Method for predicting composite joint strength, predicting device, and computer readable storage medium

Abstract

The application discloses a kind of composite joint strength prediction method, prediction device and computer readable storage medium, the composite includes metal matrix and the plastic matrix that is combined with the metal matrix by the microstructure groove of the metal matrix surface, the prediction method includes the steps of: obtaining the ratio of groove width and plastic matrix into the metal matrix depth h, the completeness of the microstructure groove β and the mean value of the surface height variance of the microstructure groove σ;According to the h, β, σ and the pre-determined composite joint strength prediction model, the composite joint strength is calculated to obtain.This prediction method can be used for the quality check of composite in batch production process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of performance testing of metal / plastic composite parts, and particularly to a composite part bonding strength prediction method, a composite part bonding strength prediction device, and a computer readable storage medium. BACKGROUND

[0002] Plastics have been favored in the manufacturing field for their light weight, easy molding, and high cost performance, but the application of plastics alone in some special occasions requiring high strength is insufficient. Therefore, composite materials and their corresponding composite molding technology have become a research hotspot in recent years.

[0003] The bonding strength of a composite part is an index that needs special attention when it is put into use. The existing testing method mainly uses random sampling for destructive measurement, and the sampling test results represent the performance of the entire batch of products. However, due to the complex process of composite part manufacturing, and the stability of the processing technology is greatly affected by factors such as equipment working environment and service life when preparing the microstructure of the metal substrate. With the change of the environment and the extension of the production time, the confidence level of using random sampling destructive measurement data to reflect the bonding strength level of the entire batch of products will be greatly affected.

[0004] Therefore, the prior art still needs to be further improved and improved. SUMMARY

[0005] The technical problem to be solved by the present application is that there is currently a lack of detection methods that match the mass production mode of composite parts.

[0006] The technical solution adopted by the present application to solve the technical problem is to provide a composite part bonding strength prediction method, the composite part comprising a metal substrate and a plastic substrate combined with the metal substrate through a microstructure groove on the surface of the metal substrate, wherein the prediction method comprises the following steps:

[0007] Obtaining the ratio h of the groove width to the depth of the plastic substrate into the metal substrate, the completeness β of the microstructure groove, and the mean value of the surface height variance of the microstructure groove

[0008] According to the h, β, and a predetermined composite part bonding strength prediction model, the composite part bonding strength is calculated.

[0009] As a preferred technical solution, the composite part bonding strength prediction method, wherein the predetermined composite part bonding strength prediction model is obtained by fitting a multivariate function according to the h, β, and the tensile force of a plurality of samples.

[0010] As a preferred technical solution, the composite joint strength prediction method, wherein the step of obtaining the surface height variance mean of the microstructure groove includes:

[0011] selecting a plurality of regions on the microstructure surface of the metal matrix, and obtaining height information of corresponding sampling points in the plurality of regions;

[0012] performing noise reduction processing on the height information of the sampling points in the plurality of regions to obtain corresponding point cloud data of each region;

[0013] performing height variance operation on all sampling points in the same column for each region point cloud data, and performing variance statistics for each column to obtain the average value of the column variance of each region;

[0014] obtaining the average value of the column variance of each region to obtain the surface height variance mean of the microstructure groove

[0015] As a preferred technical solution, the composite joint strength prediction method, wherein the step of performing height variance operation on all sampling points in the same column for each region point cloud data, and performing variance statistics for each column to obtain the average value of the column variance of each region, further includes:

[0016] selecting a height screening threshold value according to the analysis of the combination effect of the plastic and the microstructure on the surface of the metal matrix; the height screening threshold value is 30 microns;

[0017] inputting the PLY file of the point cloud data into a screening code based on the height threshold value to simplify the point cloud data.

[0018] As a preferred technical solution, the composite joint strength prediction method, wherein the step of obtaining the completeness β of the microstructure groove includes:

[0019] obtaining a topographic image of the microstructure on the surface of the metal matrix; according to the distribution of the microstructure groove, the topographic image is divided into a first region, a second region and a third region; performing gray value reading on the topographic image to obtain a gray matrix of the corresponding region;

[0020] setting a gray threshold value, and screening out groove distributions in different regions based on the gray threshold value; performing feature statistics on the groove distributions, and according to the column, the proportion of the sampling points lower than the gray threshold value in the total number of sampling points in the corresponding gray matrix is calculated to obtain statistical curve graphs of the first region, the second region and the third region;

[0021] Integrate each peak area in the statistical curve of the first region, the second region and the third region respectively to obtain statistical average values, denoted as β1, β2 and β3 respectively;

[0022] Calculate the areas of the first region, the second region and the third region, denoted as S1, S2 and S3 respectively.

