LAYER THICKNESS OPTIMIZATION AND PROGRAMMING METHOD FOR A COATING PLANT AND CORRESPONDING COATING PLANT

Description

[0001] The invention relates to a method for parameterizing and programming a program-controlled coating robot for coating components, in particular for a painting robot for painting motor vehicle body components. Furthermore, the invention relates to a correspondingly operating coating system.

[0002] In modern paint shops for painting automotive body components, rotary atomizers are typically used as applicators. These are guided by a multi-axis painting robot with serial robot kinematics along predefined robot paths over the surfaces of the automotive body components to be painted. The rotary atomizer is operated with specific application parameters (e.g., paint quantity, speed, shaping air, high voltage of the electrostatic coating agent charging, etc.).

[0003] The challenge here is programming the application parameters and robot paths for a specific vehicle body with a given shape.

[0004] A conventional approach to programming application parameters and robot paths is known as "teaching." The programmer defines the robot path along the component surface by programming specific points along the path, which are then automatically followed during painting. Furthermore, the programmer, based on their experience, defines the application parameters that the rotary atomizer adheres to when following the specified robot path. A test run is then carried out with the specified robot path and application parameters to check the painting result. Depending on the quality of the achieved painting result, the robot path and application parameters can then be adjusted to improve the painting result accordingly. In this way, an acceptable painting result can be achieved in just a few iteration steps.

[0005] The disadvantage of this common approach to programming robot paths and application parameters is that the programmer's experience is crucial for achieving a good painting result. Furthermore, it typically requires multiple iteration steps (optimization loops), each of which involves a corresponding amount of material consumption.

[0006] Another approach to programming the robot paths and application parameters involves a complete simulation of the painting result in a corresponding computer model.

[0007] The disadvantage of this approach to programming robot paths and application parameters is the fact that enormous computing power is required to achieve meaningful results regarding the painting result. Furthermore, the physical modeling of the entire painting process is extremely difficult, so the informative value of such a model-based determination of the painting result is not yet entirely satisfactory.

[0008] Regarding the technical background of the invention, reference should also be made to the following publications: US 5 521477 A, DE 101 50 826 A1, EP 0 867 233 A1, DE 102 25 276 A1, Shengrui, Y., Ligang, C.: "Modeling and Prediction of Paint Film Deposition Rate for Robotic Spray Painting", Proceedings of the 2011 IEEE International Conference on Mechatronics and Automation 2011.

[0009] Finally, a method according to the preamble of claim 1 is known from Arikan S., Balkan T.: "Modeling of paint flow rate flux for elliptical paints sprays by using experimental paint thickness distributions", Industrial Robot, Vol. 33, 2006 No. 1, ISSN 0143-991X. However, this known method is not yet completely satisfactory.

[0010] The invention is therefore based on the object of creating an improved approach for parameterizing, optimizing and programming the robot paths and the application parameters.

[0011] This object is achieved by a method according to the invention or a corresponding coating system according to the independent claims.

[0012] The method according to the invention initially provides for a robot path to be specified, as is generally known from the prior art. For example, the robot path can be defined by a plurality of path points, which are then traversed one after the other from the paint impact point of the applicator (e.g., rotary atomizer). The robot path can reflect not only the position of the individual path points in Cartesian spatial coordinates, but also the desired spatial orientation of the spray axis of the applicator used (e.g., rotary atomizer).

[0013] Furthermore, the method according to the invention provides for the specification of application parameters that the applicator and the coating robot are to adhere to during actual coating operation when moving along the robot path. The term "application parameter" used within the scope of the invention preferably encompasses the operating variables of the applicator used, such as atomizer speed, shaping air flow / shaping air pressure, high voltage of an electrostatic coating agent charge, and / or paint volume flow. However, the term "application parameters" used within the scope of the invention is not limited to the aforementioned examples of operating variables of the applicator.Furthermore, the term application parameters used in the context of the invention preferably also includes operating variables of the coating robot, such as the painting speed with which the coating robot moves the applicator over the component surface.

[0014] Within the scope of the programming method according to the invention, the painting result achieved during painting along the specified robot path with the specified application parameters is then virtually determined. However, within the scope of the invention, this virtual determination of the painting result is not carried out exclusively based on a model, but rather takes into account real spray pattern data.

[0015] For example, spray tests are conducted with the applicator in advance, with the actual coating results being measured for specific application parameters and robot paths. For example, test panels can be coated, and the coating thickness profile on the test panel is then measured. The spray pattern data obtained in this way is then stored in a database, associated with the respective application parameters and robot path.

