Method for testing weather resistance of carbon nano composite anticorrosion and anti-impact material for vehicle chassis

Abstract

The present application relates to the technical field of vehicle chassis testing, and particularly relates to a weather resistance detection method for a carbon nano composite anticorrosion and anti-impact material for a vehicle chassis. The present application divides a chassis infrared image into sub-regions, selects a heat concentration region according to the temperature prominence and shape features of the sub-regions, and takes the remaining regions as normal aging regions; divides the normal aging regions into local regions, obtains the oxidation value of the local regions according to the color change and oxygen-containing group change of the actual region on the surface of the corresponding sample before and after the aging test; determines the actual elastic modulus of the local regions based on the oxidation value, determines the mechanical performance decline degree according to the difference in the actual elastic modulus of different local regions in the chassis infrared image, and performs weather resistance detection on the vehicle chassis sample. The present application eliminates the heat concentration region, analyzes the mechanical performance decline degree through the oxidation degree of the normal aging region, and further evaluates the weather resistance, thereby improving the accuracy of the weather resistance detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle chassis testing, in particular to a weather resistance detection method of a carbon nanometer composite anticorrosion and anti-impact material for vehicle chassis. BACKGROUND

[0002] Carbon nanometer composite material combines the strength and anticorrosion properties of carbon nanometer tube or carbon nanometer fiber, and has excellent mechanical properties and anticorrosion and anti-impact properties, and has become an ideal candidate material for key components such as vehicle chassis. However, the long-term service performance of carbon nanometer composite material is affected by the coupling of multiple environmental factors such as sunlight, oxidation, and humid heat, and its stability and anti-aging ability need to be evaluated through weather resistance test.

[0003] The existing method usually uses ultraviolet accelerated aging test to simulate solar radiation, and combines high temperature and high humidity cycle to shorten the test period, and then evaluate the weather resistance. However, carbon nanometer tube or fiber has extremely high light absorption capacity and thermal conductivity, if the carbon nanometer tube or fiber in the composite material is not uniformly distributed, local area will produce heat concentration due to light-heat conversion under ultraviolet irradiation, these heat concentration areas accelerate degradation, while other areas lag behind in aging degree, making the degradation degree of different areas of the material differ greatly, and then affecting the accuracy of the weather resistance evaluation result of carbon nanometer composite material. SUMMARY

[0004] In order to solve the technical problem of weather resistance evaluation deviation caused by uneven light-heat characteristics of material in ultraviolet accelerated aging test, the purpose of the present application is to provide a weather resistance detection method of carbon nanometer composite anticorrosion and anti-impact material for vehicle chassis, and the technical scheme adopted is as follows:

[0005] The present application provides a weather resistance detection method of carbon nanometer composite anticorrosion and anti-impact material for vehicle chassis, the method comprises:

[0006] Obtaining chassis infrared image of vehicle chassis sample after aging test;

[0007] Dividing the chassis infrared image into different sub-regions, selecting the heat concentration area according to the temperature significance and shape characteristics of the sub-region, and taking the remaining area except the heat concentration area in the chassis infrared image as the normal aging area;

[0008] Dividing the normal aging area into local areas, and obtaining the oxidation value of each local area according to the color change and oxygen-containing group change of the actual area on the surface of the corresponding sample before and after the aging test;

[0009] Determining the actual elastic modulus of each local area based on the oxidation value, determining the mechanical property decline degree according to the difference of the actual elastic modulus of different local areas in the chassis infrared image, and performing weather resistance detection on the vehicle chassis sample.

[0010] Further, the selecting the heat concentration region comprises:

[0011] obtaining a minimum circumscribed rectangle of the sub-region, and taking a ratio of a length and a width of the rectangle as a shape feature;

[0012] taking a mean value of pixel values of all pixel points in the sub-region as a region temperature, and taking a ratio of the region temperature of each sub-region in the chassis infrared image and a mean value of region temperatures of all sub-regions as a temperature prominence degree of each sub-region;

[0013] obtaining a heat concentration degree of the corresponding sub-region according to the shape feature and the temperature prominence degree of each sub-region in the chassis infrared image; and selecting a sub-region greater than a preset heat concentration threshold as the heat concentration region.

[0014] Further, the obtaining the oxidation value of each local region comprises:

[0015] obtaining chassis grayscale images of the vehicle chassis sample before and after aging test respectively; mapping each local region in the chassis infrared image to the chassis grayscale images before and after aging test respectively to obtain a first mapping region and a second mapping region of each local region in sequence; and taking a difference value of region gray scales of the first mapping region and the second mapping region as a color difference value of each local region.

