Painting diagnosis system, painting diagnosis method

Abstract

The present invention provides a coating diagnostic system and a coating diagnostic method that can uniformly manage data related to processes including pretreatment in a coating line. The coating diagnostic system comprises: a data acquisition unit (31) of a pretreatment control device (3) that acquires data related to the pretreatment process when coating a workpiece (W) is performed in the pretreatment process of the coating line (PL); and a data management unit (43) of a higher-level control device (9) that associates the data related to the pretreatment process with the serial number of the workpiece (W) and registers it in a database (51).

Description

Technical Field

[0001] Embodiments of this disclosure relate to a coating diagnostic system and a coating diagnostic method. Background Technology

[0002] Patent Document 1 discloses a remote management system for a pre-coating treatment line. This remote management system includes: a processing line terminal installed on the pre-coating treatment line; and a remote management server connected to the processing line terminal via a communication line. The processing line terminal sends operational status data of the pre-coating treatment line to the remote management server. The remote management server monitors the operational status of the pre-coating treatment line based on the operational status data received from the processing line terminal.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2002-307000 Summary of the Invention

[0006] The problem the invention aims to solve

[0007] The quality of the final coating in a painting line is greatly influenced not only by the quality of the coating itself, but also by the quality of the pretreatment. Therefore, a painting diagnostic system that can uniformly manage data from all processes in a painting line, including pretreatment, is needed.

[0008] The present invention was made in view of the following problems, and its object is to provide a coating diagnostic system and coating diagnostic method that can uniformly manage data involving processes of a coating line, including pretreatment.

[0009] Technical solution

[0010] To address the aforementioned problems, according to one aspect of the present invention, a coating diagnostic system is provided, comprising: a first data acquisition unit that acquires data related to the pre-treatment process of a workpiece during the pre-treatment process of coating line; and a data management unit that associates and registers the data related to the pre-treatment process with the identification information of the workpiece.

[0011] Furthermore, according to another aspect of the present invention, a coating diagnostic method is applied, the coating diagnostic method comprising: acquiring data related to the pre-treatment process when the workpiece is pre-treated for coating in the pre-treatment process of the coating line; and establishing and registering the data related to the pre-treatment process with the identification information of the workpiece.

[0012] Beneficial effects

[0013] According to the coating diagnostic system of the present invention, data involving processes in coating lines, including pretreatment, can be managed uniformly. Attached Figure Description

[0014] Figure 1 This is a diagram illustrating an example of the overall configuration of a coating diagnostic system according to an embodiment.

[0015] Figure 2 This is a block diagram illustrating an example of the functional configuration of various control devices.

[0016] Figure 3 This is a diagram representing an example of the data content in a database.

[0017] Figure 4 This is a flowchart illustrating an example of a processing procedure executed by a higher-level control device.

[0018] Figure 5 This is a diagram illustrating an example of the overall configuration of a coating diagnostic system in a modified example where a pretreatment process is performed outside an explosion-proof area.

[0019] Figure 6 This is a diagram illustrating an example of the database data content in a variation of a pre-treatment process performed outside an explosion-proof area.

[0020] Figure 7 This is a block diagram illustrating an example of the hardware configuration of a higher-level control device. Detailed Implementation

[0021] The embodiments will now be described with reference to the accompanying drawings.

[0022] <1. Overall Structure of a Coating Diagnostic System>

[0023] Reference Figure 1 An example of the overall configuration of the coating diagnostic system 1 of the embodiment will be described.

[0024] The coating diagnostic system 1 is a system for diagnosing the quality of coating applied to workpiece W in the coating line PL. In this embodiment, the workpiece W is, for example, a car body. It should be noted that, regarding the type of workpiece W, there is no particular limitation as long as it is a workpiece that undergoes pretreatment during coating, and it can be applied to workpieces other than car bodies.

[0025] like Figure 1As shown, the coating line PL includes a pretreatment process, a coating process, and a visual inspection process. In the pretreatment process, workpieces W conveyed by conveyor 17 undergo pretreatment. In the coating process, pretreated workpieces W are coated. In the visual inspection process, coated workpieces W undergo visual inspection. In each process, operations can be performed either while workpieces W are continuously being conveyed via conveyor 17, or while the conveying of workpieces W is temporarily stopped. The pretreatment and coating processes are performed within the coating chamber PB, which is configured as an explosion-proof area.

[0026] The coating diagnostic system 1 includes a pretreatment control device 3, a coating control device 5, a visual inspection control device 7, and a higher-level control device 9.

[0027] The pretreatment control device 3 controls various equipment related to the pretreatment process. Equipment related to the pretreatment process includes, for example, a dust collector 11, a de-energizer 13, a robot 15, a conveyor 17, and an air conditioning unit 19. The dust collector 11 drives the brush 11a to rotate and remove dust adhering to the surface of the workpiece W. The de-energizer 13 generates ions and releases them to the workpiece W to remove static electricity. The robot 15 (an example of an automaton) is, for example, a vertical multi-jointed robot (e.g., a six-axis robot with six joints), which moves the dust collector 11 and de-energizer 13, mounted at its top, around the workpiece W to perform dust removal and static electricity removal operations (an example of a pre-defined operation related to the pretreatment process). The robot 15 is, for example, suspended from the ceiling or wall of the painting room PB. The conveyor 17 (an example of a conveying device) moves along the conveying direction ( Figure 1 (The direction of the middle arrow) transports the workpiece W. Air conditioning unit 19 adjusts the temperature, humidity, airflow, etc., within the painting room PB (an example of an area where pretreatment is performed).

[0028] It should be noted that multiple sets of robot 15, dust collector 11, and de-energizer 13 can be arranged around the workpiece W, for example, two sets can be arranged on the left and right sides of the workpiece W. Alternatively, dust collector 11 and de-energizer 13 can be mounted on different robots 15. Furthermore, robot 15 can be any type of robot other than a vertical multi-joint type. For example, a horizontal multi-joint type robot or a parallel linkage robot can be used. Besides general-purpose robots, specialized work machines designed specifically for pre-processing operations can also be used, such as those equipped with a driver that enables linear movement along the XYZ directions on orthogonal coordinate axes and rotation along the θ direction. Additionally, robot 15 can be placed on the ground. Furthermore, in the pre-processing stage, in addition to performing dust removal and de-energizing operations, other operations can be performed, or additional operations can be performed to replace dust removal and de-energizing operations.