[0016] Within the scope of the programming method according to the invention, the spray pattern data corresponding to the specified application parameters and the specified robot path are determined. The virtual determination of the painting result is then carried out depending on these spray pattern data, which were previously measured in real life. This involves extrapolation, for example, from the coating thickness profile on a flat test sheet to the corresponding coating thickness profile on a curved component surface. The programming method according to the invention thus determines the painting result in a quasi-hybrid form, by taking into account both real spray pattern data and an extrapolation model for converting the real spray pattern data to the real vehicle body.

[0017] The robot path and the application parameters are then optimized within the framework of the programming method according to the invention, which can be carried out in several iteration loops. If the virtually determined painting result is unsatisfactory, the application parameters and the robot path can be adjusted in order to then determine the painting result virtually for the adjusted application parameters and / or the adjusted robot path. In this way, an acceptable painting result can be achieved in just a few iteration loops, with the associated application parameters and the associated robot path then being adopted for subsequent painting operations. An advantage of this programming method according to the invention is the fact that the iteration loops for determining the acceptable application parameters and the acceptable robot path do not lead to corresponding material consumption, since the painting result is determined virtually in each case.

[0018] The variation of application parameters or path paths (robot paths) within given limits to optimize layer thickness uniformity is primarily performed automatically based on a computer algorithm. This is preferably the task of an optimization unit, which automatically compares the layer thickness uniformity achieved in the virtual determination of the painting result with a permissible layer thickness fluctuation and, if necessary, initiates iteration loops with modified application parameters and / or path paths (robot paths). The given limits result from the process windows of the application parameters that experience has shown to be favorable for industrial applications or from proven robot path concepts. In addition to or as an alternative to this automatic optimization process, the assessment of layer thickness uniformity and the variation of the application parameters or path paths can also be performed manually by the user.

[0019] It was briefly mentioned above that the spray pattern data can be determined, for example, through tests in which test sheets are coated with different sets of application parameters. The programming method according to the invention therefore also includes conducting real application tests with different application parameters and / or different robot paths, in particular by coating test sheets with the different application parameters. The spray pattern is then measured during the real application tests, and corresponding spray pattern data is determined. The spray pattern data determined in this way are then stored in the database for the various application parameters and robot paths.It is important that the spray pattern data is stored in the database in a fixed assignment to the underlying application parameters and robot paths, so that the associated spray pattern data can later be read out from the database depending on the application parameters and the robot path.

[0020] In practice, it is difficult to measure and store spray pattern data for all combinations of application parameters and robot paths. The invention therefore provides that the database contains a characteristic map in which the actually measured spray pattern data only form individual support points. The remaining characteristic map points are then interpolated from these support points.

[0021] It should also be mentioned that the painting of a motor vehicle body is usually carried out using different so-called brushes. The term "brush" used in the context of the invention preferably defines the complete set of application parameters and the robot path. For example, large-area painting is usually carried out using a so-called area brush, which is characterized by a wide shape or a wide layer thickness profile. The painting of edges or details, on the other hand, is usually carried out using a so-called edge brush, which is characterized by a narrow shape or a narrow layer thickness profile. The database with the spray pattern data should therefore preferably contain spray pattern data for both the area brush and the edge brush. Within the scope of the programming method according to the invention, the area brush is then preferably optimized in the manner generally described above.The edge brush is then optimized separately in essentially the same way.

[0022] It should also be mentioned that the edge brush can be used, for example, to paint fenders, A-pillars, B-pillars, C-pillars, tornado lines or sill areas, to name just a few examples.

[0023] The optimized surface and edge brushes can lead to unsatisfactory painting results (hot spots), for example, at module joints (transition areas between individual painting modules, e.g., between the fender and the hood). Therefore, within the scope of the invention, it is also possible to optimize the layer thickness uniformity at hot spots by locally varying the robot paths and the affected brushes, if necessary by adjusting the paint flow on / off points.

[0024] Furthermore, it should be mentioned that within the scope of the invention, preferably not only the operating parameters of the applicator (e.g. rotary atomizer) are optimized, but preferably also the robot path itself.

[0025] With regard to the spray pattern data, various possibilities exist within the scope of the invention. For example, the spray pattern data for each individual spray pattern can include at least one spray pattern characteristic value that characterizes the spray pattern. For example, this can be the well-known SB

[50] value. The SB

[50] value is the width of the layer thickness profile within which the layer thickness on the component surface is at least 50% of the maximum layer thickness SDmax. However, alternatively or additionally, it is also possible for at least one of the spray pattern characteristic values ​​to be an SDmax value that represents the maximum layer thickness.

[0026] Furthermore, it should be mentioned that the real layer thickness curve can be represented by mathematical curves (e.g. Gaussian curves).

