CN110743920A - Polishing process optimization method for improving surface appearance of galvanized automobile plate - Google Patents
Polishing process optimization method for improving surface appearance of galvanized automobile plate Download PDFInfo
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- CN110743920A CN110743920A CN201910919359.1A CN201910919359A CN110743920A CN 110743920 A CN110743920 A CN 110743920A CN 201910919359 A CN201910919359 A CN 201910919359A CN 110743920 A CN110743920 A CN 110743920A
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
- B21B37/16—Control of thickness, width, diameter or other transverse dimensions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
- B21B37/28—Control of flatness or profile during rolling of strip, sheets or plates
- B21B37/30—Control of flatness or profile during rolling of strip, sheets or plates using roll camber control
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
- B21B37/46—Roll speed or drive motor control
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
- B21B37/48—Tension control; Compression control
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
- B21B37/56—Elongation control
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
- B21B37/58—Roll-force control; Roll-gap control
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B24—GRINDING; POLISHING
- B24B—MACHINES, DEVICES, OR PROCESSES FOR GRINDING OR POLISHING; DRESSING OR CONDITIONING OF ABRADING SURFACES; FEEDING OF GRINDING, POLISHING, OR LAPPING AGENTS
- B24B5/00—Machines or devices designed for grinding surfaces of revolution on work, including those which also grind adjacent plane surfaces; Accessories therefor
- B24B5/02—Machines or devices designed for grinding surfaces of revolution on work, including those which also grind adjacent plane surfaces; Accessories therefor involving centres or chucks for holding work
- B24B5/16—Machines or devices designed for grinding surfaces of revolution on work, including those which also grind adjacent plane surfaces; Accessories therefor involving centres or chucks for holding work for grinding peculiarly surfaces, e.g. bulged
- B24B5/167—Machines or devices designed for grinding surfaces of revolution on work, including those which also grind adjacent plane surfaces; Accessories therefor involving centres or chucks for holding work for grinding peculiarly surfaces, e.g. bulged for rolls with large curvature radius, e.g. mill rolls
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Abstract
The invention discloses a finishing process optimization method for improving the surface appearance of a galvanized automobile plate, which is characterized by establishing a roughness replication model relating to a plurality of influence factors of roller roughness, unit width rolling force, rolling speed and elongation; optimizing a roughness replication model according to a sample set formed by data relations corresponding to the industrial field measured parameters and the roughness of the strip steel to obtain a roughness prediction model; and predicting and setting the roughness of the product prepared by the finishing process through a roughness prediction model, adjusting and setting the finishing process if the predicted roughness does not meet the roughness setting requirement, and finishing the strip steel according to the set finishing process if the predicted roughness meets the roughness setting requirement. By the optimization method of the finishing process, the transverse roughness Ra of the finished strip steel is less than or equal to 1.0, RPC is more than or equal to 100, Wca0.8 is less than 0.35, the qualification rate of the surface appearance of the outer plate is more than 95%, and the coating effect of the produced automobile outer plate product is better.
Description
Technical Field
The application belongs to the technical field of metal material processing, and particularly relates to a finishing process optimization method for improving the surface appearance of a galvanized automobile sheet.
Background
With the rapid development of the automobile industry in China, the localization requirement of high-surface-level galvanized automobile plates is particularly urgent. However, due to the limitation of the technical level and the lack of the production experience, the research on the analysis and control mechanism of the product surface micro-morphology is relatively less, and the personalized requirements and the technical difficulty of the process of the cold-rolled high-end hot-dip galvanized automobile sheet product are mainly shown in the following aspects:
1) the control requirements of narrow range of surface roughness and high precision of the strip steel are as follows: the client requires that the surface roughness of the strip steel meets the tolerance range of 0.6-1.0 μm, and the single-point detection values of the strip steel to be rolled are all required to be in the control range;
2) the control requirement of the high peak density Rpc value of the surface of the strip steel is as follows: for the strip steel with different elongation rates, the influence mechanisms of the surface peak density Rpc values are different, so that the requirements of customers cannot be met.
Due to the reasons, the existing finishing process parameters and the roller grinding and texturing process can only reach the conditions that the roughness Ra of the strip steel is 1.0-1.5, the RPC value is less than 80, and the surface appearance can not meet the requirements of S6 level.
Disclosure of Invention
In order to solve the technical problems, the invention provides a finishing process optimization method for improving the surface appearance of a galvanized automobile sheet, which takes the surface appearance as a core and gives consideration to roughness, RPC value and waviness.
The technical scheme adopted for achieving the aim of the invention is that the finishing process optimization method for improving the surface appearance of the galvanized automobile plate comprises the following steps:
(1) establishing a roughness replication model related to a plurality of influence factors, wherein the influence factors comprise roll roughness, unit width rolling force, rolling speed and elongation;
(2) optimizing the roughness replication model according to a sample set formed by data relations corresponding to the industrial field measured parameters and the roughness of the strip steel to obtain a roughness prediction model;
(3) and predicting the roughness of the product prepared by the finishing process through the roughness forecasting model:
a) if the predicted roughness meets the roughness setting requirement, finishing the strip steel according to the set finishing process;
b) and if the predicted roughness does not meet the roughness setting requirement, adjusting the set finishing process until the predicted roughness meets the roughness setting requirement, and finishing the strip steel according to the set finishing process.