[0029] During the pre-processing stage, when the workpiece W is pre-treated for coating, the pre-processing control device 3 acquires data related to the pre-processing stage. This data includes, for example, the coordinates of the top position of the robot 15, the torque of each joint, the top speed, the conveying speed or position of the workpiece W, the torque or rotational speed (rotational speed) of the dust collector 11, the output of the dust collector 13, and environmental data within the coating chamber PB. The conveying speed or position of the workpiece W is detected, for example, by an encoder (not shown) installed on the motor (not shown) of the conveyor 17. Environmental data within the coating chamber PB includes, for example, the temperature, humidity, or airflow (downstream) within the coating chamber PB, and is acquired through command values ​​for the air conditioning unit 19.

[0030] The painting control device 5 controls various equipment related to painting. Equipment related to painting includes, for example, the painting gun 21 and the air source device 22 (see below). Figure 2 Examples of components include a compressor, solenoid valve, air conditioner, robot 23, conveyor 17, and air conditioning unit 19. The coating gun 21 mixes paint supplied from the paint tank with air supplied from the air source device 22, blowing the paint onto the workpiece W. The air source device 22 includes, for example, a compressor, solenoid valve, and air conditioner. The solenoid valve switches the air supply from the compressor to the coating gun 21 on or off, and the air conditioner controls the air pressure. The robot 23 is, for example, a vertical multi-jointed robot (e.g., a six-axis robot with six joints), commonly used in industrial robots, moving the coating gun 21, mounted at its top, around the workpiece W. The robot 23 is, for example, suspended from the ceiling or wall of the coating chamber PB.

[0031] It should be noted that multiple sets of robot 23 and painting gun 21 can be arranged around the workpiece W, for example, two sets can be arranged on the left and right sides of the workpiece W. Furthermore, robot 23 can be any type of robot other than a vertical multi-joint type. For example, it can be a horizontal multi-joint type robot, a parallel linkage robot, or a special-purpose machine designed specifically for painting operations, other than a general-purpose robot. Additionally, robot 23 can be installed on the ground. Furthermore, the painting control device 5 can also control the temperature of the drying oven (not shown) used for post-painting drying.

[0032] When the coating control device 5 coats the workpiece W in the coating process, it acquires data related to the coating process. This data includes, for example, the coordinates of the top position of the robot 23, the torque of each joint, the top speed, the conveying speed or position of the workpiece W, the air pressure, and the drying temperature. The air pressure is acquired, for example, through a command value for the air source device 22, and the drying temperature is acquired, for example, through a command value for the drying oven.

[0033] The appearance inspection control device 7 controls various equipment related to appearance inspection. Equipment related to appearance inspection includes, for example, the inspection device 25, the robot 27, and the conveyor 17. The inspection device 25 consists, for example, of a camera or other imaging device, a film thickness gauge, etc. The quality of the coating can be diagnosed using image recognition processing, such as machine learning or deep learning, based on the images captured by the camera. The quality of the coating is diagnosed by checking whether the thickness measured by the film thickness gauge is within an appropriate range. The robot 27 is, for example, a vertical multi-jointed robot (e.g., a six-axis robot with six joints), which moves the inspection device 25 mounted on its top end around the workpiece W. The robot 27 is, for example, located on the ground.

[0034] It should be noted that multiple sets of robot 27 and inspection device 25 can be arranged around workpiece W, for example, two sets can be arranged on the left and right sides of workpiece W. Furthermore, robot 27 can be any type of robot other than a vertical articulated robot. For example, it can be a horizontal articulated robot, a parallel linkage robot, or a special-purpose machine designed specifically for visual inspection, other than a general-purpose robot. Additionally, robot 27 can be suspended from the ceiling or wall of the painting booth PB. Furthermore, it can perform visual inspections other than image diagnostics and film thickness checks. Moreover, visual inspection does not necessarily need to be automated; it can be performed by an operator or by a combination of machine and operator.

[0035] During the visual inspection process, the control device 7 for visual inspection checks the coating condition of workpiece W and acquires data related to the inspection results. This data includes, for example, the coordinates of the top position of robot 27, the presence or absence of coating defects, and the type of coating defect. Types of coating defects include, for example, sagging, poor hiding power, pitting, uneven color, and orange peel. Sagging refers to paint flowing downwards, causing localized thickening of the coating. Poor hiding power refers to localized thinning of the coating. Pitting refers to foreign matter mixed in the coating, resulting in raised areas that impair the smoothness of the coating. Uneven color refers to localized unevenness in the color of the coating. Orange peel refers to the surface of the coating exhibiting a wave-like appearance. It should be noted that the above is just one example; various other types of coating defects can also be detected.

[0036] The upper control unit 9 provides overall control of the equipment for each process of the coating line PL. Furthermore, the upper control unit 9 acquires data related to the pretreatment process from the pretreatment control unit 3, data related to the coating process from the coating control unit 5, and data related to the inspection results from the appearance inspection control unit 7. It then associates this data with the corresponding workpiece W's identification information and registers it in the database 51 (see below). Figure 2The identification information is not particularly limited as long as it can identify the workpiece W. For example, it is not a serial number, manufacturing number, batch number, etc., that is uniquely assigned to each workpiece W.

[0037] The above-described configuration of the coating diagnostic system 1 is an example and is not limited to the above. For example, a post-processing step may be provided between the coating step and the visual inspection step. In this case, a post-processing control device may be provided to acquire data related to the post-processing step when the workpiece W is post-processed. The post-processing step may be, for example, a drying step in which the workpiece W is dried in a drying oven after coating. Data related to the post-processing step may include, for example, the temperature of the drying oven.

[0038] <2. Functional Composition of Each Control Device>

[0039] Reference Figure 2 and Figure 3 An example of the functional configuration of the pretreatment control device 3, the coating control device 5, the appearance inspection control device 7, and the upper control device 9 will be explained.

[0040] like Figure 2 As shown, the pretreatment control device 3 includes a control unit 29 and a data acquisition unit 31. The control unit 29 controls the dust collector 11, the dust removal motor 13, the robot 15, etc. The data acquisition unit 31 (an example of a first data acquisition unit) acquires data related to the pretreatment process when the workpiece W is pretreated for coating. The data acquisition unit 31 acquires data related to the pretreatment process for each part of the workpiece W. Specifically, as... Figure 3 As shown, the data acquisition unit 31 acquires at least one of the following data related to the pretreatment process: the coordinates of the top position of the robot 15, the torque of each joint, the top speed, the conveying speed (or conveying position) of the conveyor 17 to the workpiece W, the torque and rotational speed of the dust collector 11, the output of the dust collector 13, and the temperature and humidity (and possibly airflow) inside the painting chamber PB. This data can be acquired in an explosion-proof environment by detecting the command values ​​generated by the control unit 29 for each device and by sensors with explosion-proof specifications installed inside the robot 15. It should be noted that data other than those mentioned above can also be acquired. For example, the data acquisition unit 31 acquires the above data along with the coordinates of the top position of the robot 15 according to each step of the job performed by the robot 15. Based on the coordinates of the top position of the robot 15, the location of the workpiece W to which the acquired data is obtained can be determined.

