CN114331195A - Process curve risk evaluation method for influencing overall length quality of hot-rolled strip steel - Google Patents
Process curve risk evaluation method for influencing overall length quality of hot-rolled strip steel Download PDFInfo
- Publication number
- CN114331195A CN114331195A CN202111681808.7A CN202111681808A CN114331195A CN 114331195 A CN114331195 A CN 114331195A CN 202111681808 A CN202111681808 A CN 202111681808A CN 114331195 A CN114331195 A CN 114331195A
- Authority
- CN
- China
- Prior art keywords
- curve
- risk
- quality
- length
- curves
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Control Of Metal Rolling (AREA)
Abstract
The invention provides a process curve risk evaluation method for influencing the overall length quality of a hot-rolled strip steel, and belongs to the technical field of metallurgy automation. The method comprises the steps of firstly, obtaining a process curve influencing the quality of hot rolled strip steel, and collecting the strip steel with qualified quality and the process curve as an excellent sample library; then, carrying out data differentiation and treatment on the curve head and tail step sections, and normalizing the length; and scaling all curves to a unit length in proportion to the longest curve; points of all the scaled curves are equidistant again, and a single group of risk coefficients are calculated according to a given threshold value of a field process table; and finally, solving the risk coefficient of the whole curve according to the single group of risk coefficients and evaluating. By the method, the reason of quality abnormity can be quickly positioned, and the precision and the efficiency of quality abnormity analysis are greatly improved.
Description
Technical Field
The invention relates to the technical field of metallurgy automation, in particular to a process curve risk evaluation method for influencing the full-length quality of hot-rolled strip steel.
Background
In the rolling process of the strip steel, a craftsman judges whether the quality of the strip steel is qualified according to a plurality of important quality indexes of the strip steel, and the judged influence factors comprise: thickness, width, FDT (finish rolling temperature), CT (coiling temperature), crown, wedge, symmetrical flatness, asymmetrical flatness. In the process of rolling the strip steel, each process procedure can generate certain influence on the quality of the strip steel, and the change of the process curve can reflect the change of the full-length rolling state of the strip steel. For example, the size of the blank and the roll gaps of the rolls in the rolling process directly influence the width and the thickness of the finished strip steel; incoming material temperature, rolling speed, cooling water quantity and the like can directly influence the strip steel FDT and CT; the roll shifting direction and the roll shifting amount affect the flatness of the finished strip steel. When the rolling process tends to be stable, the quality of the produced product is ensured, so that the parameters and curves in the rolling process of steel coils with excellent quality and the same specification and the same steel type are basically stable in a certain range, when the strip steel is influenced by the change of an external environment, for example, an incoming material slab has a water beam mark, the incoming material temperature is uneven, an incoming material temperature curve has a plurality of local extreme points, when the strip steel is rolled according to a set rolling force and a set roll gap, the rolling reduction of the strip steel is overlarge at the local maximum of the incoming material temperature curve, and the rolling reduction is too small at the local minimum, so that the thickness of the finished strip steel exceeds the limit. At present, steel mill technologists do not have a method and a tool for evaluating a process curve, and only judge the process curve through manual experience, so that the efficiency and the accuracy are low. Therefore, the process curve risk evaluation method influencing the overall length quality of the hot-rolled strip steel is provided, the reason of the abnormal quality is quickly positioned, manpower is saved, the efficiency is improved, and the accuracy is improved.
At present, the hot rolling quality control has no evaluation method for a rolling process curve, but the similarity evaluation of the curve is studied. For example, in the curve/curved surface quality quantitative evaluation method in the prior art, coordinate transformation is performed on the curve/curved surface, then wavelet fairing is performed for multiple times to obtain a reconstruction matrix, then the curve/curved surface is reconstructed, detail information is calculated, and the curve/curved surface is quantitatively evaluated through the detail information. The method evaluates the quality of the curve, and the method of the invention evaluates the quality of the curve by comparing the curve with the sample. In the method and the device for acquiring the similarity of the characteristic curves of the industrial equipment and the curve similarity calculation method in the prior art, the similarity of the curves is evaluated in a mode of calculating the weighted distance between the curves, and the abnormal positions of the curves cannot be definitely reflected.
In order to solve the problems, the invention provides a process curve risk evaluation method for influencing the overall length quality of hot-rolled strip steel, which is characterized in that a strip steel rolling process curve is collected, and a threshold value is set through a process idea to carry out risk evaluation on the curve. The process curve risk evaluation method influencing the overall length quality of the hot-rolled strip steel can realize quick positioning of the reason of the quality abnormality and improve the efficiency and accuracy of manual judgment on the abnormal rolling process.
