Programming Method for Coating Equipment and Corresponding Coating Equipment

Abstract

The present invention relates to a method for programming a program-controlled coating installation with a coating robot and an application device for coating parts, in particular for programming a painting installation with a painting robot for painting automotive body parts, comprising the steps of a) presetting or determining (S1) geometry data of the part to be coated, b) presetting (S2) robot path data of a robot path to be scanned, and c) determining (S3) suitable spray pattern data representing a layer thickness profile and determined by simulation taking into account the robot path data and the geometry data of the part to be coated. Furthermore, the present invention also encompasses an appropriately adapted coating installation.

Description

Technical Field

[0001] The present invention relates to a method for programming a program-controlled coating facility, in particular for programming a coating facility for painting automotive body parts.

Background Art

[0002] In modern coating facilities for painting automotive body parts, a rotary atomizer is usually used as the coating device, which is guided by a multi-axis painting robot above the automotive body part to be painted. The operation of the coating facility, and thus the control units of the painting robot and the rotary atomizer, are program-controlled. Therefore, the coating facility must be programmed before the actual painting operation. As part of this programming, the robot path to be scanned by the paint impact point of the rotary atomizer is planned offline, taking into account defined painting guidelines that specify, for example, the path spacing and path speed. In addition, application parameters for the rotary atomizer, such as paint flow rate, speed, shaping air flow rate, etc. (where these application parameters are defined according to defined painting guidelines and are based on the experience of experts), are defined during programming. The purpose of programming is, among other things, to achieve the most uniform layer thickness distribution possible of the paint layer on the part to be coated. For this reason, during programming, the layer thickness distribution is optimized by changing the application parameters and the trajectory of the robot path in multiple iterative loops based on painting tests on a test vehicle body. The disadvantage of known programming methods is the high cost in terms of time, painting materials, and test vehicle bodies.

[0003] On the other hand, in a newer development line that has not yet been put into practical use, attempts are being made to completely physically simulate the painting process, so that the optimization of the robot path and application parameters can then be carried out as part of the simulation. However, the disadvantage of this new development line is the high computational effort required within the simulation, so that the time required for the calculation is still not practical.

[0004] For the general technical background of the present invention, refer to Patent Document 1 and Patent Document 2.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0006] In view of the above, the present invention is based on the problem of providing an improved method for programming a program-controlled coating facility. Further, the present invention is based on the problem of providing a correspondingly adapted coating facility.

Means for Solving the Problems

[0007] This problem is solved by the method according to the present invention or the coating facility according to the present invention based on the independent claims.

[0008] The programming method according to the present invention first provides for making available geometry data representing the geometry of the part to be coated (for example, an automotive body part). Within the scope of the programming method according to the present invention, the geometry data may be measured, for example, based on an actual part, or may be specified, for example, in a file format.

[0009] Furthermore, the programming method according to the present invention provides for specifying robot path data (wherein the robot path data defines a robot path to be scanned by the paint impact points (TCP: Tool Center Point) of a coating device guided by a coating robot during a coating operation).

[0010] Here, with regard to the type of coating device, it should be mentioned that the present invention is not necessarily limited to the rotary atomizer described at the beginning. Rather, the coating device may, in contrast to an atomizer, be a print head that discharges a spatially narrowly confined coating agent jet instead of a spray jet of the coating agent. Instead, within the scope of the present invention, the coating device may, to mention just a few examples, also be an airless atomizer or an air mix atomizer.

[0011] Furthermore, the programming method according to the present invention provides that spray pattern data, which may also be referred to as a "brush curve" representing a layer thickness profile, in particular a three-dimensional layer thickness profile, generated by the coating device on the surface of the components around the paint impact point during an actual coating operation, is determined. For example, when an ideal rotary atomizer coats a flat component surface, it generates a rotationally symmetric donut-shaped layer thickness profile. And the spray pattern data can reproduce the transition of the layer thickness as a function of the radial distance from the paint impact point. The three-dimensional layer thickness profile (static spray pattern) can be converted, for example, into a two-dimensional layer thickness cross-section (dynamic spray pattern), from which the spray pattern data can be determined, for example, in the generation of characteristic diagrams. However, within the scope of the present invention, the spray pattern data may also represent a dynamic spray pattern such as a layer thickness cross-section along the coating path.

[0012] In the context of the present invention, a simulation of the coating result is performed based on the spray pattern data, where the spray pattern data is optimized until an acceptable coating result is simulated. The acceptable spray pattern data is then stored in a first data set in order to later determine appropriate coating parameters (such as paint flow, shaping air flow, rotational speed of the rotary atomizer, etc.) suitable for the actual realization of the previously determined spray pattern data.

[0013] However, for a given robot path, even the most appropriate spray pattern data may not result in an acceptable coating outcome. In such cases, it is useful to optimize not only the spray pattern data but also the robot path. Thus, in one embodiment of the present invention, it is checked whether the simulated coating result with the most appropriate spray pattern data yields an acceptable coating result. If so, optimization of the robot path is not required, and the appropriate spray pattern data determined by the simulation can be continuously used. Otherwise, the robot path is optimized, and the determination of the most appropriate spray pattern data is repeatedly looped during the simulation until the simulated coating result becomes acceptable.

