EP0994757A1 - Verfahren und einrichtung zur steuerung bzw. voreinstellung eines walzgerüstes - Google Patents
Verfahren und einrichtung zur steuerung bzw. voreinstellung eines walzgerüstesInfo
- Publication number
- EP0994757A1 EP0994757A1 EP98940053A EP98940053A EP0994757A1 EP 0994757 A1 EP0994757 A1 EP 0994757A1 EP 98940053 A EP98940053 A EP 98940053A EP 98940053 A EP98940053 A EP 98940053A EP 0994757 A1 EP0994757 A1 EP 0994757A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- rolling
- moment
- force
- model
- advance
- 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
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 and a device for controlling or presetting a rolling stand for rolling a rolled strip, the controlling or presetting of the rolling stand taking place as a function of at least one of the sizes, rolling force, rolling moment and advance.
- variables such as rolling force, rolling moment or advance or more of these variables must be known. It is possible to determine these quantities using a rolling model, in which the strip height, the run-in of the rolled strip, the strip width, the strip tension, the material hardness and / or the friction between the rolls and the rolled strip are used as input variables.
- a rolling model in which the strip height, the run-in of the rolled strip, the strip width, the strip tension, the material hardness and / or the friction between the rolls and the rolled strip are used as input variables.
- the quality requirements in particular for high-quality steels, can often not be met in such a method. Accordingly, it is an object of the invention to increase the quality of a rolled steel, in particular by maintaining thicknesses or hardness tolerances.
- the object is achieved in that the sizes or at least one of the sizes, rolling force, rolling moment and advance, on the basis of which the presetting or control of the rolling stand or the rolling train is carried out, are determined precisely.
- This modeling of the influences advantageously takes place and according to the invention by means of information processing based on neural networks.
- the object is achieved according to the invention by a method or a device for controlling and / or presetting a rolling stand for rolling a rolled strip, the controlling and / or presetting of the rolling stand depending on at least one of the sizes, rolling force, rolling moment and advance , which are calculated using a rolling model, the calculation of the sizes, rolling force, rolling moment and lead, using the rolling model as a function of at least one of the sizes, hardness of the rolled strip and friction between the rolled strip and the rolls of the rolling stand, as input variables of the rolling mo - Dells occurs, and wherein at least one of the input variables of the rolling model, in particular at least one of the variables, hardness of the rolled strip and friction between the rolled strip and the
- Rolling the roll stand is determined or corrected by means of a neural network.
- the object is achieved according to the invention by a method or a device for controlling and / or presetting a rolling stand for rolling a rolled strip, the controlling and / or presetting of the rolling stand depending on at least one of the sizes, rolling force, rolling moment and Lead, which are calculated by means of a rolling model, the calculation of the sizes, rolling force, rolling moment and lead, and at least one of the sizes, rolling force, rolling moment and lead, by means of a neural network in the sense of reducing the deviation between actual Rolling force, rolling moment or advance and through the rolling model determined rolling force, rolling moment or advance is corrected.
- FIG. 3 shows an inventive procedure at the entrance of the rolling model.
- FIG. 4 shows an inventive procedure at the exit of the rolling model
- FIG. 6 shows a training method for neural networks in a particularly advantageous embodiment
- Reference symbols FSi denote the advance on the i-th stand, MRi the rolling moment on the i-th stand, FRi the rolling force on the i-th stand, FTi the strip tension on the i-th stand, epsi the relative thickness decrease on the i-th stand, MS the material hardness, ie the hardness of the rolled strip, Vj_ the strip speed after the i-th stand, Hi the strip thickness after the i-th stand.
- the relative thickness decrease epsi results from: with H 0 : strip thickness when reeling
- the lead FSj on the i-th framework is defined as
- V w i peripheral speed of the i-th roller
- the peripheral speed of the i-th roller is calculated according to:
- V wi 2 ⁇ • R A • n ⁇ (3)
- Reference numerals 1, 2, 3, 4 and 5 denote rolling stands, reference numeral 6 a reel-off, reference numeral 7 a rolling band and reference numeral 8 a reel-up.
- 2 illustrates the physical relationships in a roll gap, which advantageously find their way into modeling with a roll model.
