EP4069448A1 - Procédé et système informatique pour prédire le retrait d'un produit métallique coulé - Google Patents

Procédé et système informatique pour prédire le retrait d'un produit métallique coulé

Info

Publication number
EP4069448A1
EP4069448A1 EP20780216.6A EP20780216A EP4069448A1 EP 4069448 A1 EP4069448 A1 EP 4069448A1 EP 20780216 A EP20780216 A EP 20780216A EP 4069448 A1 EP4069448 A1 EP 4069448A1
Authority
EP
European Patent Office
Prior art keywords
product
width
shrinkage
cast
height
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.)
Pending
Application number
EP20780216.6A
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
Publication of EP4069448A1 publication Critical patent/EP4069448A1/fr
Pending legal-status Critical Current

Links

Classifications

    • 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 of a strand cross-section, of a metal product which has been produced from liquid metal by a casting process, preferably a slab cast by a continuous casting plant.
  • the invention relates to a computer system with a memory.
  • the problem arises that 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.
  • EP2279052 B1 shows a method for continuous casting in which a mathematical simulation model is used to calculate the shrinkage. These are simplified and can only take into account changes in the melt, production parameters or other influencing parameters with a certain error range.
  • JP H07214268 A shows a method for determining the shrinkage of a slab cast from steel with the aid of a neural network.
  • the object of the invention is to provide a reliable method to predict the shrinkage between the mold cross-section and the strand cross-section produced as precisely as possible and to quickly identify incorrect input parameters.
  • 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 following are essential input parameters: a current casting width a temperature of the melt a casting speed composition of the melt.
  • the product width and / or product height of the cast metal product is output as an output.
  • the product height and the product width are those dimensions which are determined by the neural network at predetermined points within the casting plant, after the casting plant or at a given temperature - for example room temperature.
  • the multi-layered feedforward network has shown very good results.
  • the input parameters depend on the casting plant and the cast metal melt.
  • the molten metal can consist of a large number of alloying elements, 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. Furthermore, the actual width and / or actual height of the metal product is measured. If there is a discrepancy between the product width and the actual width and / or between the product height and the actual height, the neural network is used to calculate back from the output to the input parameters, and the cause of the respective discrepancy is determined.
  • the actual width and actual height are measured in the state in which the prediction of the shrinkage is made - for example when it is cold or at a predetermined position inside or outside the casting plant.
  • the predetermined position can be in front of a flame cutting machine, for example. Since the generated strand cross-section has a certain state at this point - for example has a certain temperature - this is also shown at the output of the neural network. It is also conceivable that the measurement and the determination of the shrinkage are carried out at several points with the aid of a neural network.
  • the influence of the individual input parameters on the forecast can be calculated and possible causes of deviations can be determined. If the influence of an input parameter deviates significantly from the norm, this indicates a malfunction, such as incorrect data transmission or incorrect measurement. With this design, for example, faulty measuring equipment can be quickly identified or measuring errors can be recognized.
  • LRP Layerwise Relevance Propagation
  • the relevance to the output can be determined for a given input parameter. This enables possible incorrect measurement or process data to be determined.
  • the LRP algorithm can be used to calculate back for each layer and each neuron and determine the respective relevance of the respective neuron in one layer to a neuron in the next layer. This is done until the relevance of the input layer is available. Due to the relevance of the individual input parameters of the input layer to the output, the causes of deviations from the measured result and the result predicted by the neural network can be quickly identified.
  • the characteristic relevance distribution of the sensor or input parameter m - defined by relevance scores R m (n) - of the individual input parameters are described by statistical moments for nominal operation. If the product height and the actual height or the product width and the actual width match in nominal operation, the expected relevance scores are within defined confidence intervals, characterized by the mean and a standard deviation a m for each individual input parameter. In continuous operation, standardized random variables (z-score) z m (ri) are then calculated for the individual relevance scores R m (ji) of the deviation between the measurement and the output of the neural network, as shown in equation 1.
  • a selection of the following input parameters is used for the feedforward network: a temperature of the solidification point, a current position of side walls of a mold, information on the type and quantity of alloying elements in the melt, a casting powder type, a casting height,
  • the selection of which input parameters are used for the prediction of 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, sulfur, phosphorus, titanium, chromium, nickel, bromine, arsenic and / or other alloy elements. In order to obtain the most accurate results possible, the respective proportion of the alloying elements should be available to the neural network as an input parameter.
  • the multilayer feedforward 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, there is the problem that the too high model order makes the results worse again - this is referred to as the so-called overfitting tendency.
  • An advantageous embodiment provides that the respective hidden layers each have up to 250 neurons.
  • the number of neurons 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 (ReLU), a rectangular function, a Tanh function or a Gaussian function is used as the activation function of the individual neurons.
  • the ReLU function has proven to be particularly advantageous for the hidden layers and leads to very precise results.
  • a particularly preferred embodiment provides that the shrinkage, preferably the product width and / or product 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 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. At least a current casting width, a temperature of the melt, a casting speed of the slab and a composition of the melt are required as 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. As an output, the computer system transmits the product width and / or product height of the cast metal product.
  • the actual width and / or actual height of the metal product is measured by a measuring instrument and in the event of a deviation between product width and actual width and / or product height and actual height, the computer system uses the neural network to calculate the input parameters from the output and the The cause of the respective deviation is determined.
  • 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.
  • FIG. 1 shows a schematic representation of a continuous casting plant.
  • FIG. 2 shows a neural network for predicting shrinkage.
  • FIG. 3 comparing measurement results and predicting shrinkage
  • 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.
  • a computer system 3 which is connected to a memory 4, calculates the shrinkage of the cast strand 7 with the aid of a neural network.
  • the shrinkage is then fed to a regulating, control device 9 and / or a display unit 8.
  • the regulation 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, 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 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 which have been produced by a continuous casting plant.
  • 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.

