Coating condition prediction system and coating model

Abstract

Provided is a coating condition prediction system capable of performing a coating condition determination of a coating machine in a short time and reducing loss of a coating material by determining an optimum coating condition. A coating condition prediction system according to the present invention is for predicting an optimum coating condition of a coating machine. The coating condition prediction system comprises: a coating model construction means; a film thickness value prediction means; and a coating condition prediction means. The coating model construction means accumulates data of coating conditions (X1 to X9) and shape data of workpieces, and constructs a coating model for outputting a prediction value of the film thickness value of a coating film that is formed on a surface of a workpiece. The film thickness value prediction means predicts the film thickness value by accumulating the data of the coating conditions (X1 to X9) with respect to the coating model. The coating condition prediction means predicts the optimum coating condition by performing inverse analysis using the coating model in response to an input of a targeted film thickness value and shape data of a workpiece.

Description

Painting condition prediction system, painting model

[0001] The present invention relates to a coating condition prediction system that predicts optimal coating conditions for a coating machine, and a coating model that outputs predicted values ​​for the thickness of a coating film formed on the surface of a workpiece.

[0002] Conventionally, when launching a new product, the coating conditions for a rotary atomizer electrostatic sprayer are determined based on the experience of the user (teacher) and past data such as previous coating results, and are then determined through trial and error of actual coating (actual coating trials) (see, for example, Patent Document 1).

[0003] Japanese Patent Application Laid-Open No. 2023-1804

[0004] However, in the past, actual painting trials often failed, leading to frequent redoing, such as reviewing the painting conditions. As a result, the number of work steps increased, and the time required to establish the painting quality of new products, etc. (specifically, determining the painting conditions that will result in a good-quality painted workpiece) became longer. Furthermore, even if the painting conditions were determined, they may not be optimal. In this case, there is a risk of over-misting or excessive film thickness, resulting in increased paint loss.

[0005] The present invention has been made in consideration of the above-mentioned problems, and its first object is to provide a coating condition prediction system that can determine coating conditions for a coating machine in a short period of time and reduce paint loss by determining optimal coating conditions. Also, its second object is to provide a coating model that can optimize coating conditions.

[0006] In order to solve the above problems, the invention described in claim 1 is a system for predicting optimal coating conditions for a rotary atomizer coater when a workpiece is painted using the coater, comprising: a coating model construction means for accumulating data on the coating conditions and shape data of the workpiece, and constructing a coating model that outputs a predicted value for the film thickness value of the coating film formed on the surface of the workpiece; a film thickness value prediction means for predicting the film thickness value of the coating film by accumulating data on the coating conditions in the coating model; and a coating condition prediction means for performing inverse analysis using the coating model in response to input of a target film thickness value of the coating film and shape data of the workpiece, thereby predicting the optimal coating conditions for achieving the target.

[0007] In the invention described in claim 1, by performing inverse analysis using a painting model that outputs predicted values ​​of the thickness of the paint film formed on the surface of a workpiece, it is possible to accurately predict the optimal painting conditions for the paint sprayer to achieve the target paint film thickness value. This reduces the need to review the painting conditions during actual painting trials, thereby reducing work hours and shortening the time it takes to establish the paint quality of new products, etc. Furthermore, because the optimal painting conditions can be predicted with high accuracy, it is possible to reduce the occurrence of over-misting and excessive film thickness caused by suboptimal painting conditions, thereby reducing paint waste.

[0008] The invention described in claim 2 is based on claim 1, and the coating model construction means accumulates at least one of factors related to the electrostatic coating machine, which is the coating machine, and factors related to the coating robot equipped with the electrostatic coating machine, as coating condition data, and constructs the coating model that outputs a predicted value of the film thickness value.

[0009] In the invention described in claim 2, by accumulating at least one of factors related to the electrostatic paint sprayer and factors related to the painting robot as painting condition data for the painting model, the accuracy of the target paint film thickness value is improved. Therefore, if the painting condition prediction means performs inverse analysis using the painting model, it is possible to obtain with high accuracy the optimal painting conditions to achieve the target.

[0010] The factors related to the electrostatic sprayer include, for example, at least one of a factor related to the rotary atomizing head of the electrostatic sprayer and a factor related to the energization conditions for electrostatic painting (Claim 3). In this case, the coating model construction means may select at least one factor each from the factors related to the rotary atomizing head, the factors related to the energization conditions for electrostatic painting, and the factors related to the coating robot, and store them as coating condition data (Claim 4).

