Patent Application
20250222479
The disclosure relates to a simulation method for a coating installation for coating a component by means of an applicator, in particular for a painting installation for painting motor vehicle body components with an atomizer or a print head. The disclosure also relates to a corresponding coating installation for carrying out the simulation method.
DE 10 2019 113 341 A1 and DE 10 2020 114 201 A1 disclose a simulation method that makes it possible to simulate painting processes.
In this process, geometry data is initially specified that reflects the geometry of the component to be painted. For example, this geometry data can be specified in the form of a CAD file (CAD: Computer Aided Design), whereby the CAD file represents the shape of a vehicle body to be painted.
Furthermore, general painting parameters are specified, such as the air temperature in the paint booth or paint parameters (e.g. viscosity of the paint).
In addition, a painting path is specified that is to be followed by the paint impact point of the application device used (e.g. rotary atomizer) during operation.
Furthermore, starting values of the painting parameters to be optimized are defined, which can be so-called current spray patterns, i.e. layer thickness distributions around the respective paint impact point. These current spray patterns are then superimposed as part of the simulation.
Within the scope of the disclosure, this computational superimposition of the current spray patterns can also be carried out using a projection method, i.e. the spray patterns (possibly adapted with regard to certain aspects of the coating situation) are geometrically projected onto the workpiece geometry. The current spray patterns projected onto the workpiece geometry are then superimposed. The term used in the context of the disclosure for superimposing the current spray patterns therefore also includes the projection method mentioned above.
The current spray patterns can then be optimized in a simulation loop in order to achieve the best possible painting result in the simulation. For example, one optimization goal can be to achieve a coating thickness that is as uniform as possible.
The current spray patterns used in the simulation of the coating process can be derived from reference spray patterns that are stored in a database for various reference coating situations. As part of the simulation loop, the current coating situation is first determined for each path point of the coating path. A reference spray pattern is then read from the database, which was measured in a reference painting situation that corresponds as closely as possible to the actual current painting situation. In some examples, however, the database may not contain corresponding reference spray patterns for all possible current painting situations. In practice, it is therefore intended that the current spray pattern is determined by interpolation or by mathematical adaptation of reference spray patterns stored in the database.
The known simulation method described above provides satisfactory simulation results. However, there is a need for further optimization of this known simulation method.
The disclosed technology is based on the task of creating a correspondingly improved simulation process.
Furthermore, the disclosure is based on the task of creating a correspondingly adapted coating installation which is suitable for carrying out the simulation process according to the disclosure. This task is solved by a simulation method and by a coating installation according to the claims.
The disclosure is based on the newly gained technical-physical knowledge that the quality of the coating does not only depend on the uniformity of the coating thickness. Rather, the quality of the coating is also determined by the so-called degree of wetness. In the simulation, several current spray patterns are superimposed, so that the coating in the simulation consists of several superimposed current spray patterns. Similarly, the real coating on the real component also has several overlays that originate from several spray patterns that were applied to the component surface along parallel coating paths, for example.
It is possible for the various overlays of current spray patterns to each contribute equally to the overall layer thickness. If, for example, three current spray patterns are superimposed at one point on the component surface, each current spray pattern can contribute one third to the overall coating thickness. However, it is also possible that the various overlays of current spray patterns contribute very differently to the overall layer thickness. With three overlays, for example, it is possible that the individual overlays contribute in the ratio 70%: 20%: 10% to the total layer thickness. In the context of the disclosure, the degree of wetness can then reflect the percentage of the total coating thickness that the individual overlaid layers contribute. For example, the degree of wetness can indicate what the largest percentage share of one of the overlays in the total coating thickness is.
In addition, the number of overlays of different layers can also vary within the coated component surface. For example, the coating at one location on the component surface may comprise three superimposed layers of current spray patterns, while the coating at another location on the component surface may comprise five superimposed layers of current spray patterns. The term “degree of wetness” used in the context of the disclosure can then also indicate how many superimposed layers of current spray patterns the coating comprises at the respective point on the component surface.
Furthermore, the degree of wetness can also reflect the geometric properties of the current spray patterns, which have an influence on the overall layer thickness at the respective point on the component surface.
Furthermore, within the scope of the disclosure, it is also possible for the degree of wetness to indicate how high the total layer thickness is at the respective point on the component surface. The term “degree of wetness” used in the context of the disclosure may reflect one or more of the above definitions.