Further, in step (1), the establishing a roughness replication model involving a plurality of influencing factors includes:
analyzing the influence rule of the roll parameters and the finishing process on the roughness of the finished product, and establishing a roughness replication model related to a plurality of influence factors through stepwise regression, wherein the regression equation is as follows:
Y＝a+bX1+cX2+dX3+eX4；
wherein: y is the roughness of the finished strip steel; x1 is roll roughness; x2 is the unit width rolling force; x3 is rolling speed; x4 is elongation; a. b, c, d and e are all set coefficients, and a, b, c, d and e are all smaller than 1.
Further, in the coefficients a, b, c, d, e, | b | > | a | > | d | > | c |, | b | > | a | > | e | > | c |.
Further, the coefficient a is 0.1-0.5; the coefficient b is 0.4-0.8; the coefficient c is 0.0001 to 0.0004; the coefficient d is-0.005; the coefficient e is-0.3 to 0.3.
Further, in the step (2), the optimizing the roughness replication model according to a sample set composed of data relationships corresponding to the industrial field measured parameters and the strip steel roughness to obtain a roughness prediction model includes:
(2-1) according to the roughness replication model, combining the surface topography control requirements of the galvanized automobile plate and the actual production data, considering the relationship between the roughness of the strip steel and the roughness of the roller, the roller diameter, the rolling period, the rolling force of the finishing process, the rolling speed and the elongation rate, and establishing a surface topography prediction system based on a neural network;
and (2-2) training a neural network of the surface topography prediction system according to a sample set formed by data relation corresponding to industrial field measured parameters and the roughness of the strip steel, verifying the correctness of the surface topography prediction system by using the principle of the neural network, and continuously correcting the rule and the calculation method of the surface topography prediction system according to the sample set.
Further, in the step (3), the setting finishing process includes:
controlling the initial roughness Ra of the roller to be 1.8-3.8 mu m, wherein the roughness Ra of the base roller is less than 0.4 mu m;
controlling the unit width rolling force to be 2.5 KN-7.0 KN;
controlling the rolling speed to be 100-140 m/min;
controlling the elongation rate to be 0.8% -1.6%;
controlling the rolling force of the finishing process to be 2200 KN-6900 KN and the roll bending force to be 200 KN-450 KN;
and controlling the inlet tension to be 30-60 KN and the outlet tension to be 35-65 KN.
Further, in the step (3), the controlling of the initial roughness Ra of the roll to be 2.0 μm to 2.2 μm includes:
carrying out a grinding process and a texturing process on the roller, wherein the grinding process comprises coarse grinding, semi-fine grinding, fine grinding and ultra-fine grinding, and the rotating speed of a grinding wheel in the semi-fine grinding process is 20 r/min-24 r/min;
the surface speed of the roller in the texturing process is 1600 mm/min.
Further, in the step (3), grading is carried out according to the diameter of the grinding wheel: the diameter of the grinding wheel is larger than 750mm and is the first gear, the diameter of the grinding wheel is 750 mm-650 mm and is the second gear, and different grinding technological parameters are used according to different diameters of the grinding wheel.
Based on the same inventive concept, the invention also provides a control device, which comprises a memory and a processor connected with the memory, wherein the memory is stored with program codes, and the processor is used for reading the program codes from the memory so as to execute the above-mentioned optimization method of the finishing process.
Based on the same inventive concept, the present invention also provides a computer-readable storage medium storing program code, which, when executed by a processor, can implement the above-mentioned optimization method of the finishing process.
According to the technical scheme, the polishing process optimization method for improving the surface morphology of the galvanized automobile plate, provided by the invention, establishes a roughness replication model related to a plurality of influence factors, including roller roughness, unit width rolling force, rolling speed and elongation, and the model can reflect the relationship between the strip steel roughness and the roller roughness, the rolling force, the rolling speed and the elongation; optimizing the roughness replication model to obtain a roughness prediction model according to a sample set consisting of data relations corresponding to industrial field actual measurement parameters and the roughness of the strip steel, wherein the roughness prediction model obtained by optimization can be better matched with the actual situation of field production so as to obtain a roughness prediction value extremely close to the actual situation; after the finishing process is set or a period of time is operated, the roughness of the product prepared by the finishing process can be predicted and set through the roughness prediction model, if the predicted roughness does not meet the set requirement, the finishing process can be adjusted and set in time, and the adjustment process does not need to be detected after a large number of finished products are prepared.
According to the finishing process optimization method for improving the surface appearance of the galvanized automobile plate, the finishing process parameters are set and adjusted by taking the roughness predicted value as a formulation basis, the surface appearance is taken as a core, and the roughness, the RPC value and the waviness are taken into consideration, so that the quality of the product is ensured, the uniformity of the roughness of the strip steel is less than or equal to +/-Ra0.1, the RPC is greater than or equal to 100, the Wca0.8 is less than 0.35, the qualification rate of the surface appearance of the outer plate is greater than 95%, the surface appearance meets the high requirement of the S6 level, and the coating effect of the produced automobile outer plate.
Drawings
FIG. 1 is a flow chart of a finishing process optimization method for improving the surface topography of a galvanized automobile plate in an embodiment of the invention;
FIG. 2 is a graph of the roughness replication rate data distribution in example 1;
FIG. 3 is a graph of the roughness copy rate data distribution in example 5;
fig. 4 is a display interface diagram of the control device in embodiment 6.
Detailed Description
In order to make the present application more clearly understood by those skilled in the art to which the present application pertains, the following detailed description of the present application is made with reference to the accompanying drawings by way of specific embodiments.