[0014] When optimizing the robot path, for example, the following optimization means can be executed. · Orbit conformity, for example, changing the polygon defining the orbit. · Route distance conformity. · Shortening or extending the route (e.g., at the ends). · Introduction of additional routes. · Removal of routes. · Adjusting the orientation of the applicator axis on the robot path. · Adjusting the route speed. · Changing the switch-on or switch-off points on the robot path, particularly at the ends, path turning points, and boundaries of the painting areas of different painting robots ('painting module joints'). · Optimization by brush parameterization regarding the scaling of the spray jet width or diameter, spray jet shape, paint discharge amount, and by inserting or removing the number of brushes in the related brush parameterization.

[0015] During the simulation for determining the appropriate spray pattern data, it is preferably considered the nature of the sprayer and / or bell cup forming air ring system, particularly regarding the possible paint discharge amount and the achievable spray pattern width.

[0016] However, data other than the spray pattern data may be optimized as part of the simulation. For example, the robot path sequence and paint flow on / off points may be optimized.

[0017] The programming method according to the present invention also provides a simulation of the coating result, similar to the prior art described at the beginning. However, in the coating method according to the present invention, a rather complex physical simulation itself is not performed. Rather, since the simulation according to the present invention is based on the spray pattern data and the layer thickness profile defined thereby, the simulation according to the present invention is much simpler and can therefore be executed in practical computing time.

[0018] As already described above, appropriate spray pattern data (brush curve) is available within the first data set after the simulation. However, this spray pattern data is not yet suitable for controlling the coating device. In the programming method according to the present invention, there, when the coating device moves along a specified robot path, appropriate coating parameters (e.g., the rotational speed of a rotary atomizer, paint flow, shaping air flow, etc.) suitable for realizing the appropriate spray pattern data determined previously in actual operation are preferably determined from the determined appropriate spray pattern data. Such appropriate coating parameters are then stored in the second data set.

[0019] The coating facility can then be operated with the coating parameters thus determined. During operation with the previously determined coating parameters, the previously determined appropriate spray pattern data is subsequently actually generated, which is appropriately determined based on the simulation and results in an acceptable coating result.

[0020] In a variant of the present invention, suitable spray pattern data is determined by the coating equipment operator during a simulation on the operator side, which is done, for example, automatically or by assisting user intervention. A first data set with the suitable spray pattern data determined during the simulation is then transmitted from the operator side to the manufacturer side of the coating equipment, for example, to a service provider commissioned by the manufacturer of the coating equipment. The service provider then determines suitable coating parameters based on the characteristic diagram using the suitable spray pattern data transmitted from the coating equipment operator, transmits these back to the coating equipment operator, and they then operate the coating equipment with the transmitted coating parameters.

[0021] In another variant, the determination of suitable spray pattern data within the framework of the simulation is also carried out on the operator side by the coating equipment operator, in particular automatically or by assisting user intervention. The coating equipment operator then receives, on the manufacturer side, a characteristic diagram for determining suitable coating parameters, for example, generated by a service provider commissioned by the manufacturer of the coating equipment. For example, the coating equipment operator may transmit the coating data to a service provider identifying the coating agent to be used. The service provider commissioned by the manufacturer may then select or generate a suitable coating-specific characteristic diagram and transmit it to the coating equipment operator. The operator of the coating equipment then determines suitable coating parameters from the suitable spray pattern data based on the characteristic diagram previously transmitted from the manufacturer.

[0022] As already described above, the first data set includes suitable spray pattern data that results in an acceptable coating result within the simulation. Further, the first data set with the suitable spray pattern data may further include additional coating data. For example, the following coating data that may be included in the first data set can be described at this point. ·The desired layer thickness of the coating agent layer on the surface of the component. This is preferably the layer thickness in the dry state as opposed to the layer thickness of the wet paint. ·A coating agent identifier for identifying the coating agent and / or the nature of the coating agent. For example, the coating agent identifier may indicate the solids content contained in the coating agent used or whether it is a base coat or a clear coat. ·A coating device identifier for identifying the coating device and / or the nature of the coating device. For example, the coating device identifier may indicate which type of rotary sprayer was used. ·Furthermore, the coating data may also include layer information for distinguishing different layers in a multi-layer coating. ·Another example of possible coating data is the path speed of the paint impact points along the robot path, which also affects the coating result.

[0023] And when determining the appropriate coating parameters described above, it is preferable that not only the spray pattern data included in the first data set but also the above-described coating data be considered. Furthermore, reference values for the coating efficiency of the coating device, the solids content of the coating agent, the target layer thickness, the path speed, and the path distance between adjacent coating paths may be considered.

[0024] As described above, the robot path data is considered during the simulation, and the robot path data defines the robot path to be scanned by the paint impact points of the coating device during the coating operation. Such robot path data preferably includes the following data. ·The spatial transition of the robot path, ·The path speed of the paint impact points along the robot path, ·The path distance between path portions of adjacent, overlapping, or adjoining robot paths, ·The orientation of the coating device, ·The temporal transition of the paint impact points along the robot path, or the speed of the paint impact points along the robot path, · Switch-on point and / or switch-off point of the paint flow, and / or · Number of brushes in the active state: How many brushes with corresponding brush parameterization are active along the robot path and where?

[0025] Furthermore, it should be mentioned that it is preferable that the robot path is divided into a plurality of continuous path portions that are to be continuously scanned by the paint impact points of the coating device. And it is preferable that appropriate spray pattern data is determined individually and specifically for individual path portions of the robot path. Furthermore, a set of appropriate coating parameters may be determined individually and specifically for individual path portions.

[0026] As already described above, the determination of appropriate spray pattern data (brush curves) is performed within the simulation. In a preferred embodiment of the present invention, this simulation includes a plurality of iterative steps that are executed sequentially and each includes an optimization loop.

