A method for controlling point defects on the surface of high-strength steel during continuous annealing based on water cooling process

Abstract

The present application relates to the technical field of high-strength steel processing, and specifically to a method for controlling point defects on the surface of high-strength steel continuous annealing based on a water cooling process, the method comprising: scanning the quality of residual oil and residual iron on the surface of cold-rolled plates, screening cold-rolled plates; vertically spraying and cleaning the strip steel, electrolytically cleaning the vertically sprayed and cleaned strip steel; spraying thermal barrier coating ceramic coating, depositing chromium carbide coating, introducing protective gas, performing continuous annealing treatment, controlling the parameters of the annealing treatment, pickling the strip steel after annealing treatment; nickel plating the pickled strip steel, and controlling point defects on the surface of high-strength steel continuous annealing. The present application realizes the precise control of point defects on the surface of high-strength steel continuous annealing.

Description

Technical Field

[0001] The present application relates to the technical field of high-strength steel processing, and in particular to a method for controlling surface point defects of high-strength steel during continuous annealing based on a water cooling process. Background Art

[0002] Cold-rolled advanced high-strength steels are widely used in vehicle body manufacturing. Steel grades such as DP980, DP1180, TBF1180, and TBF980 are routinely used in reinforcing components such as B-pillars and door frame reinforcements, as well as complex structural components such as front and rear longitudinal beams. With the continued improvement in the performance of cold-rolled high-strength steel, the use of water cooling processes to precisely control the phase ratios of ferrite, martensite, bainite, and retained austenite has become increasingly efficient. However, due to increasingly stringent surface quality requirements for steel plates in automobile factories, more complex annealing process curves and more diverse alloy composition designs have adversely affected the effective control of surface point defects. This has significantly limited the surface quality of high-strength steel continuous annealing using water cooling processes, hindering stable product production and the cost control and efficiency improvement of steel companies.

[0003] During the continuous annealing process of high-strength steel, the generation of surface point defects is caused by a variety of factors. From the perspective of raw materials, insufficient steel purity and differences in the original surface quality, such as the segregation of impurity elements, surface scratches or residual iron oxide scale, may all be the cause of defects. In the annealing process, improper temperature control at each stage, such as excessive heating speed resulting in excessive thermal stress, too high or too low annealing temperature causing abnormal structural transformation, unreasonable cooling speed causing thermal stress and structural inhomogeneity, etc., will cause problems. In terms of equipment factors, such as uneven cooling water flow rate in the cooling system, the cooling effect of the steel surface will be inconsistent, which will in turn cause thermal stress and structural inhomogeneity, and ultimately lead to point defects. Traditional control methods are often limited to a single means, and do not fully consider the joint interference between different influencing factors, making it difficult for the deviation of defect control in the continuous annealing process of high-strength steel to meet production needs. Summary of the Invention

[0004] In order to solve the above technical problems, the present application provides a method for controlling surface point defects of high-strength steel continuous annealing based on a water cooling process to solve the existing problems.

[0005] The method for controlling surface point defects of high-strength steel continuous annealing based on a water cooling process in this application adopts the following technical solutions:

[0006] One embodiment of the present application provides a method for controlling surface point defects in high-strength steel continuous annealing based on a water cooling process, the method comprising the following steps:

[0007] S10, scanning the quality of residual oil and residual iron on the surface of the cold-rolled sheet to screen the cold-rolled sheet;

[0008] S20, vertically spray cleaning the steel strip using a hot alkali cleaning solution, and electrolytically cleaning the vertically spray cleaned steel strip using an electrolytic cleaning alkali solution;

[0009] S30, spraying thermal barrier coating ceramic coating on the high temperature zone and the medium temperature zone of the furnace, depositing chromium carbide coating on the low temperature zone, introducing protective gas into the furnace, and continuously annealing the strip steel after electrolytic cleaning. The parameters of the annealing process are controlled by the following control method:

[0010] S31, collecting each monitoring data during the continuous annealing process of high-strength steel;

[0011] S32, based on the differences between the monitoring data and in combination with a mutation point detection algorithm, obtaining each joint response interval;

[0012] S33, obtaining an annealing control deviation coefficient of each joint response interval based on the monitoring data of the joint response interval;

[0013] S34, based on the degree of disorder of the monitoring data in the joint response interval and the annealing control deviation coefficient, obtaining a feedback compensation error for each type of monitoring data, and adjusting each type of monitoring data during the annealing process;

[0014] S40, pickling the annealed steel strip;

[0015] S50, nickel plating is performed on the strip steel after pickling to reduce the point defects on the surface of high-strength steel after continuous annealing.

