Performance control method for 420MPa-grade galvanized low-alloy high-strength steel plate and storage medium

Abstract

The invention discloses a performance control method for a 420 MPa-grade galvanized low-alloy high-strength steel plate and a storage medium. The performance control method comprises the following steps that S1, a production actual performance database is established according to historical production data; s2, establishing a causal matrix table according to the actual production database, and scoring and sorting influence programs of each process parameter in the causal matrix table on an actual product to obtain key factors; s3, collecting process parameters corresponding to the key factors and determining a control range corresponding to each key factor; and S4, analyzing and optimizing the control range of the key factors to obtain a process parameter range corresponding to steel plate performance control, and controlling the production of the steel plate by adopting the obtained process parameter range corresponding to the steel plate performance control. The production of the steel plate is controlled through the method so as to improve the performance and the qualified rate.

Description

Technical Field

[0001] The present invention relates to the field of metal material processing control, and in particular to a performance control method for a 420MPa-grade galvanized low-alloy high-strength steel plate based on a Six Sigma management method. Background Art

[0002] Low-alloy high-strength steel is made by adding trace amounts of strong carbide elements such as niobium, vanadium and titanium to low-carbon steel. Its main structures are ferrite and pearlite. It mainly relies on the precipitation strengthening of carbides and nitrides in the ferrite matrix to hinder dislocation movement and increase yield strength.

[0003] Low-alloy, high-strength steel plates are primarily used in the automotive, shipbuilding, machinery, bridge, and equipment manufacturing sectors, and are in high demand. In particular, demand and requirements for low-alloy, high-strength steel plates for automotive structural components are increasing.

[0004] Low-alloy high-strength steels primarily focus on controlling yield strength and have high yield-to-strength ratios. However, they offer relatively poor work-hardening properties, good fatigue resistance, and unstable mechanical properties. Furthermore, customer demands for increasingly high performance products are becoming increasingly stringent. Therefore, it is imperative to be able to mass-produce low-alloy high-strength steel plates that meet customer requirements and have a high pass rate.

[0005] In the existing technology, for products with performance requirements set by customers, changes in process parameters during the production process may cause the final product performance to fail to meet user requirements, resulting in the product qualification rate failing to meet the requirements. Therefore, in order to meet user needs, it is necessary to control the process parameters of steel plate performance. How to implement a production process control model corresponding to the required products for mass production is a technical problem that needs to be solved. Summary of the Invention

[0006] The present invention aims to overcome the shortcomings of existing technologies by providing a Six Sigma-based performance control model for low-alloy, high-strength steel plates. This model can be used to obtain characteristic data, such as the chemical composition and key process parameters, for products of varying specifications. This characteristic data is optimized and adjusted based on actual production conditions, and validated and solidified through batch production, resulting in low-alloy, high-strength steel with a high yield and highly stable performance, meeting market demand.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a method for controlling the performance of a 420MPa grade galvanized low-alloy high-strength steel plate, comprising the following steps:

[0008] S1. Establish a production performance database based on historical production data;

[0009] S2. Establish a cause-effect matrix based on the actual production database, and score and rank the impact of each process parameter in the cause-effect matrix on the actual product to obtain the key factors;

[0010] S3. Collect the process parameters corresponding to the key factors and determine the control range corresponding to each key factor;

[0011] S4. Analyze and optimize the control range of key factors to obtain the process parameter range corresponding to the steel plate performance control, and use the obtained process parameter range corresponding to the steel plate performance control to control the production of the steel plate.

[0012] Establishing the production performance database in step S1 includes collecting the composition and production process data of the low alloy high strength steel plate and investigating and determining characteristic data.

[0013] In step S2, scores and rankings are performed according to the actual impact, and key factors are determined through a Pareto chart.

[0014] Step S2 also includes eliminating factors from the key factors through matrix diagrams, scatter plots, and regression analysis to obtain the final key factors.

[0015] Step S4 also includes a rationality analysis of the components and process parameters corresponding to the key factors to obtain the value range of each process parameter, and then test the value range of the process parameters to verify the qualification of the product, and adjust the value range according to the test verification results and solidify it to obtain the final process parameter range corresponding to the steel plate performance control.

