CN112439796B - Rolling force automatic judgment method based on big data analysis - Google Patents
Rolling force automatic judgment method based on big data analysis Download PDFInfo
- Publication number
- CN112439796B CN112439796B CN201910816661.4A CN201910816661A CN112439796B CN 112439796 B CN112439796 B CN 112439796B CN 201910816661 A CN201910816661 A CN 201910816661A CN 112439796 B CN112439796 B CN 112439796B
- Authority
- CN
- China
- Prior art keywords
- rolling force
- deviation
- rolling
- diff
- tail
- 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.)
- Active
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B38/00—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product
- B21B38/08—Methods or devices for measuring, detecting or monitoring specially adapted for metal-rolling mills, e.g. position detection, inspection of the product for measuring roll-force
-
- 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
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B2201/00—Special rolling modes
- B21B2201/06—Thermomechanical rolling
-
- 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
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Control Of Metal Rolling (AREA)
- Force Measurement Appropriate To Specific Purposes (AREA)
Abstract
The invention relates to a rolling force automatic judgment method based on big data analysis, which comprises the following steps: step 1), data acquisition; step 2), data processing; step 3), automatically judging the rolling force; step 4), dynamic feedback processing; and 5) finishing. The invention can not only improve the labor efficiency through an online automatic judgment platform, but also quickly diagnose the reason of the quality defect of the strip steel through an algorithm, thereby providing a quick and accurate means for fault diagnosis and troubleshooting of a rolling line.
Description
Technical Field
The invention relates to a judgment method, in particular to a rolling force automatic judgment method based on big data analysis, and belongs to the technical field of hot rolling methods.
Background
In the hot continuous rolling, the situation that the rolling force for rolling the front half part of the strip steel and the rolling force for rolling the rear half part of the strip steel have larger deviation sometimes occurs, or the situation that the set value of the rolling force and the actual value of the rolling force have larger deviation sometimes can cause the defects of different types, such as substandard plate type standard deviation of the strip steel, wavy shape of the strip steel, partial thicker or thinner part of the strip steel, and the like. The phenomenon can be called by a self-learning module of the rolling model, and the learning parameters in the self-learning model are greatly adjusted, so that the phenomenon has great influence on the later rolling of the strip steel with the same specification.
The reasons for the occurrence of the rolling force deviation are mainly two points: 1. in the continuous casting area, in order to prevent cost increase and generation of iron oxide when molten steel raw material replacement is performed, a device for carrying molten steel is not completely cleaned but directly places the next planned molten steel in its vessel. This results in some billets being steel mix components, with the previous and present schedule of raw materials being mixed in the preceding segment. The mixed steel raw materials cause that the rolling force required for rolling the plate blank into the steel coil with the same thickness is different, so that the deviation value of the actual rolling force of the local strip steel and the set rolling force is larger, or the deviation value of the actual rolling force of the head and the tail of the strip steel is larger; 2. in the rolling plan making process, the steel tapping mark and the steel type specification are not matched sometimes, and in this case, the configuration parameters called by the calculation of the finish rolling setting model are not matched with the steel type specification necessarily. This results in a large deviation of the set rolling force from the actual rolling force for each stand.
Disclosure of Invention
The invention provides a rolling force automatic judging method based on big data analysis aiming at the problems in the prior art, which realizes the online diagnosis of the rolling force through an online quality automatic judging platform and blocks the defective steel coil caused by the rolling force deviation in time.
In order to achieve the above object, the technical solution of the present invention is a rolling force automatic determination method based on big data analysis, the method comprising the steps of:
step 1), data acquisition;
step 2), data processing;
step 3), automatically judging the rolling force;
step 4), dynamic feedback processing;
and 5) finishing. The invention provides a rolling force automatic judgment method based on big data, which considers that the rolling force deviation of the strip steel with different specifications is caused by the material or specification, and realizes the purpose of automatically blocking and judging the strip steel with the type deviation through an algorithm.
As an improvement of the invention, the step 1) is data acquisition; the method comprises the following specific steps: and (3) carrying out periodic rolling force data sampling on each of the n frames of the hot continuous rolling finish rolling, wherein each sampling point is 1 meter. The sampling quantity of the strip steels with different specifications is different due to the fact that the lengths of the strip steels are different.
