Description:FIELD
The present disclosure relates to the field of an automated and/ or artificial intelligence-based system for the surface coating of an object and a method thereof.
DEFINITIONS
Recirculation: The process of using the same solution more than once in a system.
Filtration: The process of separating suspended solid matter from a liquid or a solution.
Body in White (BIW): It refers to the welded sheet metal components that form the structure of the vehicle to which the other components will be married, i.e., engine, chassis, exterior, and interior trim.
Cathodic-Electrodeposition (CED): Cathodic-Electrodeposition also known as (aka) cathodic coating or cataphoretic painting is a high-quality economical coating for all submersible parts of metal.
Dry Film Thickness (DFT): Dry Film Thickness (DFT) is the thickness of a coating as measured above the substrate. It consists of a single layer or multiple layers.
BACKGROUND
Electro-deposition is a critical process for rust prevention done normally in any paint shop or electroplating/ Surface Coating Industry, followed by the application of the next layers of primers & paints. The process of painting includes storage, recirculation, and temperature control of CED paint, anolyte system, DC rectifier system, parts conveying system, etc. In the standard process of coating an object, the BIW body or object to be coated enters the CED coating tank. After entering the tank, the part is identified by sensor or data input from the Manual Input Station, or the Default profile is set, and based on its type, voltage is applied to the BIW body/Object which results in an electrochemical reaction, thus depositing CED Paint on the body is further sent to the baking oven. There are various parameter settings, which impact the coating process when Voltage is applied on the BIW body/Object.
Further, the paint is measured as Dry Film thickness (DFT) and the parameters that affect the Dry Film thickness (DFT) are Body Surface area, Applied Voltage, Paint Temperature, Paint Solids/NVM, Ash, Dipping time, Anode to Cathode ratio, Coulombic yield of Paint, etc., all of these are not considered during the process, they always set at Default settings. Only BIW / Object Surface area and Voltage applied are configured & regulated. This is the standard process of coating. As above mentioned parameters are not considered in real time this results in DFT variation, uneven coating, and over or undercoating results which results in deterioration of quality or consumption of excess paint which are all undesirable.
Therefore, there is a need for an artificial intelligence-based system for the surface coating of an object and a method thereof that alleviates the aforementioned drawbacks.
OBJECTS
Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows:
It is an object of the present disclosure to ameliorate one or more problems of the prior art or to at least provide a useful alternative.
An object of the present disclosure is to provide an artificial intelligence system that processes the surface coating of an object.
Another object of the present disclosure is to provide an artificial intelligence system that determines and maintains the level of CED solutions in real time.
Yet another object of the present disclosure is to provide an artificial intelligence system that senses the object and determines the surface area for coating.
Still another object of the present disclosure is to provide an artificial intelligence system that achieves optimal consumption of CED material/ solution for surface coating.
Yet another object of the present disclosure is to provide an artificial intelligence system that determines bath process parameters, paint parameters, and the applied voltage.
Still another object of the present disclosure is to provide a method for surface coating of an object.
Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY
The present disclosure, in one aspect, envisages an artificial intelligence-based system for the surface coating of an object. The system comprises at least a container, an AI-controlled device, a rectifier, and a microcontroller.
The container contains a Cathodic Electro-Deposition (CED) solution and has a plurality of sensors. In an embodiment, the system includes a dosing unit coupled with a container to pass and fill the container with various Chemicals, Pigments, and resin.
The plurality of sensors is configured to determine and maintain the level of CED solution in real-time, based on consumption, and parameters, and further configured to determine and maintain the bath temperature of the container in accordance with the desired threshold temperature.
In an embodiment, the plurality of sensors includes a level sensor, bath temperature sensor, pH Sensor, conductivity sensor, triggering signals, and the like.
The AI-controlled device is configured to cooperate with the container to dip the object into the container for surface coating by means of a CED processing module. The AI-controlled device has a repository, a sensing module, and a validator module.
The repository is configured to store pre-trained data of object profiles, a set of object parameters, predefined commands, and a set of artificial intelligence rules. In an embodiment, the pre-trained data of object profiles historical data of CED with their associated parameters and the set of object parameters includes temperature, solids, pH, Ash, Voltage, solution conductivity, and dipping time.
The sensing module is configured to identify and sense an object and determine the surface area of the object by means of at least one object sensor.
The validator module is configured to cooperate with the sensing module and the repository to receive the determined surface area and the pre-trained data of the object profile to determine the set of parameters for CED by means of the set of artificial intelligence rules and generate a trigger signal.
The rectifier is configured to cooperate with the validator module of an AI-controlled device to receive a trigger signal and generate controlled precise voltage for a desired dipping time to accumulate a desired Dry Film thickness (DFT) of the surface coating of the object.
The microcontroller is configured to receive the predefined commands from the repository to operate and execute one or more components of the system.