[0027] In the first optimization step, in particular, default values are preferably specified for the coating agent flow and the spray pattern as percentage values, relative values, or virtual values (reference brushes). These default values function only as initial values for the simulation of the coating result. During the subsequent simulation process based on the specified default values, it is preferably determined the deviation of the simulated layer thickness distribution from the target while the layer thickness distribution is determined. Regarding the layer thickness uniformity on a large part surface, the robot path data may already be optimized in this first optimization step.

[0028] The aforementioned initial values for the simulation may include, for example, reference values for the path distance between the central axes of directly adjacent coating paths. For example, when painting an automobile body, the coating paths are usually applied to the automobile body in a state where they are parallel to each other and overlap laterally. Therefore, the initial values for the simulation may also define the lateral overlap of directly adjacent coating paths. Furthermore, the initial values for the simulation may include reference values for the coating agent flow.

[0029] In the second optimization step, it is preferable that the layer thickness uniformity test is then carried out on the simple module joint. The surface of the part to be painted is usually divided into a plurality of coating modules that are painted in sequence. For example, the coating modules may be the hood, roof, trunk lid, fender, and door of an automobile body, and these are painted in sequence. In this case, adjacent modules are in contact with each other at the module boundary, but the simple module joint in the sense of the present invention is the boundary between adjacent modules where exactly two adjacent coating modules are in contact with each other. For example, in an automobile body, since the front side door and the rear side door can each form a coating module, the boundary between the front door and the side door forms a simple module joint. On the other hand, the joint between three or more adjacent coating modules is called a complex module joint in the context of the present invention. For example, such a complex module joint occurs at the location of the automobile body where the hood, fender, front door, and A-pillar are adjacent to each other.

[0030] In the second optimization step, the layer thickness uniformity at the above-mentioned simple module joint is first determined and compared with the target value. In the second optimization step, then, the robot path can be optimized to optimize the layer thickness uniformity at the simple module joint. This optimization can be performed in a plurality of iterative loops that are continuously executed until an acceptable improvement in layer thickness uniformity is achieved.

[0031] In the third optimization step, the layer thickness uniformity can then be optimized with the above-described complex module joints. Here, also, the robot path can be adjusted multiple times during the third optimization step for optimizing the layer thickness uniformity at the complex module joints. In the third optimization step, this optimization can be performed with a plurality of optimization loops that are sequentially executed until an acceptable layer thickness uniformity is achieved at the complex module joints.

[0032] In the fourth optimization step, the layer thickness uniformity can again be optimized with simple and / or complex module joints. However, in this fourth optimization step, what is adjusted is not the robot path but the spray pattern data (brush curve) and / or the coating agent flow. The optimization in the fourth optimization step can also be performed with a plurality of optimization loops that are sequentially executed until the simulation yields an acceptable coating result at the simple or complex module joints.

[0033] Finally, in the fifth optimization step, the layer thickness uniformity at the part edge of the part to be coated is optimized. For example, the part edge may be the edge of the engine hood to be coated or the edge of the door of the automobile body to be coated. In the fifth optimization step, the layer thickness uniformity at the part edge is simulated and compared with a specified target value for the layer thickness uniformity. Thereafter, the robot path, the spray pattern data, and / or the coating agent flow can be adjusted until the simulation yields an acceptable coating result. In the fifth optimization step, for example, the following adjustments can be made. · Optimization of the robot path: Adjustment of the path, such as overall trimming of the polygon or adjustment of the path distance, shortening and extension of the path (e.g., at the edge), additional paths, fewer paths, adjustment of the orientation of the perpendicular plate axis, adjustment of the speed, · In particular, optimization by changing the switch-on point and / or the switch-off point (GUN ON / GUN OFF) of the coating flow at the edge, the path turning point, and the boundary of the coating areas of different robots (coating module joints), · Optimization by brush parameterization regarding scaling of spray jet width or spray jet diameter, spray jet shape, paint flow rate, and by insertion or removal of the number of brushes in the relevant brush parameterization.

[0034] It should be mentioned that the above-mentioned adjustment ('adjustment screw') is possible not only within the framework of the fifth optimization step but also generally. The optimization in the fifth optimization step can also be carried out in a plurality of optimization loops that are sequentially executed until the simulation yields an acceptable coating result at the part edge.

[0035] As part of the simulation of the coating result, the coating result may be graphically displayed on the operator's screen. For example, the part to be coated may be displayed on the screen as a perspective model. The surface of the displayed model of the part to be coated may be colored depending on the position, where the coloring of the part surface on the screen reflects, for example, the deviation between a specified target value for one simulated layer thickness and another layer thickness. Such visualization of the simulated coating result on the screen provides the programmer with a quick and intuitive overview of the simulated coating result.

[0036] As described above, after determining the appropriate spray pattern data (brush curve), appropriate application parameters suitable for realizing the specified spray pattern data are determined as part of the simulation. This conversion is preferably carried out based on a multi-dimensional characteristic diagram, which associates, for example, a plurality of the following variables for a specific coating agent with each other. · In particular, as the SB50 value of the layer thickness profile, the width of the layer thickness profile. Here, the SB50 value indicates the width of the layer thickness profile in the range where the layer thickness is at least 50% of the maximum value of the layer thickness. · The shaping air flow of the coating device (e.g., rotary atomizer). · The coating agent flow of the coating device (e.g., rotary atomizer). ·The rotational speed of the rotary atomizer used as the coating device. ·The high voltage of the electrostatic charging of the coating agent. ·The path speed of the coating device (e.g., rotary atomizer) along the robot path. ·The coating distance between the coating device and the surface of the part to be coated.