Abstract

L'invention concerne un procédé et un système informatique (3) permettant de prédire le retrait d'un produit métallique qui a été produit à partir d'un métal liquide au moyen d'un processus de coulage. Le but de l'invention est de fournir un procédé destiné à prédire, le plus exactement possible, le retrait entre la section transversale du moule et la section transversale du toron produit. Ledit problème est résolu au moyen d'un réseau neuronal (10) qui est constitué d'un réseau multicouche à propagation avant comportant une pluralité de paramètres d'entrée. Les paramètres d'entrée sont des paramètres caractéristiques du processus de coulage et du produit métallique. Une largeur de produit et/ou une hauteur de produit du produit métallique coulé sont délivrées en sortie en résultat, et une largeur réelle et/ou une hauteur réelle du produit métallique sont mesurées. S'il existe un écart entre la largeur du produit et la largeur réelle et/ou entre la hauteur du produit et la hauteur réelle, un rétrocalcul du résultat aux paramètres d'entrée est effectué au moyen du réseau neuronal. L'origine de l'écart en question est ainsi déterminée.
EP20780216.6A 2019-12-05 2020-09-30 Procédé et système informatique pour prédire le retrait d'un produit métallique coulé Pending EP4069448A1 (fr)

Applications Claiming Priority (2)

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
PCT/EP2020/077406 WO2021110300A1 (fr) 2019-12-05 2020-09-30 Procédé et système informatique pour prédire le retrait d'un produit métallique coulé

Publications (1)

Publication Number Publication Date
EP4069448A1 true EP4069448A1 (fr) 2022-10-12

Family

ID=68806692

Family Applications (2)

Application Number Title Priority Date Filing Date
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 Before (1)

Application Number Title Priority Date Filing Date
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

Country Status (2)

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

Family Cites Families (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
AT506976B1 (de) 2008-05-21 2012-10-15 Siemens Vai Metals Tech Gmbh Verfahren zum stranggiessen eines metallstrangs

Also Published As

Publication number Publication date
WO2021110300A1 (fr) 2021-06-10
EP3831511A1 (fr) 2021-06-09

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