[0016] obtaining a peak size according to a change of an oxygen-containing group of an actual region on the sample surface of each local region before and after aging test.

[0017] obtaining an oxidation value of each local region in the chassis infrared image according to the color difference value and the peak size.

[0018] Further, the obtaining the peak size comprises:

[0019] obtaining 1s electron orbits of oxygen atoms of an actual region on the sample surface of each local region before and after aging test respectively, and taking peaks in a preset binding energy range of the two orbits as a first oxidation peak and a second oxidation peak respectively.

[0020] obtaining a peak size of each local region according to a difference value of areas of the second oxidation peak and the first oxidation peak and a width of the second oxidation peak.

[0021] Further, the determining the actual elastic modulus of each local region based on the oxidation value comprises:

[0022] the aging test comprises a plurality of tests; a plurality of vehicle chassis specimens of the same material as the vehicle chassis sample are subjected to the same stress after different tests to obtain elastic moduli and oxidation values of each vehicle chassis specimen after the test.

[0023] The elastic modulus of all vehicle chassis specimens at the oxidation value is differentially fitted to obtain the elastic modulus at all oxidation values, and the elastic modulus corresponding to the oxidation value of each local area in the vehicle chassis sample is taken as the actual elastic modulus of the corresponding local area.

[0024] Further, the determination of the mechanical property decline degree comprises:

[0025] The standard elastic modulus of the vehicle chassis sample is respectively subtracted from the actual elastic modulus of all local areas in the chassis infrared image, all the differences are averaged to obtain the elastic overall decline degree.

[0026] The difference between the actual elastic modulus of each two local areas in the chassis infrared image is calculated, and the average of all the differences is obtained to obtain the elastic difference degree.

[0027] According to the elastic overall decline degree and the elastic difference degree, the mechanical property decline degree is obtained.

[0028] Further, the chassis infrared image is divided into different sub-regions, comprising:

[0029] Based on the pixel value of the pixel point in the chassis infrared image, all the pixel points are clustered to obtain different clustering clusters, and the connected domain formed by the pixel points in the same clustering cluster is taken as a sub-region.

[0030] Further, the area gray value is the average of the gray values of all pixel points in the mapping area.

[0031] Further, the color difference value and the peak size are positively correlated with the oxidation value.

[0032] Further, the preset heat concentration threshold is 0.88.

[0033] The present application has the following beneficial effects:

[0034] In the embodiment of the present application, the area with heat concentration is obviously high heat and the shape of the area is long strip, the temperature significant degree of the sub-area and the shape feature are combined to select the heat concentration area, the area reserved after the heat concentration area is removed is the normal aging area, and the state represents the real weather resistance of the carbon nanometer coating, thereby solving the problem that the uneven distribution of carbon nanotubes leads to the deviation of the weather resistance evaluation of the carbon nanometer composite material in the aging test. However, the normal aging area at the random position on the surface of the vehicle chassis sample is not convenient for the mechanical property test, and the present scheme determines the mechanical property by analyzing the oxidation degree of the normal aging area, specifically: the opaque or translucent product generated by the oxidation reaction makes the sample color dark, and the functional group generated by the oxidation reaction represents the oxidation degree of the vehicle chassis, the color change of the actual area on the sample surface before and after the aging test is combined with the change of the oxygen-containing group to analyze the oxidation degree after the aging test, and the oxidation value is obtained; and the mechanical stability of the vehicle chassis sample is evaluated by the change relationship between the oxidation degree and the elastic modulus, the actual elastic modulus of the local area is determined based on the oxidation value; the weather resistance of the material can be measured by the mechanical property, the mechanical property degradation degree is determined according to the difference of the actual elastic modulus of different local areas in the chassis infrared image, the weather resistance of the sample is evaluated by using the mechanical property degradation degree, and the accuracy of the weather resistance detection is improved. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art and the advantages thereof, a brief introduction will be given to the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0036] Figure 1 The step flow chart of a weather resistance detection method of a carbon nanometer composite anticorrosion and anti-impact material for vehicle chassis provided by an embodiment of the present application;

[0037] Figure 2 The flow chart of an oxidation value acquisition method provided by an embodiment of the present application;

[0038] Figure 3 The system structure diagram of a weather resistance detection system of a carbon nanometer composite anticorrosion and anti-impact material for vehicle chassis provided by an embodiment of the present application;

[0039] Figure 4 The computer device schematic diagram of a weather resistance detection equipment of a carbon nanometer composite anticorrosion and anti-impact material for vehicle chassis provided by an embodiment of the present application. DETAILED DESCRIPTION

[0040] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined inventive objectives, the following describes in detail the specific implementation, structure, features and effects of a weather resistance detection method for a carbon nano composite anticorrosive and anti-impact material for a vehicle chassis according to the present application, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0041] Unless otherwise defined, 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.