[0037] As described above, the robot path data is specified, which determines the progress of the robot path during the actual coating operation. This robot path data may be specified by the operator, for example, based on the geometry data of the part to be coated. However, instead, the robot path data may be provided by the manufacturer and then read in file format by the coating equipment operator.

[0038] The same also applies to the geometry data of the part to be coated, which may be determined by the operator, for example, as part of the measurement process. However, instead, the geometry data may be provided by the manufacturer.

[0039] Practically, as already shown and mentioned, for the simulation method and spray pattern data, it is an advantage that, for example, the distortion of the spray jets can be at least partially avoided at edges or A-pillars. Therefore, special coating conditions and effects, such as the air flow in the booth, the air flow around the atomizer and around the object to be processed, the influence of the high voltage, etc., are automatically considered in the simulation, if necessary, at the edges of the object to be processed, the geometry of complex objects to be processed or the recesses of the sunroof.

[0040] Furthermore, it should be mentioned that the present invention does not claim the right protection only for the programming method according to the present invention described above. Rather, the present invention also claims the right protection for the coating equipment adapted correspondingly to the execution of the programming method according to the present invention.

[0041] Following the prior art, the coating equipment according to the present invention first includes at least one coating robot, at least one coating device (e.g., a rotary atomizer), and a control unit for controlling the coating device and the coating robot. And in the coating equipment according to the present invention, the control unit is designed to execute the programming method according to the present invention.

[0042] For example, the above characteristic diagram may be stored in the control unit to determine appropriate coating parameters from appropriate spray pattern data.

[0043] Furthermore, the coating equipment according to the present invention preferably has a data interface for transmitting spray pattern data and / or coating data to a coating equipment manufacturer or a service provider commissioned by the manufacturer, and receiving relevant characteristic diagrams for determining appropriate coating parameters from the coating equipment manufacturer or the service provider.

Brief Description of the Drawings

[0044] Other advantageous further embodiments of the present invention are shown in the dependent claims and will be described in more detail below in conjunction with the description of the preferred embodiments of the present invention with reference to the drawings.

[0045]

Figure 1A

Figure 1B

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Embodiment for Carrying Out the Invention

[0046] Flowcharts according to FIGS. 1A and 1B illustrating the programming method according to the present invention are described below.

[0047] In the first step S1, geometry data reflecting the geometry of the part to be painted (for example, an automobile body part) is first specified. The geometry data may be provided, for example, in a file format and easily read out. However, alternatively, the geometry data may be measured using an actual part.

[0048] In the second step S2, thereafter, robot path data defining the path of movement of the paint impact points of the coating device used on the part surface is provided. The robot path data is determined based on the geometry data of the part to be coated. Here, coating guidelines are usually considered, which may include, for example, specifications regarding the lateral distance between adjacent painting paths and specifications regarding the lateral overlap of adjacent painting paths.

[0049] In a further step S3, thereafter, appropriate spray pattern data (brush curve) is determined within the simulation, where the spray pattern data represents a layer thickness profile and results in an acceptable painting result within the simulation. For example, FIG. 3 shows the layer thickness profile of a rotary sprayer with a plurality of spray pattern data characterizing the layer thickness profile. Here, the most important value for describing the layer thickness profile as already mentioned is the SB50 value, which represents the width of the layer thickness profile in the range where the layer thickness is at least 50% of the maximum layer thickness SD MAX and. The simulation in step S3 will be described in detail later with reference to FIG. 2.

[0050] The acceptable spray pattern data determined within the simulation is then stored in the first data set in step S4.

[0051] In a further step S5, additional painting data such as the desired layer thickness, paint type, type of coating device, type of layer (base coat / clear coat), coating speed, and coating efficiency of the coating device is then determined. The painting data is then stored in the first data set together with the spray pattern data in step S6.

[0052] In step S7, a coating-specific characteristic diagram is then provided by the manufacturer so that the relevant coating parameters that actually result in the realization of the appropriate spray pattern data can be determined from the appropriate spray pattern data.

[0053] In the next step S8, the characteristic diagram is then used to determine the appropriate coating parameters from the appropriate spray pattern data and paint data. For example, the coating parameters may include the shaping air flow, high voltage charging, and paint flow of a rotary atomizer.

[0054] The appropriate coating parameters are then stored in the second data set in step S9.

[0055] In the next step S10, the coating equipment is then operated with the specified robot path data and the determined coating parameters. In the case of the optimally calculated characteristic diagram, the operation of the coating equipment with the coating parameters read from the characteristic diagram results in the spray pattern data previously determined in the simulation, so a good agreement between the simulation and the actual painting operation can be achieved.

[0056] Here, it should be mentioned that the above programming method can determine spray pattern data and related coating parameters individually for different path portions on the robot path. This means that the coating parameters do not necessarily have to be constant along the robot path. Rather, the coating parameters can vary along the robot path in order to achieve good coating results.

[0057] Hereinafter, the simulation related to step S3 in FIG. 1A will be described in more detail with reference to the flowchart of FIG. 2.

[0058] In the first step S3.1 of the simulation, default values for the spray pattern data are first specified. This can also be called a reference brush. Subsequently, the coating result is simulated based on the specified default values.

