Description:
FORM – 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(SEE SECTION 10, RULE 13)

“SYSTEM AND METHOD FOR CONTROLLING COATING TO A METAL SUBSTRATE”

BY

STEEL AUTHORITY OF INDIA LIMITED, A GOVERNMENT OF INDIA ENTERPRISE, HAVING ITS ADDRESS AT BOKARO STEEL PLANT, BOKARO STEEL CITY, BOKARO, JHARKHAND, PIN: 827001, INDIA

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.

TECHNICAL FIELD OF THE INVENTION
[0001] The present disclosure generally relates to galvanizing units particularly relates to a system for controlling coating to a metal substrate with a coating element (e.g., Zinc (Zn)) in a coating equipment (or the galvanizing unit).
BACKGROUND OF THE INVENTION
[0002] Generally, hot dip galvanizing is a method of coating by dipping. This results in coating a metal surface (e.g., steel surface) on both sides with a thin layer of zinc (Zn). It is understood that coating the metal surface with zinc prevents corrosion of the metal and thus it is economical.
[0003] In existing art, the metal (e.g., cold rolled coil/strip) is processed in the galvanizing unit through an annealing furnace provided with combustion, oxidation and reduction zones. Thereafter, the metal is fed to a zinc melt bath under protective atmosphere. The temperature profile of the furnace is maintained suitably for achieving the required mechanical properties and microstructural phases. For instance, the zinc bath temperature may be maintained around 460 degrees Celsius for proper zinc coating onto the metal surface. It is to be noted that the thickness of the zinc coating depends on one or more factors or process parameters. However, the existing galvanizing unit lacks control mechanism for controlling the factors and process parameters to provide appropriate coating to the metal surface. This results in over coating of the metal surface with zinc. In addition, the operational cost involved in coating the metal surface also increases.
[0004] Therefore, there is a need for a system and method for controlling zinc coating onto the metal surfaces in the galvanizing units, in addition to providing other technical advantages.