[0011] The fifth aspect of the present invention is characterized in that the first aspect of the present invention further comprises a visualization means for visualizing the predicted film thickness values ​​output by the painting model as a contour diagram.

[0012] In the fifth aspect of the invention, the user can intuitively know what the film thickness value of the coating film in each region is by looking at the contour diagram.

[0013] The sixth aspect of the present invention is characterized in that, in the fifth aspect, the visualization means generates a three-dimensional color contour diagram based on the shape data of the workpiece.

[0014] In the invention described in claim 6, by viewing the three-dimensional color contour diagram, the user can intuitively and accurately know the state of the coating film formed on the surface of a three-dimensional workpiece by differences in color.

[0015] The invention described in claim 7 is characterized in that, in any one of claims 1 to 6, the painting model outputs a predicted value of the coating efficiency in addition to the predicted value of the film thickness when the data of the painting conditions is input.

[0016] In the seventh aspect of the present invention, it is possible to predict the coating efficiency, which is the ratio between the amount of paint used and the amount of paint attached to the workpiece, when the workpiece is painted under the input coating conditions. In other words, it is possible to predict the extent to which paint loss can be reduced.

[0017] The invention as set forth in claim 8 is characterized in that, in any one of claims 1 to 6, the coating condition prediction means predicts the coating conditions that have a large influence on the film thickness value with priority.

[0018] In the eighth aspect of the present invention, the coating conditions that have a large effect on the coating thickness value of the coating film are predicted with priority, thereby enabling the target coating thickness value to be reached quickly.

[0019] The invention described in claim 9 is based on claim 8, and is characterized in that at least some of the painting conditions can be set as user-selected factors selected by the user, the painting conditions set as the user-selected factors are evaluation parameters that are preferentially determined by the user, and the painting condition prediction means predicts the painting conditions that are not set as the user-selected factors as the optimal painting conditions.

[0020] In the invention as set forth in claim 9, at least a part of the painting conditions can be set as evaluation parameters (user selection factors) determined in accordance with the user's wishes.

[0021] The gist of the invention described in claim 10 is a painting model that stores at least one of factors related to a rotary atomizer electrostatic paint sprayer and factors related to a painting robot equipped with the electrostatic paint sprayer as data on optimal painting conditions for the electrostatic paint sprayer, and outputs a predicted value for the film thickness of a paint film formed on the surface of a workpiece.

[0022] In the tenth aspect of the present invention, the accuracy of the predicted film thickness value is improved by accumulating at least one of factors related to the electrostatic paint sprayer and factors related to the painting robot. Therefore, by using the painting model, it is possible to optimize the film thickness value of the paint film.

[0023] As described above, according to the inventions described in claims 1 to 9, the coating conditions for the coating machine can be determined in a short period of time, and the determination of optimal coating conditions can reduce paint loss. Furthermore, according to the invention described in claim 10, the coating conditions can be optimized.

[0024] 1 is a schematic diagram showing a coating condition prediction system according to an embodiment of the present invention; 2 is a flowchart showing a process for predicting coating conditions for an electrostatic coater; 3 is a table for explaining the prediction of film thickness value and the prediction of coating efficiency; 4 is a front view of a display on which predicted values ​​of film thickness value, predicted values ​​of coating efficiency, a three-dimensional color contour diagram, etc. are displayed; 5 is a table for explaining the prediction of coating conditions by inverse analysis; 6 is a front view of a display on which coating conditions, a three-dimensional color contour diagram, etc. are displayed;

[0025] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] 1, the coating condition prediction system 1 of this embodiment is a system that predicts optimal coating conditions X1 to X9 (see FIG. 3) for an electrostatic coater 21 when an automobile body W1 (workpiece) is painted using a rotary atomizer electrostatic coater 21 in a coating facility 10. A coating robot 11 is installed within a coating area (not shown) of the coating facility 10.