- the conditions in the roll gap are advantageously modeled by a strip model, it being sufficient for reasons of symmetry to model only the upper or only the lower part of the roll stand, so that one limit of the rolling model is the axis of symmetry 23 of the rolled strip 27.
- the band 27 is divided into strips 28 in the region of the band-roller contact surface (due to the clarity, only one strip is provided with a reference number) perpendicular to the direction of movement of the band 27.
- the material tension forces F Q are calculated in the horizontal and vertical directions and adapted to one another via equilibrium conditions at the strip edges. Some material tension forces F Q are entered as examples in FIG.
- the vertical material tension forces F Q lead to a flattening 26 of the roller 21.
- the flattened roller radius R B is calculated iteratively with the aid of the strip model and a model that describes the deformation of the roller.
- the flow sheath 20 is where the material is moving at the peripheral speed of the roller 21. In front of the flow sheath, the material moves more slowly, under the flow sheath faster than the peripheral speed of the roller 21. Except at the location of the flow sheath 21, a relative movement 24, 25 therefore occurs everywhere between the work roll and the material. This relative movement 24, 25 leads to considerable frictional forces.
- the rolling model 31 determines as a function of the input variables 30 and 32 output variables 34. These output variables are rolling force, rolling moment and / or lead.
- the input variables 32 are formed by means of information processing based on neural networks or a neural network 33 as a function of input variables 35 of the neural network.
- the input variables 30 and 32 are, for example, the tensile force in the rolled strip, the strip width, the inlet thickness of the rolled strip, the hardness of the rolled strip and / or the friction between the roll and the rolled strip.
- the input variables 35 of the neural network are, for example, material-specific data such as, for example, the alloy proportions, the inlet thickness, the outlet thickness as well as characteristic data about previous processing such as thickness reduction or temperature during the previous processing.
- the rolling model 41 determines, as a function of input variables 43, such as material hardness,
- These output variables are rolling force, rolling moment and / or lead.
- the output variables 47 of the rolling model 41 are corrected by a correction block 45 as a function of correction parameters 44.
- the output variables of the correction block 45 are correspondingly corrected values for the rolling force FR, rolling torque MR and / or advance FS. It is particularly advantageous to link the output variables 47 of the rolling model 41 by multiplying them with the correction parameters 44 to corrected values for the rolling force FR, rolling moment MR or advance FS.
- the correction parameters 44 are determined by means of a neural network 42 as a function of input variables 46. 5 shows a particularly advantageous embodiment of the invention.
- Reference numeral 51 designates a rolling model.
- Input variables 64 and MS of the rolling model 51 are the material hardness MS and indicated by reference numeral 64 specific rolling strip or stand-specific data such as friction between rolls and rolling strip, tensile stress, strip width and inlet thickness of the rolling strip.
- the material hardness MS is calculated using a neural network, material network 50, as a function of certain input variables 60.
- These input variables 60 can be: alloy proportions, inlet thickness, outlet thickness, temperature and information for characterizing the preprocessing, such as, for example, the previous degree of reduction or the previous processing temperature.
- Output variables 65 of the rolling model are values for rolling force, rolling moment and / or lead.
- correction parameters FRCP, MRCP, FSCP which are calculated using a neural network, scaffolding network 52, as a function of input variables 61.
- input variables 61 include the strip thickness, the strip width and roll-specific data.
- Output variables 66 of the correction element 53 are corrected values for the rolling force, rolling moment and / or lead. These are fed to a further correction element 55, which corrects them further by means of the correction parameters FRCD, MRCD and FSCD.
- the correction parameters FRCD, MRCD, FSCD are calculated using a neural network, day network 54, as a function of input variables 62.
- These input variables include strip thickness, strip width and roll-specific data.
- Output variables 67 of the correction element 55 are corrected values for the rolling force, rolling torque and advance, which are further corrected by means of a further correction element depending on the correction parameters FRCS, MRCS and FSCS.
- the correction parameters FRCS, MRCS, FSCS are calculated using a neural network, speed network 56, as a function of input variables 63.
- the input variables 63 are the speed of the Rolled strip as well as strip thickness, strip width and roll-specific data.
- the output variable 68 of the correction element 57 are corrected values for the rolling force, rolling moment and advance, which are corrected by means of a further correction element 59 as a function of a correction factor ⁇ for fine correction and adaptation to the current rolled strip.
