PROGRAMMING PROCESS FOR A COATING SYSTEM AND CORRESPONDING COATING SYSTEM

Description

[0001] The invention relates to a method for programming a program-controlled coating system, in particular for a painting system for painting motor vehicle body components.

[0002] In modern paint shops for painting motor vehicle body components, a rotary atomizer is typically used as the application device. This atomizer is guided over the motor vehicle body components to be painted by a multi-axis painting robot. The operation of the painting shop, and thus also the control of the painting robot and the rotary atomizer, is program-controlled. Therefore, programming of the painting shop is required before the actual painting operation. During this programming, robot paths are planned offline. These paths are to be followed from a paint impact point of the rotary atomizer, taking into account predefined painting guidelines that specify, for example, the path spacing and path speed. Furthermore, application parameters for the rotary atomizer, such as paint flow rate, speed, shaping air flow, etc., are defined during programming.The application parameters are defined according to specified painting guidelines and based on the experience of the specialist personnel. The goal of the programming is, among other things, to achieve the most homogeneous coating thickness distribution possible on the component to be coated. Therefore, the coating thickness distribution is optimized during programming by varying the application parameters and the robot path paths in several iteration loops based on painting tests on test bodies. The disadvantage of this known programming method is the high expenditure of time, paint material, and test bodies.

[0003] In a newer development line, which has not yet been implemented in practice, attempts are being made to fully simulate the painting process physically, allowing the optimization of the robot path and application parameters to be carried out within a simulation. A disadvantage of this new development line, however, is the high computational effort involved in the simulation, making the time required for the calculation impractical.

[0004] For the general technical background of the invention, reference should also be made to US 2012 / 0156362 A1 and DE 196 51 716 A1.

[0005] Finally, CN 106 354 932 B discloses a method according to the preamble of claim 1. However, this known method is not yet entirely satisfactory.

[0006] The invention is therefore based on the object of creating an improved method for programming a program-controlled coating system. Furthermore, the invention is based on the object of creating a correspondingly adapted coating system.

[0007] This object is achieved by a method according to the invention or by a coating system according to the invention according to the independent claims.

[0008] The programming method according to the invention initially provides geometric data, wherein the geometric data represent the geometry of the component to be coated (e.g., a motor vehicle body component). Within the scope of the programming method according to the invention, the geometric data can be measured, for example, based on a real component or specified, for example, in file form.

[0009] Furthermore, the programming method according to the invention provides that robot path data are specified, wherein the robot path data define a robot path that is to be followed from a paint impact point (TCP: Tool Center Point) of the application device guided by the coating robot during coating operation.

[0010] It should be noted that the invention is not limited to the rotary atomizer mentioned above with regard to the type of application device. Rather, the application device can also be a pressure head, which, unlike an atomizer, does not emit a spray jet of the coating agent, but rather a spatially confined jet of coating agent. Alternatively, within the scope of the invention, it is also possible for the application device to be an airless atomizer or an airmix atomizer, to name just a few further examples.

[0011] Furthermore, the programming method according to the invention provides for the determination of spray pattern data, which can also be referred to as "brush curves," wherein the spray pattern data represent a layer thickness profile, in particular a three-dimensional layer thickness profile, which is generated by the application device on the surface of the component around the paint impact point during actual coating operations. For example, rotary atomizers, when applying a surface-normal coating to a flat component surface, generate a rotationally symmetric, donut-shaped layer thickness profile when viewed ideally. The spray pattern data can then represent the layer thickness progression as a function of the radial distance from the paint impact point. Three-dimensional layer thickness profiles (static spray patterns) can be converted, for example, into two-dimensional layer thickness cross-sections (dynamic spray patterns), from which the spray pattern data can be determined, e.g.when creating a characteristic map. However, within the scope of the invention, it is also possible for the spray pattern data to represent dynamic spray patterns, such as a layer thickness cross-section along a paint path.

[0012] Within the scope of the invention, the coating result is simulated based on the spray pattern data, whereby the spray pattern data are optimized until an acceptable coating result is simulated. The acceptable spray pattern data are then stored in a first data set in order to later determine the appropriate application parameters (e.g., paint flow, shaping air flow, rotational speed of the rotary atomizer, etc.) that are suitable for the practical implementation of the previously determined spray pattern data.

