steel plate.

[0016] K k =a+(ba)·εt c (1)

[0017] In the formula, Kk is the deformation resistance of the kth pass, MPa; a, b, c are model parameters; εt is the true strain of the strip, calculated according to formula (2).

[0018]

[0019] Where, h0 is the thickness of hot-rolled raw material, in mm; h k-1 is the entrance thickness of the kth pass, in mm; h k is the exit thickness of the kth pass, in mm; β is the coefficient, generally taken as 0.75.

[0020] Step 4: Collect data of no less than 450 rolls of strip steel to train the artificial neural network. The network input is the strip steel parameters and cold rolling parameters, and the network output is the deformation resistance K of the strip steel. a , establish an artificial neural network model.

[0021] In step 4, the strip parameters include the steel type and incoming material thickness of the high-strength hot-rolled steel plate; the cold rolling parameters include pass entrance thickness, pass exit thickness, pass entrance tension, pass exit tension, and working roll diameter.

[0022] Step 5: Using the artificial neural network model established in step 4, according to the measured data, the corresponding deformation resistance curve of the high-strength cold-rolled strip is obtained; in the deformation resistance curve of the high-strength cold-rolled strip, multiple deformation rates corresponding to the plateau period and the rapid increase period are back-calculated to obtain the corresponding deformation resistance K of the strip in the plateau period and the rapid increase period. a ;

[0023] In the step 5, preferably, the deformation rates corresponding to the deformation resistance of the strip in the plateau period and the rapid increase period are 20%, 30%, 60% and 80%.

[0024] Step 6: Calculate the reduction rate correction factor F corresponding to different true strains f :

[0025] F f =K a / K k (3)

[0026] Where, f is the serial number of the correction point, and K is the true strain corresponding to multiple deformation rates corresponding to the deformation resistance of the strip in the plateau period and the rapid increase period. The calculation formula is determined by combining the calculation method of the deformation rate and formula (2); a K is the deformation resistance of the strip; k The deformation resistance value corresponding to the true strain is calculated according to the deformation resistance model of formula (1).

[0027] In step 6, F fis the corresponding reduction rate correction coefficient; according to the formula, F1 is the reduction rate correction coefficient when the true strain is 0.2, F2 is the reduction rate correction coefficient when the true strain is 0.35, F3 is the reduction rate correction coefficient when the true strain is 0.9, and F4 is the reduction rate correction coefficient when the true strain is 1.7.

[0028] Step 7: Calculate the deformation resistance correction factor C according to formula (4).

[0029]

[0030] Where W f is the influence factor of the correction point, and the calculation method is shown in formula (5).

[0031]

[0032] In the formula, ε e is the true strain on the deformation resistance curve, which is determined by the basic deformation resistance model in step 3; ε f is the true strain of the correction point, that is, the true strain corresponding to multiple deformation rates corresponding to the deformation resistance of the strip in the plateau period and the rapid rise period, and more preferably is 0.2, 0.35, 0.9, and 1.7 respectively; η is 1.

[0033] Step 8: The modified deformation resistance model is calculated according to formula (6).

[0034] K=K k ·C (6)

[0035] K is the corrected deformation resistance, MPa.

[0036] The compensation method of the deformation resistance model of high-strength cold-rolled strip steel obtains a basic deformation resistance model by performing regression fitting on different deformation rates and their corresponding yield strengths; then an artificial neural network is trained in combination with data of no less than 450 rolls of strip steel to establish an artificial neural network model, and a deformation resistance curve is obtained. The deformation rates corresponding to the plateau period and the rapid rise period are back-calculated to obtain the corresponding deformation resistance K of the strip steel in the plateau period and the rapid rise period. a , and then calculate the reduction rate correction factor F corresponding to different true strains f , the deformation resistance correction factor C is obtained, and thus the corrected deformation resistance model K is obtained.

[0037] The present invention discloses a compensation method for a deformation resistance model of high-strength cold-rolled steel strip. By combining laboratory experimental data with actual production data, the deformation resistance model is compensated. The deformation resistance of the cold rolling process can be predicted, and then process parameters such as rolling force and bending roll force can be set. The method is then applied to cold rolling production lines to improve the quality of high-strength cold-rolled steel strip.

