Method, device, storage medium and program product for predicting roughness of a roll

Abstract

The application discloses a kind of prediction method, equipment, storage medium and program product of roughness of roll, the method comprises: in the case where the rolling process parameter of cold rolling continuous rolling mill is constant, the roll speed, strip speed and rolling length when each steel coil is rolled in the service cycle of roll are obtained;According to roll speed, strip speed and rolling length, the change trend equation of the front slip value in the stable rolling stage in the service cycle of roll is determined;According to change trend equation, the target attenuation prediction equation of roughness of roll is constructed;According to target attenuation prediction equation and the rolling length of target steel coil, the roughness of roll corresponding to the rolling length of target steel coil is predicted.Therein, by constructing the target attenuation prediction equation of roughness of roll, the roughness of roll corresponding to target steel coil at any rolling length can be predicted, the prediction of roughness of roll is not influenced by the accuracy of strip surface roughness measurement and transfer rule, with higher precision.

Description

Technical Field

[0001] This application belongs to the technical field of cold rolling mills, and particularly relates to a method, equipment, storage medium and program product for predicting roll roughness. Background Technology

[0002] During cold continuous rolling, the roughness of the rolls decreases with the increase of rolling mileage. The roughness of the rolls has an important impact on the calculation accuracy of the friction coefficient, which in turn affects the calculation accuracy of the rolling force and forward slip value through the friction coefficient.

[0003] The traditional method for calculating roll roughness involves measuring the surface roughness of the rolled strip and inferring the roll roughness by observing the transfer pattern between the roll and the strip surface roughness. However, the accuracy of the roll roughness obtained by this method is easily affected by the accuracy of the strip surface roughness measurement and the transfer pattern, which can lead to low precision. Summary of the Invention

[0004] The embodiments of this application provide a method, device, storage medium, and program product for predicting roll roughness, which can at least to some extent improve the prediction accuracy of roll roughness in cold rolling mills.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to a first aspect of the embodiments of this application, a method for predicting the roughness of a roll is provided, comprising:

[0007] Under the condition that the rolling process parameters of the cold rolling mill are constant, the roll speed, strip speed and rolling length of each steel coil during the service life of the roll are obtained;

[0008] Based on the roll speed, the strip speed, and the rolling length, determine the trend equation of the forward slip value during the stable rolling stage within the roll's service life;

[0009] Based on the aforementioned trend equation, a target attenuation prediction equation for roll roughness is constructed.

[0010] Based on the target attenuation prediction equation and the rolling length of the target steel coil, the roll roughness corresponding to the rolling length of the target steel coil is predicted.

[0011] In some embodiments, constructing a target attenuation prediction equation for roll roughness based on the trend equation includes:

[0012] The rate of decrease in roll roughness is obtained by differentiating the equation of change trend.

[0013] Based on the aforementioned attenuation rate, an initial attenuation prediction equation for roll roughness is constructed;

[0014] The coefficients of the initial attenuation prediction equation are solved based on the roughness of the upper mill rolls, the roughness of the lower mill rolls corresponding to the service cycle of the rolls, and the rolling length of each steel coil, so as to obtain the target attenuation prediction equation for the roll roughness.

[0015] In some embodiments, the service life of the rolls is the sum of the rolling cycles corresponding to each steel coil, and determining the trend equation of the forward slip value during the stable rolling phase within the service life of the rolls, based on the roll speed, the strip speed, and the rolling length, includes:

[0016] The forward slip value of each steel coil is determined based on the roll speed and the strip speed.

[0017] Based on the forward slip value of each steel coil, determine the average forward slip value of each steel coil during the stable rolling stage within the corresponding rolling cycle;

[0018] Based on the rolling length of each steel coil, the rolling mileage of the cold rolling mill in the corresponding stable rolling stage of each steel coil is determined;

[0019] The forward slip value is fitted and regressed by fitting the average forward slip value of each steel coil in the stable rolling stage within the corresponding rolling cycle and the rolling mileage of the cold rolling mill in the corresponding stable rolling stage of each steel coil to obtain the trend equation of the forward slip value.