[0016] The characteristic data includes data factors in the steel plate production process, and the data factors include steelmaking process components, hot rolling process parameters, pickling process parameters and galvanizing process parameters.

[0017] Assign weights to the output characteristics of product performance, and score each data factor on the output characteristics; sum up the weight and score of each data factor on the output characteristics to obtain the total score of each data factor, and sort the data factors according to the total score.

[0018] The total score of each data factor is the sum of the weight of each output feature and its score.

[0019] The output characteristics include yield strength, tensile strength and elongation. Each data factor is scored on the three output characteristics and multiplied by its corresponding weight.

[0020] A computer storage medium stores a computer program, wherein the computer program, when executed by a processor, executes the control method according to any one of claims 1 to 9.

[0021] The advantages of the present invention are that it analyzes existing production data to ultimately generate a reliable model. This model then generates a rational chemical composition design, which, combined with steelmaking, hot rolling, pickling, cold rolling, continuous annealing or hot-dip galvanizing, skin-passing, or leveling processes, produces low-alloy, high-strength steel plates with a high and stable mechanical property qualification rate. The process parameters derived from the analysis and control method can be controlled to ensure that the steel produced meets user requirements, reduce steel quality fluctuations, and improve qualification rates. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The following is a brief description of the contents and symbols in the drawings of the present invention:

[0023] Figure 1 This is the metallographic structure diagram of the steel plate according to the embodiment of the present invention. DETAILED DESCRIPTION

[0024] The specific implementation of the present invention will be further explained in detail below by describing the best embodiment with reference to the accompanying drawings.

[0025] This embodiment is used to control the parameters in the steel plate production process through a 420MPa grade galvanized low-alloy high-strength steel plate performance control model based on the Six Sigma method. By adjusting the process parameters of the steel plate production process through the parameters obtained by the model, the qualified rate of steel plate production can be improved, and the different needs of steel plate production can be effectively met.

[0026] This embodiment provides a 420MPa grade galvanized low alloy high strength steel plate performance control method, which is characterized by comprising the following steps:

[0027] S1. Establish a production performance database based on historical production data;

[0028] S2. Establish a cause-effect matrix based on the actual production database, and score and rank the impact of each process parameter in the cause-effect matrix on the actual product to obtain the key factors;

[0029] S3. Collect the process parameters corresponding to the key factors and determine the control range corresponding to each key factor;

[0030] S4. Analyze and optimize the control range of key factors to obtain the process parameter range corresponding to the steel plate performance control, and use the obtained process parameter range corresponding to the steel plate performance control to control the production of the steel plate.

[0031] In step S1, qualified steel plate production data is obtained based on historical production data to establish a performance database. Establishing the production performance database includes collecting the composition and production process data of low-alloy high-strength steel plates and investigating and determining characteristic data.

[0032] In step S2, scores and rankings are performed according to the actual impact, and key factors are determined through a Pareto chart.

[0033] Step S2 also includes eliminating factors from the key factors through matrix diagrams, scatter plots, and regression analysis to obtain the final key factors.

[0034] Step S4 also includes a rationality analysis of the components and process parameters corresponding to the key factors, deriving the value ranges of each process parameter, then conducting tests on the value ranges of the process parameters to verify product qualification, and adjusting and solidifying the value ranges based on the test verification results to obtain the final process parameter ranges corresponding to the steel plate performance control. Since the key factors are obtained through analysis and processing of historical data, and the value ranges of the key factors are obtained through processing, the production process parameters are controlled according to the value ranges to ensure that the production process process parameters are within the value ranges, thereby improving the qualification rate of the produced steel plates.

[0035] In this embodiment, the characteristic data includes data factors from the steel plate production process, including steelmaking process components, hot rolling process parameters, pickling process parameters, and galvanizing process parameters. The data factors include: 1) Steelmaking process components: C, Si, Mn, P, S, Ti, and Nb.

[0036] 2) Hot rolling process: heating time, furnace temperature, rough rolling pass, intermediate billet temperature, intermediate billet thickness, intermediate billet temperature drop, intermediate billet soaking, FET finishing inlet temperature, finishing rolling load distribution, finishing stand reduction rate, roll gap, finishing rolling pass, final rolling temperature FDT, cooling manifold unobstructedness, cooling mode, water seal effect, air seal effect, coiling temperature CT, pinch roll surface quality, total reduction rate.

