Welded joint temperature monitoring method under thermoplastic composite material welding scene

Abstract

The invention relates to the technical field of temperature monitoring, in particular to a welding joint temperature monitoring method in a thermoplastic composite material welding scene, which comprises the following steps: acquiring temperature data of different positions of a thermoplastic composite material in the welding scene at each acquisition moment to form a temperature image point matrix at each acquisition moment; acquiring fluctuation data of the temperature image point matrix at each temperature fluctuation moment; dividing each fluctuation data cluster of fluctuation data in the temperature image point matrix at each temperature fluctuation moment; calculating a fluctuation distribution factor at each temperature fluctuation moment; obtaining a temperature abrupt change factor of each fluctuation data cluster of the temperature image point matrix at each temperature fluctuation moment; acquiring a fluctuation abrupt change factor at each temperature fluctuation moment; acquiring the laser intensity nonuniform weight at each temperature fluctuation moment; and monitoring the temperature of the welding joint in the thermoplastic composite material welding scene based on the uneven weight of the laser intensity. The accuracy of welding joint temperature monitoring is improved.

Description

Technical Field

[0001] The present application relates to the field of temperature monitoring technology, and in particular to a method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario. Background Art

[0002] Thermoplastic composites are widely used in industrial welding because of their superior strength and stiffness, and because they can melt when heated and still maintain their original mechanical properties after cooling. When using laser welding to weld thermoplastic composites, any temperature fluctuations beyond the set limit at the weld joint may result in poor welding and defective products. Therefore, the temperature of the weld joint needs to be monitored during the welding process.

[0003] Through temperature monitoring, welders can adjust welding parameters in time to ensure that the temperature during welding is within the appropriate range, thereby ensuring welding quality. It is also possible to determine whether there are defects in the welding quality of the product during welding based on the monitored temperature data. Common temperature monitoring methods include infrared temperature measurement, thermocouple temperature measurement, fiber optic sensor temperature measurement, and infrared camera temperature measurement. Among them, thermocouple sensors are not suitable for embedding inside the structure due to their large size and are usually not used; infrared temperature measurement methods and fiber optic sensor methods measure the average temperature of a surface and cannot reflect the temperature distribution at different positions of the welding joint; infrared thermal imagers can measure the temperature distribution of the entire plane of the welding joint, so they are widely used in welding joint temperature monitoring. When the infrared thermal imager collects the surface temperature of the welding joint, the collected temperature data is easily interfered by electromagnetic waves in the environment, resulting in the collected temperature data not accurately reflecting the actual temperature of the welding joint. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a welding joint temperature monitoring method in a thermoplastic composite material welding scenario to solve the existing problems.

[0005] The welding joint temperature monitoring method in the thermoplastic composite material welding scenario of the present application adopts the following technical solution:

[0006] An embodiment of the present application provides a method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario, the method comprising the following steps:

[0007] Collect temperature data of different positions of thermoplastic composite materials at each collection time in welding scenarios to form a temperature image point matrix at each collection time;

[0008] Obtain each temperature fluctuation moment based on the discrete degree of local temperature data in the temperature image point matrix and the distance between local temperature data;

[0009] Based on the deviation of the temperature data of the welding joint, the fluctuation data of the temperature image point matrix at each temperature fluctuation moment is obtained;

[0010] Divide the fluctuation data in the temperature image point matrix at each temperature fluctuation moment into clusters of fluctuation data;

[0011] Based on the average number of elements in the fluctuation data clusters, the number of fluctuation data clusters and the distribution of fluctuation data, the fluctuation distribution factor at each temperature fluctuation moment is obtained;

[0012] Based on the fluctuation range of all fluctuation data in the neighborhood of the edge fluctuation data in the fluctuation data cluster, the temperature mutation factor of each fluctuation data cluster of the temperature image point matrix at each temperature fluctuation moment is obtained;

[0013] Based on the average of the temperature mutation factor, the fluctuation mutation factor at each temperature fluctuation moment is obtained;

[0014] Obtain the laser intensity uneven weight at each temperature fluctuation moment based on the fluctuation distribution factor and the fluctuation mutation factor;

[0015] The temperature of the weld joint in thermoplastic composite welding scenarios is monitored based on the laser intensity uneven weight.

