EP3831511A1 - Procédé et système informatique de prédiction d'une rétraction d'un produit métallique coulé - Google Patents

Procédé et système informatique de prédiction d'une rétraction d'un produit métallique coulé Download PDF

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Publication number
EP3831511A1
EP3831511A1 EP19213866.7A EP19213866A EP3831511A1 EP 3831511 A1 EP3831511 A1 EP 3831511A1 EP 19213866 A EP19213866 A EP 19213866A EP 3831511 A1 EP3831511 A1 EP 3831511A1
Authority
EP
European Patent Office
Prior art keywords
shrinkage
metal product
computer system
predicting
cast
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.)
Withdrawn
Application number
EP19213866.7A
Other languages
German (de)
English (en)
Inventor
Adnan HUSAKOVIC
Rainer Kaltseis
Daniel FUCHSHUBER
Franz Hartl
Andreas ROHRHOFER
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Primetals Technologies Austria GmbH
Original Assignee
Primetals Technologies Austria GmbH
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Primetals Technologies Austria GmbH filed Critical Primetals Technologies Austria GmbH
Priority to EP19213866.7A priority Critical patent/EP3831511A1/fr
Priority to EP20780216.6A priority patent/EP4069448A1/fr
Priority to PCT/EP2020/077406 priority patent/WO2021110300A1/fr
Publication of EP3831511A1 publication Critical patent/EP3831511A1/fr
Withdrawn legal-status Critical Current

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
    • B22D11/00—Continuous casting of metals, i.e. casting in indefinite lengths
    • B22D11/16—Controlling or regulating processes or operations
    • B22D11/168—Controlling or regulating processes or operations for adjusting the mould size or mould taper
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
    • B22D11/00—Continuous casting of metals, i.e. casting in indefinite lengths
    • B22D11/12—Accessories for subsequent treating or working cast stock in situ
    • B22D11/1206—Accessories for subsequent treating or working cast stock in situ for plastic shaping of strands
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B22—CASTING; POWDER METALLURGY
    • B22D—CASTING OF METALS; CASTING OF OTHER SUBSTANCES BY THE SAME PROCESSES OR DEVICES
    • B22D11/00—Continuous casting of metals, i.e. casting in indefinite lengths
    • B22D11/16—Controlling or regulating processes or operations