[0013] However, it can happen that, for the given robot path, even the most suitable spray pattern data does not lead to an acceptable coating result. In this case, it is sensible to optimize not only the spray pattern data, but also the robot path. In one embodiment of the invention, it is therefore checked whether the simulated coating result with the most suitable spray pattern data leads to an acceptable coating result. If this is the case, then no optimization of the robot path is necessary and the most suitable spray pattern data determined during the simulation can continue to be used. Otherwise, however, the robot path is optimized and the determination of the most suitable spray pattern data is repeated in a loop during the simulation until the simulated coating result is acceptable.

[0014] When optimizing the robot path, the following optimization measures can be carried out: Adjustment of the path, e.g. changing the polygons that define the path, adjusting the path spacing, shortening or lengthening paths (e.g. at edges), introducing additional paths, removing paths, adjusting the orientation of the applicator axis on the robot path, adjusting the path speed, changing switch-on or switch-off points on the robot path, particularly at edges, path reversal points, interfaces between painting areas of different painting robots ("painting module impacts"), optimization through brush parameterization with regard to the spray jet width or diameter, spray jet shape and scaling of the paint flow rate and by inserting or removing brush numbers with the associated brush parameterization.

[0015] When simulating to determine suitable spray pattern data, properties of the atomizer and / or the bell cup-directing air ring system are preferably also taken into account, particularly with regard to possible paint flow rates and achievable spray pattern widths.

[0016] However, other data besides the spray pattern data can also be optimized during the simulation. For example, the robot paths and the paint flow on / off points can also be optimized.

[0017] The programming method according to the invention also provides for a simulation of the coating result, as is the case with the prior art described above. However, the coating method according to the invention does not involve a physical simulation, which is significantly more complex in itself. Rather, the simulation according to the invention is based on the spray pattern data and the layer thickness profiles defined thereby, so that the simulation according to the invention is significantly simpler and can therefore be performed within a practical computing time.

[0018] It was already mentioned above that, after the simulation, the suitable spray pattern data (brush curves) are available in a first data set. However, this spray pattern data is not yet suitable for controlling the application device. Therefore, within the scope of the programming method according to the invention, it is provided that, from the determined suitable spray pattern data, corresponding suitable application parameters (e.g., speed of the rotary atomizer, paint flow, shaping air flow, etc.) are determined, which are suitable in practical operation for implementing the previously determined suitable spray pattern data when the application device follows the specified robot path. These suitable application parameters are then stored in a second data set.

[0019] The coating system can then be operated with the application parameters determined in this way. Operation with the previously determined application parameters will then produce the previously determined suitable spray pattern data, which were determined to be suitable based on the simulation and lead to an acceptable coating result.

[0020] In one variant of the invention, the suitable spray pattern data are determined during the simulation on the operator side by the coating system operator, which can be done automatically or with supporting user intervention, for example. The first data set containing the suitable spray pattern data determined during the simulation is then transmitted from the operator side to a manufacturer side of the coating system, for example to a service provider commissioned by the manufacturer of the coating system. This service provider then uses the suitable spray pattern data transmitted by the coating system operator to determine the suitable application parameters using a characteristic map and transmits these parameters back to the coating system operator, who then operates the coating system with the transmitted application parameters.

[0021] In another variant of the invention, the determination of suitable spray pattern data during the simulation also takes place on the coating system operator's side, in particular automatically or with supporting user intervention. The coating system operator then receives a characteristic map for determining the suitable application parameters, which is created by the manufacturer, for example, by a service provider commissioned by the coating system manufacturer. For example, the coating system operator can transmit coating data to the service provider that specifies the coating agent to be used. The service provider commissioned by the manufacturer can then select or create a corresponding paint-specific characteristic map and transmit it to the coating system operator.The operator of the coating system then determines the appropriate application parameters from the appropriate spray pattern data using the characteristic map that was previously transferred from the manufacturer.

[0022] As already mentioned above, the first data set contains the appropriate spray pattern data that will lead to an acceptable coating result in the simulation. Furthermore, the first data set with the appropriate spray pattern data can also contain additional coating data. For example, the following coating data can be included in the first data set: The desired layer thickness of the coating agent on the surface of the component. This is preferably the layer thickness in the dried state as opposed to the layer thickness of the wet paint. A coating agent identifier for identifying the coating agent and / or properties of the coating agent. For example, the coating agent identifier can indicate the solids content of the coating agent used, or whether it is a basecoat or a clearcoat. An application device identifier for identifying the application device and / or properties of the application device. For example, the application device identifier can indicate the type of rotary atomizer used. In addition, the coating data can also contain layer information to differentiate between different layers in a multi-layer coating.Another example of possible coating data is the path speed of the paint impact point along the robot path, which also influences the coating result.

