Coating condition prediction system and coating model

Abstract

To provide a coating condition prediction system capable of determining a coating condition for a coater within a short period and reducing a paint loss through determination of an optimal coating condition.SOLUTION: This coating condition prediction system for predicting an optimal coating condition for a coater comprises: coating model construction means; film thickness value prediction means; and coating condition prediction means. The coating model construction means accumulates data relating to coating conditions X1-X9 and workpiece shape data, and constructs a coating model that outputs a prediction value of a film thickness value of a film formed on the surface of a workpiece. The film thickness value prediction means predicts a film thickness value by accumulating the data relating to the coating conditions X1-X9 for the coating model. The coating condition prediction means predicts the optimal coating condition by performing reverse analysis using the coating model upon entry of a target film thickness value and the workpiece shape data.SELECTED DRAWING: Figure 5

Description

Technical Field

[0001] The present invention relates to a coating condition prediction system for predicting the coating conditions of an optimal coating machine and a coating model for outputting a predicted value of the film thickness of a coating film formed on the surface of a workpiece.

Background Art

[0002] Conventionally, in the startup of new products and the like, the coating conditions of a rotary atomization type electrostatic coating machine have been studied based on the experience of the user (teacher) and past data such as old coating results, and determined by trial and error of actual coating (actual coating trial) (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, conventionally, there have been many failures in actual coating trials, and there have been many reworks such as reviewing coating conditions. As a result, there is a problem that the working hours increase and the period for starting up the coating quality of new products and the like (specifically, determining the coating conditions for the workpiece to be a good coating) becomes longer. Further, even if the coating conditions are determined, there is a possibility that they are not the optimal coating conditions. In this case, there is a problem that paint loss increases because overspray, excessive film thickness, etc. may occur.

[0005] The present invention has been made in view of the above problems, and a first object is to provide a coating condition prediction system that can determine the coating conditions of a coating machine in a short period of time and reduce paint loss by determining optimal coating conditions. A second object is to provide a coating model capable of optimizing coating conditions.

Means for Solving the Problem

[0006] In order to solve the above problems, the invention according to claim 1 is a system for predicting the optimum painting conditions of the painting machine when painting a workpiece using a rotary atomization type painting machine, which accumulates data on the painting conditions and shape data of the workpiece, and constructs a painting model for outputting a predicted value of the film thickness of the paint film formed on the surface of the workpiece. The painting model construction means, the 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 the painting condition prediction means for predicting the optimum painting conditions for achieving the target by performing inverse analysis using the painting model triggered by the input of the target film thickness value of the paint film and the shape data of the workpiece. The gist of the painting condition prediction system is characterized by comprising.

[0007] In the invention according to claim 1, by performing inverse analysis using a painting model that outputs a predicted value of the film thickness of the paint film formed on the surface of the workpiece, the optimum painting conditions of the painting machine for achieving the target film thickness value of the paint film can be predicted with high accuracy. As a result, the review of the painting conditions in the actual painting trial is reduced, so that the man-hours can be reduced and the period for starting up the painting quality of new products and the like can be shortened. In addition, since the optimum painting conditions can be predicted with high accuracy, the occurrence of overspray, excessive film thickness, etc. due to non-optimum painting conditions can be reduced. Thereby, the loss of paint can be reduced.

[0008] The invention according to claim 2 is, in claim 1, characterized in that the painting model construction means accumulates at least one of factors related to the electrostatic painting machine which is the painting machine and factors related to the painting robot equipped with the electrostatic painting machine as the data on the painting conditions, and constructs the painting model for outputting the predicted value of the film thickness value.

[0009] In the invention according to claim 2, for the painting model, by accumulating at least one of the factors related to the electrostatic painting machine and the factors related to the painting robot as data of the painting conditions, the accuracy of the film thickness value of the target paint film is increased. Therefore, if the painting condition prediction means performs inverse analysis using the painting model, the optimal painting conditions for achieving the target can be obtained with high accuracy.

[0010] Note that, as the factors related to the electrostatic painting machine, for example, at least one of the factors related to the rotary atomizing head of the electrostatic painting machine and the factors related to the energization conditions of the electrostatic painting can be mentioned (claim 3). In this case, the painting model construction means may select at least one from each of the factors related to the rotary atomizing head, the factors related to the energization conditions of the electrostatic painting, and the factors related to the painting robot, and accumulate them as data of the painting conditions (claim 4).