Disclosure of Invention
The invention aims to solve the technical problem of providing a process curve risk evaluation method for influencing the overall length quality of hot-rolled strip steel, which can more intuitively reflect the state of the strip steel in the rolling process through the evaluation of the process curve and realize the quick positioning of the reason of quality abnormality.
The method comprises the following steps:
(1) acquiring process curves influencing the quality of hot rolled strip steel by using the conventional equipment, wherein the process curves comprise curves which can be measured by temperature, speed, rolling force, roll gap and the like in the rolling process, and collecting the strip steel with qualified quality and the process curves thereof as an excellent sample library;
(2) performing data discrimination and treatment on all the process curve head and tail step sections acquired in the step (1), eliminating abnormal step section data, and performing length normalization on the curve with the abnormal step section data eliminated;
(3) scaling all curves to a unit length in proportion to the longest curve;
(4) points are fetched again at equal intervals by all the zoomed curves, all sample points of each point fetching are used as a group, a threshold value is given according to a field process table, and a risk coefficient is calculated by using a single group of risk coefficient calculation method;
(5) and solving the risk coefficient of the whole curve according to the single group of risk coefficients, and sorting all the curves in a descending order according to the magnitude of the risk coefficients of the curve, wherein the process curves in the first three of the sorting are main influence factors of the current curve in the quality analysis item.
Wherein, the band steel with qualified quality in the step (1) refers to the band steel with the same steel type, width and thickness and the quality hit rate which accords with the 2 sigma principle, namely the quality hit rate is more than 95.45%.
The data discrimination and treatment of the curve head and tail step section in the step (2) specifically comprises the following steps: eliminating curve head and tail step sections, wherein the eliminated data length formula is as follows:
wherein L isCH、LCTRespectively, head-removed length and tail-removed length, hEActually measuring the thickness h for the strip steel finish rolling inletTThe target thickness of the strip steel finish rolling is obtained.
Normalizing the length of the curve with the head and the tail removed in the step (2) to a [0,1] interval, wherein the normalization calculation formula is as follows:
wherein L isniIs the length of the curve normalized, LiIs the length of the original curve, Lmax、LminThe length of the curve in the excellent sample library is the maximum value and the minimum value respectively, and i is the curve number.
In the step (3), the scaling ratio of the curve is used as a scaling multiple according to the ratio of the normalized length of the curve to the normalized length of the longest curve, namely:
wherein L ismiTo scale the curve length, LniIs the length of the curve normalized, LnmaxIs the length of the longest curve after curve normalization.
And (4) in the step (4), the re-equidistant point taking is carried out according to the length of the longest curve after the step section is removed, 1 point is taken every 1 meter, and the final total point taking number is taken as the number of the re-point taking of all the curves.
In the step (4), points which are obtained by re-taking all curves each time are taken as a group, the total group number is the number of points obtained by re-taking the curves, each group of risk coefficients is obtained by calculating by using a single group of risk coefficient calculation method, and the single group of risk coefficient calculation method is as follows:
s41: arranging all the sample points except the point taken by the current steel coil curve from small to large, and selecting a median x of the samplesCenSelecting the intermediate value of the samples as a median when the number of the samples is odd, and selecting the mean value of the two intermediate samples as the median when the number of the samples is even;
s42: the point x taken by the current steel coil curvecAnd median xCenTaking the difference, recording as the deviation delta:
δ＝|xc-xCen|
s43: comparing the deviation delta with upper and lower thresholds, defining the upper threshold as TupThe lower threshold is TlowIf the upper threshold and the lower threshold are both obtained by inquiring the field process table, a single group of Risk coefficients RiskpComprises the following steps:
in the step (5), the average value of all the single-group Risk coefficients obtained in the step (4) represents the Risk coefficient of the whole curve, and the Risk coefficient of the curve is defined as RiskC：
Wherein n is the number of single-group risk coefficients, namely the number of equidistant points of each curve, and i is the serial number of each group of single-group risk coefficients; riskpiAnd (4) sorting all the curves in a descending order according to the sizes of the risk coefficients for a single group of risk coefficients, wherein the process curves in the first three of the risk coefficient sorting are main influence factors of the current curves in the quality analysis item.
The technical scheme of the invention has the following beneficial effects:
according to the scheme, the curve of the strip steel rolling process is collected, the threshold value is set through the process idea, risk evaluation is carried out on the curve, the reason of quality abnormity can be rapidly positioned, and the efficiency and the accuracy of manual judgment on the rolling abnormity process are improved.