[0016] Further, the method for obtaining the temperature fluctuation moment is:

[0017] For the temperature image point matrix at each acquisition moment, the temperature data in the temperature image point matrix is ​​used as the input of the Otsu threshold segmentation algorithm to obtain the optimal segmentation threshold, and the temperature data greater than the optimal segmentation threshold is used as the temperature data of the welding joint;

[0018] For each acquisition moment, the sequence of all welding joint temperature data from left to right and from top to bottom in the temperature image point matrix is ​​used as the welding temperature sequence at each acquisition moment;

[0019] The joint temperature difference coefficient of the joint at each collection moment is obtained based on the discrete degree of the welding joint temperature data and the distance between the welding joint temperature data, and the collection moment when the joint temperature difference coefficient is greater than the preset fluctuation threshold is taken as the temperature fluctuation moment.

[0020] Furthermore, the calculation formula of the joint temperature difference coefficient is: S i =var i +dtw(t i ,t i―1 );where S i Indicates the joint temperature difference coefficient at the i-th acquisition moment; var i represents the variance of all welding joint temperature data at the i-th acquisition moment, t i ,t i―1They represent the welding temperature sequences at the i-th acquisition moment and the i-1-th acquisition moment respectively; dtw() represents the DTW distance.

[0021] Further, the method for obtaining the fluctuation data is:

[0022] For each temperature fluctuation moment, all welding joint temperature data of the temperature image point matrix at the temperature fluctuation moment are used as the input of the Z-score algorithm to obtain the Z score of each welding joint temperature data, and the welding joint temperature data with a Z score greater than the first preset value and less than the second preset value are used as the fluctuation data of the temperature image point matrix at each temperature fluctuation moment.

[0023] Furthermore, the method for obtaining the fluctuation data cluster is:

[0024] A density clustering algorithm is used to obtain clusters of fluctuation data of the temperature image point matrix at each temperature fluctuation moment as clusters of fluctuation data of the temperature image point matrix at each temperature fluctuation moment.

[0025] Furthermore, the calculation formula of the volatility distribution factor is: In the formula, F j is the fluctuation distribution factor at the jth temperature fluctuation moment; e j represents the mean number of elements of all fluctuation data clusters at the jth temperature fluctuation moment, k j represents the number of clusters of fluctuation data at the i-th temperature fluctuation moment, h j Represents the area of ​​the minimum circumscribed rectangle of all fluctuation data in the temperature image point matrix at the jth temperature fluctuation moment.

[0026] Further, the method for obtaining the temperature mutation factor is:

[0027] For each fluctuation data cluster of the temperature image point matrix at each temperature fluctuation moment, the range of all fluctuation data in the neighborhood with the fluctuation data with the smallest horizontal coordinate in the fluctuation data cluster as the center and the preset radius as the radius is calculated as the mutation coefficient of the fluctuation data with the smallest horizontal coordinate;

[0028] The same method as the mutation coefficient of the fluctuation data with the smallest horizontal coordinate is adopted to obtain the mutation coefficients of the fluctuation data with the largest horizontal coordinate, the fluctuation data with the largest vertical coordinate and the fluctuation data with the smallest vertical coordinate in the fluctuation data cluster, and the sum of all mutation coefficients in the fluctuation data cluster is used as the temperature mutation factor of each fluctuation data cluster of the temperature image point matrix at each temperature fluctuation moment.

[0029] Furthermore, the fluctuation mutation factor is the average of the temperature mutation factors of all fluctuation data clusters at each temperature fluctuation moment.

[0030] Furthermore, the laser intensity uneven weight is the ratio of the fluctuation distribution factor to the fluctuation mutation factor.

[0031] Further, the monitoring of the temperature of the welding joint in the thermoplastic composite material welding scenario based on the laser intensity uneven weight includes:

[0032] Calculate the Z score of the laser intensity uneven weight at each temperature fluctuation moment in the welding process of thermoplastic composite materials among the laser intensity uneven weights at all temperature fluctuation moments;

[0033] For each temperature fluctuation moment, when the Z score of the laser intensity uneven weight at the temperature fluctuation moment is greater than or equal to the third preset value, the temperature of the welding joint is abnormal; when the Z score of the laser intensity uneven weight at the temperature fluctuation moment is less than the third preset value, the temperature of the welding joint is normal.

