Painting quality inspection system and method using collaborative robot

Abstract

The present invention relates to a paint quality inspection system and method, comprising: a collaborative robot including at least one sensor, a digital light, and a control unit that controls the sensor to measure paint condition information for a specific area; and a paint quality inspection device that collects the measured paint condition information from the collaborative robot and inputs the collected paint condition information into a pre-prepared artificial intelligence model to inspect the paint quality.

Description

Technology Field

[0001] The present invention relates to a paint quality inspection system and method, and more particularly to a paint quality inspection system and method using a collaborative robot.

[0002] This research was conducted with support from the Ministry of Trade, Industry and Energy's Marine Mobility Smart Painting System Technology Development (RS-2024-00431769). Background Technology

[0004] Generally, during the shipbuilding phase, a painting process is performed on the ship's surface to prevent corrosion of the hull caused by contact with air or water. Since the lifespan of a ship depends on the quality of this painting process, it is a critical step; consequently, technology capable of inspecting the condition or quality of the painted surface is required.

[0005] Conventionally, in inspecting the condition or quality of such paintwork, methods relying on the inspector's senses, such as the sense of touch or the eye, or devices capable of measuring the thickness of the paint film have been used. However, inspection methods based on such conventional technology are performed manually by inspectors, which increases the possibility of error. Furthermore, because standards vary from inspector to inspector, it is difficult to control the paintwork condition or quality uniformly, and there is a problem that it is very inefficient because inspectors must inspect every paint surface of a large ship individually.

[0006] Therefore, research is needed on methods to manage ships by efficiently and accurately inspecting their paint condition. Prior art literature

[0008] Korean Patent Publication No. 10-2022-0160384 The problem to be solved

[0009] The present invention was devised to solve the above-mentioned problems, and the objective of the present invention is to provide a paint quality inspection system and method using a collaborative robot. means of solving the problem

[0011] A paint quality inspection system according to one embodiment of the present invention for achieving the above objective includes: a collaborative robot comprising at least one sensor, a digital light, and a control unit that controls the sensor to measure paint condition information for a specific area; and a paint quality inspection device that collects the measured paint condition information from the collaborative robot and inputs the collected paint condition information into a pre-prepared artificial intelligence model to inspect the paint quality.

[0012] A method for inspecting paint quality according to an embodiment of the present invention for achieving the above objective comprises: a step in which a collaborative robot comprising at least one sensor, a digital light, and a control unit controls the control unit so that the sensor measures paint condition information for a specific area; and a step in which a paint quality inspection device collects the measured paint condition information from the collaborative robot and inputs the collected paint condition information into a pre-prepared artificial intelligence model to inspect the paint quality. Effects of the invention

[0014] According to one aspect of the present invention described above, by providing a paint quality inspection device and method using a collaborative robot, high accuracy and fast creation speed can be achieved by utilizing multiple sensors through the implementation of various measurement angles and positions of the paint film on the ship wall to be measured.

[0015] Furthermore, paint quality inspection devices utilizing collaborative robots can enhance worker safety and increase operational efficiency, including reduced work time, by replacing human workers in enclosed painting environments where visibility is limited. In addition, collaborative robots effectively handle repetitive and high-risk tasks in place of workers while helping to maintain consistency in inspection operations.

[0016] Furthermore, AI-based inspection and supervision of painting work is possible through remote operation, unaffected by time and space; by positioning illuminance sensors and digital lighting around multi-sensors, painting defects can be recognized regardless of the internal or external working environment of the vessel, and optimal illuminance can be provided for illuminance measurements. Brief explanation of the drawing

[0018] FIG. 1 is a drawing showing a paint quality inspection system using a collaborative robot according to an embodiment of the present invention. FIG. 2 is a diagram showing an example of an implementation of the collaborative robot of FIG. 1. FIG. 3 is a diagram showing an example of the implementation of the multi-sensor and digital lighting of FIG. 2. And, FIG. 4 is a flowchart illustrating a paint quality inspection method of a paint quality inspection system using a collaborative robot according to an embodiment of the present invention. Specific details for implementing the invention

[0019] The following detailed description of the invention refers to the accompanying drawings, which illustrate specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that various embodiments of the invention are different but need not be mutually exclusive. For example, specific shapes, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in relation to one embodiment. It should also be understood that the location or arrangement of individual components within each disclosed embodiment may be changed without departing from the spirit and scope of the invention. Accordingly, the following detailed description is not intended to be limiting, and the scope of the invention is limited only by the appended claims, including all equivalents to those claimed therein, provided appropriately described. Similar reference numerals in the drawings refer to the same or similar functions across various aspects.