Drawings
FIG. 1 is a flowchart of a process curve risk assessment method of the present invention affecting the overall length quality of a hot rolled strip;
FIG. 2 is a schematic diagram illustrating a single set of risk factor calculation steps according to an embodiment of the present invention;
FIG. 3 is a roll gap graph of a strip steel F3 stand according to an embodiment of the present invention;
FIG. 4 is a graph of strip FET temperature profiles in accordance with an embodiment of the present invention;
FIG. 5 is a graph of the loop angle of strip steel F4 in an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages of the present invention more apparent, the following detailed description is given with reference to the accompanying drawings and specific embodiments.
The invention provides a process curve risk evaluation method for influencing the overall length quality of hot-rolled strip steel.
As shown in fig. 1, the method comprises the steps of:
(1) acquiring a process curve influencing the quality of hot rolled strip steel, and collecting the strip steel with qualified quality and the process curve thereof as an excellent sample library;
(2) performing data discrimination and treatment on all the process curve head and tail step sections acquired in the step (1), eliminating abnormal step section data, and performing length normalization on the curve with the abnormal step section data eliminated;
(3) scaling all curves to a unit length in proportion to the longest curve;
(4) points are fetched again at equal intervals by all the zoomed curves, all sample points of each point fetching are used as a group, a threshold value is given according to a field process table, and a risk coefficient is calculated by using a single group of risk coefficient calculation method;
(5) and solving the risk coefficient of the whole curve according to the single group of risk coefficients and evaluating.
Each group of risk coefficients in the step (4) is calculated by using a single group of risk coefficient calculation method, and the single group of risk coefficient calculation method comprises the following steps:
s41: arranging all the sample points except the point taken by the current steel coil curve from small to large, and selecting a median x of the samplesCenSelecting the intermediate value of the samples as a median when the number of the samples is odd, and selecting the mean value of the two intermediate samples as the median when the number of the samples is even;
s42: the point x taken by the current steel coil curvecAnd median xCenTaking the difference, recording as the deviation delta:
δ＝|xc-xCen|
s43: comparing the deviation delta with upper and lower thresholds, defining the upper threshold as TupThe lower threshold is TlowIf the upper threshold and the lower threshold are both obtained by inquiring the field process table, a single group of Risk coefficients RiskpComprises the following steps:
a single set of risk factor calculation method flows are shown in fig. 2.
The following description is given with reference to specific examples.
Taking curve data of a certain hot rolling 2250 production line offline coil, the steel grade is CP800, the thickness of the finished product is 4.08mm, the width of the finished product is 1250mm, the first coil is the current coil, the rest are excellent samples, and the quality analysis item is the thickness. The embodiment specifically comprises the following steps:
s1: and acquiring a roll gap curve of the current steel coil F3 rack, and adding a steel coil process curve with the same steel type, width and thickness and the mass hit rate of more than 95.45% into an excellent sample library. Specific data are shown in table 1:
TABLE 1 actual measurement of hot rolled strip F3 machine frame roll gap curve
S2: eliminating invalid data at the head and the tail of the curve:
the curve after removing the step data is shown in fig. 3, and the specific data is shown in table 2:
TABLE 2F 3 Rack roll gap culling step section Curve
Normalizing the curve length:
s3: the curve scaling is used as a scaling factor according to the ratio of the normalized length of the curve to the normalized length of the longest curve, namely:
s4: and (3) re-taking the number of points, removing the length of the step section according to the longest curve, taking 1 point every 1 meter, taking the total number of points as the number of all curves, taking all samples of each point as a group, and calculating a single group of risk coefficients according to a given threshold value of a field process table. The data of the new points are shown in table 3, and the specific process table of the upper and lower thresholds is shown in table 4:
TABLE 3F 3 frame roll gap curve repoint values
TABLE 4 roll gap deviation threshold value process table
S5: calculating the whole group of risk coefficients by using a single group of risk coefficient calculation method, wherein the single group of risk coefficient calculation method comprises the following steps:
s51: arranging all the sample points except the point taken by the current steel coil curve from small to large, and selecting a median x of the samplesCenWhen the number of the samples is odd, selecting the intermediate value of the samples as a median; when the number of samples is even, selecting the average value of the middle two samples as a median;
s52: the point x taken by the current steel coil curvecAnd median xCenThe difference is recorded as the deviation delta. Median x in group 1 samplesCen8.234, the point x taken by the current coil curvec9.336, the deviation δ:
δ＝|xc-xCen|＝|9.336-8.234|＝1.102
s53: the calculated deviation delta is between the upper and lower thresholds, i.e. Tlow＝0＜δ＝1.102＜Tup1.4, the single set of risk factors is:
the remaining single set of risk factors are calculated as above, and the single set of risk factors are shown in table 5 below:
TABLE 5 Single set of Risk coefficients
| Counting number | 1 | 2 | 3 | 4 | 5 | 6 | ... |
| Risk factor% | 78.71 | 82.06 | 78.79 | 76.22 | 69.29 | 71.80 | ... |
Taking the average value of all the single-group Risk coefficients to represent the Risk coefficient of the whole curve, and defining the Risk coefficient of the curve as RiskCThen the curve risk coefficient is:
namely, the risk coefficient of the roll gap curve of the current coiled steel strip F3 frame is 73.78%.
The risk coefficients of other process curves are calculated in the same manner and sorted as shown in table 6 below:
TABLE 6 Process Curve Risk coefficients ordering
The FET temperature curve and the F4 loop angle curve are respectively shown in fig. 4 and 5, and it can be concluded that the roll gap of the F3 frame, the FET temperature, and the F4 loop tension risk coefficient are large, and are main influence factors affecting the current strip steel thickness.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. A process curve risk evaluation method influencing the full-length quality of a hot-rolled strip steel is characterized by comprising the following steps: the method comprises the following steps:
(1) acquiring process curves influencing the quality of hot rolled strip steel by using the conventional equipment, wherein the process curves comprise temperature, speed, rolling force and roll gap curves in the rolling process, and collecting the strip steel with qualified quality and the process curves thereof as excellent sample libraries;
(2) performing data discrimination and treatment on all the process curve head and tail step sections acquired in the step (1), eliminating abnormal step section data, and performing length normalization on the curve with the abnormal step section data eliminated;
(3) scaling all curves to a unit length in proportion to the longest curve;
(4) points are fetched again at equal intervals by all the zoomed curves, all sample points of each point fetching are used as a group, a threshold value is given according to a field process table, and a risk coefficient is calculated by using a single group of risk coefficient calculation method;
(5) and solving the risk coefficient of the whole curve according to the single group of risk coefficients, and sorting all the curves in a descending order according to the magnitude of the risk coefficients of the curve, wherein the process curves in the first three of the sorting are main influence factors of the current curve in the quality analysis item.
2. The method of claim 1 for assessing risk of a process curve affecting the overall length quality of a hot rolled strip, wherein the method comprises the steps of: the band steel with qualified quality in the step (1) refers to the band steel with the same steel type, width and thickness and the quality hit rate which accords with the 2 sigma principle, namely the quality hit rate is more than 95.45%.
3. The method of claim 1 for assessing risk of a process curve affecting the overall length quality of a hot rolled strip, wherein the method comprises the steps of: the data discrimination and treatment of the head-tail step sections of all the process curves in the step (2) specifically comprises the following steps: eliminating curve head and tail step sections, wherein the eliminated data length formula is as follows:
wherein L isCH、LCTRespectively, head-removed length and tail-removed length, hEActually measuring the thickness h for the strip steel finish rolling inletTThe target thickness of the strip steel finish rolling is obtained.
4. The method of claim 1 for assessing risk of a process curve affecting the overall length quality of a hot rolled strip, wherein the method comprises the steps of: the normalized calculation formula in the step (2) is as follows:
wherein L isniIs the length of the curve normalized, LiIs the length of the original curve, Lmax、LminThe maximum value and the minimum value of the curve length in the excellent sample library are respectively, and i is a curve serial number.
5. The method of claim 1 for assessing risk of a process curve affecting the overall length quality of a hot rolled strip, wherein the method comprises the steps of: in the step (3), the scaling ratio of the curve is used as a scaling multiple according to the ratio of the normalized length of the curve to the normalized length of the longest curve, namely:
wherein L ismiTo scale the curve length, LniIs the length of the curve normalized, LnmaxIs the length of the longest curve after curve normalization.
6. The method of claim 1 for assessing risk of a process curve affecting the overall length quality of a hot rolled strip, wherein the method comprises the steps of: and (4) in the step (4), the re-equidistant point taking is carried out according to the length of the longest curve after the step section is removed, 1 point is taken in every 1 meter, and the final total point taking number is taken as the number of the re-point taking of all the curves.
