Claims:1. A method of predicting a Continuous Cooling Transformation (CCT) for steel, the method comprising:
receiving, by a system (101), a dataset comprising a plurality of attributes of a steel relating to each cooling rate, wherein the plurality of attributes comprises composition of the steel, prior heat treatment performed on the steel and austenising temperature;
determining, by the system (101), one or more stable phases of the steel along with respective start temperature and an end temperature based on a combined effect of the plurality of attributes and respective cooling rate using prediction models trained using Light Gradient Boosting Machine (LGBM) technique; and
predicting, by the system (101), a Continuous Cooling Transformation (CCT) for the steel based on the plurality of attributes, corresponding to various cooling rates and one or more stable phases using the prediction models (203).

2. The method as claimed in claim 1, wherein the composition of the steel comprises plurality of elements in weight percentages.

3. The method as claimed in claim 1, wherein a prediction model from the prediction models (203) relates to the start temperature and the end temperature for respective stable phase, and wherein the prediction models (203) are trained using training dataset of a plurality of attributes of steel to generate a respective decision tree for corresponding phases by iteratively analysing each attribute of the training dataset.

4. The method as claimed in claim 3, wherein a node in the respective decision tree represents a condition pertaining to an attribute of the plurality of attributes.

5. The method as claimed in claim 3, wherein the training dataset is processed by normalizing each attribute in the training dataset.

6. The method as claimed in claim 3, wherein the decision tree associated with each prediction model comprises a root node and one or more leaf nodes depending on number of splits in respective decision tree.

7. The method as claimed in claim 6, wherein the number of splits in the decision tree is based on entropy calculation associated with respective attribute.

8. A system (101) for predicting a Continuous Cooling Transformation (CCT) for steel, comprising:

a processor (111); and
a memory (109) communicatively coupled to the processor (111), wherein the memory (109) stores processor instructions, which, on execution, causes the processor (111) to:

receive a dataset comprising a plurality of attributes of a steel relating to each cooling rate, wherein the plurality of attributes comprises composition of the steel, prior heat treatment performed on the steel and austenising temperature;
determine one or more stable phases of the steel along with respective start temperature and an end temperature based on a combined effect of the plurality of attributes and respective cooling rate using prediction models (203) trained using Light Gradient Boosting Machine (LGBM) technique; and
predict a Continuous Cooling Transformation (CCT) for the steel based on the plurality of attributes, corresponding to various cooling rates and one or more stable phases using the prediction models (203).

9. The system (101) as claimed in claim 8, wherein the composition of the steel comprises plurality of elements in weight percentages.

10. The system (101) as claimed in claim 8, wherein a prediction model from the prediction models (203) relates to the start temperature and the end temperature for respective stable phase, and the processor trains the prediction models using a training dataset of a plurality of attributes of steel to generate a respective decision tree for corresponding phases by iteratively analysing each attribute of the training dataset.

11. The system (101) as claimed in claim 10, wherein a node in the respective decision tree represents a condition pertaining to an attribute of the plurality of attributes

12. The system (101) as claimed in claim 10, wherein the processor (111) processes the training dataset by normalizing each attribute in the plurality of training dataset.

13. The system (101) as claimed in claim 10, wherein the decision tree associated with each of the prediction model comprises a root node and one or more leaf nodes depending on number of splits in respective decision tree.

14. The system (101) as claimed in claim 13, wherein the number of splits in the decision tree is based on entropy calculation associated with respective attribute.