The present disclosure envisages a method for surface coating of an object. The method includes the following steps:
• containing, by at least one container, a Cathodic Electro-Deposition (CED) solution for surface coating of an object;
• determining and maintaining, by a plurality of sensors, the level of CED solution in real-time and the bath temperature of the container in accordance with the desired threshold temperature;
• configuring, by an AI-controlled device, to cooperate with the container to dip an object into the container for surface coating by means of a CED processing module;
• storing, by a repository, pre-trained data of object profiles, a set of object parameters, predefined commands, and a set of artificial intelligence rules;
• sensing, by a sensing module, object and determining the surface area of the object by means of at least one object sensor;
• validating, by a validating module, to cooperate with the sensing module and repository to receive determined surface area and pre-trained data of object profile to determine the set of parameters for CED by means of the set of artificial intelligence rules and generate a trigger signal;
• receiving, by a rectifier, the trigger signal;
• generating, by a rectifier, controlled precise voltage for a desired dipping time to accumulate a desired Dry Film thickness (DFT) of the surface coating of the object;
• receiving, by a microcontroller, to receive predefined commands from the repository to operate and execute one or more components of the system.
wherein the set of object parameters includes Temperature, Solids, pH, Ash, Voltage, solution conductivity, Dipping time; and
wherein the set of parameters for CED includes Body Surface area, Applied Voltage, Paint Temperature, Paint Solids/NVM, Ash, dipping time, Anode Cathode ratio, and Coulombic yield of solution.
In one aspect of the present invention, a method is disclosed to be performed through the artificial intelligence-based system for the surface coating of an object, wherein the container has a plurality of sensors. The container for undertaking the CED process module further includes a unit for anolyte solution consisting of an anolyte tank to maintain conductivity inside the tank and to withdraw acid generated during the process; and a cooling unit to keep the dialysis cell temperature maintained; a container for CED to store the CED paint to dip the object to be coated; a plurality of rinse zones; an ultrafiltration system consisting: an ultrafiltration tank; a plurality of ultrafiltration modules; a dosing system for dosing of various chemicals selected from pigments, resins; and a dump tank. The CED container is coated with insulation material, and the cooling unit consists of chillers.
In another aspect of the present invention, the container includes an anolyte tank unit consisting of demineralized water as an anolyte to maintain conductivity inside the tank and to withdraw acid generated during the process.
In another aspect of the present invention, the plurality of sensors includes a level sensor, bath temperature sensor, triggering signals, for controlling the dialysis cell temperature, pH meter sensor, and a conductivity sensor.
In another aspect of the present invention, the temperature is captured by a temperature-capturing device, the pH is measured by a pH meter device, and the conductivity is controlled by a conductivity device.
In another aspect of the present invention, the pre-trained data of object profiles is profile check data that is captured by a profile-capturing device or fetched from the repository.
In another aspect of the present invention, the dosing unit is coupled with the container to pass and fill the container with various Chemicals, Pigments, and resin.
In another aspect of the present invention, the surface coating of an object, the method (800) comprises the steps:
• containing, by at least one container, a Cathodic Electro-Deposition (CED) solution for surface coating of an object;
• determining and maintaining, by a plurality of sensors, the level of CED solution in real-time and the bath temperature of the container in accordance with the desired threshold temperature;
• configuring, by an AI-controlled device, to cooperate with the container to dip the object into the container for surface coating by means of a CED processing module;
• storing, by a repository, pre-trained data of object profiles, a set of object parameters, predefined commands, and a set of artificial intelligence rules;
• sensing, by a sensing module, senses the object and determines the surface area of the object by means of at least one object sensor;
• validating, by a validating module, to cooperate with the sensing module and the repository to receive the determined surface area and the pre-trained data of the object profile to determine the set of parameters for CED by means of the set of artificial intelligence rules and generate a trigger signal;
• receiving, by a rectifier, the trigger signal;
• generating, by a rectifier, controlled precise voltage for a desired dipping time to accumulate a desired Dry Film thickness (DFT) of the surface coating of the object; and
• receiving, by a microcontroller, to receive the predefined commands from the repository to operate and execute one or more components of the system.
In another aspect of the present invention, the CED processing in the CED processing module comprises the steps:
a. filling a CED container with a paint solution containing pigments at a predetermined pH;
b. dosing by a dosing system predetermined pigment, chemicals, and resin to the container to obtain a CED solution;
c. determining and maintaining by a plurality of sensors the level of CED solution and bath temperature;
d. dipping the object in the CED container for a predetermined dipping time period and applying a predetermined voltage with a predefined Ramp up time, controlled by the rectifier, wherein the negative terminal of the electrode is attached to the object via Busbar to obtain a painted object;
e. removing of extra paint followed by rinsing the painted object in the ultra-filtrate solution to obtain the painted object and baking the painted object at a predetermined temperature to get a Dry Film Thickness of the surface coating on the object;
f. filtering the paint solution by an ultrafiltration system to obtain impurity-free permeate/ filtrate ;
g. maintaining the pH of the CED solution through an anolyte tank filled with demineralized water and recirculating the impurity-free filtrate, paint to the container for reapplication.
In another aspect of the present invention, the container is an electrochemical or cathodic electro-deposition tank.
In another aspect of the present invention, the predetermined pH is in the range of 5 to 7.
In another aspect of the present invention, the bath temperature is in the range of 28°C to 32°C.
In another aspect of the present invention, the predetermined pigment is a paint wherein the total solids/ nonvolatile matter is in the range of 21% to 25%
In another aspect of the present invention, the predetermined pigment is a paint wherein the total solids/ nonvolatile matter is in the range of 23% to 25%.
In another aspect of the present invention, the ash percentage is in the range of 22% to 27%.
In another aspect of the present invention, the predetermined temperature is in the range of 30°C to 32°C.
In another aspect of the present invention, the predetermined voltage is in the range of 80 V to 360V.
In accordance with the present disclosure, the baking temperature is in the range of 165 °C to 200 °C.
In another aspect of the present invention, the anolyte tank is filled with demineralized water.
In another aspect of the present invention, the predetermined dipping time period is in the range of 120 seconds to 240 seconds.
BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWING
The artificial intelligence-based system for surface coating of an object and a method thereof of the present disclosure will now be described with the help of the accompanying drawings, in which:
Figure 1 illustrates a block diagram of an artificial intelligence-based system for the surface coating of an object, in accordance with the present disclosure;
Figure 2 illustrates the standard CED coating time graph, in accordance with the present disclosure;
Figure 3 illustrates the CED voltage application time chart for Model A 103 Sqm, with a change in bath temperature in accordance with the present disclosure;
Figure 4 illustrates the CED voltage application time chart for Model B 33 Sqm, in accordance with the present disclosure;
Figure 5 illustrates the CED coating process equipment, in accordance with the present disclosure;
Figure 6A &6B illustrates the basic of CED coating, in accordance with the present disclosure;
Figure 7 illustrates the function CED rectifier, in accordance with the present disclosure;
Figure 8 illustrates a flow chart depicting steps involved in a method for the surface coating of an object, in accordance with the present disclosure;
Figure 9 illustrates the graph of bath temperature change on fixed voltage and surface area, in accordance with the present disclosure;
Figure 10 illustrates the schematic diagram of the proposed invention, in accordance with the present disclosure.
LIST OF REFERENCE NUMERALS
100 - System
102 – CED Container/ Tank
102a - Plurality of sensors
102a-1 – Level sensor
102a-2 – Bath temperature sensor
102a-3 – Triggering signal sensor
102a-4 – pH meter sensor
102a-5 – Conductivity sensor
104 - AI-controlled device
104a - Repository
104b - Sensing module
104b-1- Object sensor
104c - Validator module
106 - Rectifier
108 - Microcontroller
110 - CED processing module
112 - Profile capturing device
114 – Temperature capturing device
116 – pH meter device
118 – Conductivity device
120 – Dosing unit
122 – Anolyte tank
800 - Method
DETAILED DESCRIPTION
Embodiments, of the present disclosure, will now be described with reference to the accompanying drawings.
Embodiments are provided so as to thoroughly and fully convey the scope of the present disclosure to the person skilled in the art. Numerous details are set forth, relating to specific components, and methods, to provide a complete understanding of embodiments of the present disclosure. It will be apparent to the person skilled in the art that the details provided in the embodiments should not be construed to limit the scope of the present disclosure. In some embodiments, well-known processes, well-known apparatus structures, and well-known techniques are not described in detail.
The terminology used, in the present disclosure, is only for the purpose of explaining a particular embodiment and such terminology shall not be considered to limit the scope of the present disclosure. As used in the present disclosure, the forms "a,” "an," and "the" may be intended to include the plural forms as well, unless the context clearly suggests otherwise. The terms “including,” and “having,” are open-ended transitional phrases and therefore specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not forbid the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The particular order of steps disclosed in the method and process of the present disclosure is not to be construed as necessarily requiring their performance as described or illustrated. It is also to be understood that additional or alternative steps may be employed.
When an element is referred to as being “engaged to,” "connected to," or "coupled to" another element, it may be directly engaged, connected, or coupled to the other element. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed elements.
Normally in any paint shop or electroplating / Surface Coating Industry, electro-deposition is a critical process for rust prevention and it is followed by the application of the next layers of primers & paints. The process of painting includes storage, recirculation, and temperature control of CED paint, anolyte system, DC rectifier system, parts conveying system, etc. In the standard process of coating an object, the BIW body or object to be coated enters the CED coating tank. After entering the tank, the part is identified by sensor or data input from the Manual Input Station, or the Default profile is set, and based on its type, voltage is applied to the BIW body/Object which results in the electrochemical reaction, thus depositing CED Paint on the body is further sent to baking oven.
Various parameter settings impact the coating process when Voltage is applied to the BIW body/Object.
Further, the paint is measured as Dry Film thickness (DFT) and the parameters that affect the Dry Film thickness (DFT) are Body Surface area, Applied Voltage, Paint Temperature, Paint Solids/NVM, Ash, Dipping time, Anode to Cathode ratio, Coulombic yield of Paint, etc., all of these are not considered during the process, they always set at Default settings. Only BIW / Object Surface area and Voltage applied are configured & regulated. This is the standard process of coating. As above mentioned parameters are not considered in real-time this results in DFT variation, uneven coating, and over or undercoating results which results in deterioration of quality or consumption of excess paint which are all undesirable.
In order to address the aforementioned problems, the present disclosure envisages an artificial intelligence-based system for surface coating of an object (hereinafter referred to as a “system 100”) and a method for surface coating of an object (hereinafter referred to as a “method 800”). System 100 and method 800 are now being described with reference to Figure 1 to Figure 10.
Referring to Figure 1, the artificial intelligence-based system 100 for surface coating of an object comprises at least a container 102, an AI-controlled device 104, a rectifier 106, and a microcontroller 108.
Container 102 contains a Cathodic Electro-Deposition (CED) solution and has a plurality of sensors 102a.
In an embodiment, system 100 includes a dosing unit (120) coupled with container 102 to pass and fill container 102 with various chemicals, pigments, and resin. Container 102 has an inlet and outlet tab for cleaning container 102 by using recirculation & filtration.
The plurality of sensors 102a is configured to determine and maintain the level of CED solution in real-time and further configured to determine and maintain the bath temperature of container 102 in accordance with the desired threshold temperature.
In an embodiment, the plurality of sensors 102a includes a level sensor 102a-1, bath temperature sensor 102a-2, triggering signals 102a-3, and the like.