[0042] The following describes in detail a specific scheme of a weather resistance detection method for a carbon nano composite anticorrosive and anti-impact material for a vehicle chassis according to the present application.

[0043] Embodiment 1:

[0044] The present application proposes a weather resistance detection method for a carbon nano composite anticorrosive and anti-impact material for a vehicle chassis, please refer to Figure 1 which shows a step flowchart of a weather resistance detection method for a carbon nano composite anticorrosive and anti-impact material for a vehicle chassis according to one embodiment of the present application. The method comprises:

[0045] Step S1: Obtain the chassis infrared image of the vehicle chassis sample after aging test.

[0046] An ultraviolet A-340 light source is used to emit ultraviolet rays of 315 to 400 nanometers to simulate the ultraviolet wave band of the sun to irradiate the vehicle chassis sample made of carbon nano composite material, and then the sample is placed in a high temperature and high humidity test chamber with temperature and relative humidity set to 85 degrees Celsius and 95% respectively. The vehicle chassis sample is irradiated by the light source for 8 hours in a single test cycle, and stored in the high temperature and high humidity test chamber for 4 hours. The sample is tested multiple times, and the infrared image of the sample is collected by an infrared thermal imager after all tests are completed to obtain the chassis infrared image after aging test.

[0047] Before and after the aging test starts, an RGB image of the vehicle chassis sample is collected by a camera, and the RGB image is processed by gray scale processing to obtain the chassis gray scale images before and after the aging test, respectively, so as to analyze the color change of the vehicle chassis sample before and after the aging test.

[0048] It should be noted that the weighted average gray scale processing algorithm is selected for gray scale processing in the embodiment of the present application, which is a well-known technical means to those skilled in the art.

[0049] In one implementation form of the embodiment of the application, the test number of the vehicle chassis sample for the aging test is set to 80.

[0050] It should be noted that the infrared thermal imager and the camera have the same resolution and are located at the same position when collecting images, so that the two imaging cover the same physical area, ensuring that any physical position in the sample has the same pixel coordinates in the chassis infrared image and the chassis grayscale image, and the pixel points in the chassis grayscale images before and after the aging test are one-to-one corresponding.

[0051] Step S2: dividing the chassis infrared image into different sub-regions, selecting a heat concentration region according to the temperature significance and shape features of the sub-regions, and taking the remaining region of the chassis infrared image except the heat concentration region as a normal aging region.

[0052] In the ultraviolet aging test, the vehicle chassis sample needs to be irradiated at a specific temperature and humidity. However, carbon nanotubes or fibers have extremely high light absorption capacity and thermal conductivity, and when the carbon nanotubes are unevenly distributed, heat concentration may occur. Therefore, the region where heat concentration may occur needs to be removed, and the normal aging region is retained. The vehicle chassis sample shows that the heat distribution of different parts is significantly different, and the sub-region division is helpful to more accurately analyze the temperature features. The region where heat concentration occurs will have obviously high heat, and the carbon nanomaterials that cause heat concentration generally present a long strip shape. The heat concentration region can be selected by combining the temperature significance and shape features of the sub-regions, and the region retained after removing the heat concentration region is the normal aging region, which represents the true weather resistance of the carbon nanocoating.

[0053] Step S3: dividing the normal aging region into local regions, and obtaining the oxidation value of each local region according to the color change and oxygen-containing group change of the actual region corresponding to each local region on the sample surface before and after the aging test.

[0054] The weather resistance of the vehicle chassis mainly reflects the mechanical stability, however, the surface coating material of the chassis gradually oxidizes to affect the mechanical properties, the greater the oxidation degree represents the decline of the mechanical properties of the vehicle chassis, and the oxidation degree of the normal aging area needs to be analyzed. The oxidation degree of different positions on the surface of the vehicle chassis may be different, the state of the normal aging area represents the true weather resistance of the carbon nanometer coating, in order to improve the accuracy of the oxidation degree analysis of the vehicle chassis, the normal aging area needs to be divided into multiple local areas. The ultraviolet aging test simulates the oxidation of the vehicle chassis in the real environment, the oxidation reaction often generates opaque or translucent products, which leads to the decrease of the light transmittance of the sample and the darkening of the color of the sample, at the same time, the oxidation reaction will produce oxide, hydroxyl and other functional groups, and the generation of these groups can also represent the oxidation degree of the vehicle chassis. Therefore, combined with the color change of the actual area on the surface of the sample in the local area before and after the aging test and the change of the oxygen-containing group, the oxidation degree of the local area after the aging test can be analyzed, and the oxidation value is obtained.