[0023] When determining the appropriate application parameters as mentioned above, preferably not only the spray pattern data contained in the first data set is taken into account, but also the coating data mentioned above. Furthermore, reference values for the path spacing between adjacent paint paths, the path speed, the target layer thickness, the solids content of the coating agent, and the application efficiency of the application device can also be considered.

[0024] As already mentioned above, robot path data is taken into account during the simulation. The robot path data defines the robot path to be followed from the paint impact point of the application device during the coating process. This robot path data preferably includes the following data: Spatial course of the robot path, path speed of the paint impact point along the robot path, path distance between laterally adjacent, laterally overlapping or adjacent path sections of the robot path, orientation of the application device, temporal course of the paint impact point along the robot path or speed of the paint impact point along the robot path, switch-on points and / or switch-off points for the paint flow, and / or active brush number: Where is which brush number with the corresponding brush parameterization active in the robot path?

[0025] Furthermore, it should be noted that the robot path is preferably divided into several consecutive path sections, which are to be traversed one after the other from the paint impact point of the application device. The appropriate spray pattern data is then preferably determined individually and specifically for each path section of the robot path. Furthermore, a set of suitable application parameters can also be determined individually and specifically for each path section.

[0026] As already mentioned above, the determination of suitable spray pattern data (brush curves) takes place in a simulation. In the preferred embodiment of the invention, this simulation comprises several iterative steps that are run sequentially, each containing optimization loops.

[0027] In a first optimization step, standard values for the spray pattern and the coating agent flow are preferably specified, particularly as percentage, relative, or virtual values (reference brush). These standard values serve only as starting values for simulating the coating result. In the subsequent simulation based on the specified standard values, the coating thickness distribution is then preferably determined, with deviations of the simulated coating thickness distribution from a target value being identified. With regard to coating thickness homogeneity on large component surfaces, the robot path data can already be optimized in this first optimization step.

[0028] The aforementioned starting values for the simulation can, for example, include a reference value for the path spacing between the center axes of directly adjacent coating paths. For example, when painting motor vehicle bodies, paint paths are typically applied to the vehicle body, with the paint paths running parallel to each other and overlapping laterally. The starting values for the simulation can therefore also define the lateral overlap of the directly adjacent coating paths. Furthermore, the starting values for the simulation can also include a reference value for the coating agent flow.

[0029] In a second optimization step, the layer thickness homogeneity is then preferably tested at simple module joints. For example, the surface of a component to be painted is usually divided into painting modules, which are painted one after the other. For example, the painting modules can be the hood, roof, trunk lid, fenders, and doors of a motor vehicle body, which are painted one after the other. The neighboring modules abut one another at module boundaries, with simple module joints within the meaning of the invention being those boundaries between neighboring modules where exactly two neighboring coating modules border one another. For example, in the case of a motor vehicle body, the front side door and the rear side door can each form a painting module, so that the boundary between the front door and the side door forms a simple module joint.The joints between three or more adjacent coating modules, however, are referred to as complex module joints within the scope of the invention. For example, such a complex module joint occurs at the point on a motor vehicle body where the hood, fender, front door, and A-pillar adjoin each other.

[0030] In the second optimization step, the layer thickness homogeneity at the simple module joints described above is first determined and compared with a target value. During the second optimization step, the robot path can then be optimized to optimize the layer thickness homogeneity at the simple module joints. This optimization can be performed in several iteration loops, which are run consecutively until an acceptable improvement in layer thickness homogeneity is achieved.

[0031] In a third optimization step, the layer thickness homogeneity at the complex module joints mentioned above can then be optimized. Here, too, the robot path can be adjusted several times during the third optimization step to optimize the layer thickness homogeneity at the complex module joints. This optimization can be performed in several optimization loops within the third optimization step, which are run consecutively until an acceptable layer thickness homogeneity is achieved at the complex module joints.