In an embodiment, the AI-controlled device 104 is configured to cooperate with container 102 to dip the object into container 102 for surface coating by means of a CED processing module 110. The AI-controlled device 104 controls the withdrawal of acid during the CED process from container 102. The AI-controlled device 104 has a repository 104a, a sensing module 104b, and a validator module 104c.
In another embodiment, repository 104a is configured to store pre-trained data of object profiles, a set of object parameters, predefined commands, and a set of artificial intelligence rules. The pre-trained data of object profiles are profile check data that is captured by a profile-capturing device 112 or fetched from repository 104a.
In another embodiment, the pre-trained data of object profiles historical data of CED with their associated parameters and the set of object parameters includes temperature, solids, pH, Ash, Voltage, solution conductivity, and dipping time.
In an embodiment, referring to Figure 2, the set of object parameters may include Temperature, Solids, pH, Ash, Voltage, solution conductivity, and Dipping time. The temperature is captured by a temperature-capturing device represented by a sensor 102a-2, and pH is captured by a pH meter device 102a-4. The solution conductivity is used to measure the ionic content in CED solution, wherein conductivity is controlled by a conductivity device 102a-5.
In an exemplary embodiment, the set of parameters may include temperature, solids/ NVM, pH, percentage impurity, voltage, and dipping time.
In an exemplary embodiment, the bath temperature may be 25°C to 35°C, total solids/ Non-Volatile Matter in the predetermined paint may be in the range of 20 % to 30%, pH may be in the range of 5 to 7, percentage impurity may be in the range of 20% to 30%, the voltage may be in the range of 100 V to 360 V and dipping time may be in the range of 120 seconds to 240 seconds.
In an exemplary embodiment, the voltage to be applied on the object may vary corresponding to the surface area of the object, the bath temperature may be 28°C to 32°C, and total solids/ Non-Volatile Matter in the predetermined paint may be in the range of 21 % to 25%, pH may be in the range of 5 to 7, percentage Ash may be in the range of 22% to 27%, the voltage may be in the range of 80 V to 360V, and dipping time maybe 180 seconds.
In an exemplary embodiment, the dipping time may vary based on process parameters and fixed voltage. Bath temperature may be 28°C to 32°C, total solids/ Non-Volatile Matter in the predetermined paint may be in the range of 21 % to 25%, pH may be in the range of 5.5 to 6.3, and voltage may be in the range of 110 V to 320 V.
In an exemplary embodiment, the optimal consumption of CED material may be achieved by varying the voltage to be applied on the object corresponding to the surface area of the object.
In an exemplary embodiment, the optimal consumption of CED material may be achieved by varying dipping times corresponding to the applied voltage, bath temperature, and surface area of the object.
In an exemplary embodiment, the optimal consumption of CED material may be achieved by varying the voltage corresponding to the bath parameters with fixed dipping time.
In an exemplary embodiment, the optimal consumption of CED material may be achieved by varying the dipping time based on process parameters and fixed voltage.
In an exemplary embodiment, the method provides real-time process control.
In an embodiment, the sensing module 104b is configured to identify and sense an object and determine the surface area of the object by means of at least one object sensor 104b-1. The surface area of the object is determined and calculated in 360 degrees by scanning the complete surface area of the object.
In an embodiment, the validator module 104c is configured to cooperate with the sensing module 104b and the repository 104a to receive the determined surface area and the pre-trained data of the object profile to determine the set of parameters for CED by means of the set of artificial intelligence rules and generate a trigger signal. The artificial intelligence rules are the process of computing a model's set of parameters for CED from pre-trained data of object profiles.
In another embodiment, the rectifier 106 is configured to cooperate with the validator module 104c of an AI-controlled device to receive a trigger signal and generate controlled precise voltage for a desired dipping time to accumulate a desired Dry Film thickness (DFT) of the surface coating of the object. The dipping time is based on the set of parameters for CED with a fixed precise voltage.
The microcontroller 108 is configured to receive the predefined commands from the repository to operate and execute one or more components of the system 100.
In another embodiment, the container (102) includes an anolyte tank (122) unit consisting of demineralized water as an anolyte to maintain conductivity inside the tank and to withdraw acid generated during the process.
In another embodiment, the plurality of sensors (102a) includes a level sensor (102a-1), bath temperature sensor (102a-2), triggering signals (102a-3), for controlling the dialysis cell temperature, pH meter sensor (102a-4), and a conductivity sensor (102a-5).
In another embodiment, the temperature is captured by a temperature-capturing device (114), the pH is measured by a pH meter device (116), and the conductivity is controlled by a conductivity device (118).
In another embodiment, the pre-trained data of object profiles is profile check data that is captured by a profile-capturing device (112) or fetched from the repository (104a).
In another embodiment, the dosing unit (120) is coupled with the container (102) to pass and fill the container (102) with various Chemicals, Pigments, and resin.
Referring to Figure 2, the figure illustrates the standard CED coating time chart that measures the processing time from the First Point of the Part getting dipped to the Last Point of the Part leaving the Paint bath. This is also mentioned as “First In & Last Out”. The Full Dip time is measured from the Last Point of the Part getting dipped to the Last Point of the Part leaving the Paint bath. This is also mentioned as “Last In & Last Out”. The processing time in normal cases is fixed & ranges from 120 seconds to 360 seconds and the processing time is based on various factors of part dimensions, paint parameters, and properties, product requirements, etc.
Referring to Figure 3, the figure illustrates the CED voltage application time chart for Model A 103 Square Meter (Sqm).
Referring to Figure 4, the figure illustrates the CED Voltage application time chart for Model B 33 Square Meters (Sqm).