[0041] The painting control device 5 includes a control unit 33 and a data acquisition unit 35. The control unit 33 controls the painting gun 21, the air source device 22, the robot 23, etc. The data acquisition unit 35 (an example of a second data acquisition unit) acquires data related to the painting process when painting the workpiece W. The data acquisition unit 35 acquires data related to the painting process for each part of the workpiece W. Specifically, such as... Figure 3 As shown, the data acquisition unit 31 acquires at least one of the following data related to the coating process: the coordinates of the top position of the robot 23, the torque of each joint, the top speed, the conveying speed (or conveying position) of the conveyor 17 to the workpiece W, and the air pressure of the air source device 22. This data can be acquired in an explosion-proof environment by detecting command values ​​generated by the control unit 33 for each device and by sensors installed in the robot 23 with explosion-proof specifications. It should be noted that other data (such as drying temperature) can also be acquired. The data acquisition unit 35 acquires the above data along with the coordinates of the top position of the robot 23, for example, according to each step of the operation performed by the robot 23. Based on the coordinates of the top position of the robot 23, the location of the workpiece W to which the acquired data is obtained can be determined.

[0042] The visual inspection control device 7 includes a control unit 37 and a data acquisition unit 39. The control unit 37 controls the inspection device 25, robot 27, etc. The data acquisition unit 39 (an example of a third data acquisition unit) acquires data related to the inspection results when inspecting the coating condition of workpiece W during the visual inspection process. The data acquisition unit 39 acquires data related to the inspection results for each part of workpiece W. Specifically, such as... Figure 3 As shown, the data acquisition unit 39 acquires at least one of the coordinates of the top position of the robot 27 and the inspection result as data related to the inspection result. It should be noted that other data besides the above can also be acquired. For example, the data acquisition unit 39 acquires the inspection result along with the coordinates of the top position of the robot 27 according to each step of the operation performed by the robot 27. Based on the coordinates of the top position of the robot 27, the location of the workpiece W being inspected can be determined.

[0043] It should be noted that the conveyor 17 and the air conditioning unit 19 are controlled by one or all of the control unit 29 of the pretreatment control device 3, the control unit 33 of the painting control device 5, and the control unit 37 of the appearance inspection control device 7. Alternatively, the conveyor 17 and the air conditioning unit 19 may also be controlled by a dedicated control device different from the aforementioned control devices 3, 5, and 7.

[0044] The upper control device 9 has a data acquisition unit 41, a data management unit 43, a defect cause analysis unit 45, a process improvement suggestion unit 47, and a prevention and maintenance suggestion unit 49.

[0045] The data acquisition unit 41 acquires data related to the pretreatment process from the pretreatment control device 3, data related to the coating process from the coating control device 5, and data related to the inspection results from the appearance inspection control device 7. Furthermore, the data acquisition unit 41 (an example of a fourth data acquisition unit) acquires data related to precautions in processes preceding the pretreatment process. The types of preceding processes are not particularly limited as long as they are processes preceding the pretreatment process of the coating line PL; for example, welding processes, cleaning processes, etc. Precautions include, for example, whether there were defects in the preceding processes (defective welding in specific areas, poor cleaning, etc.), whether there were irregular operations caused by the operator (the operator cleaned or swept specific areas of the workpiece surface, etc.), and whether there were any unexpected events (personal intrusion into the coating chamber, equipment malfunction, etc.). Precautions may include, for example, Figure 3 As shown, the data is recorded as simple data, for example, using bits of 0 (no precaution) or 1 (precaution) to indicate whether there is a precaution. In this case, it can be set up so that the details of the precaution are recorded separately and can be checked as needed.

[0046] The data management unit 43 associates the data obtained by the data acquisition unit 41 with the identification information (serial number, etc.) of the corresponding workpiece W and registers it in the database 51. The data obtained by the data acquisition unit 41 includes data related to the pretreatment process, data related to the coating process, data related to the inspection results, and data related to precautions.

[0047] Figure 3 The image shows an example of the data content in database 51. Figure 3 In the example shown, for the serial number (1A, 2A, ...) of workpiece W, the data of each device and the coordinates (X, Y, Z coordinates) of the robot's top position are linked and registered according to each process of the pre-processing, painting, and visual inspection processes. It should be noted that... Figure 3 In this context, the torque (S) of each robot represents the torque of the joint axis at the base (S-axis), while the torque (T) represents the torque of the joint axis at the top (T-axis). Figure 3 Although illustrations are omitted, torques are registered for joint axes located between the S-axis and T-axis (such as the L-axis, U-axis, R-axis, B-axis, etc.). Furthermore, Figure 3 The example shown illustrates data for a scenario where one robot is deployed in each process step. However, in the case of multiple robots deployed in each process step, data for each robot is registered separately for the same serial number. Furthermore, in... Figure 3In the example shown, any precautions are registered on an individual basis for workpiece W. However, it is also possible to register each part of workpiece W (based on the coordinates of the top position of the robot). Furthermore, if the air conditioning unit 19 is separately installed in the pretreatment and painting processes, environmental data can also be acquired and registered during the painting process.

[0048] When a painting defect is found in workpiece W during the visual inspection process, the defect cause analysis unit 45 analyzes the cause of the defect based on at least one of the following: data related to the pre-processing process, data related to the painting process, and data related to precautions, all associated with the serial number of workpiece W. The defect cause analysis unit 45 can assign a priority order to the analyzed data as follows: first, it analyzes the defect causes in the painting process based on the data related to the painting process; if the painting process is determined to be normal, it then analyzes the defect causes in the pre-processing process based on the data related to the pre-processing process; if the pre-processing process is determined to be normal, it finally analyzes the defect causes based on the data related to precautions. Furthermore, the priority order can be changed according to the type of defect cause found. By analyzing the data in order of decreasing probability of the defect cause, the efficiency of the analysis can be improved, and the processing load can be reduced. The analysis results obtained by the defect cause analysis unit 45 can be displayed on a display device, recorded on a suitable recording medium, or sent to other devices. Furthermore, alarms and warnings can be output based on the analysis results.

[0049] Specifically, for example, if a specific part of the workpiece W is found to have poor coating (e.g., sagging) during the visual inspection process, and if there is a tendency for the top speed of the robot 23 to decrease at the corresponding part or for the conveyor speed of the conveyor 17 to decrease during the coating process at the corresponding part, the defect analysis unit 45 may speculate that the defect is caused by the decrease in the relative speed of the coating gun 21 relative to the workpiece W, resulting in local thickening of the coating film and downward flow of the paint.