15. The system (101) as claimed in claim 10, wherein the processor (111) uses an upper and a lower limit based on a training dataset to avoid an overlap between the start temperature and the end temperature of adjacent stable phases.
, Description:TECHNICAL FIELD
The present subject matter relates generally to a field of steel structure prediction and machine learning. Particularly, but not exclusively the disclosure relates to a method and system for predicting a Continuous Cooling Transformation (CCT) for steel.
BACKGROUND
Generation of different grades of steel is an important aspect in steel industry. Typically, developing a new grade of steel demands a huge amount of prior planning. In such a scenario, it becomes extremely important to correctly predict phase/phases that are stable in a specified temperature range for a given cooling rate. Also, for a given cooling rate, an idea of start and end temperatures of the stable phases, can help to determine finish rolling temperature. These must be predicted, considering austenite grain size which affects nucleation rate and prior treatment given to the material as it largely affects Continuous Cooling Transformation (CCT) behavior. CCT refers to phase diagram which is often used when heat treating steel. These diagrams are used to represent which types of phase changes may occur in a material as it is cooled at different rates. Generally, many complexities occur during phase prediction such as, managing multiple variables describing the steel, its processing as well as presence/absence and temperature ranges of different phases.
Currently, many existing systems make use of data models to predict CCT for steels. Among these, Artificial Neural Network (ANN) is probably most utilised technique in this activity. However, these existing systems concentrate on developing the data model based on data for one or two families of steel grades. This implies that there would be different ANN models for different families of steel grades. Also, for the same dataset, there remains a scope of huge variability in an ANN architecture. Due to these numerous choices in the ANN architecture, it is difficult to find an optimum ANN model for a given dataset.
In addition, many existing systems use decision trees, boosting algorithms and ensemble methods extensively for different practical problems involving prediction using data models. However, applications of these in the field of steel CCT prediction are not known in the existing systems.
The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the disclosure and should not be taken as an acknowledgement or any form of suggestion that this information forms prior art already known to a person skilled in the art.
SUMMARY
One or more shortcomings of the prior art may be overcome, and additional advantages may be provided through the present disclosure. Additional features and advantages may be realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.

In a non-limiting embodiment of the disclosure, a method of predicting a Continuous Cooling Transformation (CCT) for steel is disclosed. The method includes receiving a dataset comprising a plurality of attributes of a steel relating to each cooling rate. The plurality of attributes comprises composition of the steel, prior heat treatment performed on the steel and austenising temperature. The method includes determining one or more stable phases of the steel along with respective start temperature and an end temperature based on a combined effect of the plurality of attributes and respective cooling rate using prediction models trained using Light Gradient Boosting Machine (LGBM) technique. Thereafter, the method includes predicting a Continuous Cooling Transformation (CCT) for the steel based on the plurality of attributes, corresponding to various cooling rates and one or more stable phases using the prediction models.

In an embodiment of the disclosure, the composition of the steel comprises plurality of elements in weight percentages.

In an embodiment of the disclosure, a prediction model from the prediction models relates to the start temperature and the end temperature for respective stable phase, and the prediction models are trained using training dataset of a plurality of attributes of steel to generate a respective decision tree for corresponding phases by iteratively analysing each attribute of the training dataset.

In an embodiment of the disclosure, a node in the respective decision tree represents a condition pertaining to an attribute of the plurality of attributes.
In an embodiment of the disclosure, the training dataset is processed by normalizing each attribute in the training dataset.

In an embodiment of the disclosure, the decision tree associated with each prediction model comprises a root node and one or more leaf nodes depending on number of splits in respective decision tree.

In an embodiment of the disclosure, the number of splits in the decision tree is based on entropy calculation associated with respective attribute.

In one non-limiting embodiment of the disclosure, a system for predicting a Continuous Cooling Transformation (CCT) for steel is disclosed. The system comprises a processor and a memory communicatively coupled to the processor, where the memory stores processor executable instructions, which, on execution, may cause the system to receive a dataset comprising a plurality of attributes of a steel relating to each cooling rate. The plurality of attributes comprises composition of the steel, prior heat treatment performed on the steel and austenising temperature. The system determines one or more stable phases of the steel along with respective start temperature and an end temperature based on a combined effect of the plurality of attributes and respective cooling rate using prediction models trained using Light Gradient Boosting Machine (LGBM) technique. Thereafter, the system predicts a Continuous Cooling Transformation (CCT) for the steel based on the plurality of attributes, corresponding to various cooling rates and one or more stable phases using the prediction models.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DIAGRAMS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and with reference to the accompanying figures, in which:

FIGURE.1 shows an exemplary embodiment for predicting a Continuous Cooling Transformation (CCT) for steel in accordance with some embodiments of the present disclosure;

FIGURE.2 shows a detailed block diagram of a system in accordance with some embodiments of the present disclosure;

FIGURE.3 shows an exemplary decision graph in accordance with some embodiments of the present disclosure;

FIGURE.4 is a flowchart illustrating a method for predicting a Continuous Cooling Transformation (CCT) for steel in accordance with some embodiments of the present disclosure;

FIGURE.5 shows an exemplary embodiment illustrating comparison score of prediction model for predicting start and end temperatures of different phases of steel in accordance with some embodiments of the present disclosure;
FIGURE.6A-6D show exemplary graphs of actual and predicted temperatures of different phases of steel in accordance with some embodiments of the present disclosure; and
FIGURE.7 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.
It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.