Referring to Figure 5 in accordance with the present embodiment, the CED process module (not numbered) comprises a unit for an anolyte solution consisting of an anolyte tank (122) to maintain conductivity inside the tank and to withdraw acid generated during the process, and a cooling unit (not numbered) to keep the dialysis cell temperature maintained;
a container for CED 102 to store the CED paint to dip the object to be coated;
a plurality of rinse zones (not numbered);
an ultrafiltration system (not numbered) consisting of an ultrafiltration tank (not numbered); and a plurality of ultrafiltration modules (not numbered);
a dosing system (not numbered) for dosing various chemicals selected from pigments, resins; and
a dump tank (not numbered);
wherein container 102 is coated with insulation material, and the cooling unit (not numbered) consists of chillers (not numbered).
Referring to Figure 5, the figure illustrates the CED process comprising the following steps:
In the first step, a CED container (102) is filled with a paint solution containing pigments at a predetermined pH.
In accordance with the present disclosure, the container is electrochemical (EC) or cathodic electrodeposition (CED) tank 102.
In accordance with the present disclosure, the CED tank (102) is filled with paint solution.
In accordance with the present disclosure, the predetermined pH is in the range of 5 to 7. In an exemplary embodiment, the pH is 5.5 to 6.3.
In the second step, predetermined pigment, chemicals, and resin are dosed by a dosing system (not numbered) to obtain a CED solution.
In accordance with the present disclosure, the predetermined pigment is a paint wherein the total solids/nonvolatile matter is in the range of 20% to 30%. In an exemplary embodiment, the total solids/nonvolatile matter is in the range of 21% to 25%.
In the third step, the bath temperature and the level of the CED solution are determined and maintained by a sensor.
In accordance with the present disclosure, the bath temperature is in the range of 25°C to 35°C. In an exemplary embodiment, the bath temperature is 30±2°C.
In the fourth step, the object is dipped in the CED container 102 for a predetermined dipping time period and a predetermined voltage controlled by the rectifier module 106, wherein the negative terminal of the electrode is attached to the object to obtain a painted object.
In accordance with the present disclosure, the predetermined dipping time period is in the range of 120 seconds to 240 seconds. In an exemplary embodiment, the predetermined time period is 180 seconds.
In accordance with the present disclosure, the predetermined voltage is in the range of 80 V to 360 V. In an exemplary embodiment, the predetermined voltage is 110 V to 320 V.
In a fifth step, the painted object is removed followed by rinsing in the rinse zone with the permeate solution and further baking at a predetermined temperature, to get a Dry Film Thickness of 16 to 18 microns of the surface coating on the object.
In accordance with the present disclosure, the baking temperature is in the range of 165 °C to 200 °C. In an exemplary embodiment, the baking temperature is 180°C.
In the sixth step, the CED solution in the CED tank is filtered by an ultrafiltration system (not numbered) and filter units (not numbered) to obtain fresh permeate impurity-free paint.
In the seventh step, the pH inside the CED tank is maintained by the demineralized water from the anolyte tank (122), and the impurity-free paint is recirculated to the CED container (102) for reapplication, and the material can be stored in a dump tank (not numbered) during Maintenance / Shutdown.
In accordance with the present disclosure, pigment, resin, and binder solution are added to a CED container aka CED tank 102 for surface coating of an object. The anolyte tank consisting of demineralized water maintains conductivity inside the tank and withdraws acid generated during the process. The level of CED solution, bath temperature is maintained by sensors 102a inside the CED tank 102. An AI device is configured to cooperate with the CED container 102 and the object is dipped in the CED container 102 for surface coating. The pre-trained data is identified by a repository 104a and a sensing module 104b. The surface area of the object is validated by a validating module 104c which cooperates with a sensing module 104b and the repository 104a. The pre-trained data related to the surface area of the object is retrieved from repository 104a to determine the set of parameters for CED.
Figure 6A and 6B refers to the basics of CED coating. Electrocoating is a coating method based on an electrochemical process depending on the principle that “opposites attract”, by allowing the object to be painted with a negative charge and the paint a positive charge. The ionic attraction results in a durable, smooth, hard paint finish on the object. The negatively charged object is dipped into the paint with positively charged particles. Paint particles are attracted by the object, where they are deposited to form a uniform coating film over the entire surface. In the absence of the applied voltage, there is no interaction of the object with the paint particles. On application of suitable voltage, the positively charged paint particle makes a uniform coating over the negatively charged object.
Referring to Figure 7, the figure illustrates the function of CED rectifier module 106 that converts input AC voltage to DC voltage. The surface area of the object is predefined by a repository 104a The DC voltage output for a predefined object is stored in repository 104a and fixed. In accordance with the object, the current is limited to maximum current and below rupture voltage. The positive terminal of the rectifier is connected to the anode cell (i.e. paint) and the negative terminal is connected to the object. The control input of the rectifier module 106 includes
• Identifying the object;
• Voltage setpoint is based on the object;
• Maximum voltage and current are fixed;
• Positioning the object in the container by means of a conveyor.
Figure 8 illustrates a flow chart depicting steps involved in method 800 for the surface coating of an object, in accordance with an embodiment of the present disclosure. Method 800 comprises the following steps:
At step 802, method 800 includes containing, by at least one container 102, a Cathodic Electro-Deposition (CED) solution for the surface coating of an object.
At step 804, method 800 includes determining and maintaining, by a plurality of sensors 102a, the level of CED solution in real-time and the bath temperature of container 102 in accordance with the desired threshold temperature.