[0055] In one implementation manner of the embodiment of the present application, the normal aging area is uniformly divided into different local areas, and the size of each local area is set to 10 pixels x 10 pixels; other embodiments can also randomly divide the normal aging area into multiple local areas.

[0056] Step S4: determining the actual elastic modulus of each local area based on the oxidation value, determining the degree of decline of the mechanical properties according to the difference of the actual elastic modulus of different local areas in the chassis infrared image, and detecting the weather resistance of the vehicle chassis sample.

[0057] Generally, the weather resistance of the material mainly reflects the anti-aging performance, the stronger the anti-aging performance of the sample, the stronger the anti-corrosion and anti-impact ability, which indicates that the mechanical properties of the sample are excellent, so the weather resistance degree of the vehicle chassis sample can be reflected by evaluating the mechanical properties. However, the elastic modulus determined by the mechanical test of the local area with small area is easily affected by the mechanical properties of the adjacent area, which leads to large data error and low reliability of the weather resistance evaluation. The greater the oxidation degree of the vehicle chassis represents the more unstable mechanical properties, which leads to the decline of the anti-impact ability, and the elastic modulus of the chassis material represents the ability of the vehicle chassis to resist deformation under impact, which reflects the mechanical stability, so the mechanical stability of the vehicle chassis sample can be evaluated by the change relationship between the oxidation degree and the elastic modulus, and the actual elastic modulus of the local area is determined based on the oxidation value.

[0058] The actual elastic modulus represents the mechanical characteristics of the local area after the aging test. As the oxidation reaction proceeds, the elastic modulus of the chassis material gradually decreases. The difference in the actual elastic modulus of different local areas means that the chassis material is not uniform, and the local stress concentration is intensified when stressed, which is easy to cause cracks in the weak area, resulting in a decrease in the overall mechanical properties. Therefore, the difference in the elastic modulus of different local areas in the chassis infrared image reflects the mechanical properties of the vehicle chassis sample, and the degree of decrease in the mechanical properties is obtained, and the weather resistance is evaluated by the degree of decrease in the mechanical properties of the vehicle chassis sample, thereby improving the accuracy of the weather resistance detection.

[0059] Preferably, in some possible implementation manners of the embodiment of the present application, the selection method of the sub-area includes: clustering all the pixel points based on the pixel values of the pixel points in the chassis infrared image to obtain different clustering clusters, and a connected domain formed by the pixel points in the same clustering cluster is recorded as a sub-area.

[0060] It should be noted that, because the pixel values of the pixel points in the same clustering cluster, i.e., the temperatures, are similar but the positions can be far apart, it is necessary to merge adjacent pixel points. In this embodiment, the K-means clustering algorithm is selected to cluster the pixel points in the chassis infrared image, wherein the K value is determined by the elbow method, and the DBSCAN algorithm or the like can also be selected for clustering.

[0061] In other embodiments of the present application, the region growing algorithm, the adaptive threshold segmentation algorithm or the like can also be selected for sub-area division.

[0062] Preferably, in some possible implementation manners of the embodiment of the present application, the selection method of the heat concentration area includes: obtaining the minimum circumscribed rectangle of the sub-area, recording the ratio of the length and the width of the rectangle as the shape feature, recording the average of the pixel values of all the pixel points in the sub-area as the area temperature, recording the ratio of the area temperature of each sub-area in the chassis infrared image to the average of the area temperatures of all the sub-areas as the temperature prominence degree of each sub-area, obtaining the heat concentration degree of the corresponding sub-area according to the shape feature and the temperature prominence degree of each sub-area in the chassis infrared image, and selecting the sub-area greater than the preset heat concentration threshold as the heat concentration area.

[0063] It should be noted that, the greater the shape feature and the temperature prominence degree, the higher the temperature of the sub-area relative to the temperature of the entire image, which means that the sub-area is obviously high in heat and the shape is closer to a long strip, and the possibility of heat concentration of the sub-area is greater, and the heat concentration degree is greater. Therefore, the shape feature and the temperature prominence degree are positively correlated with the heat concentration degree. In the embodiment of the present application, the product of the shape feature and the temperature prominence degree of each sub-area is normalized to obtain the heat concentration degree.