[0032] In a fourth optimization step, the coating thickness homogeneity can be optimized again at simple and / or complex module joints. However, in this fourth optimization step, the robot path is not adjusted, but rather the spray pattern data (brush curves) and / or the coating agent flow. Optimization in the fourth optimization step can also be performed in several optimization loops, which are run sequentially until the simulation leads to an acceptable coating result at the simple or complex module joints.

[0033] Finally, a fifth optimization step can optimize the layer thickness homogeneity at the edges of the component to be coated. For example, the component edges could be the edges of a hood to be coated or the edges of a car body door to be coated. During the fifth optimization step, the layer thickness homogeneity at the component edges is simulated and compared with a specified target value for the layer thickness homogeneity. The robot path, the spray pattern data, and / or the coating agent flow can then be adjusted until the simulation leads to an acceptable coating result. Therefore, the following adjustments can be made during the fifth optimization step: Optimization of the robot path: Adjustment of the path, e.g. general change of the polygons or adjustment with regard to path spacing, shortening and lengthening of paths (e.g. at edges), additional paths, fewer paths, adjustment of the orientation of the bell cup axis, adjustment of the speed, optimization by changing switch-on points and / or switch-off points of the paint flow (GUN ON / GUN OFF), especially at edges, path reversal points, interfaces of painting areas of different robots (painting module impacts), optimization by brush parameterization with regard to spray jet width or spray jet diameter, spray jet shape and scaling of the paint flow rate and by inserting or removing brush numbers with the associated brush parameterization.

[0034] It should be noted that the aforementioned adjustments ("adjustment screws") are possible not only within the fifth optimization step, but also in general. Optimization within the fifth optimization step can also be performed in several optimization loops, which are run consecutively until the simulation leads to an acceptable coating result at the component edges.

[0035] As part of the simulation of the coating result, the coating result can also be displayed graphically on a screen by the operator. For example, the component to be coated can be displayed as a perspective model on a screen. The surface of the displayed model of the component to be coated can be colored depending on its location, with the coloring of the component surface on the screen reflecting, for example, the local deviation between the simulated coating thickness on the one hand and a specified target value for the coating thickness on the other. This visualization of the simulated coating result on a screen provides the programmer with a quick and intuitive overview of the simulated coating result.

[0036] As already mentioned above, after determining the appropriate spray pattern data (brush curves), the simulation determines the appropriate application parameters for implementing the specified spray pattern data. This conversion is performed using a multidimensional characteristic map that, for a specific coating agent, can, for example, link several of the following parameters: Width of the coating thickness profile, particularly as the SB50 value of the coating thickness profile. The SB50 value refers to the width of the coating thickness profile within which the coating thickness is at least 50% of the maximum value of the coating thickness. Shaping air flow of the application device (e.g., rotary atomizer). Coating agent flow of the application device (e.g., rotary atomizer). Speed of the rotary atomizer used as the application device. High voltage of an electrostatic coating agent charge. Path speed of the application device (e.g., rotary atomizer) along the robot path. Coating distance between the application device and the surface of the component to be coated.

[0037] As already mentioned above, robot path data is specified, which determines the course of the robot path during the actual coating operation. This robot path data can, for example, be specified by the operator based on the geometric data of the component to be coated. Alternatively, however, it is also possible for the robot path data to be provided by the manufacturer and then imported in file format by the coating system operator.

[0038] The same applies to the geometric data of the component to be coated, which can be determined, for example, by the coating system operator during the measurement process. Alternatively, however, the geometric data can be provided by the manufacturer.

[0039] In practice, it has also been shown that, with regard to the simulation method and spray pattern data, it is advantageous to be able to at least partially circumvent spray jet distortion, for example, at edges, A-pillars, etc. Special painting situations and effects, such as air flow in the booth, air flow around the atomizer and the workpiece, high-voltage influences, etc., are therefore automatically taken into account in the simulation - where necessary - e.g., at workpiece edges, recesses for sunroofs, or complex workpiece geometries.

[0040] Furthermore, it should be noted that the invention not only claims protection for the above-described programming method according to the invention. Rather, the invention also claims protection for a correspondingly adapted coating system suitable for implementing the programming method according to the invention.

[0041] The coating system according to the invention initially comprises, in accordance with the prior art, at least one coating robot, at least one application device (e.g., rotary atomizer), and a controller that controls the application device and the coating robot. The controller in the coating system according to the invention is then configured to execute the programming method according to the invention.