[0020] The components according to the present invention are defined by functional distinction rather than physical distinction, and can be defined by the functions each performs. Each component may be implemented as hardware or as program code and processing units that perform each function, and the functions of two or more components may be included and implemented in a single component. Therefore, it should be noted that the names assigned to the components in the following embodiments are not intended to physically distinguish each component but are assigned to imply the representative function performed by each component, and that the technical concept of the present invention is not limited by the names of the components.

[0021] Preferred embodiments of the present invention will be described in more detail below with reference to the drawings.

[0022] FIG. 1 is a drawing showing a paint quality inspection system using a collaborative robot according to an embodiment of the present invention, FIG. 2 is a drawing showing an example of implementation of the collaborative robot of FIG. 1, and FIG. 3 is a drawing showing an example of implementation of the multi-sensor and digital lighting of FIG. 2.

[0023] The painted quality inspection system includes a collaborative robot (110) and a painted quality inspection device (120), wherein the collaborative robot (110) is composed of a multi-sensor (111), i.e., at least one sensor, a digital light (113), and a control unit (115).

[0024] The control unit (115) of the collaborative robot (110) controls the multi-sensor (111) to measure paint condition information for a specific area. For example, assuming a collaborative robot (110) implemented with multiple joints as shown in FIG. 2, the collaborative robot (110) can measure paint condition information through a multi-sensor located at the end and digital lights (111, 113), and this collaborative robot can additionally be equipped with a mobile robot to move freely and change the specific area to measure paint condition information. At this time, the control unit (115) calculates the angle and direction of the multi-sensor (111) for measuring paint condition information for the specific area, and can change the specific area by adjusting the input angle of the joint axis of the collaborative robot (110) according to the calculated angle and direction.

[0025] Additionally, the multi-sensor (111) may include, for example, a thermal imaging camera sensor, a vision camera sensor, and an optical-based high-frequency antenna sensor. In this case, the multi-sensor and digital lighting (111, 113) located at the end of the collaborative robot (110) of FIG. 2 may be implemented as shown in FIG. 3. That is, the thermal imaging camera sensor, the vision camera sensor, and the optical-based high-frequency antenna sensor may be placed in the center, and the digital lighting may be placed around these multi-sensors (111). Additionally, each of the sensors may share real-time information with one another through communication, and the digital lighting may measure the brightness in the measurement environment through an illuminance sensor, etc., and switch to an on state when the measured brightness is below a certain threshold to illuminate the surface of a specific area to be measured.

[0026] The paint quality inspection device (120) collects paint condition information measured from the collaborative robot (110) and inputs the collected paint condition information into a pre-prepared artificial intelligence model to inspect the paint quality. Here, the paint condition information varies depending on the sensor that measures the information, and below, through FIG. 4, the operation of inspecting the paint quality using paint condition information measured by a thermal imaging camera sensor, a vision camera sensor, and an optical-based high-frequency antenna sensor will be explained in more detail.

[0027] FIG. 4 is a flowchart illustrating a method for inspecting paint quality using a collaborative robot according to an embodiment of the present invention.

[0028] The paint quality inspection system first determines paint defects in a preset large area based on a thermal imaging camera sensor (S401). That is, the collaborative robot controls the control unit to measure paint condition information for the large area through the thermal imaging camera sensor, and the paint quality inspection device determines paint defects in the large area using the measured paint condition information. The paint condition information measured through the thermal imaging camera sensor is information that visualizes changes in the temperature distribution of the paint surface, and the paint quality inspection device can determine paint defects such as bubbles, peeling, cracks, erosion, and thickness non-uniformity by detecting differences in how the defective areas of the paint surface emit or absorb heat differently from these changes in the temperature distribution of the paint surface.

[0029] In addition, the above large area refers to an area that is pre-set considering the maximum area that the thermal imaging camera sensor can scan, and the above large area can be adjusted as much as needed depending on the scanning capability of the thermal imaging camera sensor.

[0030] Based on the judgment of S401, the paint quality inspection system inspects whether paint defects exist in a large area. (S403)

[0031] If, as a result of the inspection of S403, paint defects exist in a large area, the paint quality inspection system precisely determines paint defects in a preset detailed area within the large area based on a vision camera sensor (S405), and repeats the operation of S405 to precisely determine paint defects in the entire area (S407). That is, the collaborative robot controls the control unit to measure paint condition information for the detailed area through the vision camera sensor, and the paint quality inspection device precisely determines paint defects in the entire area using the measured paint condition information. The paint condition information measured through the vision camera sensor is image information captured by scanning the paint surface, and the paint quality inspection device can determine paint defects such as scratches, bubbles, peeling, stains, color non-uniformity, and foreign substances by analyzing this image information in real time.

