Method for resistance welding

Abstract

The invention relates to a method for resistance welding, wherein a welding process is carried out (201), in the course of which a welding electrode is pressed against a welding point of a workpiece and is energized with a welding current (201) in each case in accordance with predefined welding parameters, wherein at least one characteristic value characterizing the welding quality is determined (202) in each case in the course of the welding process, wherein a statistical analysis of the determined characteristic values is carried out (203, 204, 205, 206) after a plurality of welding processes have been carried out, and wherein it is determined on the basis of the result of the statistical analysis whether an adaptation of the predefined welding parameters should be carried out (207).

Description

TECHNICAL FIELD

[0001] The invention relates to a method for resistance welding and to a control unit, to a welding device and to a computer program for carrying out the method. BACKGROUND

[0002] By means of a welding process, like for example resistance welding, workpieces can be connected to one another in a materially bonded manner. In the course of automated white body manufacture, for example, different workpieces, for example sheets, are welded to one another by means of resistance welding by means of robot-guided welding tongs.

[0003] In the course of resistance welding, first of all in the course of a so-called force build-up phase, two welding electrodes of a welding tong are pressed against the welding points of the workpieces by means of an electrode drive until a predefined electrode force is reached. Subsequently, the actual welding process is carried out, in the course of which the welding electrodes are energized with a welding current for the duration of a welding time, as a result of which resistance heating of the two workpieces to be welded takes place between the welding electrodes and the workpieces are heated until the desired welding temperature is reached. SUMMARY

[0004] Against this background, the method for resistance welding and the control unit, the welding device and the computer program for carrying out the method according to the invention are proposed. Advantageous design solutions are the subject matter of other parts of the disclosure.

[0005] In the context of the method, welding processes are carried out in the course of which the welding electrodes are pressed against the welding points of the workpieces and are energized with a welding current, respectively, in accordance with predefined welding parameters. Thus, in the course of each welding process in the welding process, respectively, one welding point is welded.

[0006] The welding parameters, in particular, represent predefined setpoint values or time curves of setpoint values, in accordance with which the respective welding process is to be carried out. The welding parameters can in particular be electrical and / or mechanical values which relate to the movement or energization of the welding electrodes. The welding parameters for the welding processes to be carried out can be predefined, for example, in accordance with the workpieces to be welded. In particular, the welding parameters can be stored in the form of so-called welding programs, which are expediently implemented by a corresponding welding control device.

[0007] Furthermore, in the course of the welding processes, respectively, at least one characteristic value is determined which characterizes the welding quality or the welding point quality. The characteristic values, respectively, describe in particular the quality of the welding points produced and / or of the welding processes carried out. The characteristic values can be detected, for example, in the course of the respective welding process by means of measuring technology and / or derived from detected measured values.

[0008] The determined characteristic values are statistically analyzed and, on the basis of the result of this statistical analysis, it is determined whether an adaptation of the predefined welding parameters should be carried out. If this is the case, the future welding processes are expediently carried out with these adapted welding parameters.

[0009] It is therefore investigated within the scope of the method whether the currently used welding parameters lead to the desired welding quality. If this is not the case, the welding parameters are adapted in order to achieve a better welding quality in the subsequent welding processes.

[0010] The method is based on a statistical analysis of the welded welds. The respective welding control device records the course of each welding, analyzes this record on the basis of statistics. If an adaptation is to be carried out, inter alia the adaptation of the current and force characteristic curves is calculated and, if necessary, also other welding parameters, and is written into or back into the control device. The control device now continues to weld with the new parameters.

[0011] The statistical analysis is carried out after a number (N > 1) of welding processes have been carried out. Here, inter alia a statistical analysis of all last (for example last M; M and N can be identical) welding processes carried out with the current predefined welding parameters is carried out and it is checked whether an adaptation of the welding parameters should be carried out in the control device.

[0012] For example, if one or more predefined criteria are met, the adaptation is carried out. In contrast, if the one or more criteria are not met, the following number of welding processes is also continued to be carried out with the current predefined welding parameters, expediently so long until the criteria are met. It is therefore possible, inter alia, to carry out a continuous analysis.