At step 806, method 800 includes configuring, by an AI-controlled device 104, to cooperate with container 102 to dip an object into container 102 for surface coating.
At step 808, method 800 includes storing, by a repository 104a, pre-trained data of object profiles, a set of object parameters, predefined commands, and a set of artificial intelligence rules.
At step 810, method 800 includes sensing, by a sensing module 104b, to sense an object and determine the surface area of the object by means of at least one object sensor 104b-1.
At step 812, method 800 includes validating, by a validating module 104c, to cooperate with sensing module 104b and repository 104a to receive determined surface area and pre-trained data of object profile to determine the set of parameters for CED by means of the set of artificial intelligence rules and generate a trigger signal.
At step 814, method 800 includes receiving, by a rectifier 106, a trigger signal.
At step 816, method 800 includes generating, by a rectifier 106, controlled precise voltage for a desired dipping time to accumulate a desired Dry Film thickness (DFT) of the surface coating of the object.
At step 818, method 800 includes receiving, by a microcontroller 108, predefined commands from repository 104a to operate and execute one or more components of the system 100.
Referring to Figure 9, the figure illustrates the bath temperature change on fixed voltage and surface area. At constant voltage, an increase in bath temperature increases the deposition in turn increasing DFT in a fixed surface. Similarly, at constant voltage decrease in bath temperature decreases deposition in turn reducing DFT in a fixed surface.
Referring to Figure 10 illustrates the system in real-time in which a paint solution is added to a CED container 102 for surface coating of an object. Paint is added through a dosing module to obtain a CED solution. The level of CED solution, bath temperature is maintained by temperature sensor 102a-2 inside the CED tank. An AI device 104 is configured to cooperate with the CED container 102 and the object is dipped in the CED container 102 for surface coating. The pre-trained data is identified by a repository 104a and a sensing module 104b. The surface area of the object is validated by a validating module 104c which cooperates with a sensing module 104b and the repository 104a. The pre-trained data related to the surface area of the object is retrieved from repository 104a to determine the set of parameters for CED.
In an operative configuration, system 100 comprises at least a container 102, an AI-controlled device 104, a rectifier 106, and a microcontroller 108. Container 102 contains a Cathodic Electro-Deposition (CED) solution and has a plurality of sensors 102a. The plurality of sensors 102a is configured to determine and maintain the level of CED solution in real-time and further configured to determine and maintain the bath temperature of container 102 in accordance with the desired threshold temperature. The AI-controlled device 104 is configured to cooperate with container 102 to dip the object into container 102 for surface coating by means of a CED processing module 110. The AI-controlled device has a repository 104a, a sensing module 104b, and a validator module 104c. The repository 104a is configured to store pre-trained data of object profiles, a set of object parameters, predefined commands, and a set of artificial intelligence rules. The sensing module 104b is configured to identify and sense an object and determine the surface area of the object by means of at least one object sensor 104b-1. The validator module 104c is configured to cooperate with the sensing module 104b and the repository 104a to receive the determined surface area and the pre-trained data of the object profile to determine the set of parameters for CED by means of the set of artificial intelligence rules and generate a trigger signal. The rectifier is configured to cooperate with the validator module of an AI-controlled device 104 to receive a trigger signal and generate controlled precise voltage for a desired dipping time to accumulate a desired Dry Film thickness (DFT) of the surface coating of the object. The Microcontroller 108 is configured to receive the predefined commands from repository 104a to operate and execute one or more components of the system.
The present disclosure is further illustrated herein below with the help of the following non-limiting examples. The experiments disclosed under these examples herein are intended merely to facilitate an understanding of how the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the experiments should not be construed as limiting the scope of embodiments herein. These laboratory-scale experiments can be scaled up to an industrial/ commercial scale and the results obtained can be extrapolated to industrial/ commercial scale.
Example 1:
CED Process:
A standard CED coating time chart that measures the processing time from the First Point of the Part getting dipped to the Last Point of the Part leaving the Paint bath is depicted vide Figure 2. This is also mentioned as “First In & Last Out”. The Full Dip time was measured from the Last Point of the Part getting dipped to the Last Point of the Part leaving the Paint bath. This is also mentioned as “Last In & Last Out”. The processing time in normal cases was fixed & ranged from 120 seconds to 360 seconds and the processing time was based on various factors of part dimensions, paint parameters, and properties, product requirements, etc. The typical parameter for the CED process was temperature 30±2°C, solids/NVM 23±2%, pH 5.5 to 6.3, Ash 22% to 27%, voltage 110V to 320V, and dipping time 180 seconds. Other parameters include paint conductivity, object profile/ surface area to be painted, applied Vs actual voltage, and actual current. The trigger signal was received by the rectifier, which then converts input AC voltage to DC voltage for a desired dipping time based on the surface area of the object. The predefined command was sent by the repository to a microcontroller which then executes the other components of the system, depicted in Figure 10.
Figure 10 illustrates the system in real-time in which an anolyte solution was added to a CED container 102 for the surface coating of an object. The anolyte tank consisting of demineralized water maintains conductivity inside the tank and withdraws acid generated during the process. The level of CED solution, bath temperature was maintained by temperature sensor 102a-2 inside the CED tank. An AI device 104 is configured to cooperate with the CED container 102 and the object was dipped in the CED container 102 for surface coating. The pre-trained data was identified by a repository 104a and a sensing module 104b. The surface area of the object was validated by a validating module 104c which cooperates with a sensing module 104b and the repository 104a. The pre-trained data related to the surface area of the object was retrieved from repository 104a to determine the set of parameters for CED.