[0027] Furthermore, the invention provides the possibility of graphically displaying the painting result, for example, with the resulting coating thickness curve. The programmer can then directly assess the virtually determined painting result based on the display and decide whether the painting result is acceptable.

[0028] In addition to the programming method according to the invention described above, the invention also comprises a corresponding coating system which is suitable for carrying out the programming method according to the invention.

[0029] Thus, the coating system according to the invention usually comprises a coating robot and an applicator (e.g. rotary atomizer) which is guided by the coating robot and, during operation, applies the coating agent to the component (e.g. motor vehicle body).

[0030] The coating system according to the invention is characterized by a database containing spray pattern data for various application parameters and various robot paths. These data represent a real spray pattern that the applicator generates during a real coating operation with the application parameters and / or on the robot path. It should be noted that the database is usually not completely filled with the required spray pattern data at the beginning. The spray pattern data can then be subsequently determined during operation and fed into the database.

[0031] In addition, the coating system according to the invention comprises a computing unit that virtually calculates the painting result from predetermined application parameters and predetermined robot paths, taking into account the stored spray pattern data.

[0032] Furthermore, the coating system according to the invention comprises an optimization unit that optimizes the application parameters and / or the robot path, which can be done, for example, in several iteration loops. The optimization unit specifies specific application parameters and / or a specific robot path and then evaluates the virtually determined painting result. If the virtually determined painting result is acceptable, the application parameters and the robot path can be adopted for subsequent real-life coating operations. Otherwise, the optimization unit varies the application parameters and the robot path until the virtually determined painting result is acceptable.

[0033] The coating system according to the invention preferably also has a display unit (e.g. screen) in order to be able to graphically display the painting result.

[0034] Other advantageous developments of the invention are characterized in the subclaims or are explained in more detail below together with the description of the preferred embodiments of the invention with reference to the figures. They show: Figure 1 shows a flow chart to illustrate the programming method according to the invention, Figure 2 shows a schematic representation of a painting system according to the invention, Figure 3 shows a layer thickness curve that was actually measured, Figure 4 shows a layer thickness curve that was actually measured with a mathematical approximation, Figure 5 shows an example of a characteristic map with stored spray pattern data, and Figure 6 shows a flow chart to illustrate a further development of the programming method according to the invention.

[0035] The following is the flow chart according to Figure 1 which illustrates the programming method according to the invention in schematic form.

[0036] In a first step S1, a robot path is specified. For this purpose, path points can be defined that are to be traversed successively from the paint impact point of the applicator used (e.g., rotary atomizer). The individual path points can be defined, for example, using Cartesian spatial coordinates (X, Y, Z). Furthermore, the desired orientation of the spray axis of the applicator used is preferably also specified at the individual path points; for this purpose, a vector with three components (XR, YR, ZR) can be defined, for example.

[0037] In a further step S2, the complete set of application parameters is defined with which the applicator is to operate during the actual painting operation. These include, for example, the atomizer speed, the shaping air flow, the paint flow, the high voltage of the electrostatic coating agent charging, and the painting speed at which the painting robot moves the applicator over the component surface.

[0038] In step S3, spray pattern data that matches the specified robot path and the specified application parameters and was previously determined is read from a database. Alternatively, the spray pattern data can also be generated first if it has not yet been saved.

[0039] In step S4, a virtual calculation of the painting result is performed for the specified robot path and the specified application parameters, taking into account the spray pattern data read from the database. For example, the spray pattern data previously measured on a flat test sheet are converted or extrapolated to the actual painting result on a curved component surface.

[0040] In step S5 the painting result can then be visualized.

[0041] In step S6, a check is then carried out to determine whether the painting result is acceptable. One possibility for this is for the programmer to check whether the painting result is acceptable using the visualization in step S5. A preferred alternative, however, is for a computer algorithm to automatically check whether the painting result is acceptable. If this is the case, the programming process is terminated in step S7, and the specified robot path and the specified application parameters are stored for later Painting taken over.

[0042] Otherwise, further optimization is required, so in step S8, the application parameters and / or the robot path are modified. Steps S3-S7 are then repeated in an iterative loop until an acceptable painting result is achieved. This repetition in an iterative loop can be automatic or user-controlled.

[0043] Figure 2 shows a schematic, simplified representation of a painting system according to the invention for painting motor vehicle body components. The painting system initially has an applicator 1, which can be, for example, a rotary atomizer.

[0044] During operation, the applicator 1 is moved over the component surface by a multi-axis painting robot 2 with serial robot kinematics.

