Description:DESCRIPTION:
Field of the invention:
[0001] The present disclosure generally relates to the technical field of crane girder production technique, and in specific relates to a smart crane girder welding system adaptively releases welding wire and flux in controlled manner during welding to increase welding efficiency and bond strength of the weld joint.
Background of the invention:
[0002] Arc welding is a process for connecting metals that involves creation of an electric arc between an electrode and the object. Typically, the electrodes are consumable and are continually melted by the welding gun as the electrodes pass over the object. Both the electrode and some of the base metal generate a pool of weld metal during arc welding using consumable electrodes, which then freezes to form the weld.

[0003] At present, various types of arc welding techniques have developed such as MIG (metal inert gas) guns, Submerged Arc Welding (SAW), and Twin submerged arc welding thereof. Submerged Arc Welding (SAW) is an arc welding technology in which heat is created by an arc between a constantly supplied bare solid metal consumable wire or strip electrode and the work piece. A layer of granular flux obscures this process. The molten flux keeps the arc going, refines the weld metal, and shields it from contaminants. After screening, un-fused flux can be returned to the circulating system and reused.

[0004] Twin submerged arc welding is similar to regular SAW, except instead of a single thicker wire. The twin submerged arc welding employs two wires in a single power source and a single contact tip. Smaller wire distances generate undercuts owing to high electromagnetic forces, but wider wire distances reduce the heat effects between the wires and the arcs. This results in different types of welding cavities due to the low energy.

[0005] In general, the basic structure of a box girder crane includes end carriages, the hoist, motor drives and the main girder but there can be variations in the structure depending on lifting loads or the operation environment. The main welding process in box girder manufacturing is submerged arc welding. Although, the single-wire submerged arc welding effectively finishes the welding process, the welding speed is poor when the traditional method is utilized because the welding feet of the crane's main beam are usually large.

[0006] However, in the existing crane girder welding systems the cost for production of crane girder is high. In the existing crane girder welding systems the motor of driving wheels may lead to extra release of welding wire the leads to welding wire wastages. Further, the existing crane girder welding systems fails to control the welding wire and flux during welding which results in the loss of welding efficiency and bond strength of the weld joint.

[0007] Therefore, there is a need for a smart crane girder welding system adaptively releases welding wire and flux in controlled manner during welding to increase welding efficiency and bond strength of the weld joint. A smart crane girder welding system is needed that is adaptive to various types of weld metal materials, type of welding joints, and ambient welding conditions for welding wires. A smart crane girder welding system is needed that enables users to input that type of welding joint manually or automatically detect the welding operation based on the base metal position. There is a need for a smart crane girder welding system for welding crane girders that eliminates the welding wire wastages and reduces the cost of production of crane girders.
Objectives of the invention:
[0008] The primary objective of the invention is to provide a smart crane girder welding system adaptively releases welding wire and flux in controlled manner during welding to increase welding efficiency and bond strength of the weld joint.

[0009] The other objective of the invention is to provide a smart crane girder welding system that takes multiple images of the weld metal material to detect the type of weld metal material, type of welding joints, and ambient welding conditions to control releasing of welding wire and flux.

[0010] Another objective of the invention is to provide a smart crane girder welding system that either enables users to input that type of welding joint manually or automatically detect the welding operation based on the base metal position.

[0011] Another objective of the invention is to provide a welding system that controls the release of welding wire and flux to increase the welding efficiency and bond strength.

[0012] The other objective of the invention is to provide a welding system that controls releasing rate of welding wire by controlling the welding feeder driving wheels.

[0013] Yet another objective of the invention is to provide a welding system that controls the welding gun angle based on the position of the weld metals.

[0014] Further objective of the invention is to provide a smart crane girder welding system for welding crane girders that eliminates the welding wire wastages and reduces the cost of production of crane girders.