[0013] It is therefore possible, inter alia, to carry out the statistical analysis after each welding process. The number of welding processes can be fixedly predefined, for example, or can also be flexibly, for example, after each statistical analysis. For example, if a relatively strong adaptation of the welding parameters is required, the following number of welding processes can be selected to be small in order to carry out the check again after a relatively short time. In contrast, if only a relatively small adaptation of the welding parameters is required or no adaptation of the welding parameters is required at all, the following number of welding processes can be selected to be higher in order to carry out the check again only after a relatively long time.

[0014] Thus, within the scope of the method, the welding process can be carried out particularly expediently cyclically, wherein in the course of each cycle a predefined number of welding processes is carried out. Thus, in each cycle it is checked whether an adaptation of the welding parameters should take place, and if so, the following cycle is carried out with adapted welding parameters. Likewise, the check for adaptation of the welding parameters can expediently also be carried out continuously after each welding process.

[0015] Thus, the welding parameters are continuously and dynamically adapted to the respective current welding quality. Furthermore, the adaptation takes place in particular automatically, so that the welding parameters are adapted automatically and expediently without the need for manual intervention by a user. Thus, the resistance welding process can always be carried out with optimally adapted welding parameters in order to achieve the best possible welding quality.

[0016] Particularly advantageously, the at least one characteristic value characterizing the welding quality characterizes welding spatters occurring during the respective welding process, in particular at the respective time points at which the welding spatters respectively occur during the respective welding process. In this connection, by welding spatter is understood a droplet of molten metal which breaks off as a result of the extreme strength of the heat and force applied at or around the welding point. Such welding spatters have a negative effect on the welding quality and can lead to contamination of metal surfaces, in particular of the workpiece surface and of the surface of the welding device itself, for example the electrode cap. It is therefore important to be able to identify welding spatters precisely in order to be able to counteract this negative effect. By means of the method it is possible to reduce the occurrence of such welding spatters by means of the adaptive automatic adaptation of the welding parameters. Manual adaptation can expediently be dispensed with. Furthermore, it is possible to avoid so-called Q stops, i.e. production line stops due to quality problems, caused by the occurrence of excessive spatters.

[0017] In a conventional manner, welding programs in which spatters often occur are mostly parameterized manually, mostly with a small current at a specific time point in the welding process. Since spatters are material which is expelled from the welding nugget, welding devices with UIR regulation ensure that the welding time is extended in order to restructure the welding nugget and to ensure the point quality. However, the welding duration of the spatter point will be extended here, which can lead to the process on the production line being slower. In contrast, in the course of the method, by means of the statistical evaluation of the characteristic values it is possible to identify the occurrence of welding spatters and to adapt the welding parameters automatically in order to reduce or, if possible, avoid the occurrence of welding spatters in future welding processes. Thus, it is possible to improve the quality and efficiency of the welding processes carried out.

[0018] Preferably, the statistical analysis is performed depending on the number and / or rate of welding spatters occurring during the plurality of welding processes. The number and rate of welding spatters describe, inter alia, a spatter threshold value. In particular, it can be assessed first whether the number or rate of occurring spatters is high enough to warrant an adaptation of the welding parameters. If the number or rate of welding spatters occurring during the plurality of welding processes does not reach a respective predefined threshold value, it can be assessed, inter alia, that the welding parameters are selected well enough so that welding spatters occur rarely and that there is no need to adapt the welding parameters. In contrast, if the number or rate of welding spatters reaches a respective threshold value, it is expediently assessed that there is a need to adapt the welding parameters.

[0019] Preferably, the statistical analysis is performed depending on a statistical mean value, in particular depending on a median of the spatter time points at which the welding spatters respectively occur during the respective welding processes. Thus, it is expediently analyzed at which time point during the welding process the welding spatters occur on average. It can be concluded, for example, how a time profile of a setpoint value as welding parameter can be adapted in order to prevent the occurrence of welding spatters at the respective time point.