[0050] Furthermore, for example, if a specific area of ​​workpiece W is found to have poor coating (e.g., poor hiding power) during the visual inspection process, and if there is a tendency for the tip speed of robot 23 to increase at the corresponding area or for the conveyor speed of conveyor 17 to increase during the coating process at the corresponding area, the defect cause analysis unit 45 may speculate that the coating film is thinned due to the increased relative speed of the coating gun 21 relative to workpiece W. Alternatively, in this case, if the defect cause analysis unit 45 determines that there is no problem with the coating process, it may analyze data related to the pretreatment process and, if the output of motor 13 is low at the corresponding area, speculate that the coating paint did not adhere sufficiently due to insufficient de-energization of the corresponding area of ​​workpiece W.

[0051] Furthermore, for example, if a specific area of ​​workpiece W is found to have poor coating (e.g., pitting) during the visual inspection process, and the torque of the dust collector 11 tends to be low at the corresponding area during the pretreatment process, the defect analysis unit 45 might speculate that the dust collector 11's brush 11a did not make sufficient contact with the workpiece W, thus failing to adequately remove dust. It should be noted that, in cases where the coating defect is pitting, the cause is highly likely to be in the pretreatment process. Therefore, the priority can be changed to analyze data related to the pretreatment process first, rather than data related to the coating process.

[0052] Furthermore, for example, if a specific area of ​​workpiece W is found to have poor coating (e.g., uneven color) during the visual inspection process, and if the top speed of robot 23 tends to vary at the corresponding area during the coating process, or if the conveyor speed of conveyor 17 tends to vary during coating at the corresponding area, the defect cause analysis unit 45 may speculate that the uneven coating thickness is due to variations in the relative speed of coating gun 21 relative to workpiece W. Alternatively, if the defect cause analysis unit 45 determines that the coating process is without problems and analyzes data related to the pretreatment process, and if the top speed of robot 15 tends to vary at the corresponding area during the pretreatment process, or if the conveyor speed of conveyor 17 tends to vary during de-energization at the corresponding area, the defect cause analysis unit 45 may speculate that the failure to uniformly de-energize due to variations in the relative speed of de-energizer 13 relative to workpiece W is the cause of the defect.

[0053] Furthermore, for example, if a defective coating (e.g., orange peel) is found in the overall or partial coating of workpiece W during the visual inspection process, and there is a tendency for excessive downward airflow in the coating chamber PB, the defect analysis unit 45 may speculate that the defect is caused by excessively rapid solvent evaporation during the coating process. Alternatively, if there is a tendency for low humidity in the coating chamber PB, which is a state prone to static electricity generation, the defect analysis unit 45 may speculate that the defect is caused by the failure to uniformly remove static electricity from workpiece W during the pretreatment process.

[0054] Furthermore, for example, if a specific part of workpiece W is found to have poor coating (e.g., dent) during the visual inspection process, and it is determined that there is no problem in either the coating process or the pretreatment process, the defect cause analysis unit 45 analyzes the data related to precautions and speculates that, for example, the cause of the defect is oil adhesion caused by poor cleaning of the corresponding part during the cleaning process or by irregular operation caused by the operator in the corresponding part.

[0055] Based on the analysis results of the defect cause analysis unit 45, the process improvement suggestion unit 47 suggests improvements to the operating conditions of equipment related to the pretreatment or painting processes. Furthermore, the process improvement suggestion unit 47 can also suggest improvements related to processes preceding the pretreatment processes. The suggestions made by the process improvement suggestion unit 47 can be displayed on a display device, recorded on a suitable recording medium, or sent to other devices. In addition, alarms and warnings can be output based on the suggestions.

[0056] Specifically, if the defect cause analysis unit 45 deduces that, for example, a decrease in the top speed of the robot 23 at the corresponding part during the painting process or a decrease in the conveying speed of the conveyor 17 during the painting of the corresponding part is the cause of the painting defect (e.g., sagging), the process improvement suggestion unit 47 suggests process improvements based on condition settings, such as increasing the top speed of the robot 23 at the corresponding part or increasing the conveying speed of the conveyor 17.

[0057] Furthermore, for example, if the defect cause analysis unit 45 deduces that the increase in the top speed of the robot 23 at the corresponding part during the painting process or the increase in the conveyor speed of the conveyor 17 during the painting of the corresponding part is the cause of the painting defect (e.g., poor hiding power), the process improvement suggestion unit 47 may suggest process improvements based on condition settings, such as reducing the top speed of the robot 23 at the corresponding part or reducing the conveyor speed of the conveyor 17. Alternatively, if the defect cause analysis unit 45 deduces that there is no problem in the painting process but the low output of the motor 13 at the corresponding part during the pretreatment process is the cause of the defect, the process improvement suggestion unit 47 may suggest process improvements based on condition settings, such as increasing the output of the motor 13 at the corresponding part.

[0058] Furthermore, for example, if the defect cause analysis unit 45 deduces that the low torque of the dust collector 11 in the corresponding part during the pretreatment process is the cause of coating defects (e.g., pitting), the process improvement suggestion unit 47 may suggest a change based on condition settings, such as changing the position of the dust collector 11 in the corresponding part (coordinates of the top position of the robot 15) to the position where the brush 11a is in full contact with the workpiece W.

[0059] Furthermore, for example, if the defect cause analysis unit 45 deduces that the coating defect (e.g., uneven color) is caused by variations in the tip speed of the robot 23 at the corresponding location during the coating process or variations in the conveyor speed of the conveyor 17 during coating at the corresponding location, the process improvement suggestion unit 47 may suggest process improvements based on condition settings, such as stabilizing the tip speed of the robot 23 at the corresponding location or stabilizing the conveyor speed of the conveyor 17. Alternatively, if the defect cause analysis unit 45 deduces that the coating process is not problematic but the coating defect is caused by variations in the tip speed of the robot 15 at the corresponding location during the pretreatment process or variations in the conveyor speed of the conveyor 17 during power removal at the corresponding location, the process improvement suggestion unit 47 may suggest process improvements based on condition settings, such as stabilizing the tip speed of the robot 15 at the corresponding location or stabilizing the conveyor speed of the conveyor 17.

[0060] Furthermore, for example, if the defect cause analysis unit 45 deduces that excessive airflow in the painting chamber PB is the cause of poor coating (e.g., orange peel), the process improvement suggestion unit 47 may suggest process improvements based on condition settings, such as reducing the airflow in the painting chamber PB. Alternatively, if the low humidity in the painting chamber PB is deduced as the cause of poor coating (e.g., orange peel), the process improvement suggestion unit 47 may suggest process improvements based on condition settings, such as increasing the humidity in the painting chamber PB.