[0016] Furthermore, the mass of the surface residual oil is 200-300 mg / m² per side, and the mass of the surface residual iron is 30-50 mg / m² per side.

[0017] Furthermore, the concentration of the hot alkali washing solution is 6% to 8%, and the temperature is 75 to 85°C.

[0018] Furthermore, the spray pressure of the vertical spray cleaning is 23 MPa, and the spray flow rate is 700 L / h.

[0019] Furthermore, the concentration of the electrolytic cleaning alkali solution is 10% to 12% and the temperature is 75 to 85°C.

[0020] Furthermore, the method for obtaining the joint response interval is:

[0021] For each type of monitoring data, the monitoring data at each collection moment are sequenced in chronological order as the monitoring data sequence of each type of monitoring data, and the difference calculation results between each element in the monitoring data sequence and the preset standard monitoring data are calculated respectively, and the sequence composed of the difference calculation results is used as the deviation data sequence of each type of monitoring data;

[0022] The deviation data sequence of each type of monitoring data is used as each row element in the matrix, and the deviation data sequences of all types of monitoring data together form a deviation data matrix;

[0023] The deviation data matrix is ​​taken as input, and the Radon algorithm is used to map the deviation data matrix vertically to obtain the comprehensive deviation data sequence of the deviation data matrix. The abnormal data detection algorithm is used to obtain the mutation points in the comprehensive deviation data sequence. The collection time corresponding to the mutation point is used as the segmentation time point. The time intervals are divided based on the segmentation time point, and the time period between each segmentation time point is used as each joint response interval.

[0024] Furthermore, the method for obtaining the annealing control deviation coefficient is:

[0025] In the deviation data matrix, the matrix composed of all elements in each joint response interval is used as each sub-matrix of the deviation data matrix in each joint response interval; the rank of each sub-matrix of the deviation data matrix is ​​calculated;

[0026] For each submatrix of the deviation data matrix, calculate the mean of all data in each row of data in the submatrix, and use the sequence composed of the mean of each row of data in the submatrix as the deviation accumulation sequence of each submatrix;

[0027] The calculation formula of the annealing control deviation coefficient is: Where, Indicates the Annealing control deviation coefficient of the joint response interval; and Respectively represent and The cumulative sequence of deviations of the submatrices of the joint response intervals, express and Euclidean distance between them; and Respectively represent and The rank of the submatrix of the joint response intervals, represents an exponential function with a natural constant as its base; represents the number of joint response intervals.

[0028] Furthermore, the method for obtaining the feedback compensation error is:

[0029] Each monitoring data in each joint response interval is used as the input of the entropy weight method to obtain the relative change weight of each monitoring data in each joint response interval;

[0030] For each monitoring data, calculate the product of the relative change weight of each monitoring data in each joint response interval and the annealing control deviation coefficient of the joint response interval, and obtain a normalized result of all the products; use the normalized result of the product as the feedback compensation deviation coefficient of the annealing processing parameter control of each monitoring data in each joint response interval;

[0031] The calculation formula of the feedback compensation error is: Where, represents the feedback compensation error of the i-th monitoring data; represents the mean of all data of the i-th monitoring data in the p-th joint response interval; and They represent the feedback compensation deviation coefficients of the i-th monitoring data in the p-th and t-th joint response intervals respectively; represents the number of joint response intervals.

