CN108108752A - Process data processing method in a kind of cold continuous rolling production actual achievement - Google Patents
Process data processing method in a kind of cold continuous rolling production actual achievement Download PDFInfo
- Publication number
- CN108108752A CN108108752A CN201711347869.3A CN201711347869A CN108108752A CN 108108752 A CN108108752 A CN 108108752A CN 201711347869 A CN201711347869 A CN 201711347869A CN 108108752 A CN108108752 A CN 108108752A
- Authority
- CN
- China
- Prior art keywords
- process data
- group
- data
- actual achievement
- processing method
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 123
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 50
- 238000005096 rolling process Methods 0.000 title claims abstract description 43
- 238000003672 processing method Methods 0.000 title claims abstract description 16
- 230000001133 acceleration Effects 0.000 claims abstract description 12
- 238000001914 filtration Methods 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims abstract description 7
- 229910000831 Steel Inorganic materials 0.000 claims abstract description 6
- 238000003064 k means clustering Methods 0.000 claims abstract description 6
- 239000010959 steel Substances 0.000 claims abstract description 6
- 238000012790 confirmation Methods 0.000 claims abstract description 3
- 238000013480 data collection Methods 0.000 claims description 21
- 241001269238 Data Species 0.000 claims description 11
- 238000010606 normalization Methods 0.000 claims description 9
- 238000005452 bending Methods 0.000 claims description 5
- 238000003801 milling Methods 0.000 claims description 4
- 238000005303 weighing Methods 0.000 claims description 3
- 229910017435 S2 In Inorganic materials 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 5
- 239000000463 material Substances 0.000 description 2
- 238000004886 process control Methods 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 241000277275 Oncorhynchus mykiss Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000002688 persistence Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 238000013000 roll bending Methods 0.000 description 1
- 238000003466 welding Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/211—Selection of the most significant subset of features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06398—Performance of employee with respect to a job function
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C3/00—Registering or indicating the condition or the working of machines or other apparatus, other than vehicles
- G07C3/005—Registering or indicating the condition or the working of machines or other apparatus, other than vehicles during manufacturing process
-
- 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)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Evolutionary Computation (AREA)
- Tourism & Hospitality (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Business, Economics & Management (AREA)
- Artificial Intelligence (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Manufacturing & Machinery (AREA)
- Probability & Statistics with Applications (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Control Of Metal Rolling (AREA)
- Metal Rolling (AREA)
Abstract
The invention discloses a kind of cold continuous rollings to produce process data processing method in actual achievement, in continuous belt steel production process, gathering strip production technology data and carrying out subsequent processing to data, it is characterised in that including two steps：Step S1：In current strip production process, from primary PLC system acquisition process data, acquisition mode is to be acquired by grade outlet length；Step S2：After the completion of current strip production, to the total data of the current strip of step S1 acquisitions carries out acceleration deceleration state filtering successively, k means clustering algorithms are classified, the confirmation of the sum of normalized, data point minimum range, obtain representing the process data of production actual achievement.Data are representative more preferable, and the operating mode of data reflection is most stable, while avoids the numerical value difference between different technical parameters.
Description
Technical field
The present invention relates to operation of rolling data processing fields, and in particular to process data in a kind of cold continuous rolling production actual achievement
Processing method.
Background technology
Cold continuous rolling products application is extensive, and all trades and professions, to the dimensional accuracy of cold continuous rolling product, surface appearance, plate face is straight
The requirements such as degree, physical property are continuously improved, everything is required for ensuring in production process using more preferably process system, wherein really
Determine the premise that process data in cold continuous rolling production actual achievement is process optimization.
Cold continuous rolling uses multilevel control system.Wherein second level system is Process Control System.It gathers level-one basis certainly
Dynamicization system meter signal, completes material tracking and data collection, and generation production actual achievement is simultaneously stored in database, is then sent to three
Grade production management system.Production actual achievement is the basis of steel mill's production management, while its process data is that technologists carry out
The important evidence of quality analysis.