[0020] In some embodiments, determining the average forward slip value of each steel coil during the stable rolling stage within the corresponding rolling cycle, based on the forward slip value of each steel coil, includes:

[0021] For each steel coil, the forward slip value corresponding to the highest continuous roll speed is selected from the forward slip values ​​of the steel coil;

[0022] The average value of the selected forward slip is determined as the average forward slip value of the steel coil during the stable rolling stage within the corresponding rolling cycle.

[0023] In some embodiments, determining the rolling mileage of the cold rolling mill in the stable rolling stage corresponding to each steel coil based on the rolling length of each steel coil includes:

[0024] For each steel coil, the sum of the rolling lengths from the first steel coil to the first steel coil is determined as the rolling mileage of the cold rolling mill in the stable rolling stage corresponding to the steel coil.

[0025] In some embodiments, predicting the roll roughness corresponding to the rolling length of the target steel coil based on the target attenuation prediction equation and the rolling length of the target steel coil includes:

[0026] The rolling mileage of the cold rolling mill is determined based on the rolling length of the target steel coil.

[0027] By substituting the rolling mileage of the cold rolling mill into the target attenuation prediction equation, the roll roughness corresponding to the rolling length of the target steel coil is obtained.

[0028] In some embodiments, the rolling process parameters include: pass reduction rate, maximum roll speed, front tension, back tension, stand exit thickness, emulsion information, and steel grade and specifications of the coil.

[0029] According to a second aspect of the embodiments of this application, a roll roughness prediction device is provided, including a processor and a memory, the memory storing computer program instructions executable by the processor, wherein when the processor executes the computer program instructions, it implements the steps of the method described in any of the first aspects above.

[0030] According to a third aspect of the embodiments of this application, a computer-readable storage medium is provided, wherein computer program instructions are stored therein, and when executed by a processor, the computer program instructions cause the processor to perform the steps of the method as described in any of the first aspects above.

[0031] According to a fourth aspect of the embodiments of this application, a computer program product is provided, including a computer program that, when executed by a processor, causes the processor to perform the steps of the method as described in any of the first aspects above.

[0032] In this application, under the condition that the rolling process parameters of a cold rolling mill are constant, the roll speed, strip speed, and rolling length are obtained when rolling each steel coil during the roll's service life. Based on the roll speed, strip speed, and rolling length, a trend equation for the change of the forward slip value in the stable rolling stage within the roll's service life is determined. Based on the trend equation, a target attenuation prediction equation for roll roughness is constructed. Based on the target attenuation prediction equation and the rolling length of the target steel coil, the roll roughness corresponding to the rolling length of the target steel coil is predicted. Specifically, by constructing the target attenuation prediction equation for roll roughness, the roll roughness corresponding to the target steel coil at any rolling length can be predicted. The prediction of roll roughness is not affected by the accuracy of strip surface roughness measurement and transfer patterns, and has high accuracy.

[0033] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0035] Figure 1 A flowchart illustrating a method for predicting roll roughness in one embodiment is shown;

[0036] Figure 2 It shows Figure 1 A schematic diagram of the rolling speed of various steel coils;

[0037] Figure 3 It shows Figure 2 A schematic diagram showing the forward slip value corresponding to the roll speed of each steel coil in the process;

[0038] Figure 4 It shows Figure 2 A schematic diagram of the roll speeds during the stable rolling stage of each steel coil within the corresponding rolling cycle;

[0039] Figure 5 It shows Figure 3 A schematic diagram of the forward sliding average value of each steel coil during the stable rolling stage within the corresponding rolling cycle;

[0040] Figure 6 A block diagram of a device for predicting roll roughness in one embodiment is shown;

[0041] Figure 7 A schematic diagram of a device for predicting roll roughness in one embodiment is shown. Detailed Implementation

[0042] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0044] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0045] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0046] To enable those skilled in the art to better understand this application, the application scenarios involved in this application will be briefly described first.