[0042] For example, the above-mentioned characteristic map can be stored in the control system in order to determine the appropriate application parameters from the appropriate spray pattern data.

[0043] Furthermore, the coating system according to the invention preferably has a data interface in order to transmit the spray pattern data and / or the coating data to the coating system manufacturer or a service provider commissioned by the manufacturer and to receive the associated characteristic map for determining the suitable application parameters from the coating system manufacturer or from the service provider.

[0044] 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 embodiment of the invention with reference to the figures. Figure 1A , 1B show a flow chart to illustrate the programming method according to the invention. Figure 2 shows a flow chart to illustrate the simulation of the coating result within the framework of the programming method according to the invention. Figure 3shows an example of a layer thickness profile generated by a rotary atomizer. Figure 4 shows different layer thickness profiles when varying the paint flow. Figure 5 shows an example of a characteristic map for linking spray pattern data on the one hand and application parameters on the other. Figure 6 shows a schematic representation of a painting system according to the invention. Figure 7 shows a variation of Figure 1A .

[0045] The following is the flow chart according to the Figure 1A and 1B which illustrates the programming method according to the invention.

[0046] In a first step (S1), geometric data representing the geometry of the component to be painted (e.g., a vehicle body component) is specified. The geometric data can be provided in file form, for example, and easily read out. Alternatively, it is also possible to measure the geometric data based on a real component.

[0047] In a second step S2, robot path data is then specified. The robot path data defines the movement path of the paint impact point of the application device used on the component surface. The robot path data is determined based on the geometric data of the component to be coated. Painting guidelines are typically taken into account, which may, for example, include specifications regarding the lateral spacing of adjacent painting paths and the lateral overlap of adjacent painting paths.

[0048] In a further step S3, suitable spray pattern data (brush curves) are determined within the framework of a simulation, whereby the spray pattern data represent a layer thickness profile and lead to an acceptable painting result in the simulation. For example, Figure 3 A layer thickness profile of a rotary atomizer with several spray pattern data that characterize the layer thickness profile. The most important value for describing the layer thickness profile is the SB50 value, which represents the width of the layer thickness profile within which the layer thickness is at least 50% of the maximum layer thickness SD MAX. The simulation in step S3 will be described in more detail later using Figure 2 described.

[0049] The acceptable spray pattern data determined during the simulation are then stored in a first data set in step S4.

[0050] In a further step S5, additional painting data are determined, such as the desired coating thickness, the paint type, the application device type, the coating type (basecoat / clearcoat), the web speed, and the application efficiency of the application device. These painting data are then stored in the first data set in a step S6, together with the spray pattern data.

[0051] In a step S7, a paint-specific characteristic map is then provided by the manufacturer so that the corresponding application parameters can be determined from the suitable spray pattern data, which in practice lead to the realization of the suitable spray pattern data.

[0052] In the next step (S8), the characteristic map is then used to determine the appropriate application parameters from the appropriate spray pattern data and the paint data. For example, the application parameters can include the shaping air flow of a rotary atomizer, the high-voltage charging, and the paint flow.

[0053] The appropriate application parameters are then stored in a second data set in step S9.

[0054] In the next step S10, the paint shop is operated using the specified robot path data and the determined application parameters. With an optimally calculated characteristic map, operating the paint shop with the application parameters read from the characteristic map then results in the spray pattern data previously determined during the simulation, thus achieving a good match between the simulation and the actual paint shop operation.

[0055] It should be noted that the programming method described above can determine the spray pattern data and the associated application parameters individually for different sections of the robot path. This means that the application parameters do not have to be constant along the robot path. Rather, the application parameters can be varied along the robot path to achieve a good coating result.

[0056] In the following, the simulation according to step S3 in Figure 1A described in more detail, referring to the flow chart according to Figure 2 reference is made.

[0057] In the first step (S3.1) of the simulation, standard values are specified for the spray pattern data, which can also be referred to as the reference brush. Subsequently, the coating result is simulated based on the specified standard values.

[0058] In a second optimization step S3.2, the layer thickness homogeneity is then checked at simple module joints between two adjacent modules. In the context of the invention, the term simple module joints refers to the boundaries between exactly two adjacent painting 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 during the simulation until a maximum improvement in layer thickness homogeneity is achieved at the simple module joints. The second optimization step S3.2 can therefore comprise several iteration loops that are run through one after the other. It is important that the layer thickness homogeneity at the simple module joints is evaluated and used to optimize the robot path.