Producing actual achievement mainly includes essential information and process data information, and wherein essential information includes finished product reel number, entrance
Reel number, specification weight, steel grade, production start over time, teams and groups etc..Process data information includes speed, thickness, and tension rolls
Power processed, bending roller force etc..In actual production, two level acquires series of process data from level-one (primary PLC system), these techniques
If data all preserve, data volume is too big, is not suitable for long-term preserve.Then a set of generation is selected from this series of process data
The process data of table as the process data of production actual achievement, carries out persistence.
Produce the definite generally use following manner of process data in actual achievement：
(1) process data of length direction intermediate point is chosen.
Or the process data of (2) access speed maximum.
Or (3) all select median to the parameters of this series of process, form a set of process data.
Or (4) are all averaged the parameters of this series of process, form a set of process data.
However, mode (1) only can it be as the representative value of process data, and often under Part load
It is optimal.Mode (2) is perhaps effective when producing strict implement speed regulation plan rolling, but in producing inevitably
Speed control fluctuates, it is caused to be possible to inaccurate.The parameters for a set of process data that mode (3) and (4) are formed are not
Same place is derived from, it is when being analyzed as subsequent technique and improper.
In short, aforesaid way is excessively random during process data is determined, it is representative also and bad.
The content of the invention
For defect in the prior art, the technical problem to be solved in the present invention is to provide a kind of production of cold continuous rolling is real
Process data processing method in achievement, data are representative more preferable, and the operating mode of data reflection is most stable, while avoid different process ginseng
Numerical value difference between number.
In order to solve the above technical problems, the technical solution adopted by the present invention is as follows：
Process data processing method in a kind of cold continuous rolling production actual achievement, in continuous belt steel production process, gathering strip
Production technology data simultaneously carry out subsequent processing to data, it is characterised in that including two steps：Step S1：Current strip production process
In, from primary PLC system acquisition process data, acquisition mode is to be acquired by grade outlet length；Step S2：Current strip life
After the completion of production, acceleration deceleration state filtering is carried out successively to the total data of the current strip of step S1 acquisitions, k-means is clustered
Algorithm is classified, the confirmation of the sum of normalized, data point minimum range, obtains representing the process data of production actual achievement.
Further, the specific features of step S2 data processings are as follows：
S2.1：The data of step S1 acquisitions are formed in whole process data collection a G1, whole process data collection G1, filtering
It is the process data for accelerating or slowing down to fall acceleration deceleration state, and it is normal technique number only to retain acceleration deceleration state
According to obtaining filtering technique data set G2；
S2.2：First group of subset parameter in these process datas is chosen from filtering technique data set G2, using k-means
Clustering algorithm is classified, and obtains the first group cluster process data collection G3 and the second group cluster process data collection G4；Choose first
In group cluster process data collection G3 and the second group cluster process data collection G4, that larger group of average outlet strip speed is false
Larger group of G5 of average outlet strip speed is set to, is further processed；
S2.3：From larger group of G5 of average outlet strip speed, second group of subset parameter in these process datas is chosen,
It is normalized, obtains normalization process data collection G6；
S2.4：The distance between any two process data point in normalization process data collection G6 is calculated, is then united respectively
The sum of each distance of process data point away from other all data points is counted, finds that minimum data point of the sum of distance average
Corresponding original process data are the process data in optimal production actual achievement in outlet larger group of G5 of strip speed.
Further, the process data item gathered in step S1 includes but not limited to：Acceleration deceleration state；It is fast before and after each rack
Degree, tension, thickness；The roll-force of each rack, roll torque, rolling power, tilting value；Working roll linear velocity；Each roll it is curved
Roller power, roll shifting amount；Plate shape.
Further, the medium outlet modes of step S1 are arranged to the frequency that outlet often shuts out 3-6 meters of length strips.
Further, the production of step S2 strips completes that effective flying shear shear event occurs after referring to milling train.Further,
When choosing first group of subset parameter in these process datas from filtering technique data set G2 in step S2, first group of subset parameter
Refer to the related process parameters of utmostly influence of rolled stability.