[0020] Preferably, the statistical analysis is performed depending on a scatter, in particular a variance and / or a standard deviation, of the spatter time points at which the welding spatters respectively occur during the respective welding processes. Here, the scatter is inter alia helpful to determine whether the welding parameters leading to welding spatters have a stable characteristic in order to be able to implement an appropriate adaptation type. A multi-modal welding program can lead to welding spatters at a plurality of time points and require a more complex adaptation accordingly.

[0021] Advantageously, the statistical analysis is performed depending on a median of the spatter time points a difference of a product of a standard deviation σ of the spatter time points and a predefinable constant k This difference represents a particularly expedient evaluation parameter by means of which an adaptation of the welding parameters can be performed. The value of the constant k can be expediently chosen, for example by the user himself. If, for example a so-called pre-phase of the welding electrode energization is present, the welding current intensity can be reduced, for example, in a time range from the beginning of the welding process up to the spatter time point plus k times the standard deviation or up to the end of the first sequence or weld block. If, for example a post-phase, for example, is present, a negative ramp, for example, can be added to the existing current parameterization.

[0022] Preferably, the statistical analysis is performed according to a test, in particular a chi-square test, in order to determine whether the occurrence of welding spatters during a plurality of welding processes corresponds to a random distribution. In particular, in the course of the test it is statistically determined whether the occurrence of spatters is sufficiently randomly distributed. By means of this test it can be ruled out, in particular, that the welding parameters are incorrectly adapted. In particular, it is tested whether the occurrence of spatters is randomly distributed over all weld points welded with the welding parameters, or whether there is a systematic accumulation or recurrence of a plurality of welding spatters. A positive test result means, in particular, that the occurrence of spatters is sufficiently randomly distributed and that an adaptation of the welding parameters should be made. A negative test result indicates, in particular, that the welding parameters are correct, but can have been used incorrectly. In this case, in particular, no adaptation of the welding parameters is made.

[0023] Advantageously, the statistical analysis is also performed in dependence on the material of the workpiece and / or the properties of the welding electrode. Thus, it is expedient to take into account in the course of the analysis both the material to be welded and the welding electrode itself, since these can have an influence on the occurrence of welding spatters. In particular, the course of each welding is recorded expediently with the aid of corresponding statistical parameters, and the corresponding recordings are analyzed statistically on the basis of each combination of material, welding torch, welding program and spatter time point.

[0024] Particularly preferably, the predefined welding parameters include the electrode force and / or the welding current and / or the welding voltage and / or the welding time and / or a time profile of the electrode force and / or a time profile of the welding current and / or a time profile of the welding voltage. Thus, the welding parameters to be adapted relate to mechanical parameters and electrical parameters or time profiles thereof.

[0025] The method is particularly advantageously suitable for body-in-white production, in particular for automated welding processes in the course of body-in-white production, preferably in the course of motor vehicle production. Here, in particular sheet metal parts are welded to one another in order to produce a body of a motor vehicle. In the course of the production of a single body, up to several thousand weld points can be processed automatically (for example approximately 5000 weld points for a medium-sized vehicle). By means of the method it is possible to weld the individual weld points with the best possible quality.

[0026] A control unit (computing unit) according to the application, for example a welding control device of a welding apparatus, is in particular programmed in a program technology in order to carry out the method according to the application. The control unit or welding control device can be configured, for example, as an SPS (store-programmable control), an NC (numerical control) or a CNC (computerized numerical control).

[0027] The welding device for resistance welding according to the application has, inter alia, a welding tongs with a welding electrode and an electrode drive for moving the welding electrode. A robot can also be provided, inter alia, in order to manipulate the welding tongs, for example in the case of a pneumatic welding tongs. Furthermore, the welding device comprises a preferred design of the control unit according to the application.