[0023] According to the formula, the integrity β of the microstructure groove is obtained; the formula is as follows:

[0024]

[0025] As a preferred technical solution, the composite joint strength prediction method, wherein the gray threshold is 60.

[0026] The ratio h of the groove width to the depth of the plastic matrix into the metal matrix is obtained as follows:

[0027] By obtaining the topographic image of the microstructure of the metal matrix surface, combining the magnification of the image and the corresponding graphic scale, the width data A of the microstructure groove is obtained, and the depth B of the plastic into the metal matrix is obtained by metallographic microscope or CT scanning.

[0028] The data is substituted into the formula: h = A / B to obtain the corresponding ratio h of the groove width to the depth of the plastic matrix into the metal matrix.

[0029] As a preferred technical solution, the composite joint strength prediction method, wherein the composite joint strength prediction model is The correlation coefficient is 0.98.

[0030] Secondly, a composite joint strength prediction device comprises:

[0031] The obtaining module is configured to obtain the ratio h of the groove width to the depth of the plastic matrix into the metal matrix, the integrity β of the microstructure groove and the mean square deviation of the surface height of the microstructure groove.

[0032] The calculating module is configured to obtain the composite joint strength according to the h, β, and the pre-determined composite joint strength prediction model.

[0033] As a preferred technical solution, the composite joint strength prediction device, wherein the pre-determined composite joint strength prediction model is obtained by multivariate regression fitting according to the h, β, and the tensile force of a plurality of samples.

[0034] In a third aspect, a computer readable storage medium, wherein the computer readable storage medium comprises a stored executable program, wherein when the executable program is run, the device in which the storage medium is located is controlled to perform the prediction method of any one of the above.

[0035] Beneficial effects: compared with the prior art, the prediction method of the composite part bonding strength provided by the present application can be used for quality checking of the composite part in the mass production process, and the prediction method has high precision and simple detection process, and avoids the problem that the confidence level of replacing the bonding strength of the whole batch of products with the destructive measurement data of random sampling is damaged. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 is the model design idea flowchart in the present application;

[0037] Figure 2 is the path schematic diagram of scanning on the surface of the metal base by using the nanosecond laser in the present application;

[0038] Figure 3 is the microstructure groove height point cloud diagram in the present application;

[0039] Figure 4 is the column variance statistical broken line diagram in the present application;

[0040] Figure 5 is the distribution schematic diagram of the laser processing area in the present application, wherein "1" represents the first area, "2" represents the second area, and "3" represents the third area;

[0041] Figure 6 is the scanning electron microscope (SEM) morphology diagram corresponding to different areas in Figure 5

[0042] Figure 7 is the groove distribution schematic diagram in different areas;

[0043] Figure 8 is the groove proportion statistical curve diagram in different areas;

[0044] Figure 9 is the groove distribution area statistical diagram in different areas;

[0045] Figure 10 is the structure schematic diagram of the prediction device provided by the present application. DETAILED DESCRIPTION

[0046] ​The application provides a composite bonding strength prediction method, a composite bonding strength prediction device and a computer readable storage medium. In order to make the purpose, technical scheme and advantages of the application more clear and explicit, the application is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the application and not to limit the application. In the following examples, the experimental methods without specific conditions are selected according to the conventional methods and conditions or the product instructions. In addition, it should be understood that after reading the content of the application, those skilled in the art can make various modifications or changes to the application, and these equivalent forms also fall within the scope of the appended claims.

[0047] The application provides a composite bonding strength prediction method, a composite bonding strength prediction device and a computer readable storage medium. In order to make the purpose, technical scheme and advantages of the application more clear and explicit, the application is further described in detail below. It should be understood that the specific embodiments described herein are only used to explain the application and not to limit the application. In the following examples, the experimental methods without specific conditions are selected according to the conventional methods and conditions or the product instructions. In addition, it should be understood that after reading the content of the application, those skilled in the art can make various modifications or changes to the application, and these equivalent forms also fall within the scope of the appended claims.