[0059] In the second optimization step S3.2, the layer thickness uniformity is then tested at a simple module joint between two adjacent modules. In the context of the present invention, the term simple module joint refers to the boundary exactly between two adjacent coating modules. For example, the boundary between the front side door and the rear side door forms such a simple module joint. The robot path is then optimized within the framework of the simulation until the maximum improvement in layer thickness uniformity at the simple module joint is achieved. Therefore, the second optimization step S3.2 may include a plurality of iterative loops that are executed sequentially. Here, it is important that the layer thickness uniformity at the simple module joint is evaluated and used for the optimization of the robot path.

[0060] In the third optimization step S3.3, the layer thickness uniformity at complex module joints between more than two adjacent coating modules may then be checked. The term "complex module joint" refers to the boundary between more than two adjacent coating modules. For example, the boundaries between the fender, hood, front side door, and A-pillar of an automobile body form such complex module joints. In the third optimization step S3.3, the robot path is iteratively optimized until the maximum improvement in layer thickness uniformity at the complex module joint is achieved.

[0061] In the fourth optimization step S3.4, the layer thickness uniformity at complex and / or simple module joints is determined again and used as an optimization criterion. However, it is the spray pattern data (brush curve), not the robot path, that is optimized until the maximum improvement in layer thickness uniformity at the simple or complex module joint is achieved.

[0062] In the fifth optimization step S3.5, the layer thickness uniformity at the part edge is then checked and used as an optimization criterion. For example, the layer thickness uniformity at the part edge of the engine hood may be checked and considered. During optimization, the spray pattern data and / or the robot path may then be optimized until the maximum improvement in layer thickness uniformity at the part edge is achieved. Here again, the optimization may include a plurality of iterative loops that are executed sequentially.

[0063] FIG. 3 shows the layer thickness profile typically generated by a rotary atomizer.

[0064] FIG. 4 shows the corresponding layer thickness profile of the rotary atomizer when the paint flow is changed from 70% to 130%.

[0065] FIG. 5 shows a characteristic diagram that can be used in the context of the present invention to determine appropriate coating parameters from spray pattern data. The spray pattern data in the characteristic diagram shown in FIG. 5 is the SB50 value, while the coating parameters are the shaping air flow and the coating flow rate. However, within the scope of the present invention, a multi-dimensional characteristic diagram that associates a larger number of spray pattern data or coating parameters may also be used.

[0066] FIG. 6 shows, in a highly simplified list form, the coating equipment according to the present invention that is suitable for executing the programming method according to the present invention.

[0067] Therefore, the coating equipment according to the present invention first includes a coating equipment control unit 1 following a known coating equipment, which controls a coating robot and a coating device (for example, a rotary sprayer) with certain coating parameters during operation. The coating equipment control unit 1 receives robot path data as an input variable for specifying the progress of the robot path, where the robot path data may be specified according to a coating guideline.

[0068] Furthermore, the coating equipment control unit 1 receives geometry data reflecting the geometry of the part to be coated.

[0069] Finally, the coating equipment control unit 1 receives coating data reflecting, for example, the type of paint used.

[0070] During the coating operation, the coating equipment control unit 1 then controls the coating robot and the coating device appropriately using the coating parameters determined by the programming method according to the present invention.

[0071] For this purpose, a simulation tool 2 is provided, which also receives coating data, geometry data, and robot path data, and determines appropriate spray pattern data as part of the simulation, which results in an acceptable coating result within the simulation as described above.

[0072] Furthermore, as will be described in detail later, the painting equipment has a characteristic diagram element 3 for determining coating parameters for the painting equipment control unit 1.

[0073] Here, the painting equipment according to the present invention additionally has a data interface 4, which is used to transmit appropriate spray pattern data and painting data to the painting equipment manufacturer or a service provider commissioned by the painting equipment manufacturer, and these parties also have a data interface 5 for this purpose. And on the manufacturer side, an appropriate characteristic diagram may be read from the painting data and the transmitted spray pattern data transmitted from the characteristic diagram memory 6 and transmitted to the painting equipment operator, and the painting equipment operator stores the appropriate characteristic diagram in the characteristic diagram memory 3. And the characteristic diagram stored in the characteristic diagram memory 3 enables appropriate coating parameters to be determined from the simulated spray pattern data.

[0074] Since FIG. 7 shows a modified example of the flowchart according to FIG. 1A, in order to avoid repetition, reference is first made to the above description of FIG. 1A for the reference signs.

[0075] The special feature of this modified example lies in the processing steps S4 and S5 inserted into the processing sequence. The method according to FIG. 1A assumes that the robot path is fixed and not changed during the simulation. However, for a specific fixed robot path, it may also happen that even the best possible spray pattern data ('brush curve') does not result in an acceptable coating result. This can be the case, for example, when the specified robot path is particularly demanding in terms of coating technology. In such a case, it makes sense to also optimize the robot path.

[0076] Therefore, in step S4, after the best possible spray pattern data is determined, a check is made to determine whether the simulated painting result is acceptable. If so, the procedure can continue to step S6 as described above with reference to FIG. 1A.

[0077] On the one hand, if the simulated painting result is not acceptable even with the best possible spray pattern data, the robot path is optimized in step S5, and steps S3, S4, and S5 are repeated until the simulation with the best possible spray pattern data and the optimized robot path results in an acceptable coating result. If, in such a case, it may be possible to proceed to step S6 as described above with reference to FIG. 1A.

[0078] The present invention is not necessarily limited to the above-described preferred embodiments. Rather, several variations and modifications are possible using the concept of the present invention, and these are also included within the scope of the present invention. In particular, the present invention claims the protection of the subject matter and features of the dependent claims independently of the claims to which they respectively refer, in particular without the features of the independent claims. Therefore, the present invention includes various aspects of the invention that enjoy rights independently of each other.