[0038] Compared with the prior art, the compensation method of the deformation resistance model of high-strength cold-rolled strip steel described in the present invention has the beneficial effect of: back-calculating the deformation resistance value of the high-strength cold-rolled strip steel in the "plateau period" of the deformation resistance curve through artificial neural network technology, obtaining the deformation resistance correction factor C, and correcting the deformation resistance model. The corrected deformation resistance model has higher accuracy and can more accurately reflect the strain hardening characteristics of high-strength steel. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is the deformation resistance curve of high-strength cold-rolled strip steel.

[0040] Figure 2 This is the deformation resistance curve of Example 1 before and after correction.

[0041] Figure 3 This is the deformation resistance curve of Example 2 before and after correction.

[0042] Figure 4 This is the deformation resistance curve of Example 3 before and after correction. DETAILED DESCRIPTION

[0043] The present invention is further described in detail below in conjunction with embodiments.

[0044] The embodiment of the present invention uses high-strength hot-rolled pickled plate as raw material (tensile strength ≥ 600MPa) and is applied to a six-roll cold rolling mill. The basic parameters of the rolling mill are: working roll diameter: 270-310mm; intermediate roll diameter: 510-560mm; support roll diameter: 1310-1450mm.

[0045] Example 1

[0046] Prepare DP600 high-strength hot-rolled pickled plate with a thickness of 3.5 mm and a width of 1250 mm:

[0047] Step 1: Conduct a rolling experiment on an experimental rolling mill to prepare high-strength cold-rolled steel plates with different deformation rates. The deformation rates are 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, and 85%.

[0048] Step 2: Use a tensile testing machine to measure the yield strength of high-strength cold-rolled steel plates with different deformation rates. Take the average value of three samples for each deformation rate. The results are shown in Table 1.

[0049] Table 1 DP600 rolling-tensile test results

[0050] Deformation rate Yield strength(MPa) Deformation rate Yield strength(MPa) Deformation rate Yield strength(MPa) 0％ 365 35％ 658 65％ 820 10％ 525 40％ 673 70％ 850 15％ 580 45％ 713 75％ 890 20％ 597 50％ 728 80％ 915 25％ 597 55％ 778 85％ 965 30％ 633 60％ 758 —— ——

[0051] Step 3: Perform regression fitting on each deformation rate and the corresponding yield strength according to the model structure of formula (1) to obtain the basic deformation resistance model of DP600 as shown in formula (7).

[0052] K k =370.53+(806.15-370.53)·εt 0.46733 (7)

[0053] Among them, K k is the deformation resistance of the kth pass, that is, the yield strength of the corresponding kth pass, MPa. εt is the true strain of the strip, which is converted according to the formula of deformation rate and combined with the thickness of the hot-rolled raw material h0;

[0054]

[0055] Step 4: 485 strip steel data were collected to train the artificial neural network. The network input was steel type, hot rolling material thickness, pass entry and exit thickness, pass entry and exit tension, and work roll diameter. The network output was the strip steel deformation resistance K. a .

[0056] Step 5: Using the artificial neural network model established in step 4, the corresponding deformation resistance curve of high-strength cold-rolled strip is obtained according to the measured data; the deformation rates corresponding to the "plateau period" and "rapid rise period" are determined according to the deformation resistance curve for back calculation, specifically, the deformation resistance K of the strip when the back calculation deformation rates are 20%, 30%, 60% and 80% a They correspond to the deformation resistance K when the deformation rate is 20% a The deformation resistance K is 575MPa and the deformation rate is 30%. a The deformation resistance K is 625MPa and the deformation rate is 60%. a The deformation resistance K is 773MPa and the deformation rate is 80%. a It is 940MPa.

[0057] Step 6: Calculate the reduction rate correction factor F f , f is the serial number of the correction point, which are 1 (deformation rate 20%), 2 (deformation rate 30%), 3 (deformation rate 60%), and 4 (deformation rate 80%). f =K a / K e , where K e The deformation resistance value corresponding to the true strain is calculated according to the basic deformation resistance model of DP600 (7). After calculation, F1~F4 are 1.04076, 1.01959, 1.01581, and 0.95428 respectively.

[0058] Step 7: Calculate the deformation resistance correction factor C according to equations (4) and (5). Where ε in equation (5) e Determined by the points fitted by the deformation resistance model curve.