[0011] The gist of the invention according to claim 5 is that, in claim 1, it includes visualization means for visualizing the predicted value of the film thickness value output by the painting model as a contour diagram.

[0012] In the invention according to claim 5, by looking at the contour diagram, the user can intuitively know what value the film thickness value of the paint film in which area will be.

[0013] The gist of the invention according to claim 6 is that, in claim 5, the visualization means generates a three-dimensional color contour diagram based on the shape data of the workpiece.

[0014] In the invention according to claim 6, by looking at the three-dimensional color contour diagram, the user can intuitively and accurately know the state of the paint film formed on the surface of the three-dimensional workpiece by the difference in color.

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

[0016] In the invention according to claim 7, when the workpiece is painted under the input painting conditions, it is possible to predict the coating efficiency, which is the ratio of the amount of paint used to the paint adhered to the workpiece. That is, it is possible to predict how much the loss of paint can be reduced.

[0017] The gist of the invention according to claim 8 is that, in any one of claims 1 to 6, the coating condition prediction means preferentially predicts the coating conditions having a high influence degree on the film thickness value.

[0018] In the invention according to claim 8, by preferentially predicting the coating conditions having a high influence degree on the film thickness value of the coating film, it is possible to quickly reach the target film thickness value.

[0019] The gist of the invention according to claim 9 is that, in claim 8, at least a part of the coating conditions can be set as user selection factors selected by the user, the coating conditions set as the user selection factors are evaluation parameters preferentially determined by the user, and the coating condition prediction means predicts the coating conditions not set as the user selection factors as the optimum coating conditions.

[0020] In the invention according to claim 9, at least a part of the coating conditions can be set as evaluation parameters (user selection factors) determined according to the user's wishes.

[0021] The gist of the invention according to claim 10 is 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 on 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.

[0022] In the invention according to claim 10, by accumulating at least one of the factors related to the electrostatic coating machine and the factors related to the coating robot, the accuracy of the predicted value of the film thickness value is increased. Therefore, by using the coating model, it becomes possible to optimize the film thickness value of the coating film.

Effect of the Invention

[0023] As described in detail above, according to the inventions according to claims 1 to 9, it is possible to determine the coating conditions of the coating machine in a short period of time, and by determining the optimal coating conditions, it is possible to reduce the loss of the coating material. Further, according to the invention according to claim 10, it becomes possible to optimize the coating conditions.

Brief Description of the Drawings

[0024]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Mode for Carrying Out the Invention

[0025] Hereinafter, an embodiment embodying the present invention will be described in detail based on the drawings.

[0026] As shown in FIG. 1, the painting condition prediction system 1 of the present embodiment is a system that predicts the optimal painting conditions X1 to X9 (see FIG. 3) of the electrostatic painting machine 21 when painting the automobile body W1 (workpiece) using a rotary atomization type electrostatic painting machine 21 in the painting facility 10. Further, a painting robot 11 is installed in the painting area (not shown) of the painting facility 10.

[0027] The painting robot 11 includes a main body 12, an arm 13 extending from the main body 12, and an electrostatic painting machine 21 attached to the tip of the arm 13. A rotary atomizing head 23 is rotatably attached to the front of the painting machine main body 22 that constitutes the electrostatic painting machine 21. Note that paint P1 is supplied to the painting machine main body 22, and the paint P1 supplied to the painting machine main body 22 is supplied to the rotary atomizing head 23. Then, the rotary atomizing head 23 rotates by an air motor (not shown) provided in the painting machine main body 22, so that the paint P1 is atomized and sprayed onto the automobile body W1. Further, a current is applied to the rotary atomizing head 23 and the paint P1 from a current generator (not shown). The electrostatic painting machine 21 of the present embodiment is a high deposition painting machine (so-called iX painting machine) that performs painting in a state where paint particles (paint P1) are atomized by an electric force.

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

[0029] As shown in FIG. 1, the painting condition prediction system 1 includes a personal computer 30, and the personal computer 30 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, and the like. Further, a keyboard 35 of the personal computer 30 and a display 36 of the personal computer 30 are electrically connected to the CPU 32. Note that the display 36 of the present embodiment is a display with a touch panel. Furthermore, the CPU 32 is electrically connected to the painting robot 11 and the electrostatic painting machine 21, and controls them by various drive signals. Also, a program for controlling the painting condition prediction system 1 is stored in the ROM 33.