[0032] Furthermore, the adjustment of each monitoring data during the annealing process includes:

[0033] The mean value of the feedback compensation deviation coefficients of all monitoring data is used as the judgment threshold, and the mean value of the feedback compensation deviation coefficients of all monitoring data in the joint response interval where the current acquisition moment is located is compared with the judgment threshold. If it is greater than or equal to the judgment threshold, the feedback compensation error of the monitoring data is used as the input of the automatic control system programmable controller PLC; if it is less than the judgment threshold, the absolute value of the difference between the mean value of the feedback compensation deviation coefficients of all monitoring data in the joint response interval where the current acquisition moment is located and the judgment threshold is used as the input of the automatic control system programmable controller PLC, and each type of monitoring data adjusted during the annealing process is output.

[0034] Furthermore, the nickel layer thickness of the nickel plating is 15-20 mg / m².

[0035] This application has at least the following beneficial effects:

[0036] During the continuous annealing process of high-strength steel based on the water-cooling process, the generation of surface point defects is mainly due to the complex surface contamination sources of the cold-hardened finished products produced by the cold rolling unit, and it is difficult for subsequent units to completely remove these contamination sources, resulting in frequent accumulation of oxides on the surface of the furnace rollers in the furnace; and in actual production, during the continuous annealing process under the water-cooling process, there is a joint interference phenomenon between various parameters, which seriously interferes with the stability of the strip annealing parameters, causes stress changes, and then causes point defects on the strip surface. In response to the above problems, this application first carries out the inspection and screening of raw materials in sequence, pre-treats the surface of the raw materials, and adopts vertical spray cleaning and electrolytic cleaning technology to effectively remove impurities on the surface of the raw materials. Secondly, during the annealing process, the long-term impact of the combined interference that the annealing parameters may suffer over a longer time span is fully taken into account, and the feedback errors that occur during the control process are deeply analyzed and precisely adjusted. The beneficial effect is that, from the perspective of full-process surface control, a coupled design is carried out, which effectively reduces the incidence of point defects on the annealing surface caused by pollutant interference and the long-term impact of control deviations during the actual treatment process, thereby achieving precise control of point defects on the surface of continuous annealing of high-strength steel, and effectively ensuring product quality and production stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0038] Figure 1 A flow chart of a method for controlling surface point defects in high-strength steel continuous annealing based on a water cooling process according to one embodiment of the present application;

[0039] Figure 2 This is a flow chart of annealing process parameter control provided by one embodiment of the present application. DETAILED DESCRIPTION

[0040] To further illustrate the technical means and effectiveness of this application to achieve the intended invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of the method for controlling surface point defects in high-strength steel continuous annealing based on a water-cooling process proposed in this application. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0041] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0042] The specific scheme of the method for controlling surface point defects of high-strength steel continuous annealing based on water cooling process provided by the present application is described in detail below with reference to the accompanying drawings.

[0043] One embodiment of the present application provides a method for controlling surface point defects of high-strength steel continuous annealing based on a water cooling process. Specifically, the following method for controlling surface point defects of high-strength steel continuous annealing based on a water cooling process is provided. Figure 1 , the method comprises the following steps:

[0044] This application proposes a method for controlling surface point defects in high-strength steel continuous annealing based on a water cooling process, which specifically includes raw material inspection and screening, surface pretreatment, vertical spray cleaning, electrolytic cleaning, continuous annealing treatment, continuous annealing roller surface treatment, furnace atmosphere design, pickling and nickel flash treatment. The specific control process is as follows:

[0045] Step S10, scanning the surface oil residue and surface iron residue of the cold-rolled sheet to screen the cold-rolled sheet: using a laser scanning surface defect detector, a comprehensive inspection of the cold-rolled sheet is performed, and according to the preset quality standards, the cold-rolled sheet that meets the requirements of surface oil residue of 200-300 mg / m² per side and surface iron residue of 30-50 mg / m² per side is screened.

[0046] Step S20, use hot alkali washing solution to vertically spray clean the strip, and use electrolytic cleaning alkali solution to electrolytically clean the vertically spray cleaned strip: equipped with multiple sets of high-pressure nozzles, the nozzle layout is optimized according to the strip width and transmission speed, and the nozzle angle is accurately adjusted by the automatic control system (adjustable within the range of ±15°), the spray pressure is 23MPa, and the spray flow rate is 700L / h. The hot alkali washing solution is sprayed onto the strip under high pressure to achieve all-round, uniform and efficient cleaning of the strip surface; the concentration of the hot alkali washing solution is 6%~8%, and the temperature of the hot alkali washing solution is 75~85℃.