- the output variables of the correction element 59 are corrected values for the rolling force FR, rolling moment MR and advance FS.
- the correction elements 53, 55, 57, 59 can be multipliers, for example. In principle, other correction strategies can also be used. Such correction strategies or links of neural networks that can be used for the given application can be found in DE 1 96 14 31.
- the material network 50 provides the material hardness MS e.g. in the form of the regression parameters MSI, MSO and MSE described in FIG.
- the stand network 52 provides stand-specific correction factors FRCP, MRCP and FSCP for rolling force, rolling moment and advance.
- the material network and the scaffold network are advantageously trained with data which represent the material and the roll stand over the life of the roll stand.
- the daily network 54 provides the correction factors FRCD, MRCD and FSCD for rolling force, rolling moment and advance, which describe the relatively small changes in accordance with the day form of the roll stand.
- the training of the daily network 56 takes place with young data sets, e.g. Records that are less than three days old.
- the speed network 56 supplies the speed-dependent correction factors FRCS, MRCS and FSCS for rolling force, rolling moment and advance.
- the speed network particularly compensates for friction-specific deviations.
- the friction between the roller and the rolled strip depends heavily on the Belt speed. The higher the belt speed, the smaller the friction, since a lubricating film forms between the rolled belt and the rollers with increasing speed.
- MSE, MSI and MSO represent the material hardness corresponding to MS in FIG. 5.
- the meaning of MSE, MSI and MSO is explained in FIG. FR, MR, FS, ß, FRCL, MRCL, FSCL, FRCD, MRCD, FSCD, FRCS, MRCS and
- Reference numerals 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. men.
- Reference symbol 81 denotes a scaffolding network with an associated learning or training algorithm, reference symbol 82 denotes a day network with associated learning or training algorithm and reference symbol 83 denotes a speed network with associated learning algorithm.
- Reference numeral 70 denotes a data memory or a database in which data AC, FRA, MRA and FSA are stored, which form characteristic data for a representative cross section of all the strips rolled in the corresponding rolling stand / rolling mill.
- FRA, MCA and FSA are the actual values for rolling force, rolling moment and lead over a long period of time, eg over the life of the roll stand. They are formed from the roll-specific data AC.
- Function block 80 designates an inverted rolling model and a regression model, the actual material hardness on the individual stands of the rolling mill being determined from the data AC by means of the inverted rolling model and the actual values for using the regression model from the values for material hardness of the individual stands the parameters MSE, MSI and MSO are formed.
- Material networks 76, 77, 78 are trained for MSO, MSI and MSE.
- a rolling model 79 calculates values for the rolling force, rolling moment and advance.
- the input variables 90 correspond to the input variables 64 from FIG. 5.
- the stand network 81 is trained as a function of the input variables 87 of the data AC, FRA, MRA and FSA and the output variables of the rolling model 79.
- a correction block 53 By means of a correction block 53
- the output variables of the rolling model 79 are corrected with the correction parameters FRCL, MRCL and FSCL, which the stand network 81 outputs.
- the daily network 82 is trained with the output variables of the correction block 53, the input variables 88 and the data DC, FRD, MRD and FSD.
- the output variables of the daily network 82 are the correction parameters FRCD, MRCD and FSCD, which are 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 correspond to the data AC, FRA, MRA and FSA, and in contrast to the data AC, FRA, MRA and FSA they only represent rolled strips from the last day or the last days.
- the output variables of the correction block 55, the input variables 89 and the data ACC are input variables into the speed network 83 or its learning algorithm. Furthermore, correction parameters FRCS, MSCS and FSCS are entered into the speed network 83 or its learning algorithm
- Speed correction element 85 can be determined.
- the speed correction element 85 transforms correction parameters FRC, FSC and MSC normalized to a standard speed with respect to the current speed of the rolled strip.
- the ACC data correspond to the AC data, but only represent the current rolled strip. Accordingly, the database or data storage 72 contains only the data for the current rolled strip.
- Output variables of the speed network 83 are correction parameters FRCS, MRCS, FSCS, which go into a further correction block 57. The output of this correction block goes into a further correction block 59.