When determining the degree of wetness, several or all locally involved spray patterns can be taken into account within the scope of the disclosure (e.g. mean value of the proportions, ratio of the proportions, . . . ) or only one specific locally involved spray pattern (e.g. the one that leads to the highest degree of wetness at the location under consideration, the one that is the last to be overcoated, . . . ).
It should be mentioned here that different current spray patterns can be used when calculating the simulated coating result than when calculating the degree of wetness.
The simulation method according to the disclosure is preferably suitable for a painting installation for painting motor vehicle body components using an atomizer (e.g. rotary atomizer) or a print head. However, the disclosure is not limited to use in a painting installation, but can also be realized in connection with a coating installation that applies other coating agents, such as adhesive, insulating material or sealant, to name just a few examples.
Furthermore, the disclosure is also not limited for use in a painting installation that paints vehicle body components. With regard to the components to be coated, the disclosure is therefore not limited to motor vehicle body components.
Furthermore, the disclosure is not only suitable for simulations in coating installations that use an atomizer (e.g. rotary atomizer) or a print head as applicator. The principle according to the disclosure is also generally applicable in this respect.
The simulation method according to the disclosure initially provides that geometry data are specified which reflect the geometry of the component to be coated. For example, these geometry data can be read from a component file in the form of CAD data (CAD: Computer Aided Design) of the component to be coated. Alternatively, it is also possible to generate the geometry data of the component to be coated by measuring a real component.
Furthermore, the simulation method according to the disclosure also provides, in accordance with the known simulation method described at the beginning, that general coating parameters are initially specified, such as coating agent parameters (e.g. viscosity), applicator type, bell cup type or path spacing of the adjacent coating paths. These general coating parameters are preferably specified by the user or read out from a data memory. It should be mentioned here that these general coating parameters do not have to be optimized as part of the simulation process according to the disclosure. However, it is also possible within the scope of the disclosure for the general coating parameters to be optimized as well.
In addition, starting values for coating parameters to be optimized are then defined. The coating parameters to be optimized initially comprise a coating path, which consists of numerous path points and is to be followed by the paint impact point of the applicator (e.g. rotary atomizer) in coating operation, as is known per se from the prior art. It should be noted here that the term “path point” is to be understood generally in the context of the disclosure and preferably refers to the temporal or spatial discretization of the path trajectory (e.g. one path point every x milliseconds or one path point every y millimeters).
In addition, the coating parameters to be optimized also include so-called current spray patterns for the individual path points of the coating path, whereby the current spray patterns reflect the coating thickness distribution on the component around the paint impact point on the component.
It should be mentioned here that the starting values of the coating parameters to be optimized do not have to be specified by the user, but can be defined by the program. For example, the starting values of the current spray patterns can be derived from reference spray patterns that were previously determined for various coating situations, for example by coating test sheets, as is known from the prior art.
The simulation method according to the disclosure then provides for the program-controlled execution of several steps in a simulation loop, whereby the individual steps are carried out for the individual path points of the coating path.
First, a simulated coating result is calculated as part of the simulation loop by superimposing the actual current spray patterns for the individual points of the coating path. This superimposition of the current spray patterns can also be carried out using a projection method, for example, as described in detail below.
In the simulation loop, the simulation result is then checked in a next step, whereby, for example, the uniformity of the resulting layer thickness is evaluated as a quality parameter. However, the degree of wetness in the individual points of the component surface is determined and taken into account as a quality parameter.
The next step in the simulation loop is to adjust the coating parameters to be optimized (e.g. current spray patterns, coating path) in order to optimize the simulated coating result.
The simulation loop is then repeated until the simulated coating result is satisfactory and the degree of wetness in the individual points of the component surface determined during the simulation is also acceptable.
The procedure explained above is described again below in other words in order to avoid misunderstandings. The user can assign different (or the same) reference spray patterns to different path sections. With regard to the first simulation run, these would be the starting values specified by the user (e.g. in a brush table with spray pattern width and scaling factor for the spray pattern height). Depending on the painting situation on the workpiece, these reference spray patterns can be automatically adapted by the program to create current spray patterns, which are then used for the simulation. With regard to the first simulation run, these would be the starting values that are automatically determined by the program based on the reference spray patterns specified by the user and the painting situation on the workpiece. If the first simulation result is not satisfactory, the user changes the assignment of reference spray patterns (e.g. wider spray pattern, higher spray pattern, . . . ), i.e. he changes the start values from the first simulation run. Consequently, the automatically defined current spray patterns used for the simulation also change. This continues until a satisfactory coating thickness result is achieved.