[0034] This application has at least the following beneficial effects:

[0035] This application aims to solve the problem that infrared thermal imagers are easily affected by electromagnetic interference when monitoring the surface temperature of welding joints, resulting in inaccurate temperature data. A welding joint temperature monitoring method in a thermoplastic composite welding scenario is proposed. First, based on the temperature changes before and after the temperature data and the temperature unevenness at the current moment, the joint temperature difference coefficient is constructed to accurately detect the moment when the temperature fluctuation occurs, thereby improving the accuracy of temperature monitoring; then, based on the distribution characteristics and mutation characteristics of the fluctuation data, the laser intensity uneven weight is constructed to determine whether the current fluctuation data is caused by uneven laser intensity, thereby eliminating the influence of electromagnetic interference on the temperature data and making the temperature monitoring of the welding joint more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 A flow chart of a method for monitoring temperature of a welded joint in a thermoplastic composite welding scenario provided in this application;

[0038] Figure 2 Flowchart for obtaining uneven weights for laser intensity. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, characteristics and effects of the welding joint temperature monitoring method in the thermoplastic composite material welding scenario proposed in the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0041] The specific scheme of the welding joint temperature monitoring method in the thermoplastic composite material welding scenario provided by the present application is described in detail below with reference to the accompanying drawings.

[0042] An embodiment of the present application provides a method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario. Specifically, the following method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario is provided. Figure 1 , the method comprises the following steps:

[0043] Step S1, collecting temperature data of different positions of the thermoplastic composite material at each collection time in a welding scene, and forming a temperature image point matrix at each collection time.

[0044] The principle of laser welding is to use a highly focused laser beam to heat and melt the thermoplastic composite material at the joint, so that the material forms a fusion when cooling. Laser welding methods are divided into quasi-synchronous welding, synchronous welding and mask welding. Among them, synchronous welding uses a laser transmitter with multiple laser ports, and adjusts the direction and shape of the laser beam through optical devices. The laser beam welds along the contour line of the welding layer, so that the entire contour line is melted and bonded together at the same time.

[0045] During the process of welding thermoplastic composites using synchronous laser welding, an infrared thermal imager is installed above the welding area of ​​the thermoplastic composite material so that the infrared thermal imager can directly observe the temperature distribution on the surface of the welded joint.

[0046] Furthermore, an infrared thermal image of the thermoplastic composite material at each acquisition moment is obtained by an infrared thermal imager, and a matrix composed of temperature data of each pixel in the infrared thermal image according to the position of the pixel is used as a temperature image point matrix at each acquisition moment.

[0047] Specifically, the data in the temperature image point matrix represents the surface temperature of the object within the observation range of the infrared thermal imager. Due to the noise interference in the environment, the collected temperature image point matrix may contain noise. The sampled median filtering algorithm of this embodiment denoises the collected temperature image point matrix. The implementer can select other denoising methods according to the actual situation. The median filtering algorithm is a well-known technology, and the specific process will not be repeated.

[0048] Step S2, obtaining each temperature fluctuation moment based on the discrete degree of local temperature data in the temperature image point matrix and the distance between local temperature data; obtaining the fluctuation data of the temperature image point matrix at each temperature fluctuation moment based on the deviation of the temperature data of the welding joint; dividing the fluctuation data in the temperature image point matrix at each temperature fluctuation moment into each fluctuation data cluster; obtaining the fluctuation distribution factor at each temperature fluctuation moment based on the average number of elements in the fluctuation data cluster, the number of fluctuation data clusters and the distribution of fluctuation data.

[0049] Generally speaking, any object above absolute zero will generate infrared radiation. The higher the temperature, the stronger the infrared radiation intensity. Infrared thermal imagers can capture the infrared radiation emitted by objects, thereby intuitively displaying the temperature distribution of the welding area. During the laser welding process, the operation of the laser and other electrical equipment in the environment will emit electromagnetic interference, which will cause the temperature data collected by the infrared imager to be inaccurate. Therefore, the temperature data collected by the infrared thermal imager needs to be processed.