Example 1:
the embodiment provides a finishing process optimization method for improving the surface appearance of a galvanized automobile plate, which is used for finishing a galvanized automobile outer plate of a certain model, and the finishing process optimization method comprises the following steps:
(1) a roughness replication model is established that relates to a plurality of influencing factors, including roll roughness, unit width rolling force, rolling speed and elongation.
Specifically, in this embodiment, the influence rule of the roll parameters and the finishing process on the roughness of the finished product is analyzed in the historical data, a roughness replication model relating to a plurality of influence factors is established through stepwise regression, and the regression equation is as follows: y ═ 0.235+0.665X1+0.000117X2-0.00417X3-0.181X 4;
wherein: y is the roughness of the finished product strip steel, and is mum; x1 is the roughness of the roller, mu m; x2 is the unit width rolling force, KN; x3 is rolling speed; x4 is elongation.
Through calculation, as shown in fig. 2, the roughness replication rate of the roughness replication model is 50% to 74%, and the average replication rate is 62%.
(2) And optimizing the roughness replication model according to a sample set formed by data relations corresponding to the industrial field measured parameters and the strip steel roughness to obtain a roughness prediction model. The specific content of the step is as follows:
(2-1) according to the roughness replication model, combining the surface topography control requirements of the galvanized automobile plate and the actual production data, considering the relationship between the roughness of the strip steel and the roughness of the roller, the diameter of the roller, the rolling period, the rolling force of the finishing process, the rolling speed and the elongation rate, and establishing a surface topography prediction system based on a neural network;
and (2-2) training a neural network of the surface morphology forecasting system according to a sample set consisting of data relations corresponding to the industrial field measured parameters and the roughness of the strip steel, verifying the correctness of the surface morphology forecasting system by using the principle of the neural network, continuously correcting the rules and the calculation method of the surface morphology forecasting system according to the sample set, generating a model extremely approaching to the actual situation, and then forecasting by using the trained network model.
(3) The finishing process specifically comprises the following steps:
(3-1) setting a finishing process, wherein the specific parameters of the finishing process are as follows:
the initial roughness Ra of the roller is 1.8-3.8 μm, and the roughness Ra of the base roller is less than 0.4 μm;
the unit width rolling force is 2.5 KN-7.0 KN;
the rolling speed is 100 m/min-140 m/min;
the elongation is 0.8% -1.6%;
the rolling force of the finishing process is 2200 KN-6900 KN, and the roller bending force is 200 KN-450 KN;
the inlet tension is 30-60 KN, and the outlet tension is 35-65 KN.
The concrete parameters of the finishing process corresponding to the steel grade processed in the embodiment are as follows:
the initial roughness Ra of the roller is 2.0-2.4 μm, and the roughness Ra of the base roller is less than 0.4 μm;
the unit width rolling force is 2.5 KN-4.0 KN;
the rolling speed is 110 m/min-120 m/min;
the elongation is 1.1% -1.3%;
the rolling force of the finishing process is 4900 KN-5900 KN, the median value of the rolling force is 5256KN, and the roll bending force is 300 KN-450 KN;
the inlet tension is 45-50 KN, and the outlet tension is 55-60 KN.
In a specific application example, the finishing process parameters of the galvanized automobile outer panel of the type are shown in the table 1:
TABLE 1 galvanizing line finishing parameters
As can be seen from the above table, the finishing process of the present embodiment uses a large rolling force and a small tension, so as to cover the defects after galvanization and improve the surface quality. The small-roughness roller is used, so that a higher RPC value can be obtained, the rolling force is increased, the tension is reduced, and the roughness of the strip steel is ensured to be within a reasonable range.
And carrying out a grinding process and a texturing process on the roller for controlling the initial roughness Ra of the roller, wherein the grinding process comprises coarse grinding, semi-fine grinding, fine grinding and ultra-fine grinding, and the rotating speed of a grinding wheel in the semi-fine grinding process is 20 r/min-24 r/min.
Because the diameter of the grinding wheel has great influence on the surface quality and the process requirement of the roller, the embodiment performs grading according to the diameter of the grinding wheel: the diameter of the grinding wheel is larger than 750mm and is the first gear, the diameter of the grinding wheel is 750 mm-650 mm and is the second gear, different grinding process parameters are used according to different diameters of the grinding wheel, and the grinding parameters can be finely adjusted in a small range due to the difference of rollers and the continuous consumption of the grinding wheel.
In this example, the specific grinding process of the grinding wheel is shown in tables 2, 3 and 4:
TABLE 2 grinding procedure for grinding wheels having a diameter of 750mm or more
TABLE 3 grinding procedure for grinding wheel diameter 750mm 650mm
TABLE 4 ultra-precision grinding Process parameters
The roll uses a super-finishing process flow to improve roughness uniformity, roughness retention and RPC value from the roll.
The specific parameters of the texturing process are shown in table 5:
TABLE 5 texturing Process parameters
The surface speed of the roller in the roughening process is 1600mm/min, so that the vibration of the roller in the roughening process can be reduced, and the uniformity of the surface roughness is improved.
(3-2) predicting the roughness of the product prepared by the setting of the finishing process through a roughness forecasting model:
a) if the predicted roughness meets the roughness setting requirement, finishing the strip steel according to a set finishing process;
b) and if the predicted roughness does not meet the roughness setting requirement, adjusting the set finishing process until the predicted roughness meets the roughness setting requirement, and finishing the strip steel according to the set finishing process.