[0027] The painting robot 11 includes a main body 12, an arm 13 extending from the main body 12, and an electrostatic sprayer 21 attached to the tip of the arm 13. A rotary atomizing head 23 is rotatably attached to the front of the sprayer main body 22, which constitutes the electrostatic sprayer 21. Paint P1 is supplied to the sprayer main body 22, which then supplies the paint P1 to the rotary atomizing head 23. An air motor (not shown) provided within the sprayer main body 22 rotates the rotary atomizing head 23, atomizing the paint P1 and spraying it onto the automobile body W1. An electric current is applied to the rotary atomizing head 23 and the paint P1 from a current generator (not shown). The electrostatic sprayer 21 of this embodiment is a high-transfer sprayer (a so-called iX sprayer) that sprays paint by atomizing paint particles (paint P1) using electrical power.

[0028] Next, the electrical configuration of the coating condition prediction system 1 will be described.

[0029] As shown in FIG. 1 , the painting condition prediction system 1 includes a personal computer 30, which includes a control device 31 for overall control of the entire system. The control device 31 is composed of a CPU 32, a ROM 33, a RAM 34, an input / output circuit, etc. The CPU 32 is electrically connected to a keyboard 35 and a display 36 of the personal computer 30. In this embodiment, the display 36 is a display with a touch panel. The CPU 32 is also electrically connected to the painting robot 11 and the electrostatic paint sprayer 21, and controls them using various drive signals. The ROM 33 stores a program for controlling the painting condition prediction system 1.

[0030] Next, a method for predicting the coating conditions X1 to X9 using the coating condition prediction system 1 will be described.

[0031] First, the CPU 32 accumulates data on the painting conditions X1 to X9 and shape data (three-dimensional data) of the automobile body W1, and constructs a painting model that outputs predicted values ​​for paint film thickness (film thickness distribution). Specifically, first, data (experimental data) showing the relationship between the paint film formation position (predetermined position on the surface of the automobile body W1) and the paint film thickness value is accumulated. Next, a basic two-dimensional basic painting model is constructed from the experimental data. To improve accuracy, the experimental data is fitted to each parameter value of the painting model, and the fitted parameters are converted into functions to obtain a highly accurate two-dimensional basic painting model. Next, the obtained painting model is three-dimensionalized, thereby expanding the two-dimensional basic painting model into a three-dimensional extended painting model. Furthermore, a painting model that can predict the ratio of paint consumption to the paint P1 applied to the automobile body W1 (i.e., coating efficiency) is constructed by linearly interpolating the painting model equation parameters for each condition using previously collected paint consumption data (paint consumption data). At this point, when the data for the painting conditions X1 to X9 are input, the painting model outputs a predicted value for the coating efficiency in addition to a predicted value for the film thickness. In other words, the CPU 32 functions as a "painting model construction means." The painting model is constructed in a process separate from the painting line for the automobile body W1 (offline). The CPU 32 also stores the constructed painting model in the RAM 34.

[0032] Next, the CPU 32 performs processing to predict the characteristics of the paint film to be formed on the surface W2 of the automobile body W1. In step S10 shown in Figure 2, the user inputs (stores) data on the optimum painting conditions X1 to X9 (see Figure 3) for the electrostatic atomizer 21 into the painting model stored in the RAM 34. The CPU 32 also inputs (stores) shape data of the automobile body W1 into the painting model stored in the RAM 34.

[0033] Of the coating conditions X1 to X9, the data for coating conditions X1 to X4 are factors related to the electrostatic sprayer 21. Of the coating conditions X1 to X4, which are factors related to the electrostatic sprayer 21, the data for coating conditions X1 to X3 are factors related to the rotary atomizing head 23 of the electrostatic sprayer 21, and the data for coating condition X4 is a factor related to the energization conditions for electrostatic coating. Coating condition X1 is the discharge rate (cc / min) of the coating material P1 from the rotary atomizing head 23. Coating condition X2 is the rotation speed (rpm) of the air motor provided in the electrostatic sprayer 21. Coating condition X3 is the flow rate (L / min) of air discharged from the periphery of the rotary atomizing head 23. In this embodiment, the air discharged from the periphery of the rotary atomizing head 23 has the same function as shaping air (atomization of the coating material P1 and formation of a coating pattern). The coating condition X4 is the current in constant current control, specifically the current value (μA) of the current that charges the rotary atomizing head 23 and the coating material P1, thereby enabling a stable supply of current to the rotary atomizing head 23 and the coating material P1.