OBJECTIVE OF THE INVENTION
[0005] The main objective of the present invention is to provide a system and method for predicting thickness/weight of a coating element (such as zinc (Zn)) for a metal substrate based on process parameters, thereby controlling consumption of the coating element (Zn) by avoiding excess coating onto the metal substrate.
SUMMARY OF THE INVENTION
[0006] An aspect of the present invention is to address at least the above-mentioned problems and/or disadvantages and to provide at least the advantages described below.
[0007] Accordingly, in one aspect of the present disclosure, a system for controlling coating of a metal substrate is disclosed. The system includes a data analysis module. The data acquisition module is configured to identify one or more parameters associated with coating of the metal substrate based at least on training data. The one or more parameters includes at least a line speed, air pressure, dynamic air knife gap and a horizontal position. The system includes a prediction module. The prediction module is communicably coupled to the data analysis module. Further, the prediction module is configured to determine an optimum coating weight of a coating element to be coated onto the metal substrate. The prediction module is implemented using a multivariate logistic regression technique for determining the optimum coating weight of the coating element based at least on the one or more parameters. The system further includes a coating controller communicably coupled to the prediction module. The coating controller is configured to compute a horizontal position set point and a pressure set point for coating the metal substrate with the optimum coating weight of the coating element determined by the prediction module.
[0008] Accordingly, in one aspect of the present invention, a method for controlling coating of a metal substrate is disclosed. The method performed by a system includes identifying one or more parameters associated with coating of the metal substrate based at least on training data. The one or more parameters includes at least a line speed, air pressure, dynamic air knife gap and a horizontal position. The method includes predicting an optimum coating weight of a coating element to be coated onto the metal substrate based at least on the one or more parameters. The optimum coating weight of the coating element is determined by implementing a multivariate logistic regression technique. Further, the method includes computing a horizontal position set point and a pressure set point for coating the metal substrate with the optimum coating weight of the coating element. The method further includes facilitating display of the optimum coating weight, the horizontal position set point and the pressure set point on a human machine interface (HMI) associated with a coating equipment for coating the metal substrate.
[0009] Other aspects, advantages, and salient features of the invention will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the annexed drawings, discloses exemplary embodiments of the invention.
BRIEF DESCRIPTION OF ACCOMPANYING DRAWINGS
[0010] The detailed description is described with reference to the accompanying figures. 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 drawings to reference features and modules.
[0011] FIG. 1 illustrates a simplified block diagram representation of a system architecture for coating a metal substrate with a coating element, in accordance with an embodiment of the present disclosure;
[0012] FIG. 2 is an example representation of coating region in a coating equipment depicting coating the metal substrate with the coating element, in accordance with an embodiment of the present disclosure;
[0013] FIGS. 3A and 3B illustrate comparison results of predicted optimum coating weight and actual coating weight being deposited by the coating equipment onto the metal substrate, in accordance with an embodiment of the present disclosure; and
[0014] FIG. 4 is a flowchart depicting a method for controlling coating of the metal substrate with the coating element, in accordance with an embodiment of the present disclosure.
[0015] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative methods embodying the principles of the present disclosure. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION OF THE INVENTION
[0016] The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the invention as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
[0017] The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used by the inventor to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention as defined by the appended claims and their equivalents.
[0018] It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a component surface” includes reference to one or more of such surfaces. References in the specification to “one embodiment” or “an embodiment” mean that a particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
[0019] By the term “substantially” it is meant that the recited characteristic, parameter, or value need not be achieved exactly, but that deviations or variations, including for example, tolerances, measurement error, measurement accuracy limitations and other factors known to those of skill in the art, may occur in amounts that do not preclude the effect the characteristic was intended to provide.
[0020] Figures discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way that would limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged communications system. The terms used to describe various embodiments are exemplary. It should be understood that these are provided to merely aid the understanding of the description, and that their use and definitions in no way limit the scope of the invention. Terms first, second, and the like are used to differentiate between objects having the same terminology and are in no way intended to represent a chronological order, unless where explicitly stated otherwise. A set is defined as a non-empty set including at least one element.
[0021] In the following description, for purpose of explanation, specific details are set forth in order to provide an understanding of the present disclosure. It will be apparent, however, to one skilled in the art that the present disclosure may be practiced without these details. One skilled in the art will recognize that embodiments of the present disclosure, some of which are described below, may be incorporated into a number of systems. However, the systems and methods are not limited to the specific embodiments described herein. Further, structures and devices shown in the figures are illustrative of exemplary embodiments of the presently disclosure and are meant to avoid obscuring of the presently disclosure.