Further, strip muzzle velocity is chosen in step S2 as first group of subset parameter or the last rack working roll of selection
Linear velocity is as first group of subset parameter or selection strip muzzle velocity and plate shape collectively as one group of subset parameter.
Further, in step S2, the k values of k-means clustering algorithms are determined by cold continuous rolling speed, for distinguishing high regime
And low speed segment.
Further, in step S2, second group of subset parameter refers to the related process parameters for weighing rolling data stability.
Further, in step S2, second group of subset parameter includes at least：Export strip speed, exit thickness, entrance thickness
Degree, plate shape value, each rack roll-force, each rack working roll, intermediate calender rolls bending roller force.
Compared with prior art, the present invention has the advantages that：
(1) all process datas have been taken into full account, and have eliminated the influence of the unstable periods such as acceleration and deceleration, data choosing
The representativeness selected is more preferable.
(2) process data in high speed stable rolling stage is obtained using hierarchical cluster, the operating mode of these data is most stable.
(3) the numerical value difference between different technical parameters is avoided using normalization algorithm.
Description of the drawings
Fig. 1 typical case's cold continuous rolling muzzle velocity figure.
Specific embodiment
In order to which the purpose of the present invention, technical solution and advantage is more clearly understood, with reference to embodiment into traveling
One step is described in detail.It should be understood that this embodiment is only the one side that the present invention applies, it is not used to limit this hair
It is bright.
Usually, cold continuous rolling produced by the way of 5 racks either 6 rack continuous rollings roll select four-roller or
Six-high cluster mill.In general the strip welding that strip is rolling on uncoiler in uncoiling, with production line, afterwards into work
Set reaches milling train.Need to collect the technological parameter of this strip at this time, until its afterbody leaves last rack, so repeatedly
It carries out.
Cold continuous rolling speed regulation is as shown in Figure 1.Muzzle velocity currently with firm afterbody and next strip steel head usually with
Relatively low speed rolls to prevent broken belt, and the middle part of current strip is generally rolled using fair speed, to improve production efficiency.
In raising speed and down speeding procedure, one section of raising speed section and reduction of speed section have been corresponded to respectively.As seen from the figure, speed is divided into two typical cases
Section, high speed stable rolling area and low speed cross weld seam rolling zone.The desired process data that represents of technologist must be at a high speed
Most representative data in stable rolling area.
Strip is identified in the material tracking module of Process Control System, obtains the essential information of strip, and when this band
In steel production process, its technique information is collected.The acquisition scheme of mainstream is periodical collection technology data, collection period 500
Millisecond or 1 second.But always change due to strip speed, the data that this acquisition mode obtains are along strip length direction
Upper distribution is simultaneously uneven.Process data to subsequently determining production actual achievement is unfavorable.
Certain embodiment technical solution comprises the following steps：
Step S1：In current strip production process, from primary PLC collection technology data.Acquisition mode is to be grown by grade outlets
Degree is acquired.
The data item wherein gathered includes：Acceleration deceleration state；Speed before and after each rack, tension, thickness；The rolling of each rack
Power, roll torque, rolling power, tilting value；Working roll linear velocity；The bending roller force of each roll, roll shifting amount；Plate shape；Deng
Wherein acquisition mode is to be acquired by grade outlet length.General selection often rolls 5 meters of acquisitions and once (exports out
Carry out the frequency of 5 meters of acquisitions once).
Step S2：After the completion of current strip production, data are handled with the process data represented.
Wherein strip production completes to refer to that effective flying shear shear event occurs after milling train occurs.
It is as follows for the specific numerical procedure of step 2：
S2.1：In whole process data collection G1, filtering acceleration deceleration state is the technique number for accelerating or slowing down
According to.It is normal process data only to retain acceleration deceleration state, obtains process data collection G2.
S2.2：First group of subset parameter in these process datas is chosen to G2, is divided using k-means clustering algorithms
Class, wherein k values are 2 (K values are fixed as 2, as long as being to separate high regime and low speed segment the reason for selected as 2).Obtain two groups
Process data collection G3 and G4.It chooses in G3 and G4, that larger group of average outlet strip speed, it is assumed that for G5, carry out next
Step processing.