- a parameter ⁇ which is stored in a memory 84, also forms the input variable of the correction block 59.
- the output of the correction block 59 are corrected values for the rolling force FR, rolling torque MR and advance FS.
- the adaptive values for rolling force FRA, rolling moment MRA, advance FSA used for training the neural networks or the correction values FRC, FSC and MSC for rolling force, advance and rolling moment used for training the neural networks are shown in
- the training of the neural networks thus takes place in a long-term learning part 73 in a day or short-term learning part 74 and in a speed learning part 75.
- Reference numeral 70 designates a database corresponding to FIG. 6, AC roll-specific data (cf. FIG. 6), reference numeral 100 a material network with a learning algorithm, 101 a regression model and reference number 102 a rolling model.
- the material hardness MS ⁇ ... n on the individual stands as well as optionally the rolling strip temperature T sr ip and the total thickness reduction eps ⁇ ... n assigned to the individual rolling stands from the material network 100.
- the regression parameters MSU, MSI, MSE the material network 100, which consists of one or more neural networks, gives the material rialhardening MS ⁇ ...
- the regression parameter MST is a parameter representing the temperature dependency, which can optionally be calculated if the temperature T sr ip of the rolled strip also enters the material network 100. This parameter is particularly advantageous when the method according to the invention is not used for cold rolling, but for hot rolling.
- FRi f FR (B, Hi-i, FTi, MSO, MSI, MSE, ⁇ , v wi ) (5)
- MRi f MR (B, Hi_ ⁇ , Hi, FTi- !, T t MSO, MSI, MSE, ⁇ , v wi ) (6) and for the advance
- F ⁇ Si f F s (B, Hi_ ⁇ , Hi, FTi-i, FTi, MSO, MSI, MSE, ⁇ , v wi ) (7)
- FRi, MRi and FSi indicate the estimates of the respective models.
- B denotes the band width, Hi_ ⁇ the band thickness in front of the i-th stand, Hi the band thickness behind the i-th stand, FTi_ ⁇ the band tension in front of the i-th stand, FTi the band-pull behind the i-th stand and v W i the circumferential speed of the work rolls in the i-th frame.
- FRC, MRC and FSC are calculated
- FRi S , MRi ⁇ t and FSist are the current values for rolling force, rolling moment and lead.
- FRA, MRA and FSA are calculated from:
- the coefficient of friction ⁇ is e.g. stored in a table.
- either the material hardness or the friction can be determined. It is also conceivable to determine both variables using a neural network. However, it has been shown that it is usually sufficient to use only one of the two unknowns, material hardness or to determine friction using a neural network. If, for example, the material hardness is determined according to the invention by means of a neural network and used for the (rough) estimated values for the friction, the material network is able to correct the errors in relation to the rolling force, the rolling moment or the advance caused by inaccurate knowledge of the friction between the rolled strip and roller arise to correct.
- the neural network determines a bad estimate for the material hardness, but that this deviation from the actual material hardness compensates for the error in the coefficient of friction.
- the method according to the invention obtains a suboptimal value for the material hardness, a particularly precise value for the rolling force, rolling moment and
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Control Of Metal Rolling (AREA)
Description
Claims
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 true EP0994757A1 (de) | 2000-04-26 |
EP0994757B1 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) |
Families Citing this family (4)
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 |
CN109351785B (zh) * | 2018-11-28 | 2020-09-08 | 北京首钢冷轧薄板有限公司 | 一种轧制力优化方法及装置 |
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 WO PCT/DE1998/001740 patent/WO1999002282A1/de active IP Right Grant
- 1998-06-24 DE DE59813227T patent/DE59813227D1/de not_active Expired - Lifetime
- 1998-06-24 AT AT98940053T patent/ATE310592T1/de active
- 1998-06-24 EP EP98940053A patent/EP0994757B1/de not_active Expired - Lifetime
Non-Patent Citations (1)
Title |
---|
See references of WO9902282A1 * |
Also Published As
Publication number | Publication date |
---|---|
EP0994757B1 (de) | 2005-11-23 |
DE19728979A1 (de) | 1998-09-10 |
ATE310592T1 (de) | 2005-12-15 |
WO1999002282A1 (de) | 1999-01-21 |
DE59813227D1 (de) | 2005-12-29 |
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