[0079] [Appendix] [Appendix 1] A method for programming a program-controlled coating facility equipped with a coating robot and an application device for coating parts, in particular for programming a coating facility equipped with a painting robot for painting automotive body parts, comprising: a) a step (S1) of specifying or determining geometry data representing the geometry of the part to be coated; b) a step (S2) of specifying robot path data, b1) the robot path data defines a robot path to be scanned by paint impact points of the application device guided by the coating robot during a coating operation, and b2) the robot path is defined based on the specified geometry data of the part to be coated, step (S2); c) a step (S3) of determining appropriate spray pattern data, c1) The spray pattern data represents a layer thickness profile, in particular a three-dimensional layer thickness profile, generated by the coating device on the surface of the part around the paint impact point during an actual coating operation, and c2) The determined spray pattern data is intended to achieve an acceptable coating result during an actual coating operation when coating the part along the robot path, c3) The appropriate spray pattern data is determined by a simulation taking into account the geometry data of the part to be coated and the robot path data, and c4) The appropriate spray pattern data is stored in a first data set (S4), step, A method having steps (S1 - S3) such as these.

[0080] [Appendix 2] a) A step of checking the simulated coating result after determining the possible spray pattern data, b) An optimization step of a given robot path if the checking indicates that the coating result is not acceptable, c) A step of repeating the determination of the possible spray pattern data and the optimization of the robot path until the simulated coating result becomes acceptable, A method according to Appendix 1 having steps such as these.

[0081] [Appendix 3] a) A step (S8) of determining appropriate coating parameters for operating the coating device, wherein a1) The appropriate coating parameters are determined from the spray pattern data included in the first data set based on a characteristic diagram, a2) The determined appropriate coating parameters result in the appropriate spray pattern data during the actual operation of the coating device when the robot path is scanned, and a3) The appropriate coating parameters are stored in the second data set (S9), process, and b) The step of operating the coating facility (S10), wherein b1) the coating robot is controlled according to the robot path data such that the paint impact point of the coating device scans a predetermined robot path on the surface of the part to be coated, and b2) the coating device is controlled with the appropriate coating parameters included in the second data set, step The method according to appendix 1 or 2, comprising such steps.

[0082] [Appendix 4] a) The determination of the appropriate spray pattern data is performed by the coating facility operator, in particular, automatically or by assisting user intervention, within the framework of a simulation on the operator side, b) The first data set with the appropriate spray pattern data determined during the simulation is transmitted from the operator side to the manufacturer side of the coating facility, in particular, to the service provider, c) The determination of the appropriate coating parameters is performed on the manufacturer side, in particular, by the service provider, based on the characteristic diagram and the appropriate spray pattern data, and d) The second data set with the appropriate coating parameters is transmitted from the manufacturer side to the operator side. The method according to appendix 3.

[0083] [Appendix 5] a) The determination of the appropriate spray pattern data is performed on the operator side, by the coating facility operator, in particular, automatically or by assisting user intervention, within the framework of a simulation, b) The characteristic diagram for determining the appropriate coating parameters is generated on the manufacturer side, in particular, by the service provider. c) The characteristic diagram for determining the appropriate coating parameters is transmitted by the service provider to the operator side, and, d) The determination of the appropriate coating parameters is performed on the operator side based on the characteristic diagram and the appropriate spray pattern data, The method according to Appendix 3.

[0084] [Appendix 6] a) The first data set with the appropriate spray pattern data includes a1) The desired layer thickness of the coating agent layer on the surface of the part, in particular the layer thickness in the dry state, a2) A coating agent identifier for identifying the coating agent and / or the properties of the coating agent, a3) A coating device identifier for identifying the coating device and / or the properties of the coating device, a4) Layer information for distinguishing different layers in a multi-layer coating, in particular for distinguishing a base coat layer and a clear coat layer, and / or a5) The path speed of the paint impact points along the robot path, and also includes coating data such as, and / or b) When determining the appropriate coating parameters, not only the spray pattern data included in the first data set but also the coating data included in the first data set are preferably considered, and also the coating efficiency of the coating device as an empirical value, the solid content of the coating agent, the target layer thickness, the reference value of the path speed, and the reference value of the path interval. The method according to any one of Appendices 1 to 5.

[0085] [Appendix 7] The robot path data includes a) The spatial movement of the robot path, and / or b) The path speed of the paint impact points along the robot path, and / or c) The path interval between path portions of the robot path that are adjacent, overlapping, or adjacent laterally, and / or d) the orientation of the coating device, and / or, e) the time evolution of the paint impact point along the robot path, or the speed of the paint impact point along the robot path, and / or, f) the switch-on point and / or switch-off point of the paint flow, A method according to any one of Appendices 1 to 6, comprising data of

[0086] [Appendix 8] a) a step of dividing the robot path into a plurality of consecutive path portions to be continuously scanned by the paint impact point of the coating device, b) a step of determining an appropriate spray pattern for each individual path portion of the robot path, c) a step of determining, for each individual path portion of the robot path, an appropriate coating agent flow, particularly as a percentage value, relative value, or virtual value, such as steps in the simulation, d) The first data set with the appropriate spray pattern data for each individual path portion of the robot path each includes an appropriate spray image and, particularly, the appropriate coating agent flow as a percentage value, relative value, or virtual value, A method according to Appendix 6.