[0047] Using the steel strip as an electrode, gas is released on the strip surface, and an electrolytic cleaning solution is used to deeply clean the strip surface. The electrolysis current density is 0.3-0.5A / dm², the voltage is 4-6V, the concentration of the electrolytic cleaning solution is 10%-12%, and the temperature is 75-85°C. A circulating pump continuously delivers the electrolytic cleaning solution at a flow rate of 800-1200L / h. The filter is automatically backwashed regularly to remove impurities in the electrolytic cleaning solution, ensuring the cleanliness and conductivity of the electrolyte, improving cleaning effectiveness and stability, and achieving deep cleaning.

[0048] Specifically, in this application, the parameters of 8 embodiments for raw material inspection and screening, surface pretreatment, vertical spray treatment, and electrolysis treatment are shown in Table 1:

[0049] Table 1 Process parameters of the embodiment

[0050]

[0051] In step S30, a thermal barrier coating ceramic coating is sprayed on the high-temperature and medium-temperature zones of the furnace, and a chromium carbide coating is deposited on the low-temperature zone. A protective gas is introduced into the furnace, and the strip steel after electrolytic cleaning is continuously annealed. The tension coefficient is selected: for the high-temperature zone (700°C to 900°C) and medium-temperature zone (400°C to 700°C) in the furnace zone, an automated thermal barrier coating spraying equipment is used to spray the ceramic coating, wherein the spraying speed is 50-80 mm / s and the spraying thickness is 0.3-0.5 mm to ensure coating uniformity and adhesion.

[0052] Chromium carbide coatings are sprayed in the low-temperature range (200°C to 400°C) using a chemical vapor deposition (CVD) process. The deposition temperature is 400-500°C, the chromium source gas flow rate is 50-100 sccm, and the deposition time is 3-5 hours. This ensures the coating has the appropriate hardness (HV 1500-2000) and wear resistance. Different replacement cycles are established for different temperature zones: 11,500 km for the high-temperature zone, 15,000 km for the low-temperature zone, and 18,400 km for the medium-temperature zone.

[0053] Into the furnace and Shielding gas flow rate, where The injection volume is 4.5%~5% (volume fraction), The injection amount is 95%~95.5% to ensure the effective coverage and reduction effect of the protective gas, and prevent oxidation and point defects on the surface of the steel plate and furnace roller.

[0054] The tension coefficient is set according to the specifications of the strip steel, as shown in Table 2:

[0055] Table 2 Tension coefficients in different process sections

[0056]

[0057] During the annealing process, the automatic control system is used to control and adjust the annealing parameters to ensure that the strip is heated stably and the tension changes stably during the annealing process. The process of optimizing the control by the automatic control system at different process stages is as follows:

[0058] Step S31: collecting each monitoring data during the continuous annealing process of high-strength steel.

[0059] Sensors are used to collect each type of monitoring data during the continuous annealing process of high-strength steel. In this embodiment, the types of monitoring data include strip tension data, furnace temperature data, and strip transmission speed data. Tension sensors, temperature sensors, and speed sensors are used for data collection, respectively. Each type of monitoring data has a preset standard value, which is used as the preset standard monitoring data. In this embodiment, the interval between each collection time is 1 second. The implementer may select other values ​​based on actual conditions. Furthermore, each type of monitoring data is subjected to denoising. In this embodiment, Wiener filtering is used for denoising. Wiener filtering is a well-known technology and is not described in detail in this embodiment.

[0060] At this point, the acquisition of each type of monitoring data during the continuous annealing process of high-strength steel has been completed.

[0061] Step S32: Based on the differences between the monitoring data sequences and in combination with the mutation point detection algorithm, each joint response interval is obtained.