[0045] The applicator 1 and the painting robot 2 are controlled by a robot controller 3, with the robot controller 3 controlling the painting robot 2 so that the applicator 1 follows a predetermined robot path with its paint impact point. The robot controller 3 controls the applicator 1 with specific application parameters (e.g., atomizer speed, paint flow, etc.).

[0046] It should be noted that the robot controller 3 can be distributed across several control units.

[0047] The painting system according to the invention additionally has a database 4 in which spray pattern data for a variety of different application parameters and robot paths are stored. These spray pattern data were previously measured in spray tests, with the application parameters and the robot path being varied accordingly.

[0048] In addition, the painting system according to the invention has an optimization unit 5, which initially specifies a set of application parameters and a specific robot path as a starting point for the optimization and forwards these to a computing unit 6. The computing unit 6 then reads the associated spray pattern data from the database 4 depending on the specified application parameters and the specified robot path. The computing unit 6 then calculates the painting result on the component surface from the spray pattern data and reports the painting result back to the optimization unit 5. The optimization unit 5 then graphically displays the painting result on a display unit 7. In addition, the optimization unit 5 can vary and optimize the application parameters and the robot path in an iteration loop, whereby the painting result is virtually determined and evaluated in each case.The optimization is therefore preferably carried out automatically by the optimization unit 5.

[0049] Figure 3 shows a layer thickness profile generated by a rotary atomizer on the component surface, with the layer thickness profile being based on a real measurement. Within the framework of the programming method according to the invention, spray pattern parameters, such as the SB

[50] value or the maximum value SD max , can then be calculated from the real layer thickness profile. The SB

[50] value indicates the width of the layer thickness profile within which the layer thickness is at least 50% of the maximum layer thickness SD max .

[0050] Figure 4 shows a corresponding layer thickness curve, namely on the one hand a real measured layer thickness curve 8 and on the other hand a mathematically approximated layer thickness curve 9. For example, the real measured layer thickness curve 8 can be simulated by Gaussian curves.

[0051] Figure 5 shows a stored characteristic map with spray pattern data for different paint volumes and different shaping air flows. The stored spray pattern data consists of different SB

[50] values ​​for the different paint volumes and the different shaping air flows.

[0052] Figure 6 shows a further development of the programming method according to the invention.

[0053] In the first step (S1), a surface brush (main brush) is specified. The surface brush defines all application parameters and the robot path for painting large areas of the vehicle body, such as a roof.

[0054] In a step S2, the painting result is then determined virtually in the manner described above, taking into account the spray pattern data read from the database.

[0055] In a step S3 it is then checked whether the layer thickness level of the layer thickness uniformity is acceptable.

[0056] In step S4, it is then checked on this basis whether the painting result is acceptable.

[0057] If this is not the case, the application parameters of the surface brush are varied within given limits in a step S5 and the above-mentioned steps S2-S4 are repeated in an iteration loop until an acceptable painting result for the surface brush is obtained.

[0058] In steps S6-S10, the same process is then carried out for an edge brush, which is used to paint the edges and edge areas of the body.

[0059] Finally, in steps S11-S15, the application parameters and robot paths for painting hot spots are checked and optimized. These are problematic surface areas of vehicle bodies that are difficult to paint. List of reference symbols:

[0060] 1Applicator 2Painting robot 3Robot control 4Database 5Optimization unit 6Calculation unit 7Display unit 8Actual measured layer thickness curve 9Mathematically approximated layer thickness curve

Claims

1. Method for programming a program-controlled coating robot (2) for coating components, in particular a painting robot (2) for painting motor vehicle body components, having the following steps: a) Specification of a robot path which is to be traversed by a paint impact point of an applicator (1) guided by the coating robot (2) in coating operation, b) specification of application parameters which are to be maintained by the applicator (1) and the coating robot (2) in real coating operation during movement along the robot path, wherein the term application parameters preferably includes the operating parameters of the applicator used, as for example the atomizer rotary speed, shaping air flow, shaping air pressure, high voltage of an electrostatic coating agent charging system and / or paint volume flow, c) virtual determination of a coating result for the predetermined robot path and the predetermined application parameters, with the following steps for the virtual determination of the coating result: c1) reading out real spray pattern data from a database (4) as a function of the predetermined robot path and / or the predetermined application parameters, the real spray pattern data read out from the database (4) reproducing a spray pattern which the applicator (1) generates during a real coating operation with the predetermined application parameters and / or on the predetermined robot path, with the following steps for the determination of the spray pattern data: c1a) carrying out real application tests with different application parameters and different robot paths, c1b) measuring the spray pattern during the real application tests and determining the corresponding spray pattern data for the different application parameters and robot paths, and c1c) storing the spray pattern data for the various application parameters and robot paths in the database (4), and c2) determining the coating result taking into account the real spray pattern data generated or read out from the database (4), wherein d) the database contains a characteristic map in which the real measured spray pattern data only form individual interpolation points, and e) the remaining map points are interpolated from these interpolation points, the method further comprises the following steps: Optimizing the robot path and the application parameters as a function of the determined virtual coating result, optimizing the robot path and the application parameters depending on the determined virtual coating result, visualization of the coating result, checking the coating result, if the coating result is acceptable: adoption of the specified robot path and the specified application parameters for subsequent coating.