[0022] Various embodiments of the present disclosure are further described with reference to FIG. 1 to FIG. 4.
[0023] FIG. 1 illustrates a simplified block diagram representation of a system architecture (100) for coating a metal substrate with a coating element, in accordance with an embodiment of the present disclosure. Although the system architecture (100) is depicted to include one or a few components, modules, or devices arranged in a particular arrangement in the present disclosure, it should not be taken to limit the scope of the present disclosure. The system architecture (100) is hereinafter interchangeably referred to as “the architecture (100)”. In one example, the architecture (100) may be implemented in continuous galvanizing lines for coating a metal substrate (see, (202) of FIG. 2). The metal substrate (202) may include steel, cold rolled coil/ring, iron and the like. The most common method of galvanizing is hot-dip galvanizing. It is apparent to a person skilled in the art that the galvanizing process or the hot-dip galvanizing involves coating the metal substrate with a coating element (204) such as zinc (Zn). Thus, it is to be understood that the architecture (100) is deployed in the continuous galvanizing lines for coating the metal substrate (202) with Zn (i.e., the coating element (204)) while the metal substrate (202) is running on the line or the coating region of a coating equipment which will be further explained in detail.
[0024] The architecture (100) includes a system (102) for controlling coating of the metal substrate (202) in the continuous galvanizing line. In particular, the system (102) predicts/determines an optimum coating weight of the coating element (204) (or zinc) to be coated onto the metal substrate (202). In other words, the system (102) corresponds to a zinc coating weight prediction system for galvanized coils (i.e., the metal substrate (202)) at hot dip galvanizing line. It is useful in precise prediction, monitoring and controlling of zinc coating weight in the galvanized products. Further, the system (102) may be implemented using Machine Learning (ML), Internet of Things (IoT) and Cloud Computing for real-time control of coating weight based on predictive analytics which is further explained in detail.
[0025] The system (102) includes one or more modules such as a data acquisition module (104), a data logging module (106), a data analysis module (108), a prediction module (110) and a coating controller (112).
[0026] The data acquisition module (104) is configured to acquire datasets from one or more resources. The datasets may include laboratory reports of coating results. The one or more resources may include automation programmable logic controllers (PLCs) (not shown in figures) of the coating equipment. For example, the automation PLCs may be a non-Internet of Things (IoT) compliant Level-1 automation system PLCs associated with the coating equipment. The data acquisition module (104) may be implemented using one or more hardware components such as computer, Network Interface Card (NIC) or Ethernet cards, and the like. In addition, the data acquisition module (104) is configured to acquire data from circuitry or other electronic modules of the coating equipment in real-time. In other words, the data acquisition module (104) performs live data acquisition from the PLCs associated with the coating equipment.
[0027] The data logging module (106) is communicably coupled to the data acquisition module (104). The data logging module (106) is configured to receive the datasets from the data acquisition module (104) and logs in a database (not shown in figures) associated with the system (102). For example, the database may be a cloud database. In other words, the data logging module (106) is a middleware for logging critical data (or the datasets) captured by the data acquisition module (104) to the cloud database. The datasets may contain one or more parameters contributing for zinc coating weight and target zinc coating weight for the metal substrate (202). The data logging module (106) can be implemented using Node-RED programming tool. To that effect, the data logging module (106) along with the data acquisition module (104) acquires the datasets from the one or more resources as explained above and creates the training data.
[0028] As explained above, the data acquisition module (104) performs live data acquisition. For instance, the data is captured for every meter of the metal substrate (202) or the strip in a ten meters window near coating region using tracing data of the line. In case of live data acquisition, the data logging module (106) is configured to update the training data in real-time which will be explained further in detail.
[0029] The data analysis module (108) is configured to identify the one or more parameters associated with coating of the metal substrate (202) (or coiled ring) based on the training data. In fact, the data analysis module (108) identifies the parameters affecting zinc coating weight in the galvanized steel strip (or the metal substrate (202)). The data analysis module (108) determines the parameters based on one or more machine learning (ML) models. The one or more machine learning (ML) models associated with the data analysis module comprising a combination of unsupervised machine learning (ML) models and supervised machine learning (ML) models. For example, the unsupervised ML models may be based on a Principal Component Analysis (PCA) and the supervised ML models may be based on a Linear Discriminant Analysis (LDA). The parameters affecting the zinc coating weight (or coating thickness) may include, but not limited to, a line speed, air pressure, dynamic air knife (DAK) gap and a horizontal position. The parameters affecting the optimum coating weight of the coating element (204) (or the zinc coating weight) may be represented as:
W = f (Ls, p, h, g) ……… (Eq. 1)
Where,
W = Zinc coating weight,
Ls = Line speed,
p = Air Pressure in Dynamic Air Knife (DAK)
h = Horizontal distance between DAK and strip, and
g = Air pressure of DAK chamber.

