Abnormal welding spot detection method and system in spot welding process

Abstract

The invention discloses a method and system for detecting abnormal welding spots in the spot welding process. The method comprises the steps that a time sequence data sequence collected in real time in the welding process is obtained; calculating a first-order difference sequence of the time sequence data sequence; screening a target data segment representing a stable trend based on the first-order difference sequence; performing linear fitting on the first K data points of the target data segment to obtain a first trend direction; performing linear fitting on the last K data points of the target data segment to obtain a second trend direction; calculating the variable quantity of the first trend direction and the second trend direction; and when the variable quantity exceeds a preset threshold value, it is judged that welding is abnormal, and an alarm is triggered. The stable trend section is screened through the differential sequence, noise interference is effectively eliminated, and it is ensured that the quality criterion is based on reliable data. And whether the welding spot is abnormal or not is quickly and accurately judged, so that real-time and non-destructive spot welding quality on-line monitoring is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of spot welding diagnostic evaluation, in particular to an abnormal welding spot detection method and system in a spot welding process. BACKGROUND

[0002] Spot welding technology is a widely used welding method, which melts and connects two workpieces by placing them under a certain electrode pressure and using the heat generated by the current passing through the contact surface. Although this method is simple and effective, in the actual welding process, the quality of spot welding is affected by many uncontrollable factors, including current, welding pressure and workpiece material, making it difficult to accurately control the quality of the welding spot. In addition, the nugget inside the welding spot cannot be directly observed due to its closed state, further increasing the difficulty of extracting quality information.

[0003] Currently, the detection technology for spot welding quality has been developed to some extent, mainly including cutting detection method, ultrasonic detection method and visual detection method. Among them, although the cutting detection method can directly observe the nugget morphology of the welding spot, its destructive, time-consuming and high cost limit its use in industrial production. The ultrasonic detection method relies on echo signal analysis to detect nugget defects, requires the use of coupling agent, and has high requirements for surface flatness, and is suitable for offline sampling inspection. The visual detection method can only detect defects on the surface of the welding spot, cannot evaluate the internal quality, and cannot provide comprehensive quality information.

[0004] In summary, the existing spot welding quality detection technology still needs to be improved in terms of detection efficiency, accuracy and real-time performance, and a new technology that can realize real-time online monitoring is urgently needed to improve these problems. SUMMARY

[0005] The purpose of the present application is to overcome the shortcomings of the prior art. In order to achieve the above purpose, an abnormal welding spot detection method and system in a spot welding process are used to solve the problems raised in the background technology.

[0006] An abnormal welding spot detection method in a spot welding process, comprising the following steps:

[0007] Obtaining a time series data sequence collected in real time during the welding process;

[0008] Calculating the first-order difference sequence of the time series data sequence;

[0009] Filtering a target data segment representing a stable trend based on the first-order difference sequence;

[0010] Linearly fitting the first K data points of the target data segment to obtain a first trend direction;

[0011] linearly fitting the last K data points of the target data segment to obtain a second trend direction;

[0012] calculating a change amount of the first trend direction and the second trend direction;

[0013] when the change amount exceeds a preset threshold, determining a welding abnormality and triggering an alarm.

[0014] As a further aspect of the present application: the time series data sequence is a welding resistance value sequence.

[0015] As a further aspect of the present application: before calculating the first-order difference sequence, invalid data points with a value of zero in the time series data sequence are removed.

[0016] As a further aspect of the present application: the target data segment representing a stable trend includes:

[0017] removing data points with a difference value greater than zero in the first-order difference sequence, and retaining a continuous data segment as a target data segment.

[0018] As a further aspect of the present application: the stable trend is a monotonically decreasing trend of data values.

[0019] As a further aspect of the present application: the step of obtaining the first trend direction and the second trend direction includes:

[0020] linearly fitting the selected data points by least squares method to calculate the angle between the fitting straight line and the time axis.

[0021] As a further aspect of the present application: the change amount is the difference α between the fitting straight line angle Q1 of the first K data points and the fitting straight line angle Q2 of the last K data points, where α = Q1-Q2.

[0022] As a further aspect of the present application: the preset threshold is dynamically adjusted according to the welding material type and the electrode pressure.

[0023] As a further aspect of the present application: the specific steps of triggering an alarm include at least one of:

[0024] outputting the position number of the abnormal welding spot in real time;

[0025] interrupting the welding production line operation;

[0026] storing the abnormal data record.