According to the measurement, the Ra, RPC and Wca of the steel grade produced by the finishing process are 0.82 μm, 105 and 0.326, and all meet the requirements of customers on the surface topography of the automobile outer plate.
Example 2:
the embodiment provides a finishing process optimization method for improving the surface appearance of a galvanized automobile plate, which is used for finishing another type of galvanized automobile outer plate, and referring to fig. 1, the finishing process optimization method comprises the following steps:
(1) a roughness replication model is established that relates to a plurality of influencing factors, including roll roughness, unit width rolling force, rolling speed and elongation.
Specifically, in this embodiment, the influence rule of the roll parameters and the finishing process on the roughness of the finished product is analyzed in the historical data, a roughness replication model relating to a plurality of influence factors is established through stepwise regression, and the regression equation is as follows: y ═ 0.28+0.665X1+0.00018X2-0.00476X3-0.151X 4;
wherein: y is the roughness of the finished product strip steel, and is mum; x1 is the roughness of the roller, mu m; x2 is the unit width rolling force, KN; x3 is rolling speed; x4 is elongation.
The roughness replication rate of the roughness replication model is 52-72% and the average replication rate is 61% through calculation.
(2) And optimizing the roughness replication model according to a sample set formed by data relations corresponding to the industrial field measured parameters and the strip steel roughness to obtain a roughness prediction model. The specific content of the step is as follows:
(2-1) according to the roughness replication model, combining the surface topography control requirements of the galvanized automobile plate and the actual production data, considering the relationship between the roughness of the strip steel and the roughness of the roller, the diameter of the roller, the rolling period, the rolling force of the finishing process, the rolling speed and the elongation rate, and establishing a surface topography prediction system based on a neural network;
and (2-2) training a neural network of the surface morphology forecasting system according to a sample set consisting of data relations corresponding to the industrial field measured parameters and the roughness of the strip steel, verifying the correctness of the surface morphology forecasting system by using the principle of the neural network, continuously correcting the rules and the calculation method of the surface morphology forecasting system according to the sample set, generating a model extremely approaching to the actual situation, and then forecasting by using the trained network model.
(3) The finishing process specifically comprises the following steps:
(3) the finishing process specifically comprises the following steps:
(3-1) setting a finishing process, wherein the specific parameters of the finishing process are as follows:
the initial roughness Ra of the roller is 1.8-3.8 μm, and the roughness Ra of the base roller is less than 0.4 μm;
the unit width rolling force is 2.5 KN-7.0 KN;
the rolling speed is 100 m/min-140 m/min;
the elongation is 0.8% -1.6%;
the rolling force of the finishing process is 2200 KN-6900 KN, and the roller bending force is 200 KN-450 KN;
the inlet tension is 30-60 KN, and the outlet tension is 35-65 KN.
The concrete parameters of the finishing process corresponding to the steel grade processed in the embodiment are as follows:
the initial roughness Ra of the roller is 3.4-3.8 μm, and the roughness Ra of the base roller is less than 0.4 μm;
the unit width rolling force is 5.0 KN-7.0 KN;
the rolling speed is 120 m/min-140 m/min;
the elongation is 1.4% -1.6%;
the rolling force of the finishing process is 5900 KN-6900 KN, the median value of the rolling force is 6301KN, and the bending force is 420 KN-450 KN;
the inlet tension is 55-60 KN, and the outlet tension is 60-65 KN.
In a specific application example, the finishing process parameters of the galvanized automobile outer panel of the type are shown in the table 6:
TABLE 6 galvanizing line finishing parameters
As can be seen from the above table, the finishing process of the present embodiment uses a large rolling force and a small tension, so as to cover the defects after galvanization and improve the surface quality. The small-roughness roller is used, so that a higher RPC value can be obtained, the rolling force is increased, the tension is reduced, and the roughness of the strip steel is ensured to be within a reasonable range.
The grinding process and the texturing process for the roller are performed to control the initial roughness Ra of the roller, and the specific contents of the grinding process and the texturing process are the same as those of embodiment 1, and are not described herein again.
According to the measurement, the Ra of the steel grade produced by the finishing process is 0.9 μm, the RPC is 113, and the Wca is 0.313, which all meet the requirements of customers on the surface appearance of the automobile outer plate.
Example 3:
the embodiment provides a finishing process optimization method for improving the surface appearance of a galvanized automobile plate, which is used for finishing another type of galvanized automobile outer plate, and referring to fig. 1, the finishing process optimization method comprises the following steps:
(1) a roughness replication model is established that relates to a plurality of influencing factors, including roll roughness, unit width rolling force, rolling speed and elongation.
Specifically, in this embodiment, the influence rule of the roll parameters and the finishing process on the roughness of the finished product is analyzed in the historical data, a roughness replication model relating to a plurality of influence factors is established through stepwise regression, and the regression equation is as follows: y ═ 0.26+0.725X1+0.00029X2-0.00426X3-0.181X 4;
wherein: y is the roughness of the finished product strip steel, and is mum; x1 is the roughness of the roller, mu m; x2 is the unit width rolling force, KN; x3 is rolling speed; x4 is elongation.
The roughness replication rate of the roughness replication model is 54-79% and the average replication rate is 65% through calculation.