[0030] The prediction module (110) is communicably coupled to the data analysis module (108). The prediction module is configured to determine the optimum coating weight of the coating element (204) to be coated onto the metal substrate (202). The prediction module (110) is implemented using a multivariate logistic regression technique for determining the optimum coating weight of the coating element (204) based at least on the one or more parameters. In an embodiment, the prediction module (110) may be deployed using Node-red platform. In some embodiments, the prediction module (110) may use tools and techniques such as Analysis of Variance (ANOVA), Message Queuing Telemetry Transport (MQTT) protocol. Further, the prediction module (110) implemented using logistic regression technique determines the optimum coating weight based on the following equation (Eq. 2).
W = A.(Ls) + B.(p) + C.(h)+ D.(g) ……… (Eq. 2).
Wherein, A, B, C, D are a first set of regression coefficients.

[0031] The training parameters affecting the coating and their combined effects may be collated in all possible combinations in the training data. Thus, the prediction module (110) trained with the training data computes an equation (Eq. 2) for zinc coating weight based on the parameters. In other words, the prediction module (110) predicts/determines the optimum coating weight of the coating element (204) (i.e., zinc (Zn)) based on a combination of the parameters the first set of regression coefficients associated with the parameters.
[0032] The coating controller (112) is communicably coupled to the prediction module (110). The coating controller (112) computes a horizontal position set point and a pressure set point for coating the metal substrate (202) with the optimum coating weight of the coating element (204) determined by the prediction module (110). The pressure set point for the desired coating weight is determined using the following equation (Eq. 3).
P= X. (Ls) + Y.(WT) + Z.(h)+ W.(g) ……… (Eq. 3)
Wherein,
P is the pressure set point, and
X, Y, Z and W correspond to a second set of regression coefficients.