[0037] 3) Acid rolling process: reduction rate.

[0038] 4) Galvanizing process: process section speed, heating temperature, soaking temperature, slow cooling temperature, rapid cooling temperature, zinc pot entry temperature, skin-pass elongation, rolling force, roll surface quality, straightening elongation, sampling position, and thickness.

[0039] The output characteristics of product performance are weighted and scored for each data factor. The weight and score of each data factor on the output characteristic are summed to obtain the total score for each data factor, and the data factors are sorted by total score. The total score for each data factor is the sum of the weight of each output characteristic multiplied by its score. Output characteristics include yield strength, tensile strength, and elongation. Each data factor is scored for each of these three output characteristics and multiplied by its corresponding weight.

[0040] Since yield strength, tensile strength and elongation are very important for steel indicators, they are divided into three output characteristics: yield strength, tensile strength and elongation. Then, they are scored according to the importance of the data factors. The three indicators, yield strength, tensile strength and elongation, have their own weights. The weights of yield strength, tensile strength and elongation in the data factors are multiplied by the corresponding scores to obtain three products. The sum of the three products is then added together to obtain the sum of the data factors. The scores are sorted and processed according to the sum to finally obtain the key factors. The values of the key factors are statistically analyzed to obtain the value range of the key factors. The value range is then optimized to obtain the final key component and key process parameter corresponding key factor value range comparison table. Production control is carried out according to the comparison table to improve the pass rate.

[0041] In this embodiment, a computer storage medium is also provided, which stores a computer program. When the computer program is run by a processor, the control method of this embodiment is executed, thereby achieving control over steel plate production and further improving the qualified rate of 420MPa grade galvanized low-alloy high-strength steel plate production.

[0042] The purpose of this invention is to analyze existing production data and ultimately obtain a reliable model; and to obtain a reasonable chemical composition design through the model, and to produce low-alloy high-strength steel plates with a high and stable mechanical property qualification rate by combining steelmaking, hot rolling, pickling, continuous annealing, hot-dip galvanizing, and skin-passing processes.

[0043] 1) Analyze the existing low-alloy high-strength steel plate production data through Six Sigma tools, establish a database, and obtain a reliable model.

[0044] 2) The model can be used to obtain characteristic data such as different chemical compositions and key process parameters of products of different specifications to meet special needs and develop new products.

[0045] 3) Ensures the performance stability of low-alloy high-strength steel plates, reduces enterprise costs and improves the pass rate.

[0046] The technical problem to be solved by the present invention is to provide a performance control model for 420MPa grade galvanized low alloy high strength steel plate based on the Six Sigma method. The main scheme is as follows:

[0047] 1. Establish a production performance database, that is, collect the composition and production process data of low-alloy high-strength steel plates, investigate and determine the characteristic data; establish a cause-and-effect matrix, score and rank according to the actual degree of influence, and determine the key factors through Pareto charts; eliminate factors and establish models through matrix diagrams, scatter plots, and regression analysis; analyze the rationality of the composition and process parameters simulated by the model and optimize the model; derive the required specifications, product composition, and process and conduct experiments, and adjust and solidify according to the test results.

[0048] 2. The characteristic data is 40 factors:

[0049] 1) Steelmaking process components: C, Si, Mn, P, S, Ti, Nb.

[0050] 2) Hot rolling process: heating time, furnace temperature, rough rolling pass, intermediate billet temperature, intermediate billet thickness, intermediate billet temperature drop, intermediate billet soaking, FET finishing inlet temperature, finishing rolling load distribution, finishing stand reduction rate, roll gap, finishing rolling pass, final rolling temperature FDT, cooling manifold unobstructedness, cooling mode, water seal effect, air seal effect, coiling temperature CT, pinch roll surface quality, total reduction rate.

[0051] 3) Acid rolling process: reduction rate.

[0052] 4) Galvanizing process: process section speed, heating temperature, soaking temperature, slow cooling temperature, rapid cooling temperature, zinc pot entry temperature, skin-pass elongation, rolling force, roll surface quality, straightening elongation, sampling position, and thickness.