[0062] During the four key stages of the continuous annealing process for high-strength steel—heating, slow cooling, rapid cooling, and aging—the dynamic changes in the three key process parameters, temperature, strip transmission speed, and strip tension, interact with each other. Rapid temperature fluctuations can cause corresponding fluctuations in strip tension, which in turn affect strip transmission speed, leading to significant stress changes within the system. These stress changes can alter the steel's surface microstructure, potentially causing uneven grain growth and the formation of localized protrusions or depressions on the surface, ultimately resulting in point defects.

[0063] It can be seen that during the continuous annealing process of high-strength steel, the more significant the combined interference effect between temperature changes, strip transmission speed changes, and strip tension changes, the higher the probability of point defects appearing on the surface of the high-strength steel obtained after continuous annealing. In view of the deviation characteristics presented by the combined interference effect of different monitoring data during the high-strength continuous annealing process, feedback control and adjustment are implemented for the process parameters at each stage of continuous annealing. The specific calculation and analysis process is as follows:

[0064] During the continuous annealing process, the traditional monitoring data deviation feedback mechanism has limitations. Generally speaking, the feedback of monitoring data such as temperature, tension and speed in the space where the strip is located is judged only based on the instantaneous response characteristics at the current moment, that is, simply considering the error of these data deviating from the preset value at the current moment. However, in the actual processing process, the joint interference between different monitoring data is not instantaneous, but continues to change dynamically over a long period of time. This long-term continuous change will have a significant impact on the adjustment of the parameter control response, resulting in large control errors;

[0065] Therefore, based on the above analysis, for each type of monitoring data, the sequence of monitoring data at each collection moment in chronological order is used as the monitoring data sequence of each type of monitoring data, and the difference calculation results between each element in the monitoring data sequence and the preset standard monitoring data are calculated respectively, and the sequence composed of the difference calculation results is used as the deviation data sequence of each type of monitoring data.

[0066] Furthermore, in order to compare the response relationship of monitoring data deviation in different time ranges, the deviation data sequence of each type of monitoring data is used as the row elements in the matrix, and the deviation data sequences of all types of monitoring data together form a deviation data matrix. In the deviation data matrix, each column element corresponds to the deviation of different monitoring data at each collection moment.

[0067] Furthermore, the deviation data matrix is ​​used as input, and the Radon algorithm is used to perform vertical mapping on the deviation data matrix to obtain a comprehensive deviation data sequence of the deviation data matrix. The LOF algorithm is used to obtain the mutation points in the comprehensive deviation data sequence. The acquisition time corresponding to the mutation point is used as the segmentation time point. The time intervals are divided based on the segmentation time point, and the time period between each segmentation time point is used as each joint response interval. It should be noted that the time period between the start time of data acquisition and the first segmentation time point and the time period between the last segmentation time point and the end time of data acquisition are also used as joint response intervals.

[0068] It should be noted that the purpose of performing anomaly detection after vertical mapping is to accurately delineate the intervals of deviation response changes in the monitoring data by integrating the nonlinear variation characteristics of the combined interference effects between all monitoring data. The Radon algorithm and the LOF algorithm are well-known technologies and are not described in detail in this embodiment.

[0069] Step S33 : obtaining the annealing control deviation coefficient of each joint response interval based on the monitoring data of the joint response interval.

[0070] Furthermore, the deviation data matrix is ​​divided according to the divided joint response intervals. The specific division method is as follows: in the deviation data matrix, the matrix composed of all elements in each joint response interval is used as each submatrix of the deviation data matrix in each joint response interval. The rank of each submatrix of the deviation data matrix is ​​calculated. The larger the value of the rank, the greater the degree of influence of the joint interference effect within the corresponding joint response interval during the control process, that is, the more significant the nonlinear characteristics between the deviations of different monitoring data;

[0071] Furthermore, for each submatrix of the deviation data matrix, the mean of all data in each row of data in the submatrix is ​​calculated, and the sequence composed of the mean of each row of data in the submatrix is ​​used as the deviation accumulation sequence of each submatrix; it should be noted that the deviation accumulation sequence reflects the cumulative deviation of the combined interference effect of different monitoring data deviations in the corresponding joint response interval; based on the nonlinear characteristics and deviation accumulation characteristics of the annealing processing monitoring data in different joint response intervals, the annealing control deviation coefficient is calculated, and the calculation formula is: Where, Indicates the Annealing control deviation coefficient of the joint response interval; and Respectively represent and The cumulative sequence of deviations of the submatrices of the joint response intervals, express and Euclidean distance between them; and Respectively represent and The rank of the submatrix of the joint response intervals, represents an exponential function with the natural constant e as the base; represents the number of joint response intervals; wherein, the Euclidean distance is a well-known technology and will not be described in detail in this embodiment.