[0015] Further objective of the invention is to provide a welding system that detects the weld material, welding operation and ambient welding conditions, and sends a feedback to the controller to control the motor of driving wheels.
Summary of the invention:
[0016] The present disclosure proposes a smart crane girder welding system. The following presents a simplified summary in order to provide a basic understanding of some aspects of the claimed subject matter. This summary is not an extensive overview. It is not intended to identify key/critical elements or to delineate the scope of the claimed subject matter. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

[0017] In order to overcome the above deficiencies of the prior art, the present disclosure is to solve the technical problem to provide a smart crane girder welding system adaptively releases welding wire and flux in controlled manner during welding to increase welding efficiency and bond strength of the weld joint.

[0018] According to an aspect, the invention provides a smart crane girder welding system that controls the usage of welding wire and flux and eliminates wastages of welding wire. The system comprises an image capturing module, a processing module, a communication module, and a control module. The image capturing module is configured with an image sensor to capture an image of a workpiece that needs to be welded. In specific, the image sensor includes either a charge-coupled device or an electron-multiplying charge-coupled device or a complementary metal-oxide-semiconductor or any other image sensor.

[0019] The processing module is configured to process the image to detect welding conditions using a machine learning algorithm, and thereby transmit a control signal based on the detected welding conditions to the communication module. In specific, the welding conditions include type of material of the workpiece, position of the workpiece with respect to a based metal, type of weld joint and ambient welding conditions. The type of weld joint includes butt joint, lap joint, seam joint, T-weld joint, and corner welds joint, thereof. Further, the type of weld joint is determined either by detecting the position of the workpiece using the image sensor or manually entered by an operator.

[0020] The communication module is configured to receive the control signal from the processing module and transmit the control signal to the control module. The control module is configured to receive the control signal to control plurality of driving wheels of a wire feeder, plurality of valves of a welding flux funnel, and a first motor of a welding gun to perform welding operation. The wire feeder includes at least one welding wire disc that is configured to store at least one welding wire. In specific, the control module includes a controller that controls a second motor to drives the driving wheels to release the welding wire in controlled manner for welding.

[0021] According to another aspect, the invention provides a welding process using a smart crane girder welding system. At first, an image of workpiece is captured using the image sensor through the image capturing module. Next, the captured image is processed through the processing module to detect welding conditions using a machine learning algorithm. Next, a control signal is transmitted based on the welding conditions through the communication module to the control module. Finally, the control signal is received by the control module to control the driving wheels, the valves and the first motor, to release the welding wire through the wire feeder, to release flux from the welding flux funnel, and the angle of welding gun respectively.

[0022] Further, objects and advantages of the present invention will be apparent from a study of the following portion of the specification, the claims, and the attached drawings.
Detailed description of drawings:
[0023] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate an embodiment of the invention, and, together with the description, explain the principles of the invention.

[0024] FIG. 1 illustrates an exemplary block diagram of smart crane girder welding system in accordance to an exemplary embodiment of the invention.

[0025] FIG. 2 illustrates exemplary method of controlling welding operation using a smart crane girder welding system in accordance to an exemplary embodiment of the invention.
Detailed invention disclosure:
[0026] Various embodiments of the present invention will be described in reference to the accompanying drawings. Wherever possible, same or similar reference numerals are used in the drawings and the description to refer to the same or like parts or steps.

[0027] The present disclosure has been made with a view towards solving the problem with the prior art described above, and it is an object of the present invention to provide a smart crane girder welding system adaptively releases welding wire and flux in controlled manner during welding to increase welding efficiency and bond strength of the weld joint.

[0028] According to an exemplary embodiment of the invention, FIG. 1 refers to an exemplary block diagram of smart crane girder welding system 100. The system 100 comprises an image capturing module 102, a processing module 104, a communication module 106, and a control module 108. The image capturing module 102 is configured to capture an image of a workpiece using an image sensor. In specific, the image sensor includes either a charge-coupled device or an electron-multiplying charge-coupled device or a complementary metal-oxide-semiconductor or any other image sensor. Further, ambient conditions for welding the workpiece are monitored by the image capturing module 102.