[0030] Next, a method for predicting the painting conditions X1 to X9 using the painting 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 a predicted value of the film thickness value (film thickness distribution) of the paint film. Specifically, first, data (experimental data) showing the relationship between the formation position of the paint film (a predetermined position on the surface of the automobile body W1) and the film thickness value of the paint film is accumulated. Next, after constructing a two-dimensional basic form painting model as a basis from the experimental data, in order to improve the accuracy, further fitting (rubbing) is performed between the experimental data and each parameter value of the painting model, and then the function of the fitted parameters is performed. By doing so, a highly accurate two-dimensional basic form painting model can be obtained. Next, by three-dimensionalizing the obtained painting model, the two-dimensional basic form painting model is extended to a three-dimensional extended type painting model. Further, using the data on the paint usage amount (paint usage amount data) taken in advance, by linearly interpolating the painting model formula parameters under each condition, a painting model that can predict the ratio of the paint usage amount to the paint P1 attached to the automobile body W1 (that is, the painting efficiency) is constructed. At this point, when data on the painting conditions X1 to X9 is input to the painting model, the painting model outputs a predicted value of the painting efficiency in addition to the predicted value of the film thickness value. That is, the CPU 32 has a function as "painting model construction means". Note that the construction of the painting model is performed in a process separate from the painting line of the automobile body W1 (offline). Further, the CPU 32 stores the constructed painting model in the RAM 34.

[0032] Next, the CPU 32 performs processing for predicting the characteristics of the paint film formed on the surface W2 of the automobile body W1. In step S10 shown in FIG. 2, the user inputs (accumulates) data on the painting conditions X1 to X9 (see FIG. 3) of the optimal electrostatic painting machine 21 to the painting model stored in the RAM 34. Further, the CPU 32 inputs (accumulates) the shape data of the automobile body W1 to the painting model stored in the RAM 34.

[0033] Among the painting conditions X1 to X9, the data of the painting conditions X1 to X4 are factors related to the electrostatic painting machine 21. Among the painting conditions X1 to X4 that are factors related to the electrostatic painting machine 21, the data of the painting conditions X1 to X3 are factors related to the rotary atomizing head 23 of the electrostatic painting machine 21, and the data of the painting condition X4 are factors related to the energization condition of the electrostatic painting. The painting condition X1 is the discharge amount (cc / min) of the paint P1 from the rotary atomizing head 23. The painting condition X2 is the rotation speed (rpm) of the air motor provided in the electrostatic painting machine 21. The painting condition X3 is the air flow rate (L / min) discharged from around the rotary atomizing head 23. In this embodiment, the air discharged from around the rotary atomizing head 23 has the same function as the shaping air (atomization of the paint P1, formation of the painting pattern). Also, the painting condition X4 is the current in the constant current control, specifically, the current value (μA) of the current that charges the rotary atomizing head 23 and the paint P1. Thereby, a current can be stably supplied to the rotary atomizing head 23 and the paint P1.

[0034] Furthermore, among the painting conditions X1 to X9, the data of the painting conditions X5 to X9 are factors related to the painting robot 11 equipped with the electrostatic painting machine 21. The painting condition X5 is the distance of the electrostatic painting machine 21 (gun), specifically, the distance (mm) between the tip surface of the rotary atomizing head 23 and the surface W2 of the automobile body W1. The painting condition X6 is the moving speed (mm / sec) of the electrostatic painting machine 21 (gun). The painting condition X7 is the number of painting times (times) by the electrostatic painting machine 21. The painting condition X8 is the pitch (mm) of the painting locus with respect to the automobile body W1. The pitch of the painting locus means, for example, the distance when shifting in the horizontal direction when moving the electrostatic painting machine 21 in the order of downward → horizontal direction → upward → horizontal direction → downward →... while painting. Also, the painting condition X9 is the angle (°) of the electrostatic painting machine 21 (gun) with respect to the surface W2 of the automobile body W1.

[0035] Note that the input of painting conditions X1 to X9 is performed, for example, by the user operating the keyboard 35. Then, the CPU 32 performs control to display painting conditions X1 to X5 and X9 among the input painting conditions X1 to X9 on the left side of the display 36 (see Fig. 4). Also, the CPU 32 performs control to display the sequential analysis button 41 (see Fig. 4) at a position directly below painting conditions X1 to X5 and X9 on the left side of the display 36. Note that the sequential analysis button 41 is an icon in an oval shape, with a picture of a right-facing triangular arrow and the word "Calculation" attached to it.