[0028] It is also advantageous for the method according to the application to be implemented in the form of a computer program or computer program product with program code for carrying out all the method steps, since this leads to particularly low costs, especially when the controller used is also used for other tasks and is therefore already present. Suitable data carriers for providing the computer program are, inter alia, magnetic, optical and electronic memories, such as, for example, hard disks, flash memories, EEPROMs, DVDs, etc. The program can also be downloaded via a computer network (Internet, Intranet, etc.). BRIEF DESCRIPTION OF DRAWINGS

[0029] Further advantages and design solutions of the application result from the description and the drawings.

[0030] It goes without saying that the features mentioned above and those yet to be explained below can be used not only in the combinations indicated, but also in other combinations or on their own, without leaving the scope of the present application.

[0031] The application is schematically illustrated in the drawings by means of an example and is described in detail below with reference to the drawings.

[0032] Figure 1 A preferred design of the welding device according to the application is schematically illustrated, which is provided for carrying out a preferred embodiment of the method according to the application.

[0033] Figure 2 A preferred embodiment of the method according to the application is schematically illustrated as a block diagram.

[0034] Figure 3 The time-dependent change curves of the welding spatter quantity, the welding resistance and the current value are schematically illustrated, which can be the basis for a preferred embodiment of the method according to the application. DETAILED DESCRIPTION

[0035] In Figure 1 In particular, a welding device for resistance welding is schematically illustrated and designated 100.

[0036] With the welding device 100 it is possible to join workpieces 120 to one another by means of resistance welding. In particular, the workpieces 120 are welded to one another in the course of a body-in-white production, wherein in particular a body of a motor vehicle is produced. Here, for example, two sheets 121 and 122 made of aluminum are welded to one another as workpieces.

[0037] The welding device 100 has a welding tongs 110 with two welding electrodes 111 and 112. An electrode drive 130 is provided in order to move the welding electrodes 111, 112. In Figure 1 particular, the welding tongs 110 are shown as a servo-electric welding tongs, which has an electrode drive 130 configured as a servo motor. It is also conceivable that the electrode drive 130 can be configured as an electric motor, a hydraulic motor or a pneumatic motor, for example.

[0038] In the course of a resistance welding process, during a so-called force build-up phase, the welding electrodes 111 and 112 are pressed with an electrode force by means of the electrode drive 130 against the sheets 121 and 122 at the welding point 125. Subsequently, during the actual welding process, the welding electrodes 111 and 112 are energized with a welding current for the duration of a welding time, whereby a resistance heating of the sheets 121 and 122 at the welding point 125 is achieved and a liquefaction of the surface of the workpieces 121, 122 occurs.

[0039] Furthermore, the welding device 100 has a control unit (welding control) 140, which can be configured as an SPS (store-programmable control), for example. The control unit 140 is provided for the actuation of the electrode drive 130 and the welding tongs 110, which is indicated by the reference numerals 151 and 152, and in order to thereby regulate the welding process. For this purpose, a corresponding control program or welding program 141 is implemented in the control unit 140, by means of which the electrode drive 130 and the welding tongs 110 are actuated in accordance with pre-specified welding parameters. These welding parameters can include, for example, a time profile of the electrode force and of the welding current.

[0040] Furthermore, the control unit 140 is provided for the analysis of the executed welding process or of the quality of the resulting welding point 125 and for the adaptation of the welding parameters in the event of a quality failure. For this purpose, the control unit 140 is in particular provided in a program-technical manner for the execution of a preferred embodiment of the method according to the application, which is shown schematically as a block diagram in Figure 2 and is explained below with reference to Figure 1 and 2

[0041] ​According to a preferred embodiment, the welding process is periodically executed. In the course of each cycle, a predefined number of welding processes is executed and statistically analyzed separately. Based on the analysis, the welding parameters are adapted as required and the subsequent cycle is executed with the adapted welding parameters.

[0042] In step 201, a respective predefined number of welding processes is executed, wherein in the course of each of the welding processes the welding electrode 111, 112 is pressed against the welding point 125 of the workpiece 121, 122 and energized with a welding current separately according to predefined welding parameters. For example, 1000 to 2000 welding processes can be executed.