2. Method according to claim 1, characterized in a) that the application parameters optionally define a surface brush or an edge brush, b) that the surface brush is used for coating large component surfaces, c) that the edge brush is used for coating small component surfaces, in particular for coating the following component surfaces of a motor vehicle body: c1) Fender, c2) A-pillar, c3) B-pillar, c4) C-pillar, c5) tornado line, c6) sill area, and d) that the database (4) contains spray pattern data for both the surface brush and the edge brush.

3. Method according to claim 2, characterized in a) that the database (4) contains spray pattern data for variations of the surface spray with varied application parameters, in particular for variations of at least one of the following application parameters: a1) atomizer rotational speed, a2) shaping air flow or shaping air pressure, a3) high voltage of an electrostatic coating agent charging, a4) paint volume flow, a5) coating speed at which the coating robot (2) moves the applicator (1) over the component, and / or b) that the database (4) contains spray pattern data for variations of the edge brush with varied application parameters, in particular for variations of at least one of the following application parameters: b1) atomizer rotational speed, b2) shaping air flow or shaping air pressure, b3) high voltage of an electrostatic coating agent charge, b4) paint volume flow, b5) coating speed at which the coating robot (2) moves the applicator (1) over the component.

4. Method according to one of the preceding claims, characterized by the following steps: a) determining the coating result for the different variations of the surface brush; and b) selecting the variation of the surface brush that leads to the best coating result.

5. Method according to any of the preceding claims, characterized by the following steps: a) virtual determination of the coating result for the different variations of the edge-brush, and b) selection of the variation of the edge-brush that leads to the best coating result.

6. Method according to any of the preceding claims, characterized by the following steps: a) virtually determining the coating result for the different variations of the robotic path; and b) selecting the variation of the robot trajectory that leads to the best coating result.

7. Method according to one of the preceding claims, characterized in that the spray pattern data for the individual spray patterns each comprise at least one spray pattern characteristic value, in particular an SB[50] value or an SDmax-value.

8. Method according to one of the preceding claims, characterized in a) that the spray pattern data reproduce a coating thickness profile, and / or b) that the coating thickness profile is represented by mathematical curves, in particular Gaussian curves.

9. Method according to one of the preceding claims, characterized in a) that the coating result is represented graphically with a coating thickness distribution, and / or b) that the coating result is calculated taking into account the read-out spray pattern data and the application parameters.

10. Method according to one of the preceding claims, characterized in that the application parameters determine the following operating variables of the applicator (1) and / or the coating robot (2): a) atomizer rotational speed of a rotary atomizer (1) forming the applicator (1), b) shaping air flow or shaping air pressure of shaping air delivered to form a spray of the applicator (1), c) high voltage of an electrostatic coating agent charge, d) coating volume flow delivered by the applicator (1), e) drive air flow or drive air pressure for driving a compressed air turbine of a rota-tion atomizer (1) forming the applicator (1), f) drawing speed at which the coating robot (2) moves the applicator (1) over the component to be coated, and / or g) application distance between the applicator (1) and the component to be coated.

11. Coating installation for coating components with a coating agent, in particular for painting motor vehicle body components, having a) a coating robot (2), b) an applicator (1) which is guided by the coating robot (2) and adapted to apply the coating agent to the component during operation, c) a database (4) which contains spray pattern data for various application parameters and various robot paths, which reproduce a spray pattern which the applicator (1) generates during a real coating operation with the application parameters and / or on the robot path, and d) a computing unit (6) which is adapted to virtually calculate a coating result from predetermined application parameters and a predetermined robot path and in doing so taking into account the stored spray pattern data for the predetermined application parameters and the predetermined robot path, wherein e) the database contains a characteristic map in which the real measured spray pattern data only form individual interpolation points, and f) the computing unit is adapted to interpolate the remaining map points from these interpolation points, and the coating system is adapted to carry out the method according to any of claims 1-10.

12. Coating system according to claim 11, characterized by a display unit (7) for graphical visualization of the virtually determined coating result.