EP0994757B1 - Verfahren und einrichtung zur steuerung bzw. voreinstellung eines walzgerüstes - Google Patents
Verfahren und einrichtung zur steuerung bzw. voreinstellung eines walzgerüstes Download PDFInfo
- Publication number
- EP0994757B1 EP0994757B1 EP98940053A EP98940053A EP0994757B1 EP 0994757 B1 EP0994757 B1 EP 0994757B1 EP 98940053 A EP98940053 A EP 98940053A EP 98940053 A EP98940053 A EP 98940053A EP 0994757 B1 EP0994757 B1 EP 0994757B1
- Authority
- EP
- European Patent Office
- Prior art keywords
- rolling
- neural network
- model
- fsi
- fri
- 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.)
- Expired - Lifetime
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Classifications
-
- 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
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B37/00—Control devices or methods specially adapted for metal-rolling mills or the work produced thereby
- B21B37/48—Tension control; Compression control
- B21B37/52—Tension control; Compression control by drive motor control
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B2265/00—Forming parameters
- B21B2265/12—Rolling load or rolling pressure; roll force
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B2265/00—Forming parameters
- B21B2265/20—Slip
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B21—MECHANICAL METAL-WORKING WITHOUT ESSENTIALLY REMOVING MATERIAL; PUNCHING METAL
- B21B—ROLLING OF METAL
- B21B2275/00—Mill drive parameters
- B21B2275/10—Motor power; motor current
- B21B2275/12—Roll torque
Definitions
- the invention relates to a method for controlling and / or Presetting of a roll stand for rolling a rolled strip According to the preamble of claim 1. It further relates to a corresponding device for carrying out the method.
- a method according to the preamble of claim 1 and a Corresponding device are for example from the suaufsatz “Experiences with the use of neural networks in the Walzwerksautomatmaschine” by D. Lindhoff et al., Published in Stahl und Eisen, Volume 114 (1994), No. 4, pages 49 to 53, known.
- From DE 195 27 521 C1 is a method of control and / or presetting a rolling stand for rolling a Metal band known in which the control and / or presetting of the roll stand in dependence on a rolling force, a rolling moment or an overfeed takes place, wherein the Rolling force, the rolling moment and / or the lead by means of a Roll model at least depending on the hardness of the Metal band and / or the friction between the metal band and the rolls of the rolling stand are calculated.
- the object is achieved by a method with the Features of claim 1 and a device with the features of claim 6 solved.
- reference symbols FS i denote the lead on the ith frame
- MR i the rolling moment on the ith frame
- FR i the rolling force on the ith frame
- FT i the strip tension on the ith frame
- eps i the relative decrease in thickness on the i -th framework
- MS the material hardness, ie the hardness of the rolled strip
- V i the belt speed after the ith frame
- H i the strip thickness after the ith frame.
- Reference numerals 1, 2, 3, 4 and 5 denote rolling stands, reference numerals 6 a uncoiler, reference numeral 7 a rolled strip and 8, a coiler.
- FIG. 2 illustrates the physical relationships in a roll gap, which are advantageously included in the modeling with a rolling model.
- the conditions in the nip are advantageously modeled by a strip model, where it is sufficient for reasons of symmetry to model only the upper or only the lower part of the roll stand, so that a boundary of the rolling model is the axis of symmetry 23 of the rolled strip 27.
- the strip 27 is split in the region of the contact surface strip - roller in strips 28 (due to the clarity, only one strip is provided with a reference numeral) perpendicular to the direction of movement of the strip 27.
- the material tension forces F Q are calculated in the horizontal and vertical directions and adjusted to each other via equilibrium conditions at the strip edges.
- some material tension forces F Q are entered by way of example.
- the vertical material tension forces F Q lead to a flattening 26 of the roller 21.
- the calculation of the flattened roller radius R B is carried out iteratively with the aid of the strip model and a model which describes the deformation of the roller.
- the flow sheath 20 is the location where the material is straight with the peripheral speed of the roller 21 moves. In front the flow sheath 20, the material moves slower, behind the flow sheath 20 faster than the peripheral speed the roller 21. Except at the location of the flow sheath 20 occurs accordingly everywhere between work roll 21 and material one Relative movement 24, 25 on. This relative movement 24, 25 leads to considerable friction forces.