[0072] It should be noted that the larger the calculated annealing control deviation coefficient is, the more significant the nonlinear characteristics of the deviation response of different monitoring data are during the annealing process, and the more serious the influence of the deviation accumulation on the joint interference effect is, which may lead to fluctuations in the control feedback deviation over a longer time range during the annealing process.

[0073] Step S34 : Based on the degree of disorder of the monitoring data in the joint response interval and the annealing control deviation coefficient, a feedback compensation error of each monitoring data is obtained, and each monitoring data in the annealing process is adjusted.

[0074] The above analysis shows that in the continuous annealing process of high-strength steel, the response of the control error of its parameters is not only reflected in the instantaneous characteristics of the current control moment, but also in the longer control range, the more significant the joint response deviation of different monitoring data is, the greater the possibility of the occurrence of feedback error deviation.

[0075] Furthermore, each type of monitoring data in each joint response interval is used as the input of the entropy weight method to obtain the relative change weight of each type of monitoring data in each joint response interval. It should be noted that the larger the relative change weight, the more significant the dynamic change characteristics of the data in the row corresponding to the relative change weight under the influence of the joint interference within the time range of the current joint response interval.

[0076] Based on the time difference of each monitoring data in the control process affected by the joint interference during the annealing process, the deviation of the feedback error of each monitoring data is analyzed, and the feedback compensation error is calculated based on the analysis results. , the acquisition method is as follows:

[0077] Specifically, for each monitoring data, the product of the relative change weight of each monitoring data in each joint response interval and the annealing control deviation coefficient of the joint response interval is calculated to obtain the normalized results of all the products; the normalized results of the products are used as the feedback compensation deviation coefficient of the annealing processing parameter control of each monitoring data in each joint response interval. The larger the feedback compensation deviation coefficient is, the greater the degree of parameter deviation in the control process is affected by the joint interference within the corresponding time range.

[0078] Furthermore, based on the joint interference effect characteristics of each monitoring data in different time ranges up to the current moment in the control process, feedback error correction analysis is performed to calculate the feedback compensation error. The calculation formula is: Where, represents the feedback compensation error of the i-th monitoring data; represents the mean of all data of the i-th monitoring data in the p-th joint response interval; and They represent the feedback compensation deviation coefficients of the i-th monitoring data in the p-th and t-th joint response intervals respectively; represents the number of joint response intervals.

[0079] It should be noted that the larger the calculated feedback compensation error is, the greater the impact of the combined interference of fluctuations in different monitoring data over a longer period of time up to the current moment, resulting in a decrease in the accuracy of the feedback error at the current moment and a larger compensation correction value.

[0080] At this point, the feedback compensation error of each monitoring data during the high-strength steel annealing process was obtained.

[0081] The feedback compensation error of each monitoring data is used as the input of the programmable controller PLC of the automatic control system. The programmable controller uses the PID control algorithm to control and adjust each monitoring data in the annealing process according to the feedback compensation error of the different monitoring data received. It should be noted that in the real-time control process, for the adjustment of the feedback error of each monitoring data, the influence of the joint interference effect is judged at the current acquisition moment. Specifically, the mean of the feedback compensation deviation coefficients of all monitoring data is used as the judgment threshold, and the mean of the feedback compensation deviation coefficients of all monitoring data in the joint response interval where the current acquisition moment is located is compared with the judgment threshold. If it is greater than or equal to the judgment threshold, the feedback compensation error of the monitoring data is used as the input of the programmable controller PLC of the automatic control system; if it is less than the judgment threshold, the absolute value of the difference between the mean of the feedback compensation deviation coefficients of all monitoring data in the joint response interval where the current acquisition moment is located and the judgment threshold is used as the input of the programmable controller PLC of the automatic control system, and each monitoring data adjusted in the annealing process is output. The purpose is to avoid the influence of the joint interference fluctuation of different monitoring data on the control deviation over a long time range during the control process. Among them, the annealing process parameter control flow chart is as follows: Figure 2 shown.