[0047] Traditional methods for calculating roll roughness attenuation measure the surface roughness of the rolled strip and infer the roll roughness based on the roughness transfer pattern between the roll and the strip surface. However, the accuracy of this method is susceptible to inaccuracies due to the limitations of the strip surface roughness measurement and the accuracy of the transfer pattern. The roll roughness prediction method provided in this application considers that rolling process parameters affect the friction coefficient by influencing the thickness of the lubricating oil film. Under constant rolling process parameters and with stable lubrication, the factor affecting the friction coefficient during rolling is the attenuation of roll roughness. The factors affecting the friction coefficient can be derived backwards using the following Hill formula:

[0048]

[0049]

[0050]

[0051] Where μ is the coefficient of friction, P is the actual rolling force, b is the strip width, and k pLet H be the strip yield strength related to the steel grade, H be the inlet strip thickness, h be the outlet strip thickness, r be the pass reduction rate, tf be the pre-tension, tb be the post-tension, and R′ be the roll flattening radius. According to the formula, under stable rolling process conditions, the change in friction coefficient is caused by the decrease in roll roughness.

[0052] According to Stone's formula:

[0053]

[0054] Where Δh is the thickness difference at the stand exit, R is the roll radius, and f is the forward slip value, a correspondence between the friction coefficient and forward slip can be established. This leads to the derivation that, under stable rolling conditions, the factor affecting the change in forward slip value is the change in roll roughness. Therefore, the trend equation of the forward slip value during the stable rolling phase within the roll's service life is used as the attenuation trend equation for roll roughness. Based on this, a target attenuation prediction equation for roll roughness is determined. Furthermore, this target attenuation prediction equation is used to predict the roll roughness when rolling target steel coils in a cold rolling mill, thus obtaining a roll roughness with high accuracy.

[0055] Figure 1 A flowchart illustrating a method for predicting roll roughness in one embodiment is shown. Figure 1 As shown, a method for predicting the roughness of a roll is provided, which may include the following steps 101 to 104.

[0056] In step 101, with the rolling process parameters of the cold rolling mill constant, the roll speed, strip speed and rolling length are obtained when rolling each steel coil during the service life of the rolls.

[0057] Rolling process parameters are related to the coefficient of friction by affecting the thickness of the lubricating oil film. These parameters may include: pass reduction rate, maximum roll speed, front tension, back tension, stand exit thickness, emulsion information, and the steel grade and specifications of the coil. Emulsion information may include details such as the cleanliness, stability, chemical properties, concentration, and temperature of the emulsion.

[0058] It should be noted that, according to the production planning rules of cold rolling mills, the steel coils rolled within a single roll service cycle typically have the same steel grade and specifications to ensure the stability of the rolled steel coil's deformation resistance. Therefore, in the embodiments of this application, the steel grades and specifications of the steel coils rolled within the roll service cycle are the same.

[0059] It is understandable that the service life of the rolls is the sum of the rolling cycles corresponding to each steel coil. Within the corresponding rolling cycle of each steel coil, both the roll speed and the strip speed change. In the implementation process, it is necessary to obtain multiple roll speeds and multiple strip speeds corresponding to each steel coil. The roll speed can be acquired through a high-precision roll encoder, and the strip speed can be acquired through a speed measuring instrument.

[0060] In step 102, the trend equation of the forward slip value during the stable rolling stage within the roll service life is determined based on the roll speed, strip speed, and rolling length.

[0061] It is understandable that the service life of the rolls is the sum of the rolling cycles corresponding to each steel coil.

[0062] In some embodiments, the forward slip value of each steel coil can be determined based on the roll speed and strip speed; the average forward slip value of each steel coil in the stable rolling stage within the corresponding rolling cycle can be determined based on the forward slip value of each steel coil; the rolling mileage of the cold rolling mill in the stable rolling stage corresponding to each steel coil can be determined based on the rolling length of each steel coil; the average forward slip value of each steel coil in the stable rolling stage within the corresponding rolling cycle and the rolling mileage of the cold rolling mill in the stable rolling stage corresponding to each steel coil are fitted and regressed to obtain the trend equation of the forward slip value.

[0063] The forward slip value can be calculated using the following formula:

[0064]

[0065] Where f is the forward sliding value, v h v is the speed of the strip. h This refers to the speed of the rolling mill rolls.