- FIG. 3 shows an improvement of the output variables of a rolling model 32 by changing input variables 32 of the rolling model 31 by means of a neural network based Information Processing 33.
- the rolling model 31 determines depending on the Input variables 30 and 32 Output variables 34. These output variables are rolling force, rolling moment and / or overfeed.
- the Inputs 32 are made by one on neural networks based information processing or a neural Network 33 as a function of input values 35 of the neural Network 33 is formed.
- the input variables 30 and 32 are z. As the tensile force in the rolled strip, the bandwidth, the inlet thickness of the rolled strip, the hardness of the rolled strip and / or the friction between roller and rolled strip.
- the input variables 35 of the neural network z.
- B. material-specific Data such as B. the alloying shares, the inlet thickness, the outlet thickness and characteristics of a previous processing such as B. Thickness reduction or temperature in the preceding Processing.
- FIG. 4 shows another possibility for improving the output variables a rolling model 41 by correcting the output variables 47 of the rolling model 41. Also the procedure as shown in FIG 4 is not the subject of the present invention Invention.
- the rolling model 41 is determined as a function of Input variables 43, such as material hardness, friction between Rolls and rolled strip, tension, belt width or inlet thickness rolled strip, outputs 47. These outputs are rolling force, rolling moment and / or overfeed.
- the output variables 47 of the rolling model 41 are replaced by a correction block 45 corrected in response to correction parameters 44.
- Output variables of the correction block 45 are corresponding corrected values for rolling force FR, rolling moment MR and / or lead FS.
- the Correction parameters 44 are by means of a neural network 42 determined as a function of input variables 46.
- reference numeral 51 denotes a rolling model.
- Input variables 64 and MS of the rolling model 51 are the material hardness MS as indicated by reference numeral 64 rolling stock or scaffold-specific data such. B. friction between Rolls and rolled strip, tension, belt width and inlet thickness of the rolled strip.
- the material hardness MS is using a neural network, material network 50, depending on certain Input variables 60 are calculated. These input variables 60 can be: alloy parts, inlet thickness, outlet thickness, Temperature as well as information for the characterization of preprocessing such as B. previous degree of reduction or previous processing temperature.
- Outputs 65 of the Rolling model 51 are values for rolling force, rolling moment and / or Overfeed.
- correction block 53 in dependence corrected by correction parameters FRCP, MRCP, FSCP, by means of a neural network, scaffolding network 52, depending on of inputs 61.
- input variables 61 are u. a. the tape thickness, the bandwidth as well roll-specific data.
- Output variables 66 of the correction element 53 are corrected values for rolling force, rolling moment and / or Overfeed. These are fed to a further correction element 55, this by means of the correction parameters FRCD, MRCD and FSCD further corrected.
- the correction parameters FRCD, MRCD, FSCD are using a neural network, daytime network 54, calculated as a function of input variables 62.
- Input variables are u. a. Strip thickness, belt width and roll-specific Dates.
- Output variables 67 of the correction element 55 are corrected values for rolling force, rolling moment and overfeed, by means of another correction element in dependence of correction parameters FRCS, MRCS and FSCS on Getting corrected.
- the correction parameters FRCS, MRCS, FSCS be using a neural network, speed network 56, calculated as a function of input variables 63.
- the input variables 63 are the speed of the rolled strip as well u. a. Tape thickness, bandwidth and roll specific data.
- output 68 of the correction element 57 are corrected values for rolling force, rolling moment and overfeed, by means of a another correction element 59 as a function of a correction factor ⁇ for fine correction and adaptation to the current Rolled strip to be corrected.
- Output variables of the correction element 59 are corrected values for rolling force FR, rolling moment MR and lead FS.
- the correction members 53, 55, 57, 59 can z. B. multipliers. Basically, others come too Corrective strategies in question. Such correction strategies or joins of neural networks that are for the given Application can be used are known.
- the material network 50 provides the material hardness MS z. In Form of the regression parameters MSI, MSO described in FIG and MSE.