[0087] [Appendix 9] a) In a first optimization step, particularly for the coating agent flow and the spray pattern as percentage values, relative values, or virtual values, default values are specified, and the coating results obtained as a result of the default values are simulated, and / or, b) In a second optimization step, the layer thickness uniformity at a simple module joint between exactly two adjacent coating modules on the surface of the part to be coated is tested, and particularly, the robot path is optimized to improve the layer thickness uniformity at the simple module joint between, for example, between the fender and the hood, between the front door and the rear door, and between the modules of the roof of an automobile body, and / or, c) In the third optimization step, test the layer thickness uniformity at the complex module joints between more than two adjacent coating modules on the surface of the part to be coated, and, in particular, optimize the robot path and / or d) In the fourth optimization step, test the layer thickness uniformity at the simple and / or complex module joints between adjacent coating modules on the surface of the part to be coated, and, in order to improve the layer thickness uniformity at the simple and / or complex module joints, d1) spray pattern data, and / or d2) in particular, optimize variables such as the coating agent flow as a percentage value, relative value, or virtual value, and / or e) In the fifth optimization step, test the layer thickness uniformity at the part edge of the part to be coated, and, in order to improve the layer thickness uniformity at the part edge, e1) robot path, e2) spray pattern data, and / or e3) in particular, optimize variables such as the coating agent flow as a percentage value, relative value, or virtual value, in such iterative optimization steps as those described in Appendix 7 or 8, the simulation is executed. The method according to Appendix 7 or 8, in which the simulation is executed in such iterative optimization steps.

[0088] [Appendix 10] The default values for the first optimization step of the simulation are a) reference values for the path distance between the central axes of directly adjacent coating paths, b) the overlap of directly adjacent coating paths, and / or c) in particular, reference values for the coating agent flow as a percentage value, relative value, or virtual value, which are specified depending on variables such as those described in Appendix 8.

[0089] [Appendix 11] The simulated coating result is graphically shown on the operator's screen, in particular, by a perspective view of the part to be coated as a model and depending on the simulated local layer thickness, in particular, by a position-dependent coloring of the surface of the model corresponding to the deviation between one of the simulated local layer thicknesses and a predetermined reference value for the other layer thickness, according to the method described in any one of Appendices 1 to 10.

[0090] [Appendix 12] The characteristic diagram for determining the appropriate coating parameters in relation to a specific coating agent and / or the coating device used is a) the width of the layer thickness profile, in particular, the SB50 value of the layer thickness profile, b) the shaping air flow of the coating device, c) the coating agent flow of the coating device, d) the rotational speed of the rotary atomizer used as the coating device, e) the high voltage of the charging of the electrostatic coating agent, f) the path speed of the coating device along the robot path, g) the coating distance between the coating device and the surface of the part to be coated, linking variables such as these to each other, according to the method described in any one of Appendices 1 to 11.

[0091] [Appendix 13] a) The robot path data is, in particular, specified on the operator side by the coating equipment operator, either automatically or by assisting user intervention, and / or b) The geometry data of the part to be coated is, in particular, specified on the operator side by the coating equipment operator, either automatically or by assisting user intervention. According to the method described in any one of Appendices 1 to 12.

[0092] [Appendix 14] A coating facility for coating components, in particular for painting automotive body components, comprising: a) at least one coating robot, b) at least one application device guided by the coating robot, and c) a control unit for controlling the application device and the coating robot, characterized in that d) the control unit executes the method according to any one of appendices 1 to 13.

[0093] [Appendix 15] The coating facility according to appendix 14, wherein the characteristic diagram is stored in the control unit for determining the appropriate application parameters from the appropriate spray pattern data.

[0094] [Appendix 16] The coating facility according to appendix 14 or 15, further comprising a data interface for transmitting the first data set with the appropriate spray pattern data to the service provider and receiving the second data set with the appropriate application parameters from the service provider.

Explanation of reference numerals

[0095] 1 Coating facility control unit 2 Simulation tool 3 Characteristic diagram element 4 Data interface 5 Data interface 6 Characteristic diagram memory

