JP2005118757A - Coating defect analyzing system, coating defect analyzing method and computer program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a coating defect analyzing system by which the clarification of causes of coating defects and the presentation of the preventive measures are efficiently carried out, an analyzing method and a computer program. <P>SOLUTION: The coating defect analyzing system is for deciding the coating defect preventive measures based on coating defect data including the kinds and the frequency of the occurrence of the coating defects or the degree of the coating defects and coating condition data including at least one of coating color, a material to be coated and a coating part and is provided with an input means 1 for collected coating data, a storing means 2 for storing the coating data, a data analyzing means for analyzing the actual condition of the occurrence of the coating defects in every kinds of the coating defects based on the coating date, a cause judging means for judging the cause of the occurrence of the coating defects based on the analyzing result of the coating data, a coating defect preventive measures deciding means for deciding the coating defect occurrence preventive measures based on the coating defect preventive measure data base stored in the storing means 2 using the judging result of the occurrence cause as a base and an output means for coating defect preventive measures. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a paint defect analysis system, a paint defect analysis method, and a computer program for operating a paint defect analysis system that occur in a paint process of vehicles such as automobiles, home appliances, and building materials.

  Paints are usually applied to the surface of vehicles such as passenger cars, household appliances such as refrigerators and washing machines, and building materials such as outer wall materials. This coating protects the underlying steel plate and puts a great deal of weight on finishing it with an aesthetic appearance. Therefore, in the painting process in the production line, the coating film has bumps (projections on the coating surface), different colors (difference between the target color and the coating color), sagging (part where the coating film droops locally), Great care is taken to prevent coating defects such as repellency (parts of the paint surface that are indented or unpainted), or pits (pinholes). In addition, when a defect occurs in the coating film, the cause must be clarified as soon as possible and countermeasures must be taken. Therefore, many man-hours are required to prevent coating defects.

  FIG. 1 is a diagram showing an example of a process in a painting line for an automobile body. The coating film of an automobile is generally composed of a three-layer structure of undercoat, intermediate coat, and topcoat. First, pretreatment such as degreasing the base steel sheet and forming a phosphorylated film is performed. Next, undercoating is performed by electrodeposition coating or the like. After rinsing with water, the undercoat paint is baked in a drying oven. If the coating film has defects such as flaws, it is removed by polishing and then washed with water as necessary. Thereafter, the coating process is completed through intermediate coating by electrostatic coating, baking, polishing and washing with water, top coating, and baking as necessary.

  As a countermeasure for preventing the occurrence of coating defects in the coating process as described above, an “electrodeposition coating apparatus” for preventing dust and dirt from being included in the coating film in electrodeposition coating has been disclosed (for example, Patent Documents). 1). In this apparatus, measures are taken to prevent dust and deposits from adhering to the coating film by improving the flow of the electrodeposition liquid in the electrodeposition tank. This apparatus is one means for suppressing the occurrence of coating defects in the electrodeposition coating process. However, it is an apparatus for improving a part of the painting process. Moreover, the said patent document 1 does not disclose a means for routinely managing grasp and prevention of coating defects.

  In the painting line shown in FIG. 1, there is a source of coating defects in almost all processes. Therefore, in order to obtain a coating film with excellent aesthetic appearance, an operation method is usually employed in which coating defects are inspected before and after each process, and coating defects do not occur in each process. Further, in the case of automobiles, what defects have occurred and how much have occurred for each unit such as bonnet, door, fender, roof, etc., are collected as data. Furthermore, since different car models flow on the same painting line and painting is performed with different paint colors, the painting conditions are different. Therefore, painting condition data for each vehicle is also collected.

  As described above, the coating object and the coating conditions in the coating line usually change every day, and the coating defects also change every day. Therefore, enormous amounts of data are always collected, and when a coating defect that requires countermeasures is found, it is required to analyze the data, elucidate what the cause is, and take appropriate measures.