[0034] Furthermore, among the coating conditions X1 to X9, the data for coating conditions X5 to X9 are factors related to the coating robot 11 equipped with the electrostatic sprayer 21. Coating condition X5 is the distance (mm) of the electrostatic sprayer 21 (gun), specifically, the distance (mm) between the tip of the rotary atomizing head 23 and the surface W2 of the automobile body W1. Coating condition X6 is the movement speed (mm / sec) of the electrostatic sprayer 21 (gun). Coating condition X7 is the number of coatings (times) performed by the electrostatic sprayer 21. Coating condition X8 is the pitch (mm) of the coating path relative to the automobile body W1. Note that the pitch of the coating path refers to the distance shifted laterally when, for example, the electrostatic sprayer 21 is moved downward → horizontally → upward → horizontally → downward → ... while coating. Furthermore, coating condition X9 is the angle (°) of the electrostatic sprayer 21 (gun) relative to the surface W2 of the automobile body W1.

[0035] The coating conditions X1 to X9 are input, for example, by the user operating the keyboard 35. The CPU 32 then controls the display of the input coating conditions X1 to X5 and X9 on the left side of the display 36 (see FIG. 4). The CPU 32 also controls the display of a forward analysis button 41 (see FIG. 4) on the left side of the display 36, directly below the coating conditions X1 to X5 and X9. The forward analysis button 41 is an oval icon with a picture of a triangular arrow pointing to the right and the word "Calculation."

[0036] When data for coating conditions X1 to X9 are accumulated for the coating model, the CPU 32 shown in FIG. 1 predicts the coating thickness value of the paint film in response to the user's operation of the forward analysis button 41. In other words, the CPU 32 functions as a "coating thickness value prediction means." When data for coating conditions X1 to X9 are input, the CPU 32 predicts the coating thickness value as well as the transfer efficiency in response to the user's operation of the forward analysis button 41. Furthermore, in step S20 shown in FIG. 2, the CPU 32 outputs a predicted coating thickness value A1 (see FIG. 4) and a predicted transfer efficiency value A2 (see FIG. 4) from the coating model. The predicted values ​​A1 and A2 are displayed in the lower right portion of the display 36. Also, in the lower right portion of the display 36, to the right of the predicted coating thickness value A1, the Y coordinate A3 (see FIG. 4) of the location on the automobile body W1 where the predicted value A1 is located is displayed. This allows the user to confirm (simulate) the film thickness values ​​and coating efficiencies for the input coating conditions X1 to X9.

[0037] The CPU 32 also controls the visualization (generation) of the predicted film thickness values ​​A1 output by the paint model as a three-dimensional color contour diagram C1 (see FIG. 4) based on the shape data of the automobile body W1, and displays the resulting image on the right side of the display 36. In other words, the CPU 32 functions as a "visualization means." A graph 43 showing the relationship between the X-direction dimension at the Y coordinate A3 and the film thickness value at the Y coordinate A3 is displayed on the right side of the three-dimensional color contour diagram C1 on the right side of the display 36.

[0038] Next, the CPU 32 shown in Figure 1 performs processing to predict the optimal coating conditions X1 to X9 for the electrostatic atomizer 21 to achieve the target. Specifically, first, in step S30 shown in Figure 2, the user inputs the target coating thickness value (see "Target Coating Thickness" in Figure 5) into the coating model stored in RAM 34. The CPU 32 also inputs shape data for the automobile body W1 into the coating model stored in RAM 34.

[0039] The target film thickness value is input by, for example, the user operating the keyboard 35. Then, the CPU 32 controls the display of the input film thickness value (target film thickness) in the upper left portion of the display 36 (see FIG. 6). The CPU 32 also controls the display of an inverse analysis button 51 (see FIG. 6) in the lower left portion of the display 36. The inverse analysis button 51 is an oval icon with a picture of a triangular arrow pointing to the right and the word "Calculation."

[0040] When the target film thickness and shape data of the automobile body W1 are input for the paint model, the CPU 32 shown in FIG. 1 performs an inverse analysis (inverse analysis algorithm) using the paint model in response to the user's operation of the inverse analysis button 51. Specifically, using Bayesian optimization and a genetic algorithm, the analysis is performed to minimize the root mean square error (rms error) of the parameters (evaluation function) for the most efficient and optimal objective variable for the target film thickness. This predicts the optimal painting conditions X1 to X9 for achieving the target. In other words, the CPU 32 functions as a "painting condition prediction means."