First group of subset parameter refers to the related process parameters of utmostly influence of rolled stability.It is chosen in the present embodiment
Strip muzzle velocity be first group of subset parameter, muzzle velocity be weigh whether the important symbol of stable rolling.It can certainly
Last rack working roll linear velocity is selected as first group of subset parameter.Or selection strip muzzle velocity and plate shape are collectively as one
Group subset parameter.
K values selected as 2 is determined by cold continuous rolling speed regulation.
K-means clustering algorithms (k=2) step is as follows in the present embodiment：
1. randomly choosing one group of process data in G2, G2 middle outlets speed and just selected process data are then searched
That maximum group process data of muzzle velocity difference, and this two groups of process datas are chosen as initial clustering center of mass point.
2. for each process data of G2, it is poor with the muzzle velocity of the two barycenter that them are calculated respectively, with some
The speed difference smaller of barycenter, then being divided into this barycenter, this is a kind of.
3. determining new center of mass point, the muzzle velocity of new center of mass point is the outlet speed of all process datas of class where it
The average of degree.
4. 2 and 3 are repeated until center of mass point no longer changes.
S2.3：Second group of subset parameter in these process datas is chosen to G5, is normalized, obtains process data collection
G6。
Second group of subset parameter refers to the related process parameters for weighing rolling data stability.Second group of son in the present embodiment
Collection parameter includes：Outlet strip speed, exit thickness, inlet thickness, plate shape value, each rack roll-force, each rack working roll, in
Between roll bending power.
Normalization is with the following method：
x*Represent the value after normalization, x represents the value before normalization, and μ represents average value, and σ represents variance.
This method for normalizing can evade indivedual extraordinary noise data to whole influence.
S2.4：The distance between any two process data in G6 is calculated, then adds up each process data away from other institutes
There is the sum of distance of data.The original process data for finding the corresponding G5 of that minimum point of the sum of distance are optimal life
Produce the process data in actual achievement.
Distance selects Euclidean distance d, and formula is as follows：
D represents the distance of p (any one process data) to q (another arbitrary process data) point in formula, and p and q have
There is n feature (n is natural number).Euclidean distance best suits rolling data disposition.
Claims (10)
1. a kind of process data processing method in cold continuous rolling production actual achievement, in continuous belt steel production process, acquisition strip to be given birth to
Production. art data simultaneously carry out subsequent processing to data, it is characterised in that including two steps：Step S1：In current strip production process,
From primary PLC system acquisition process data, acquisition mode is to be acquired by grade outlet length；Step S2：Current strip production
After finishing, acceleration deceleration state filtering is carried out successively to the total data of the current strip of step S1 acquisitions, k-means clusters are calculated
Method is classified, the confirmation of the sum of normalized, data point minimum range, obtains representing the process data of production actual achievement.
2. process data processing method in cold continuous rolling production actual achievement according to claim 1, it is characterised in that step S2 numbers
It is as follows according to the specific features of processing：
S2.1：The data of step S1 acquisitions are formed in whole process data collection a G1, whole process data collection G1, are filtered out and are added
Deceleration regime is the process data for accelerating or slowing down, and it is normal process data only to retain acceleration deceleration state,
Obtain filtering technique data set G2；
S2.2：First group of subset parameter in these process datas is chosen from filtering technique data set G2, is clustered using k-means
Algorithm is classified, and obtains the first group cluster process data collection G3 and the second group cluster process data collection G4；First group is chosen to gather
In class process data collection G3 and the second group cluster process data collection G4, that larger group of average outlet strip speed, it is assumed that be
Larger group of G5 of average outlet strip speed, is further processed；
S2.3：From larger group of G5 of average outlet strip speed, second group of subset parameter in these process datas is chosen, is carried out
Normalization obtains normalization process data collection G6；
S2.4：The distance between any two process data point in normalization process data collection G6 is calculated, then statistics is every respectively
The sum of a distance of process data point away from other all data points finds that minimum data point of the sum of distance in average outlet
Corresponding original process data are the process data in optimal production actual achievement in larger group of G5 of strip speed.