[0043] Furthermore, according to step 202, at least one characteristic value characterizing the welding quality is determined separately in the course of each of the welding processes. The characteristic values in particular characterize welding spatters occurring during the respective welding process. For example, the spatter time points at which welding spatters occur separately during the respective welding process are determined as characteristic values. After a predefined number of welding processes has been executed, the determined characteristic values, i.e. the determined spatter time points, are statistically analyzed.

[0044] In particular, first in step 203, the number and rate of welding spatters are determined during a plurality of welding processes. In step 204, it is checked whether the number and the rate respectively reach a predefined threshold value. If this is not the case, this indicates that the welding parameters are selected well enough so that only very few welding spatters occur. In this case, no adaptation of the welding parameters takes place and the following plurality of welding processes is executed with unchanged welding parameters.

[0045] In contrast, if the number of welding spatters or the rate of welding spatters respectively reaches a predefined threshold value, this indicates that a spatter threshold value is exceeded and an adaptation of the welding parameters should take place in order to reduce the occurrence of welding spatters.

[0046] In this case, in step 205, it is checked whether the occurrence of welding spatters conforms to a random distribution. In particular, for this purpose, a chi-squared test is carried out. This test should rule out that the welding program is selected incorrectly for the welding parameter adaptation.

[0047] For this purpose, it is checked whether the spatter occurrence is randomly distributed over all welding points welded with the welding program or whether there is a systematic accumulation or reappearance of a plurality of welding spatters.

[0048] A negative test result indicates that the parameterization of the welding program is correct, but that it can have been used for the wrong application. In this case no adaptation of the welding parameters takes place, but rather the program is suitably exchanged. In contrast, a positive test result indicates that the spatters occur sufficiently randomly distributed. In this case an adaptation of the welding parameters takes place.

[0049] For this purpose, in step 206 the median of the spatter time points is determined from the determined characteristic values, i.e. from the spatter time points and the standard deviation σ. According to the difference of the median from the product of the standard deviation and a predefinable constant k In step 207 it is determined how the welding parameters are to be adapted in order to reduce the occurrence of welding spatters in the subsequent welding process.

[0050] With these adapted welding parameters a predefined number of welding processes is re-executed. The welding parameters are thus continuously and dynamically adapted to the respective current welding quality. This adaptation is particularly suitably carried out automatically without the need for manual intervention by the user.

[0051] According to a preferred embodiment, a statistical analysis can also be carried out after each welding process. In this case, at least one characteristic value characterizing the welding quality is also determined in the course of each welding process. In particular, as set forth above with respect to step 202, the spatter time points at which a welding spatter respectively occurs are determined as this characteristic value. In particular in this case, the above-described test is carried out after each welding process according to steps 203 to 207. Here, after each welding process according to step 203 the number and the rate of welding spatters occurring during the executed welding process are determined, the welding process being suitably executed with the same welding parameters. As set forth with respect to step 204, it is tested whether this number and this rate respectively reach a predefinable threshold value. If this is not the case, the next welding process is executed with unchanged welding parameters. If, in contrast, this is the case, a chi-squared test is carried out according to step 205. In a negative test result in particular the program is exchanged, in a positive test result an adaptation of the welding parameters is suitably carried out, in particular as set forth above according to steps 206 and 207. The next welding process is executed with these adapted welding parameters.

[0052] Figure 3 A time curve of a quantity is schematically shown, which can be the basis for a preferred embodiment of the method according to the application.

[0053] In Figure 300, the number of welding spatters n that occur during one cycle of the welding process is plotted with respect to the duration t of the corresponding welding process. Bars 310 represent the number of welding spatters that occur at specific points in time during the corresponding welding process.

[0054] Furthermore, the welding resistance R is plotted in Figure 300 with respect to duration t. Curve 320 represents a reference curve for the welding resistance during a high-quality welding process without welding spatter. Conversely, curve 330 represents the welding resistance during a welding process with welding spatter.