It has already been mentioned above that the coating parameters to be optimized (e.g. current spray patterns, coating path) are optimized as part of the simulation loop. This optimization can, for example, be performed by an operator based on experience. In some embodiments, however, the coating parameters to be optimized can be adjusted in the simulation loop using artificial intelligence (AI).
At the start of the simulation loop, starting values for the current spray patterns in the individual points of the coating path are specified. When determining the current spray patterns, the respective current coating situation can be taken into account. For example, the current coating situation is characterized by the coating distance (i.e. the distance between the applicator and the component surface), the component geometry at the paint impact point and similar coating parameters. Depending on this current coating situation in the individual path points of the coating path, the associated current spray patterns can then be determined with the aid of a spray pattern database in which reference spray patterns for different coating situations are stored.
For example, the stored reference spray patterns can be determined in spray pattern tests in which test sheets are coated in different reference coating situations. The coating thickness distribution on the test sheets is then measured and stored in the spray pattern database with the associated coating parameters that define the respective reference coating situation. The current spray patterns can then be derived from the stored reference spray patterns, which can also be done, for example, by interpolating various stored reference spray patterns. If, for example, the current coating situation does not correspond exactly to the reference coating situation of the reference spray patterns stored in the spray pattern database, two or more reference spray patterns can be interpolated that were determined for similar coating situations.
However, the current spray patterns do not necessarily have to be determined from the stored reference spray patterns by interpolation from several stored reference spray patterns. Alternatively, it is also possible to determine a current spray pattern by mathematically adjusting a stored reference spray pattern. This adaptation of the reference spray patterns stored in the database according to the current coating situation can also be carried out by an algorithm, for example by means of artificial intelligence (AI).
In practice, the adjustment can be carried out automatically using correction or scaling factors if the current coating situation is a geometric edge (e.g. coating path on workpiece edge), as a certain percentage of the coating agent beams (projection beams) then pass by the workpiece. The adjusted spray pattern can then be projected onto the workpiece surface.
The current coating situation and the reference coating situation can, for example, be defined by at least one of the following variables:
It has already been mentioned above that the reference spray patterns stored in the spray pattern database can be determined by coating tests on test sheets before the simulation loop. The coating thickness distribution measured on the test sheets is then stored in the spray pattern database as a reference spray pattern in an assignment to the respective reference coating situation.
It should also be mentioned that the reference spray patterns stored in the spray pattern database can be either dynamic or static spray patterns. Dynamic spray patterns are measured as a result of coating processes in which the applicator moves relative to the component (e.g. test sheet). Static spray patterns, on the other hand, are measured as a result of coating processes in which the applicator is stationary relative to the component (e.g. test sheet).
In the simulation loop, it is then possible to continuously check at which points of the coating path the adjustment of the coating parameters to be optimized has led to a change in the coating parameters. This means that the coating parameters are not usually changed at all path points in the various runs of the simulation loop. The simulation then only needs to be updated in those path points in which the optimization of the coating parameters has actually led to a change. In the context of the disclosure, it is therefore not necessary for the simulation loop to extend over all path points of the coating path in each run.
Furthermore, it should be mentioned that the above-mentioned general coating parameters may comprise at least one of the following variables:
The coating parameters to be optimized can, for example, include at least one of the following variables:
In the context of the disclosure, the simulated coating result can then be displayed graphically on a screen to enable an operator to make a simple assessment. However, it is also possible for the simulated coating result to be evaluated automatically, for example by artificial intelligence (AI).
After completion of the simulation process according to the disclosure, optimized coating parameters are then available for the individual path points of the coating path. These optimized coating parameters can then be transferred to a control system of the coating installation so that the control system then controls the coating installation accordingly in real coating operation. In practice, the optimized coating parameters are thus converted into real control variables for controlling the coating installation.
This conversion (“forward translation”) of the optimized coating parameters into real control variables for controlling the coating installation can relate to the commissioning or optimization of existing systems or existing coatings. However, within the scope of the disclosure, it is also possible for existing control variables of the coating installation to be read in by the simulation computer and converted into start parameterizations for the simulation. Control variables for the control of the coating installation that have already been tested in real life are used here as starting values for coating parameters to be optimized (“backward translation”). The term “conversion of the optimized coating parameters into real control variables for controlling the coating installation” used in the context of the disclosure is therefore to be understood in general terms.