[0059] Step 8: According to formula (6), the basic deformation resistance model of formula (7) is modified. The deformation resistance model curves before and after the modification are as follows: Figure 2 As shown in the figure, the correlation coefficient of the modified DP600 deformation resistance model is increased by 0.0038, the average absolute error is reduced by 5.3MPa, and the maximum absolute error is reduced by 9.9MPa.

[0060] Example 2

[0061] Prepare DP780 high-strength hot-rolled pickled plate with a thickness of 4.0 mm and a width of 1050 mm:

[0062] Step 1: Conduct a rolling experiment on an experimental rolling mill to prepare high-strength cold-rolled steel plates with different deformation rates. The deformation rates are 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, and 85%.

[0063] Step 2: Use a tensile testing machine to measure the yield strength of high-strength cold-rolled steel plates with different deformation rates. Take the average value of three samples for each deformation rate. The results are shown in Table 2.

[0064] Table 2 DP780 rolling-tensile test results

[0065] Deformation rate Yield strength(MPa) Deformation rate Yield strength(MPa) Deformation rate Yield strength (MPa) 0％ 380 35％ 708 65％ 873 10％ 613 40％ 768 70％ 890 15％ 645 45％ 778 75％ 950 20％ 688 50％ 815 80％ 988 25％ 688 55％ 865 85％ 1055 30％ 700 60％ 880 —— ——

[0066] Step 3: Perform regression fitting on each deformation rate and the corresponding yield strength according to the model structure of formula (1), and obtain the basic deformation resistance model of DP780 as shown in formula (8).

[0067] K k =389.18+(883.69-389.18)·εt 0.40441 (8)

[0068] Step 4: 473 strip steel data were collected to train the artificial neural network. The network input was steel type, hot rolling material thickness, pass entry and exit thickness, pass entry and exit tension, and work roll diameter. The network output was the strip steel deformation resistance K. a .

[0069] Step 5: Using the artificial neural network model established in step 4, according to the measured data, the deformation resistance K of the strip steel when the deformation rate is 20%, 30%, 60% and 80% is calculated. a They are: deformation resistance K when the deformation rate is 20%a The deformation resistance K is 675MPa and the deformation rate is 30%. a The deformation resistance K is 680MPa and the deformation rate is 60%. a The deformation resistance K is 870MPa and the deformation rate is 80%. a It is 1000MPa.

[0070] Step 6: Calculate the reduction rate correction factor F f , f is the serial number of the correction point, which are 1 (deformation rate 20%), 2 (deformation rate 30%), 3 (deformation rate 60%), and 4 (deformation rate 80%). f =K a / K e , where K e The deformation resistance value corresponding to the true strain is calculated according to the basic deformation resistance model formula (8) of DP780. After calculation, F1~F4 are 1.00219, 1.04797, 0.99203, and 0.97181 respectively.

[0071] Step 7: Calculate the deformation resistance correction factor C according to equations (4) and (5).

[0072] Step 8: According to formula (6), the basic deformation resistance model of formula (8) is modified. The deformation resistance model curves before and after the modification are as follows: Figure 3 As shown in the figure, the correlation coefficient of the modified DP780 deformation resistance model is increased by 0.0035, the average absolute error is reduced by 4.7MPa, and the maximum absolute error is reduced by 8.5MPa.

[0073] Example 3

[0074] Prepare QP980 high-strength hot-rolled pickled plate with a thickness of 4.5mm and a width of 1150mm:

[0075] Step 1: Conduct a rolling experiment on an experimental rolling mill to prepare high-strength cold-rolled steel plates with different deformation rates. The deformation rates are 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, and 85%.

[0076] Step 2: Use a tensile testing machine to measure the yield strength of high-strength cold-rolled steel plates with different deformation rates. Take the average value of three samples for each deformation rate. The results are shown in Table 3.

[0077] Table 3 QP980 rolling-tensile test results

[0078] Deformation rate Yield strength(MPa) Deformation rate Yield strength (MPa) Deformation rate Yield strength (MPa) 0％ 630 35％ 1100 65％ 1290 10％ 920 40％ 1130 70％ 1325 15％ 940 45％ 1210 75％ 1360 20％ 1020 50％ 1220 80％ 1355 25％ 965 55％ 1235 85％ 1480 30％ 1050 60％ 1245 —— ——

[0079] Step 3: Perform regression fitting on each deformation rate and the corresponding yield strength according to the model structure of formula (1), and obtain the basic deformation resistance model of DP780 as shown in formula (9).