[0055] Furthermore, Figure 300 shows the values... and The interval between, where, σ is the median of the splash time points, σ ​​is the standard deviation of the splash time points, and σ is the value of 1 chosen for the constant k.

[0056] As mentioned above, based on the parameters To determine how to adapt the welding parameters for the next welding process cycle. For example, if evaluating parameters... The value during the first 150ms of the welding process is as follows: Figure 3 If the welding is in the so-called pre-stage as shown, the welding electrical intensity decreases as shown in Figure 400.

[0057] In Figure 400, the current value ΔI is plotted relative to the duration t of the welding process, with the welding current intensity being reduced using this current value ΔI as a welding parameter. Curve 410 represents the change of this current value over time. Here, no adjustment of the welding current intensity is performed during stage 420. In stage 430, the welding current intensity is reduced, and in stage 440, the welding process continues with a correspondingly reduced welding current intensity.

[0058] Alternatively, the regression model is used to predict the probability and timing of weld spatter for the next weld point to be welded. If the probability of spatter occurrence for the next weld, estimated by the regression model, is higher than a user-defined limit, welding parameters are automatically adapted for the next point. In this way, many parameters can be adapted to take action before spatter occurs without limiting spot quality.

[0059] The regression model is based on a recurrent neural network, among others, and it identifies itself which of the input parameters are to be considered for their output / target values. All recorded data from the welding control device, e.g. time series of reference and / or actual curves, welding parameters used for the weld, and data derived such as time periods between individual welds, can be provided to the model. The output of the model can be the probability of spatter and the time point, where a gradient with respect to the input parameters can be formed. With this gradient, the parameters can be automatically adapted in order to minimize the probability of spatter occurring.

Claims

1. A method for resistance welding, wherein, Performing a welding process, during the course of which a welding electrode (111, 112) is pressed against a welding point (125) of a workpiece (121, 122) and is energized with a welding current in accordance with predefined welding parameters, wherein at least one characteristic value characterizing the welding quality is determined during the course of the welding process, wherein a statistical analysis of the determined characteristic values is performed after a plurality of welding processes have been performed, and wherein it is determined on the basis of the result of the statistical analysis whether an adaptation of the predefined welding parameters is performed, characterized in that the statistical analysis is performed on the basis of a median value of the time points of occurrence of welding spatters during the respective welding process and the divergence, and the statistical analysis is performed on the basis of the difference of the median value of the time points of occurrence of welding spatters and the product of the standard deviation of the time points of occurrence of welding spatters and a constant that can be predefined from one another.

2. The method according to claim 1, wherein the at least one characteristic value characterizing the welding quality characterizes welding spatters occurring during the respective welding process.

3. The method according to claim 1, wherein the statistical analysis is performed depending on a number and / or a rate of welding spatters occurring during the plurality of welding processes.

4. The method of any one of claims 1 to 3, wherein, The statistical analysis is performed depending on a chi-squared test whether occurrences of welding spatters during the plurality of welding processes comply with a random distribution.

5. The method of any one of claims 1 to 3, wherein, The statistical analysis is further performed depending on a material of the workpieces (121, 122) and / or properties of the welding electrodes (111, 112).

6. The method according to any one of claims 1 to 3, wherein the pre-defined welding parameters comprise one or more of the following parameters: - electrode force; - welding current; - welding voltage; - welding time; - a time profile of the electrode force; - a time profile of the welding current intensity, and - a time profile of the welding voltage.

7. The method of any one of claims 1 to 3, wherein, The statistical analysis is performed depending on a chi-squared test whether occurrences of welding spatters during the plurality of welding processes comply with a random distribution.

8. A control unit (140) configured to perform the method according to any one of claims 1 to 7.

9. A welding device (100) having a control unit (140) according to claim 8.

10. A computer program product causing a control unit (140) to perform the method according to any one of claims 1 to 7 when the computer program product is implemented on the control unit (140).

11. A machine-readable storage medium having stored thereon the computer program product according to claim 10.

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