Classifications

• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B19/00—Program-control systems
  • G05B19/02—Program-control systems electric
  • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
  • G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
  • B05B—SPRAYING APPARATUS; ATOMISING APPARATUS; NOZZLES
  • B05B12/00—Arrangements for controlling delivery; Arrangements for controlling the spray area
  • B05B12/08—Arrangements for controlling delivery; Arrangements for controlling the spray area responsive to condition of liquid or other fluent material to be discharged, of ambient medium or of target ; responsive to condition of spray devices or of supply means, e.g. pipes, pumps or their drive means
  • B05B12/084—Arrangements for controlling delivery; Arrangements for controlling the spray area responsive to condition of liquid or other fluent material to be discharged, of ambient medium or of target ; responsive to condition of spray devices or of supply means, e.g. pipes, pumps or their drive means responsive to condition of liquid or other fluent material already sprayed on the target, e.g. coating thickness, weight or pattern
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
  • B05B—SPRAYING APPARATUS; ATOMISING APPARATUS; NOZZLES
  • B05B12/00—Arrangements for controlling delivery; Arrangements for controlling the spray area
  • B05B12/004—Arrangements for controlling delivery; Arrangements for controlling the spray area comprising sensors for monitoring the delivery, e.g. by displaying the sensed value or generating an alarm
  • B05B12/006—Pressure or flow rate sensors
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
  • B05B—SPRAYING APPARATUS; ATOMISING APPARATUS; NOZZLES
  • B05B12/00—Arrangements for controlling delivery; Arrangements for controlling the spray area
  • B05B12/08—Arrangements for controlling delivery; Arrangements for controlling the spray area responsive to condition of liquid or other fluent material to be discharged, of ambient medium or of target ; responsive to condition of spray devices or of supply means, e.g. pipes, pumps or their drive means
  • B05B12/082—Arrangements for controlling delivery; Arrangements for controlling the spray area responsive to condition of liquid or other fluent material to be discharged, of ambient medium or of target ; responsive to condition of spray devices or of supply means, e.g. pipes, pumps or their drive means responsive to a condition of the discharged jet or spray, e.g. to jet shape, spray pattern or droplet size
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
  • B05B—SPRAYING APPARATUS; ATOMISING APPARATUS; NOZZLES
  • B05B12/00—Arrangements for controlling delivery; Arrangements for controlling the spray area
  • B05B12/08—Arrangements for controlling delivery; Arrangements for controlling the spray area responsive to condition of liquid or other fluent material to be discharged, of ambient medium or of target ; responsive to condition of spray devices or of supply means, e.g. pipes, pumps or their drive means
  • B05B12/085—Arrangements for controlling delivery; Arrangements for controlling the spray area responsive to condition of liquid or other fluent material to be discharged, of ambient medium or of target ; responsive to condition of spray devices or of supply means, e.g. pipes, pumps or their drive means responsive to flow or pressure of liquid or other fluent material to be discharged
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
  • B05B—SPRAYING APPARATUS; ATOMISING APPARATUS; NOZZLES
  • B05B13/00—Machines or plants for applying liquids or other fluent materials to surfaces of objects or other work by spraying, not covered by groups B05B1/00 - B05B11/00
  • B05B13/02—Means for supporting work; Arrangement or mounting of spray heads; Adaptation or arrangement of means for feeding work
  • B05B13/04—Means for supporting work; Arrangement or mounting of spray heads; Adaptation or arrangement of means for feeding work the spray heads being moved during spraying operation
  • B05B13/0431—Means for supporting work; Arrangement or mounting of spray heads; Adaptation or arrangement of means for feeding work the spray heads being moved during spraying operation with spray heads moved by robots or articulated arms, e.g. for applying liquid or other fluent material to three-dimensional [3D] surfaces
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B05—SPRAYING OR ATOMISING IN GENERAL; APPLYING FLUENT MATERIALS TO SURFACES, IN GENERAL
  • B05B—SPRAYING APPARATUS; ATOMISING APPARATUS; NOZZLES
  • B05B13/00—Machines or plants for applying liquids or other fluent materials to surfaces of objects or other work by spraying, not covered by groups B05B1/00 - B05B11/00
  • B05B13/02—Means for supporting work; Arrangement or mounting of spray heads; Adaptation or arrangement of means for feeding work
  • B05B13/04—Means for supporting work; Arrangement or mounting of spray heads; Adaptation or arrangement of means for feeding work the spray heads being moved during spraying operation
  • B05B13/0431—Means for supporting work; Arrangement or mounting of spray heads; Adaptation or arrangement of means for feeding work the spray heads being moved during spraying operation with spray heads moved by robots or articulated arms, e.g. for applying liquid or other fluent material to three-dimensional [3D] surfaces
  • B05B13/0433—Means for supporting work; Arrangement or mounting of spray heads; Adaptation or arrangement of means for feeding work the spray heads being moved during spraying operation with spray heads moved by robots or articulated arms, e.g. for applying liquid or other fluent material to three-dimensional [3D] surfaces the work being vehicle components, e.g. vehicle bodies