Example 2:
CED Voltage application time charts:
With reference to standard paint specifications & trial parameters for Model A of 103 square meters (Sqm) surface area was derived for eg., at 32oC, application voltage was 100% for Time T1.
But when the Paint Temperature was increased to 33oC in conventional process application voltage was 100% for Time T1, which was undesirable.
So for Time T1, Temperature 33oC, the applied voltage should be V2, i.e. 95%V1. This has been detailed vide Figure 3.
With reference to standard Paint Specifications & trial parameters for Model B of 33 Sqm area surface was derived e.g., at 32oC, application Voltage be 100% for Time T1.
When the Paint Temperature was increased to 330C in conventional Process application Voltage be 100% for Time T1, Model B which has a lower surface area resulted in very high DFT, which was totally undesirable.
Hence, with Model B in Frame of Selection, the application Time would be T2, at Temperature 33oC, and the applied voltage shall be V3 i.e. 80% V1. All these setpoints will be auto-derived by an AI algorithm which will self-learn during operation phases. Model B is revealed vide Figure 4.
Example 3:
Effect of different parameters on the DFT:
The effect of different parameters on the Dry Film Thickness (DFT) was studied the result of the study is summarized in Table 1
Surface Area Voltage Bath Temperature Paint Specific Parameters DFT Resultant
A1 V1 T1 S1 Standard Desirable
A1 V1 < T1 S1 Lower Undesirable
A1 V1 > T1 S1 Higher Undesirable
< A1 V1 T1 S1 Higher Undesirable
> A1 V1 T1 S1 Lower Undesirable
A1 V1 T1 < S1 Lower Undesirable
A1 V1 T1 > S1 Higher Undesirable
The object profile was identified by a repository and a sensing module to determine the surface area of the object. The surface area of the object was validated by a validating module which cooperates with a sensing module and the repository. The temperature was optimized with respect to the object profile. It was observed that lowering the bath temperature while keeping the surface area of the object, voltage, and other paint-specific parameters such as pH, conductivity, and solids constant results in lower DFT while increasing the bath temperature resulted in higher DFT which was undesirable.
Lowering the area of the object, while keeping bath temperature, voltage, and paint-specific parameters constant results in higher DFT, while increasing the surface area of the object while keeping bath temperature, voltage, and paint-specific parameters constant results in lower DFT, both of which were undesirable.
Lowering the paint-specific parameters, while keeping object area, applied voltage, and bath temperature constant such as pH and conductivity resulted in lower DFT which was undesirable. Similarly increasing the paint-specific parameters, while keeping all other parameters such as object area, applied voltage, and bath temperature constant resulted in higher DFT, which was not desirable.
Corresponding to this, Figure 9 illustrates the bath temperature change on fixed voltage and surface area. At constant voltage, an increase in bath temperature increases the deposition in turn increasing DFT in a fixed surface. Similarly, at constant voltage decrease in bath temperature decreases deposition in turn reducing DFT in a fixed surface.
The foregoing description of the embodiments has been provided for purposes of illustration and is not intended to limit the scope of the present disclosure. Individual components of a particular embodiment are generally not limited to that particular embodiment, but, are interchangeable. Such variations are not to be regarded as a departure from the present disclosure, and all such modifications are considered to be within the scope of the present disclosure.
TECHNICAL ADVANCEMENTS
The present disclosure described herein above has several technical advantages including, but not limited to, the realization of an artificial intelligence-based system 100 for surface coating of an object and method thereof that:
• the thickness of the coating prevents corrosion;
• prevents from rusting;
• the coated surfaces are durable for the long term;
• coating of the object is highly resistant;
• optimized DFT is achieved; and
• optimizing paint usage.
The aspect herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
The foregoing description of the specific embodiments so fully reveals the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
The use of the expression “at least” or “at least one” suggests the use of one or more elements or ingredients or quantities, as the use may be in the embodiment of the disclosure to achieve one or more of the desired objects or results.
Any discussion of devices, articles, or the like that has been included in this specification is solely for the purpose of providing a context for the disclosure. It is not to be taken as an admission that any or all of these matters form a part of the prior art base or were common general knowledge in the field relevant to the disclosure as it existed anywhere before the priority date of this application.
While considerable emphasis has been placed herein on the components and parts of the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiment as well as other embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be interpreted merely as illustrative of the disclosure and not as a limitation. , Claims:WE CLAIM:
1. An Artificial intelligence-based system (100) for the surface coating of an object, said system (100) comprising:
• at least one container (102) containing a Cathodic Electro-Deposition (CED) solution for surface coating of an object, wherein said container (102) comprises:
o a plurality of sensors (102a) configured to determine and maintain the level of CED solution in real-time, and further configured to determine and maintain the bath temperature of said container (102) in accordance with the desired threshold temperature;
• an AI-controlled device (104) configured to cooperate with said container (102) to dip said object into said container (102) for surface coating by means of a CED processing module (110), wherein said AI-controlled device (104) comprises:
o a repository (104a) configured to store pre-trained data of object profiles, a set of object parameters, predefined commands, and a set of artificial intelligence rules;
o a sensing module (104b) configured to identify and sense said object and determine the surface area of said object by means of at least one object sensor (104b-1);
o a validator module (104c) configured to cooperate with said sensing module (104b) and said repository (104a) to receive said determined surface area and said pre-trained data of object profile to determine the set of parameters for CED by means of said set of artificial intelligence rules and generate a trigger signal;
• a rectifier (106) configured to cooperate with said validator module (104c) of said AI-controlled device (104) to receive said trigger signal and generate controlled precise voltage for a desired dipping time to accumulate a desired Dry Film thickness (DFT) of the surface coating of the object; and
• a microcontroller (108) configured to receive said predefined commands from said repository (104a) to operate and execute one or more components of the system (100);
wherein said set of object parameters includes Temperature, Solids, pH, Ash, Voltage, solution conductivity, and Dipping time; and
wherein said set of parameters for CED includes Body Surface area, Applied Voltage, Paint Temperature, Paint Solids/NVM, Ash, dipping time, Anode to Cathode ratio, Coulombic yield of solution.