[0050] In the process of welding thermoplastic composites using synchronous laser welding, the weld joints of thermoplastic composites receive laser light at the same time, and the weld contours melt at the same time. Under normal circumstances, the temperature distribution at the weld joint at different times should be similar. When the temperature fluctuates, it may be due to temperature measurement errors caused by electromagnetic interference and laser radiation, or it may be due to fluctuations in the actual temperature at the weld joint.

[0051] Specifically, the temperature at the welding joint will be significantly higher than that in other areas. Therefore, for the temperature image point matrix at each acquisition time, the temperature data in the temperature image point matrix is ​​used as the input of the Otsu threshold segmentation algorithm to obtain the optimal segmentation threshold, and the temperature data greater than the optimal segmentation threshold is used as the welding joint temperature data. Among them, the Otsu threshold segmentation algorithm is a well-known technology and will not be described in detail in this embodiment.

[0052] For each acquisition moment, a sequence of all welding joint temperature data from left to right and from top to bottom in the temperature image point matrix is ​​used as the welding temperature sequence at each acquisition moment.

[0053] Furthermore, the joint temperature difference coefficient is constructed to characterize the temperature fluctuation degree of the welding joint at each acquisition time. The calculation formula is: Si =var i +dtw(t i ,t i―1 );where S i Indicates the joint temperature difference coefficient at the i-th acquisition moment; var i represents the variance of all welding joint temperature data at the i-th acquisition moment, t i ,t i―1 They represent the welding temperature sequences at the i-th acquisition moment and the i-1-th acquisition moment respectively; dtw() represents the DTW distance.

[0054] It should be noted that var i The larger the value of , the greater the difference in the temperature data collected in the welding joint at the i-th collection time; dtw(t i ,t i―1 ) is larger, indicating that the temperature at the i-th acquisition moment has changed significantly compared with the temperature at the previous moment, which further indicates that the temperature at the i-th acquisition moment has fluctuated significantly; the joint temperature difference coefficient S i The larger the value of , the greater the fluctuation of the temperature data collected in the welding joint at the i-th collection time; conversely, the smaller the fluctuation of the temperature data collected in the welding joint at the i-th collection time.

[0055] For each collection moment, when the joint temperature difference coefficient at the collection moment is greater than the preset fluctuation threshold, the collection moment is taken as the temperature fluctuation moment. In this embodiment, the preset fluctuation threshold is 5, and the implementer can select other values ​​according to actual conditions.

[0056] Furthermore, it is necessary to determine whether the cause of the temperature fluctuation is the temperature measurement error caused by electromagnetic interference, or the change in the actual temperature of the welding joint. Specifically, during the operation of the infrared thermal imager, other electrical equipment in its environment will emit electromagnetic interference. This electromagnetic interference will cause the electronic components inside the infrared thermal imager to work unstably, thereby causing fluctuations in the temperature data. When the temperature data is subject to electromagnetic interference, it will appear as a large data fluctuation in the temperature data, and the area occupied by the fluctuating data will be smaller but more widely distributed, and will occur in the entire welding joint area, resulting in noise in the data in the welding joint area. When the temperature data is not subject to electromagnetic interference, the temperature data fluctuation usually occurs due to the uneven laser intensity emitted by lasers at different positions. This data fluctuation often does not occur in the entire welding joint area, but only a small part of the welding joint has temperature changes, then the area occupied by the fluctuating data will be larger and more concentrated.

[0057] For each temperature fluctuation moment, all the temperature data of the welding joints in the temperature image point matrix at the temperature fluctuation moment are used as the input of the Z-score algorithm to obtain the Z score of each welding joint temperature data, and the welding joint temperature data with a Z score greater than the first preset value and less than the second preset value is used as the fluctuation data of the temperature image point matrix at each temperature fluctuation moment. In this embodiment, the value of the first preset value is 2, and the value of the second preset value is -2. The implementer can select other values ​​according to actual conditions.

[0058] Further, the DCP density clustering algorithm is used to obtain the clustering clusters of the fluctuation data of the temperature image point matrix at each temperature fluctuation moment, as each fluctuation data clustering cluster of the temperature image point matrix at each temperature fluctuation moment. Among them, the Z-score algorithm and the DCP density clustering algorithm are well-known technologies and are not described in detail in this embodiment.