[0064] The normalization processing in the embodiment of the present application can also use a normalization method such as a Sigmoid function and function conversion, which is not limited herein.

[0065] In one implementation of the embodiment of the present application, the preset heat concentration threshold is set to 0.88.

[0066] Preferably, in some possible implementations of the embodiment of the present application, the method for obtaining the oxidation value can refer to Figure 2 Fig. 3 shows a flowchart of a method for obtaining an oxidation value according to an embodiment of the present application, and the method comprises the following steps:

[0067] Step S310: Obtain chassis gray scale images of a vehicle chassis sample before and after aging test respectively; map each local region in the chassis infrared image to the chassis gray scale images before and after aging test respectively, and sequentially obtain a first mapping region and a second mapping region of each local region; and record a difference value between region gray scales of the first mapping region and the second mapping region as a color difference value of each local region.

[0068] It should be noted that the region gray scale is a mean value of gray scales of all pixel points in the mapping region, which presents the overall gray scale of the mapping region. The region gray scales of the first mapping region and the second mapping region are obtained according to the above method. The oxidation reaction usually generates opaque or translucent products, which leads to a decrease in light transmittance of the sample, so that the gray scale of the first mapping region after aging test is smaller than that of the second mapping region before aging test, and the greater the difference between the region gray scales of the first mapping region and the second mapping region, the higher the oxidation degree of the local region.

[0069] It should be noted that the local region is one-to-one corresponding to the pixel points in the first mapping region and the second mapping region, i.e., the pixel coordinates are the same.

[0070] Step S320: Obtain the peak scale according to changes in oxygen-containing groups of the actual region on the sample surface of each local region before and after aging test.

[0071] Preferably, in some possible implementations of the embodiment of the present application, the method for obtaining the peak scale comprises: obtaining 1s electron orbits of oxygen atoms of the actual region on the sample surface of each local region before and after aging test respectively, recording peaks in a preset binding energy range of the two orbits as a first oxidation peak and a second oxidation peak respectively; and obtaining the peak scale of each local region according to a difference value between areas of the second oxidation peak and the first oxidation peak and a width of the second oxidation peak.

[0072] It should be noted that before the vehicle chassis sample is subjected to the ultraviolet aging test, the area of the first oxidation peak may mainly come from a small amount of oxidation or adsorbed oxygen of the vehicle chassis sample itself, and after the aging test, the sample surface undergoes an oxidation reaction to generate more oxygen-containing groups, so that the area of the second oxidation peak is greater than that of the first oxidation peak. Therefore, the greater the difference between the area of the second oxidation peak and the area of the first oxidation peak, the larger the scale of the peak, which means that the more oxygen-containing groups are generated by oxidation of the sample, and the more serious the oxidation degree. The sample may generate various oxygen-containing groups during the oxidation reaction in the ultraviolet aging test, and the binding energies of these oxygen-containing groups are slightly different, resulting in peak broadening. Therefore, the greater the width of the second oxidation peak, the larger the scale of the peak, and the higher the oxidation degree. In summary, the difference between the area of the second oxidation peak and the area of the first oxidation peak and the width of the second oxidation peak are positively correlated with the scale of the peak. In the embodiment of the present application, the product of the difference between the area of the second oxidation peak and the area of the first oxidation peak and the width of the second oxidation peak of each local region is taken as the scale of each local region. The local region with a larger scale has a higher oxidation degree during the ultraviolet aging test.

[0073] In this embodiment, the 1s electron orbit of oxygen atoms is obtained by using X-ray photoelectron spectroscopy technology.

[0074] Since the binding energy range of oxygen elements is generally 530 to 535 electron volts, the preset binding energy range in this embodiment is 530 to 535 electron volts.

[0075] Step S330: According to the color difference value and the peak scale, the oxidation value of each local region in the chassis infrared image is obtained.

[0076] It should be noted that the color difference value and the peak scale are used to analyze the oxidation degree of the local region by analyzing the color change and the change of oxygen-containing groups of the local region before and after the aging test. When the color difference and the peak scale of the local region are both larger, the oxidation degree of the local region is higher, and the oxidation value is larger. Therefore, the color difference value and the peak scale are positively correlated with the oxidation value. In the embodiment of the present application, the product of the color difference value and the peak scale of each local region is taken as the oxidation value.