[0080] K k =626.57+(1273.84-626.57)·εt 0.36474 (9)

[0081] Step 4: 467 strip steel data were collected to train the artificial neural network. The network input was steel type, hot rolling material thickness, pass entry and exit thickness, pass entry and exit tension, and working roll diameter. The network output was the strip steel deformation resistance K. a .

[0082] Step 5: Using the artificial neural network model established in step 4, according to the measured data, the deformation resistance K of the strip steel when the deformation rate is 20%, 30%, 60% and 80% is calculated. a They are: deformation resistance K when the deformation rate is 20% a The deformation resistance K is 1010MPa and the deformation rate is 30%. a The deformation resistance K is 1040MPa and the deformation rate is 60%. a The deformation resistance K is 1250MPa and the deformation rate is 80%. a It is 1590MPa.

[0083] Step 6: Calculate the reduction rate correction factor F f , f is the serial number of the correction point, which are 1 (deformation rate 20%), 2 (deformation rate 30%), 3 (deformation rate 60%), and 4 (deformation rate 80%). f =K a / K e , where K e The deformation resistance values ​​corresponding to the true strain are calculated according to the basic deformation resistance model of DP780. F1~F4 are 1.00688, 1.02685, 0.99955, and 0.86604 respectively.

[0084] Step 7: Calculate the deformation resistance correction factor C according to equations (4) and (5).

[0085] Step 8: According to formula (6), the basic deformation resistance model of formula (9) is modified. The deformation resistance model curves before and after the modification are as follows: Figure 4 As shown in the figure, the correlation coefficient of the modified QP980 deformation resistance model is increased by 0.0027, the average absolute error is reduced by 3.9MPa, and the maximum absolute error is reduced by 7.7MPa.

[0086] Comparative Example 1

[0087] Same as Example 1, except that:

[0088] In step 1, a rolling experiment is carried out on an experimental rolling mill to prepare high-strength cold-rolled steel plates with different deformation rates. The deformation rates are 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%. The deformation resistance model obtained in this comparative example cannot truly reflect the plateau period and the rapid rise period, and is prone to lack of data, thereby increasing the absolute error.

[0089] Comparative Example 2

[0090] If only formula (1) is used as the deformation resistance model, it cannot reflect the real strain hardening characteristics of the material, and the setting deviation of process parameters such as rolling force is large, which will cause strip shape defects.

Claims

1. A method for compensating a deformation resistance model of high-strength cold-rolled strip steel, characterized in that: The compensation method of the deformation resistance model of high-strength cold-rolled strip steel obtains a basic deformation resistance model by performing regression fitting on different deformation rates and their corresponding yield strengths; then an artificial neural network is trained with data of no less than 450 coils of strip steel to establish an artificial neural network model, and a deformation resistance curve is obtained. The multiple deformation rates corresponding to the plateau period and the rapid rise period are back-calculated, and the deformation resistance K of the strip steel in the corresponding plateau period and the rapid rise period is obtained. a , and then calculate the reduction rate correction factor F corresponding to different true strains f , the deformation resistance correction factor C is obtained, and thus the corrected deformation resistance model K is obtained.

2. The method for compensating the deformation resistance model of high-strength cold-rolled strip steel according to claim 1, characterized in that: The determination method of the basic deformation resistance model is as follows: Step 1: Conduct rolling experiments on the impurity-removed high-strength hot-rolled steel plates at different deformation rates to obtain high-strength cold-rolled steel plates with different deformation rates; the deformation rate varies in the range of 10 to 85%; and the different deformation rates vary in increments of 2 to 5%; Step 2: Stretching the high-strength cold-rolled steel plates with different deformation rates to obtain the yield strength of the high-strength cold-rolled steel plates with each deformation rate; Step 3: regress each deformation rate and the corresponding yield strength according to the deformation resistance model of formula (1) to obtain the model parameters a, b, c in formula (1), thereby determining the basic deformation resistance model of the high-strength hot-rolled steel plate; K k =a+(ba)·εt c (1) In the formula, K k is the deformation resistance of the kth pass, MPa; a, b, c are model parameters; εt is the true strain of the strip.