DETAILED DESCRIPTION

In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.
The terms “comprises,” “comprising,” “includes” or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that includes a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.
Disclosed herein is a method and system for predicting a Continuous Cooling Transformation (CCT) for steel. Generally, in steel industry, when heat treatment is applied to the steel up to a particular temperature and cooled at a particular cooling rate, different phases in the microstructure of steel may be obtained depending on chemistry of steel and the cooling rate applied to the steel. A final output of the steel may include a combination of plurality oh phases such as, ferrite, pearlite, bainite and martensite phase. The CCT represents phase diagram which is often used during heat treatment of the steel. These diagrams are used to represent which types of phase changes may occur in a material as it is cooled at different rates. Thus, development of a new grade prediction of phases is crucial to evaluate properties of steel. Prediction of the phase/phases are linked with the cooling rate. For a given cooling rate, a start and end temperatures of the phases may aid to determine finish rolling temperature. Currently, complexities exist in predicting the phases of the steel with respect to diverse grades due to multiple variables describing the steel, its processing as well as the presence /absence and temperature ranges of the plurality of phases.
Therefore, to solve the above problem, the present disclosure discloses determining stable phases of steel along with respective start temperature and an end temperature based on a combined effect of different attributes and respective cooling rate of the steel, using prediction models trained using Light Gradient Boosting Machine (LGBM) technique. The CCT for the steel is predicted based on the attributes, corresponding to various cooling rates and the stable phases using the prediction models. Thus, the present disclosure enables prediction of the CCT for various grades of steel using LGBM technique. The present disclosure provides accurate prediction of the stable phases of the steel which aids in predicting the CCT of the steel.

In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

FIGURE.1 shows an exemplary embodiment for predicting a Continuous Cooling Transformation (CCT) for steel in accordance with some embodiments of the present disclosure.

FIGURE.1 shows an environment 100 which includes a system 101 connected to a database 103 associated with a steel plant through a communication network 105. The system 101 is used for predicting the CCT of the steel. In some embodiments, the system 101 may be configured in a remote location. In some other embodiments, the system 101 may be locally configured. The system 101 may include an Input/Output (I/O) interface 107, a memory 109, a processor 111 and prediction models 113, as shown in the FIGURE.1. The I/O interface 107 may receive a dataset from the database 103. In an embodiment, the system 101 may be any computing device such as, desktop computer, server, and the like. In an embodiment, the system 101 may include a Human Machine Interface (HMI) (not shown explicitly in FIGURE.1) to provide a visual indication. As an example, the HMI may display the CCT of the steel, to operators and the like.

The system 101 receives the dataset comprising a plurality of attributes of steel relating to each cooling rate. The plurality of attributes includes composition of the steel, prior heat treatment performed on the steel and austenising temperature. In an embodiment, the dataset obtained from the database 103 may be digitised from a plurality of predetermined CCT diagrams. The composition of the steel comprises plurality of elements in weight percentages. For instance, the plurality of elements may include Carbon(C), Silicon (Si), Manganese (Mn), Phosphorus(P), Sulphur(S), Chromium (Cr), Molybdenum (Mo), Nickel (Ni), Aluminium (Al), Vanadium(V), Titanium (Ti), and the like. The prior heat treatment for the steel may include for instance hot rolling. On receiving the dataset of the plurality of attributes, the system 101 may determine one or more stable phases of the steel along with respective start temperature and an end temperature. The phases of the steel may include ferrite, pearlite, bainite and martensite phase. The system 101 may determine the one or more stables phases based on a combined effect of the plurality of attributes and respective cooling rate using prediction models which are pretrained using Light Gradient Boosting Machine (LGBM) technique. LGBM is a machine learning which makes use of decision trees for regression predictions.

Particularly, during training, the system may generate a prediction model for each start temperature and the end temperature for respective phases. That is, the system 101 may generate a prediction model for each start and end temperature of ferrite, pearlite, bainite and martensite phases. Further, the prediction models are trained using training dataset of the plurality of attributes of steel. The system 101 may initially process the training dataset by normalizing each attribute in the training dataset. Then, the prediction models are trained to generate a respective decision tree for corresponding phases by iteratively analysing each processed attribute of the training dataset. The decision tree includes a plurality of nodes, where each node in the respective decision tree represents a condition pertaining to an attribute of the plurality of attributes.