As an improvement of the present invention, the step 2) data processing includes, specifically as follows,
the data collected by the n racks are subjected to deviation processing to obtain n groups of arrays, each array comprises a data sampling set of one rack, and a data processing algorithm is as follows:
step 21) calculating rolling force deviation values of the n frames;
diff[i]＝|force act [i]-force set [i]|；
wherein force act [i]For actual rolling force array, force set [i]To set the rolling force array, diff1[ i ]]For the rolling force deviation value array, i is a temporary variable, T is the total number of sampling points, 0<i<T；
Step 22) taking m points of the head and storing the m points into an array data1[ j]In which
The total number of sampling points is counted, and the tail m points are taken and stored in an array data2[ j [ ]]Middle, head-to-tail deviation values;
diff2[j]＝data1[j]-data2[j]。
as an improvement of the present invention, in the step 3), the rolling force is automatically determined, specifically as follows:
step 31): and (3) judging the rolling force deviation, wherein when the strip steel is rolled in an ideal state, the set value and the actual value of the rolling force are equal, and in the actual rolling process, the quality judgment system performs the following real-time judgment:
calculating the mean deviation value of the rolling force:
if 0<diff ave <150 (ton), which shows that the mean value of the rolling force deviation is in a normal range, and the rolling force is judged to be qualified;
if diff ave >150 (ton), which indicates that the mean value of the rolling force deviation exceeds the normal range, and the rolling force deviation is judged to be unqualified, and the blocking reason is that the steel tapping mark is not matched with the steel grade specification;
step 32) judging the local rolling force deviation, wherein 10 points are taken as the local rolling force deviation judgment, T-10 groups of data are shared, and the calculation method of each group of data is as follows
If 0<diff group [g]<150 (ton), which indicates that the mean deviation of the rolling force is within a normal range, and the rolling force is judged to be qualified;
if diff group [g]>150 (ton), which indicates that the mean value of the rolling force deviation exceeds the normal range, and the rolling force deviation is judged to be unqualified, and the blocking reason is that the steel tapping mark is not matched with the steel grade specification;
step 33) judging the rolling force deviation of the head section and the tail section, taking 20 points of each of the head section and the tail section as 2 groups of data in total for judging the rolling force deviation of the head section and the tail section, and calculating the mean value of the rolling force deviation of the head section
Rolling force deviation of tail sectionValue calculation
If 0<|diff head -diff tail |<100 (ton), which indicates that the head-tail deviation of the rolling force is in a normal range, and the rolling force is judged to be qualified;
if diff head -diff tail |>100 (ton), which indicates that the head and tail deviation of the rolling force exceeds the normal range, and the billet is judged to be unqualified, and the blockage reason is that the billet is a steel mixing component;
step 34) rolling force total judgment, namely judging that the rolling force is always unqualified when one unqualified rolling force occurs in the steps, and judging that the rolling force is qualified when all the unqualified rolling force are qualified.
As an improvement of the invention, the step 4) dynamic feedback processing is specifically as follows, when the determination result is unqualified, the rolling force self-learning model of the block of strip steel should not actively learn and change the learning parameters, and the determination result is fed back to the rolling force self-learning model of the rolling line.
Compared with the prior art, the invention has the advantages that 1) the technical scheme is beneficial to relevant workers to analyze the reasons of the defective strip steel, and effectively eliminates the quality problems of the strip steel, such as thickness, plate shape and the like, caused by the blocking of the rolling force; 2) the real-time online rolling force automatic judgment is carried out on the strip steel, so that the data information in the rolling process can be stored and processed, manual operation is not needed, and the strip steel is more accurate.
Drawings
FIG. 1 is a diagram of the logical relationship of the various modules of the system;
FIG. 2 is a rolling force deviation graph;
fig. 3 is a dynamic feedback flow chart.
The specific implementation mode is as follows:
for the purpose of enhancing an understanding of the present invention, the present embodiment will be described in detail below with reference to the accompanying drawings.
Example 1: referring to fig. 1 to 3, a rolling force automatic determination method based on big data analysis includes the following steps:
step 1) data acquisition;
step 2), data processing;
step 3), automatically judging the rolling force;
step 4), dynamic feedback processing;
and 5) finishing.
The step 1) data acquisition; the method comprises the following specific steps: and (3) carrying out periodic rolling force data sampling on each of the n frames of the hot continuous rolling finish rolling, wherein each sampling point is 1 meter. The sampling quantity of the strip steels with different specifications is different due to the fact that the lengths of the strip steels are different.