[0041] Next, in step S40 shown in Figure 2, the CPU 32 controls the display 36 to display the predicted coating conditions in the lower right portion. For example, in "Case 1" shown in Figure 5, the CPU 32 controls the display 36 to predict coating conditions X1 to X9 with priority, since these conditions have a large influence on the coating thickness. Specifically, the CPU 32 prioritizes evaluation parameters among the coating conditions X1 to X9 and determines, with priority, the values ​​that minimize the evaluation parameters as the optimal coating conditions. The CPU 32 then controls the display 36 to display the predicted coating conditions X1 to X9.

[0042] 5, at least some of the coating conditions X1 to X9 (specifically, coating conditions X6 and X8) can be set as user selection factors 61, which are selected by the user through operation of the keyboard 35. The coating conditions X6 and X8 set as user selection factors 61 are evaluation parameters that are prioritized based on the user's preferences. In this case, the CPU 32 predicts the coating conditions X1 to X5, X7, and X9, which are not set as user selection factors 61, as optimal coating conditions. Specifically, the CPU 32 defines the coating conditions X1 to X5, X7, and X9 as evaluation parameters and determines the values ​​that minimize each evaluation parameter.

[0043] In the lower right portion of the display 36, to the right of the coating conditions X1 to X9, the Y coordinate A3 of the location on the automobile body W1 where the predicted value A1 is found, the predicted film thickness A1, and the predicted coating efficiency A2 are displayed. Furthermore, a three-dimensional color contour diagram C1 (see FIG. 6) is displayed in the upper right portion of the display 36, and a graph 43 is displayed to the right of the three-dimensional color contour diagram C1. This allows the user to confirm (simulate) the target film thickness value.

[0044] 2, the CPU 32 then transfers (installs) the predicted painting conditions X1 to X9 to the painting robot 11. In the following step S60, the CPU 32 controls the in-line painting of the actual automobile body W1 based on the painting conditions X1 to X9 transferred to the painting robot 11. Specifically, the CPU 32 controls the rotary atomizing head 23 and the charging of the paint P1 based on the painting condition X4. The CPU 32 also controls the electrostatic atomizer 21 to spray the paint P1 onto the automobile body W1 based on the painting conditions X1 to X3 and X5 to X9 (actual painting trial). As a result, a coating film is formed on the surface W2 of the automobile body W1, enabling quality confirmation.

[0045] Therefore, according to this embodiment, the following effects can be obtained.

[0046] (1) In the present embodiment, the coating condition prediction system 1 performs inverse analysis using a coating model that outputs a predicted value A1 of the coating thickness value of the coating film formed on the surface W2 of the automobile body W1. This allows for highly accurate prediction of the coating conditions X1 to X9 of the electrostatic sprayer 21 that are optimal for achieving a target coating thickness value. This eliminates the need to revisit the coating conditions X1 to X9 during actual coating trials, thereby reducing the number of work steps and shortening the time required to establish coating quality for new products (specifically, determining the coating conditions that will result in a good-quality coating on the automobile body W1). Furthermore, because the optimal coating conditions X1 to X9 can be predicted with high accuracy, the occurrence of over-mist and excessive coating thickness, which are caused by non-optimal coating conditions X1 to X9, can be reduced. This improves the coating quality of the automobile body W1 and reduces the loss of paint P1. Furthermore, because the coating condition prediction system 1 of this embodiment is applied to a high-transfer coating sprayer, the occurrence of over-mist and the loss of paint P1 can be more reliably reduced.

[0047] (2) When data for the coating conditions X1 to X9 are input, the coating model of this embodiment outputs a predicted value A1 for the paint film thickness and a predicted value A2 for the coating efficiency. This makes it possible to search for and predict the conditions under which the automobile body W1 can be painted within the ideal film thickness range determined by the physical properties of the paint P1, and which also provide the highest coating efficiency. In other words, it is possible to predict not only the conditions under which the quality of the paint film is improved, but also the conditions under which loss of the paint P1 can be reduced.

[0048] (3) In this embodiment, the reverse analysis is performed using a painting model that encompasses the characteristics of the electrostatic paint sprayer 21 as a data source, so there is no need to re-learn (specifically, to prepare data showing the relationship between the painting conditions X1 to X9 and the film thickness values ​​of the paint film).