[0059] According to the above analysis, a fluctuation distribution factor is constructed to characterize the similarity between the distribution characteristics of the fluctuation data collected in the welding joint at each temperature fluctuation moment and the distribution characteristics of the fluctuation data when the laser intensity is uneven. The calculation formula is: In the formula, F j is the fluctuation distribution factor at the jth temperature fluctuation moment; e j represents the mean number of elements of all fluctuation data clusters at the jth temperature fluctuation moment, k j represents the number of clusters of fluctuation data at the jth temperature fluctuation moment, h j Represents the area of ​​the minimum circumscribed rectangle of all fluctuation data in the temperature image point matrix at the jth temperature fluctuation moment.

[0060] It should be noted that when e j When the value of is large, it means that the area occupied by each fluctuation data cluster at the jth temperature fluctuation moment is large, which further indicates that the temperature data fluctuation at the jth temperature fluctuation moment is more likely to be caused by uneven laser intensity; when k j The smaller the value of is, the smaller the number of fluctuation data clusters at the jth temperature fluctuation moment is, which further indicates that the temperature data fluctuation at the jth temperature fluctuation moment is more likely to be caused by laser unevenness; j The smaller the value is, the more concentrated the fluctuation data at the jth temperature fluctuation moment is, which means that the temperature data fluctuation at the jth temperature fluctuation moment is more likely to be the data fluctuation caused by laser unevenness, and the larger the value of the fluctuation distribution factor obtained at this time; conversely, the smaller the value of the fluctuation distribution factor obtained is.

[0061] Step S3, based on the fluctuation range of all fluctuation data in the neighborhood of the edge fluctuation data in the fluctuation data clustering cluster, obtain the temperature mutation factor of each fluctuation data clustering cluster of the temperature image point matrix at each temperature fluctuation moment; based on the average of the temperature mutation factor, obtain the fluctuation mutation factor at each temperature fluctuation moment; based on the fluctuation distribution factor and the fluctuation mutation factor, obtain the laser intensity uneven weight at each temperature fluctuation moment.

[0062] When the infrared thermal imager is not subject to electromagnetic interference, if the temperature fluctuates due to uneven laser intensity, the temperature change between the high temperature and the low temperature is gradual, and the data with temperature fluctuations changes gradually relative to the data of its surrounding positions, without sudden changes; when the infrared thermal imager is subject to electromagnetic interference, the data with temperature fluctuations may change suddenly relative to the data of its surroundings.

[0063] Furthermore, in order to reflect the degree of mutation of the fluctuation data at each temperature fluctuation moment, for each fluctuation data clustering cluster of the temperature image point matrix at each temperature fluctuation moment, the range of all fluctuation data in the neighborhood with the fluctuation data with the smallest horizontal coordinate in the fluctuation data clustering cluster as the center and a preset radius as the radius is calculated as the mutation coefficient of the fluctuation data with the smallest horizontal coordinate. Similarly, the mutation coefficients of the fluctuation data with the largest horizontal coordinate, the fluctuation data with the largest vertical coordinate and the fluctuation data with the smallest vertical coordinate in the fluctuation data clustering cluster are obtained, and the sum of all mutation coefficients in the fluctuation data clustering cluster is taken as the temperature mutation factor of each fluctuation data clustering cluster of the temperature image point matrix at each temperature fluctuation moment.

[0064] It should be noted that, for each fluctuation data cluster of the temperature image point matrix at each temperature fluctuation moment, the values ​​of the mutation coefficients of the fluctuation data with the smallest horizontal coordinate, the fluctuation data with the largest horizontal coordinate, the fluctuation data with the largest vertical coordinate, and the fluctuation data with the smallest vertical coordinate reflect the degree of mutation of the fluctuation data at the edge of the fluctuation data cluster. The larger the value of the mutation coefficient, the greater the degree of mutation, and the greater the value of the temperature mutation factor of each fluctuation data cluster obtained at this time; conversely, the smaller the value of the temperature mutation factor obtained.

[0065] Furthermore, for each temperature fluctuation moment, the mean of the temperature mutation factors of all fluctuation data clusters is calculated as the fluctuation mutation factor at each temperature fluctuation moment.

[0066] Furthermore, for each temperature fluctuation moment, the ratio of the fluctuation distribution factor to the fluctuation mutation factor is calculated as the laser intensity uneven weight at each temperature fluctuation moment. The laser intensity uneven weight acquisition flow chart is as follows: Figure 2 shown.