[0027] The technical scheme of the second aspect: a diagnostic system using the abnormal welding spot detection method in the spot welding process according to any one of the above aspects, comprising:

[0028] A data acquisition module is configured to acquire a time series of data in real time through a welding electrode;

[0029] A data processing module is configured to perform an abnormal welding point detection step;

[0030] An alarm execution module is configured to trigger an alarm action in response to an abnormality determination result.

[0031] Compared with the prior art, the present application has the following technical effects:

[0032] By using the above technical solution, the time series of data in the welding process is acquired in real time, the first-order differential sequence is calculated, and the target data segment representing the stable trend is screened out; the first K data points and the last K data points of the target data segment are linearly fitted respectively to obtain the first trend direction and the second trend direction; the change amount of the two is calculated, and when the change amount exceeds a preset threshold, the welding abnormality is determined and the alarm is triggered. By screening the stable trend segment through the differential sequence, noise interference is effectively eliminated, and the quality criterion is based on reliable data; only real-time resistance time series data needs to be analyzed, without the need to damage the sample or rely on complex equipment, which is suitable for high-speed real-time monitoring on the production line. The resistance data collected during spot welding can be used to intelligently analyze the change trend of the resistance curve, quickly and accurately determine whether the welding point is abnormal, and thus realize real-time and non-destructive online monitoring of spot welding quality. BRIEF DESCRIPTION OF DRAWINGS

[0033] The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings:

[0034] Figure 1 The figure is a schematic diagram of the step structure of the abnormal welding point detection method of the disclosed embodiment of the present application;

[0035] Figure 2 The figure is a flow chart of the abnormal welding point detection method of the disclosed embodiment of the present application;

[0036] Figure 3 The figure is a schematic diagram of the resistance curve in the normal welding process of the disclosed embodiment of the present application;

[0037] Figure 4 The figure is a schematic diagram of the resistance curve in the abnormal welding process of the disclosed embodiment of the present application. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0039] Please refer to Figure 1 and Figure 2 In the embodiment of the present application, an abnormal welding spot detection method in a spot welding process comprises the following steps:

[0040] S1, acquiring a time series data sequence collected in real time during welding;

[0041] In the embodiment, the time series data sequence is a welding resistance value sequence.

[0042] For example, in a certain automobile door panel spot welding production line, a welding controller collects resistance data of a certain welding spot in real time, i.e., a welding resistance value sequence, to obtain an original sequence.

[0043] As shown in FIG. 1, Figure 3 the figure is a schematic diagram of a normal resistance curve in a welding process.

[0044] As shown in FIG. 2, Figure 4 the figure is a schematic diagram of an abnormal resistance curve in a welding process.

[0045] S2, calculating a first-order difference sequence of the time series data sequence;

[0046] In the embodiment, before calculating the first-order difference sequence, invalid data points with a value of zero in the time series data sequence are removed.

[0047] For example, based on the welding resistance value sequence obtained in step S1, data points with a resistance value of 0 are deleted to eliminate invalid data caused by abnormal electrode contact in S1.

[0048] At the same time, a first-order difference sequence diff is calculated according to the welding resistance value sequence.

[0049] S3, screening a target data segment representing a stable trend based on the first-order difference sequence;

[0050] In the embodiment, the screening of the target data segment representing a stable trend comprises:

[0051] Data points with a difference value greater than zero in the first-order difference sequence are deleted, and a continuous data segment is retained as a target data segment.

[0052] The stable trend is a monotonically decreasing trend of data values.

[0053] For example, data points with diff>0 in S3 are deleted (a descending trend segment is retained) to obtain a continuous stable interval.

[0054] S4, linearly fitting the first K data points of the target data segment to obtain a first trend direction;

[0055] In the embodiment, the steps of obtaining the first trend direction and the second trend direction comprise:

[0056] The selected data points are subjected to least square linear fitting to calculate the angle between the fitted straight line and the time axis.

[0057] For example, based on the first 20 data points in the obtained continuous stable interval, a least square fitting straight line equation is calculated to obtain an angle Q1 with the X axis.

[0058] S5, linear fitting is performed on the last K data points of the target data segment to obtain a second trend direction.

[0059] For example, based on the last 20 data points in the obtained continuous stable interval, a least square fitting straight line equation is also calculated to obtain an angle Q2.

[0060] S6, the change amount of the first trend direction and the second trend direction is calculated.

[0061] In this embodiment, the change amount is the difference a between the fitting straight line angle Q1 of the first K data points and the fitting straight line angle Q2 of the last K data points, where a = Q1-Q2.