[0082] Specifically, the continuous annealing tension and furnace roller cycle parameters of the eight embodiments during the continuous annealing process are shown in Table 3:

[0083] Table 3 Continuous annealing process parameters

[0084]

[0085] Step S40, pickling the annealed steel strip: After continuous annealing, the steel strip is pickled using dilute hydrochloric acid with a pickling concentration of 12-16%, an acid flow rate of 300-500 L / min, a temperature of 60-70°C, and a pickling time of 10-15 seconds to ensure uniform immersion and reaction of the steel strip in the pickling tank.

[0086] Step S50, nickel plating is performed on the pickled steel strip to control the point defects on the surface of the high-strength steel continuous annealing: after pickling, the steel strip is flash nickel treated, the preset nickel layer thickness is 15-20 mg / m², the nickel plating time is 35 minutes, the current density is 0.8-1.2 A / dm², and the plating solution composition is the main salt nickel sulfate concentration of 250-300 g / L, the buffer boric acid concentration is 30-40 g / L and an appropriate amount of additives, to achieve precise nickel plating, ensure the improvement of the surface quality of the steel strip, and effectively prevent the occurrence of point defects and subsequent corrosion problems.

[0087] Specifically, the parameter settings for the furnace atmosphere design, pickling and nickel flash treatment in the eight embodiments of the present application are shown in Table 4:

[0088] Table 4 Example parameter settings

[0089]

[0090] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the above descriptions are of specific embodiments of the present application. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0091] The various embodiments in this application are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0092] The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them. Modifications to the technical solutions described in the aforementioned embodiments, or equivalent replacements of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A method for controlling surface point defects of high-strength steel continuous annealing based on a water cooling process, characterized in that: The method comprises the following steps: S10, scanning the quality of residual oil and residual iron on the surface of the cold-rolled sheet to screen the cold-rolled sheet; S20, using a hot alkali washing solution to vertically spray wash the steel strip, and using an electrolytic washing alkali solution to electrolytically wash the vertically spray washed steel strip; S30, spraying thermal barrier coating ceramic coating on the high temperature zone and the medium temperature zone of the furnace area, depositing chromium carbide coating on the low temperature zone, introducing protective gas into the furnace, continuously annealing the strip steel after electrolytic cleaning, and controlling the parameters of the annealing treatment, and the control method is: S31, collecting each monitoring data during the continuous annealing process of high-strength steel; S32, based on the difference between the monitoring data and in combination with the mutation point detection algorithm, obtaining each joint response interval; S33, obtaining an annealing control deviation coefficient of each joint response interval based on the monitoring data of the joint response interval; S34, based on the disorder degree of the monitoring data in the joint response interval and the annealing control deviation coefficient, obtaining a feedback compensation error of each monitoring data, and adjusting each monitoring data in the annealing process; S40, pickling the strip steel after annealing; S50, nickel plating is performed on the strip steel after pickling to reduce the point defects on the surface of high-strength steel continuous annealing.

2. The method for controlling surface point defects of high-strength steel continuous annealing based on water cooling process according to claim 1, characterized in that: The mass of the surface residual oil is 200-300 mg / m² per side, and the mass of the surface residual iron is 30-50 mg / m² per side.

3. The method for controlling surface point defects of high-strength steel continuous annealing based on water cooling process according to claim 1, characterized in that: The hot alkali washing solution has a concentration of 6% to 8% and a temperature of 75 to 85°C.

4. The method for controlling surface point defects of high-strength steel continuous annealing based on water cooling process according to claim 1, characterized in that: The spray pressure of the vertical spray cleaning is 23 MPa, and the spray flow rate is 700 L / h.