[0067] When the value of the fluctuation mutation factor is small, it means that the temperature change of the fluctuation data at the temperature fluctuation moment is relatively gentle compared with the temperature data at the adjacent position, which means that the fluctuation data at the temperature fluctuation moment is more likely to be the fluctuation data caused by uneven laser intensity; when the value of the fluctuation distribution factor is large, it means that the distribution characteristics of the fluctuation data at the temperature fluctuation moment are more similar to the distribution characteristics of the fluctuation data when the laser intensity is uneven, which means that the temperature data at the temperature fluctuation moment is more likely to be the fluctuation data caused by uneven laser intensity. At this time, the larger the value of the laser intensity uneven weight obtained is, the more likely the fluctuation data at the temperature fluctuation moment is the fluctuation data caused by uneven laser intensity; otherwise, it means that the fluctuation data at the temperature fluctuation moment is less likely to be the fluctuation data caused by uneven laser intensity.

[0068] Step S4, monitoring the temperature of the welding joint in the thermoplastic composite material welding scenario based on the laser intensity uneven weight.

[0069] Furthermore, in order to reflect the degree to which the temperature data at the moment of temperature fluctuation deviates from the normal situation due to the uneven laser intensity, the Z score of the laser intensity uneven weight at each temperature fluctuation moment in the welding process of the thermoplastic composite material is calculated in the laser intensity uneven weight at all temperature fluctuation moments. The Z score is obtained by the Z-score algorithm.

[0070] For each temperature fluctuation moment, when the Z score of the laser intensity uneven weight at the temperature fluctuation moment is greater than or equal to the third preset value, the fluctuation data at the temperature fluctuation moment is the fluctuation data caused by the laser intensity unevenness, indicating that the temperature of the welding joint is abnormal. At this time, an early warning is issued to remind the staff to adjust the welding parameters in time so that the temperature at the welding joint returns to normal; when the Z score of the laser intensity uneven weight at the temperature fluctuation moment is less than the third preset value, the temperature fluctuation data at the temperature fluctuation moment is the temperature fluctuation caused by electromagnetic interference, not the temperature fluctuation caused by the laser intensity unevenness. The temperature at the welding joint is normal, and there is no need to adjust the relevant parameters of the laser, and no early warning is issued. In this embodiment, the value of the third preset value is 2, and the implementer can select other values ​​according to actual conditions.

[0071] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0073] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Modifications to the technical solutions recorded in the aforementioned embodiments, or equivalent replacement of some of the technical features therein, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario, characterized in that: The method comprises the following steps: Collect temperature data of different positions of thermoplastic composite materials at each collection time in welding scenarios to form a temperature image point matrix at each collection time; Obtain each temperature fluctuation moment based on the discrete degree of local temperature data in the temperature image point matrix and the distance between local temperature data; Based on the deviation of the temperature data of the welding joint, the fluctuation data of the temperature image point matrix at each temperature fluctuation moment is obtained; Divide the fluctuation data in the temperature image point matrix at each temperature fluctuation moment into clusters of fluctuation data; Based on the average number of elements in the fluctuation data clusters, the number of fluctuation data clusters and the distribution of fluctuation data, the fluctuation distribution factor at each temperature fluctuation moment is obtained; Based on the fluctuation range of all fluctuation data in the neighborhood of the edge fluctuation data in the fluctuation data cluster, the temperature mutation factor of each fluctuation data cluster of the temperature image point matrix at each temperature fluctuation moment is obtained; Based on the average of the temperature mutation factor, the fluctuation mutation factor at each temperature fluctuation moment is obtained; Obtain the laser intensity uneven weight at each temperature fluctuation moment based on the fluctuation distribution factor and the fluctuation mutation factor; The temperature of the weld joint in thermoplastic composite welding scenarios is monitored based on the laser intensity uneven weight.