[0061] Based on data related to the pretreatment process or the coating process, the Preventive Maintenance Recommendation Unit 49 recommends preventive maintenance for equipment related to the pretreatment process or the coating process. The recommendations made by the Preventive Maintenance Recommendation Unit 49 can be displayed on a display device, recorded on an appropriate recording medium, or sent to other devices. Furthermore, alarms and warnings can be output based on the recommendations.

[0062] Specifically, it is recommended to replace the dust collector 11 if its torque exceeds a specified value and shows an upward trend during the pretreatment process. This allows for replacement before the dust collector 11 malfunctions or breaks, preventing unexpected shutdowns of the coating line PL and associated defects in the workpiece W. Furthermore, compared to periodically replacing the dust collector 11, this maximizes its effective utilization.

[0063] It should be noted that the aforementioned processing units are not limited to examples of these processing divisions. For example, processing can be achieved using fewer processing units (e.g., a single processing unit) or through further subdivided processing units. Furthermore, regarding the functions of the host control device 9 based on the aforementioned processing units, it can be achieved using the CPU (Central Processing Unit) 901 (see below) described later. Figure 7 The program executed by the ASIC can be used to implement the ASIC, and some or all of its functions can also be implemented by actual devices such as ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), and other circuits.

[0064] <3. Controller Processing Procedure>

[0065] Reference Figure 4 An example of a processing procedure performed by the host control device 9 will be described.

[0066] In step S10, the upper control device 9 acquires data related to precautions for processes preceding the preprocessing process through the data acquisition unit 41, and associates the data related to precautions with the serial number of the workpiece W through the data management unit 43 and registers it in the database 51.

[0067] In step S20, the upper control device 9 performs a pre-processing process on the workpiece W via the pre-processing control device 3, acquires data related to the pre-processing process from the pre-processing control device 3 via the data acquisition unit 41, and associates the data related to the pre-processing process with the serial number of the workpiece W via the data management unit 43 and registers it in the database 51.

[0068] In step S30, the upper control device 9 performs a coating process on the workpiece W via the coating control device 5, acquires data related to the coating process from the coating control device 5 through the data acquisition unit 41, and establishes an association between the data related to the coating process and the serial number of the workpiece W through the data management unit 43 and registers it in the database 51.

[0069] In step S40, the upper control device 9 performs an appearance inspection process on the workpiece W via the appearance inspection control device 7, acquires data related to the inspection results from the appearance inspection control device 7 via the data acquisition unit 41, and associates the data related to the inspection results with the serial number of the workpiece W via the data management unit 43 and registers it in the database 51.

[0070] In step S50, the upper control device 9 determines whether there is a coating defect based on the data related to the inspection results obtained in step S40 above. If there is no coating defect (step S50: No), proceed to step S80 described later. On the other hand, if there is a coating defect (step S50: Yes), proceed to the next step S60.

[0071] In step S60, the upper control device 9, through the defect cause analysis unit 45, analyzes the cause of the defect based on at least one of the following: data related to the pre-processing process, data related to the coating process, and data related to precautions, all of which are associated with the serial number of the workpiece W with the coating defect. The analysis results can be displayed, recorded, sent, etc. Furthermore, alarms and warnings can be output based on the analysis results.

[0072] In step S70, the upper control device 9, through the process improvement suggestion unit 47, based on the analysis results from step S60, suggests improvements to the operating conditions of equipment related to the pretreatment or coating processes, and improvements related to processes preceding the pretreatment processes. The suggestions can be displayed, recorded, and sent. Furthermore, alarms and warnings can be output based on the suggestions.

[0073] In step S80, the upper control device 9, through the preventive maintenance suggestion unit 49, suggests preventive maintenance for equipment related to the pretreatment process or the coating process based on data related to the pretreatment process or the coating process. The suggestions can be displayed, recorded, and sent. Furthermore, alarms and warnings can be output based on the suggestions. This concludes the flowchart.

[0074] The above-described process is an example; at least some of the processes described can be deleted or modified, or additional processes can be added. The order of at least some of the processes can be changed, or multiple processes can be combined into a single process.

[0075] <4. Effects of the Implementation Method>

[0076] As explained above, in the coating diagnostic system 1 of this embodiment, when the workpiece W is pre-treated for coating in the pre-treatment process of the coating line PL, the data acquisition unit 31 of the pre-treatment control device 3 acquires data related to the pre-treatment process, and the data management unit 43 of the upper control device 9 establishes an association between the data related to the pre-treatment process and the serial number of the workpiece W and registers it. Therefore, data related to the processes of the coating line PL, including the pre-treatment process, can be associated with the workpiece W for unified management. As a result, if a coating defect is found in the workpiece W, the cause of the defect in the pre-treatment process can be analyzed based on the data related to the pre-treatment process, or suggestions for improvement of the pre-treatment process can be made, which can help to efficiently improve the processes of the coating line PL.

[0077] Furthermore, in this embodiment, the coating diagnostic system 1 may also include a data acquisition unit 35. When the workpiece W is coated in the coating process after the pretreatment process, the data acquisition unit 35 acquires data related to the coating process. The data management unit 43 can associate the data related to the coating process and the data related to the pretreatment process together with the serial number of the workpiece W and register them.

[0078] In this scenario, data from each process of the coating line PL, including pretreatment and coating processes, can be linked to the workpiece W for unified management. As a result, if a coating defect is found on workpiece W, the causes of the defect can be analyzed based on data related to the coating process, or suggestions for improvement can be made, thus contributing to the efficient improvement of the coating line PL's processes.

[0079] Furthermore, in this embodiment, the coating diagnostic system 1 may also have a data acquisition unit 39. When the data acquisition unit 39 inspects the coating status of the workpiece W in the appearance inspection process after the coating process, it acquires data related to the inspection results. The data management unit 43 can associate and register the data related to the inspection results, the data related to the pretreatment process, and the data related to the coating process together with the serial number of the workpiece W.

[0080] In this scenario, data from each process of the coating line PL, including pretreatment and coating processes, as well as inspection results of the coating status, can be linked to the workpiece W for unified management. As a result, if a coating defect is found on workpiece W, the causes of the defect in the pretreatment or coating processes can be analyzed or improvements can be suggested based on the data related to the pretreatment and coating processes linked to the inspection results. This contributes to the efficient improvement of each process constituting the coating line PL.

[0081] Furthermore, in this embodiment, the coating diagnostic system 1 may also have a defect cause analysis unit 45, which analyzes the defect cause based on at least one of the data related to the pretreatment process and the data related to the coating process that are associated with the serial number of the workpiece W when a coating defect is found in the appearance inspection process.