5. The method for controlling surface point defects of high-strength steel continuous annealing based on water cooling process according to claim 1, characterized in that: The concentration of the electrolytic cleaning alkali solution is 10%-12% and the temperature is 75-85°C.

6. The method for controlling surface point defects of high-strength steel continuous annealing based on water cooling process according to claim 1, characterized in that: The method for obtaining the joint response interval is: For each type of monitoring data, a sequence of monitoring data at each collection moment in time order is used as a monitoring data sequence of each type of monitoring data, and a difference calculation result between each element in the monitoring data sequence and the preset standard monitoring data is calculated respectively, and a sequence composed of the difference calculation results is used as a deviation data sequence of each type of monitoring data; The deviation data sequence of each type of monitoring data is used as each row element in the matrix, and the deviation data sequences of all types of monitoring data together form a deviation data matrix; The deviation data matrix is ​​taken as input, and the Radon algorithm is used to map the deviation data matrix vertically to obtain the comprehensive deviation data sequence of the deviation data matrix. The abnormal data detection algorithm is used to obtain the mutation points in the comprehensive deviation data sequence. The collection time corresponding to the mutation point is used as the segmentation time point, and each time interval is divided based on the segmentation time point. The time period between each segmentation time point is used as each joint response interval.

7. The method for controlling surface point defects of high-strength steel continuous annealing based on water cooling process according to claim 6, characterized in that: The method for obtaining the annealing control deviation coefficient is: In the deviation data matrix, the matrix composed of all elements in each joint response interval is used as each sub-matrix of the deviation data matrix in each joint response interval; Calculate the rank of each sub-matrix of the deviation data matrix; For each submatrix of the deviation data matrix, the mean of all data in each row of data in the submatrix is ​​calculated, and the sequence composed of the mean of each row of data in the submatrix is ​​used as the deviation accumulation sequence of each submatrix; The calculation formula of the annealing control deviation coefficient is: ; In the formula, Indicates Annealing control deviation coefficient for the joint response interval; and Respectively represent and The cumulative sequence of deviations of the submatrices of the joint response intervals, express and Euclidean distance between them; and Respectively represent and The rank of the submatrix of joint response intervals, represents an exponential function with a natural constant as base; represents the number of joint response intervals.

8. The method for controlling surface point defects of high-strength steel continuous annealing based on water cooling process according to claim 7, characterized in that: The method for obtaining the feedback compensation error is: Each monitoring data in each joint response interval is used as the input of the entropy weight method to obtain the relative change weight of each monitoring data in each joint response interval; For each monitoring data, the product of the relative change weight of each monitoring data in each joint response interval and the annealing control deviation coefficient of the joint response interval is calculated to obtain the normalized results of all the products; the normalized results of the products are used as the feedback compensation deviation coefficient of the annealing processing parameter control of each monitoring data in each joint response interval; The calculation formula of the feedback compensation error is: ; In the formula, represents the feedback compensation error of the i-th monitoring data; represents the mean of all data of the i-th monitoring data in the p-th joint response interval; and They represent the feedback compensation deviation coefficients of the i-th monitoring data in the p-th and t-th joint response intervals respectively; represents the number of joint response intervals.

9. The method for controlling surface point defects of high-strength steel continuous annealing based on water cooling process according to claim 1, characterized in that: The step of adjusting each monitoring data during the annealing process includes: The mean value of the feedback compensation deviation coefficients of all monitoring data is used as the judgment threshold, and the mean value of the feedback compensation deviation coefficients of all monitoring data in the joint response interval where the current acquisition moment is located is compared with the judgment threshold. If it is greater than or equal to the judgment threshold, the feedback compensation error of the monitoring data is used as the input of the automatic control system programmable controller PLC; if it is less than the judgment threshold, the absolute value of the difference between the mean value of the feedback compensation deviation coefficients of all monitoring data in the joint response interval where the current acquisition moment is located and the judgment threshold is used as the input of the automatic control system programmable controller PLC, and each type of monitoring data adjusted during the annealing process is output.

10. The method for controlling surface point defects of high-strength steel continuous annealing based on water cooling process according to claim 1, characterized in that: The nickel layer thickness of the nickel plating is 15-20 mg / m².

Images