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B17/00—Systems involving the use of models or simulators of said systems
  • G05B17/02—Systems involving the use of models or simulators of said systems electric
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B19/00—Program-control systems
  • G05B19/02—Program-control systems electric
  • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
  • G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by quality surveillance of production
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B23/00—Testing or monitoring of control systems or parts thereof
  • G05B23/02—Electric testing or monitoring
  • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
  • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
  • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
  • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
  • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
  • G06N20/20—Ensemble learning
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N3/00—Computing arrangements based on biological models
  • G06N3/02—Neural networks
  • G06N3/04—Architecture, e.g. interconnection topology
  • G06N3/044—Recurrent networks, e.g. Hopfield networks
  • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/31—From computer integrated manufacturing till monitoring
  • G05B2219/31357—Observer based fault detection, use model
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/32—Operator till task planning
  • G05B2219/32194—Quality prediction
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/33—Director till display
  • G05B2219/33002—Artificial intelligence AI, expert, knowledge, rule based system KBS
• • G—PHYSICS
  • G05—CONTROLLING; REGULATING
  • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
  • G05B2219/00—Program-control systems
  • G05B2219/30—Nc systems
  • G05B2219/45—Nc applications
  • G05B2219/45013—Spraying, coating, painting
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
  • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
  • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

• the invention relates to an operating method for a coating system for coating components (e.g. motor vehicle body components) with a coating agent (e.g. paint) using an applicator (e.g. rotary atomizer).
• a coating agent e.g. paint
• an applicator e.g. rotary atomizer
• the painting process is quality controlled to ensure that the painting result satisfies certain standards.
• quality properties of the paint applied are measured here, such as layer thickness, evenness, hue, brightness, hardness, degree of crosslinking and degree of gloss, to name just a few examples.
• process values e.g. high voltage of an electrostatic paint charge, paint flow, shaping air flow, etc.
• This adjustment of the process values of the painting process to improve the quality of the painting process has hitherto been carried out manually by an expert on the basis of the expert's empirical knowledge.
• the causes of possible quality defects are also determined manually by changing process values according to the try-and-error principle, with the influence of the change on the quality of the painting process being evaluated in each case.
• This type of quality control is error-prone and highly dependent on the experience of the expert entrusted with it.
• Coating systems in which process values are determined are known from WO 2020/141372 A1, CN 112 246 469 A, EP 2 095 336 B1 and DE 197 56 467 A1. Error states can then be detected by evaluating the process values. However, this is not yet fully satisfactory.
• the invention is therefore based on the object of improving quality control in a coating system (e.g. painting system) for coating components (e.g. motor vehicle body components).
• a coating system e.g. painting system
• coating components e.g. motor vehicle body components
• the operating method according to the invention is generally suitable for a coating system for coating components with a coating agent using an applicator.
• the coating system is a painting system for painting motor vehicle body components with a paint, in which case an atomizer (e.g. rotary atomizer) can be used as the application device.
• an atomizer e.g. rotary atomizer
• the invention is not limited to paints with regard to the applied coating composition.
• the applied coating agent can also be an adhesive, a sealant or an insulating material, to name just a few examples.
• the invention is not limited to an atomizer with regard to the type of applicator. Rather, another applicator can also be used within the scope of the invention, such as a print head or a so-called sealing applicator.
• the invention is not limited to motor vehicle body components, which are painted in the preferred exemplary embodiment of the invention. Rather, the operating method according to the invention is generally suitable for coating components of different types.
• components eg motor vehicle body components
• a coating agent in accordance with the prior art (e.g. paint) coated.
• component-related process values e.g. paint flow, directing air flow, charging voltage of an electrostatic paint charge, etc.
• diverse process values can arise and be evaluated, as will be described later in detail.
• the coating of the individual components results in a component-related coating quality, i.e. the individual components are coated with an individual coating quality.