[0029] The processing module 104 is configured to process the image to detect welding conditions using a machine learning algorithm, and thereby transmit a control signal based on the detected welding conditions to the communication module 106. After processing, the processing module 104 detects the welding conditions such as type of material of the workpiece, position of the workpiece with respect to a base metal, type of weld joint and ambient welding conditions.

[0030] In specific, type of weld joint is determined either by detecting the position of the workpiece using the image sensor or manually entered by the operator. The type of weld joint includes butt joint, lap joint, seam joint, T-weld joint, and corner welds joint, thereof. The type of material of the workpiece includes aluminum, copper, and nickel, thereof.

[0031] For instance, the image capturing module 102 captures multiple images of the workpiece in every orientation and transmits the images to the processing module 104. The processing module 104 processes the captured images by using the machine learning algorithm such as convolutional neural networks to detect the welding conditions. The convolutional neural networks disclosed in the embodiment are not confined only to convolutional neural networks it includes any other machine learning algorithm with similar functions are also considered. The processing module 104 detects the position of the workpiece with respect to the base metal, and the ambient condition for welding is continuously monitored and thereof. The captured images, the position of the workpiece and the ambient condition, the type of weld joint, and the type of material are displayed to an operator through a display unit.

[0032] Further, the processing module 104 detects the type of weld joint, i.e., position of the workpiece with respect to the base metal. For example, if the processing module 104 detects the workpiece is placed at center of the based metal, and in perpendicular position with respect to the based metal, then a T-weld joint is determined as the type of weld joint by the processing module 104. Then, the system 100 operates accordingly to form a T-weld joint.

[0033] In case, the operator desires to do lap joint, then the operator is enabled to enter the desired welding joint in to the system 100 through an input unit. After the operator enters the desires type of weld joint into the system 100. The system 100 operates accordingly to form the lap joint. Further, the ambient condition, and the type of weld joint, and the type of material are displayed to the operator on the display unit.

[0034] The communication module 106 is configured to receive the control signal from the processing module 104 and transmit the control signal to the control module 108. The control module 108 is configured to receive the control signal to control plurality of driving wheels of a wire feeder, plurality of valves of a welding flux funnel, and a first motor of a welding gun to perform welding operation. In specific, the first motor is configured changed the angle of the welding gun. The wire feeder includes at least one welding wire disc that is configured to store at least one welding wire. In specific, the control module 108 includes a controller to control a second motor that drives the driving wheels of the wire feeder to release the welding wire in controlled manner for welding.

[0035] For instance, the wire feeder feeds the desired amount of welding wire in a controlled manner based on the control signal. Then, based on the control signal the valves attached to the welding flux funnel are controlled to allow the desired amount of welding flux required for welding the workpiece. In specific, the amount of the welding wire and the amount of welding flux required for welding the workpiece are controlled by the controller based on the control signal. Further, based on the position of workpiece, the first motor turns the welding gun to achieve an optimal angle for welding the workpiece.

[0036] According to another exemplary embodiment of the invention, FIG. 2 refers to an exemplary method 200 of controlling welding operation using a smart crane girder welding system. At step 202, an image of workpiece, is captured using the image sensor through the image capturing module. Further, ambient conditions for welding are monitored. At step 204, the captured image is processed through the processing module to detect welding conditions using a machine learning algorithm. Further, the type of weld joint is determined either by detecting the position of the workpiece using the image sensor or manually entered by the operator

[0037] At step 206, a control signal is transmitted based on the welding conditions through the communication module to the control module. At step 208, the control signal is received by the control module to control the driving wheels, the valves and the first motor, to release the welding wire through the wire feeder, to release flux from the welding flux funnel, and the angle of welding gun respectively.

[0038] Further, the wire is released and passed through the wire feeder in a controlled manner based on the control signal sent from the control module. Then, a required amount of the welding flux is discharged from the welding flux funnel based on the control signal, and based on the position of workpiece, the welding gun angle is also controlled to perform the welding.