[0048] S10, obtaining the ratio h of the groove width and the depth of the plastic base into the metal base, the integrity β of the microstructure groove and the mean value of the surface height variance of the microstructure groove

[0049] Combination Figure 1 In the embodiment, the plastic and the metal material are combined. In order to better combine, the microstructure with a certain structure is prepared on the surface of the metal base, for example, the micron-level groove is prepared on the metal surface by using a solid nanosecond laser, and the microstructure groove is formed on the metal surface. When the plastic is combined with the metal, the plastic enters the microstructure groove, so that the bonding strength of the composite can be improved. It is found through research that the narrower the groove is, the more difficult the plastic flows into the inside of the groove. Therefore, the width of the groove is an important factor affecting the plastic entering the inside of the groove. In order to comprehensively evaluate the result of the plastic entering the microstructure groove of the metal base, while ensuring the stability and effectiveness of the evaluation index, the index is de-dimensioned, that is, the ratio of the groove width and the depth of the plastic base into the microstructure groove of the metal base is used as one of the evaluation parameters of the bonding strength prediction model.

[0050] In the embodiment, the integrity β of the microstructure groove is an index set to quantify the influence of the integrity and continuity of the laser-prepared micro groove on the bonding performance of the composite.

[0051] The integrity β of the microstructure groove is used as a main factor for predicting the bonding strength of the composite, which can cope with the interference of factors such as equipment working environment and service life on the stability of the processing technology during the preparation of the microstructure of the metal base.

[0052] S20, obtaining the bonding strength of the composite according to the h, β and and a predetermined composite joint strength prediction model, to obtain the composite joint strength.

[0053] The following implementation case will use 5052 aluminum alloy as the metal matrix and 30% glass fiber reinforced polyphenylene sulfide (PPS) as the plastic matrix; a solid nanosecond laser is used for metal surface processing; and an injection molding machine is used for in-situ metal-plastic connection.

[0054] 5052 aluminum alloy is used as the metal matrix, and a solid nanosecond laser is used to prepare micron-level grooves on the metal surface according to the scanning path shown in Figure 2 The processing area of the aluminum alloy surface is 24.8x20mm 2 .

[0055] The microstructure morphology information of the aluminum alloy processed surface is collected, and corresponding calculation and processing means are adopted for quantitative conversion of the characteristic indexes. The specific implementation form is as follows:

[0056] I. Mean of surface height variance of microstructure groove

[0057] The laser confocal microscope (LCM) is used to observe the aluminum alloy processed surface, 5 areas in the processed area are randomly selected, the height information of the corresponding sampling points in the area is obtained, and the sampling data points are output after noise reduction processing by MATLAB. As shown in Figure 3 According to the analysis of the microstructure joint effect of plastic and metal matrix surface, the height screening threshold value (which is related to the maximum depth of plastic entering the microstructure groove) is selected, the PLY file of the point cloud is input into the screening code based on the height threshold value to further simplify the point cloud, and the height variance of all sampling points in the same column is calculated, and the variance of each column is counted to obtain the column variance statistical line chart as shown in Figure 4 The average value of the column variance of the area is obtained. In the same way, the average value of the column variance of the corresponding five areas is obtained, and the mean value of the data of the five areas is processed to finally obtain This parameter can reflect the uniformity of the actual contact surface of plastic and metal microstructure to some extent.

[0058] II. Microstructure groove integrity β

[0059] Due to the interference of factors such as the power output stability of the laser itself, the material uniformity of the material itself, and the flatness of the processed surface, and due to the differences in phase resistance of different materials to different power energy and the flowability of the molten metal, the grooves will be connected to each other during laser processing. According to the observation of the processing area by SEM as shown in Figure 5 , the following can be obtainedFigure 6 The topography image shown.

[0060] In order to quantify the degree of influence, the microstructure groove integrity β is set, and the specific value of the index will be solved according to the gray distribution of the image file, and the solving steps are as follows:

[0061] ① Use MATLAB to extract the gray value of the above SEM image, and output the corresponding gray matrix.