• the invention provides that the component-related process values of the coating system are at least partially determined. This means that, for example, when painting a motor vehicle body, the process values with which this motor vehicle body is painted are determined. This then enables quality control, as will be described in detail below.
• the invention preferably provides that component-related quality values are then determined for the individual coated components, which reflect the coating quality of the individual components. At least one quality value or preferably a set of quality values is determined for each coated component.
• the invention now additionally provides that quality-relevant abnormalities of the process values are determined in order to be able to detect coating errors in the coating of the individual components within the framework of a predictive operation in the coating of the components.
• coating defects should therefore not only be determined by evaluating the measured quality values, i.e. afterwards, but beforehand by determining quality-relevant abnormalities in the process values.
• the determination of the quality-relevant abnormalities of the process values in the context of the prediction operation is preferably carried out using a machine learning algorithm, i.e. using artificial intelligence (KI).
• the invention provides that the position of the coating defects corresponding to the quality-relevant abnormalities on the component surface of the coated components is determined by evaluating the process values. It is therefore determined which position on the component surface was just being coated when the quality-relevant abnormalities of the process values occurred.
• the quality-relevant anomalies that can lead to coating defects are determined on the one hand.
• the position of the coating defect on the component surface is also determined. Determining the position of the coating defects on the component surface makes it easier to eliminate defects and enables the coating defects to be displayed graphically on a screen, as will be described in detail below.
• the association between the coating errors on the one hand and the quality-relevant anomalies of the process values on the other hand facilitates the optimization of the process values to improve the coating quality, so that less experience of the operator is required.
• the components are graphically represented in the form of a graphical component representation on a screen.
• the motor vehicle body components to be painted can be displayed on the screen, for example, in a perspective view or in other views (e.g. side view, top view, rear view).
• the previously determined coating defects can then be marked on the graphic representation of the component according to the position of the coating defect. If, for example, it was previously determined that there is a coating defect on the left front fender of a motor vehicle body, this coating defect is also marked accordingly on the graphic representation of the motor vehicle body on the screen on the front left fender.
• This graphical display makes it easier for the operator to identify and eliminate errors by adjusting the process values accordingly.
• the graphic representation of the component on the screen can be two-dimensional (e.g. top view, side view, rear view or front view) or three-dimensional (perspective view).
• the identified quality-relevant abnormalities of the process values are preferably stored in a database together with the associated quality values, which enables an evaluation.
• the quality-relevant abnormalities of the process values are preferably determined by a machine learning algorithm, which can be trained as part of a training operation.
• This training operation of the machine learning algorithm preferably takes place before the actual prediction operation, ie separately from the actual painting process.
• the training operation of the machine learning algorithm it is also possible for the training operation of the machine learning algorithm to take place during the prediction operation, ie during the actual painting process.
• a training operation takes place before the actual painting process in order to teach the machine learning algorithm.
• the machine learning algorithm can then be further optimized as part of the normal painting process.
• the training of the machine learning algorithm in the training mode usually includes several steps. In this way, process values are first determined in a coating operation. In addition, the associated quality values are determined at the coating company. The determined process values and the determined quality values are then stored in an association in a database. The machine learning algorithm can then be trained using the process values stored in the database and the quality values stored in the database.
• the invention preferably also provides that an optimization suggestion is determined, which indicates how the process parameters can be optimized to avoid a coating error that has occurred.
• the optimization suggestion is preferably determined automatically and preferably also implemented automatically. If, for example, the analysis of the process values and the analysis of the coating defects shows that the paint flow was too large, the optimization suggestion could provide for the paint flow to be reduced.
• the optimization suggestion is preferably also displayed visually. Within the scope of the invention, there is also the possibility that the optimization suggestion is only displayed, whereupon the operator of the paint shop can then decide whether to accept and implement the optimization suggestion.
• process values used in the context of the invention is to be understood in general terms and can include setpoint values and/or actual values of the operating variables of the individual devices in the coating system.
• process values can be at least one of the following operating parameters of the coating system:
• drive variables of a robot drive for driving a coating robot in particular target value and/or actual value of position, angle, speed and/or torque
• Path data of a robot movement in particular target values and/or actual values of path position and/or path speed of the applicator in space along a robot path
• Valve sizes of a valve in particular target values and/or actual values of a coating agent valve for controlling the flow of coating agent, in particular for a valve for controlling the flow of paint, solvent, water, sealant, insulating material or adhesives,
• Wear variables in particular the count of a wear counter or operating hours which are preferably stored on a machine component, in particular in an RFID tag (RFID: radio frequency and identification),
• composition of the coating agent in particular color, color number, color code, type of adhesive, viscosity, storage temperature, application temperature, batch,
• the components to be coated are preferably coated in a plurality of coating tracks running next to one another, as is known per se from the prior art.
• the coating tracks running next to each other then overlap at their edges and form a continuous coating film on the component.
• the process values can be determined here individually for the individual coating webs in order to be able to carry out quality control individually for each of the coating webs. However, it is also possible for the process values to relate to the currently coated coating web and at least one of the adjacent coating webs.
• quality values are determined which reflect the quality of the coating process.
• these quality values can be one or more of the following:
• the coating system according to the invention initially has at least one applicator (e.g. rotary atomizer) which is used to apply the coating agent (e.g. paint) to a component (e.g. motor vehicle body part).
• an applicator e.g. rotary atomizer
• the coating agent e.g. paint
• a component e.g. motor vehicle body part
• the coating system according to the invention has at least one coating robot to move the applicator.
• the coating robot and the applicator are controlled by a control system that is known per se from the prior art.
• control system is designed to carry out the operating method according to the invention.
• a corresponding control program is usually stored in the control system, which when executed on the control system executes the operating method according to the invention.
• control system preferably has a number of different system components which fulfill different functions.
• the individual system components can also be concentrated as software modules in a single computer. Alternatively, however, it is also possible for the individual system components to be implemented as separate hardware components.
• control system of the coating system according to the invention can have the following system components:
• At least one robot controller for controlling the coating robot, with the robot controller or an additional detection device supplying at least some of the process values, • at least one further controller in addition to the robot controller, in particular in the form of a cell controller that controls a robot cell, this further controller supplying at least some of the process values,
• connection computer • a connection computer, the connection computer receiving the process values from the at least one robot controller and/or the additional controller and forwarding them to the database computer, and receiving the quality values from the quality value computer and forwarding them to the database computer.
• a Kl computer which receives the process values and the associated quality values from the database computer and uses the machine learning algorithm to determine the quality-relevant abnormalities of the process values and the associated position on the component, and the determined quality-relevant abnormalities for storage in the database the database computer,
• the correlations between the recorded process values and the quality data are preferably recognized by training a binary or multi-class classifier (multi-class in the sense of classifying different types of paint defects, e.g. lean, craters, etc.)
• the assignment of process values to the measuring points of the quality measurements is preferably carried out via the robot paths that are also recorded.
• the process values are preferably considered as explanatory characteristics for which the distance from the applicator to the measuring point does not exceed a defined measured value.
• aggregations can be formed from the time series in order to reduce the complexity of the classifier.
• the following machine learning algorithms are particularly suitable for the classifier: gradient boosting, LSTM (long short-term memory), artificial neural network, SVM (support vector machine).
• the calibration as well as the actual execution of the training process is preferably carried out with the aid of the software tools mentioned according to "Best Practices" for training a classifier, i.e. the invention does not require any new procedure in this respect.
• FIG. 1 shows a flowchart to clarify the training mode of the machine learning algorithm for detecting the quality-relevant abnormalities of the process values.
• FIG. 2 shows a flowchart to clarify the prediction operation during the actual painting process.
• FIG. 3 shows a schematic representation of a painting system according to the invention.
• FIG. 4 shows a screen display with a perspective view of a motor vehicle body and a marking of coating defects.
• Figure 5 shows a modification of Figure 4.
• FIG. 1 shows the training operation of the machine learning algorithm
• process values are measured and recorded in a coating operation in a first step S1.
• the process values can involve a large number of operating variables from devices that are involved in the painting process. For example, this can be the paint flow, the directing air flow, the charging voltage of an electrostatic paint charge or the path speed of the painting robot, to name just a few examples.
• a large number of different process values are preferably measured and recorded in order to make the evaluation of the process values as meaningful as possible.
• quality values are then detected and recorded, which reflect the quality of the painting process.
• these quality values can reflect the layer thickness, evenness, color tone, hardness, degree of gloss or other properties of the applied salmon.
• the previously determined process values are then stored in a database together with the quality values that were also determined, specifically in an assignment to one another.
• the process values and the quality values can each be saved with a time stamp, which makes later evaluation easier.
• the machine learning algorithm can then be trained using the process values stored in the database and the quality values also stored in the database in order to be able to identify quality-related abnormalities in the process values.
• a first step S1 process values are again measured and recorded, these process values occurring in the normal painting process.
• the previously trained machine learning algorithm then analyzes the measured process values and determines quality-related abnormalities that indicate coating defects.
• the position on the component that is to be assigned to the quality-relevant abnormalities of the process values is then determined.
• the abnormalities determined in the process values are then stored in a database together with the position on the component in a step S4.
• a next step S5 the abnormalities of the process values are then graphically represented on a component representation in order to enable the user to carry out an error analysis and to make it easier to eliminate errors.
• FIG. 3 The schematic representation of a painting system according to the invention in FIG. 3 is now described below.
• the painting system according to the invention has a number of painting robots 1-4, each of which is controlled by a robot controller 5-8.
• a separate cell controller 9 which controls the individual devices in a painting cell (paint booth) at a higher level.
• the robot controller 5-8 and the cell controller 9 are connected to a connection computer 10, which enables data to be exchanged.
• the connection computer 10 thus receives numerous process values from the robot controllers 5-8 and also from the cell controller 9, such as target values and actual values from devices within the respective painting cell.
• connection computer 10 is connected to a quality value computer 11, which provides quality values that have been measured and reflect the quality of the painting process. These quality values are essentially used to train a machine learning algorithm to identify quality-related abnormalities in the process values.
• connection computer 10 is connected to a database computer 12 which receives the process values and the associated quality values from the connection computer 10 .
• the database computer 12 is in turn connected to a AI computer 13 in which a machine learning algorithm determines quality-related abnormalities in the process values and reports them back to the database computer 12 .
• the database computer 12 is also connected to a display computer 14, which has a screen and shows a graphical representation of the painted motor vehicle body components with any painting defects on the screen, as will be described in detail below.
• FIG. 4 shows an exemplary display of a screen 15 of the display computer 14 with a body display 16.
• the individual painting paths 17 along which the atomizer paints the motor vehicle body are also shown graphically.
• inconspicuous points 18 and conspicuous points 19 are marked on the car body representation 16, with the conspicuous points 19 indicating coating defects with a high degree of probability, as can be seen from the evaluation of the measured process values.
• an optimization recommendation 20 is displayed on the screen 15 .
• the optimization recommendation 20 consists in increasing the atomizer speed of the rotary atomizer from 50,000 rpm to 55,000 rpm.
• this is merely an example to illustrate the invention. The operator of the paint shop can then adopt the optimization recommendation 20 and implement it.
• FIG. 5 shows a modification of FIG. 5 with a different representation of the body 16, which in this case is only two-dimensional and includes two side views, a top view and a rear view. Otherwise, to avoid repetition, reference is made to the above description.

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• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• General Physics & Mathematics (AREA)
• Automation & Control Theory (AREA)
• Theoretical Computer Science (AREA)
• General Engineering & Computer Science (AREA)
• Software Systems (AREA)
• Artificial Intelligence (AREA)
• Mathematical Physics (AREA)
• Evolutionary Computation (AREA)
• Manufacturing & Machinery (AREA)
• Data Mining & Analysis (AREA)
• Computing Systems (AREA)
• Quality & Reliability (AREA)
• Robotics (AREA)
• Medical Informatics (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Analytical Chemistry (AREA)
• Chemical & Material Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• Molecular Biology (AREA)
• General Health & Medical Sciences (AREA)
• Computational Linguistics (AREA)
• Biophysics (AREA)
• Biomedical Technology (AREA)
• Health & Medical Sciences (AREA)
• Fluid Mechanics (AREA)
• Spray Control Apparatus (AREA)
• Application Of Or Painting With Fluid Materials (AREA)
• Electrostatic Spraying Apparatus (AREA)

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Citations (6)

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• 2021
  • 2021-08-17 DE DE102021121320.7A patent/DE102021121320A1/de active Pending
• 2022
  • 2022-08-01 WO PCT/EP2022/071520 patent/WO2023020821A1/de not_active Ceased
  • 2022-08-01 CN CN202280055881.XA patent/CN117836733A/zh active Pending
  • 2022-08-01 US US18/682,456 patent/US20250135480A1/en active Pending
  • 2022-08-01 EP EP22758512.2A patent/EP4338024B1/de active Active
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  • 2022-08-01 JP JP2024509114A patent/JP2024531310A/ja active Pending
  • 2022-08-01 KR KR1020247005384A patent/KR20240051933A/ko active Pending

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