Definitions

  • the present invention relates to the field of metal casting processes.
  • the invention relates to a method for predicting a shrinkage, preferably a shrinkage width and / or a shrinkage height of a strand cross-section, of a metal product which was produced by a casting process from liquid metal, preferably a slab cast by a continuous casting plant.
  • the invention relates to a computer system with a memory.
  • a specified dimension of the cast product - in the cold state - has to be set during and / or before the casting process.
  • the cast product undergoes shrinkage, which results in a reduction in the dimensions of the cast product, which are influenced by the casting and quality parameters of the metal melt.
  • ferrostatic pressure also acts on a strand shell, which leads to so-called creep.
  • a slab cast by the continuous caster experiences an increase in width.
  • a change in the structure of the metal during cooling also influences a change in the dimensions.
  • the object of the invention is to provide a reliable method for predicting the shrinkage as precisely as possible.
  • the object is achieved by a neural network which consists of a multi-layer feedforward network.
  • the neural network has a large number of input parameters, which are characteristic parameters of the casting process and the metal product.
  • the output is the shrinkage of the cast metal product.
  • the multi-layered feedforward network has shown very good results.
  • the input parameters depend very much on the casting system and the cast metal melt.
  • the molten metal can be made from a variety of Alloy elements exist, each of which can have a different influence on the shrinkage.
  • any cooling devices and other system-specific features also have an influence on the shrinkage.
  • a multilayer feedforward network also has at least one hidden layer. The output of these hidden layers is not visible from the outside.
  • the neural network is trained for each casting plant, for example by recording measurement data during commissioning and feeding it to the neural network accordingly.
  • the selection of which input parameters are used to predict the shrinkage depends on the molten metal and its composition.
  • the casting plant is also of crucial importance.
  • the molten metal usually has several alloy elements such as carbon, silicon, manganese, titanium, chromium, nickel, bromine, arsenic and / or other alloy elements. To as much as possible To obtain exact results, the respective proportion of the alloying elements should be available to the neural network as an input parameter.
  • the multilayer feedforeard network has at least two hidden layers, particularly preferably at least three hidden layers.
  • the use of at least two hidden layers enables a very good prediction of the strand shrinkage.
  • noise and additional non-linearities are also taken into account.
  • the number of layers used depends very much on the particular casting plant. In most cases, having two layers hidden will give the best results. However, in some cases - especially with more complex systems - it can be advantageous to use more than two shifts. If too many hidden layers are used, the problem arises that the too high model order makes the results worse again - this is known as the overfitting tendency.
  • An advantageous embodiment provides that the respective hidden layers each have up to 250 neurons.
  • the number depends on the number of input parameters. It has been found that with a number of up to 250 neurons, very good predictions for the shrinkage can be achieved.
  • An expedient embodiment provides that a rectified linear unit, a rectangular function, a Tanh function or a Gaussian function is used as the activation function of the individual neurons.
  • the Retified Linear Unit (RELI) function has proven to be particularly advantageous and leads to very precise results. But it is also a rectangle, a Tanh or a Gaussian function conceivable to get the good results of the shrinkage. It is possible for several different activation functions to be used in the neural network. It can therefore be advantageous that not all neurons have the same activation function.
  • a particularly preferred embodiment provides that the shrinkage, preferably the shrinkage width and / or shrinkage height, is fed to a control and / or regulating device of the continuous casting plant.
  • the prediction of the shrinkage - i.e. the dimensions of the cast slab - can be used directly for the control and / or regulating device in order to make the desired settings on the continuous casting plant. By directly including this forecast, it is always possible to react to changed conditions - such as changed composition or temperature of the molten metal.
  • changed conditions - such as changed composition or temperature of the molten metal.
  • one or more parameters of the continuous casting plant - for example the casting width - can be adjusted accordingly.
  • control and / or regulating device controls and / or regulates the position of the side walls of a mold.
  • the described method allows the position of the side walls of a mold to be controlled or regulated in a particularly simple manner so that the cast slab has the desired dimensions in the cooled state.
  • the actual width and / or actual height of the metal product is measured. If there is a discrepancy between the shrinkage width and actual width and / or between the shrinkage height and the actual height, the output is used to calculate the input parameters back to the input parameters and the cause of the respective deviation is determined.
  • the actual width and actual height are measured in the state in which the prediction of the shrinkage is made - for example, in the cooled state.
  • the input parameters can be calculated and thus possible deviations can be determined. Due to the incorrect input parameter, the cause, such as incorrect data transmission or incorrect measurement, can be determined.
  • This advantageous embodiment enables faulty measuring equipment to be identified quickly, for example, or measuring errors to be recognized.
  • the object is also achieved by a computer system of the type mentioned above.
  • the computer system has a memory which contains a neural network which consists of a multilayered feedforward network. This has a large number of input parameters.
  • the output is the shrinkage of a cast metal product.
  • the computer system has inputs for measurement data and / or other data which are used as input parameters.
  • the computer system is connected to a display device, a control device and / or a regulating device in order to transmit the shrinkage to the latter. With this computer system the shrinkage of a metal product can be predicted very well.
  • the multilayer feedforward network has at least two hidden layers, particularly preferably at least three hidden layers.
  • Another preferred embodiment provides that the computer system is connected to a continuous casting plant for casting slabs and operating data are used as input parameters.
  • the open-loop and / or closed-loop control of a position of side walls of a mold is carried out by the control and / or regulating device.
  • a continuous casting plant 1 is shown schematically.
  • Liquid metal 6 is poured into a mold 2 and a cast strand 7 is then withdrawn from the mold.
  • the shrinkage is then fed to a regulating, control device 9 and / or a display unit 8.
  • the regulating and / or control device 9 can regulate and / or control the continuous casting plant 1 due to the shrinkage. This is done, for example, by adjusting the side walls of the mold 2.
  • the computer system 3 receives input parameters via inputs 5.
  • These input parameters can be transferred from measuring instruments 5a, 5b, from memory 4 via memory line 5b and / or from a higher-level control system of the industrial plant.
  • the parameters recorded by measuring instruments are, for example, the measured strand dimensions, temperature of the melt, casting speed and / or parameters of the cooling section.
  • a composition of the melt can either be stored in the memory or transmitted through the higher-level control system of the industrial plant.
  • measurement data can also be stored in the memory 4, which data can be used to learn the neural network.
  • the neural network 10 allows the shrinkage of a cast metal product to be determined very precisely.
  • the neural network 10 consists of an input layer 11.
  • the input layer 11 transfers important parameters of the casting process and of the liquid metal to the neural network 11. These important parameters include the current temperature of the melt, the current casting speed, a current casting width, a current angular position of the side walls of the mold, a temperature above the solidification point of the melt, alloying elements, casting powder type and / or parameters of the cooling section. These parameters are also used to train the neural network.
  • the neural network 10 also consists of a first hidden layer 12 and a second hidden layer 13, each of which has a large number of neurons 15.
  • the number of neurons 15 of each hidden layer depends on the input parameters. If the input layer consists of fourteen input parameters, the first hidden layer 12 and the second hidden layer 13 each have around 250 neurons 15, for example.
  • the neural network 10 is completed by the output layer 14.
  • the output layer 14 outputs the shrinkage.
  • the shrinkage can be indicated by the ratio of the cast width to that in the cooled state. Of course, the shrinkage in height, length or other dimensions can also be determined.
  • FIG. 3 shows the shrinkage of slabs that were produced by a continuous caster.
  • a first curve shows the shrinkage, which was determined with measurement data (16).
  • a second curve represents a prediction (17) of the shrinkage, which was determined by the neural network.
  • the shrinkage is shown as the ratio of the cast width - the width set on the mold - to the width in the cooled state at 25 ° C.