[0077] Preferably, in some possible implementation manners of the embodiment of the present application, the method for obtaining the actual elastic modulus comprises: the aging test comprises several tests; several vehicle chassis specimens made of the same material as the vehicle chassis sample are subjected to the same stress after different tests, and the elastic modulus and the oxidation value of each vehicle chassis specimen after the test are obtained; the elastic modulus of all the vehicle chassis specimens at the oxidation value is subjected to difference fitting to obtain the elastic modulus at all the oxidation values, and the elastic modulus corresponding to the oxidation value of each local region in the vehicle chassis sample is taken as the actual elastic modulus of the corresponding local region.

[0078] It should be noted that the number of tests of different vehicle chassis samples is different to analyze different aging degrees, for example, the first vehicle chassis sample is tested for 2 times, the second vehicle chassis sample is tested for 5 times, and the like. The method for obtaining the actual elastic modulus includes: constructing a two-dimensional space with the oxidation value as the horizontal axis and the elastic modulus as the vertical axis, mapping the oxidation value and the elastic modulus of all tests into the two-dimensional space to obtain corresponding scattered points, performing curve fitting on all scattered points in the two-dimensional space to obtain a fitting curve, and marking the vertical coordinate of the corresponding point on the fitting curve as the actual elastic modulus of the corresponding local area for each local area of the vehicle chassis sample. The curve fitting method is the least square method, and the calculation method of the elastic modulus is a known technology, which will not be described here.

[0079] It should be noted that the oxidation value of the vehicle chassis sample and the oxidation value of the single local area are obtained by the same method.

[0080] Preferably, in some possible implementation manners of the embodiment of the present application, the method for obtaining the mechanical property decline degree includes: calculating the difference between the standard elastic modulus of the vehicle chassis sample and the actual elastic modulus of all local areas in the chassis infrared image, averaging all the differences to obtain an elastic overall decline degree; calculating the difference between the actual elastic modulus of each two local areas in the chassis infrared image, averaging all the differences to obtain an elastic difference degree; and obtaining the mechanical property decline degree according to the elastic overall decline degree and the elastic difference degree.

[0081] It should be noted that the greater the difference between the standard elastic modulus and the actual elastic modulus of the local area, and the greater the difference between the actual elastic modulus of the two local areas in the chassis infrared image, the greater the decline degree of the mechanical stability of the local area after the ultraviolet aging test, and the worse the weather resistance of the chassis. Therefore, the elastic overall decline degree and the elastic difference degree are positively correlated with the mechanical property decline degree. In the embodiment of the present application, the product of the elastic overall decline degree and the elastic difference degree is normalized to obtain the mechanical property decline degree. The greater the mechanical property decline degree of the vehicle chassis sample, the worse the weather resistance.

[0082] It should be noted that the standard elastic modulus is the elastic modulus of the vehicle chassis sample before the aging test, and the stress applied in the elastic modulus test is equal to the stress applied to the vehicle chassis sample.

[0083] In the embodiment of the present application, the normalization processing is performed by using the Norm function, and normalization methods such as maximum and minimum normalization and function transformation can also be selected, which are not limited here.

[0084] In the embodiment of the present application, when the degree of decline of the mechanical property of the vehicle chassis sample is greater than the preset property threshold, the weather resistance of the vehicle chassis sample is qualified; when the degree of decline of the mechanical property of the vehicle chassis sample is less than or equal to the preset property threshold, the weather resistance of the vehicle chassis sample is unqualified, and the interface bonding force between the carbon nanomaterial and the matrix can be enhanced by means such as chemical modification, physical adsorption, copolymer or crosslinking of high molecular chains. For example, the surface of the carbon nanomaterial is provided with strong affinity by surface modification, the chemical bonding between the carbon nanomaterial and the matrix is enhanced, and thus the interface strength is improved. Or a multilayer film structure or a gradient composite structure is adopted to enhance the interface stability and avoid short-term oxidation reaction.

[0085] In one implementation manner of the embodiment of the present application, the preset property threshold is set to 0.7.

[0086] Up to now, the present application is completed.

[0087] Embodiment 2:

[0088] The present application provides a weather resistance detection system for a carbon nanocomposite anticorrosion and anti-impact material for a vehicle chassis, please refer to Figure 3 , which shows a system structure diagram of a weather resistance detection system for a carbon nanocomposite anticorrosion and anti-impact material for a vehicle chassis provided by one embodiment of the present application, and the system comprises:

[0089] The data acquisition module 510 is configured to acquire the chassis infrared image of the vehicle chassis sample after the aging test.