- the skeleton network 52 provides framework-specific correction factors FRCP, MRCP and FSCP for rolling force, rolling moment and Overfeed.
- the material network 50 and the skeleton network 52 become advantageously trained with data, material and Represent the rolling stand over the life of the roll stand.
- the daily network 54 supplies the correction factors FRCD, MRCD and FSCD for rolling force, rolling moment and overfeed, which is the relative small changes according to the daily form of the mill stand describe. Accordingly, the training of the day network is done 54 with young records, z. For example, records that are not older than three days.
- the speed network 56 provides the speed-dependent Correction factors FRCS, MRCS and FSCS for rolling force, Rolling moment and advance. With the speed network 56 In particular, friction-specific deviations are compensated.
- the friction between roller and rolled strip is very strong from the belt speed. The friction is i. a. like this smaller, the higher the belt speed is, as between Rolled strip and rolls with increasing speed Lubricating film forms.
- FIG. 6 shows a training method for an inventive Structure according to FIG. 5.
- MSE, MSI represent this and MSO the material hardness according to MS in FIG 5.
- the meaning MSE, MSI and MSO is explained in FIG. FR, MR, FS, ⁇ , FRCL, MRCL, FSCL, FRCD, MRCD, FSCD, FRCS, MRCS and FSCS have the same meaning as in FIG 5.
- the input variables 86, 87, 88, 89 correspond to the input variables 60, 61, 62, 63 in FIG 5.
- Reference numerals 76, 77, 78 denote material networks with the associated training or learning algorithms.
- Reference numeral 81 denotes a scaffolding net with an associated one Learning or training algorithm
- Reference numeral 83 a speed network with associated Learning algorithm.
- Reference numeral 70 denotes a data memory or a database, in the data AC, FRA, MRA and FSA stored, the characteristics of a representative Cross-section of all rolled in the corresponding mill / rolling mill Form bands.
- FRA, MCA and FSA are the actual ones Values for rolling force, rolling moment and advance over one long period, e.g. over the life of the mill stand, considered. They are formed from the roll-specific data AC.
- Function block 80 denotes an inverted one Walzmodell and a regression model, whereby by means of the inverted Roll model from the data AC the actual material hardness determined on the individual stands of the rolling mill is and where by means of the regression model of the Values for material hardness of the individual scaffolds the actual Values for the parameters MSE, MSI and MSO are formed.
- MSO the material networks 76, 77, 78 are trained.
- a rolling model calculates 79 values for rolling force, Rolling moment and advance.
- the input quantities correspond 90 the input variables 64 of FIG. 5
- the skeleton network 81 becomes dependent on the input variables 87 the data AC, FRA, MRA and FSA as well as the output quantities of the Roll model 79 trains.
- a correction block 53 (see FIG 5) are the output variables of the roll model 79 with the correction parameters FRCL, MRCL and FSCL, which are the skeleton network 81 issues, corrected.
- Output variables of the daily network 82 are the correction parameters FRCD, MRCD and FSCD, the input variables in a correction block 55, by means of which the output variables of the correction block 53 are corrected.
- the parameters DC, FRD, MRD and FSD from the database 71 the data AC, FRA, MRA and FSA, in contrast to to the data AC, FRA, MRA and FSA only rolling belts of the last one Represent the day or the last days.
- the outputs of the correction block 55, the input variables 89 and the ACC data are input variables in the speed network 83 and its learning algorithm. Furthermore, go in the speed network 83 or its learning algorithm correction parameters FRCS, MSCS and FSCS using a Speed correction element 85 are determined. there transforms the velocity correcting member 85 to a Standard speed normalized correction parameters FRC, FSC and MSC with respect to the actual speed of the rolled strip.
- the data ACC correspond to the data AC, but only represent the current rolled strip. Contains accordingly the database or the data memory 72 only the data for the current rolled strip.
- Output variables of the velocity network 83 are correction parameters FRCS, MRCS, FSCS, which are in enter a further correction block 57. The output of this Correction block enters a further correction block 59.
- a parameter ⁇ which is stored in a memory 84.
- Output of the correction block 59 are corrected values for rolling force FR, rolling moment MR and lead FS.