2. The system (100) as claimed in claim 1, wherein said container (102) for undertaking the CED process module comprises:
a. a cooling unit to keep the dialysis cell temperature maintained;
b. a container for CED to store the CED paint to dip the object to be coated;
c. a cooling unit;
d. a plurality of rinse zones; and
e. an ultrafiltration system consisting:
an ultrafiltration tank;
a plurality of ultrafiltration modules;
f. a dosing system for dosing of various chemicals selected from pigments, resins; and
g. a dump tank
wherein:
a) said container (102) is coated with insulation material;
b) said cooling unit consists of chillers.
3. The system (100) as claimed in claim 1, wherein said container (102) includes an anolyte tank (122) unit consisting of demineralized water as anolyte to maintain conductivity inside the tank and to withdraw acid generated during the process.
4. The system (100) as claimed in claim 1, wherein said plurality of sensors (102a) includes a level sensor (102a-1), bath temperature sensor (102a-2), triggering signals (102a-3), for controlling the dialysis cell temperature, pH meter sensor (102a-4) and a conductivity sensor (102a-5).
5. The system (100) as claimed in claim 1, wherein said temperature is captured by a temperature capturing device (114), and said pH is measured by a pH meter device (116), said conductivity is controlled by a conductivity device (118).
6. The system (100) as claimed in claim 1, wherein said pre-trained data of object profiles is a profile check data which is captured by a profile capturing device (112) or fetched from said repository (104a).
7. The system (100) as claimed in claim 1 and 2, wherein said dosing unit (120) is coupled with said container (102) to pass and fill the said container (102) with various Chemicals, Pigments, and resin.
8. A method (800) for the surface coating of an object, said method (800) comprises the steps of:
• containing, by at least one container (102), a Cathodic Electro-Deposition (CED) solution for surface coating of an object;
• determining and maintaining, by a plurality of sensors (102a), the level of CED solution in real-time and the bath temperature of the container (102) in accordance with the desired threshold temperature;
• configuring, by an AI-controlled device (104), to cooperate with said container (102) to dip said object into said container (102) for surface coating by means of a CED processing module (110);
• storing, by a repository (104a), pre-trained data of object profiles, a set of object parameters, predefined commands, and a set of artificial intelligence rules;
• sensing, by a sensing module (104b), sense said object and determine the surface area of said object by means of at least one object sensor (104b-1), (104b-2), (104b-3);
• validating, by a validating module (104c), to cooperate with said sensing module (104b) and said repository (104a) to receive said determined surface area and said pre-trained data of object profile to determine the set of parameters for CED by means of said set of artificial intelligence rules and generate a trigger signal;
• receiving, by a rectifier (106), said trigger signal;
• generating, by a rectifier (106), controlled precise voltage for a desired dipping time to accumulate a desired Dry Film thickness (DFT) of the surface coating of the object; and
• receiving, by a microcontroller (108), to receive said predefined commands from said repository (104a) to operate and execute one or more components of the system (100).

9. The method as claimed in claim 7, wherein the CED processing in the CED processing module comprising the steps of:
a. filling a CED container (102) with a paint solution at a predetermined pH;
b. dosing by a dosing system predetermined pigment, chemicals and resin to obtain a CED solution;
c. determining and maintaining by a plurality of said sensors (102a) the level of CED solution and bath temperature;
d. dipping said object in said container for a predetermined dipping time period and applying a predetermined voltage controlled by said rectifier, wherein the negative terminal of the electrode is attached to the object to obtain a painted object;
e. removing of extra paint followed by rinsing said painted object in the permeate solution to obtain the painted object and baking said painted object at a predetermined temperature to get a Dry Film Thickness of the surface coating on said object;
f. filtering said paint solution by an ultrafiltration system to obtain impurity free Paint & permeate ; and
g. maintaining the pH of said CED solution through an anolyte tank filled with demineralized water and recirculating the impurity-free filtrate, paint to the container for reapplication.

10. The method as claimed in claim 8, wherein:
a. said container is electro chemical or cathodic electro deposition tank;
b. said predetermined pH is in the range of 5 to 7; and
c. said bath temperature is in the range of 28°C to 32°C.

11. The method as claimed in claim 8, wherein :
a. said predetermined pigment is a paint wherein the total solids/ nonvolatile matter is in the range of 21% to 25%;
b. said ash percentage is in the range of 22% to 27%;
c. said predetermined voltage is in the range of 80 V to 360 V; and
d. said predetermined dipping time period is in the range of 120 second to 240 seconds.

Dated this 11th day of December, 2023

_______________________________
MOHAN RAJKUMAR DEWAN, IN/PA – 25
of R.K.DEWAN & CO.
Authorized Agent of Applicant

TO,
THE CONTROLLER OF PATENTS
THE PATENT OFFICE, AT CHENNAI