[0066] Figure 2 It shows Figure 1 A schematic diagram of the roll speeds for various steel coils. Figure 3 It shows Figure 2 A schematic diagram showing the forward slip values ​​corresponding to the roll speeds of various steel coils. (See also...) Figure 2 and Figure 3 Each steel coil has a rolling cycle of 15 minutes. The roll speed of each steel coil changes continuously within the corresponding rolling cycle. It usually rises to the highest roll speed and maintains it for a period of time before decreasing. Therefore, the calculated forward slip value also changes continuously.

[0067] It is understandable that each steel coil has a stable rolling stage within its corresponding rolling cycle, during which the roll speed reaches its maximum and lasts for a relatively long period. Figure 4 It shows Figure 2 A schematic diagram showing the roll speeds of each steel coil during the stable rolling stage within its corresponding rolling cycle. (See diagram below.) Figure 4 As shown, the roll speed of each steel coil is the maximum roll speed in the stable rolling stage, but the rolling time of different steel coils in the stable rolling stage varies.

[0068] In some embodiments, for each steel coil, the forward slip value corresponding to the highest continuous roll speed can be selected from the forward slip values ​​of the steel coil; the average value of the selected forward slip values ​​is determined as the average forward slip value of the steel coil during the stable rolling stage within the corresponding rolling cycle.

[0069] Figure 5 It shows Figure 3 A schematic diagram showing the average forward slip of each steel coil during the stable rolling stage within its corresponding rolling cycle. (See diagram below.) Figure 5 As shown, the forward slip value of each steel coil in the stable rolling stage within the corresponding rolling cycle decreases with the increase of rolling time.

[0070] After determining the average forward slip value of the steel coil in the stable rolling stage within the corresponding rolling cycle, the average forward slip value of each steel coil in the stable rolling stage within the corresponding rolling cycle can be fitted with the rolling mileage of the cold rolling mill in the corresponding stable rolling stage of each steel coil to obtain the trend curve of the forward slip value. Then, the trend curve is regressed to obtain the trend equation.

[0071] In the implementation process, for each steel coil, the sum of the rolling lengths from the first steel coil to the first steel coil can be determined as the rolling mileage of the cold rolling mill in the stable rolling stage corresponding to that steel coil.

[0072] Taking the rolling length of the first steel coil as 9.44 km, the second steel coil as 9.37 km, and the third steel coil as 9.31 km as examples, the rolling mileage of the cold rolling mill in the stable rolling stage corresponding to the first steel coil is 9.44 km, the rolling mileage of the stable rolling stage corresponding to the second steel coil is 18.81 km, and the rolling mileage of the stable rolling stage corresponding to the third steel coil is 28.12 km.

[0073] The average forward slip value of each steel coil during the stable rolling stage within the corresponding rolling cycle and the rolling mileage of the cold rolling mill during this stable rolling stage can be referenced in Table 1 below:

[0074] Table 1

[0075] Rolling distance (km) 9.44 18.81 28.12 37.45 46.81 56.14 65.37 74.77 Average forward slip (%) 1.06 1.01 0.99 0.96 0.93 0.93 0.93 0.91 Rolling distance (km) 84.13 93.48 102.63 111.72 121.03 130.15 139.52 148.94 Average forward slip (%) 0.89 0.88 0.88 0.84 0.85 0.81 0.79 0.81 Rolling distance (km) 157.84 167.23 176.66 186.09 195.38 204.58 Average forward slip (%) 0.76 0.75 0.74 0.74 0.75 0.75

[0076] After obtaining the average forward slip and rolling mileage of each steel coil during the stable rolling stage within the corresponding rolling cycle, the trend curve of the forward slip value can be obtained by fitting the average forward slip and rolling mileage. Regression on this trend curve yields the trend equation y for the forward slip value. f (x)=(4E-6)x2 -0.0024x+1.058, where x is the rolling mileage of the cold rolling mill, and y f (x) is the forward sliding value.