[0082] In this case, the cause of the coating defect can be efficiently analyzed regardless of whether it is caused by the pretreatment process, the coating process, or both.

[0083] In addition, in this embodiment, the defect cause analysis unit 45 may analyze the defect causes in the coating process based on data related to the coating process, and if the coating process is determined to be normal, analyze the defect causes in the pretreatment process based on data related to the pretreatment process.

[0084] In this scenario, if the painting process is deemed normal, the pretreatment process can be diagnosed; if the cause of the defect is found within the painting process, the diagnosis of the pretreatment process can be omitted. In other words, a priority order can be assigned to the diagnoses of each process. This reduces the processing load compared to always diagnosing both the pretreatment and painting processes.

[0085] Furthermore, in this embodiment, the coating diagnostic system 1 may also include a process improvement suggestion unit 47, which suggests improvements to the operating conditions of equipment related to the pretreatment process or the coating process based on the analysis results of the defect cause analysis unit 45.

[0086] In this case, if a coating defect is found in workpiece W, effective improvements can be recommended for each process that constitutes the coating line PL.

[0087] Furthermore, in this embodiment, the coating diagnostic system 1 may also include a preventive maintenance recommendation unit 49, which recommends preventive maintenance for equipment related to the pretreatment process or the coating process based on data related to the pretreatment process or data related to the coating process.

[0088] In this case, effective preventative maintenance can be recommended for the equipment used in each process of the coating line PL. This allows for replacement of equipment before it malfunctions or breaks down, preventing unexpected shutdowns of the coating line PL and the associated defects in the workpieces W. Furthermore, compared to periodically replacing equipment, the equipment can be utilized more efficiently.

[0089] Alternatively, in this embodiment, the data acquisition unit 31 may acquire data related to the pre-processing steps for each part of the workpiece W.

[0090] In this case, for example, if a specific area of ​​workpiece W is found to have poor coating, the cause of the defect in the pretreatment process can be analyzed based on data related to the pretreatment process corresponding to that area, or suggestions can be made to improve the corresponding part of the pretreatment process. Therefore, the cause of coating defects can be analyzed efficiently, and each process constituting the coating line PL can be improved efficiently.

[0091] Furthermore, in this embodiment, during the pre-processing step, the multi-joint robot 15 performs a pre-processing-related operation on the workpiece W, and the data acquisition unit 31 can acquire at least one of the top position, joint torque, and top speed of the robot 15 as data related to the pre-processing step.

[0092] In this case, the causes of defects in the pre-processing process can be analyzed or process improvements can be suggested based on data related to the robot 15 operating in the pre-processing process. For example, if a specific area of ​​workpiece W is found to have poor coating (e.g., uneven color) during the visual inspection process, and there is a tendency for the tip speed of the robot 15 to vary at the corresponding area, it can be inferred that the defect is due to the inability to uniformly remove static electricity caused by the relative speed variation of the de-energizer 13 relative to the workpiece W. In this case, process improvements based on changes to the conditions, such as stabilizing the tip speed of the robot 15 at the corresponding area, can be suggested.

[0093] Furthermore, in this embodiment, in the pre-processing step, the workpiece W conveyed by the conveyor 17 is pre-processed, and the data acquisition unit 31 can acquire the conveying speed or conveying position of the conveyor 17 on the workpiece W as data related to the pre-processing step.

[0094] In this case, the causes of defects in the pre-processing process can be analyzed or process improvements can be suggested based on data related to the conveyor 17 operating in the pre-processing process. For example, if a specific area of ​​workpiece W is found to have poor coating (e.g., uneven color) during the inspection process, and there is a tendency for the conveyor 17's conveying speed to fluctuate during the de-energization of the corresponding area in the pre-processing process, it can be inferred that the defect is due to the inability to uniformly de-energize the motor 13 relative to workpiece W. In this case, process improvements based on changes to the conditions, such as stabilizing the conveyor 17's conveying speed in the pre-processing process, can be suggested.

[0095] Furthermore, in this embodiment, in the pretreatment process, the dust removal operation of the dust removal machine 11 driven by rotating the brush 11a is performed to remove dust from the surface of the workpiece W. The data acquisition unit 31 can acquire at least one of the torque and rotation speed of the dust removal machine 11 as data related to the pretreatment process.

[0096] In this case, the causes of defects in the pretreatment process can be analyzed or process improvements can be suggested based on data related to the dust collector 11 operating in the pretreatment process. For example, if a specific area of ​​the workpiece W is found to have poor coating (e.g., pitting) during the visual inspection process, and if there is a tendency for the torque of the dust collector 11 to be low at the corresponding area, it can be inferred that the cause of the defect is that the brush 11a of the dust collector 11 is not making sufficient contact with the workpiece W and is therefore not effectively removing dust. In this case, process improvements based on condition settings, such as changing the position of the dust collector 11 at the corresponding area to a position where the brush 11a makes sufficient contact with the workpiece W, can be suggested.

[0097] Furthermore, in this embodiment, during the pretreatment process, the static electricity removal operation of the workpiece W is performed by the destatic generator 13, and the data acquisition unit 31 can acquire the output of the destatic generator 13 as data related to the pretreatment process.

[0098] In this case, the causes of defects in the pretreatment process can be analyzed or process improvements can be suggested based on data related to the de-energizer 13 operating in the pretreatment process. For example, if poor coating (e.g., poor hiding power) is found in a specific area of ​​workpiece W during the visual inspection process, and if there is a tendency for the output of the de-energizer 13 to be low in the corresponding area, it can be inferred that the defect is due to insufficient de-energization of the corresponding area of ​​workpiece W, resulting in insufficient paint adhesion. In this case, process improvements based on condition settings, such as increasing the output of the de-energizer 13 in the corresponding area, can be suggested.

[0099] Furthermore, in this embodiment, the data acquisition unit 31 can acquire at least one of the temperature, humidity, and airflow in the painting chamber PB as data related to the pretreatment process.

[0100] In this situation, the causes of defects in the pretreatment process can be analyzed or process improvements can be suggested based on environmental data within the coating chamber PB. For example, if a workpiece W is found to have overall or partial coating defects (e.g., orange peel) during the visual inspection process, and there is a tendency for excessive downward airflow in the coating chamber PB, it can be inferred that the defect is caused by excessively rapid solvent evaporation during the coating process. In this case, process improvements based on condition settings, such as reducing the airflow in the coating chamber PB, can be suggested. Alternatively, if there is a tendency for low humidity in the coating chamber PB, which is prone to static electricity generation, it can be inferred that the defect is caused by insufficient uniform removal of static electricity from the workpiece W during the pretreatment process. In this case, process improvements based on condition settings, such as increasing the humidity in the coating chamber PB, can be suggested.