3. The method for compensating the deformation resistance model of high-strength cold-rolled strip steel according to claim 2, characterized in that: In the step 1, the high-strength hot-rolled steel plate after impurities removal is a high-strength hot-rolled steel plate with a tensile strength of ≥600MPa after pickling.

4. The method for compensating the deformation resistance model of high-strength cold-rolled strip steel according to claim 2, characterized in that: In the step 1, the deformation rate is calculated as follows: deformation rate = ((entrance thickness - exit thickness) / (entrance thickness)) × 100%.

5. The method for compensating the deformation resistance model of high-strength cold-rolled strip steel according to claim 2, characterized in that: In the step 2, at least three tensile specimens are prepared for each deformation rate of the high-strength cold-rolled steel sheets with different deformation rates; the yield strength corresponding to the high-strength cold-rolled steel sheets with each deformation rate is the average value of the three tensile specimens.

6. The method for compensating the deformation resistance model of high-strength cold-rolled strip steel according to claim 2, characterized in that: In step 3, the true strain εt of the strip is calculated according to formula (2): Where, h0 is the thickness of hot-rolled raw material, in mm; h k-1 is the entrance thickness of the kth pass, in mm; h k is the exit thickness of the kth pass, in mm; β is the coefficient, generally taken as 0.

75.

7. The method for compensating the deformation resistance model of high-strength cold-rolled strip steel according to claim 1, characterized in that: The method for determining the deformation resistance correction factor C is: Step 4: Collect data of no less than 450 rolls of strip steel to train the artificial neural network. The network input is the strip steel parameters and cold rolling parameters, and the network output is the deformation resistance K of the strip steel. a , establish an artificial neural network model; Step 5: Using the artificial neural network model established in step 4, according to the measured data, the corresponding deformation resistance curve of the high-strength cold-rolled steel strip is obtained; the corresponding deformation rates of the plateau period and the rapid rise period in the deformation resistance curve of the high-strength cold-rolled steel strip are back-calculated to obtain the corresponding deformation resistance K of the steel strip in the plateau period and the rapid rise period. a ; Step 6: Calculate the reduction rate correction factor F corresponding to different true strains f : F f =K a / K k (3) Where, f is the serial number of the correction point, and K is the true strain corresponding to multiple deformation rates corresponding to the deformation resistance of the strip in the plateau period and the rapid increase period. The calculation formula is determined by combining the calculation method of the deformation rate and formula (2); a K is the deformation resistance of the strip; k The deformation resistance value corresponding to the true strain is calculated according to the deformation resistance model of formula (1); Step 7: Calculate the deformation resistance correction factor C according to formula (4); Where W f is the influence factor of the correction point, and the calculation method is shown in formula (5); In the formula, ε e is the true strain on the deformation resistance curve, which is determined by the basic deformation resistance model in step 3; ε f is the true strain of the correction point, that is, the true strain corresponding to multiple deformation rates corresponding to the deformation resistance of the strip in the plateau period and the rapid increase period respectively; η is taken as 1.

8. The method for compensating the deformation resistance model of high-strength cold-rolled strip steel according to claim 7, characterized in that: In step 4, the strip parameters include the steel type and incoming material thickness of the high-strength hot-rolled steel plate; the cold rolling parameters include pass entrance thickness, pass exit thickness, pass entrance tension, pass exit tension, and working roll diameter.

9. The method for compensating the deformation resistance model of high-strength cold-rolled strip steel according to claim 7, characterized in that: In the step 5, the deformation rates corresponding to the deformation resistance of the steel strip in the plateau period and the rapid increase period are 20%, 30%, 60% and 80%; And / or, in step 6, F f is the corresponding reduction rate correction coefficient; according to the formula, F1 is the reduction rate correction coefficient when the true strain is 0.2, F2 is the reduction rate correction coefficient when the true strain is 0.35, F3 is the reduction rate correction coefficient when the true strain is 0.9, and F4 is the reduction rate correction coefficient when the true strain is 1.7; And / or, in step 7, ε f The values ​​are 0.2, 0.35, 0.9 and 1.7 respectively.

10. The method for compensating the deformation resistance model of high-strength cold-rolled strip steel according to claim 1, characterized in that: The modified deformation resistance model K is calculated according to formula (6); K=K k ·C (6) K is the corrected deformation resistance, MPa.

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