[0036] Then, when data of painting conditions X1 to X9 is accumulated in the painting model shown in Fig. 1, the CPU 32 shown in Fig. 1 predicts the film thickness value of the paint film upon the user operating the sequential analysis button 41. That is, the CPU 32 has a function as "film thickness value prediction means". Also, when data of painting conditions X1 to X9 is input, the CPU 32 predicts the painting efficiency in addition to the film thickness value of the paint film upon the user operating the sequential analysis button 41. Furthermore, in step S20 shown in Fig. 2, the CPU 32 outputs a predicted value A1 (see Fig. 4) of the film thickness value and a predicted value A2 (see Fig. 4) of the painting efficiency from the painting model. Note that the predicted values A1 and A2 are displayed in the lower right part of the display 36. Also, at a position on the right side of the predicted value A1 of the film thickness value in the lower right part of the display 36, the Y coordinate A3 (see Fig. 4) of the location in the automobile body W1 where the predicted value A1 is obtained is displayed. Thereby, the user can confirm (simulate) the film thickness value and the painting efficiency for the input painting conditions X1 to X9.

[0037] Further, the CPU 32 performs control to visualize (generate) the predicted value A1 of the film thickness value output by the painting model as a three-dimensional color contour diagram C1 (see FIG. 4) based on the shape data of the automobile body W1 and display it on the right side portion of the display 36. That is, the CPU 32 has a function as "visualization means". Note that on the right side portion of the display 36, at the right side position of the three-dimensional color contour diagram C1, a graph 43 showing the relationship between the dimension in the X direction at the Y coordinate A3 and the film thickness value at the Y coordinate A3 is displayed.

[0038] Next, the CPU 32 shown in FIG. 1 performs a process for predicting the painting conditions X1 to X9 of the electrostatic painting machine 21 that are optimal for achieving the target. Specifically, first, in step S30 shown in FIG. 2, the user inputs the target film thickness value (see "target film thickness" in FIG. 5) into the painting model stored in the RAM 34. Further, the CPU 32 inputs the shape data of the automobile body W1 into the painting model stored in the RAM 34.

[0039] Note that the input of the target film thickness value is performed, for example, by the user operating the keyboard 35. Then, the CPU 32 performs control to display the input film thickness value (target film thickness) on the upper left portion of the display 36 (see FIG. 6). Further, the CPU 32 performs control to display the inverse analysis button 51 (see FIG. 6) on the lower left portion of the display 36. Note that the inverse analysis button 51 is an icon having an elliptical shape, and is provided with a picture of a rightward-facing triangular arrow and the character "Calculation".

[0040] And when the target film thickness value and the shape data of the automobile body W1 are input for the painting model, the CPU 32 shown in FIG. 1 performs inverse analysis (inverse analysis algorithm) using the painting model upon the operation of the inverse analysis button 51 by the user. Specifically, using the Bayesian optimization method and the genetic algorithm, for the target film thickness, analysis is performed such that the parameters (evaluation function) in the most efficient and optimal objective variables are minimized in terms of the root mean square error. Thereby, the optimal painting conditions X1 to X9 for achieving the target are predicted. That is, the CPU 32 has a function as "painting condition prediction means".

[0041] Next, in step S40 shown in FIG. 2, the CPU 32 performs control to display the predicted painting conditions in the lower right part of the display 36. For example, in "Case 1" shown in FIG. 5, the CPU 32 performs control to preferentially predict the painting conditions X1 to X9 that have a high influence on the film thickness value. Specifically, the CPU 32 positions the priority evaluation parameters among the painting conditions X1 to X9, and preferentially determines the value with the minimum evaluation parameter as the optimal painting condition. Then, the CPU 32 performs control to display the predicted painting conditions X1 to X9 on the display 36.

[0042] Also, in "Case 2" shown in FIG. 5, at least a part of the painting conditions X1 to X9 (specifically, painting conditions X6, X8) can be set as the user selection factor 61 selected by the operation of the keyboard 35 by the user. Note that the painting conditions X6, X8 set as the user selection factor 61 are the evaluation parameters preferentially determined according to the user's wishes. In this case, the CPU 32 predicts the painting conditions X1 to X5, X7, X9 that are not set as the user selection factor 61 as the optimal painting conditions. Specifically, the CPU 32 positions the painting conditions X1 to X5, X7, X9 as evaluation parameters, and determines the values such that each evaluation parameter is minimized.