[0059] In a third optimization step S3.3, the coating thickness homogeneity can then be checked at complex module joints between more than two adjacent painting modules. The term "complex module joints" refers to the boundaries between more than two adjacent painting modules. For example, the boundary between the fender, the hood, the front side door, and the A-pillar of a vehicle body constitutes such a complex module joint. In the third optimization step S3.3, the robot path is then iteratively optimized again until a maximum improvement in coating thickness homogeneity is achieved at the complex module joints.

[0060] In a fourth optimization step (S3.4), the layer thickness homogeneity at the complex and / or simple module joints is again determined and used as an optimization criterion. However, in this case, the robot path is not optimized, but rather the spray pattern data (brush curves) until a maximum improvement in layer thickness homogeneity is achieved at the simple or complex module joints.

[0061] In a fifth optimization step (S3.5), the layer thickness homogeneity at the component edges is then checked and used as an optimization criterion. For example, the layer thickness homogeneity at the edges of a car hood can be checked and taken into account. During the optimization process, the spray pattern data and / or the robot path can then be optimized until a maximum improvement in layer thickness homogeneity at the component edges is achieved. Here, too, the optimization can involve multiple iteration loops that are run consecutively.

[0062] Figure 3 shows a layer thickness profile as typically produced by a rotary atomizer.

[0063] Figure 4 shows a corresponding layer thickness profile of a rotary atomizer when the paint flow changes from 70% to 130%.

[0064] Figure 5shows a characteristic map that can be used within the scope of the invention to determine suitable application parameters from the spray pattern data. The spray pattern data in the characteristic map according to Figure 5 around the SB 50 value, while the application parameters here are the shaping air flow and the paint quantity. However, within the scope of the invention, multidimensional characteristic maps can also be used that link a larger number of spray pattern data or application parameters.

[0065] Figure 6 shows in a highly simplified and thematic form a painting system according to the invention which is suitable for carrying out the programming method according to the invention.

[0066] Thus, the painting system according to the invention, in accordance with known painting systems, initially comprises a painting system controller 1, which, during operation, controls a painting robot and an application device (e.g., a rotary atomizer) with specific application parameters. The painting system controller 1 receives robot path data as input variables, which specify the course of a robot path. The robot path data can be specified according to painting guidelines.

[0067] In addition, the paint shop control 1 receives geometric data that reflects the geometry of the component to be coated.

[0068] Finally, the paint shop control 1 receives painting data, which, for example, indicate the type of paint used.

[0069] During painting operation, the painting system control 1 then controls the painting robot and the application device accordingly, using application parameters that are defined using the programming method according to the invention.

[0070] For this purpose, a simulation tool 2 is provided, which also receives the painting data, the geometry data and the robot path data and, within the framework of a simulation, determines suitable spray pattern data that lead to an acceptable coating result in the simulation, as described above.

[0071] In addition, the painting system has a characteristic map element 3 to determine the application parameters for the painting system control 1, as will be described in detail below.

[0072] The painting system according to the invention now additionally has a data interface 4, which serves to transmit the suitable spray pattern data and the painting data to the painting system manufacturer or a service provider commissioned by the manufacturer, which also has a data interface 5 for this purpose. On the manufacturer's side, a suitable characteristic map can then be read from a characteristic map memory 6 from the transmitted painting data and the transmitted spray pattern data and transmitted to the painting system operator, who then stores the suitable characteristic map in the characteristic map memory 3. The characteristic map stored in the characteristic map memory 3 then enables the determination of the suitable application parameters from the simulated spray pattern data.

[0073] Figure 7 shows a modification of the flow chart according to Figure 1A , so that to avoid repetition, first refer to the above description Figure 1A is referred to.

[0074] A special feature of this modification is the process steps S4 and S5, which are inserted into the process flow. The process proceeds according to Figure 1A assumes that the robot path is fixed and will not be changed during the simulation. However, it may happen that for a specific, fixed robot path, even the best-suited spray pattern data ("brush curves") do not lead to an acceptable coating result. This can be the case, for example, if the specified robot path is particularly demanding from a coating perspective. In such cases, it is advisable to also optimize the robot path.

[0075] In step S4, after determining the most suitable spray pattern data, it is checked whether the simulated painting result is acceptable. If this is the case, the method can continue with step S6, as described above with reference to Figure 1Awas described.