[0090] The normal aging area selection module 520 is configured to divide the chassis infrared image into different sub-areas, select a heat concentration area according to the temperature significance and shape features of the sub-areas, and take the remaining areas in the chassis infrared image except the heat concentration area as the normal aging area.

[0091] The oxidation analysis module 530 is configured to divide the normal aging area into local areas, and acquire the oxidation value of each local area according to the color change and oxygen-containing group change of the actual area on the surface of the corresponding sample before and after the aging test.

[0092] The weather resistance detection module 540 is configured to determine the actual elastic modulus of each local area based on the oxidation value, determine the degree of decline of the mechanical property according to the difference of the actual elastic modulus of different local areas in the chassis infrared image, and perform weather resistance detection on the vehicle chassis sample.

[0093] It should be noted that the device provided in the above embodiment is only used as an example for the division of the above functional modules, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, the weather resistance detection system and the weather resistance detection method for the carbon nanometer composite anticorrosive and anti-impact material for vehicle chassis provided in the above embodiment belong to the same concept, and the specific implementation process is described in the method embodiment, which will not be repeated here.

[0094] Embodiment 3:

[0095] Figure 4 The computer device schematic diagram of the weather resistance detection device for the carbon nanometer composite anticorrosive and anti-impact material for vehicle chassis provided in an embodiment of the application. As shown in the example, Figure 4 the computer device includes a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602, wherein when the processor 602 executes the computer program 603, the computer device can execute any of the above-mentioned weather resistance detection methods for the carbon nanometer composite anticorrosive and anti-impact material for vehicle chassis.

[0096] In addition, the embodiment of the application also protects a device, which can include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to execute the weather resistance detection method for the carbon nanometer composite anticorrosive and anti-impact material for vehicle chassis provided in the embodiment of the application.

[0097] The embodiment can divide the device into functional modules according to the above method examples, for example, each functional module can be corresponding, or two or more functions can be integrated in one processing module, and the integrated module can be realized in the form of hardware. It should be noted that the division of the modules in the embodiment is illustrative, and is only a logical function division, and another division mode can be used in actual implementation.

[0098] It should be understood that the device provided in the embodiment is used to execute the above-mentioned weather resistance detection method for the carbon nanometer composite anticorrosive and anti-impact material for vehicle chassis, and thus the same effect as the above-mentioned implementation method can be achieved.

[0099] In the case of using integrated units, the device can include a processing module and a storage module. When the device is applied to the equipment, the processing module can be used to control and manage the actions of the equipment. The storage module can be used to support the equipment to execute mutual program codes and the like.

[0100] The processing module can be a processor or a controller, which can realize or execute various exemplary logical blocks, modules and circuits contained in combination with the disclosure of the present application. The processor can also be a combination of computing functions, such as one or more microprocessor combinations, digital signal processing (DSP) and microprocessor combinations, etc. The storage module can be a memory.

[0101] Embodiment 4:

[0102] The embodiment also provides a computer readable storage medium, which stores computer program codes, when the computer program codes are run on a computer, the computer is caused to execute the above-mentioned related method steps to realize the weather resistance detection method of the carbon nano composite anticorrosive and anti-collision material for vehicle chassis provided in the above-mentioned embodiment.

[0103] Embodiment 5:

[0104] The embodiment also provides a computer program product, when the computer program product is run on a computer, the computer is caused to execute the above-mentioned related steps to realize the weather resistance detection method of the carbon nano composite anticorrosive and anti-collision material for vehicle chassis provided in the above-mentioned embodiment.

[0105] The device, computer readable storage medium, computer program product or chip provided in the embodiment are used to execute the corresponding method provided above, so the beneficial effects achieved by the device, computer readable storage medium, computer program product or chip can refer to the beneficial effects of the corresponding method provided above, which will not be repeated here.

[0106] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules or units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed mutual ones can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0107] It should be noted that: the above-mentioned sequence of the embodiments of the present application is only for description, not representing the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0108] The various embodiments described in this specification are presented by way of example, and each embodiment is not inherently more important than any other embodiment.

Claims

1. A method for detecting weather resistance of a carbon nano-composite anticorrosion and anti-impact material for a vehicle chassis, characterized by, The method comprises: obtaining an infrared image of the vehicle chassis sample after the aging test; dividing the chassis infrared image into different sub-regions, selecting a heat concentration region according to the temperature prominence and shape features of the sub-regions, and taking the remaining regions in the chassis infrared image except the heat concentration region as normal aging regions; dividing the normal aging regions into local regions, and obtaining the oxidation value of each local region according to the color change and oxygen-containing group change of the actual region on the surface of the corresponding sample before and after the aging test; determining the actual elastic modulus of each local region based on the oxidation value, determining the mechanical property degradation degree according to the difference of the actual elastic modulus of different local regions in the chassis infrared image, and detecting the weather resistance of the vehicle chassis sample.