3. process data processing method in cold continuous rolling production actual achievement according to claim 1 or 2, it is characterised in that step S1
The process data item of middle acquisition includes but not limited to：Acceleration deceleration state；Speed, tension, thickness before and after each rack；Each rack is rolled
Power processed, roll torque, rolling power, tilting value；Working roll linear velocity；The bending roller force of each roll, roll shifting amount；Plate shape.
4. process data processing method in cold continuous rolling production actual achievement according to claim 1 or 2, it is characterised in that step S1
Medium outlet mode is arranged to the frequency that outlet often shuts out 3-6 meters of length strips.
5. process data processing method in cold continuous rolling production actual achievement according to claim 1 or 2, it is characterised in that step S2
Strip production completes that effective flying shear shear event occurs after referring to milling train.
6. process data processing method in cold continuous rolling production actual achievement according to claim 2, it is characterised in that in step S2
When choosing first group of subset parameter in these process datas from filtering technique data set G2, first group of subset parameter refers to maximum
The related process parameters of degree influence of rolled stability.
7. process data processing method in cold continuous rolling production actual achievement according to claim 2, it is characterised in that in step S2
Strip muzzle velocity is chosen as first group of subset parameter or select end rack working roll linear velocity as first group of subset parameter,
Or selection strip muzzle velocity and plate shape are collectively as one group of subset parameter.
8. process data processing method in cold continuous rolling production actual achievement according to claim 2, it is characterised in that in step S2,
The k values of k-means clustering algorithms are determined by cold continuous rolling speed, for distinguishing high regime and low speed segment.
9. process data processing method in cold continuous rolling production actual achievement according to claim 2, it is characterised in that in step S2,
Second group of subset parameter refers to the related process parameters for weighing rolling data stability.
10. process data processing method in cold continuous rolling production actual achievement according to claim 2, it is characterised in that step S2
In, second group of subset parameter includes at least：Outlet strip speed, exit thickness, inlet thickness, plate shape value, each rack roll-force,
Each rack working roll, intermediate calender rolls bending roller force.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711347869.3A CN108108752B (en) | 2017-12-15 | 2017-12-15 | Process data processing method in cold continuous rolling production achievement |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711347869.3A CN108108752B (en) | 2017-12-15 | 2017-12-15 | Process data processing method in cold continuous rolling production achievement |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108108752A true CN108108752A (en) | 2018-06-01 |
| CN108108752B CN108108752B (en) | 2022-01-11 |
Family
ID=62216169
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711347869.3A Active CN108108752B (en) | 2017-12-15 | 2017-12-15 | Process data processing method in cold continuous rolling production achievement |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108108752B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112108523A (en) * | 2020-08-12 | 2020-12-22 | 北京首钢自动化信息技术有限公司 | Method and system for processing surface defects of strip steel in cold continuous rolling production |
| CN114367547A (en) * | 2021-12-28 | 2022-04-19 | 北京首钢自动化信息技术有限公司 | Statistical method and device for rolling data |
| CN115018246A (en) * | 2022-04-19 | 2022-09-06 | 北京科技大学 | Hot-rolled working roll on-machine selection method and roll on-machine expert system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101025767A (en) * | 2007-03-21 | 2007-08-29 | 燕山大学 | Friction coefficient forecasting and setting method for cold-continuous-rolling high-speed rolling process |
| CN101934289A (en) * | 2009-06-30 | 2011-01-05 | 上海宝信软件股份有限公司 | Adjusting method of stainless-steel cold continuous-rolling roll gap |
| US20110113847A1 (en) * | 2009-11-05 | 2011-05-19 | Mitsubishi -Hitachi Metals Machinery, Inc. | Cluster-type multistage rolling mill |