Collecting the above data and reviewing the line to prevent coating defects requires a lot of man-hours for manufacturing personnel, and elucidates the causes of coating defects based on the collected data and measures to prevent them It requires a lot of man-hours for paint / painting engineers. In particular, when a coating defect occurs, a paint / painting engineer must quickly consider a countermeasure, and therefore, the paint / painting engineer has a very long time to be bound by the coating line. In addition, analyzing data and elucidating the cause requires a lot of knowledge and experience, so you have to rely on a limited number of skilled engineers.
JP-A-6-272092

  The present invention has been made to solve the above-described problems, and can reduce the man-hours required for elucidating the cause of coating defects and determining and presenting countermeasures, as well as accurately clarifying the causes and presenting countermeasures. An object of the present invention is to provide a computer program for operating a paint defect analysis system, a paint defect analysis method, and a paint defect analysis system.

The coating defect analysis system (1) according to the present invention includes coating defect data including the type and number or degree of coating defects before and / or after a predetermined coating process, as well as the coating color, coating object, and coating site. A coating defect analysis system for determining a measure for preventing the occurrence of the coating defect based on coating condition data including at least one of the coating conditions, wherein the coating defect data and the coating condition data are collected. Data input means for inputting paint data, storage means for storing the paint data input by the data input means, and paint for each type of paint defect based on the paint data stored in the storage means Data analysis means for analyzing the actual state of defect occurrence, cause determination means for determining the cause of occurrence of the coating defect based on the analysis result by the data analysis means, and the cause Based on the determination result by the constant unit, on the basis of the storage means or another coating defect prevention database stored in the storage means, and determining painting defect prevention determination means for prevention measures of the paint defects,
Output means for outputting a determination result by the coating defect prevention measure determination means.

  Further, the coating defect analysis system (2) according to the present invention is the coating defect analysis system (1), wherein the coating defect data is data of change over time of the coating defect related to the type and the number or degree of occurrence of the coating defects. The data analysis means classifies the types of the coating defects and / or the number or degree of occurrence of the coating defects over time on the basis of the coating defect temporal change data, It is characterized by having an analysis function.

  Further, in the coating defect analysis system (3) according to the present invention, in the coating defect analysis system (1) or (2), the coating defect data includes data for each painting surface having a different inclination in the coating object. The data analysis means has a function of analyzing the coating defect for each painted surface having a different inclination.

  The method for analyzing coating defects according to the present invention includes coating defect data including the type and number or degree of coating defects before and / or after a predetermined coating process, and at least one of a coating color, a coating object, and a coating site. A painting defect prevention method for collecting painting condition data including one painting condition and determining a measure for preventing the occurrence of the painting defect based on the collected painting data, the painting defect data and the painting condition Based on the step of inputting the paint data including data from the data input means, the step of storing the paint data input from the data input means in the storage means, and the paint data stored in the storage means, A step of analyzing the actual state of occurrence of the coating defect for each type of the coating defect, and a step of determining the cause of the occurrence of the coating defect based on the analysis result by the data analysis means. And, based on the determination result by the cause determining means, determining the occurrence of the coating defect prevention measure based on the coating defect prevention measure database stored in the storage means or another storage means; The method includes a step of outputting the determination result of the countermeasure for preventing the occurrence of the coating defect from the output.

  In addition, the coating defect analysis method (2) according to the present invention is the coating defect analysis method (1), wherein the coating defect data is data of change over time of the coating defects related to the type and the number or degree of occurrence of the coating defects. And classifying the types of the coating defects and / or the number or degree of occurrence of the coating defects over time based on the time-dependent change data, and analyzing each band according to time. It is said.

  Further, the paint defect analysis method (3) according to the present invention is the paint defect analysis method (1) or (2), wherein the paint defect data includes data for each paint surface having a different inclination in the paint object. The coating defect is analyzed for each painted surface having a different inclination.

  The computer program for a coating defect analysis system according to the present invention includes coating defect data including the type and the number or degree of coating defects before and / or after a predetermined coating process, as well as the coating color, coating object, and coating site. A computer program for a coating defect analysis system for determining measures for preventing the occurrence of a coating defect based on coating condition data including at least one of the coating conditions, the computer program being input from a data input means to the computer The paint data including the paint defect data and the paint condition data is read, the read data is stored in the storage means, and based on the paint data stored in the storage means, the paint defect is classified by type of the paint defect. The actual condition of the occurrence is analyzed, the analysis result is stored in the storage means, and the coating result is based on the analysis result stored in the storage means. Based on the coating defect prevention countermeasure database stored in the storage means or another storage means, based on the determination result of the cause of occurrence of the coating defects, the cause of occurrence of the defects is determined. And the operation of outputting the determination result of the coating defect prevention measure is executed.

  According to the coating defect analysis system (1) or the coating defect analysis method (1) according to the present invention, the analysis of the coating data, the determination of the cause of the coating defect, and the determination of the countermeasure for preventing the coating defect are executed by the computer. In addition, the time required for the treatment is remarkably shortened, and even when there is no engineer skilled in the paint / painting technique, it is possible to promptly present an appropriate coating defect prevention measure. In addition, the labor required for input and analysis of painting data, elucidation of the cause of painting defects, and determination of preventive measures, etc. is greatly reduced, so that significant labor saving is achieved. Furthermore, since it is possible to analyze data from various angles for a large amount of data, it is possible to improve the analysis accuracy and the determination accuracy of the coating defect prevention measure.

  In addition, according to the coating defect analysis system (2) or the coating defect analysis method (2) according to the present invention, detailed analysis can be performed based on temporal change or time-dependent data. It is possible to elucidate the cause of painting defects with higher accuracy. This is particularly effective when a high-quality coating film is required.

  Moreover, according to the coating defect analysis system (3) or the coating defect analysis method (3) according to the present invention, it is possible to perform a detailed analysis based on the coating data for each painted surface having a different inclination of the coating object. Therefore, it is possible to elucidate the cause of occurrence of coating defects with higher accuracy. In particular, this is effective when a high-quality coating film is required for a three-dimensional automobile or home appliance.

  Hereinafter, a coating defect analysis system according to an embodiment of the present invention will be described in detail with reference to the drawings. Since the coating defect analysis method according to the present invention is a method executed by the coating defect analysis system, the coating defect analysis method will be described as being included in the coating defect analysis system.

  FIG. 2 is a block diagram showing a configuration of the entire coating defect analysis system according to the embodiment (1). The coating defect analysis system includes a data input unit 1, a storage unit 2, a control unit 3, and an output unit 4. Furthermore, the control unit 3 and the storage unit 2 and the control unit 3 and the output unit 4 are electrically connected, and the control unit 3 and the data input unit 1 are electrically connected to means such as a recording medium. Alternatively, a signal or data can be transmitted and received by a communication means such as a telephone line or the Internet.

  The data input unit 1 may be provided on the painting line side or on the control unit 3 side. Therefore, the data transmission / reception between the data input unit 1 and the control unit 3 is suitable for a recording medium or communication means when the data input unit 1 is provided on the painting line side, and provided on the control unit 3 side. Where possible, an electrical connection is suitable.

  The data input unit 1 is an input unit for painting data collected on the painting line, and an input device such as a keyboard, a touch panel, an OCR reader, or a mark sheet reader can be used. As described above, since there is a great deal of data collected in the painting line, it is most efficient and reliable to write data directly on the mark sheet (OMR) and read the data with a mark sheet reader on the production line. The read coating data is directly transmitted to the storage unit 2 or temporarily stored in a recording medium such as a CD, a DVD, an MD, or a magnetic tape, and then stored in the storage unit 2.

  The storage unit 2 includes a data file 2a, a data analysis result file 2b, and a paint defect occurrence cause-paint defect prevention measure file 2c (paint defect prevention measure database). The data file 2a is a file that stores painting data input from the data input unit 1 described above. This file contains painting data collected on the painting line, that is, painting defect data relating to painting defects including the type and number or degree of painting defects before and / or after a given process of the painting process, and painting data. Painting condition data including at least one painting condition of a color (including a clear color), a painting object, and a painting part is stored.