The step 2) of data processing is specifically as follows,
the data collected by the n racks are subjected to deviation processing to obtain n groups of arrays, each array comprises a data sampling set of one rack, and a data processing algorithm is as follows:
step 21) calculating rolling force deviation values of the n frames;
diff[i]＝|force act [i]-force set [i]|；
wherein for act [i]For actual rolling force array, force set [i]To set the rolling force array, diff1[ i ]]Is a rolling force deviation value array, i is a temporary variable, T is the total number of sampling points, 0<i<T；
Step 22) fetch m points of head and store the m points into an array data1 j]In which
The total number of sampling points is taken, and m tail points are taken and stored into an array data2[ j]Middle, head-tail deviation value;
diff2[j]＝data1[j]-data2[j]。
the step 3) of automatically judging the rolling force comprises the following specific steps:
step 31): and (3) judging the rolling force deviation, wherein when the strip steel is rolled in an ideal state, the set value and the actual value of the rolling force are equal, and in the actual rolling process, the quality judgment system performs the following real-time judgment:
calculating the mean deviation value of the rolling force:
if 0<diff ave <150 (ton), which shows that the mean value of the rolling force deviation is in a normal range, and the rolling force is judged to be qualified;
if diff ave >150 (ton), which indicates that the mean value of the rolling force deviation exceeds the normal range, and the rolling force deviation is judged to be unqualified, and the blocking reason is that the steel tapping mark is not matched with the steel grade specification;
step 32) judging the local rolling force deviation, taking 10 points as the local rolling force deviation judgment, sharing T-10 group data,
each group of data is calculated by
If 0<diff group [g]<150 (ton), which indicates that the mean deviation of the rolling force is within a normal range, and the rolling force is judged to be qualified;
if diff group [g]>150 (ton), which indicates that the mean value of the rolling force deviation exceeds the normal range, and the rolling force deviation is judged to be unqualified, and the blocking reason is that the steel tapping mark is not matched with the steel grade specification;
step 33) judging the rolling force deviation of the head section and the tail section, taking 20 points of each of the head section and the tail section as 2 groups of data in total for judging the rolling force deviation of the head section and the tail section, and calculating the mean value of the rolling force deviation of the head section
Calculation of mean deviation of rolling force of tail section
If 0<|diff head -diff tail |<100 (ton), which indicates that the head-tail deviation of the rolling force is in a normal range, and the rolling force is judged to be qualified;
if diff head -diff tail |>100 (ton), which indicates that the head and tail deviation of the rolling force exceeds the normal range, and the billet is judged to be unqualified, and the blockage reason is that the billet is a steel mixing component;
step 34) rolling force total judgment, namely judging that the rolling force is always unqualified when one unqualified rolling force occurs in the steps, and judging that the rolling force is qualified when all the unqualified rolling force are qualified.
And 4) dynamically feeding back, specifically, when the judgment result is unqualified, the rolling force self-learning model of the strip steel should not actively learn and change the learning parameters, and the judgment result is fed back to the rolling force self-learning model of the rolling line.
The application example is as follows:
take a hot rolled steel coil product with a coil number of 2100290800 in a hot rolled plate mill of a certain steel mill enterprise as an example. The steel tapping mark of the strip steel is GT5360A2, the slab thickness is 230mm, the slab width is 1200mm, the target thickness is 11.75mm, and the target width is 1150 mm. The control steps are as follows:
step 1), data acquisition;
each of the 7 stands of the hot continuous rolling finish rolling is periodically sampled by rolling force data, and 384 groups of data are obtained in a sampling point of 5 meters.
Step 2), data processing;
the data collected by the 7 racks is subjected to deviation processing to obtain 7 groups of arrays, and each array comprises a data sampling set of one rack. The data processing algorithm is as follows:
step 21): calculating the rolling force deviation value of 7 stands
diff[i]＝|force act [i]-force set [i]|
Wherein for act [i]For actual rolling force array, force set [i]To set the rolling force array, diff1[ i ]]For the rolling force deviation value array, i is a temporary variable, and T is the total number of sampling points 384, 0<i<T。
Step 22): fetch head m points and store into array data1[ j ]]In which
Take m points at tail and store them in array data2[ j ]]Deviation of head and tail
diff2[j]＝data1[j]-data2[j]
Step 3) the rolling force is automatically judged,
step 31): and judging the rolling force deviation. When the strip steel is rolled in an ideal state, the set value of the rolling force is equal to the actual value. In the actual rolling process, the quality judgment system makes the following real-time judgment:
calculating the deviation mean value of the rolling force:
the rolling force deviation of the strip steel is 0-150 (tons), which indicates that the mean value of the rolling force deviation is in a normal range, and the strip steel is judged to be qualified;
step 32) judging the local rolling force deviation, wherein 10 points are taken as the local rolling force deviation judgment, T-10 groups of data are shared, and the calculation method of each group of data is as follows
The local rolling force deviation of the strip steel is 0-150 (tons), and the average value of the rolling force deviation is in a normal range, and the strip steel is judged to be qualified;
and step 33) judging the rolling force deviation of the first section and the tail section. Taking 20 points of the first section and the tail section as the total 2 groups of data for the head and tail rolling force deviation judgment, and calculating the mean value of the first section rolling force deviation
Calculation of mean deviation of rolling force of tail section
Of the strip head -diff tail |>100 (ton), which indicates that the head and tail deviation of the rolling force exceeds the normal range, the billet is judged to be unqualified, and the blockage reason is that the billet is a steel mixing component.