2. The method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario according to claim 1, characterized in that: The method for obtaining the temperature fluctuation moment is: For the temperature image point matrix at each acquisition moment, the temperature data in the temperature image point matrix is ​​used as the input of the Otsu threshold segmentation algorithm to obtain the optimal segmentation threshold, and the temperature data greater than the optimal segmentation threshold is used as the temperature data of the welding joint; For each acquisition moment, the sequence of all welding joint temperature data from left to right and from top to bottom in the temperature image point matrix is ​​used as the welding temperature sequence at each acquisition moment; The joint temperature difference coefficient of the joint at each collection moment is obtained based on the discrete degree of the welding joint temperature data and the distance between the welding joint temperature data, and the collection moment when the joint temperature difference coefficient is greater than the preset fluctuation threshold is taken as the temperature fluctuation moment.

3. The method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario according to claim 2, characterized in that: The calculation formula of the joint temperature difference coefficient is: S i =var i +dtw(t i ,t i―1 );where S i Indicates the joint temperature difference coefficient at the i-th acquisition moment; var i represents the variance of all welding joint temperature data at the i-th acquisition moment, t i ,t i―1 They represent the welding temperature sequences at the i-th acquisition moment and the i-1-th acquisition moment respectively; dtw() represents the DTW distance.

4. The method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario according to claim 2, characterized in that: The method for obtaining the fluctuation data is: For each temperature fluctuation moment, all welding joint temperature data of the temperature image point matrix at the temperature fluctuation moment are used as the input of the Z-score algorithm to obtain the Z score of each welding joint temperature data, and the welding joint temperature data with a Z score greater than the first preset value and less than the second preset value are used as the fluctuation data of the temperature image point matrix at each temperature fluctuation moment.

5. The method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario according to claim 4, characterized in that: The method for obtaining the fluctuation data cluster is as follows: A density clustering algorithm is used to obtain clusters of fluctuation data of the temperature image point matrix at each temperature fluctuation moment as clusters of fluctuation data of the temperature image point matrix at each temperature fluctuation moment.

6. The method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario according to claim 1, characterized in that: The calculation formula of the volatility distribution factor is: In the formula, F j is the fluctuation distribution factor at the jth temperature fluctuation moment; e j represents the mean number of elements of all fluctuation data clusters at the jth temperature fluctuation moment, k j represents the number of clusters of fluctuation data at the jth temperature fluctuation moment, h j Represents the area of ​​the minimum circumscribed rectangle of all fluctuation data in the temperature image point matrix at the jth temperature fluctuation moment.

7. The method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario according to claim 1, characterized in that: The method for obtaining the temperature mutation factor is: For each fluctuation data cluster of the temperature image point matrix at each temperature fluctuation moment, the range of all fluctuation data in the neighborhood with the fluctuation data with the smallest horizontal coordinate in the fluctuation data cluster as the center and the preset radius as the radius is calculated as the mutation coefficient of the fluctuation data with the smallest horizontal coordinate; The same method as the mutation coefficient of the fluctuation data with the smallest horizontal coordinate is adopted to obtain the mutation coefficients of the fluctuation data with the largest horizontal coordinate, the fluctuation data with the largest vertical coordinate and the fluctuation data with the smallest vertical coordinate in the fluctuation data cluster, and the sum of all mutation coefficients in the fluctuation data cluster is used as the temperature mutation factor of each fluctuation data cluster of the temperature image point matrix at each temperature fluctuation moment.

8. The method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario according to claim 1, characterized in that: The fluctuation mutation factor is the average of the temperature mutation factors of all fluctuation data clusters at each temperature fluctuation moment.

9. The method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario according to claim 1, characterized in that: The laser intensity uneven weight is the ratio of the fluctuation distribution factor to the fluctuation mutation factor.

10. The method for monitoring the temperature of a welded joint in a thermoplastic composite material welding scenario according to claim 4, characterized in that: The method of monitoring the temperature of a welding joint in a thermoplastic composite material welding scenario based on the laser intensity uneven weight includes: Calculate the Z score of the laser intensity uneven weight at each temperature fluctuation moment in the welding process of thermoplastic composite materials among the laser intensity uneven weights at all temperature fluctuation moments; For each temperature fluctuation moment, when the Z score of the laser intensity uneven weight at the temperature fluctuation moment is greater than or equal to the third preset value, the temperature of the welding joint is abnormal; when the Z score of the laser intensity uneven weight at the temperature fluctuation moment is less than the third preset value, the temperature of the welding joint is normal.

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