  In the data analysis result file 2b, the results of data aggregation and analysis using the painting data stored in the data file 2a are filed. For example, the number or degree of occurrence of coating defects by coating process and their changes over time, as well as by coating object, by coating color, by coating site, and by production volume (production volume of coating objects and paint usage) The analysis results such as the relationship between the coating conditions such as somewhat different and the type / number of occurrences or degree of coating defects are stored.

  The cause of painting defect-painting defect prevention countermeasure file (hereinafter abbreviated as “painting defect prevention countermeasure file”) 2c is the most characteristic and important file according to the present embodiment, and this painting defect prevention countermeasure file 2c. Is a compilation of the causes of paint defects and preventive measures for paint defects corresponding to the causes. This file is used when determining the cause of occurrence of a coating defect based on the analysis result stored in the data analysis result file 2b and determining a specific countermeasure from the determination result.

  FIG. 3 is a diagram showing a part of the coating defect prevention measures corresponding to the cause of the occurrence of the coating defects (pops) stored in the coating defect prevention countermeasure file 2c in a table format. The table shown in Fig. 3 is an example of the cause of the occurrence of flaws in the automobile painting line and the countermeasures for preventing them. Specific measures are listed. This table was created based on the results of a specific painting line. In other words, it is a compilation of effective measures taken by paint / painting engineers in each case based on a variety of data related to the occurrence of irregularities.

  For example, if the size of the bumps is small, such as a predetermined size (for example, less than 1 mm), the size is small, so it was generated in the process before the discovered process and was overlooked. Judge that it is. FIG. 4 is a diagram illustrating an example of a coating defect prevention measure corresponding to the cause when it is determined that the cause of the occurrence is “missing oversight in the previous process”. The countermeasures shown in FIG. 4 are summarized from past data accumulation, and it corresponds to the countermeasure column corresponding to “(b) oversight in the previous process” in the table shown in FIG. Listed in the right column. In this way, the file in which the relationship between the cause of occurrence of the coating defect and the prevention measure is arranged for each defect type is the coating defect prevention measure file 2c.

  In addition, the classification of the cause of the occurrence of coating defects and the coating defect prevention measures corresponding to each cause vary depending on various factors such as the type of coating defects, the object to be painted, the configuration of the painting line, the painting environment, It is better to analyze and decide for each case.

  In addition, although the case where the data file 2a, the data analysis result file 2b, and the coating defect prevention countermeasure file 2c are provided in the storage unit 2 is shown, it is an example for convenience of explanation, and these are not necessarily There is no need to divide the files into files, nor do they need to be tabulated. The format of the memory | storage part 2 should just be a form suitable for operation | movement, such as an analysis by a computer. Moreover, although the said memory | storage part 2 was shown separated from the control part 3 in FIG. 2, both may be comprised integrally.

  The control unit 3 includes a CPU 3a and has a configuration and functions as a computer. In addition to controlling the operation of the entire system, it also has a function of analyzing the above-mentioned coating data, determining the cause of occurrence of coating defects, and determining countermeasures for preventing coating defects, and outputting countermeasures for preventing coating defects.

  Further, the output unit 4 is a means for outputting the output of concrete coating defect prevention measures finally determined and, if necessary, the coating defect data, the coating condition data, the analysis result of the coating defect occurrence cause, and the like. For example, a printer, a display such as a liquid crystal or a CRT, or a recording medium such as an FD or a CD. These output means may be configured to be able to transmit and receive directly to the painting line via communication means such as a telephone line or the Internet.

  FIG. 5 is a flowchart showing a schematic procedure for determining a countermeasure for preventing a coating defect based on the painting data collected in the painting line. Examples of the painting line to be used include painting lines for automobiles including passenger cars, home appliances such as refrigerators and washing machines, and building materials such as outer wall materials.

  First, before and / or after a predetermined process in these painting lines, painting data including painting defect data such as the type and number or degree of painting defects and painting condition data is collected (step S1). As described above, the collected coating data is written on a mark sheet or the like, and in the case of a mark sheet, it is read and input by a mark sheet reader which is the data input unit 1 (see FIG. 2) (step S2). The painting data is stored in the data file 2a of the storage unit 2 (step S3).