For instance, the condition may be threshold associated with the attribute. Particularly, the decision tree associated with each prediction model includes a root node and one or more leaf nodes depending on number of splits in respective decision tree. The number of splits in the decision tree is based on entropy calculation associated with respective attribute, which is explained in subsequent figures of the description. In an embodiment, the system 101 may use an upper and a lower limit based on the training dataset to avoid an overlap between the start temperature and the end temperature of adjacent stable phases. Thereafter, the system 101 may predict the CCT for the steel based on the plurality of attributes, corresponding to various cooling rates and one or more stable phases using the prediction models.
A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

FIGURE.2 shows a detailed block diagram of a system in accordance with some embodiments of the present disclosure.

In some implementations, the system 101 may include data 200 and modules 213. As an example, the data 200 is stored in the memory 109 associated with the system 101. In some embodiments, the data 200 may include input data 201, prediction models 203, steel phase data 205, CCT data 207 and other data 209. In some embodiments, the data 200 may be stored in the memory 109 in form of various data structures.

The input data 201 may include the dataset comprising the plurality of attributes of the steel relating to each cooling rate. The plurality of attributes includes the composition of the steel, prior heat treatment performed on the steel and austenising temperature.
The prediction models 203 includes the prediction model generated for each start temperature and the end temperature for respective phases. For instance, the prediction models 203 includes eight prediction model corresponding to start and end temperature of each of the four phases of the steel (i.e., ferrite, pearlite, bainite and martensite phase). The prediction models 203 also includes respective decision trees generated during the training.

The steel phase data 205 may include information regarding the stable phases determined for the steel. The information may include the phase along with respective start temperature and an end temperature value.

The CCT data 207 may include the CCT predicted for the steel.

The other data 209 may be stored data, including temporary data and temporary files, generated by the modules 213 for performing the various functions of the system 101.

In an embodiment, the data 200 in the memory 109 are processed by the one or more modules 213 present within the memory 109 of the system 101.

One or more modules 213 along with the data 200 functions to predict the CCT of the steel. In one implementation, the one or more modules 213 may include, but are not limited to, a receiving module 215, a training module 217, a steel phase determination module 219, a prediction module 221, and one or more other modules 223.

In an embodiment, the one or more modules 213 may be implemented as dedicated units. As used herein, the term module refers to an application specific integrated circuit (ASIC), an electronic circuit, a field-programmable gate arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and/or other suitable components that provide the described functionality. In some implementations, the one or more modules 213 may be communicatively coupled to the processor 111 for performing one or more functions of the system 101. The said modules 213 when configured with the functionality defined in the present disclosure will result in a novel hardware.

The receiving module 215 may receive the dataset from the database 103 regarding the plurality of attributes of the steel. The plurality of attributes comprises composition of the steel, prior heat treatment performed on the steel and austenising temperature. In an embodiment, the dataset obtained may be digitised from various predefined CCT diagrams. As an example, the dataset may include twenty-one columns. Out of the twenty columns, the first 13 columns are associated with the attributes. Among these attributes, first 11 columns may represent the weight percentage of different elements in the steel. For instance, the elements may include Carbon(C), Silicon (Si), Manganese (Mn), Phosphorus(P), Sulphur(S), Chromium (Cr), Molybdenum (Mo), Nickel (Ni), Aluminium (Al), Vanadium(V) and Titanium (Ti). Other two columns (12th and 13th column) include austenitizing temperature and cooling rates.
The training module 217 may train the prediction models 203 associated with start and end temperature of each phase. The training module 217 may train the prediction models 203 using the training dataset of the plurality of attributes of steel to generate the respective decision tree for corresponding phases by iteratively analysing and parsing each attribute of the training dataset. The training module 217 may train the prediction models using the Light Gradient Boosting Machine (LGBM) technique. The training module 101 may initially process the training dataset by normalizing and discretising each attribute in the training dataset. Then, the training module 217 may perform feature gain/ entropy calculation associated with respective attribute using one or more techniques. Feature gain calculation is shown in below equation.
……………………………………………………… (1)