[0049] The above embodiment may be modified as follows.

[0050] In the above embodiment, the CPU 32 stores factors related to the electrostatic paint sprayer 21 and factors related to the painting robot 11 as data on painting conditions (painting conditions X1 to X9). However, the CPU 32 may store only factors related to the electrostatic paint sprayer 21 as data on painting conditions (painting conditions X1 to X4), or may store only factors related to the painting robot 11 as data on painting conditions (painting conditions X5 to X9). The factor related to the painting robot 11 may be at least one of the distance between the rotary atomizing head 23 and the automobile body W1 (painting condition X5), the movement speed of the electrostatic paint sprayer 21 (painting condition X6), the number of coats (painting condition X7), the pitch of the paint path relative to the automobile body W1 (painting condition X8), and the angle of the electrostatic paint sprayer 21 relative to the automobile body W1 (painting condition X9).

[0051] In the above embodiment, the factors related to the electrostatic sprayer 21 consisted of factors related to the rotary atomizing head 23 of the electrostatic sprayer 21, which provide data for the coating conditions X1 to X3, and factors related to the energization conditions for electrostatic coating, which provide data for the coating condition X4. However, the factors related to the electrostatic sprayer 21 may consist solely of factors related to the rotary atomizing head 23, or may consist solely of factors related to the energization conditions for electric coating. Furthermore, the factor related to the rotary atomizing head 23 may be at least one of the amount of paint P1 discharged from the rotary atomizing head 23 (coating condition X1), the rotation speed of the air motor (coating condition X2), and the flow rate of air discharged from around the rotary atomizing head 23 (coating condition X3).

[0052] In the above embodiment, the factor related to the energization conditions for electrostatic painting, which is the data for the painting condition X4, is the current in constant current control, specifically the current value for charging the rotary atomizing head 23 and the paint P1. However, the factor related to the energization conditions for electrostatic painting may also be the voltage in constant voltage control, specifically the voltage value for the current for charging the rotary atomizing head 23 and the paint P1.

[0053] The CPU 32 in the above embodiment may select at least one from each of the factors related to the rotary atomizing head 23 (painting conditions X1 to X3), the factor related to the energization conditions for electrostatic painting (painting condition X4), and the factor related to the painting robot 11 (painting conditions X5 to X9), and store the selected factors as painting condition data.

[0054] In the above embodiment, the CPU 32 controls the display 36 to display a three-dimensional color contour diagram C1 that visualizes the predicted film thickness value A1 output by the coating model. However, the CPU 32 may control the display 36 to display a monochrome contour diagram or a two-dimensional contour diagram.

[0055] In the above embodiment, a three-dimensional color contour diagram C1 that visualizes the predicted film thickness value A1 output by the paint model is displayed on the display 36, but the three-dimensional color contour diagram C1 does not have to be displayed.

[0056] In the above embodiment, of the coating conditions X1 to X9, only the coating conditions X1 to X5 and X9 were displayed on the display 36 (see FIGS. 4 and 6). However, the types of coating conditions displayed on the display 36 may be increased or decreased. Furthermore, all of the coating conditions X1 to X9 may be displayed on the display 36, or not all of the coating conditions X1 to X9 may be displayed on the display 36.

[0057] In the above embodiment, the paint film thickness value and coating efficiency were predicted in response to the user's operation of the forward analysis button 41. However, the forward analysis button 41 may be omitted, and the paint film thickness value and coating efficiency may be predicted in response to the accumulation of data on the painting conditions X1 to X9 for the painting model. Similarly, in the above embodiment, the paint film thickness value and coating efficiency were predicted in response to the user's operation of the inverse analysis button 51, by performing inverse analysis using the painting model. However, the inverse analysis button 51 may be omitted, and the paint film thickness value and coating efficiency may be predicted in response to the input of a target paint film thickness value and shape data for the automobile body W1 for the painting model.

[0058] In the above embodiment, a high transfer coating sprayer (a so-called iX sprayer) is used as the sprayer, but a normal electrostatic rotary atomizer sprayer or a sprayer that does not use static electricity may also be used.