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Continuous Casting (AREA)
EP19213866.7A 2019-12-05 2019-12-05 Procédé et système informatique de prédiction d'une rétraction d'un produit métallique coulé Withdrawn EP3831511A1 (fr)

Priority Applications (3)

Application Number Priority Date Filing Date Title
EP19213866.7A EP3831511A1 (fr) 2019-12-05 2019-12-05 Procédé et système informatique de prédiction d'une rétraction d'un produit métallique coulé
EP20780216.6A EP4069448A1 (fr) 2019-12-05 2020-09-30 Procédé et système informatique pour prédire le retrait d'un produit métallique coulé
PCT/EP2020/077406 WO2021110300A1 (fr) 2019-12-05 2020-09-30 Procédé et système informatique pour prédire le retrait d'un produit métallique coulé

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP19213866.7A EP3831511A1 (fr) 2019-12-05 2019-12-05 Procédé et système informatique de prédiction d'une rétraction d'un produit métallique coulé

Publications (1)

Publication Number Publication Date
EP3831511A1 true EP3831511A1 (fr) 2021-06-09

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EP19213866.7A Withdrawn EP3831511A1 (fr) 2019-12-05 2019-12-05 Procédé et système informatique de prédiction d'une rétraction d'un produit métallique coulé
EP20780216.6A Pending EP4069448A1 (fr) 2019-12-05 2020-09-30 Procédé et système informatique pour prédire le retrait d'un produit métallique coulé

Family Applications After (1)

Application Number Title Priority Date Filing Date
EP20780216.6A Pending EP4069448A1 (fr) 2019-12-05 2020-09-30 Procédé et système informatique pour prédire le retrait d'un produit métallique coulé

Country Status (2)

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EP (2) EP3831511A1 (fr)
WO (1) WO2021110300A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH07214268A (ja) * 1994-02-02 1995-08-15 Nippon Steel Corp 連続鋳造設備におけるスラブ幅制御方法
EP2279052B1 (fr) 2008-05-21 2016-11-09 Primetals Technologies Austria GmbH Procédé de coulée continue d'une barre métallique

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH07214268A (ja) * 1994-02-02 1995-08-15 Nippon Steel Corp 連続鋳造設備におけるスラブ幅制御方法
EP2279052B1 (fr) 2008-05-21 2016-11-09 Primetals Technologies Austria GmbH Procédé de coulée continue d'une barre métallique

Also Published As

Publication number Publication date
WO2021110300A1 (fr) 2021-06-10
EP4069448A1 (fr) 2022-10-12

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