  Next, based on the stored painting data, the painting data necessary for determining the cause of the painting defect is analyzed on a daily basis, for example (step S4), and the result is stored in the data analysis result file 2b. Stored (step S5). Further, the cause of the paint defect is determined using the analysis result of the paint data (step S6), and when the cause of the paint defect is specified, the paint defect prevention measure is taken using the defect prevention measure file 2c. It is determined (step S7), and the result (paint defect prevention measure) is output (step S8).

  The collection of data in the above step S1 does not necessarily have to be performed in every process in the painting line. For example, FIG. 1 shows an example of an automobile painting process, but it is not necessary to collect painting data in all the processes shown in FIG. The position at which painting data is collected is determined in advance according to the configuration, characteristics, painting object, etc. of each painting line. The main paint data to be collected are paint defect data and paint condition data such as the kind (including size) of paint defects and the number or degree of occurrence.

  The types of coating defects include butts, sagging, armpits, different colors, luster and repellency. However, it is not necessary to inspect all types of coating defects and collect data, and it may be selected as appropriate according to the painting line, painting object, and the like. For example, in the case of an automobile, in particular, there is often an emphasis on prevention of occurrence of irregularities, and in that case, data is collected for irregularities.

  The data relating to the coating conditions include the object to be coated, the coating color, the presence or absence of polishing after painting, the production volume, the production speed, the drying conditions, the coating machine, and the like. However, it is not necessary to collect all of these data, and the data to be collected may be selected according to the configuration, characteristics, painting object, etc. of the painting line. In addition, regarding the data of the object to be painted, for example, if it is an automobile, the type of the vehicle, if it is a household electric product, the classification of the product or shape when the product or shape is different, and the data of the coating color, metallic, solid, It is preferable to include a paint system category such as clear.

  The painting data regarding the type and number of occurrences of paint defects, the degree of painting, and the painting conditions should be collected for each individual when a small individual is continuous, such as a car. When the products are continuous, data should be collected in a predetermined lot unit or a predetermined time interval. In either case, data is collected at intervals within a predetermined range as necessary so that the actual state of occurrence of coating defects can be analyzed over time.

  The analysis of the painting data in step S4 is performed mainly by transmission / reception of signals and calculation between the control unit 3 and the storage unit 2, and these are executed under the control of the CPU 3a. For example, the following analysis is performed. For each process, the type, size, number of occurrences, and degree of coating defects are tabulated. In this case, if necessary, an analysis is also performed on the tendency of change over time together with the total amount or the average value. These analyzes are also performed for each paint color and each paint object. In addition, the effects of the amount of production and / or the production speed, the presence / absence of polishing of the coating film, the presence / absence of drying (baking), etc., on the occurrence of coating defects are also analyzed as necessary. The analysis result is stored in the data analysis result file 2b.

  6A and 6B are flowcharts showing an example of a procedure for determining the cause of occurrence of a coating defect in step S6. Each step shown in FIGS. 6A and 6B is mainly performed by transmission / reception of signals between the control unit 3 and the storage unit 2 and calculation, and these are executed under the control of the CPU 3a. Moreover, FIG. 6A and FIG. 6B illustrate the case where the coating defect to be processed is irregular.

  First, the data analysis result is read from the data analysis result file 2b (step S11). In FIG. 6A, the data analysis results are displayed to be collectively read in step S11. However, the target data may be read for each subsequent determination step. In step S12, it is determined whether or not the size of the chip generated as a coating defect is a predetermined size (for example, 1 mm) or more. If it is equal to or larger than the predetermined size, it will not be overlooked in the inspection in the process prior to the normally detected process, so the cause of the occurrence of the defect is specified as “occurred in the detected process” (step S13). On the other hand, if the size is less than the predetermined size, the cause of the occurrence of blistering is specified as being caused by “oversight in the previous process” (step S14). Since the anti-fouling measures corresponding to the cause of these flaws are as shown in FIG. 3, detailed description is omitted. It should be noted that, when the cause of occurrence of the flaws is identified, a flaw prevention measure may be output and the step may be terminated, but usually other causes are also confirmed as described below.