[0059] In the above embodiment, an automobile body W1 is exemplified as a workpiece to be painted using the electrostatic paint sprayer 21, but the present invention is not limited to this. For example, the workpiece may be an automobile interior part such as an instrument panel, console box, or armrest, or an automobile exterior part such as a bumper or an aerodynamic part (spoiler, etc.). Furthermore, the workpiece does not necessarily have to be an automobile part.

[0060] Next, in addition to the technical ideas set forth in the claims, the technical ideas grasped by the above-described embodiments will be listed below.

[0061] (1) A coating condition prediction system according to claim 4, characterized in that the factor related to the rotary atomizing head is at least one of the amount of paint discharged from the rotary atomizing head, the rotation speed of an air motor provided in the electrostatic sprayer, and the flow rate of air discharged from around the rotary atomizing head; the factor related to the energization conditions of electrostatic coating is the current value or voltage value of the current that charges the rotary atomizing head and the paint; and the factor related to the coating robot is at least one of the distance between the rotary atomizing head and the workpiece, the movement speed of the electrostatic sprayer, the number of coats applied, the pitch of the coating trajectory relative to the workpiece, and the angle of the electrostatic sprayer relative to the workpiece.

[0062] (2) A coating condition prediction system according to claim 1, wherein the shape data of the workpiece is three-dimensional data.

[0063] 1...Painting condition prediction system 11...Painting robot 21...Electrostatic paint sprayer as a paint sprayer 23...Rotary atomizing head 32...CPU as a paint model construction means, film thickness value prediction means, painting condition prediction means and visualization means 61...User selection factor A1...Predicted value of film thickness value A2...Predicted value of coating efficiency C1...Three-dimensional color contour diagram as a contour diagram W1...Automobile body as workpiece W2...Surface of workpiece X1 to X9...Painting conditions

Claims

1. A system for predicting the optimal painting conditions of a painting machine when painting a workpiece using a rotary atomization type painting machine, the system comprising: a painting model construction means for accumulating data on the painting conditions and shape data of the workpiece, and constructing a painting model for outputting a predicted value of the film thickness of a paint film formed on the surface of the workpiece; a film thickness value prediction means for predicting the film thickness value of the paint film by accumulating data on the painting conditions for the painting model; and a painting condition prediction means for predicting the optimal painting conditions for achieving the target by performing inverse analysis using the painting model upon input of the target film thickness value of the paint film and the shape data of the workpiece. A painting condition prediction system characterized by comprising the above.

2. The painting condition prediction system according to claim 1, wherein the painting model construction means accumulates at least one factor related to an electrostatic painting machine which is the painting machine and a factor related to a painting robot equipped with the electrostatic painting machine as data on the painting conditions, and constructs the painting model for outputting the predicted value of the film thickness value.

3. The painting condition prediction system according to claim 2, wherein the factor related to the electrostatic painting machine is at least one of a factor related to a rotary atomization head of the electrostatic painting machine and a factor related to energization conditions of electrostatic painting.

4. The painting condition prediction system according to claim 3, wherein the painting model construction means selects at least one factor from each of the factor related to the rotary atomization head, the factor related to the energization conditions of electrostatic painting, and the factor related to the painting robot, and accumulates the same as data on the painting conditions.

5. The painting condition prediction system according to claim 1, further comprising a visualization means for visualizing the predicted value of the film thickness value output by the painting model as a contour diagram.

6. The painting condition prediction system according to claim 5, wherein the visualization means generates a three-dimensional color contour diagram based on the shape data of the workpiece.

7. The painting condition prediction system according to any one of claims 1 to 6, wherein the painting model outputs a predicted value of the painting efficiency in addition to the predicted value of the film thickness value when data on the painting conditions is input.

8. The coating condition prediction system according to any one of claims 1 to 6, wherein the coating condition prediction means preferentially predicts the coating conditions having a high degree of influence on the film thickness value.

9. At least a part of the coating conditions can be set as a user selection factor selected by the user. The coating conditions set as the user selection factor are evaluation parameters preferentially determined by the user. The coating condition prediction means predicts the coating conditions not set as the user selection factor as the optimum coating conditions. The coating condition prediction system according to claim 8.

10. A coating model characterized by accumulating at least one of factors related to a rotary atomization type electrostatic coating machine and factors related to a coating robot equipped with the electrostatic coating machine as data of the coating conditions of the optimum electrostatic coating machine, and outputting a predicted value of the film thickness value of the coating film formed on the surface of the workpiece.

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