- the adaptive values for rolling force FRA, rolling moment MRA, leading FSA used for training the neural networks and the correction values FRC, FSC and MSC for training of the neural networks for rolling force, overfeed and rolling moment become dependent on estimated values determined, which are calculated by means of a rolling model depending on the known data sets.
- the training of neural networks thus takes place in a long-term learning part 73, a day or short-term learning part 74 as well a speed learning part 75.
- FIG. 7 shows an alternative training of the material network, wherein the material hardnesses are used for n rolling stands.
- reference numeral 70 denotes a database corresponding to FIG. 6, AC roll-specific data (see FIG. 6), reference numeral 100 a material network with a learning algorithm, 101 a regression model, and reference numeral 102 a rolling model.
- the material hardnesses MS 1... N at the individual stands and optionally the rolled strip temperature T strip and the total thickness reduction eps 1... N assigned to the individual rolling stands are output from the material network 100.
- the regression parameters MSU, MSI, MSE the material network 100, which consists of one or more neural networks, outputs the material hardnesses MS 1...
- the regression parameter MST is a parameter representing the temperature dependency, which can optionally be calculated if the temperature T strip of the rolled strip also enters the material network 100. This parameter is particularly advantageous if the method according to the invention is not used for cold rolling but for hot rolling.
- B denotes the band width, H i-1 the band thickness before the ith frame, H i the band thickness behind the ith frame, FT i-1 the band tension before the ith frame, FT i the band tension behind the i-th frame scaffold and v Wi the peripheral speed of the work rolls in the i-th scaffold.
- FRC FR is FR ⁇ ⁇ FRCS FRCD ⁇ FRCV
- MRC MR is MR ⁇ ⁇ MRCS MRCD ⁇ MRCV
- FSC FS is FS ⁇ ⁇ FSCS FSCD ⁇ FSCV where FR is , MR is and FS is the current values for rolling force, rolling moment and overfeed.
- either the material hardness or the friction can be determined. It is further conceivable to determine both quantities by means of a neural network. However, it has been shown that it is usually is sufficient, only one of the two unknowns, material hardness or friction, by means of a neural network. If the material hardness z. B. according to the invention by means of a neural network and for friction (rough) Estimates are used, the material network is able to the errors with respect to the rolling force, the rolling moment or the lead caused by inaccurate knowledge of the friction between Rolled strip and roller arise, correct.
Description
- FIG 1
- die physikalischen Verhältnisse einer Walzstraße sowie den Zusammenhang zwischen Dickenreduktion und Materialhärte,
- FIG 2
- die physikalischen Verhältnisse in einem Walzspalt,
- FIG 3
- ein Vorgehen am Eingang des Walzmodells,
- FIG 4
- ein Vorgehen am Ausgang des Walzmodells,
- FIG 5
- ein erfindungsgemäßes Vorgehen am Ein- und Ausgang des Walzmodells,
- FIG 6
- ein Trainingsverfahren für neuronale Netze in einer besonders vorteilhaften Ausgestaltung,
- FIG 7
- ein alternatives Trainingsverfahren für ein neuronales Netz zur Bestimmung der Materialhärte und
Hi: Banddicke nach dem i-ten Gerüst, i = 1,2,3,4,5 bei 5 Gerüsten.
RAW = Walzenradius der i-ten Walze und
ni = Drehzahl der i-ten Walze.