[0077] It should be noted that the trend curve of the forward slip value obtained by fitting the average forward slip value and the rolling mileage fully considers the influence of the forward slip value at a low roll speed on the forward slip value in the stable rolling stage, and therefore can more accurately reflect the trend of the forward slip value.

[0078] In step 103, a target attenuation prediction equation for roll roughness is constructed based on the trend equation.

[0079] In some embodiments, the derivative of the trend equation can be calculated to obtain the attenuation rate of the roll roughness; based on the attenuation rate, an initial attenuation prediction equation for the roll roughness is constructed; the coefficients of the initial attenuation prediction equation are solved based on the upper roll roughness, lower roll roughness, and rolling length of each steel coil corresponding to the roll service cycle to obtain the target attenuation prediction equation for the roll roughness.

[0080] The equation for the changing trend of the aforementioned forward slip value is y f (x)=(4E-6)x 2 Taking -0.0024x+1.058 as an example, differentiating this equation yields the decay rate y' of the roll roughness. f (x) = 2(4E-6)x - 0.0024. The initial attenuation prediction equation Y(x) = ky' is constructed using the attenuation rate. f (x)x+b, where x is the rolling mileage of the cold rolling mill, and y f (x) represents the roll roughness, and k and b are the coefficients to be solved. Assuming the upper roll roughness corresponding to the roll's service life is 0.2 and the lower roll roughness is 0.165, and the sum of the rolling lengths of all steel coils (i.e., the total rolling mileage) is 204.58 kilometers, substituting these three data points into the initial attenuation prediction equation, we can obtain the coefficients k and b. Substituting the obtained coefficients k and b into the initial attenuation prediction equation, we obtain the target attenuation prediction equation for the roll roughness: Y(x) = 4.48235(4E-7)x 2 -5.37888(1E-4)x+0.2.

[0081] In step 104, the roll roughness corresponding to the rolling length of the target steel coil is predicted based on the target attenuation prediction equation and the rolling length of the target steel coil.

[0082] In some embodiments, the rolling mileage of the cold rolling mill can be determined based on the rolling length of the target steel coil; the rolling mileage of the cold rolling mill can be substituted into the target attenuation prediction equation to obtain the roll roughness corresponding to the rolling length of the target steel coil.

[0083] Understandably, by using the trend equation of the forward slip value, where the rolling process parameters are constant and the rolling is in a stable rolling phase, as the trend equation for roll roughness decay, the decay rate of roll roughness can be calculated using the trend equation of the forward slip value. Then, an initial decay prediction equation can be constructed using the decay rate. By solving the coefficients of the initial decay prediction equation using the upper mill roll roughness, the lower mill roll roughness, and the total rolling mileage, an accurate target decay prediction equation can be obtained. In actual rolling processes, when a cold rolling mill rolls the same steel grade and specification of target steel coils using the same rolling process, the sum of the rolling lengths from the first steel coil to the target steel coil can be used as the rolling mileage of the cold rolling mill. This rolling mileage can then be substituted into the target decay prediction equation to predict the roll roughness corresponding to the rolling length of the target steel coil.

[0084] In the aforementioned method for predicting roll roughness, under the condition that the rolling process parameters of the cold rolling mill are constant, the roll speed, strip speed, and rolling length are obtained when rolling each steel coil during the roll's service life. Based on the roll speed, strip speed, and rolling length, the trend equation for the change of the forward slip value in the stable rolling stage within the roll's service life is determined. Based on the trend equation, a target attenuation prediction equation for roll roughness is constructed. Based on the target attenuation prediction equation and the rolling length of the target steel coil, the roll roughness corresponding to the rolling length of the target steel coil is predicted. Specifically, by constructing the target attenuation prediction equation for roll roughness, the roll roughness corresponding to the target steel coil at any rolling length can be predicted. The prediction of roll roughness is not affected by the accuracy of strip surface roughness measurement and transfer patterns, and has high accuracy.

[0085] The following describes an embodiment of the apparatus described in this application, which can be used to execute the roll roughness prediction method described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the roll roughness prediction method described in the above applications.