  Next, the process proceeds to step S15, where it is determined whether or not the lump is fibrous. If it is fibrous, the cause of the lump is identified as “clothing or the like” (step S16). If it is not fibrous, the process proceeds to step S17 to determine whether or not the solid is solid. If it is solid, the cause of the occurrence of blisters is identified as being caused by “paint-mixed blisters, paint film fragments, dust” (step S18). If it is not solid, the process proceeds to step S19, where it is determined whether or not the production amount is small. If the production amount is not small, the process proceeds to step S21, where it is determined whether or not the production amount is large. If the production amount is large, the cause of the irregularity is identified as being caused by “all areas in the generation environment” (step S22). ). When the production amount is not large, the process proceeds to step S23, and it is determined whether or not it is unique to a specific painting object. If it is peculiar to a specific object, it is identified as being caused by “corresponding object characteristic (work standard)” (step S24).

  If it is not specific to a specific object, the process further proceeds to step S25 shown in FIG. 6B, where it is determined whether or not there is more paint after drying. The cause is identified as “furnace” (step S26). If there is less after drying, the cause is identified as “paint soft blister” (heat flowable blister mixed in the paint) (step S27).

  Further, when confirming the change over time of the occurrence of irregularities, the process proceeds to step S28, where it is determined whether or not there is a tendency to increase over time. Is identified as the cause (step S29). If not, the process proceeds to step S30, where it is determined whether or not there is a tendency to decrease over time. If the tendency is to decrease, “occurs during line operation” is specified (step S31). If not, the process proceeds to step S32 to determine whether there is a change from increase to decrease or decrease to increase over time. (Step S33).

  After passing through the above steps, other determinations are made as necessary (step S34), and the identification of all occurrences of defects is completed.

  6A and 6B, the cause of the fluff is identified, and based on the result, as described above, the relationship between the cause of the defect and the preventive measure is organized and stored. Based on the defect prevention measure file 2c (see FIG. 3), the prevention measure for the defect is determined, and the result (the occurrence prevention measure) is output. Note that FIG. 3 exemplifies a flaw occurrence prevention measure corresponding to the flaw occurrence cause identified in steps S13, S14, S16, S26 and S27.

  The procedure shown in FIGS. 6A and 6B is an example in the case where the coating defect is rough. The optimum judgment is made according to the characteristics of the painting line and the painting process, the form of the painting object, the type of painting defect to be prevented, and the like. It is better to set items. Further, the order of the procedures shown in FIGS. 6A and 6B is not particularly limited, and the most efficient order in the system should be selected.

  In addition, in the steps from S11 to S32, when the cause of the irregularity becomes clear on the way, it is possible to appropriately skip the step.

  In addition, by time-dependent band described in this specification means to divide the passage of time into two or more bands (areas), and the division of the passage of time is in minutes, hours, days, It can be either weekly or monthly.

  FIG. 7 is a graph showing a part of the paint data analysis function included in the paint defect analysis system according to Embodiment (2). In the graph shown in FIG. 7, the horizontal axis represents time and the vertical axis represents the number of occurrences of irregularities, and the transition of the occurrence number of irregularities in a specific process is illustrated for a predetermined period (for example, one day). As shown in FIG. 7, the average number of occurrences is about 9 for the entire average, about 7 for each of the areas (bands over time) 1 and 3 and the average for area 2. There are about 19 cases. When the number of occurrences of the irregularities is viewed as an overall average value without being classified by area as shown in FIG. 7, even if an abnormality occurs in a specific area, it is often impossible to clearly grasp the abnormality. However, by examining the changes over time and dividing the analysis into areas with a large number of occurrences and areas with a few occurrences, it is possible to accurately grasp abnormalities in the occurrence of coating defects and perform a more sophisticated analysis.

  For example, in the case shown in FIG. 7, since the number of occurrences of spots in the area 2 is large, the analysis is performed by paying attention to the painting data included in this area. Areas other than area 2 can also be used as data for comparison with area 2 and can be excluded from the analysis when there is no need for comparison. In any case, since it is clear that the number of occurrences of irregularities in area 2 is large, it is estimated that there is some cause. Therefore, by analyzing the painting data included in the area 2 intensively, it is possible to accurately elucidate the cause of the occurrence of irregularities.