Step 34) total rolling force determination. The rolling force data deviation of the strip steel is extracted, the first half part of the rolling force deviation of the strip steel is generally higher, and the rolling force is judged to be unqualified.
And 4) carrying out dynamic feedback processing. And when the judgment result is unqualified, the rolling force self-learning model of the strip steel does not actively learn and change the learning parameters. And simultaneously feeding back the judgment result to the rolling force self-learning model of the rolling line.
And 5) finishing.
It should be noted that the above-mentioned embodiments are not intended to limit the scope of the present invention, and all equivalent modifications and substitutions based on the above-mentioned technical solutions are within the scope of the present invention as defined in the claims.
Claims (2)
1. A rolling force automatic judgment method based on big data analysis is characterized by comprising the following steps:
step 1), data acquisition;
step 2), data processing;
step 3), automatically judging the rolling force;
step 4), dynamic feedback processing;
step 5) ending;
the step 1) of data acquisition comprises the following specific steps: carrying out periodic rolling force data sampling on each of n frames of hot continuous rolling finish rolling, wherein 1 m is a sampling point;
the step 2) of data processing is specifically as follows,
the data collected by the n racks are subjected to deviation processing to obtain n groups of arrays, each group of arrays comprises a data sampling set of one rack, and a data processing algorithm is as follows:
step 21) calculating the rolling force deviation values of the n frames:
diff[i]＝|force act [i]-force set [i]|；
wherein for act [i]For actual rolling force array, force set [i]To set the rolling force array, diff [ i ]]For the rolling force deviation value array, i is a temporary variable, T is the total number of sampling points, 0<i<T；
Step 22) taking m points of the head and storing the m points into an array data1[ j]In which
The total number of sampling points is taken, and m tail points are taken and stored into an array data2[ j]Middle, head to tail deviation value:
diff2[j]＝data1[j]-data2[j]；
the step 3) of automatically judging the rolling force comprises the following specific steps:
step 31): and (3) judging the rolling force deviation, wherein when the strip steel is rolled in an ideal state, the set value and the actual value of the rolling force are equal, and in the actual rolling process, the judging system makes the following real-time judgment:
calculating the mean deviation of the rolling force:
if 0<diff ave <150 tons, which indicates that the mean value of the rolling force deviation is within a normal range, and the rolling force is judged to be qualified;
if diff ave >150 tons, which indicates that the mean value of the rolling force deviation exceeds the normal range, is judged to be unqualified, and the reason for blockade is that the steel tapping mark is not matched with the steel grade specification;
step 32) local rolling force deviation judgment, taking 10 points as local rolling force deviation judgment, and calculating each group of data according to the method that
If 0<diff group [g]<150 tons, which indicates that the mean value of the rolling force deviation is within a normal range, and the rolling force is judged to be qualified;
if diff group [g]>150 tons, which indicates that the mean value of the rolling force deviation exceeds the normal range, is judged to be unqualified, and the reason for blockade is that the steel tapping mark is not matched with the steel grade specification;
step 33) judging the rolling force deviation of the first section and the tail section, taking 20 points of the first section and the tail section as the judgment of the deviation of the rolling force of the head and the tail, and totally having 2 groups of data, wherein the mean value of the deviation of the rolling force of the first section is
Mean value of rolling force deviation of tail section
If 0<|diff head -diff tail |<100 tons, which indicates that the head and tail deviation of the rolling force is within the normal range, and judgesDetermining to be qualified;
if diff head -diff tail |>100 tons, which indicates that the head and tail deviation of the rolling force exceeds the normal range, is judged to be unqualified, and the blockage reason is that the steel billet is a steel mixing component;
step 34) rolling force total judgment, wherein if one disqualification occurs in the steps, the rolling force total judgment is disqualified, and if all the disqualifications are qualified, the rolling force total judgment is qualified.