[0062] ② Set the gray threshold, the purpose is to filter out the groove part through the gray threshold (the depth of the microstructure is negatively correlated with the gray value of the image), so as to Figure 6 Take the topography image as an example, it can be filtered and processed through the corresponding gray threshold to obtain the groove distribution diagram shown in Figure 7

[0063] ③ Use MATLAB again to perform feature statistics on the groove distribution diagram shown in Figure 7 According to the proportion of sampling points below the gray threshold in the whole column sampling points in the corresponding gray matrix, the corresponding statistical curve diagram is finally output as shown in Figure 8

[0064] ④ Integrate the peak area of the above curve diagram by image J, and finally calculate the statistical average value. Figure 8 The three curve diagrams in the middle can obtain β1, β2, β3 respectively.

[0065] ⑤ Use image J to perform area statistics on the processing area distribution diagram shown in Figure 5 The statistical results are shown in Figure 9 , and the areas S1, S2, S3 of the feature regions "1, 2, 3" are obtained respectively.

[0066] ⑥ Figure 9 The area in a is the total area of the processing area S 总 、 Figure 9 The white area area in b is the area S1 of "1" topography, Figure 9 The red area area in c is the area S2 of "2" topography. Combined with the proportion factor in step ④ and the following formula, the microstructure groove integrity β of the metal sample is obtained.

[0067] S3=S 总 -S1-S2 (1)

[0068]

[0069] ​​(3) The aluminum alloy metal piece is placed in a Germany BOY precision injection molding machine with a locking force of 250 kN, and 30% glass fiber reinforced PPS is used as the injection material, and the injection conditions are as follows: melt temperature 320 DEG C, injection pressure 65 MPa, holding pressure 55 MPa, upper / lower mold temperature 95 / 80 DEG C, mold opening time 5 s, holding time 3 s.

[0070] (4) The groove width of the SEM observation image is recorded and counted, the result is A, the cross section of the metal-plastic connecting piece is observed by a metallographic microscope, the depth of the plastic into the microstructure groove is measured, and recorded as B, and the ratio result h is obtained by A / B.

[0071] (5) The above steps (1)-(4) are repeated, and the corresponding three index data of a certain number of samples are obtained and recorded in Table 1, and the origin is used for multiple function regression fitting of the prediction model.

[0072] Table 1: Sample data record

[0073]

[0074] The multiple function regression fitting result is The model correlation coefficient is 0.98.

[0075] In the present application, three quantitative indicators are set to measure the bonding characteristics of the composite, and the indicator parameters are converted into corresponding data information by taking corresponding calculation means, the data information is introduced into the prediction model to obtain the corresponding bonding strength (herein the bonding tension is used as the strength representation form), and then the bonding strength of the prediction model is judged according to the industry standard, if it is higher than the industry standard, it is judged as a qualified product, otherwise it is not qualified. The prediction method can be used for quality checking of the composite in batch production process. The prediction method greatly reduces the complexity and calculation cost of prediction because it only contains three parameters.

[0076] Based on the above composite bonding strength prediction method, the present application provides a computer readable storage medium, the computer readable storage medium stores one or more programs, the one or more programs can be executed by one or more processors to implement the steps in the composite bonding strength prediction method as any one of the above.

[0077] Based on the above composite bonding strength prediction method, the present application further provides a composite bonding strength prediction device, such as Figure 10As shown, it includes at least one processor (processor) 20, a display screen 21, and a memory 22 (memory), and may also include a communication interface (Communications Interface) 23 and a bus 24. Among them, the processor 20, the display screen 21, the memory 22 and the communication interface 23 can communicate with each other through the bus 24. The display screen 21 is configured to display the preset user guidance interface in the initial setting mode, the communication interface 23 can transmit information, and the processor 20 can call the logic instructions in the memory 22 to execute the method in the above embodiment. In addition, when the logic instructions in the above memory 22 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The memory 22, as a computer-readable storage medium, can be configured to store software programs, computer executable programs, such as program instructions or modules corresponding to the method in the embodiment of the present disclosure. The processor 20 executes functional applications and data processing by running the software programs, instructions or modules stored in the memory 22, that is, implementing the method in the above embodiment. The memory 22 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 22 may include a high-speed random access memory and may also include a non-volatile memory. For example, a variety of media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, may also be a transient storage medium. In addition, the specific process of the above-mentioned storage medium and the multiple instruction processors in the device loading and executing them has been described in detail in the above-mentioned method and will not be repeated here.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for predicting the bonding strength of a composite part, wherein the composite part comprises a metal substrate and a plastic substrate bonded to the metal substrate via microstructured grooves on the surface of the metal substrate, characterized in that: The prediction method comprises the following steps: Obtain the ratio h of the width of the microstructure groove to the depth of the plastic matrix entering the metal matrix, the integrity β of the microstructure groove, and the mean value of the surface height variance of the microstructure groove ; According to the h, β, and a predetermined composite component bonding strength prediction model to calculate the composite component bonding strength.

2. The method for predicting the bonding strength of composite parts according to claim 1, characterized in that: The predetermined composite component bonding strength prediction model is composed of the h, β, and tension, which are obtained through multivariate function regression fitting.

3. The method for predicting the bonding strength of a composite part according to claim 1, wherein: Obtain the mean value of the surface height variance of the microstructure groove The steps include: Selecting a plurality of regions on the surface of the metal substrate having the microstructure, and obtaining height information of corresponding sampling points within the plurality of regions; Perform noise reduction on the height information of the sampling points in several areas to obtain the corresponding point cloud data of each area; Performing height variance calculations on all sampling points in the same column of the point cloud data of each region, and performing variance statistics on each column to obtain the average value of the variance of each region column; The average value of the obtained variance of each region column is taken to obtain the surface height variance mean of the microstructure groove .

4. The method for predicting the bonding strength of a composite part according to claim 3, characterized in that: In the step, the height variance calculation of all sampling points in the same column of the point cloud data of each region is performed respectively, and the variance statistics of each column are performed to obtain the average value of the variance of each region column, and the above also includes: A height screening threshold is selected based on an analysis of the bonding effect between the plastic and the surface microstructure of the metal substrate; The PLY file of the point cloud data is input into a screening code based on a height threshold to simplify the point cloud data.

5. The method for predicting the bonding strength of composite parts according to claim 1, characterized in that: The step of obtaining the integrity β of the microstructure groove comprises: Acquire a topographic image of the microstructure on the surface of the metal substrate; divide the topographic image into a first region, a second region, and a third region according to the distribution of the microstructure grooves; read the grayscale value of the topographic image to obtain a grayscale matrix of the corresponding region; Setting a grayscale threshold and screening groove distributions in different regions based on the grayscale threshold; performing feature statistics on the groove distribution, and counting, by column, the proportion of sampling points in the corresponding grayscale matrix that are below the grayscale threshold to the total number of sampling points in the entire column, to obtain statistical curve graphs for the first region, the second region, and the third region; Integrating the peak areas in the statistical curves of the first region, the second region, and the third region, respectively, to obtain statistical average values, which are recorded as β1, β2, and β3; The areas of the first region, the second region, and the third region are calculated and recorded as S1, S2, and S3 respectively; S 总 is the total area of ​​the processing area; The integrity β of the microstructure groove is calculated according to the formula; the formula is as follows: 。 6. The method for predicting the bonding strength of composite parts according to claim 5, characterized in that: The grayscale threshold is 60.

7. The method for predicting the bonding strength of composite parts according to claim 1, characterized in that: The step of obtaining the ratio h of the width of the microstructure groove to the depth of the plastic matrix entering the metal matrix includes: By acquiring a topographic image of the microstructure on the surface of the metal substrate, and combining the image magnification and the corresponding graphic ratio, the width data A of the microstructure groove is obtained, and the depth B of the plastic penetrating into the metal substrate is obtained by metallographic microscopy or CT scanning; Substituting the data into the formula: h=A / B, the ratio h of the corresponding groove width to the depth of the plastic matrix entering the metal matrix is ​​obtained.

8. The method for predicting the bonding strength of composite parts according to claim 2, characterized in that: The composite component bonding strength prediction model is: , the model correlation coefficient is 0.

98.

9. A device for predicting the bonding strength of composite parts, characterized in that include: The acquisition module is used to obtain the ratio h of the width of the microstructure groove to the depth of the plastic matrix entering the metal matrix, the integrity β of the microstructure groove, and the mean value of the surface height variance of the microstructure groove. ; A calculation module is used to calculate the and a predetermined composite component bonding strength prediction model to obtain the composite component bonding strength; The predetermined composite component bonding strength prediction model is composed of the h, β, and tension, obtained through multiple regression fitting; The composite part includes a metal matrix and a plastic matrix combined with the metal matrix through microstructure grooves on the surface of the metal matrix.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein when the executable program is run, the device where the storage medium is located is controlled to execute the prediction method according to any one of claims 1 to 8.

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