[0032] Meanwhile, if the inspection results of S403 show that no paint defects exist in the large-area region, the paint quality inspection system proceeds to S407 to precisely determine paint defects for the entire area.

[0033] Based on the precise judgment of S405 and S407, the paint quality inspection system inspects whether paint defects exist in detailed areas. (S409)

[0034] If no paint defects exist in the detailed area as a result of the inspection of S409, the paint quality inspection system measures the paint film thickness through an optical-based high-frequency antenna sensor (S411) and inspects whether paint defects exist according to the paint film quality conditions (S413). That is, the collaborative robot controls the control unit to measure the paint film thickness through the optical-based high-frequency antenna sensor and provides the measured paint film thickness to the paint quality inspection device, and the paint quality inspection device compares the measured paint film thickness with the paint film thickness required under preset paint film quality conditions to inspect whether paint defects exist.

[0035] To briefly explain the principle of measuring film thickness using the aforementioned optical-based high-frequency antenna sensor, the optical-based high-frequency antenna sensor emits electromagnetic waves in the high-frequency band onto the surface of the coating and can measure the thickness of the film by analyzing the signal reflected from within.

[0036] In addition, the film thickness required under the above film quality conditions can be set by considering the presence of a corrosive environment, climatic conditions, high risk of wear, high-intensity UV exposure, electrical requirements, etc. For example, assuming ship painting, the film thickness required for the ship's exterior surface exposed to a corrosive environment and the interior surface not exposed may be set differently, and the thickness required for the ship's interior surface may also be set differently depending on the intended use of the space.

[0037] If no coating defects exist according to the coating quality conditions as a result of the inspection of S413, the coating quality inspection device selects the coating thickness measured through S411 and stores it in a pre-prepared management table (S415), and stores and outputs the judgment result based on the inspection result of S413 (S417). The management table stores and manages not only the coating thickness measured through S411 but also the coating thickness required by the coating quality conditions, and the coating thickness required by the coating quality conditions stored in the management table can be fine-tuned at any time as needed by reflecting the temperature, humidity, etc. at that time.

[0038] Meanwhile, if a paint defect exists in a detailed area as a result of the inspection of S409, or if a paint defect exists according to the film quality conditions as a result of the inspection of S413, the paint quality inspection system proceeds to S417 to store and output the corresponding judgment result.

[0039] Figure 4 illustrates an example of a collaborative robot determining paint defects and measuring film thickness through a thermal imaging camera sensor, a vision camera sensor, and an optical-based high-frequency antenna sensor. The collaborative robot may further be equipped with an illuminance sensor, and can adjust the brightness of the area measured by the vision camera sensor according to the illuminance (amount of light) measured by the illuminance sensor. That is, if the measured illuminance is lower than the illuminance required for measurement by the vision camera sensor, the collaborative robot controls the digital lighting to adjust the brightness of the measurement area. In this case, if the illuminance of the measurement area is lower than the required illuminance even though the collaborative robot has controlled its own digital lighting to the maximum illuminance, it may request assistance from other collaborative robots in the vicinity. That is, it may search for a collaborative robot among adjacent collaborative robots that is equipped with digital lighting capable of covering the additionally required illuminance, and request assistance from at least one of the searched collaborative robots to receive the additionally required illuminance.

[0040] These collaborative robots are each deployed individually within a large area to determine paint defects and measure the thickness of the paint film within the corresponding large area, and the collaborative robots may also exchange and share their determination and measurement results with one another.

[0041] As another example, one may assume a case where multiple collaborative robots, such as the first to nth collaborative robots, are deployed within a large area. In this case, the first collaborative robot determines paint defects in the large area and transmits the determination result to the remaining second to nth collaborative robots. The second to nth collaborative robots measure paint condition information for detailed areas based on their positions and feed back the measurement result to the first collaborative robot. The first collaborative robot may then aggregate the feedback measurement results to determine paint defects for the entire area.

[0042] Additionally, the collaborative robot may be further equipped with a collision detection sensor, and through the collision detection sensor, it can acquire distance information with a person or object (an adjacent collaborative robot) within the detection radius. The collaborative robot may also set a safety radius that is wider than the working radius, which is the maximum range it can operate, and narrower than the detection radius, which is the range detectable by the collision detection sensor.