2. The rolling force automatic determination method based on big data analysis according to claim 1, wherein the step 4) dynamically feeds back the processing, specifically, when the determination result is unqualified, it indicates that the rolling force self-learning model of the strip should not actively learn and change the learning parameters, and feeds back the determination result to the rolling force self-learning model of the rolling line.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910816661.4A CN112439796B (en) | 2019-08-30 | 2019-08-30 | Rolling force automatic judgment method based on big data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910816661.4A CN112439796B (en) | 2019-08-30 | 2019-08-30 | Rolling force automatic judgment method based on big data analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112439796A CN112439796A (en) | 2021-03-05 |
| CN112439796B true CN112439796B (en) | 2022-09-06 |
Family
ID=74735266
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910816661.4A Active CN112439796B (en) | 2019-08-30 | 2019-08-30 | Rolling force automatic judgment method based on big data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112439796B (en) |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102294364A (en) * | 2010-06-22 | 2011-12-28 | 宝山钢铁股份有限公司 | Method for presetting rolling force of extremely-thin board temper mill |
| CN103100564A (en) * | 2011-11-10 | 2013-05-15 | 上海优控科技有限公司 | Novel rolling process self-adaptive control method |
| CN104070070B (en) * | 2013-03-27 | 2017-02-08 | 宝山钢铁股份有限公司 | Control method for improving rolling force of precisely rolled strip steel and thickness precision through tension compensation |
| CN104324951B (en) * | 2013-07-22 | 2016-08-24 | 宝山钢铁股份有限公司 | Single chassis starts rolling force setup and control method |
| CN104338757B (en) * | 2013-07-31 | 2017-07-28 | 宝山钢铁股份有限公司 | A kind of method for controlling mill star-up rolling sequence roll-force |
| CN104898430B (en) * | 2015-06-03 | 2017-07-18 | 北京首钢自动化信息技术有限公司 | Single stand cold mill rolling force model parameter optimization method based on data mining |
| CN108326049B (en) * | 2017-12-22 | 2019-06-07 | 中冶南方工程技术有限公司 | A kind of self-learning method of Continuous Cold-Rolling Force |
| CN108311544B (en) * | 2018-03-07 | 2019-09-03 | 首钢集团有限公司 | A kind of roll-force Parameter Self-learning method and device |
-
2019
- 2019-08-30 CN CN201910816661.4A patent/CN112439796B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN112439796A (en) | 2021-03-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108787749B (en) | Hot rolling production plan early warning method | |
| CN102553940B (en) | Method for controlling width of plate blank by using stokehold width gauge | |
| CN107179749A (en) | Hot dip zinc product whole process method of quality control | |
| CN111666653B (en) | Online judging method for setting precision of strip steel finish rolling model | |
| CN102591286A (en) | Online rolling plan dynamic pre-analysis and self-adjustment system and method | |
| CN110434172B (en) | Load distribution calculation method for continuous rolling of furnace coil and finishing mill group | |
| CN107442574A (en) | A kind of five Stands Cold Tandem Mills group automated system gets rid of the control method of frame rolling | |
| CN109201745A (en) | A kind of same plate difference control method of cold-reduced sheet | |
| CN107931329A (en) | A kind of improvement CSP double fluids change the control method of specification rolling force model precision | |
| EP2603333A1 (en) | Real-time determination method for temperature and geometry of a hot metal hot in a finishing train | |
| CN114888092B (en) | Cold rolling deformation resistance prediction method based on cross-process data platform | |
| CN105268748A (en) | Hot rolling load distribution method based on product classification | |
| CN110014043A (en) | One kind can adjust de-scaling compress control method for belt steel surface classification dynamic | |
| CN111229829B (en) | Full-multiple-length bar processing equipment and process | |
| CN112439796B (en) | Rolling force automatic judgment method based on big data analysis | |
| WO2012034875A2 (en) | Method for determining control variables of a rolling train comprising a plurality of roll stands for rolling a metal strip | |
| CN104772340A (en) | Warning control method for abnormal fluctuations of incoming hot rolled slab convexity and wedge shape | |
| CN107695107B (en) | A kind of method for tracking and managing of Stand Mill roughing mill setting data | |
| CN109550794B (en) | Feedforward control method for outlet temperature of hot rolling and finish rolling | |
| CN105618700A (en) | Method for accurately positioning continuously cast blank outlier caused by gate replacement | |
| CN111080835B (en) | Hot-rolled strip steel wedge defect diagnosis method and system based on gray comprehensive correlation degree | |
| CN207057282U (en) | Water tank parameter on-line tuning system based on rolled piece head and tail temperature difference | |
| CN111482466B (en) | Method for setting acceleration of rolling mill | |
| CN114029346A (en) | Roll gap leveling and correcting method of finishing mill group suitable for free schedule rolling | |
| CN110842024B (en) | Method and device for shearing strip steel |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |