A method for predicting crown of rough rolling intermediate billet based on measured work roll wear

Abstract

The present invention discloses a method for predicting the convexity of a roughing intermediate billet based on measured work roll wear, which relates to the field of hot rolling control technology and includes the following steps: determining the steel grade and rolling parameters based on actual rolling data; dividing the work rolls into discrete units and calculating the wear of the discrete units according to a formula; setting the calculated wear equal to the measured wear based on the measured wear of each discrete unit to obtain the calculation coefficient in the formula; recording the measured wear under different conditions and obtaining the average value of the formula coefficient using a moving average method; establishing a simulation model based on the actual wear of the roughing work rolls and the rolling conditions, inputting the discrete points of the wear curve and the real-time rolling conditions into the model, and obtaining a curve for the convexity of the intermediate billet. The present invention predicts the convexity of the roughing intermediate billet in real time based on the current rolled steel grade and rolling conditions, which can guide production personnel in rationally scheduling production or promptly replacing the roughing work rolls to avoid excessive convexity of the intermediate billet, which results in scrap steel in the finishing process.

Description

Technical Field

[0001] The invention belongs to the technical field of hot rolling control, and in particular is a method for predicting the crown of a rough rolling intermediate billet based on actual wear measurement of working rolls. Background Art

[0002] The roughing process of the 2250 hot rolling mill converts raw slabs into relatively thin intermediate bars. The crown accuracy of the intermediate bar affects the stability of subsequent finishing and the quality of the finished plate shape. Wear of the roughing work rolls directly impacts intermediate bar crown accuracy. The roughing work rolls process a large amount of steel during a single lifespan, often exceeding 60,000 tons, and the slab widths range from 950mm to 2050mm. When the roughing work rolls wear out from rolling a large number of narrow-section slabs in the early stages, excessive intermediate bar crown can occur when rolling wider slabs later in the production process. This can lead to uncontrolled undulations between the finishing stands and, in severe cases, scrap steel accidents. Excessive intermediate bar crown is typically avoided by shortening the work roll change cycle. However, when rolling a wide range of slab sizes, accurate assessment of the wear of each work roll is impossible, making timely replacement of the roughing work rolls difficult. Summary of the Invention

[0003] The object of the present invention is to provide a method for predicting the convexity of a rough rolling intermediate billet based on the measured wear of the working rolls, so as to solve at least one aspect of the problems and defects raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides a method for predicting the crown of a rough rolling intermediate billet based on measured wear of a work roll, comprising the following steps:

[0005] Step 1: Divide all steel grades into a grades based on wear impact data, and determine the total number of rolling passes b, the rolling length l of each pass, and the rolling force f for each slab based on actual rolling data;

[0006] Step 2: Divide the roughing work roll surface into multiple discrete units of width c evenly along the roll body according to the grinder curve accuracy c and number them. Determine the discrete unit number that contacts the slab during rolling according to the slab width.

[0007] Step 3: Calculate the wear amount w of a discrete unit on the rough rolling work roll after rough rolling of the nth slab of grade A steel n , the formula is as follows:

[0008]

[0009] Where: w n : wear loss of a discrete unit on the work roll after rough rolling of the nth slab; β: wear loss adjustment coefficient; D: diameter-related wear coefficient; δ a: wear influence coefficient of grade a steel; b: total number of rolling passes; l k : rolling length of the kth pass; f k : rolling force of the kth pass;

[0010] Step 4: Calculate the total wear W of a discrete unit after rolling m slabs during the entire working cycle of the roughing roll j :

[0011]

[0012] Where: W j : the total wear of a discrete unit on the roughing work roll; m: the total number of slabs in roughing; w k : the wear amount of a discrete unit on the work roll after rough rolling of the kth slab;

[0013] Step 5: Count the actual wear W of each discrete unit on the work roll during the machine cycle s , let W j =W s , each discrete unit corresponds to a set of wear coefficients, including wear adjustment coefficient, diameter-related wear coefficient and wear influence coefficient;

[0014] Step 6: Count the measured wear of the work rolls under m different rolling plans, calculate the m groups of wear coefficients corresponding to each discrete unit, use the moving average method to calculate the mean of each coefficient, and then derive the wear calculation formula for each discrete unit;

[0015] Step 7: Calculate the wear amount of each discrete unit of the work roll after rough rolling of each slab in the rolling plan according to the calculation formula, and obtain the real-time calculated roll shape curve discrete points of the rough rolling work roll after rolling each slab in the rolling plan;

[0016] Step 8: Establish a simulation model based on the actual wear of the rough rolling work roll and the rolling conditions. Input the discrete points of the wear curve and the real-time rolling conditions into the simulation model, calculate and obtain the relationship curve between the work roll wear, rolling force and intermediate billet crown, and predict the intermediate billet crown in real time based on the relationship curve and industrial control.

[0017] Furthermore, the wear impact data includes hardness, rough rolling inlet temperature, and rough rolling outlet temperature of different grades of steel.

[0018] Furthermore, the multiple rough rolling conditions include rough rolling of the same steel grade under different working roll wear conditions and rough rolling of different steel grades under the same working roll wear conditions.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. The present invention calculates the crown variation law of the intermediate billet under various rough rolling conditions, establishes the relationship curve between rough rolling work roll wear, rolling force and intermediate billet crown, and makes real-time prediction of the rough rolling intermediate billet crown in combination with the current rolled steel grade and rolling conditions. This can guide production personnel to reasonably schedule production or replace the rough rolling work rolls in time to avoid excessive crown of the intermediate billet leading to scrap steel in finishing.

[0021] 2. The present invention predicts the convexity of the rough rolling intermediate billet based on the actual wear of the rough rolling working rolls in combination with the current rolled steel grade and rolling conditions. It conforms to production practice and has strong applicability. It can predict the convexity of the intermediate billet in real time when rolling a variety of steel grades of different sizes and grades. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] To facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0023] Figure 1 The present invention is a flowchart of a method for predicting the crown of a rough rolling intermediate billet based on the measured wear of the work rolls;

[0024] Figure 2 This is a rough rolling intermediate billet crown prediction method based on the actual wear of the working rolls, which shows the actual wear data of the rough rolling working rolls with the same roll number and different roll periods;

[0025] Figure 3 This is a diagram showing the division of discrete units of the working roll along the roll body direction in a rough rolling intermediate billet crown prediction method based on measured working roll wear;

[0026] Figure 4 This is a schematic diagram of the relationship between working roll wear, rolling force and intermediate billet crown in a rough rolling intermediate billet crown prediction method based on actual working roll wear. DETAILED DESCRIPTION

[0027] To further clarify the objectives, technical solutions, and advantages of the present invention, the technical solutions of the present invention are further described below through examples and in conjunction with the accompanying drawings. Throughout the specification, identical or similar reference numerals indicate identical or similar components. The following description of the embodiments of the present invention, with reference to the accompanying drawings, is intended to explain the overall inventive concept of the present invention and should not be construed as limiting the present invention's method for predicting crown of a roughing intermediate bar based on measured work roll wear.

[0028] In addition, in the following detailed description, for ease of explanation, numerous specific details are set forth to provide a comprehensive understanding of the disclosed embodiments. However, it is apparent that one or more embodiments can be practiced without these specific details. In other cases, well-known structures and devices are shown in diagrammatic form to simplify the accompanying drawings.

[0029] According to an overall technical concept of the present invention, Figure 1 As shown, a method for predicting the crown of a rough rolling intermediate billet based on the measured wear of the working roll is provided, comprising the following steps:

[0030] Step 1: Divide all steel grades into a grades based on wear impact data, and determine the total number of rolling passes b, the rolling length l of each pass, and the rolling force f for each slab based on actual rolling data;

[0031] Specifically, there are more than 700 steel grades actually rolled on site. All steel grades are divided into 8 grades according to the hardness, rough rolling inlet temperature and rough rolling outlet temperature of different steel grades, corresponding to 8 different steel wear coefficients δ a The number of rough rolling passes for each slab is 7, and the rolling length and rolling force of each pass are recorded according to the actual rolling data.

[0032] Step 2: Divide the roughing work roll surface into multiple discrete units of width c evenly along the roll body according to the grinder curve accuracy c and number them. Determine the discrete unit number that contacts the slab during rolling according to the slab width.

[0033] Specifically include: Figure 3 As shown in the figure, the length of the roughing work roll is L = 2550mm, the work roll profile accuracy in the grinding machine system is c = 12.75mm, so the number of discrete units of the work roll is L / c = 200, and the discrete unit numbers from the roughing work roll transmission side to the operating side are N D100 Decrease to N D1 , N O1 Increment to N O100 The axial interval coordinates of the working roll corresponding to each discrete unit are [-1275~1262.25], the upper and lower limits of the interval decrease by c to [-12.75~0], and the upper and lower limits of the interval [0~12.75] increase by c to [1262.25~1275], so that the number and coordinate interval of each discrete unit are obtained.

[0034] Since the center line of the slab coincides with the center line of the roughing work roll, the discrete unit numbers corresponding to the transmission side and the operating side of the slab are:

[0035] N DE =N Dx , N OE =N Oy ;

[0036] Wherein, x=y=Int(B / 2c), B is the strip width, and the rounding method of the Int function is rounding up.

[0037] Step 3: Calculate the wear amount w of a discrete unit on the rough rolling work roll after rough rolling of the nth slab of grade A steel n , the formula is as follows:

[0038]

[0039] Specifically: the initial value of the wear adjustment coefficient β is set to 1.0*10 -6 The diameter-related wear coefficient D has little effect on the roll wear in a single rolling cycle, and its initial value is set to 1; the value of a ranges from 1 to 8;

[0040] Step 4: Calculate the total wear W of a discrete unit after rolling m slabs during the entire working cycle of the roughing roll j :

[0041]

[0042] Step 5: Count the actual wear W of each discrete unit on the work roll during the machine cycle s , let W j =W s Each discrete unit corresponds to a set of wear coefficients, including the wear adjustment coefficient β, the diameter-related wear coefficient D, and the wear influence coefficient δ of different grades of steel a ;

[0043] like Figure 2 As shown in FIG, the actual wear diagram of the roughing working roll with the same roll number but different roll periods is shown. The horizontal axis is the roll body coordinate, and each roll body coordinate point corresponds to the measured wear of each discrete unit.

[0044] Step 6: Count the measured wear of the work rolls under m different rolling plans, calculate m groups of wear coefficients corresponding to each discrete unit, use the moving average method to calculate the mean of each wear coefficient, and then use the mean of each wear coefficient to derive the wear calculation formula for each discrete unit;

[0045] Step 7: Calculate the wear amount of each discrete unit of the work roll after rough rolling of each slab in the rolling plan according to the wear amount calculation formula of each discrete unit, and obtain the real-time calculated roll shape curve discrete points of the rough rolling work roll after rolling each slab in the rolling plan;

[0046] Step 8: Establish a simulation model based on the actual wear of the rough rolling work roll and the rolling conditions. Input the discrete points of the wear curve and the real-time rolling conditions into the simulation model, calculate and obtain the relationship curve between the work roll wear, rolling force and intermediate billet crown, and predict the intermediate billet crown in real time based on the relationship curve and industrial control.

[0047] like Figure 4 As shown, Figure 4The following curve shows the relationship between roughing work roll wear, rolling force, and intermediate bar crown. The width in the figure represents the workpiece width. As workpiece width increases, the contact area and rolling force increase. Therefore, with other conditions remaining unchanged, the workpiece width reflects the rolling force. The curve reveals the trend in intermediate bar crown: when the rolling force remains unchanged, work roll wear is positively correlated with intermediate bar crown. After work roll wear reaches 0.23 mm, intermediate bar crown becomes positively correlated with rolling force.

[0048] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for predicting the crown of a rough rolling intermediate billet based on measured wear of work rolls, characterized in that: The following steps are involved: Step 1: Divide all steel grades into a grades according to the wear impact data, and determine the total number of rolling passes b and the rolling length of each pass for each slab according to the actual rolling data. and rolling force ; Step 2: Divide the roughing work roll surface into multiple discrete units of width c evenly along the roll body according to the grinder curve accuracy c and number them. Determine the discrete unit number that contacts the slab during rolling according to the slab width. Step 3: Calculate the wear of a discrete unit on the roughing work roll after rough rolling of the nth slab of grade A steel , the formula is as follows: ； in: : the wear amount of a discrete unit on the work roll after rough rolling of the nth slab; : wear adjustment coefficient; D: diameter-related wear coefficient; : Wear influence coefficient of grade a steel; b: Total number of rolling passes; : rolling length of the kth pass; : rolling force of the kth pass; Step 4: Calculate the total wear of a discrete unit after rolling m slabs during the entire working cycle of the roughing roll : ； in: : the total wear of a discrete unit on the roughing work roll; m: the total number of slabs in roughing; : the wear amount of a discrete unit on the work roll after rough rolling of the kth slab; Step 5: Count the actual wear of each discrete unit on the work roll during the machine cycle ,make , each discrete unit corresponds to a set of wear coefficients, including wear adjustment coefficient, diameter-related wear coefficient and wear influence coefficient; Step 6: Count the measured wear of the work rolls under m different rolling plans, calculate the m groups of wear coefficients corresponding to each discrete unit, use the moving average method to calculate the mean of each coefficient, and then derive the wear calculation formula for each discrete unit; Step 7: Calculate the wear amount of each discrete unit of the work roll after rough rolling of each slab in the rolling plan according to the calculation formula, and obtain the real-time calculated roll shape curve discrete points of the rough rolling work roll after rolling each slab in the rolling plan; Step 8: Establish a simulation model based on the actual wear of the rough rolling work roll and the rolling conditions. Input the discrete points of the wear curve and the real-time rolling conditions into the simulation model, calculate and obtain the relationship curve between the work roll wear, rolling force and intermediate billet crown, and predict the intermediate billet crown in real time based on the relationship curve and industrial control.

2. The method for predicting crown of rough rolling intermediate billet based on measured work roll wear according to claim 1, characterized in that: The wear impact data include the hardness of different grades of steel, rough rolling inlet temperature and rough rolling outlet temperature.

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