7. The method of claim 1 for assessing risk of a process curve affecting the overall length quality of a hot rolled strip, wherein the method comprises the steps of: in the step (4), points obtained by re-taking all curves each time are taken as a group, the total group number is the curve re-taking point number, each group of risk coefficients is obtained by calculation by using a single group of risk coefficient calculation method, and the single group of risk coefficient calculation method is as follows:
s41: arranging all the sample points except the point taken by the current steel coil curve from small to large, and selecting a median x of the samplesCenSelecting the intermediate value of the samples as a median when the number of the samples is odd, and selecting the mean value of the two intermediate samples as the median when the number of the samples is even;
s42: the point x taken by the current steel coil curvecAnd median xCenTaking the difference, recording as the deviation delta:
δ＝|xc-xCen|
s43: comparing the deviation delta with the upper and lower thresholds to determineUpper bound of TupThe lower threshold is TlowIf the upper threshold and the lower threshold are both obtained by inquiring the field process table, a single group of Risk coefficients RiskpComprises the following steps:
8. the method of claim 1 for assessing risk of a process curve affecting the overall length quality of a hot rolled strip, wherein the method comprises the steps of: in the step (5), the average value of all the single-group Risk coefficients obtained in the step (4) represents the Risk coefficient of the whole curve, and the Risk coefficient of the curve is defined as RiskC：
Wherein n is a single group of risk coefficient number, namely the number of equidistant points of each curve; riskpiI is a single group of risk coefficients and each group of serial numbers.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111681808.7A CN114331195A (en) | 2021-12-27 | 2021-12-27 | Process curve risk evaluation method for influencing overall length quality of hot-rolled strip steel |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111681808.7A CN114331195A (en) | 2021-12-27 | 2021-12-27 | Process curve risk evaluation method for influencing overall length quality of hot-rolled strip steel |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114331195A true CN114331195A (en) | 2022-04-12 |
Family
ID=81023549
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111681808.7A Pending CN114331195A (en) | 2021-12-27 | 2021-12-27 | Process curve risk evaluation method for influencing overall length quality of hot-rolled strip steel |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114331195A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114580982A (en) * | 2022-05-07 | 2022-06-03 | 昆仑智汇数据科技(北京)有限公司 | Method, device and equipment for evaluating data quality of industrial equipment |
-
2021
- 2021-12-27 CN CN202111681808.7A patent/CN114331195A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114580982A (en) * | 2022-05-07 | 2022-06-03 | 昆仑智汇数据科技(北京)有限公司 | Method, device and equipment for evaluating data quality of industrial equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108817103B (en) | Steel rolling model steel family layer classification optimization method | |
| CN107179749A (en) | Hot dip zinc product whole process method of quality control | |
| CN104438350B (en) | Strip steel mechanical performance online detection and control method in leveling process | |
| CN113434994B (en) | Method for predicting cold rolling deformation resistance based on hot rolling process parameters of hot rolled original plate | |
| CN102658298B (en) | Plate-shape quality online judgment method applicable to hot-rolled thin strip steel | |
| CN112749505A (en) | Hot rolled strip section shape prediction method based on mechanism fusion data | |
| CN111666653B (en) | Online judging method for setting precision of strip steel finish rolling model | |
| CN111563686A (en) | Cold-rolled silicon steel quality judgment method based on full-process data | |
| CN110802114A (en) | Rolling force method for cold-rolled plate strip | |
| CN114818456B (en) | Prediction method and optimization method for full-length deformation resistance of cold continuous rolling strip steel | |
| CN112287550B (en) | Strip steel head thickness difference process parameter optimization method based on principal component analysis controller | |
| CN114331195A (en) | Process curve risk evaluation method for influencing overall length quality of hot-rolled strip steel | |
| CN110653271B (en) | Automatic grading device and grading method for roll surface quality of hot-rolled strip steel coiling pinch roll | |
| CN115815345A (en) | Mechanism collaborative prediction method and system for predicting mechanical property of full-flow hot-rolled strip steel | |
| CN114896735A (en) | Partial least square improved hot-rolled strip steel head wave defect reason identification method | |
| CN105127214B (en) | Mill elastic deformation prediction method in rolling process of four-high mill | |
| CN110646424A (en) | Rapid quantitative evaluation method for metallographic structure anisotropy | |
| CN111366702A (en) | Online intelligent accurate slitting system and online intelligent accurate slitting method for non-oriented silicon steel | |
| CN113780852B (en) | Diagnosis method for quality defects in plate and strip rolling process | |
| CN116108932A (en) | Method for establishing fusion model of steel production process data and mechanism | |
| CN112828053B (en) | Method for detecting full-length plate shape of cooled strip steel | |
| CN111744968B (en) | Cross-unit alignment method for steel coil data | |
| CN105195523A (en) | Method for improving calculation precision of temperature of intermediate billet in hot rolling | |
| CN106651945B (en) | A kind of silicon steel section thickness difference quality measurement patterned area analysis method | |
| CN117574215B (en) | Hot rolled strip steel plate shape prediction control method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |