Method for controlling a plurality of joining stations arranged in series in a motor vehicle production plant, computer program product and motor vehicle

Abstract

A method is described for controlling a plurality of joining stations arranged in series in a motor vehicle production plant and for producing at least partially based on joining parameter setpoints, wherein at least one measured value is recorded on a produced or partially produced motor vehicle, which is compared with at least one corresponding setpoint, whereby a deviation of the measured value from the setpoint is determined, wherein if at least one deviation is greater than a permissible tolerance, the joining parameter setpoints of two or more than two joining stations are adjusted depending on the measured value and / or the deviation, wherein a relationship between the at least one measured value and / or the at least one deviation is established by a correlation algorithm.where the joining parameter setpoints are adjusted taking into account the correlation algorithm, and the correlation algorithm is a kinematic matrix.

Description

[0001] The text describes a method for controlling a plurality of joining stations arranged in series in a motor vehicle production plant, a computer program product, and a motor vehicle.

[0002] Methods for controlling a plurality of joining stations arranged in series in a motor vehicle production plant, computer program products and motor vehicles of the type mentioned above are known in the prior art.

[0003] The production of car bodies in the automotive industry has been characterized by a high degree of automation for several decades, a key feature of modern production facilities across all manufacturers. Particularly in body-in-white production – the manufacturing of a vehicle's load-bearing structure – fully automated production lines are used extensively. This automation is a crucial prerequisite for the economical production of car bodies, even at locations with high labor costs. This high degree of automation is achieved through the widespread use of industrial robots. These robots enable precise, reproducible movements and contribute significantly to process reliability and cost-effectiveness.

[0004] For high-volume vehicle models, automakers typically employ fully automated production lines for key joining processes, such as assembling, bolting, welding, riveting, or bonding body components and add-ons. These production lines not only reduce the personnel requirements in series production itself but also contribute to minimizing rework. Rework on car bodies is generally necessary to meet the tight tolerances specified for the final vehicle. These tolerances must be maintained either through manual rework or—preferably—by establishing a stable and repeatable automated manufacturing process.

[0005] The technical challenge arises particularly from the fact that a motor vehicle consists of several thousand individual parts. Consequently, manufacturing inaccuracies can accumulate along the process chain, potentially leading to non-compliant end products in the worst-case scenario. Therefore, it is essential that all intermediate assemblies within the production chain are manufactured and assembled with high precision.

[0006] Adaptive control systems, collectively known in the industry as Smart Adaptive Control (SAC), have recently gained importance. SAC refers to control concepts capable of dynamically adjusting process parameters in real time based on sensor feedback and data-driven analysis. Artificial intelligence (AI) methods are increasingly being employed in this context. Such systems are able to detect and compensate for even the smallest process deviations—for example, those resulting from tolerance variations in components, tool wear, or temperature fluctuations. This ensures consistent process quality even under varying production conditions. Currently, these SAC systems operate using one or, at most, two production stations arranged in series.

[0007] A key feature of SAC systems is their ability for continuous self-optimization. Based on historical and current process data, patterns can be identified, control strategies adapted, and thus greater process robustness achieved. As a result, SAC systems contribute to reducing rework, increasing production quality, and meeting the requirements of networked and flexible manufacturing.

[0008] From DE 100 24 763 A1, a method for automatically detecting, checking, and correcting the gap dimension between an add-on part, preferably a vehicle door or a vehicle flap, on a vehicle body in a production line is disclosed. The add-on part is installed in the designated mounting opening of the vehicle body with a gap to the vehicle body and / or to an adjacent add-on part. The invention further relates to a device for carrying out the method. The gap dimension is determined between the corresponding edges of the add-on part and the adjacent add-on part or the vehicle body. The gap dimension of each gap is checked for deviations from the permissible tolerances and / or for deviations from the other gap dimensions of the add-on part, and the deviations are automatically corrected. The invention further relates to a device for carrying out the method.

[0009] Furthermore, WO 2021 / 257 988 A1 discloses a manufacturing system comprising one or more stations, a monitoring platform, and a control module. Each station of one or more stations is configured to perform at least one step in a multi-stage manufacturing process for a component. The monitoring platform is configured to monitor the component's progress throughout the multi-stage manufacturing process. The control module is configured to dynamically adjust the processing parameters of each step in the multi-stage manufacturing process to achieve a desired final quality parameter for the component.

[0010] DE 10 2007 009 275 A1 relates to a manufacturing robot system for processing workpieces with machining carried out by a manufacturing robot, wherein an automatic inspection of the workpiece is carried out after the machining step has been completed.

[0011] Finally, DE 102 41 746 A1 describes a method for quality assessment and process monitoring in cyclic production processes, distinguishing between a setup phase I, a setup phase II and a working phase, and performing a quality assessment of the products manufactured in the cyclic production process based on a set of quality characteristics, wherein the first setup phase includes automatic operating point optimization, generation of an initial training data set, automatic characteristic value selection and a self-generating process model that is transferred to the working phase.

[0012] A disadvantage of the state of the art is that the emergence of manufacturing tolerances is very complex and is partly caused by a combination of different factors at various joining stations.

[0013] The task is therefore to further develop methods for controlling a plurality of joining stations arranged in series in a motor vehicle production plant, computer program products and motor vehicles of the type mentioned above, in such a way that better manufacturing quality is possible in a complex manufacturing process.

[0014] The problem is solved by a method for controlling a plurality of joining stations arranged in series in a motor vehicle production plant according to claim 1, a computer program product according to dependent claim 9, and a motor vehicle according to dependent claim 10. Further embodiments and developments are the subject of the dependent claims.

[0015] A method for controlling a plurality of joining stations arranged in series in a motor vehicle production plant is described, wherein the motor vehicle production plant is configured to successively transport a motor vehicle from one joining station to the next, wherein at least one production step is carried out at each joining station, and thereby production is carried out at least partially based on joining parameter setpoints, wherein at least one measured value is recorded on a produced or partially produced motor vehicle, which is compared with at least one corresponding setpoint, whereby a deviation of the measured value from the setpoint is detected, wherein, in the case of at least one deviation greater than a permissible tolerance, the joining parameter setpoints of two or more than two joining stations are adjusted depending on the measured value and / or the deviation.wherein a relationship between the at least one measured value and / or the at least one deviation is established by a correlation algorithm, wherein the joining parameter target values ​​are adjusted taking the correlation algorithm into account.

[0016] This method makes it possible to identify and reduce complex relationships between the different tasks of various assembly stations in the automotive production plant and the resulting tolerances. It allows for the determination of the respective influences of several consecutive or non-consecutive assembly stations on the measured values ​​and tolerances, and the minimization of deviations.

[0017] For example, if the correlation algorithm finds a correlation between the first, fourth and fifth joining stations, the corresponding joining parameter setpoints can be adjusted accordingly.

[0018] The entire vehicle production plant can be optimized in this way, reducing the need for manual rework and increasing the quality of the produced vehicle.

[0019] In a first further embodiment, it is provided that at least one measured value is a gap dimension.

[0020] According to the invention, the correlation algorithm is a kinematic matrix.

[0021] The kinematic matrix is ​​a key concept for describing the relationships in systems with many degrees of freedom. It establishes the mathematical relationship between the movements of the individual components of such a system and the resulting movement of the end effector in space. Specifically, it describes how changes to the target values ​​of the joining parameters affect the measured values.

[0022] The term "kinematic matrix" usually refers to a Jacobian matrix. This matrix is ​​derived from the kinematic structure of the system, that is, from the geometric relationships that connect the components (e.g., joints and links of a joining station).

[0023] In the context of motion planning and control within production, the kinematic matrix allows for both forward and backward analysis. Forward analysis is used to determine the movement of an end effector at an assembly station based on given joint movements relative to the vehicle. Backward analysis, on the other hand, allows the calculation of the joint movements required to achieve a desired end effector movement.

[0024] In a further, more advanced embodiment, it is planned that the calculation of the kinematic matrix will be carried out using a recursive least squares algorithm.

[0025] A recursive least-squares algorithm is a mathematical method for the continuous estimation of unknown parameters in a linear model. It attempts to find the best possible match between a measured output signal and the known input signals of a system by adjusting the model parameters to minimize the deviation—that is, the error—between the model prediction and the measured value.

[0026] The term "recursive" here means that the estimators are calculated step by step: With each new measurement, the joining parameter target values ​​can be updated without having to reprocess all previous data.

[0027] The algorithm's central function is to calculate an improved joining parameter target value from the current measurement, the previous joining parameter target value, and the kinematic matrix. The kinematic matrix is ​​also updated with each iteration and serves to control the strength of the correction: if the uncertainty is large, the new measurement is given greater weight; if the uncertainty is small, the estimate remains more stable.

[0028] This recursive structure makes the algorithm very efficient, as only a few computational steps are required for each new measurement. Therefore, the recursive least-squares algorithm is particularly well-suited for the present procedure.

[0029] In a further refinement, it is provided that a filter with P-behavior is used to smooth the kinematic matrix.

[0030] A filter with proportional behavior (P-behavior) can, in conjunction with a kinematic matrix, smooth and stabilize the system. However, calculations based on a kinematic matrix can produce unstable, oscillating, or highly fluctuating results under numerically unfavorable conditions, which can lead to quality problems.

[0031] A filter with proportional behavior (P-filter) counteracts these effects by proportionally attenuating signals, thereby reducing high-frequency components or sudden changes. This is particularly advantageous when the input values ​​are derived from numerical calculations, such as when determining joining parameter setpoints using finite differences of measured values, as noise in the values ​​often occurs in such cases. The P-filter smooths out this noise by suppressing rapid changes and thus stabilizing the algorithm's response. One could therefore say that the P-filter, acting as a controller, eliminates all or at least a large proportion of the unknown influences that cannot be detected via measured values.

[0032] In a further refinement, it is provided that the kinematic matrix is ​​not diagonalizable.

[0033] This takes into account complex relationships between neighboring and more distant joining stations.

[0034] In a further, more advanced version, it is envisaged that the process will be carried out continuously.

[0035] In this context, continuous means after each manufacturing and transport step, in which a motor vehicle is transported from one assembly station to the next.

[0036] In a further refined embodiment, it is provided that at least one joining station is aligned at alignment points on a body of the motor vehicle using at least one joining parameter setpoint.

[0037] A first independent subject matter relates to a computer program product comprising a computer-readable storage medium on which instructions are embedded which, when executed by at least one computing unit, cause that at least one computing unit to be equipped to execute the procedure of the aforementioned type.

[0038] The process can be executed on one or more computing units, so that certain process steps are executed on one computing unit and other process steps on at least one other computing unit, whereby calculated data can be transmitted between the computing units if necessary.

[0039] Another independent item relates to a motor vehicle manufactured using the method described above.

[0040] Further advantages, features, and details will become apparent from the following description, in which – possibly with reference to the drawing – at least one embodiment is described in detail. Identical, similar, and / or functionally equivalent parts are marked with the same reference numerals.

[0041] They show schematically: Fig. 1 a motor vehicle production plant with several joining stations and a control system; Fig. 2 a sectional view of the body of a partially assembled motor vehicle, as well as Fig. 3 a kinematics matrix.

[0042] Fig. Figure 1 shows a motor vehicle production plant. Figure 2.

[0043] Motor vehicle production plant 2 is used for the production of motor vehicles 4, 4'. Motor vehicle production plant 2 can have a specific task in a longer production line, for example, the assembly of the doors of the motor vehicles 4, 4'.

[0044] The motor vehicle production plant 2 consists of a plurality of joining stations 6, 8, 10, 12, each of which has at least one of its own actuators 6.1, 8.1, 10.1, 12.1, e.g. a robot arm.

[0045] Furthermore, the motor vehicle production plant 2 has a control unit 14 which is connected via a signal link 14.1 to the joining stations 6, 8, 10, 12 in order to monitor and control or regulate the joining stations 6, 8, 10, 12.

[0046] The control unit 14 is further connected to a gap measuring system 16, which determines at least one gap dimension on the motor vehicle 4' produced by the joining stations 6, 8, 10, 12.

[0047] The control unit 14 is configured to draw conclusions about the target values ​​of the joining parameters for the individual joining stations 6, 8, 10, and 12 based on the measured values ​​of the gap measuring system 16. This means determining which deviations in the gap dimensions are caused by which (one or more) of the joining stations 6, 8, 10, and 12. For this purpose, the control unit 14 uses a kinematic matrix 14.2, which is continuously optimized after each production step. The kinematic matrix 14.2 operates according to a recursive least-squares algorithm, the behavior of which is dampened by a P-filter 14.3 to such an extent that no oscillating or otherwise borderline behavior occurs.

[0048] Fig. Figure 2 shows an enlarged section of a body section of the motor vehicle 4'.

[0049] In this case, a measured value M of a gap dimension lies above a target value S and exhibits a deviation A that is greater than a permissible tolerance. This means that the control unit 14 in the kinematic matrix 14.2 must change the joining parameter target values ​​using the regressive algorithm and, during the next measurements—i.e., when the next vehicle has passed through the various joining stations 6, 8, 10, 12—check whether the measured value M is now within an acceptable deviation A around the target value S.

[0050] By continuously repeating the process over time, deviations A can be minimized and other effects can also be taken into account, e.g., changed tolerances in the components to be installed or possible changes in the corresponding joining stations 6, 8, 10, 12.

[0051] The joining parameter setpoints serve to position the corresponding joining stations 6, 8, 10, 12 relative to the motor vehicle 4 to be produced. For this purpose, the joining stations 6, 8, 10, 12 use alignment points 4.1 on the body of the motor vehicle 4 to be produced, which represent a unique positional reference for the motor vehicle 4 and on the basis of which the respective joining station sets the respective actuator 6.1, 8.1, 10.1, 12.1 based on the joining parameter setpoints.

[0052] Fig. Figure 3 shows a kinematic matrix 14.2.

[0053] The kinematic matrix 14.2 is a square matrix with a number of rows and columns that can correspond to the number of parameters to be set in the motor vehicle production plant 2, in the simplest case, for example, one parameter per joining station 6, 8, 10, 12. The corresponding kinematic matrix 14.2 has diagonal entries, each relating to individual joining stations, and off-diagonal entries that describe the relationships between different parameters to be set.

[0054] Although the invention has been further illustrated and explained in detail by means of preferred embodiments, the invention is not limited by the disclosed examples, and other variations can be derived from them by a person skilled in the art without departing from the scope of protection of the invention. It is therefore clear that a multitude of possible variations exist. It is also clear that the embodiments mentioned as examples are truly only examples and are not to be understood in any way as limiting, for example, the scope of protection, the possible applications, or the configuration of the invention.Rather, the preceding description and the description of the figures enable the person skilled in the art to implement the exemplary embodiments in concrete terms, whereby the person skilled in the art, with knowledge of the disclosed inventive concept, can make various changes, for example with regard to the function or the arrangement of individual elements mentioned in an exemplary embodiment, without leaving the scope of protection defined by the claims and their legal equivalents, such as a further explanation in the description. Reference symbol list 2 Motor vehicle production plant 4, 4' motor vehicle 4.1 Alignment point 6, 8, 10, 12 Joining station 6.1, 8.1, 10.1, 12.1 Actuator 14 Control 14.1 Signal connection 14.2 Kinematics Matrix 14.3 P-Filter 16 Gap measuring system A deviation M measured value S setpoint P11, P12, P13, P22, ..., P44 Joining parameter setpoint

Claims

Method for controlling a plurality of joining stations (6, 8, 10, 12) arranged in series in a motor vehicle production plant (2), wherein the motor vehicle production plant (2) is configured to successively transport a motor vehicle (4, 4') from one joining station (6, 8, 10, 12) to the next, wherein at least one production step is carried out at each joining station (6, 8, 10, 12), and thereby produce at least partially on the basis of joining parameter setpoints (P11, P12, P13, P22, ..., P44), wherein at least one measured value (M) is recorded on a produced or partially produced motor vehicle (4, 4'), which is compared with at least one corresponding setpoint (S), wherein a deviation (A) of the measured value (M) from the setpoint (S) is determined, wherein if at least one deviation (A) is greater than a permissible tolerance, the Joining parameter setpoints (P11, P12, P13, P22, ..., P44) of two or more than two joining stations (6, 8, 10, 12) are adjusted depending on the measured value (M) and / or the deviation (A), wherein a relationship between the at least one measured value (M) and / or the at least one deviation (A) is established by a correlation algorithm (14.2), wherein the joining parameter setpoints (P11, P12, P13, P22, ..., P44) are adjusted taking into account the correlation algorithm (14.2), characterized in that the correlation algorithm (14.2) is a kinematic matrix (14.2). Method according to claim 1, characterized in that the at least one measured value is a gap dimension (M). Method according to claim 1 or 2, characterized in that the calculation of the kinematic matrix (14.2) is carried out using a recursive least squares algorithm. Method according to one of claims 1 to 3, characterized in that a filter with P-behavior (14.3) is used to smooth the kinematic matrix (14.2). Method according to one of claims 1 to 4, characterized in that the kinematic matrix (14.2) is not diagonalizable. Method according to one of the preceding claims, characterized in that the method is carried out continuously. Method according to one of the preceding claims, characterized in that at least one joining station (6, 8, 10, 12) is aligned at alignment points (4.1) on a body of the motor vehicle (4, 4') using at least one joining parameter setpoint (P11, P12, P13, P22, ..., P44). Computer program product comprising a computer-readable storage medium on which instructions are embedded which, when executed by at least one computing unit, cause the at least one computing unit to be configured to execute the method according to one of the preceding claims. Motor vehicle (4, 4') manufactured according to the method of any one of claims 1 to 7 .

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