(2) And optimizing the roughness replication model according to a sample set formed by data relations corresponding to the industrial field measured parameters and the strip steel roughness to obtain a roughness prediction model. The specific content of the step is as follows:
(2-1) according to the roughness replication model, combining the surface topography control requirements of the galvanized automobile plate and the actual production data, considering the relationship between the roughness of the strip steel and the roughness of the roller, the diameter of the roller, the rolling period, the rolling force of the finishing process, the rolling speed and the elongation rate, and establishing a surface topography prediction system based on a neural network;
and (2-2) training a neural network of the surface morphology forecasting system according to a sample set consisting of data relations corresponding to the industrial field measured parameters and the roughness of the strip steel, verifying the correctness of the surface morphology forecasting system by using the principle of the neural network, continuously correcting the rules and the calculation method of the surface morphology forecasting system according to the sample set, generating a model extremely approaching to the actual situation, and then forecasting by using the trained network model.
(3) The finishing process specifically comprises the following steps:
(3) the finishing process specifically comprises the following steps:
(3-1) setting a finishing process, wherein the specific parameters of the finishing process are as follows:
the initial roughness Ra of the roller is 1.8-3.8 μm, and the roughness Ra of the base roller is less than 0.4 μm;
the unit width rolling force is 2.5 KN-7.0 KN;
the rolling speed is 100 m/min-140 m/min;
the elongation is 0.8% -1.6%;
the rolling force of the finishing process is 2200 KN-6900 KN, and the roller bending force is 200 KN-450 KN;
the inlet tension is 30-60 KN, and the outlet tension is 35-65 KN.
The concrete parameters of the finishing process corresponding to the steel grade processed in the embodiment are as follows:
the initial roughness Ra of the roller is 1.8-2.0 μm, and the roughness Ra of the base roller is less than 0.4 μm;
the unit width rolling force is 2.5 KN-3.0 KN;
the rolling speed is 100 m/min-110 m/min;
the elongation is 0.8% -1.0%;
the rolling force of the finishing process is 2200KN to 3000KN, the median value of the rolling force is 2543KN, and the roll bending force is 200KN to 250 KN;
the inlet tension is 30-35 KN, and the outlet tension is 35-40 KN.
In a specific application example, the finishing process parameters of the galvanized automobile outer panel of the type are shown in the table 7:
TABLE 7 galvanizing line finishing parameters
As can be seen from the above table, the finishing process of the present embodiment uses a large rolling force and a small tension, so as to cover the defects after galvanization and improve the surface quality. The small-roughness roller is used, so that a higher RPC value can be obtained, the rolling force is increased, the tension is reduced, and the roughness of the strip steel is ensured to be within a reasonable range.
The grinding process and the texturing process for the roller are performed to control the initial roughness Ra of the roller, and the specific contents of the grinding process and the texturing process are the same as those of embodiment 1, and are not described herein again.
Through measurement, the Ra, RPC and Wca of the steel grade produced by the finishing process are 0.84 μm, 132 and 0.275, and all meet the requirements of customers on the surface topography of the automobile outer plate.
Example 4:
the embodiment provides a finishing process optimization method for improving the surface appearance of a galvanized automobile plate, which is used for finishing another type of galvanized automobile outer plate, and referring to fig. 1, the finishing process optimization method comprises the following steps:
(1) a roughness replication model is established that relates to a plurality of influencing factors, including roll roughness, unit width rolling force, rolling speed and elongation.
Specifically, in this embodiment, the influence rule of the roll parameters and the finishing process on the roughness of the finished product is analyzed in the historical data, a roughness replication model relating to a plurality of influence factors is established through stepwise regression, and the regression equation is as follows: y ═ 0.25+0.695X1+0.00034X2-0.00478X3-0.202X 4;
wherein: y is the roughness of the finished product strip steel, and is mum; x1 is the roughness of the roller, mu m; x2 is the unit width rolling force, KN; x3 is rolling speed; x4 is elongation.
Through calculation, the roughness replication rate of the roughness replication model is 60% -82%, and the average replication rate is 72%.
(2) And optimizing the roughness replication model according to a sample set formed by data relations corresponding to the industrial field measured parameters and the strip steel roughness to obtain a roughness prediction model. The specific content of the step is as follows:
(2-1) according to the roughness replication model, combining the surface topography control requirements of the galvanized automobile plate and the actual production data, considering the relationship between the roughness of the strip steel and the roughness of the roller, the diameter of the roller, the rolling period, the rolling force of the finishing process, the rolling speed and the elongation rate, and establishing a surface topography prediction system based on a neural network;
and (2-2) training a neural network of the surface morphology forecasting system according to a sample set consisting of data relations corresponding to the industrial field measured parameters and the roughness of the strip steel, verifying the correctness of the surface morphology forecasting system by using the principle of the neural network, continuously correcting the rules and the calculation method of the surface morphology forecasting system according to the sample set, generating a model extremely approaching to the actual situation, and then forecasting by using the trained network model.
(3) The finishing process specifically comprises the following steps:
(3) the finishing process specifically comprises the following steps:
(3-1) setting a finishing process, wherein the specific parameters of the finishing process are as follows:
the initial roughness Ra of the roller is 1.8-3.8 μm, and the roughness Ra of the base roller is less than 0.4 μm;
the unit width rolling force is 2.5 KN-7.0 KN;
the rolling speed is 100 m/min-140 m/min;
the elongation is 0.8% -1.6%;
the rolling force of the finishing process is 2200 KN-6900 KN, and the roller bending force is 200 KN-450 KN;
the inlet tension is 30-60 KN, and the outlet tension is 35-65 KN.