[0053] As shown in Table 1, it is a cause-effect matrix table. According to the cause-effect matrix table, the data factor is taken as X, the yield strength, tensile strength and elongation are taken as Y1, Y2 and Y3 respectively, and then a weight is given to each Y1, Y2 and Y3. In this embodiment, the weight coefficients are 10, 8 and 10 respectively. Then, the data factor is scored in the three directions of yield strength, tensile strength and elongation. After scoring, the total score of each data factor is obtained. The comprehensive score is the weight of yield strength, tensile strength and elongation multiplied by the corresponding score. Then the sum of the three products is the total score of the data factor, that is, the total = 10*yield strength score + 8*tensile strength score + 10*elongation score.

[0054] Table 1 Cause and effect matrix

[0055]

[0056]

[0057] 3. In this embodiment, the three output characteristics are respectively selected as yield strength, tensile strength, and elongation, and a certain weight number (1 to 10) is assigned to the importance of the three output characteristics of the performance; the 40 factors are scored and sorted according to the relevance (0, 1, 3, 9), and the key factors are screened and determined through the Pareto chart.

[0058] 4. Collect data on the identified key factors, eliminate factors and establish models through analysis methods such as matrix diagrams, scatter plots, and regression to obtain the final key factor data. The model is the production process model composed of the key factor data.

[0059] 5. Analyze the rationality of the components and process parameters simulated by the model, optimize the model, analyze the rationality of the process parameters obtained through the model, filter out or modify the value range of some parameters to achieve model optimization.

[0060] 6. Based on the model, the composition and process of the required specification product are derived and tested. Adjustments and solidification are made based on the test results. The quality of the produced product is verified based on the test. If the quality is qualified, the value range of the process production parameters in the model is directly solidified. Otherwise, the value range of the process parameter is fine-tuned based on the experimental results. Ultimately, a value range of the process parameter that can be used for production with a high pass rate is obtained and controlled according to the value range, thereby achieving production control with a high pass rate. As shown in Table 2, the parameter value range table of the model finally obtained in this embodiment is a model of the key components and key process parameters of 420MPa-grade galvanized low-alloy high-strength steel plate. Controlling production based on the parameters in this model can meet the user's required pass rate of 420MPa-grade galvanized low-alloy high-strength steel plate.

[0061] Table 2 Key components and key process parameters of 420MPa grade galvanized low alloy high strength steel plate

[0062]

[0063]

[0064] The purpose of the present invention is to analyze existing production data and ultimately obtain a reliable model; and to obtain a reasonable chemical composition design through the model, and to produce low-alloy high-strength steel plates with a high and stable mechanical property qualification rate in combination with steelmaking, hot rolling, pickling, cold rolling, continuous annealing or hot-dip galvanizing, skin pass or flattening processes.

[0065] According to the present invention, a performance control model for low-alloy high-strength steel plates based on the Six Sigma method is obtained, and this patent is specifically explained through examples.

[0066] According to the composition and process parameter table derived from the model, the whole process of 420 MPa grade galvanized low alloy high strength steel plate from steelmaking, hot rolling, pickling, cold rolling, continuous hot dip galvanizing, and skin passing was formulated, and finally 420 MPa grade galvanized low alloy high strength steel plate with a high pass rate was produced. Its main chemical composition is shown in Table 3, the main process parameters are shown in Table 4, and the mechanical properties are shown in Table 5.

[0067] Table 3 Chemical composition of the examples, wt%

[0068] C Si Mn P S ALS Nb N Example 1 0.0824 0.0104 1.3052 0.0097 0.0059 0.0363 0.0451 0.0028 Example 2 0.0792 0.0164 1.3078 0.0124 0.0069 0.0368 0.0521 0.0028 Example 3 0.0742 0.0215 1.2948 0.0105 0.003 0.035 0.0478 0.0028 Example 4 0.0784 0.0345 1.313 0.0112 0.0018 0.0352 0.0493 0.0028 Example 5 0.0742 0.0164 1.2738 0.0101 0.0046 0.0351 0.0466 0.0026 Example 6 0.0773 0.0142 1.2946 0.0137 0.0049 0.0355 0.05 0.0028 Example 7 0.0784 0.0345 1.2575 0.0094 0.0021 0.0382 0.0479 0.0028 Example 8 0.0795 0.0223 1.3155 0.0091 0.0046 0.0394 0.0526 0.0028