[0086] Figure 6 A block diagram of a roll roughness prediction device in one embodiment is shown. Figure 6As shown, the roll roughness prediction device of this application embodiment may include: a data acquisition module 601, a trend equation determination module 602, a target attenuation prediction equation determination module 603, and a roll roughness prediction module 604. The data acquisition module 601 is used to acquire the roll speed, strip speed, and rolling length when rolling each steel coil within the roll's service life, under the condition that the rolling process parameters of the cold rolling mill are constant. The trend equation determination module 602 is used to determine the trend equation of the forward slip value in the stable rolling stage within the roll's service life based on the roll speed, the strip speed, and the rolling length. The target attenuation prediction equation determination module 603 is used to construct a target attenuation prediction equation for roll roughness based on the trend equation. The roll roughness prediction module 604 is used to predict the roll roughness corresponding to the rolling length of the target steel coil based on the target attenuation prediction equation and the rolling length of the target steel coil.

[0087] In some embodiments, the target attenuation prediction equation determination module 603 is further configured to perform derivative calculation on the change trend equation to obtain the attenuation rate of the roll roughness; construct an initial attenuation prediction equation for the roll roughness based on the attenuation rate; and solve the coefficients of the initial attenuation prediction equation based on the upper roll roughness, lower roll roughness and rolling length of each steel coil corresponding to the roll service cycle to obtain the target attenuation prediction equation for the roll roughness.

[0088] In some embodiments, the service life of the rolls is the sum of the rolling cycles corresponding to each steel coil. The trend equation determination module 602 is further configured to determine the forward slip value of each steel coil based on the roll speed and the strip speed; determine the average forward slip value of each steel coil in the stable rolling stage within the corresponding rolling cycle based on the forward slip value of each steel coil; determine the rolling mileage of the cold rolling mill in the stable rolling stage corresponding to each steel coil based on the rolling length of each steel coil; and fit and regress the average forward slip value of each steel coil in the stable rolling stage within the corresponding rolling cycle and the rolling mileage of the cold rolling mill in the stable rolling stage corresponding to each steel coil to obtain the trend equation of the forward slip value.

[0089] In some embodiments, the trend equation determination module 602 is further configured to, for each steel coil, select the forward slip value corresponding to the highest continuous roll speed from the forward slip values ​​of the steel coil; and determine the average value of the selected forward slip values ​​as the average forward slip value of the steel coil during the stable rolling stage within the corresponding rolling cycle.

[0090] In some embodiments, the trend equation determination module 602 is further configured to determine, for each coil, the sum of the rolling lengths from the first coil to the first coil as the rolling mileage of the cold rolling mill in the stable rolling stage corresponding to the coil.

[0091] In some embodiments, the roll roughness prediction module 604 is further configured to determine the rolling mileage of the cold rolling mill based on the rolling length of the target steel coil; and to input the rolling mileage of the cold rolling mill into the target attenuation prediction equation to obtain the roll roughness corresponding to the rolling length of the target steel coil.

[0092] In some embodiments, the rolling process parameters include: pass reduction rate, maximum roll speed, front tension, back tension, stand exit thickness, emulsion information, and steel grade and specifications of the coil.

[0093] Based on the same inventive concept, embodiments of this application also provide a device for predicting roll roughness, referencing... Figure 7 The diagram shows a schematic of the structure of a roll roughness prediction device according to an embodiment of this application. The roll roughness prediction device includes one or more memories 704, one or more processors 702, and at least one computer program (computer program instruction) stored in the memory 704 and executable on the processor 702. When the processor 702 executes the computer program, it implements the method described above.

[0094] Among them, Figure 7 In this document, a bus architecture (represented by bus 700) is used. Bus 700 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 702 and memory represented by memory 704. Bus 700 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 705 provides an interface between bus 700 and receiver 701 and transmitter 703. Receiver 701 and transmitter 703 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 702 is responsible for managing bus 700 and general processing, while memory 704 can be used to store data used by processor 702 during operation.

[0095] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, cause the processor to perform the steps of the method described above.

[0096] Based on the same inventive concept, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0097] The functions described herein may be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions may be stored as one or more instructions or codes on or transmitted via a computer-readable medium. Other examples and embodiments are within the scope and spirit of this application and the appended claims. For example, due to the nature of software, the functions described above may be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Furthermore, the functional units may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.