[0101] Furthermore, in this embodiment, the coating diagnostic system 1 may also have a data acquisition unit 41, which acquires data related to precautions in processes preceding the pretreatment process. In this case, the data management unit 43 can associate and register the data related to precautions and the data related to the pretreatment process together with the serial number of the workpiece W.

[0102] In this case, data such as whether there is poor welding or cleaning, or whether there is irregular operation caused by the operator, are pre-registered as precautions using bits, etc. Thus, if a coating defect is found, the cause of the defect in a process earlier than the pretreatment process can be analyzed based on the data related to the precautions, or suggestions can be made to improve the preceding process.

[0103] Furthermore, in this embodiment, the workpiece W can be set as the body of a car. In this case, data from each process of the painting line PL that constitutes the car body can be linked to each car body for unified management.

[0104] <5. Variations>

[0105] The embodiments disclosed herein are not limited to those described above, and various modifications can be made without departing from their spirit and technical concept. Examples of such modifications will be described below.

[0106] (5-1. Case where pretreatment processes are carried out outside the explosion-proof area)

[0107] In the above embodiments, the pretreatment and coating processes were performed in the coating room PB, which is an explosion-proof area. However, for example, as Figure 5 As shown, the pretreatment process can also be performed outside the painting chamber PB. In this case, special non-explosion-proof sensors installed outside the equipment can be used in the pretreatment process. For example, such as... Figure 5 As shown, in addition to the dust collector 11 and the de-electrostatic motor 13, an electrostatic sensor 53 is also pre-installed at the top of the robot 15. During the pre-processing stage, the charge on the workpiece W is detected after the de-electrostatic operation. The data acquisition unit 31 of the pre-processing control device 3 also acquires the charge on the workpiece W as data related to the pre-processing stage. Figure 6 As shown, the data management unit 43 of the upper control device 9 establishes an association between the charge of workpiece W and the corresponding serial number of workpiece W and registers it in the database 51.

[0108] According to this modified example, if a specific area is found to have poor coating (e.g., poor hiding power) during the visual inspection process, and there is a tendency for the charge on the workpiece W to be high in the corresponding area, the defect cause analysis unit 45 can infer that the high charge is causing insufficient paint adhesion as the cause of the defect. In this case, the process improvement suggestion unit 47 can suggest process improvements based on condition settings, such as increasing the output of the motor 13 at that area to reduce the charge on the corresponding area. Furthermore, in addition to the charge detection described above, various processes using special sensors that cannot be used in explosion-proof areas can be performed in the pre-processing process.

[0109] (5-2. Obtaining the amount or size of dust removed)

[0110] Although no detection is performed in the above embodiment, it is also possible to, for example, adopt a configuration that can attract dust removed by the dust collector 11 and collect it through a filter, so that the collected dust can be detected by a laser sensor, camera, etc., and the amount of dust collected on each workpiece can be monitored by, for example, by the data management unit 43 performing the accumulation of the number of detections, the measurement of the total weight of the collected dust, and the measurement of the size. Alternatively, the weight of the collected dust can be detected by measuring the weight of the filter. The data acquisition unit 31 acquires at least one of the amount and size of the dust removed by the dust collector 11, and the data management unit 43 associates the amount and size of the dust with the serial number of the corresponding workpiece W and registers it in the database 51.

[0111] According to this modified example, the causes of defects in the pretreatment process can be analyzed based on the amount or size of the acquired dust, or used for process improvement and preventive maintenance. For example, in the case of individual workpieces W that are of the same type but have drastically different dust collection amounts and sizes, the defect cause analysis unit 45 can detect the possibility of irregularities occurring in the pretreatment process. Furthermore, if the dust collection amount tends to decrease, it can be presumed that the cause is wear of the brush 11a of the dust collector 11 or clogging of the filter, and the preventive maintenance recommendation unit 49 can recommend replacing the brush 11a and the filter.

[0112] (5-3. Acquiring sound data)

[0113] For example, if the pretreatment process is located outside the painting room PB, a microphone or other sound input device can be installed at the pretreatment process to acquire sound data as data related to the pretreatment process. The data management unit 43 associates the sound data with the serial number of the corresponding workpiece W and registers it in the database 51. Thus, compared to obtaining the airflow through command values, the actual airflow in the working environment can be detected by detecting, for example, the sound of wind generated by the downstream flow. Furthermore, for example, if an abnormal noise different from the normal working sound is detected, the possibility of certain malfunctions or adverse conditions can be detected.

[0114] <6. Hardware Configuration Example of a Host Control Device>

[0115] Reference Figure 7 The hardware configuration example of the upper control device 9 will be explained.

[0116] like Figure 7 As shown, the host control device 9 includes, for example, a CPU 901, a ROM (Read-Only Memory) 903, a RAM (Random Access Memory) 905, an application-specific integrated circuit 907 such as an ASIC or FPGA, an input device 913, an output device 915, a recording device 917, a driver 919, a connection port 921, and a communication device 923. These components are connected via a bus 909 and an input / output interface 911 in a manner that allows them to exchange signals with each other.

[0117] The program can be pre-recorded in a recording device such as ROM 903, RAM 905, or hard disk 917.

[0118] The program can be temporarily or non-temporarily (permanently) pre-recorded on portable recording media 925 such as floppy disks, various CDs (CompactDisk), MO discs (Magnet Optical Disk), DVDs (Digital Video Disc), and semiconductor memory. Such recording media 925 can also be provided as so-called packaged software. In this case, the program recorded on these recording media 925 can be read by the drive 919 and recorded on the recording device 917 via the input / output interface 911, bus 909, etc.

[0119] The program can also be pre-recorded on a download website, other computers, other recording devices, etc. (not shown). In this case, the program is transmitted via a network such as a LAN (Local Area Network) or the Internet, and the communication device 923 receives the program. Then, the program received by the communication device 923 can be recorded on the aforementioned recording device 917 via the input / output interface 911, bus 909, etc.

[0120] The program can also be pre-recorded on a suitable external connection device 927. In this case, the program can be transmitted via a suitable connection port 921 and recorded on the recording device 917 via an input / output interface 911, bus 909, etc.

[0121] CPU 901 executes various processes according to the program recorded in the recording device 917, thereby realizing the processing based on the data acquisition unit 41, data management unit 43, defect cause analysis unit 45, process improvement suggestion unit 47, preventive maintenance suggestion unit 49, etc. CPU 901 can, for example, directly read the program from the recording device 917 for execution, or execute it after temporarily loading the program into RAM 905. For example, when CPU 901 receives a program via communication device 923, driver 919, or connection port 921, it can directly execute the received program without recording it in the recording device 917.