Furthermore, it should be mentioned that the disclosure does not only claim protection for the simulation method according to the disclosure described above. Rather, the disclosure also claims protection for a corresponding coating installation which is suitable for carrying out the simulation method according to the disclosure. In addition to at least one coating robot with an applicator (e.g. rotary atomizer) and a control system, the coating installation according to the disclosure also has a simulation computer on which a simulation program is stored, which executes the simulation method according to the disclosure when it is carried out.
In general, the simulation can also be carried out on an “offline” computer (e.g. office laptop) (e.g. planning department, offline department, training department, . . . ), and the coating parameters found can then be transferred to the control system later/if required, for example.
The flow chart shown in
In a first step S1, a file containing a definition of the geometry of the motor vehicle body to be painted is first read in. For example, this file can be provided by the manufacturer of the respective motor vehicle as a CAD file.
In a further step S2, general painting parameters are then set, such as the following painting parameters:
In a further step S3, starting values of the painting parameters to be adjusted or optimized are then specified under program control for a subsequent simulation run. For example, the painting parameters to be optimized can be the following painting parameters:
After the first run of the simulation loop, the painting parameters to be optimized for the next simulation run are then adjusted in step S4. This adjustment can be made, for example, based on experience by an operator or by artificial intelligence (AI).
In the next step S5, those path points of the robot path are then determined where the adjustment of the painting parameters to be optimized has led to a significant change in the painting situation. This is useful so that the simulation loop described in detail below does not have to include all path points of the robot path, including those path points in which the adjustment of the painting parameters to be optimized does not lead to a significant change.
In the following steps S6, S7 and S8, a simulation loop is then run through, which extends over all path points of the coating path in which the coating parameters have been significantly changed.
The first step S6 provides for current spray patterns to be determined according to the respective current painting situation in the individual path points. For example, reference spray patterns can be read from a spray pattern database for this purpose. These reference spray patterns can, for example, be determined beforehand by coating test sheets. When determining the current spray patterns corresponding to the respective current painting situation, a check is then first made to see whether a reference spray pattern is stored in the spray pattern database that was measured in a reference painting situation that corresponds exactly to the current painting situation. If this is the case, the stored reference spray pattern can be read out and accepted as the current spray pattern.
In some examples, however, this may not be possible. Instead, in practice, the current spray pattern is derived by calculation from one or more stored reference spray patterns that have been measured in similar reference coating situations. This adaptation of the stored reference spray patterns to determine the current spray patterns to be used in accordance with step S7 can be carried out using artificial intelligence (AI), for example. For example, a projection method can also be used, as already mentioned.
In the next step S8, the painting result including the degree of wetness is simulated on the basis of the following variables:
The next step S9 then checks whether the simulated painting result is satisfactory. If this is not the case, the painting parameters to be optimized are adjusted again for the next simulation run in step S4.
Otherwise, the optimized painting parameters are saved in the next step S10 and can then be used to control the painting installation in real painting operation.
Finally,
In the illustration according to
However, the degree of wetness can also indicate what percentage of the total coating thickness the individual layers of the current spray patterns account for.
The schematic diagram shown in
In addition, a simulation computer 14 is shown, which is used to carry out the simulation method according to the disclosure. For this purpose, the simulation computer 14 is connected to a database computer 15, which contains a spray pattern database with stored reference spray patterns.
On the input side, the simulation computer 14 first receives the geometric data of the vehicle bodies to be painted.
In addition, the simulation computer 14 also receives general painting parameters on the input side.
Furthermore, the simulation computer 14 receives starting values of the painting parameters to be optimized on the input side. These starting values can include, for example, the painting path and current spray patterns for the individual points of the painting path.
The simulation computer 14 then transmits the respective current painting situation to the database computer 15, which determines a suitable current spray pattern corresponding to the respective current painting situation, usually by adapting or interpolating stored reference spray patterns. The database computer 15 then supplies a suitable current spray pattern to the simulation computer 14 for the individual path points of the painting path. The simulation computer 14 can then, together with the database computer 15, carry out the simulation process shown in
| Number | Date | Country | Kind |
|---|---|---|---|
| 10 2022 108 004.8 | Apr 2022 | DE | national |
This application is a national stage of, and claims priority to, Patent Cooperation Treaty Application No. PCT/EP2023/058669, filed on Apr. 3, 2023, which application claims priority to German Application No. DE 10 2022 108 004.8, filed on Apr. 4, 2022, which applications are hereby incorporated herein by reference in their entireties.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2023/058669 | 4/3/2023 | WO |