The concrete parameters of the finishing process corresponding to the steel grade processed in the embodiment are as follows:
the initial roughness Ra of the roller is 2.8-3.2 mu m, and the roughness Ra of the base roller is less than 0.4 mu m;
the unit width rolling force is 4.5 KN-5.0 KN;
the rolling speed is 110 m/min-130 m/min;
the elongation is 1.2% -1.4%;
the rolling force of the finishing process is 4500 KN-5000 KN, the median value of the rolling force is 4765KN, and the roll bending force is 345 KN-380 KN;
the inlet tension is 45-55 KN, and the outlet tension is 55-60 KN.
In a specific application example, the finishing process parameters of the galvanized automobile outer panel of the type are shown in the table 8:
TABLE 8 galvanized wire finishing parameters
As can be seen from the above table, the finishing process of the present embodiment uses a large rolling force and a small tension, so as to cover the defects after galvanization and improve the surface quality. The small-roughness roller is used, so that a higher RPC value can be obtained, the rolling force is increased, the tension is reduced, and the roughness of the strip steel is ensured to be within a reasonable range.
The grinding process and the texturing process for the roller are performed to control the initial roughness Ra of the roller, and the specific contents of the grinding process and the texturing process are the same as those of embodiment 1, and are not described herein again.
Through measurement, the Ra of the steel grade produced by the finishing process is 0.88 μm, the RPC is 133, and the Wca is 0.31, which all meet the requirements of customers on the surface topography of the automobile outer plate.
Example 5:
the embodiment provides a finishing process optimization method for improving the surface appearance of a galvanized automobile plate, which is used for finishing another type of galvanized automobile outer plate, and referring to fig. 1, the finishing process optimization method comprises the following steps:
(1) a roughness replication model is established that relates to a plurality of influencing factors, including roll roughness, unit width rolling force, rolling speed and elongation.
Specifically, in this embodiment, the influence rule of the roll parameters and the finishing process on the roughness of the finished product is analyzed in the historical data, a roughness replication model relating to a plurality of influence factors is established through stepwise regression, and the regression equation is as follows: y ═ 0.486+0.432X1+0.000208X2+0.0031X3+0.220X 4;
wherein: y is the roughness of the finished product strip steel, and is mum; x1 is the roughness of the roller, mu m; x2 is the unit width rolling force, KN; x3 is rolling speed; x4 is elongation.
Through calculation, as shown in fig. 3, the roughness replication rate of the roughness replication model is 41% to 56%, and the average replication rate is 49%.
(2) And optimizing the roughness replication model according to a sample set formed by data relations corresponding to the industrial field measured parameters and the strip steel roughness to obtain a roughness prediction model. The specific content of the step is as follows:
(2-1) according to the roughness replication model, combining the surface topography control requirements of the galvanized automobile plate and the actual production data, considering the relationship between the roughness of the strip steel and the roughness of the roller, the diameter of the roller, the rolling period, the rolling force of the finishing process, the rolling speed and the elongation rate, and establishing a surface topography prediction system based on a neural network;
and (2-2) training a neural network of the surface morphology forecasting system according to a sample set consisting of data relations corresponding to the industrial field measured parameters and the roughness of the strip steel, verifying the correctness of the surface morphology forecasting system by using the principle of the neural network, continuously correcting the rules and the calculation method of the surface morphology forecasting system according to the sample set, generating a model extremely approaching to the actual situation, and then forecasting by using the trained network model.
(3) The finishing process specifically comprises the following steps:
(3) the finishing process specifically comprises the following steps:
(3-1) setting a finishing process, wherein the specific parameters of the finishing process are as follows:
the initial roughness Ra of the roller is 1.8-3.8 μm, and the roughness Ra of the base roller is less than 0.4 μm;
the unit width rolling force is 2.5 KN-7.0 KN;
the rolling speed is 100 m/min-140 m/min;
the elongation is 0.8% -1.6%;
the rolling force of the finishing process is 2200 KN-6900 KN, and the roller bending force is 200 KN-450 KN;
the inlet tension is 30-60 KN, and the outlet tension is 35-65 KN.
The concrete parameters of the finishing process corresponding to the steel grade processed in the embodiment are as follows:
the initial roughness Ra of the roller is 1.8-2.5 mu m, and the roughness Ra of the base roller is less than 0.4 mu m;
the unit width rolling force is 2.5 KN-3.0 KN;
the rolling speed is 110 m/min-120 m/min;
the elongation is 0.8% -1.2%;
the rolling force of the finishing process is 2300KN to 3600KN, the median value of the rolling force is 2618KN, and the bending roll force is 300KN to 450 KN;
the inlet tension is 40-45 KN, and the outlet tension is 50-55 KN.
In a specific application example, the finishing process parameters of the galvanized automobile outer panel of the type are shown in the table 9:
TABLE 9 galvanizing line finishing parameters
As can be seen from the above table, the finishing process of the present embodiment uses a large rolling force and a small tension, so as to cover the defects after galvanization and improve the surface quality. The small-roughness roller is used, so that a higher RPC value can be obtained, the rolling force is increased, the tension is reduced, and the roughness of the strip steel is ensured to be within a reasonable range.
The grinding process and the texturing process for the roll are performed to control the initial roughness Ra of the roll, and the details of the grinding process and the texturing process are the same as those of embodiment 1, and are not described herein again.