[0043] Note that at the lower right part of the display 36, at the right side position of the painting conditions X1 to X9, the Y coordinate A3 of the location in the automobile body W1 that becomes the predicted value A1, the predicted value A1 of the film thickness value, and the predicted value A2 of the painting efficiency are displayed. Further, a three-dimensional color contour diagram C1 (see FIG. 6) is displayed in the upper right part of the display 36, and a graph 43 is displayed at the right side position of the three-dimensional color contour diagram C1. Thereby, the user can confirm (simulate) the film thickness value of the target coating film.

[0044] Thereafter, in step S50 shown in FIG. 2, the CPU 32 transfers (installs) the predicted painting conditions X1 to X9 to the painting robot 11. In the subsequent step S60, the CPU 32 performs control to actually paint the automobile body W1 in-line based on the painting conditions X1 to X9 transferred to the painting robot 11. Specifically speaking, the CPU 32 performs control to charge the rotary atomizing head 23 and the paint P1 based on the painting condition X4. Further, the CPU 32 performs control to spray the paint P1 from the electrostatic painting machine 21 onto the automobile body W1 based on the painting conditions X1 to X3, X5 to X9 (actual painting trial). As a result, a coating film is formed on the surface W2 of the automobile body W1, and quality confirmation becomes possible.

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

[0046] (1) In the painting condition prediction system 1 of this embodiment, by performing inverse analysis using a painting model that outputs a predicted value A1 of the film thickness of a paint film formed on the surface W2 of the automobile body W1, the optimal painting conditions X1 to X9 of the electrostatic painting machine 21 to achieve the target film thickness value of the paint film can be predicted with high accuracy. As a result, since the review of the painting conditions X1 to X9 in the actual painting trial is reduced, the man-hours can be reduced, and the period for starting the painting quality of new products, etc. (specifically, the determination of the painting conditions under which the automobile body W1 becomes a painting good product) can be shortened. Also, since the optimal painting conditions X1 to X9 can be predicted with high accuracy, the occurrence of overspray, excessive film thickness, etc. due to the painting conditions not being the optimal painting conditions X1 to X9 can be reduced. As a result, the painting quality of the automobile body W1 is improved, and the loss of the paint P1 can be reduced. Furthermore, since the painting condition prediction system 1 of this embodiment is applied to a high deposition painting machine, the occurrence of overspray and the loss of the paint P1 can be more reliably reduced.

[0047] (2) The painting model of this embodiment outputs a predicted value A2 of the painting efficiency in addition to the predicted value A1 of the film thickness of the paint film when data of the painting conditions X1 to X9 are input. As a result, it is 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, etc., and which have the highest painting efficiency. That is, it is possible to predict not only the conditions for improving the quality of the paint film but also the conditions for reducing the loss of the paint P1.

[0048] (3) In this embodiment, since inverse analysis is performed using a painting model that covers the characteristics of the electrostatic painting machine 21 as a data source, it is not necessary to perform re-learning (specifically, preparation of data showing the relationship between the painting conditions X1 to X9 and the film thickness of the paint film, etc.).

[0049] Note that the above embodiment may be modified as follows.

[0050] · The CPU 32 in the above embodiment stored factors related to the electrostatic coating machine 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 the factors related to the electrostatic coating machine 21 as data on painting conditions (painting conditions X1 to X4), or may store only the factors related to the painting robot 11 as data on painting conditions (painting conditions X5 to X9). Note that the factors 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 moving speed of the electrostatic coating machine 21 (painting condition X6), the number of painting passes (painting condition X7), the pitch of the painting locus with respect to the automobile body W1 (painting condition X8), and the angle of the electrostatic coating machine 21 with respect to the automobile body W1 (painting condition X9).