[0069] Table 4 Key process parameters of the embodiment production

[0070]

[0071] Table 5 Example product performance

[0072] Yield strength MPa Tensile strength Mpa <![CDATA[A 80 Elongation %]]> Example 1 450 534 24.5 Example 2 454 542 25 Example 3 438 524 24.5 Example 4 438 536 25 Example 5 441 527 25 Example 6 487 565 23.5 Example 7 432 534 19 Example 8 459 550 21.5

[0073] Figure 1 From the above examples 1-8, it can be seen that the 420MPa grade steel strip produced by the present invention has a structure mainly composed of ferrite + pearlite, and its mechanical properties include yield strength of 420-520MPa, tensile strength of 470-590MPa, and A 80 The elongation is ≥17%, achieving a good match between strength and formability. This patented performance control model is applicable not only to the production of hot-dip galvanized steel sheets, but also to the production of other coated products that require annealing, such as those coated with aluminum silicon, zinc aluminum magnesium, and alloys.

[0074] Obviously, the specific implementation of the present invention is not limited to the above-mentioned methods. As long as various non-substantial improvements are made using the method concept and technical solution of the present invention, they are all within the scope of protection of the present invention.

Claims

1. A method for controlling the performance of a 420MPa grade galvanized low alloy high strength steel plate, characterized by: The steps include: S1. Establish a production performance database based on historical production data; S2. Establish a cause-effect matrix based on the actual production database, and score and rank the impact of each process parameter in the cause-effect matrix on the actual product to obtain the key factors; S3. Collect the process parameters corresponding to the key factors and determine the control range corresponding to each key factor; S4. Analyze and optimize the control range of key factors to obtain the process parameter range corresponding to the steel plate performance control, and use the obtained process parameter range corresponding to the steel plate performance control to control the production of the steel plate.

2. A 420MPa grade galvanized low alloy high strength steel plate performance control method according to claim 1, characterized in that: Establishing the production performance database in step S1 includes collecting the composition and production process data of the low alloy high strength steel plate and investigating and determining characteristic data.

3. The method for controlling the performance of a 420 MPa grade galvanized low alloy high strength steel plate according to claim 1, wherein: In step S2, scores and rankings are performed according to the actual impact, and key factors are determined through a Pareto chart.

4. The method for controlling the properties of a 420 MPa grade galvanized low alloy high strength steel plate according to claim 1, wherein: Step S2 also includes eliminating factors from the key factors through matrix diagrams, scatter plots, and regression analysis to obtain the final key factors.

5. The method for controlling the properties of a 420 MPa grade galvanized low alloy high strength steel plate according to claim 1, wherein: Step S4 also includes a rationality analysis of the components and process parameters corresponding to the key factors to obtain the value range of each process parameter, and then test the value range of the process parameters to verify the qualification of the product, and adjust the value range according to the test verification results and solidify it to obtain the final process parameter range corresponding to the steel plate performance control.

6. A method for controlling properties of a 420 MPa grade galvanized low alloy high strength steel plate according to any one of claims 1 to 5, characterized in that: The characteristic data includes data factors in the steel plate production process, and the data factors include steelmaking process components, hot rolling process parameters, pickling process parameters and galvanizing process parameters.

7. A 420MPa grade galvanized low alloy high strength steel plate performance control method according to claim 6, characterized in that: Assign weights to the output characteristics of product performance, and score each data factor on the output characteristics; sum up the weight and score of each data factor on the output characteristics to obtain the total score of each data factor, and sort the data factors according to the total score.

8. A method for controlling properties of a 420 MPa grade galvanized low alloy high strength steel plate according to claim 7, characterized in that: The total score of each data factor is the sum of the weight of each output feature and its score.

9. The method for controlling the properties of a 420 MPa grade galvanized low alloy high strength steel plate according to claim 7, wherein: The output characteristics include yield strength, tensile strength and elongation. Each data factor is scored on the three output characteristics and multiplied by its corresponding weight.

10. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and when the computer program is executed by the processor, the control method according to any one of claims 1 to 9 is executed.

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