Claims

1. A method for programming a program-controlled coating facility comprising a coating robot and an application device for coating components, comprising: a) a step (S1) of specifying or determining geometry data representing the geometry of the component to be coated; b) a step (S2) of specifying robot path data, wherein b1) the robot path data defines a robot path to be scanned by paint impact points of the application device guided by the coating robot during a coating operation, and b2) the robot path is defined based on the specified geometry data of the component to be coated, step (S2); c) a step (S3) of determining appropriate spray pattern data, wherein c1) the spray pattern data represents a layer thickness profile generated by the application device on the surface of the component around the paint impact points during an actual coating operation, and c2) the determined appropriate spray pattern data is intended to achieve an acceptable coating result during an actual coating operation when coating the component along the robot path, and c3) the appropriate spray pattern data is determined by simulation taking into account the geometry data of the component to be coated and the robot path data, and c4) the appropriate spray pattern data is stored in a first data set (S4), step; having steps (S1 - S3) such as; d) a step (S8) of determining appropriate coating parameters for operating the application device, wherein d1) the appropriate coating parameters are determined from the spray pattern data contained in the first data set based on a characteristic diagram, d2) the determined appropriate coating parameters result in the appropriate spray pattern data during an actual operation of the application device when the robot path is scanned, and d3) the appropriate coating parameters are stored in a second data set (S9), step, and e) a step (S10) of operating the coating facility, wherein e1) the coating robot is controlled according to the robot path data such that the paint impact points of the application device scan a predetermined robot path on the surface of the component to be coated, and Step e2), in which the coating device is controlled with the appropriate coating parameters included in the second data set A method further comprising steps such as this **Claim 2** a) The determination of the appropriate spray pattern data is carried out by the coating equipment operator within the framework of a simulation on the operator side b) The first data set with the appropriate spray pattern data determined during the simulation is transmitted from the operator side to the manufacturer side of the coating equipment c) The determination of the appropriate coating parameters is carried out on the manufacturer side based on the characteristic diagram and the appropriate spray pattern data, and d) The second data set with the appropriate coating parameters is transmitted from the manufacturer side to the operator side The method according to claim 1 **Claim 3** a) The determination of the appropriate spray pattern data is carried out by the coating equipment operator on the operator side within the framework of a simulation b) The characteristic diagram for determining the appropriate coating parameters is generated on the manufacturer side c) The characteristic diagram for determining the appropriate coating parameters is transmitted by the service provider to the operator side, and d) The determination of the appropriate coating parameters is carried out on the operator side based on the characteristic diagram and the appropriate spray pattern data The method according to claim 1 **Claim 4** a) The first data set with the appropriate spray pattern data includes a1) The desired layer thickness of the coating agent layer on the surface of the part a2) A coating agent identifier for identifying the coating agent and / or the properties of the coating agent a3) A coating device identifier for identifying the coating device and / or the properties of the coating device a4) Layer information for distinguishing different layers in a multi-layer coating, and / or a5) The path speed of the paint impact points along the robot path Also includes coating data such as this, and / or b) When determining the appropriate coating parameters, not only the spray pattern data included in the first data set but also the coating data included in the first data set are preferably considered, as well as the coating efficiency of the coating device as an empirical value, the solid content of the coating agent, the target layer thickness, the reference value of the path speed, and the reference value of the path interval The method according to any one of claims 1 to 3.

5. The robot path data includes a) the spatial progression of the robot path, and / or b) the path speed of the paint impact points along the robot path, and / or c) the path interval between path portions of the robot path that are adjacent, overlapping, or adjoining laterally, and / or d) the orientation of the coating device, and / or e) the temporal progression of the paint impact points along the robot path, or the speed of the paint impact points along the robot path, and / or f) the switch-on point and / or switch-off point of the paint flow, The method according to any one of claims 1 to 4, comprising data of.

6. a) a step of dividing the robot path into a plurality of consecutive path portions to be successively scanned by the paint impact points of the coating device, b) a step of determining an appropriate spray pattern for individual ones of the path portions of the robot path, c) a step of determining an appropriate coating agent flow for individual ones of the path portions of the robot path, having the steps in the simulation such as d) the first data set with the appropriate spray pattern data for individual ones of the path portions of the robot path each includes an appropriate spray image and the appropriate coating agent flow, The method according to claim 4.

7. a) In a first optimization step, default values are specified for the coating agent flow and the spray pattern, and the coating result obtained as a result of the default values is simulated, and / or b) In a second optimization step, the layer thickness uniformity at a simple module joint between exactly two adjacent coating modules on the surface of the part to be coated is tested, and the robot path is optimized to improve the layer thickness uniformity at the simple module joint, and / or c) In a third optimization step, the layer thickness uniformity at a complex module joint between more than two adjacent coating modules on the surface of the part to be coated is tested, and the robot path is optimized to improve the layer thickness uniformity at the complex module joint, and / or d) In the fourth optimization step, test the layer thickness uniformity at simple and / or complex module joints between adjacent coating modules on the surface of the part to be coated, and in order to improve the layer thickness uniformity at the simple and / or complex module joints, d1) Spray pattern data, and / or d2) Coating agent flow, Optimize variables such as, and / or e) In the fifth optimization step, test the layer thickness uniformity at the part edge of the part to be coated, and in order to improve the layer thickness uniformity at the part edge, e1) Robot path, e2) Spray pattern data, and / or e3) Coating agent flow, Optimize variables such as, The simulation is executed in iterative optimization steps such as, the method according to claim 5 or 6.

8. The default values for the first optimization step of the simulation are a) A reference value for the path distance between the central axes of directly adjacent coating paths, b) The overlap of directly adjacent coating paths, and / or c) A reference value for the coating agent flow, Identified depending on variables such as, the method according to claim 7.

9. The simulated coating results are graphically shown on the operator's screen, the method according to any one of claims 1 to 8.

10. The characteristic diagram for determining the appropriate coating parameters in relation to a specific coating agent and / or the coating device used is a) The width of the layer thickness profile, b) The shaping air flow of the coating device, c) The coating agent flow of the coating device, d) The rotational speed of the rotary atomizer used as the coating device, e) The high voltage of the charging of the electrostatic coating agent, f) The path speed of the coating device along the robot path, g) The coating distance between the coating device and the surface of the part to be coated, Link variables such as to each other, the method according to any one of claims 1 to 9.

11. a) The robot path data is specified on the operator side by the coating equipment operator, and / or b) The geometry data of the part to be coated is specified on the operator side by the coating equipment operator, The method according to any one of claims 1 to 10.

12. A coating equipment for coating parts, a) at least one coating robot, b) at least one coating device guided by the coating robot, and c) a control unit for controlling the coating device and the coating robot, characterized in that it has d) the control unit executes the method according to any one of claims 1 to 11, a coating facility.

13. The coating facility according to claim 12, wherein the characteristic diagram is stored in the control unit to determine the appropriate coating parameters from the appropriate spray pattern data.

14. The coating facility according to claim 12 or 13, having a data interface for transmitting the first data set with the appropriate spray pattern data to a service provider and receiving the second data set with the appropriate coating parameters from the service provider.

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