According to the measurement, the Ra of the steel grade produced by the finishing process is 0.93 μm, the RPC is 101, and the Wca is 0.28, which all meet the requirements of customers on the surface appearance of the automobile outer plate.
Example 6:
based on the same inventive concept, the present embodiment provides a control apparatus, which includes a memory and a processor connected to the memory, wherein the memory stores program codes, and the processor is configured to read the program codes from the memory to execute the optimization method of the optical finishing process in any one of embodiments 1 to 5.
The control equipment can be specifically a PLC controller, an industrial personal computer and the like. The embodiment adopts the industrial personal computer, and the control page of the industrial personal computer is shown in figure 4.
Example 7:
based on the same inventive concept, the present embodiment provides a computer-readable storage medium storing program codes, which, when executed by a processor, can implement the optimization method of the above-mentioned embodiment 1 to 5.
Through the embodiment, the invention has the following beneficial effects or advantages:
the invention provides a finishing process optimization method for improving the surface appearance of a galvanized automobile plate, which searches the relation of influencing the surface appearance of strip steel, such as the roughness of the strip steel, RPC value and finishing process parameters aiming at the finishing process of the galvanized automobile plate, and forms a set of finishing process parameters aiming at the finishing machine of the galvanized automobile outer plate. Compared with the idea of rolling with a small rolling force and a large roughness roller and the idea of rolling with a large rolling force and a small roughness roller, the method adopts the roller with large rolling force and small roughness and the finishing process parameters to control the surface of the strip steel to be more exquisite and has stronger covering capability on defects. Aiming at the control idea that the galvanized automobile outer plate uses large rolling force and small roughness, the rolling force of the galvanized finishing machine is increased, so that the quality defects of zinc flow lines, zinc ash zinc slag, caterpillar and the like on the surface of the strip steel are effectively improved, the copying efficiency of the microcosmic appearance characteristic of the roller can be improved, and the distinctness of image, the oil storage property and the oil coating uniformity of the strip steel are improved. After the finishing process disclosed by the invention, the transverse roughness of the strip steel can reach Ra of less than or equal to 1.0, RPC of more than or equal to 100, Wca0.8 of less than 0.3, the qualification rate of the surface appearance of the outer plate is more than 95%, and the coating effect of the produced automobile outer plate product is better.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A finishing process optimization method for improving the surface appearance of a galvanized automobile plate is characterized by comprising the following steps:
(1) establishing a roughness replication model related to a plurality of influence factors, wherein the influence factors comprise roll roughness, unit width rolling force, rolling speed and elongation;
(2) optimizing the roughness replication model according to a sample set formed by data relations corresponding to the industrial field measured parameters and the roughness of the strip steel to obtain a roughness prediction model;
(3) and predicting the roughness of the product prepared by the finishing process through the roughness forecasting model:
a) if the predicted roughness meets the roughness setting requirement, finishing the strip steel according to the set finishing process;
b) and if the predicted roughness does not meet the roughness setting requirement, adjusting the set finishing process until the predicted roughness meets the roughness setting requirement, and finishing the strip steel according to the set finishing process.
2. The finishing process optimization method for improving the surface topography of a galvanized automobile plate as claimed in claim 1, wherein: in step (1), the establishing of the roughness replication model involving a plurality of influencing factors includes:
analyzing the influence rule of the roll parameters and the finishing process on the roughness of the finished product, and establishing a roughness replication model related to a plurality of influence factors through stepwise regression, wherein the regression equation is as follows:
Y＝a+bX1+cX2+dX3+eX4；
wherein: y is the roughness of the finished strip steel; x1 is roll roughness; x2 is the unit width rolling force; x3 is rolling speed; x4 is elongation; a. b, c, d and e are all set coefficients, and a, b, c, d and e are all smaller than 1.
3. The finishing process optimization method for improving the surface topography of a galvanized automobile plate as claimed in claim 2, wherein: in the coefficients a, b, c, d, e, | b | > | a | > | d | > | c |, | b | > | a | > | e | > | c |.
4. The finishing process optimization method for improving the surface topography of a galvanized automobile plate as claimed in claim 3, wherein: the coefficient a is 0.1-0.5; the coefficient b is 0.4-0.8; the coefficient c is 0.0001 to 0.0004; the coefficient d is-0.005; the coefficient e is-0.3 to 0.3.
5. The finishing process optimization method for improving the surface topography of a galvanized automobile plate as claimed in claim 1, wherein: in the step (2), the optimizing the roughness replication model according to a sample set composed of data relations corresponding to the industrial field measured parameters and the strip steel roughness to obtain a roughness prediction model includes:
(2-1) according to the roughness replication model, combining the surface topography control requirements of the galvanized automobile plate and the actual production data, considering the relationship between the roughness of the strip steel and the roughness of the roller, the roller diameter, the rolling period, the rolling force of the finishing process, the rolling speed and the elongation rate, and establishing a surface topography prediction system based on a neural network;
and (2-2) training a neural network of the surface topography prediction system according to a sample set formed by data relation corresponding to industrial field measured parameters and the roughness of the strip steel, verifying the correctness of the surface topography prediction system by using the principle of the neural network, and continuously correcting the rule and the calculation method of the surface topography prediction system according to the sample set.