[0062] Specifically, the angle difference a = Q1-Q2 of S4 and S5 is calculated.

[0063] S7, when the change amount exceeds a preset threshold, an abnormal welding point is determined and an alarm is triggered.

[0064] In this embodiment, the preset threshold is dynamically adjusted according to the welding material type and the electrode pressure.

[0065] For example, if the preset threshold is determined to be θ = 0.1° according to the actual situation, and |a| = 0.13° is obtained, then |a| > θ, and the abnormal welding point is determined:

[0066] At the same time, an alarm is triggered, and the specific steps of triggering the alarm include one or a combination of the following ways:

[0067] Real-time output of the abnormal welding point position number;

[0068] Interrupt the welding production line operation;

[0069] Store the abnormal data record.

[0070] The technical solution of the second aspect: a diagnostic system using the abnormal welding point detection method in any one of the above embodiments, comprising:

[0071] A data acquisition module for real-time acquisition of time series data through a welding electrode;

[0072] A data processing module for performing the abnormal welding point detection steps;

[0073] An alarm execution module is configured to trigger an alarm action in response to the abnormality determination result.

[0074] Advantages of the embodiment:

[0075] 1. First-order difference processing is introduced to remove invalid data points:

[0076] In the data preprocessing stage, by calculating the first-order difference of resistance data and deleting the data points with a difference greater than 0, noise interference and abnormal fluctuations are effectively eliminated, and the data stability and analysis accuracy are improved.

[0077] 2. Quality determination by comparing the trends before and after the same curve:

[0078] A straight line is fitted using the least squares method, and the angle between the straight line and the X-axis is calculated. The consistency of the trends before and after is determined to identify abnormal solder joints.

[0079] 3. Effectively improve the detection efficiency and real-time performance:

[0080] Without damaging the sample or relying on complex imaging equipment and additional equipment investment, quality judgment can be completed by collecting resistance data during the welding process, which is suitable for online detection in the production line.

[0081] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents, and all should be included within the scope of protection of the present application.

Claims

1. A method for detecting abnormal weld spots during spot welding, characterized in that: The following steps are involved: Obtaining the time series data sequence collected in real time during the welding process; Calculating a first-order difference sequence of the time series data sequence; screening a target data segment representing a stable trend based on the first-order difference sequence; Performing linear fitting on the first K data points of the target data segment to obtain a first trend direction; Performing linear fitting on the last K data points of the target data segment to obtain a second trend direction; Calculating the amount of change between the first trend direction and the second trend direction; When the change exceeds a preset threshold, it is determined that welding is abnormal and an alarm is triggered.

2. The method for detecting abnormal weld spots during spot welding according to claim 1, wherein: The time series data sequence is a welding resistance value sequence.

3. The method for detecting abnormal weld spots during spot welding according to claim 1 or 2, wherein: Before calculating the first-order difference sequence, invalid data points with a value of zero in the time series data sequence are eliminated.

4. The method for detecting abnormal weld spots during spot welding according to claim 1, wherein: The target data segment for screening the stable trend includes: Data points with difference values ​​greater than zero in the first-order difference sequence are deleted, and continuous data segments are retained as target data segments.

5. The method for detecting abnormal weld spots during spot welding according to claim 4, wherein: The stable trend is a monotonically decreasing trend of data values.

6. The method for detecting abnormal weld spots during spot welding according to claim 1, wherein: The step of acquiring the first trend direction and the second trend direction includes: Perform the least squares linear fitting on the selected data points and calculate the angle between the fitting line and the time axis.

7. The method for detecting abnormal weld spots during spot welding according to claim 6, wherein: The variation is the difference α between the angle Q1 of the fitted straight line of the first K data points and the angle Q2 of the fitted straight line of the last K data points, where α=Q1-Q2.

8. The method for detecting abnormal weld spots during spot welding according to claim 1, wherein: The preset threshold is dynamically adjusted according to the welding material type and electrode pressure.

9. The method for detecting abnormal weld spots during spot welding according to claim 1, wherein: The specific steps of triggering the alarm include at least one of the following: Output abnormal welding spot location number in real time; Interrupt welding production line operation; Store abnormal data records.

10. A diagnostic system using the method for detecting abnormal weld spots during spot welding according to any one of claims 1 to 9, characterized in that: include: A data acquisition module is used to collect time series data sequences in real time through welding electrodes; A data processing module, configured to execute abnormal solder joint detection steps; The alarm execution module is used to trigger the alarm action in response to the abnormal judgment result.

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