Claims (6)
- Verfahren zur Steuerung und/oder Voreinstellung eines Walzgerüsts (1 - 5) zum Walzen eines Metallbandes (7),wobei die Steuerung und/oder Voreinstellung des Walzgerüsts (1 - 5) in Abhängigkeit von einer Walzkraft (FRi), einem Walzmoment (MRi) und/oder einer Voreilung (FSi) erfolgt,wobei die Walzkraft (FRi), das Walzmoment (MRi) und/oder die Voreilung (FSi) mittels eines Walzmodells (51) zumindest in Abhängigkeit von der Härte (MS) des Metallbandes (7) und/oder der Reibung zwischen dem Metallband (7) und den Walzen des Walzgerüsts (1 - 5) berechnet werden,wobei mindestens eine der Eingangsgrößen (FRi, MRi, FSi, FT0 - FT5, H0 - H4, MS) des Walzmodells (51) mittels eines neuronalen Netzes (50) ermittelt oder korrigiert wird,wobei die Walzkraft (FRi), das Walzmoment (MRi) und/oder die Voreilung (FSi) mittels eines weiteren neuronalen Netzes (52, 54, 56) korrigiert werden,dass das weitere neuronale Netz (52, 54, 56) ein Gerüstnetz (52) umfasst, das mit walzspezifischen Daten, die einen Mittelwert über die Lebensdauer des Walzgerüsts (1 - 5) bilden, trainiert wird, unddass das weitere neuronale Netz (52, 54, 56) mindestens ein ergänzendes neuronales Netz (54, 56) umfasst, das Einflüsse mit Zeitkonstanten im Bereich eines Tages bis weniger Tage berücksichtigt.
- Verfahren nach Anspruch 1,
dadurch gekennzeichnet, dass die mittels des neuronalen Netzes (51) ermittelte oder korrigierte Eingangsgröße (FRi, MRi, FSi, FT0 - FT5, H0 - H4, MS) die Härte (MS) des Metallbandes (7) und/oder die Reibung zwischen dem Metallband (7) und den Walzen des Walzgerüsts (1 - 5) ist. - Verfahren nach Anspruch 1 oder 2
dadurch gekennzeichnet, dass das neuronale Netz (50), gegebenenfalls auch das weitere neuronale Netz (52, 54, 56), nachtrainiert wird bzw. werden. - Verfahren nach einem der Ansprüche 1 bis 3,
dadurch gekennzeichnet, dass das ergänzende neuronale Netz (54, 56) auch ein Geschwindigkeitsnetz (56) umfasst, das den Einfluss der Geschwindigkeit (v0 - v5) des Metallbandes (7) auf die Walzkraft (FRi), das Walzmoment (MRi) und/oder die Voreilung (FSi) berücksichtigt. - Verfahren nach Anspruch 4,
dadurch gekennzeichnet, dass das Geschwindigkeitsnetz (56) mit Daten des aktuellen Metallbandes (7) trainiert wird. - Einrichtung zur Durchführung eines Verfahrens nach einem der Ansprüche 1 bis 5, mit einem Walzmodell (51), einem dem Walzmodell (51) vorgeordneten neuronalen Netz (50) und einem dem Walzmodell (51) nachgeordneten weiteren neuronalen Netz (52, 54, 56),wobei mittels des Walzmodells (51) eine Walzkraft (FRi), ein Walzmoment (MRi) und/oder eine Voreilung (FSi) zumindest in Abhängigkeit von der Härte (MS) eines Metallbandes (7) und/oder der Reibung zwischen dem Metallband (7) und den Walzen eines Walzgerüsts (1 - 5) berechenbar sind,wobei mindestens eine der Eingangsgrößen (FRi, MRi, FSi, FT0 - FT5, H0 - H4, MS) des Walzmodells (51) mittels des vorgeordneten neuronalen Netzes (50) ermittelbar oder korrigierbar ist,wobei die Walzkraft (FRi), das Walzmoment (MRi) und/oder die Voreilung (FSi) mittels des weiteren neuronalen Netzes (52, 54, 56) korrigierbar sind,wobei das weitere neuronale Netz (52, 54, 56) ein Gerüstnetz (52) umfasst, das mit walzspezifischen Daten, die einen Mittelwert über die Lebensdauer des Walzgerüsts (1 - 5) bilden, trainiert wird,wobei das weitere neuronale Netz (52, 54, 56) mindestens ein ergänzendes neuronales Netz (54, 56) umfasst, das Einflüsse mit Zeitkonstanten im Bereich eines Tages bis weniger Tage berücksichtigt, undwobei anhand der Walzkraft (FRi), des Walzmoments (MRi) und/oder der Voreilung (FSi) das Walzgerüst (1 - 5) steuer- oder voreinstellbar ist.