[0122] CPU 901 can also perform various processing based on signals and information input from input devices 913 such as mouse, keyboard, microphone (not shown), etc., as needed.

[0123] The CPU 901 may also output the results of the above processing from an output device 915, such as a display device or a sound output device. The CPU 901 may also send the processing results via a communication device 923 or a connection port 921 as needed. The CPU 901 may also record the processing results on the recording device 917 or the recording medium 925.

[0124] In addition to the above description, methods based on the above embodiments and variations can also be appropriately combined. Furthermore, although not all examples have been given, the above embodiments and variations are embodiments and variations that can be implemented with various changes without departing from their spirit.

[0125] The problems and effects to be solved by the above-described implementation methods and variations are not limited to those described above. Through these implementation methods or variations, problems not described above may also be solved or effects not described above may also be achieved. Sometimes, only a portion of the described problems or only a portion of the described effects may be solved.

[0126] Explanation of reference numerals in the attached figures

[0127] 1: Coating diagnostic system;

[0128] 11: Dust collector;

[0129] 11a: brush;

[0130] 13: Except for the motor;

[0131] 15: Robot (Automaton);

[0132] 17: Conveyor (conveyor device);

[0133] 31: Data Acquisition Department (First Data Acquisition Department);

[0134] 35: Data Acquisition Department (Second Data Acquisition Department);

[0135] 39: Data Acquisition Department (Third Data Acquisition Department);

[0136] 41: Data Acquisition Department (Fourth Data Acquisition Department);

[0137] 43: Data Management Department;

[0138] 45: Defect Cause Analysis Department;

[0139] 47: Process Improvement Suggestion Department;

[0140] 49: Prevention and Maintenance Recommendation Department;

[0141] PB: Painting booth (the area where pretreatment is performed);

[0142] PL: Painting line;

[0143] W: Workpiece.

Claims

1. A coating diagnostic system, wherein the coating diagnostic system comprises: The first data acquisition unit acquires data related to the pre-processing step and the coordinates of the top position of the automatic machine when the multi-joint type automatic machine performs pre-processing on the workpiece in the pre-processing step of the coating line. The data management department associates and registers data related to the pre-processing step with the identification information of the workpiece and the coordinates of the top position of the automatic machine. The second data acquisition unit acquires data related to the coating process when coating the workpiece in the coating process after the pretreatment process. The third data acquisition unit acquires data related to the inspection results when inspecting the coating status of the workpiece in the inspection process after the coating process. as well as The defect cause analysis unit, when a coating defect is found in a specific part of the workpiece during the inspection process, analyzes the cause of the defect based on data related to the pre-processing process that is associated with the workpiece's identification information and the coordinates corresponding to the specific part. The defect cause analysis unit changes the priority order of the analyzed data according to the type of defect cause found.

2. The coating diagnostic system according to claim 1, wherein, The data management department will associate and register the data related to the coating process and the data related to the pretreatment process with the identification information.

3. The coating diagnostic system according to claim 2, wherein, The data management department will associate and register the data related to the inspection results, the pretreatment process, and the coating process with the identification information.

4. The coating diagnostic system according to claim 1, wherein, The defect analysis unit analyzes the causes of defects in the coating process based on data related to the coating process. If the coating process is determined to be normal, the unit analyzes the causes of defects in the pretreatment process based on data related to the pretreatment process.

5. The coating diagnostic system according to claim 1, further comprising: The process improvement suggestion department, based on the analysis results of the defect cause analysis department, suggests improvements to the operating conditions of equipment related to the pretreatment process or the coating process.

6. The coating diagnostic system according to claim 1, further comprising: The preventive maintenance recommendation department recommends preventive maintenance for equipment related to the pretreatment process or the coating process based on data related to the pretreatment process or the coating process.

7. The coating diagnostic system according to claim 1, wherein, The first data acquisition unit acquires data related to the pre-processing step for each part of the workpiece.

8. The coating diagnostic system according to claim 1, wherein, In the pretreatment process, a multi-joint type automatic machine performs prescribed operations on the workpiece related to the pretreatment. The first data acquisition unit acquires at least one of the top position, joint torque, and top speed of the automaton as data related to the pre-processing step.

9. The coating diagnostic system according to claim 1, wherein, In the pre-processing step, the workpiece conveyed by the conveying device undergoes pre-processing. The first data acquisition unit acquires the conveying speed or conveying position of the workpiece by the conveying device as data related to the pre-processing step.

10. The coating diagnostic system according to claim 1, wherein, In the pretreatment process, a dust removal operation is performed by a dust collector driven by rotating brushes to remove dust from the surface of the workpiece. The first data acquisition unit acquires at least one of the torque and rotational speed of the dust collector as data related to the pretreatment process.

11. The coating diagnostic system according to claim 10, wherein, The first data acquisition unit acquires at least one of the amount and size of the dust removed by the dust collector as data related to the pretreatment process.

12. The coating diagnostic system according to claim 1, wherein, In the pretreatment process, an electrostatic discharge operation is performed on the workpiece by using a de-electrode motor. The first data acquisition unit acquires the output of the motor as data related to the preprocessing step.

13. The coating diagnostic system according to claim 12, wherein, In the pretreatment process, the charge on the workpiece is detected after the static electricity removal operation. The first data acquisition unit acquires the charge of the workpiece as data related to the pre-processing step.

14. The coating diagnostic system according to claim 1, wherein, The first data acquisition unit acquires at least one of the temperature, humidity, and airflow of the area where the pretreatment of the workpiece is performed as data related to the pretreatment process.

15. The coating diagnostic system according to claim 1, further comprising: The fourth data acquisition unit acquires data related to precautions in processes preceding the preprocessing process. The data management department will associate and register the data related to the precautions and the data related to the pre-processing steps with the identification information.

16. The coating diagnostic system according to any one of claims 1 to 15, wherein, The workpiece is the body of a car.

17. A coating diagnostic method, wherein the coating diagnostic method comprises: When a multi-joint type automatic machine performs pre-treatment of a workpiece for coating in the pre-treatment process of a coating line, it acquires data related to the pre-treatment process and the coordinates of the top position of the automatic machine. The data related to the pre-processing step is associated with the identification information of the workpiece and the coordinates of the top position of the automatic machine and then registered. When coating the workpiece in the coating process following the pretreatment process, data related to the coating process is acquired; When inspecting the coating status of the workpiece in the inspection process following the coating process, data related to the inspection results are acquired; as well as If a coating defect is found in a specific area of ​​the workpiece during the inspection process, the cause of the defect is analyzed based on data related to the pre-processing process, which is associated with the workpiece's identification information and the coordinates corresponding to the specific area. In analyzing the causes of the defects, the priority order of the analyzed data is changed according to the type of defect discovered.

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