2. The weather resistance detection method of the carbon nano composite anticorrosive and anti-impact material for vehicle chassis according to claim 1, characterized in that, The method comprises: obtaining the minimum circumscribed rectangle of the sub-region, taking the ratio of the length and width of the rectangle as the shape feature; taking the average pixel value of all pixel points in the sub-region as the region temperature, and taking the ratio of the region temperature of each sub-region in the chassis infrared image to the average region temperature of all sub-regions as the temperature prominence of each sub-region; obtaining the heat concentration degree of the corresponding sub-region according to the shape feature and temperature prominence of each sub-region in the chassis infrared image; selecting the sub-region greater than the preset heat concentration threshold as the heat concentration region.

3. The weather resistance detection method of the carbon nano composite anticorrosive and anti-impact material for vehicle chassis according to claim 1, characterized in that, The method comprises: obtaining the chassis gray scale image of the vehicle chassis sample before and after the aging test respectively; mapping each local region in the chassis infrared image to the chassis gray scale image before and after the aging test respectively, and sequentially obtaining the first mapping region and the second mapping region of each local region; taking the difference between the region gray scale of the first mapping region and the second mapping region as the color difference value of each local region; obtaining the peak size according to the change of the oxygen-containing group of the actual region on the surface of the corresponding sample before and after the aging test; obtaining the oxidation value of each local region in the chassis infrared image according to the color difference value and the peak size.

4. The weather resistance detection method of the carbon nano composite anticorrosive and anti-impact material for vehicle chassis according to claim 3, characterized in that, The method comprises: obtaining the 1s electron orbit of oxygen atom of the actual region on the surface of the corresponding sample before and after the aging test respectively for each local region, and taking the peaks in the preset binding energy range of the two orbits as the first oxidation peak and the second oxidation peak; obtaining the peak size of each local region according to the area difference between the second oxidation peak and the first oxidation peak and the width of the second oxidation peak.

5. The method for detecting weather resistance of a carbon nano-composite anticorrosion and impact-resistant material for a vehicle chassis according to claim 1, characterized in that, The method comprises: The aging test comprises several tests; the same stress is applied to several vehicle chassis specimens with the same material as the vehicle chassis sample after different tests, and the elastic modulus and oxidation value of each vehicle chassis specimen after the test are obtained; the elastic modulus of all vehicle chassis specimens at different oxidation values is fitted by difference, and the elastic modulus corresponding to the oxidation value of each local region in the vehicle chassis sample is taken as the actual elastic modulus of the corresponding local region.

6. The method for detecting weather resistance of a carbon nano-composite anticorrosion and impact-resistant material for a vehicle chassis according to claim 1, characterized in that, The method comprises: The standard elastic modulus of the vehicle chassis sample is respectively subtracted from the actual elastic modulus of all local regions in the chassis infrared image, all the differences are averaged, and the elastic overall decline degree is obtained; The difference between the actual elastic modulus of each two local regions in the chassis infrared image is calculated, and the average of all the differences is obtained, and the elastic difference degree is obtained; According to the elastic overall decline degree and the elastic difference degree, the mechanical property decline degree is obtained.

7. The method for detecting weather resistance of a carbon nano-composite anticorrosion and impact-resistant material for a vehicle chassis according to claim 1, characterized in that, The chassis infrared image is divided into different sub-regions, including: Based on the pixel value of the pixel point in the chassis infrared image, all the pixel points are clustered to obtain different clustering clusters, and the connected domain formed by the pixel points in the same clustering cluster is regarded as a sub-region.

8. The weather resistance detection method of the carbon nano composite anticorrosive and anti-impact material for vehicle chassis according to claim 3, characterized in that, The region gray value is the average value of the gray values of all pixel points in the mapping region.

9. The method for detecting weather resistance of a carbon nano-composite anticorrosion and impact-resistant material for a vehicle chassis according to claim 3, characterized in that, The color difference value and the peak size are positively correlated with the oxidation value.

10. The method for detecting weather resistance of a carbon nano-composite anticorrosion and impact-resistant material for a vehicle chassis according to claim 2, characterized in that, The preset heat concentration threshold is 0.88.

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