[0039] Numerous advantages of the present disclosure may be apparent from the discussion above. In accordance with the present disclosure, a smart crane girder welding system adaptively releases welding wire and flux in controlled manner during welding to increase welding efficiency and bond strength of the weld joint is disclosed.

[0040] The proposed smart crane girder welding system takes multiple images of the workpiece material to detect the type of workpiece material, type of welding joint, and ambient welding conditions to control releasing of welding wire and flux. The proposed smart crane girder welding system either enables users to input that type of welding joints manually or automatically detect the welding operation based on the base metal position. The proposed welding system controls the release of welding wire and flux to increase the welding efficiency and bond strength.

[0041] The proposed welding system controls releasing rate of welding wire by controlling the welding feeder driving wheels. The proposed welding system controls the welding gun angle based on the position of workpieces. The proposed welding system detects the weld material, welding operation, and ambient welding conditions, and sends a feedback to the controller to control the motor of the driving wheels.

[0042] According to another exemplary embodiment of the invention, the proposed welding system can by controlled remotely from a long distance, without interference of human at the site of welding. The proposed smart crane girder welding system that controls the usage of welding wire and flux and eliminates wastages of welding wire. The proposed system can be integrated with augmented reality technology. The proposed smart crane girder welding system improves welding efficiency and bond strength based on various parameters such as size and type of weld, edge preparation, metal thickness, reinforcement members and distortion.

[0043] It will readily be apparent that numerous modifications and alterations can be made to the processes described in the foregoing examples without departing from the principles underlying the invention, and all such modifications and alterations are intended to be embraced by this application. , Claims:CLAIMS:
We Claim:
1. A smart crane girder welding system, comprising:
an image capturing module configured with at least one image sensor to capture at least one image of at least one workpiece to be welded;
a processing module configured to process said at least one image to detect welding conditions using a machine learning algorithm, and thereby transmit a control signal based on said detected welding conditions;
a communication module configured to receive and transmit said control signal; and
a control module configured to receive said control signal from said communication module and control plurality of driving wheels of at least one wire feeder, plurality of valves of at least one welding flux funnel, and a first motor of at least one welding gun,
whereby said smart crane girder welding system adaptively releases welding wire and flux in controlled manner during welding to increase welding efficiency and bond strength of said weld joint.
2. The smart crane girder welding system as claimed in claim 1, wherein said welding conditions include type of material of said at least one workpiece, position of said at least one workpiece, type of weld joint and ambient welding conditions.
3. The smart crane girder welding system as claimed in claim 2, wherein said type of weld joint is determined either by detecting the position of said at least one workpiece using said at least one image sensor or manually entered by an operator.
4. The smart crane girder welding system as claimed in claim 2, wherein said type of weld joint includes butt joint, lap joint, seam joint, T-weld joint, and corner welds joint, thereof.
5. The smart crane girder welding system as claimed in claim 1, wherein said at least one wire feeder includes at least one welding wire disc configured to store at least one welding wire.
6. The smart crane girder welding system as claimed in claim 1, wherein said control module includes a controller that controls a second motor of said plurality of driving wheels to release at least one welding wire in controlled manner.
7. The smart crane girder welding system as claimed in claim 1, wherein said at least one image sensor includes either a charge-coupled device or an electron-multiplying charge-coupled device or a complementary metal-oxide-semiconductor or any other image sensor.
8. A method of controlling welding operation using a smart crane girder welding system, comprising:
capturing at least one image of at least one workpiece to be welded using at least one image sensor through an image capturing module;
processing said captured at least one image from said image capturing module to detect welding conditions using a machine learning algorithm through a processing module;
transmitting a control signal based on said welding conditions through a communication module, and
receiving said control signal and controlling plurality of driving wheels of at least one wire feeder, plurality of valves of at least one welding flux funnel, and a first motor of at least one welding gun through a control module.