[0076] If, however, the simulated coating result is not acceptable even with the best-suited 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-suited spray pattern data and the optimized robot path leads to an acceptable coating result. If this is the case, the process can proceed to step S6, as described above with reference to Figure 1A has already been described. List of reference symbols

[0077] 1Paint shop control 2Simulation tool 3Characteristic map element 4Data interface 5Data interface 6Characteristic map memory

Claims

1. Method for programming a program-controlled coating installation with a coating robot and an application device for coating components, in particular for programming a painting installation with a painting robot for painting motor vehicle body components, having the following steps (S1-S3): a) Specification or determination of geometry data, the geometry data representing the geometry of the component to be coated (S1), b) specification of robot path data (S2), b1) the robot path data defining a robot path which is to be traversed by a paint impact point of the application device guided by the coating robot in coating operation, and b2) the robot path being defined on the basis of the predetermined geometry data of the component to be coated, c) determination of suitable spray pattern data (S3), c1) the spray pattern data representing a layer thickness profile, in particular a three-dimensional layer thickness profile, which is generated by the application device on the surface of the component around the paint impact point in real coating operation, and c2) wherein the determined spray pattern data are intended to achieve an acceptable coating result in real coating operation when coating the component along the predetermined robot path, c3) said suitable spray pattern data being determined by a simulation which takes into account the predetermined robot path data and the geometry data of the component to be coated, wherein the determination of the application parameters is carried out for the operation of the application device after the simulation, c4) wherein the spray pattern data are optimized during the simulation until an acceptable coating result is simulated, and c5) wherein the suitable spray pattern data are stored in a first data set (S4), characterized by the following steps: d) determining suitable application parameters for operating the application device (S8), d1) wherein the suitable application parameters are determined from the spray pattern data contained in the first data set on the basis of a characteristic diagram, d2) wherein the determined suitable application parameters lead to the suitable spray pattern data during real operation of the application device when the robot path is traversed, and d3) wherein the suitable application parameters are stored in a second data set (S9), and e) operating the coating installation (S10), e1) wherein the coating robot is controlled according to the robot path data so that the paint impact point of the application device traverses the predetermined robot path on the surface of the component to be coated, and e2) wherein the application device is controlled with the suitable application parameters contained in the second data set.

2. Method according to claim 1, characterized in, a) that the determination of the suitable spray pattern data takes place within the framework of the simulation on the operator side at the coating installation operator, in particular automatically or with an assisting user intervention, b) that the first data set with the suitable spray pattern data determined in the course of the simulation is transferred from the operator side to a manufacturer side of the coating installation, in particular at a service provider, c) that the determination of the suitable application parameters is carried out on the manufacturer's side on the basis of the characteristic diagram and the suitable spray pattern data, in particular by the service provider, and d) that the second data set with the suitable application parameters is transmitted from the manufacturer side to the operator side.

3. Method according to one of the preceding claims, characterized in, a) that the determination of the suitable spray pattern data is carried out within the framework of the simulation on the operator side at the coating installation operator, in particular automatically or with an assisting user intervention, b) that the characteristic diagram for determining the suitable application parameters is created on the manufacturer's side, in particular by the service provider, c) that the characteristic diagram for determining the suitable application parameters is transmitted by the service provider to the operator side, and d) that the determination of the suitable application parameters is carried out on the operator side on the basis of the characteristic diagram and the suitable spray pattern data.

4. Method according to one of the preceding claims, characterized in, a) that the first data set with the suitable spray pattern data also contains the following coating data: a1) the desired coating thickness of the coating agent layer on the surface of the component, in particular the coating thickness in the dried state, a2) a coating agent identifier for identifying the coating agent and / or properties of the coating agent, a3) an application device identifier for identifying the application device and / or properties of the application device, a4) layer information for distinguishing between different layers in a multilayer coating, in particular for distinguishing between a basecoat layer and a clearcoat layer, and / or a5) path speed of the paint impact point along the robot path, and / or b) that, in determining the suitable application parameters, not only the spray pattern data contained in the first data set are taken into account, but also the coating data contained in the first data set and preferably also a reference value for the path spacing, a reference value for the path speed, the target layer thickness, the solids content of the coating agent and the application efficiency of the application device as an assumed empirical value.