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
DE19728979 | 1997-07-07 | ||
DE19728979A DE19728979A1 (de) | 1997-07-07 | 1997-07-07 | Verfahren und Einrichtung zur Steuerung bzw. Voreinstellung eines Walzgerüstes |
PCT/DE1998/001740 WO1999002282A1 (de) | 1997-07-07 | 1998-06-24 | Verfahren und einrichtung zur steuerung bzw. voreinstellung eines walzgerüstes |
Publications (2)
Publication Number | Publication Date |
---|---|
EP0994757A1 EP0994757A1 (de) | 2000-04-26 |
EP0994757B1 true EP0994757B1 (de) | 2005-11-23 |
Family
ID=7834906
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP98940053A Expired - Lifetime EP0994757B1 (de) | 1997-07-07 | 1998-06-24 | Verfahren und einrichtung zur steuerung bzw. voreinstellung eines walzgerüstes |
Country Status (4)
Country | Link |
---|---|
EP (1) | EP0994757B1 (de) |
AT (1) | ATE310592T1 (de) |
DE (2) | DE19728979A1 (de) |
WO (1) | WO1999002282A1 (de) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109351785A (zh) * | 2018-11-28 | 2019-02-19 | 北京首钢冷轧薄板有限公司 | 一种轧制力优化方法及装置 |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
ATE227615T1 (de) | 1999-03-23 | 2002-11-15 | Siemens Ag | Verfahren und einrichtung zur bestimmung der walzkraft in einem walzgerüst |
DE102004003514A1 (de) | 2004-01-23 | 2005-08-11 | Sms Demag Ag | Verfahren zum Erhöhen der Prozessstabilität, insbesondere der absoluten Dickengenauigkeit und der Anlagensicherheit, beim Warmwalzen von Stahl- oder NE-Werkstoffen |
CN108984836B (zh) * | 2018-06-12 | 2022-12-02 | 中冶南方工程技术有限公司 | 一种轧制损失力矩的计算方法 |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP3136183B2 (ja) * | 1992-01-20 | 2001-02-19 | 株式会社日立製作所 | 制御方法 |
FR2688428B1 (fr) * | 1992-03-13 | 1996-06-21 | Lorraine Laminage | Dispositif de commande d'un outil d'ecrouissage par laminage leger d'une tole. |
JPH05293516A (ja) * | 1992-04-17 | 1993-11-09 | Mitsubishi Heavy Ind Ltd | 圧延機の圧延荷重推定方法 |
DE4416317B4 (de) * | 1993-05-17 | 2004-10-21 | Siemens Ag | Verfahren und Regeleinrichtung zur Regelung eines materialverarbeitenden Prozesses |
JPH07246411A (ja) * | 1994-03-09 | 1995-09-26 | Toshiba Corp | 圧延機のロールギャップ補正装置 |
DE19527521C1 (de) * | 1995-07-27 | 1996-12-19 | Siemens Ag | Lernverfahren für ein neuronales Netz |
DE19641431A1 (de) * | 1996-10-08 | 1998-04-16 | Siemens Ag | Verfahren und Einrichtung zur Identifikation bzw. Vorausberechnung von Prozeßparametern eines industriellen zeitvarianten Prozesses |
-
1997
- 1997-07-07 DE DE19728979A patent/DE19728979A1/de not_active Ceased
-
1998
- 1998-06-24 AT AT98940053T patent/ATE310592T1/de active
- 1998-06-24 DE DE59813227T patent/DE59813227D1/de not_active Expired - Lifetime
- 1998-06-24 EP EP98940053A patent/EP0994757B1/de not_active Expired - Lifetime
- 1998-06-24 WO PCT/DE1998/001740 patent/WO1999002282A1/de active IP Right Grant
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109351785A (zh) * | 2018-11-28 | 2019-02-19 | 北京首钢冷轧薄板有限公司 | 一种轧制力优化方法及装置 |
Also Published As
Publication number | Publication date |
---|---|
DE19728979A1 (de) | 1998-09-10 |
EP0994757A1 (de) | 2000-04-26 |
ATE310592T1 (de) | 2005-12-15 |
WO1999002282A1 (de) | 1999-01-21 |
DE59813227D1 (de) | 2005-12-29 |
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