As an example, the one or more technique may include Gini index. Further, one attribute from the plurality of attributes is selected as the root node and a split is performed based on entropy calculation associated with respective attribute. Further, the training module 217 may calculate mean square error to evaluate accuracy of regression tree. Particularly, the training module 217 may train the prediction models to generate the respective decision tree for corresponding phases by iteratively analysing each processed attribute of the training dataset. The decision tree includes a plurality of nodes, where each node in the respective decision tree represents a condition pertaining to an attribute of the plurality of attributes. For instance, the training module 217 may generate eight decision trees for the start and end temperature of four phases of the steel. The decision tree associated with each prediction model comprises the root node and one or more leaf nodes depending on the number of splits in respective decision tree. FIGURE.3 shows an exemplary decision graph in accordance with some embodiments of the present disclosure. Figure.3 shows an exemplary decision tree associated with a phase of the steel with start and end temperatures. As shown, the cooling rate is selected as the root node and the tree is split into left and right leaf nodes iteratively depending on the threshold values associated with the respective attributes.
Returning to FIGURE.2, the steel phase determination module 219 may determine the one or more stable phases of the steel along with respective start temperature and end temperature. Typically, when the steel is heated to a particular temperature (i.e., the austenising temperature) and gradually cooled at particular cooling rate, then depending on the chemistry/composition and the cooling rate, different phases in the microstructure are obtained. The steel phase determination module 219 may determine the one or more stable phases of the steel along with respective start temperature and end temperature by identifying the combined effect of the plurality of attributes and respective cooling rate on each other by using the prediction models 203 using the Light Gradient Boosting Machine (LGBM) technique. Considering previous example with 21 columns, in which last 8 columns pertain to the start and end temperatures of each of these phases for the specific input conditions (1st 11 columns). In an embodiment, when any particular phase is absent, the start and end temperatures are represented by null value. FIGURE.5 shows exemplary embodiment showing scores of prediction model for prediction of start and end temperatures of different phases of steel in accordance with some embodiments of the present disclosure. As shown, the FIGURE.5 represents R2 score (i.e., the parameter to compare/check the goodness of a regression model) for prediction of start and the end temperatures of the one or more phases in different steel composition.
Returning to FIGURE.2, the prediction module 221 may predict the CCT of the steel based on the plurality of attributes, corresponding to various cooling rates and one or more stable phases using the prediction models. Further, the prediction module 221 may predict an absence of one or more phases for one or more cooling conditions.
FIGURE.4 is a flowchart illustrating a method for predicting a Continuous Cooling Transformation (CCT) for steel in accordance with some embodiments of the present disclosure.

As illustrated in FIGURE.4, the method 400 comprises one or more blocks illustrating a method of predicting a Continuous Cooling Transformation (CCT) for steel in accordance with some embodiments of the present disclosure. The method 400 may be described in the general context of computer-executable instructions. Generally, computer-executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform functions or implement abstract data types.

The order in which the method 400 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 400. Additionally, individual blocks may be deleted from the methods without departing from scope of the subject matter described herein. Furthermore, the method 400 can be implemented in any suitable hardware, software, firmware, or combination thereof.

At block 401, the method 400 may include receiving, by the receiving module 215, the dataset comprising the plurality of attributes of the steel relating to each cooling rate. The plurality of attributes comprises composition of the steel, prior heat treatment performed on the steel and the austenising temperature. The composition of the steel comprises plurality of elements in weight percentages.

At block 403, the method 400 may include determining, by the steel phase determination module 219, the one or more stable phases of the steel along with respective start temperature and the end temperature based on the combined effect of the plurality of attributes and respective cooling rate, using the prediction models 203 which are trained using Light Gradient Boosting Machine (LGBM) technique. The prediction models 203 are trained using training dataset of the plurality of attributes of steel to generate respective decision tree for corresponding phases by iteratively analysing each attribute of the training dataset. The decision tree associated with each prediction model comprises the root node and the one or more leaf nodes depending on the number of splits in respective decision tree. In an embodiment, the number of splits in the decision tree is based on the entropy calculation associated with respective attribute.

At block 405, the method 400 may include predicting, by the prediction module 221, the Continuous Cooling Transformation (CCT) for the steel based on the plurality of attributes, corresponding to various cooling rates and one or more stable phases using the prediction models 203. FIGURE.6A-6D show exemplary graphs of actual and predicted temperatures of different phases of steel in accordance with some embodiments of the present disclosure. As shown, FIGURE 6A-6D shows graphical representation of the actual versus predicted temperatures of ferrite start and end temperature, pearlite start and end temperature, bainite start and end temperature and martensite start and end temperature, respectively.
FIGURE.7 is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.
In some embodiments, FIGURE.7 illustrates a block diagram of an exemplary computer system 700 for implementing embodiments consistent with the present disclosure. In some embodiments, the computer system 700 can be the system 101 that comprises a processor (also referred as a processor 702 in this FIGURE.7) that is used for predicting a Continuous Cooling Transformation (CCT) for steel. The processor 702 may include at least one data processor for executing program components for executing user or system-generated business processes. The processor 702 may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.
The processor 702 may be disposed in communication with input devices 711 and output devices 712 via I/O interface 701. The I/O interface 701 may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial bus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE), WiMax, or the like), etc.
Using the I/O interface 701, computer system 700 may communicate with input devices 711 and output devices 712.
In some embodiments, the processor 702 may be disposed in communication with a communication network 709 via a network interface 703. The network interface 703 may communicate with the communication network 709. The network interface 703 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. Using the network interface 703 and the communication network 709, the computer system 700 may communicate with the database 103.
The communication network 709 can be implemented as one of the different types of networks, such as intranet or Local Area Network (LAN) and such within the organization. The communication network 709 may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other.
Further, the communication network 709 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc. In some embodiments, the processor 702 may be disposed in communication with a memory 705 (e.g., RAM, ROM, etc. not shown in FIGURE.7) via a storage interface 704. The storage interface 704 may connect to memory 705 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fibre channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.
The memory 705 may store a collection of program or database components, including, without limitation, a user interface 706, an operating system 707, a web browser 708 etc. In some embodiments, the computer system 700 may store user/application data, such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.
Operating system 707 may facilitate resource management and operation of computer system 700. Examples of operating systems include, without limitation, APPLE® MACINTOSH® OS X®, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTION® (BSD), FREEBSD®, NETBSD®, OPENBSD, etc.), LINUX® DISTRIBUTIONS (E.G., RED HAT®, UBUNTU®, KUBUNTU®, etc.), IBM®OS/2®, MICROSOFT® WINDOWS® (XP®, VISTA®/7/8, 10 etc.), APPLE® IOS®, GOOGLETM ANDROIDTM, BLACKBERRY® OS, or the like. User interface 706 may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces may provide computer interaction interface elements on a display system operatively connected to computer system 700, such as cursors, icons, check boxes, menus, scrollers, windows, widgets, etc. Graphical User Interfaces (GUIs) may be employed, including, without limitation, Apple® Macintosh® operating systems’ Aqua®, IBM® OS/2®, Microsoft® Windows® (e.g., Aero, Metro, etc.), web interface libraries (e.g., ActiveX®, Java®, Javascript®, AJAX, HTML, Adobe® Flash®, etc.), or the like.
Computer system 700 may implement web browser 708 stored program components. Web browser 708 may be a hypertext viewing application, such as MICROSOFT® INTERNET EXPLORER®, GOOGLETM CHROMETM, MOZILLA® FIREFOX®, APPLE® SAFARI®, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browsers 708 may utilize facilities such as AJAX, DHTML, ADOBE® FLASH®, JAVASCRIPT®, JAVA®, Application Programming Interfaces (APIs), etc. Computer system 700 may implement a mail server stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP, ACTIVEX®, ANSI® C++/C#, MICROSOFT®,. NET, CGI SCRIPTS, JAVA®, JAVASCRIPT®, PERL®, PHP, PYTHON®, WEBOBJECTS®, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT® exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system 700 may implement a mail client stored program component. The mail client may be a mail viewing application, such as APPLE® MAIL, MICROSOFT® ENTOURAGE®, MICROSOFT® OUTLOOK®, MOZILLA® THUNDERBIRD®, etc.
Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.
An embodiment of the present disclosure enables prediction of the CCT for various grades of steel using LGBM technique. The present disclosure provides prediction of the stable phases of the steel which aids in predicting the CCT of the steel.
Equivalents:
A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention. When a single device or article is described herein, it will be apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be apparent that a single device/article may be used in place of the more than one device or article, or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.

The specification has described a system and a method for determining and operating caster at an optimum casting speed in real-time. The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that on-going technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words "comprising," "having," "containing," and "including," and other similar forms are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.
Referral numerals
Reference Number Description
100 Environment
101 System
103 Database
105 Communication network
107 I/O interface
109 Memory
111 Processor
113 Prediction models
200 Data
201 Input data
203 Prediction models
205 Steel phase data
207 CCT data
209 Other data
213 Modules
215 Receiving module
217 Training module
219 Steel phase determination module
221 Prediction module
223 Other modules
700 Exemplary computer system
701 I/O Interface of the exemplary computer system
702 Processor of the exemplary computer system
703 Network interface
704 Storage interface
705 Memory of the exemplary computer system
706 User interface
707 Operating system
708 Web browser
709 Communication network
711 Input devices
712 Output devices