  For example, out of the painting data included in area 2, pay attention to the size, type (thread, solid, etc.), object to be painted, paint color, production speed, etc. It is effective to analyze including comparison with. The area can be classified based on the identification of a time zone in which the number of occurrences of irregularities is large relative to the overall average value, or the determination of discontinuity (such as a sudden change) in the number of occurrences of irregularities.

  FIG. 8 is a table showing a part of the paint data analysis function included in the paint defect analysis system according to Embodiment (3). In the case of a painted object that is a three-dimensional product and has different inclination surfaces (for example, a horizontal surface and a vertical surface), such as home appliances such as automobiles, refrigerators, and washing machines, paint each painted surface. By collecting and analyzing defect data, the performance of the system can be further improved.

  The table shown in FIG. 8 (a) is divided into a horizontal plane (H) and a vertical plane (V) of the object to be coated according to the area described in the above embodiment (2). The average number of occurrences is shown. Moreover, FIG.8 (b) is the table | surface which evaluated the result shown by Fig.8 (a). From the results shown in FIGS. 8 (a) and 8 (b), in areas 1 and 3, the number of occurrences is small, and no clear difference is recognized between H and V. In area 2, it is clearly recognized that the number of bumps is large and H is more than V. Therefore, in this case, the cause of the occurrence of irregularities can be specified as “falling object in the generation environment” in the time zone corresponding to the area 2. If H is less than V, the cause of the irregularity can be identified as “because of inappropriate work standards and equipment characteristics”.

  In the above example, the analysis is performed by dividing the horizontal plane and the vertical plane for each area shown in the embodiment (2). However, the analysis is not necessarily performed for each area. However, more accurate analysis can be performed by analyzing by area. Further, the measurement surface does not necessarily have to be a horizontal surface and a vertical surface, and may be divided by surfaces having different inclinations.

  The coating defect analysis system according to the above-described embodiments (1) to (3) may be a system that is attached to the painting line and operates online, and is installed and collected at a position different from the painting line. It is good also as an off-line form which analyzes painting data. In addition, the data input unit 1 and the output unit 4 are installed in the painting line, and other devices including the storage unit 2 and the control unit 3 are installed in a place separated from the painting line, and a telephone line, the Internet, etc. between them. It is also possible to perform remote control by connecting with the communication means.

  Also, the paint defect analysis system according to the present invention is applied to a paint line in which paint data is not accumulated, that is, a paint data is collected and a paint defect prevention measure has not been taken based on the data. In this case, the coating defect prevention measure file 2c shown in FIG. 3 does not exist. In such a case, a preliminary survey is first conducted on a new line, data on that line is collected, and paint defects such as similar paint lines and similar paint objects for which data has already been accumulated are collected. It is recommended to modify the prevention measure file. Furthermore, even if it is a new painting line without the accumulation of painting data, it is possible to improve the painting defect prevention countermeasure file so that it is suitable for the painting line while accumulating data. An analysis system can be applied.

  A computer program according to the present invention is for causing a computer to execute the coating defect analysis system according to Embodiments (1) to (3). Since the contents executed by the computer are the same as the operations performed in the paint defect analysis system, description thereof is omitted.

It is a figure which shows an example of the process in the painting line of the body of a motor vehicle. It is a block diagram which shows the structure of the whole coating defect analysis system which concerns on embodiment (1). It is the figure which summarized the coating defect prevention countermeasure corresponding to the generation | occurrence | production cause of the coating defect (pops) stored in the coating defect prevention countermeasure file in the table | surface form, and shows the one part. FIG. 10 is a diagram illustrating an example of a coating defect prevention measure corresponding to the cause when it is determined that the cause of the occurrence is “missing oversight in the previous process”. It is a flowchart which shows the general | schematic procedure until it determines the prevention countermeasure of a coating defect based on the coating data extract | collected by the coating line. It is a flowchart which shows an example of the procedure which determines the generation | occurrence | production cause of a coating defect, and is a figure which shows the procedure to step S24. It is a flowchart which shows an example of the procedure which determines the generation | occurrence | production cause of a coating defect, and is a figure which shows the procedure of step S25-S34. It is a graph which shows a part of analysis function of the coating data with which the coating defect analysis system which concerns on embodiment (2) is provided. It is a table | surface which shows a part of analysis function of the coating data with which the coating defect analysis system which concerns on embodiment (3) is provided.