| CN102069094A (en) * | 2010-11-16 | 2011-05-25 | 北京首钢自动化信息技术有限公司 | Data mining-based plate shape control key process parameter optimization system |
-
2017
- 2017-12-15 CN CN201711347869.3A patent/CN108108752B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101025767A (en) * | 2007-03-21 | 2007-08-29 | 燕山大学 | Friction coefficient forecasting and setting method for cold-continuous-rolling high-speed rolling process |
| CN101934289A (en) * | 2009-06-30 | 2011-01-05 | 上海宝信软件股份有限公司 | Adjusting method of stainless-steel cold continuous-rolling roll gap |
| US20110113847A1 (en) * | 2009-11-05 | 2011-05-19 | Mitsubishi -Hitachi Metals Machinery, Inc. | Cluster-type multistage rolling mill |
| CN102069094A (en) * | 2010-11-16 | 2011-05-25 | 北京首钢自动化信息技术有限公司 | Data mining-based plate shape control key process parameter optimization system |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112108523A (en) * | 2020-08-12 | 2020-12-22 | 北京首钢自动化信息技术有限公司 | Method and system for processing surface defects of strip steel in cold continuous rolling production |
| CN114367547A (en) * | 2021-12-28 | 2022-04-19 | 北京首钢自动化信息技术有限公司 | Statistical method and device for rolling data |
| CN114367547B (en) * | 2021-12-28 | 2024-02-06 | 北京首钢自动化信息技术有限公司 | Statistical method and device for rolling data |
| CN115018246A (en) * | 2022-04-19 | 2022-09-06 | 北京科技大学 | Hot-rolled working roll on-machine selection method and roll on-machine expert system |
| CN115018246B (en) * | 2022-04-19 | 2023-01-20 | 北京科技大学 | Hot-rolled working roll on-machine selection method and roll on-machine expert system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108108752B (en) | 2022-01-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108108752A (en) | Process data processing method in a kind of cold continuous rolling production actual achievement | |
| CN107179749A (en) | Hot dip zinc product whole process method of quality control | |
| CN107377634A (en) | A kind of hot-strip exports Crown Prediction of Media method | |
| CN105740467A (en) | Mining method for C-Mn steel industry big data | |
| CN103123483B (en) | Rolling deformation resistance prediction system of new steel based on clustering analysis | |
| CN108213086B (en) | A method of realizing hot-strip slight center wave rolling | |
| CN103962391B (en) | Rolling load optimization method for hot continuous finishing mill group | |
| CN114818456B (en) | Prediction method and optimization method for full-length deformation resistance of cold continuous rolling strip steel | |
| CN108655186A (en) | Roll-force presetting method based on artificial neural network and mathematical model | |
| CN101477579A (en) | Roll-shaped curve design method of high-strength steel temper mill | |
| CN109261724A (en) | A method of improving preset model precision under multi items rolling mode | |
| CN109894482A (en) | A kind of cold continuous rolling Starting control optimization method and device | |
| CN108856305A (en) | A kind of non-orientation silicon steel production mill load distribution method | |
| CN1091008C (en) | Interlinked control method for plate-band rolling course based on coordination law of plate shape and plate thickness | |
| CN107900114B (en) | The method and device that a kind of pair of cold-rolled strip steel shape quality is evaluated | |
| CN105032945B (en) | A kind of hot tandem plate shape and plate convexity Comprehensive Control merit rating method | |
| CN106269911A (en) | A kind of roughing pressure Control System of Load Distribution method and roughing control system | |
| CN113118869B (en) | Method and system for determining use mileage of cutting edge of circle shear and industrial control equipment | |
| CN110052507B (en) | Strip steel coiling thickness control method and device | |
| CN112474815B (en) | Method and device for controlling rolling process | |
| CN107363101B (en) | A kind of other judgment method of hot-strip mathematical model data Layer | |
| CN107486587B (en) | Thinning compensation method for improving control precision of shearing setting model | |
| CN112246878B (en) | Thickness judgment system and judgment method for pickling coil hot rolling process | |
| CN109772897A (en) | A kind of setting control method improving hot-continuous-rolling strip steel overall length convexity and wedge-shaped precision | |
| CN115392104A (en) | Method for predicting mechanical property of cold-rolled continuous annealing strip steel based on annealing process |
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 |