[0033] Thus, the coating controller (112) determines the pressure set point based on a combination of the parameters, the optimum coating weight of the coating element (204) (i.e., zinc (Zn)) and their associated second set of regression coefficients. The coating element (204) is hereinafter interchangeably referred to as ‘Zn (204)’ or ‘zinc (204)’. It is to be noted that the weight and thickness of the coating element (zinc) getting coated onto the metal substrate (steel) depends on the parameters such as, line speed, horizontal distance between the air knife the metal substrate, and pressure of the impinging air and lip gap of the air knife (e.g., fixed at 1.0mm). Upon predicting the coating weight based on the above parameters, the system (102) calculates the optimum setpoints for horizontal position, air pressure and distance. Further, all the above-mentioned parameters are adjustable by the operators based on the line conditions. Thus, if one/more factors are manually altered by the operator, the other factors are automatically recalculated so as to ensure the predicted coating value to the desired coating weight. This results in dynamic control of the coating to the metal substrate.
[0034] In addition, the coating controller (112) transmits the optimum coating weight to a human machine interface (HMI) (120), thereby displaying the optimum coating weight of the coating element (204) to an operator of the coating equipment. In some embodiments, the HMI (120) may also display the horizontal position set point and the pressure set point and the parameters.
[0035] Further, the coating controller (112) integrates the system (102) with at least one programmable logic controller (PLC) (114) associated with the coating equipment. More specifically, the coating controller (112) transmits a trigger signal (exemplarily represented as “Tr”) to the PLCs (114). The trigger signal includes at least the horizontal position set point and the pressure set point for coating the metal substrate (202) with the optimum coating weight of zinc.
[0036] The at least one PLCs (114) include a dynamic air knife (DAK) PLC (114b) and a process-PLC (114a). The process-PLC (114a) and the DAK-PLC (114b) are communicably coupled to each other. The process-PLC (114a) transmits line speed corresponding to movement of the metal substrate (202) across a coating region in the coating equipment to the DAK-PLC (114b). Further, the DAK-PLC (114b) receives the trigger signal from the coating controller (112) as explained above. The trigger signal facilitates the DAK-PLC (114b) operates one or more operating components of the coating equipment based at least on the horizontal position set point, the pressure set point and line speed corresponding to movement of the metal substrate (202) across a coating region in the coating equipment, for coating the metal substrate (202) with the optimum coating weight of zinc (204).
[0037] The one or more operating components of the coating equipment include DAK air pressure control valves (116) and a DAK horizontal position control servo (118). The DAK-PLC (114b) operates the DAK air pressure control valves (116) based on the air pressure set point and the dynamic variations of the parameters. Further, the DAK-PLC (114b) operates the DAK horizontal position control servo (118) based on the horizontal position set point. As a result, the metal substrate (202) is coated with the optimum coating weight of the coating element (204).
[0038] Referring to FIG. 2, the PLCs (114) adjusts an upper lip and a lower lip positioned in the coating region (or DAK chamber) of the coating equipment based on the above- mentioned parameters, the air pressure set point and the horizontal position set point. Thereafter, the coating element (204) in molten state (i.e., molten zinc) is directed through a gap defined by the upper lip and the lip in the coating region. The gap defined between the upper lip and the lower lip corresponds to DAK gap or lip gap. Thus, the substrate (i.e., strip) is coated with the optimum coating weight of zinc (or the coating element (204)) based on the effect of the aforementioned parameters as explained with reference to FIG. 1. Further, coating the metal substrate (202) (or the strip) with the molten zinc by directing the molten zinc through the lip gap conforms to knife effect.
[0039] Referring to FIG. 1, the data logging module (106) receives at least the parameters (i.e., line speed, horizontal position, air pressure, gas pressure) from the PLCs (114a) and (114b) and the optimum coating weight of the coating element (204) as feedback in real-time. More specifically, the data acquisition module (104) acquires the parameters such as the line speed from the process-PLC (114a) and the horizontal position, air pressure from the DAK-PLC (114b) as feedback in real-time after each instance of coating the strip or the metal substrate (202) with the predicted optimum coating weight of zinc. Thereafter, the data acquisition module (104) transmits the parameters to the data logging module (106). Further, the data logging module (106) receives the optimum coating weight from the prediction module (110) in real time. The data logging module (106) updates the training data with the above-mentioned data received in real-time for improving the accuracy in determining the optimum coating weight of the coating element (204). In addition, the first and second set of regression coefficients are re-calculated at regular intervals (e.g., each month of line run) to incrementally enhance the accuracy of the system (102). In an example, the system (102) may predict the optimum coating weight of zinc (204) with an accuracy of 99.18% and a tolerance of +2 GSM.
[0040] In an embodiment, the HMI (120) may display a comparison table of the optimum coating weight of zinc predicted by the prediction module (110), the target coating weight and actual coating weight deposited by the coating equipment based on the predicted optimum coating weight (as shown in FIG. 3A). It is to be noted that the coating weight of zinc (204) is represented using ‘GSM’. In one example, the target coating weight is selected to be (120) GSM which is standard metric, and the predicted optimum coating weight is (118), and the actual coating weight deposited onto the metal substrate (202) (or strip) is 119. Thus, its is understood that the prediction module (110) is able to predict the optimum coating weight with the accuracy of 99.188% with a tolerance of +/- 2 GSM. In another embodiment, the HMI (120) may display the comparison of the predicted optimum coating weight and the actual coating weight of the coating element (204) over the time using a graphical representation (as shown in FIG. 3B).
[0041] FIG. 4 is a flow diagram depicting a method (400) for controlling coating to the metal substrate (202), in accordance with an embodiment of the present disclosure. The various steps and/or operations of the flow diagram, and combinations of steps/operations in the flow diagram, may be implemented by, for example, hardware, firmware, a processor, circuitry and/or by an apparatus such as the system architecture (100). The method (400) starts at (402).
[0042] At operation (402), the method (400) includes identifying, by a system (102), one or more parameters associated with coating of the metal substrate (202) based at least on training data. The one or more parameters include at least a line speed, air pressure, dynamic air knife gap and a horizontal position.
[0043] At operation (404), the method (400) includes predicting, by the system (102), an optimum coating weight of a coating element (204) to be coated onto the metal substrate (202) based at least on the one or more parameters. The optimum coating weight of the coating element (204) is determined by implementing a multivariate logistic regression technique.
[0044] At operation (406), the method (400) includes computing, by the system (102), a horizontal position set point and a pressure set point for coating the metal substrate (202) with the optimum coating weight of the coating element (204).
[0045] At operation (408), the method (400) facilitating, by the system (102), display of the optimum coating weight, the horizontal position set point and the pressure set point on a human machine interface (HMI) associated with a coating equipment for coating the metal substrate (202). Further, the one or more operations performed by the system (102) for controlling coating of the metal substrate (202), are explained with references to FIGS. 1 to 3A-3B, therefore they are not reiterated herein for the sake of brevity.
[0046] In an advantageous aspect of the present disclosure, an accurate zinc coating weight prediction system is provided to eliminate excess or overcoating of metal substrate (202) (e.g., cold rolled coils or metal strip) with the coating element (204) (such as zinc) in a continuous galvanized line.
[0047] The various embodiments described above are specific examples of a single broader invention. Any modifications, alterations or the equivalents of the above-mentioned embodiments are pertaining to the same invention as long as they are not falling beyond the scope of the invention as defined by the appended claims. It will be apparent to a skilled person that the embodiments described above are specific examples of a single broader invention which may have greater scope than any of the singular descriptions taught. There may be many alterations made in the invention without departing from the spirit and scope of the invention.
[0048] In the foregoing detailed description of embodiments of the invention, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the detailed description of embodiments of the invention, with each claim standing on its own as a separate embodiment.
[0049] It is understood that the above description is intended to be illustrative, and not restrictive. It is intended to cover all alternatives, modifications and equivalents as may be included within the scope of the invention as defined in the appended claims. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the invention should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein,” respectively.
, Claims:
1. A system (102) for controlling coating to a metal substrate (202), comprising:
a data analysis module (108) configured to identify one or more parameters associated with coating of the metal substrate based, at least in part, on training data, the one or more parameters comprising at least a line speed, air pressure, dynamic air knife gap and a horizontal position;
a prediction module (110) communicably coupled to the data analysis module, the prediction module configured to determine an optimum coating weight of a coating element (204) to be coated onto the metal substrate (202), wherein the prediction module is implemented using a multivariate logistic regression technique for determining the optimum coating weight of the coating element based at least on the one or more parameters; and
a coating controller (112) communicably coupled to the prediction module, the coating controller configured to compute a horizontal position set point and a pressure set point for coating the metal substrate with the optimum coating weight of the coating element determined by the prediction module.

2. The system (102) as claimed in claim 1, wherein the coating controller is configured to transmit a trigger signal (Tr) comprising the horizontal position set point and the pressure set point to at least one programmable logic controller (PLC) (114) associated with a coating equipment for coating the metal substrate.

3. The system (102) as claimed in claim 2, wherein the trigger signal facilitates the at least one PLC to operate one or more operating components of the coating equipment based at least on the horizontal position set point, the pressure set point and line speed corresponding to movement of the metal substrate across a coating region in the coating equipment, for coating the metal substrate with the optimum coating weight of the coating element.

4. The system (102) as claimed in claim 2, wherein the system (102) is configured to facilitate displaying of the optimum coating weight, the horizontal position set point and the pressure set point on a human machine interface (HMI) (120) associated with the coating equipment.

5. The system (102) as claimed in claim 1, wherein the data analysis module determines the one or more parameters based, at least in part, on one or more machine learning (ML) models, the one or more machine learning (ML) models associated with the data analysis module comprising a combination of unsupervised machine learning (ML) models and supervised machine learning (ML) models, wherein the unsupervised ML models correspond a Principal Component Analysis (PCA) and the supervised ML models correspond to a Linear Discriminant Analysis (LDA).

6. The system (102) as claimed in claim 1, further comprises:
a data acquisition module (104) configured to acquire datasets from one or more resources, the datasets comprising laboratory reports of coating results; and
a data logging module (106) communicably coupled to the data acquisition module, wherein the data logging module creates the training data for training the prediction module to determine the optimum coating weight of the coating element onto the metal substrate.

7. The system (102) as claimed in claim 6, wherein the data logging module is configured to receive at least the one or more parameters from at least one programmable logic controller (PLC) of a coating equipment and the optimum coating weight of the coating element as feedback in real-time, thereby updating the training data for determining the optimum coating weight of the coating element.

8. The system (102) as claimed in claim 1, wherein the optimum coating weight of the coating element is determined based on a combination of the one or more parameters and a first set of regression coefficients associated with the one or more parameters, and wherein the pressure set point is determined based on a combination of the one or more parameters, the optimum coating weight of the coating element and their associated second set of regression coefficients.

9. A method (400) for controlling coating to a metal substrate (202), comprising:
identifying (402), by a system (102), one or more parameters associated with coating of the metal substrate based, at least in part, on training data, the one or more parameters comprising at least a line speed, air pressure, dynamic air knife gap and a horizontal position;
predicting (404), by the system (102), an optimum coating weight of a coating element (204) to be coated onto the metal substrate based at least on the one or more parameters, wherein the optimum coating weight of the coating element is determined by implementing a multivariate logistic regression technique;
computing (406), by the system (102), a horizontal position set point and a pressure set point for coating the metal substrate with the optimum coating weight of the coating element; and
facilitating (408), by the system (102), display of the optimum coating weight, the horizontal position set point and the pressure set point on a human machine interface (HMI) (120) associated with a coating equipment for coating the metal substrate.

10. The method (400) as claimed in claim 9, further comprising:
transmitting, by the system, a trigger signal (Tr) comprising the horizontal position set point and the pressure set point to at least one programmable logic controller (PLC) (114) of the coating equipment for coating the metal substrate,
wherein the trigger signal facilitates the at least one PLC to operate one or more operating components of the coating equipment based at least on the horizontal position set point, the pressure set point and line speed corresponding to movement of the metal substrate across a coating region in the coating equipment, for coating the metal substrate with the optimum coating weight of the coating element.

11. The method (400) as claimed in claim 9, further comprising:
acquiring, by the system, datasets from one or more resources, the datasets comprising laboratory reports of coating results; and
creating the training data for training the prediction module to determine the optimum coating weight of the coating element onto the metal substrate based at least on the datasets.