Explanation of symbols

1 Data Input Unit 2 Storage Unit 3 Control Unit 4 Output Unit

Claims (7)

Coating defect data including the type and number or degree of coating defects before and / or after a predetermined coating process, and coating condition data including at least one of the coating color, the object to be coated, and the coating site Based on the paint defect analysis system for determining measures to prevent the occurrence of the paint defect,
Data input means for inputting the collected coating data including the coating defect data and the coating condition data;
Storage means for storing the paint data input by the data input means;
Based on the coating data stored in the storage means, data analysis means for analyzing the actual state of occurrence of coating defects for each type of coating defects;
Based on the analysis result by the data analysis means, cause determination means for determining the cause of occurrence of the coating defect,
Based on the determination result by the cause determination means, based on the coating defect prevention countermeasure database stored in the storage means or another storage means, countermeasure determination means for determining the occurrence of the coating defect prevention measures,
A coating defect analysis system comprising: output means for outputting a determination result by the countermeasure determination means. The coating defect data includes time-dependent data of coating defects related to the type and number or degree of the coating defects,
A function in which the data analysis means classifies the type of the coating defect and / or the number or degree of occurrence of the coating defect over time based on the coating defect temporal change data, and analyzes each band separately. The coating defect analysis system according to claim 1, wherein The painting defect data includes data for each painting surface having a different inclination in the painting object,
3. The coating defect analysis system according to claim 1, wherein the data analysis unit has a function of analyzing the coating defect for each painted surface having a different inclination. 4. Coating defect data including the type and number or degree of coating defects before and / or after a predetermined coating process, and coating condition data including at least one of the coating color, the object to be coated, and the coating site It is a coating defect analysis method for determining measures to prevent the occurrence of the coating defects based on the collected coating data.
Inputting the paint data including the paint defect data and the paint condition data from data input means;
Storing the coating data input from the data input means in a storage means;
Based on the data stored in the storage means, analyzing the occurrence of coating defects by type of coating defects;
Determining the cause of the occurrence of the coating defect based on the analysis result by the data analysis means;
Based on the determination result by the cause determining means, determining the occurrence of the coating defect prevention measures based on the coating defect prevention countermeasure database stored in the storage means or another storage means;
A method for analyzing a coating defect, comprising a step of outputting a determination result of the countermeasure for preventing the occurrence of the coating defect from an output. The coating defect data includes time-dependent change data of the coating defect related to the type and number or degree of the coating defects,
The time-dependent difference in the type and / or generation number or degree of the coating defect is classified according to a time-dependent band based on the time-dependent change data of the coating defect, and is analyzed for each band. Item 5. The coating defect analysis method according to Item 4. The painting defect data includes data for each painting surface having a different inclination in the painting object,
6. The coating defect analysis method according to claim 4, wherein the coating defect is analyzed for each painted surface having a different inclination. Coating defect data including the type and number or degree of coating defects before and / or after a predetermined coating process, and coating condition data including at least one of the coating color, the object to be coated, and the coating site Based on the above, a computer program for a coating defect analysis system for determining measures for preventing the occurrence of the coating defect,
On the computer,
The paint data including the paint defect data and the paint condition data input from the data input means are read,
Storing the read data in a storage means;
Based on the coating data stored in the storage means, analyze the actual state of occurrence of coating defects for each type of coating defects, and store the analysis results in the storage means,
Based on the analysis result stored in the storage means, the cause of the occurrence of the coating defect is determined,
Based on the determination result of the cause of occurrence of the coating defect, based on the coating defect prevention countermeasure database stored in the storage means or another storage means, to determine the occurrence of the coating defect prevention measures,
A computer program for a paint defect analysis system, characterized in that an operation for outputting a judgment result of the paint defect prevention measure is executed.

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