[0043] In this case, the collaborative robot stops its operation to prevent the risk of collision when a person or object is located within a set safety radius, or when it is determined that there is a high probability of approaching the safety radius by calculating the speed and direction of the person or object from the distance information. In addition, the movement trajectory of the collaborative robot can be controlled in a complex ship interior environment through such a collision detection sensor.

[0044] The paint quality inspection method using a collaborative robot according to the present invention can be implemented in the form of program instructions that can be executed through various computer components and recorded on a computer-readable recording medium. The computer-readable recording medium may include program instructions, data files, data structures, etc., either individually or in combination.

[0045] The program instructions recorded on the above-mentioned computer-readable recording medium may be those specifically designed and configured for the present invention, or they may be those known and available to those skilled in the art of computer software.

[0046] Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, and flash memory.

[0047] Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware device may be configured to operate as one or more software modules to perform processing according to the present invention, and vice versa.

[0048] Although various embodiments of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments described above. It is understood that various modifications can be made by those skilled in the art without departing from the essence of the invention as claimed in the claims, and such modifications should not be understood individually from the technical spirit or perspective of the present invention. Explanation of the symbols

[0050] 110: Collaborative Robot 111: Multi-sensor 113: Digital Lighting 115: Control unit 120: Paint quality inspection device

Claims

Claim 1 A collaborative robot comprising at least one sensor, a digital light, and a control unit that controls the sensor to measure paint status information for a specific area; A paint quality inspection device that collects the measured paint condition information from the collaborative robot and inputs the collected paint condition information into a pre-prepared artificial intelligence model to inspect the paint quality; wherein the collaborative robot controls the control unit to measure paint condition information for a pre-set large area using a thermal imaging camera sensor, and the paint quality inspection device determines paint defects for the large area using the measured paint condition information; if paint defects exist in the large area, the collaborative robot controls the control unit to measure paint condition information for a pre-set detailed area within the large area using a vision camera sensor; and the paint quality inspection device precisely determines paint defects for the entire area by repeating the operation of precisely determining paint defects for the detailed area using the measured paint condition information; and if paint defects do not exist in the detailed area, the collaborative robot controls the control unit to measure the paint film thickness using an optical-based high-frequency antenna sensor and provides the measured paint film thickness to the paint quality inspection device, and the paint quality inspection device determines paint defects by comparing the measured paint film thickness with pre-set paint film quality conditions, and A paint quality inspection system that stores and outputs the judgment result if a paint defect exists. Claim 2 A paint quality inspection system according to claim 1, wherein the control unit calculates the angle and direction of the sensor for measuring paint condition information for the specific area, and adjusts the input angle of the joint axis of the collaborative robot according to the calculated angle and direction. Claim 3 delete Claim 4 delete Claim 5 delete Claim 6 In claim 1, the paint quality inspection device selects and stores the measured film thickness in a management table when the paint defect does not exist, and the paint quality inspection system stores and outputs the judgment result. Claim 7 A collaborative robot comprising at least one sensor, a digital light, and a control unit controls the control unit so that the sensor measures painting status information for a specific area; The method includes the step of a paint quality inspection device collecting the measured paint condition information from the collaborative robot and inputting the collected paint condition information into a pre-prepared artificial intelligence model to inspect the paint quality; the step of controlling the control unit includes the step of controlling the control unit so that the collaborative robot measures paint condition information for a pre-set large area through a thermal imaging camera sensor; the step of inspecting the paint quality includes the step of the paint quality inspection device determining paint defects for the large area using the measured paint condition information; if paint defects exist in the large area, the method further includes the step of the collaborative robot measuring paint condition information for a pre-set detailed area within the large area through a vision camera sensor; and the step of the paint quality inspection device repeating the operation of precisely determining paint defects for the detailed area using the measured paint condition information to precisely determine paint defects for the entire area; if paint defects do not exist in the detailed area, the method further includes the step of the collaborative robot measuring the paint film thickness through an optical-based high-frequency antenna sensor and providing it to the paint quality inspection device; and the paint quality inspection device [determining] the measured paint film thickness to a pre-set paint quality A paint quality inspection method comprising the step of determining paint defects by comparing with conditions, and further including the step of storing and outputting the determination result if said paint defects exist. Claim 8 A method for inspecting paint quality according to claim 7, wherein the step of controlling the control unit is to calculate the angle and direction of the sensor for measuring paint condition information for the specific area, and to adjust the input angle of the joint axis of the collaborative robot according to the calculated angle and direction. Claim 9 delete Claim 10 delete Claim 11 delete Claim 12 A paint quality inspection method according to claim 7, further comprising the step of, when the paint quality inspection device determines that no paint defect exists, selecting and storing the measured film thickness in a management table, and storing and outputting the judgment result.

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