5. Method according to one of the preceding claims, characterized in that the robot path data contain the following data: a) spatial course of the robot path, and / or b) path speed of the paint impact point along the robot path, and / or c) path spacing between laterally adjacent, laterally overlapping or adjacent path sections of the robot path.

6. Method according to claim 4, characterized by the following steps in the simulation: a) Subdivision of the robot path into a plurality of successive path sections which are to be traversed in succession by the paint impact point of the application device, b) determination of a suitable spray pattern for the individual path sections of the robot path, c) determination of a suitable coating agent flow for the individual path sections of the robot path, in particular as a percentage, relative or virtual value, d) wherein the first data set with the suitable spray pattern data for the individual path sections of the robot path each contain the suitable spray image and the suitable coating agent flow, in particular as a percentage, relative or virtual value,7. Method according to claim 5 or 6, characterized in that the simulation is carried out in the following iterative optimization steps: a) in a first optimization step, specification of default values for the spray pattern and the coating agent flow, in particular as a percentage, relative or virtual value, and simulation of the resulting coating result with the default values, and / or b) in a second optimization step, testing the coating thickness homogeneity at simple module joints between exactly two adjacent coating modules on the surface of the component to be coated and optimizing the robot path to improve the coating thickness homogeneity at the simple module joints, in particular between the fender and the hood, between the front door and the rear door and between the modules of a roof of a motor vehicle body, and / or c) in a third optimization step, testing the layer thickness homogeneity at complex module joints between more than two adjacent coating modules on the surface of the component to be coated and optimizing the robot path to improve the layer thickness homogeneity at the complex module joints, in particular at the junction of fender, hood, front door, A-pillar or between rear fender and rear pillar, and / or d) in a fourth optimization step, testing of the coating thickness homogeneity at simple and / or complex module joints between adjacent coating modules on the surface of the component to be coated and optimization of the following variables for improving the coating thickness homogeneity at the simple and / or complex module joints: d1) spray pattern data, and / or d2) coating agent flow, in particular as a percentage, relative or virtual value, and / or e) in a fifth optimization step, testing of the coating thickness homogeneity at component edges of the component to be coated and optimization of the following variables for improving the coating thickness homogeneity at the component edges: e1) Robot path, e2) spray pattern data, and / or e3) coating agent flow, in particular as a percentage, relative or virtual value.

8. Method according to claim 6, characterized in that the default values for the first optimization step of the simulation are specified as a function of the following variables: a) reference value for the path distance between center axes of directly adjacent coating paths, b) overlap of directly adjacent coating paths, and / or c) reference value for the coating agent flow, in particular as a percentage, relative or virtual value.

9. Method according to one of the preceding claims, characterized in that the simulated coating result is represented graphically on a screen on the operator side, in particular with a perspective representation of the component to be coated as a model and with a location-dependent coloring of the surface of the represented model as a function of the simulated local coating thickness, in particular corresponding to the deviation between the simulated local coating thickness on the one hand and a predetermined reference value for the coating thickness on the other hand.

10. Method according to one of the preceding claims, characterized in that the characteristic diagram for determining the suitable application parameters in relation to a specific coating agent and / or the coating device used links the following variables with one another: a) Width of the coating thickness profile, in particular SB50 value of the coating thickness profile, b) shaping air flow of the application device, c) coating agent flow of the application device, d) rotational speed of a rotary atomizer used as application device, e) high voltage of an electrostatic coating agent charge, f) path speed of the application device along the robot path, g) coating distance between the application device and the surface of the component to be coated.

11. Method according to one of the preceding claims, characterized in, a) that the robot path data are specified on the operator side at the coating installation operator, in particular automatically or with an assisting user intervention, and / or b) that the geometry data of the component to be coated is specified on the operator side by the coating installation operator, in particular automatically or with an assisting user intervention.

12. Coating installation for coating components, in particular for painting motor vehicle body components, having a) at least one coating robot, b) at least one application device which is guided by the coating robot, and c) a control which controls the application device and the coating robot, characterized in d) that the control executes the method according to one of the preceding claims.

13. Coating installation according to claim 12, characterized in that the characteristic diagram is stored in the control in order to determine the suitable application parameters from the suitable spray pattern data.

14. Coating installation according to claim 12 or 13, characterized by a data interface for transmitting the first data set with the suitable spray pattern data to the service provider and for receiving the second data set with the suitable application parameters from the service provider.