[0098] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0099] The units described as separate components may or may not be physically separate. Similarly, the components of the control device may or may not be physical units; they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0100] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing computer program instructions, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0101] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for predicting the roughness of a rolling mill roll, characterized in that, include: Under the condition that the rolling process parameters of the cold rolling mill are constant, the roll speed, strip speed and rolling length of each steel coil during the service life of the roll are obtained; Based on the roll speed, the strip speed, and the rolling length, determine the trend equation of the forward slip value during the stable rolling stage within the roll's service life; Based on the aforementioned trend equation, a target attenuation prediction equation for roll roughness is constructed. Based on the target attenuation prediction equation and the rolling length of the target steel coil, the roll roughness corresponding to the rolling length of the target steel coil is predicted; wherein, constructing the target attenuation prediction equation for roll roughness based on the trend equation includes: The rate of decrease in roll roughness is obtained by differentiating the equation of change trend. Based on the aforementioned attenuation rate, an initial attenuation prediction equation for roll roughness is constructed; The coefficients of the initial attenuation prediction equation are solved based on the roughness of the upper mill rolls, the roughness of the lower mill rolls corresponding to the service cycle of the rolls, and the rolling length of each steel coil, so as to obtain the target attenuation prediction equation for the roll roughness.

2. The method for predicting roll roughness according to claim 1, characterized in that, The service life of the roll is the sum of the rolling cycles corresponding to each steel coil. The step of determining the trend equation for the forward slip value during the stable rolling phase within the service life of the roll, based on the roll speed, the strip speed, and the rolling length, includes: The forward slip value of each steel coil is determined based on the roll speed and the strip speed. Based on the forward slip value of each steel coil, determine the average forward slip value of each steel coil during the stable rolling stage within the corresponding rolling cycle; Based on the rolling length of each steel coil, the rolling mileage of the cold rolling mill in the corresponding stable rolling stage of each steel coil is determined; The forward slip value is fitted and regressed by fitting the average forward slip value of each steel coil in the stable rolling stage within the corresponding rolling cycle and the rolling mileage of the cold rolling mill in the corresponding stable rolling stage of each steel coil to obtain the trend equation of the forward slip value.

3. The method for predicting roll roughness according to claim 2, characterized in that, The step of determining the average forward slip value of each steel coil during the stable rolling stage within the corresponding rolling cycle, based on the forward slip value of each steel coil, includes: For each steel coil, the forward slip value corresponding to the highest continuous roll speed is selected from the forward slip values ​​of the steel coil; The average value of the selected forward slip is determined as the average forward slip value of the steel coil during the stable rolling stage within the corresponding rolling cycle.

4. The method for predicting roll roughness according to claim 2, characterized in that, The step of determining the rolling mileage of the cold rolling mill in the stable rolling stage corresponding to each steel coil based on the rolling length of each steel coil includes: For each steel coil, the sum of the rolling lengths from the first steel coil to the first steel coil is determined as the rolling mileage of the cold rolling mill in the stable rolling stage corresponding to the steel coil.

5. The method for predicting roll roughness according to claim 2, characterized in that, The step of predicting the roll roughness corresponding to the rolling length of the target steel coil based on the target attenuation prediction equation and the rolling length of the target steel coil includes: The rolling mileage of the cold rolling mill is determined based on the rolling length of the target steel coil. By substituting the rolling mileage of the cold rolling mill into the target attenuation prediction equation, the roll roughness corresponding to the rolling length of the target steel coil is obtained.

6. The method for predicting roll roughness according to claim 1, characterized in that, The rolling process parameters include: pass reduction rate, maximum roll speed, front tension, back tension, stand exit thickness, emulsion information, and steel grade and specifications of the coil.

7. A device for predicting the roughness of a roll, comprising a processor and a memory, characterized in that, The memory stores computer program instructions that can be executed by the processor, and when the processor executes the computer program instructions, it implements the steps of the method as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, cause the processor to perform the steps of the method as described in any one of claims 1 to 6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program product is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 6.

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