[0051] · In the above embodiment, the factors related to the electrostatic coating machine 21 consisted of factors related to the rotary atomizing head 23 of the electrostatic coating machine 21, which are data on painting conditions X1 to X3, and factors related to the energization conditions of the electrostatic coating, which are data on painting condition X4. However, the factors related to the electrostatic coating machine 21 may consist only of factors related to the rotary atomizing head 23, or may consist only of factors related to the energization conditions of the electrostatic coating. Also, the factors related to the rotary atomizing head 23 may be at least one of the discharge amount of the paint P1 from the rotary atomizing head 23 (painting condition X1), the rotational speed of the air motor (painting condition X2), and the flow rate of the air discharged from around the rotary atomizing head 23 (painting condition X3).

[0052] · In the above embodiment, the factor related to the energization conditions of the electrostatic coating, which is data on painting condition X4, was the current in the constant current control, specifically, the current value of the current that charges the rotary atomizing head 23 and the paint P1. However, the factor related to the energization conditions of the electrostatic coating may be the voltage in the constant voltage control, specifically, the voltage value of the current that charges the rotary atomizing head 23 and the paint P1.

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

[0054] ·In the above embodiment, the CPU 32 performed control to display, on the display 36, the three-dimensional color contour diagram C1 formed by visualizing the predicted value A1 of the film thickness value output by the painting model. However, the CPU 32 may perform control to display a monochrome contour diagram on the display 36, or may perform control to display a two-dimensional contour diagram on the display 36.

[0055] ·In the above embodiment, the three-dimensional color contour diagram C1 formed by visualizing the predicted value A1 of the film thickness value output by the painting model was displayed on the display 36, but the three-dimensional color contour diagram C1 may not be displayed.

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

[0057] · In the above embodiment, the film thickness value and the coating efficiency of the coating film were predicted upon the operation of the forward analysis button 41 by the user. However, the forward analysis button 41 may be omitted, and the film thickness value and the coating efficiency may be predicted when data of coating conditions X1 to X9 are accumulated for the coating model. Similarly, in the above embodiment, upon the operation of the reverse analysis button 51 by the user, the reverse analysis using the coating model was performed to predict the optimum coating conditions X1 to X9 for achieving the objective. However, the reverse analysis button 51 may be omitted, and the coating conditions X1 to X9 may be predicted when the target film thickness value and the shape data of the automobile body W1 are input for the coating model.

[0058] · In the above embodiment, a high deposition coating machine (so-called iX coating machine) was used as the coating machine, but a normal electrostatic rotary atomization type coating machine may be used, or a coating machine of a type that does not utilize static electricity may be used.

[0059] · In the above embodiment, the automobile body W1 was exemplified as the work to be coated using the electrostatic coating machine 21, but it is not limited thereto. For example, automotive interior parts such as instrument panels, console boxes, and armrests may be used as the work, or automotive exterior parts such as bumpers and aerodynamic add-on parts (spoilers, etc.) may be used as the work. Further, the work does not necessarily have to be an automotive part.

[0060] Next, in addition to the technical idea described in the claims, the technical ideas grasped by the above-described embodiment are listed below.

[0061] (1) In claim 4, the factor related to the rotary atomizing head is at least one of the paint discharge amount from the rotary atomizing head, the rotational speed of the air motor provided in the electrostatic coater, and the air flow rate discharged from around the rotary atomizing head; the factor related to the energization condition of the electrostatic coating is the current value or voltage value of the current for charging the rotary atomizing head and the paint; the factor related to the painting robot is at least one of the distance between the rotary atomizing head and the workpiece, the moving speed of the electrostatic coater, the number of painting passes, the pitch of the painting trajectory with respect to the workpiece, and the angle of the electrostatic coater with respect to the workpiece. A painting condition prediction system characterized by this.

[0062] (2) In claim 1, the shape data of the workpiece is three-dimensional data. A painting condition prediction system characterized by this.

Explanation of Signs

[0063] 1…Painting condition prediction system 11…Painting robot 21…Electrostatic coater as a coater 23…Rotary atomizing head 32…CPU as painting 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 a workpiece W2…Surface of the workpiece X1~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, painting model construction means for accumulating data on the painting conditions and shape data of the workpiece, and constructing a painting model that outputs a predicted value of the film thickness of the paint film formed on the surface of the workpiece; 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; painting condition prediction means for predicting the optimal painting conditions for achieving the target by performing inverse analysis using the painting model triggered by the 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:

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

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

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

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

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

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

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 influence degree on the film thickness value.

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

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 on the coating conditions of the optimum electrostatic coating machine, and outputting a predicted value of the film thickness value of a coating film formed on the surface of a workpiece.

Images