6. The finishing process optimization method for improving the surface topography of a galvanized automobile plate as claimed in claim 1, wherein: in the step (3), the setting finishing process comprises:
controlling the initial roughness Ra of the roller to be 1.8-3.8 mu m, wherein the roughness Ra of the base roller is less than 0.4 mu m;
controlling the unit width rolling force to be 2.5 KN-7.0 KN;
controlling the rolling speed to be 100-140 m/min;
controlling the elongation rate to be 0.8% -1.6%;
controlling the rolling force of the finishing process to be 2200 KN-6900 KN and the roll bending force to be 200 KN-450 KN;
and controlling the inlet tension to be 30-60 KN and the outlet tension to be 35-65 KN.
7. The finishing process optimization method for improving the surface topography of a galvanized automobile plate as claimed in claim 6, wherein: in the step (3), the controlling of the initial roughness Ra of the roller to be 2.0-2.2 μm comprises the following steps:
carrying out a grinding process and a texturing process on the roller, wherein the grinding process comprises coarse grinding, semi-fine grinding, fine grinding and ultra-fine grinding, and the rotating speed of a grinding wheel in the semi-fine grinding process is 20 r/min-24 r/min;
the surface speed of the roller in the texturing process is 1600 mm/min.
8. The finishing process optimization method for improving the surface topography of a galvanized automobile plate as claimed in claim 7, wherein: in the step (3), grading is carried out according to the diameter of the grinding wheel: the diameter of the grinding wheel is larger than 750mm and is the first gear, the diameter of the grinding wheel is 750 mm-650 mm and is the second gear, and different grinding technological parameters are used according to different diameters of the grinding wheel.
9. A control apparatus characterized by: comprising a memory having program code stored thereon and a processor coupled to the memory for reading the program code from the memory to perform the method of optimizing a finishing process according to any one of claims 1-8.
10. A computer-readable storage medium characterized by: the computer-readable storage medium stores program code that, when executed by a processor, may implement the method of optimizing a finishing process of any of claims 1-8.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009226517A (en) * | 2008-03-21 | 2009-10-08 | Nisshin Steel Co Ltd | Grinding method of roll |
| CN104624669A (en) * | 2015-02-10 | 2015-05-20 | 北京科技大学 | Roughness predication and control method for belt steel produced by electric spark textured working roller |
| CN106825069A (en) * | 2017-03-22 | 2017-06-13 | 宁波宝新不锈钢有限公司 | A kind of cold-strip steel high accuracy plate shape surface roughness on-line intelligence control method |
| CN106825068A (en) * | 2017-01-13 | 2017-06-13 | 北京科技大学 | A kind of Forecasting Methodology of operation of rolling belt steel surface roughness |
| CN107234135A (en) * | 2016-03-28 | 2017-10-10 | 上海梅山钢铁股份有限公司 | One kind is applied to hot tandem and exports belt steel surface roughness control method |
| CN107442575A (en) * | 2016-03-28 | 2017-12-08 | 上海梅山钢铁股份有限公司 | A kind of Forecasting Methodology of strip wet jetting piles surface roughness |
| CN109127740A (en) * | 2017-06-27 | 2019-01-04 | 上海梅山钢铁股份有限公司 | A kind of forecasting procedure of Cold-strip Steel Surface roughness |
| CN109753632A (en) * | 2018-11-01 | 2019-05-14 | 北京理工大学 | A Surface Roughness Monitoring Model and Construction Method Based on Data Mining |
-
2019
- 2019-09-26 CN CN201910919359.1A patent/CN110743920B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009226517A (en) * | 2008-03-21 | 2009-10-08 | Nisshin Steel Co Ltd | Grinding method of roll |
| CN104624669A (en) * | 2015-02-10 | 2015-05-20 | 北京科技大学 | Roughness predication and control method for belt steel produced by electric spark textured working roller |
| CN107234135A (en) * | 2016-03-28 | 2017-10-10 | 上海梅山钢铁股份有限公司 | One kind is applied to hot tandem and exports belt steel surface roughness control method |
| CN107442575A (en) * | 2016-03-28 | 2017-12-08 | 上海梅山钢铁股份有限公司 | A kind of Forecasting Methodology of strip wet jetting piles surface roughness |
| CN106825068A (en) * | 2017-01-13 | 2017-06-13 | 北京科技大学 | A kind of Forecasting Methodology of operation of rolling belt steel surface roughness |
| CN106825069A (en) * | 2017-03-22 | 2017-06-13 | 宁波宝新不锈钢有限公司 | A kind of cold-strip steel high accuracy plate shape surface roughness on-line intelligence control method |
| CN109127740A (en) * | 2017-06-27 | 2019-01-04 | 上海梅山钢铁股份有限公司 | A kind of forecasting procedure of Cold-strip Steel Surface roughness |
| CN109753632A (en) * | 2018-11-01 | 2019-05-14 | 北京理工大学 | A Surface Roughness Monitoring Model and Construction Method Based on Data Mining |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111399452A (en) * | 2020-03-19 | 2020-07-10 | 首钢京唐钢铁联合有限责任公司 | Leveling process optimization method for improving peak density of household appliance plate strip steel |
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| CN111889513B (en) * | 2020-06-30 | 2022-07-08 | 武汉钢铁有限公司 | Thin slab continuous casting and rolling dummy rolling method and control system thereof |
| CN113118218B (en) * | 2021-04-25 | 2022-09-06 | 上海枫元金属科技有限公司 | Production method of super-hard precise stainless steel strip sand blasting roughened surface for elastic sheet |
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