Method for detecting homogeneity of energetic material

Abstract

The application discloses a kind of detection methods of energetic material mixing uniformity, belong to material uniformity inspection technical field, including steps S1: energetic material is mixed with tracer to obtain mixed sample with different mass ratio, image is illuminated and photographed;Step S2: image is analyzed, and mixed sample with the linear relationship between tracer concentration and the color index is selected as standard sample;Step S3: the standard sample of different concentration is added to mixing device and is mixed, and image is photographed;Step S4: the image of the standard sample after mixing is sampled and the variation coefficient of color index is calculated to evaluate mixing uniformity.The method provided by the application can realize real-time monitoring and detection analysis of the mixing process of energetic material, which is simple, easy to operate and efficient, and can evaluate the mixing uniformity of energetic material by obtaining the image of energetic material with tracer, thus widening the application of online detection in the field of energetic material detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of material uniformity inspection, and particularly to a detection method for mixing uniformity of energetic materials. BACKGROUND

[0002] Energetic materials can store energy and release a large amount of energy under certain conditions, and are mainly used in military, aerospace, coal mine and other fields. Energetic materials are multi-component formula systems, and the mixing uniformity between components is an important indicator affecting the quality stability and application reliability of energetic materials, so it is necessary to quickly and accurately detect the mixing uniformity of energetic materials.

[0003] At present, the detection of mixing uniformity mainly uses electronic tomography, energy dispersive X-ray spectrometer and scanning electron microscope for offline detection, but the offline detection method has problems of low detection efficiency, large human operation error, small sample interval and low representativeness. Therefore, an online detection method can be used, which can monitor in real time and feedback to the operator in time, and has high work efficiency. However, the current online detection methods relying on temperature, electrical conductivity and optical image have high requirements for tracers. If fluorescent materials are used as tracers, there will be problems such as poor stability, poor water resistance, poor high temperature resistance, poor compatibility with the detected material, etc. This will cause the luminescent performance of the fluorescent material to decrease significantly when it is used for detecting the mixing uniformity of energetic slurry, and the detection signal cannot be collected. Therefore, it is necessary to establish an online detection method suitable for detecting the mixing uniformity of energetic materials to realize real-time monitoring and detection analysis of the mixing process of energetic materials, reduce the negative effects of tracers, environment and equipment in the monitoring process, and improve the accuracy of online detection results. SUMMARY

[0004] The present application aims to overcome the problem that the online detection method in the prior art has high requirements for tracers, environment and equipment, and provides a detection method for mixing uniformity of energetic materials.

[0005] To achieve the above-mentioned purpose, the technical scheme of the present application is: a detection method for mixing uniformity of energetic materials is provided, comprising,

[0006] Step S1: mixing energetic materials with tracers at different mass ratios to obtain a mixed sample, illuminating the mixed sample, and taking an image after the tracers develop color to obtain a mixed sample image;

[0007] Step S2: analyzing the mixed sample image and obtaining a color index, analyzing the change of the average value of the color index with the mass ratio of the tracer, and selecting a mixed sample with a linear relationship between the concentration of the tracer and the color index as a standard sample;

[0008] Step S3: take different concentrations of the standard sample into the mixing device for mixing, and image shooting, to obtain the standard sample mixed image;

[0009] Step S4: sampling the standard sample mixed image and obtaining the color index, calculating the coefficient of variation of the color index, and evaluating the mixing uniformity by the coefficient of variation.

[0010] In an embodiment, the tracer in step S1 is selected from rhodamine 6b, modified strontium aluminate, fluorescein isothiocyanate, and the modifier is selected from silicon dioxide, PMMA, and aluminum oxide.

[0011] In an embodiment, the mass percentage of the tracer in step S1 is 1-15%.

[0012] In an embodiment, the energetic material in step S1 is selected from octogen and photosensitive resin, octogen, aluminum powder and photosensitive resin, hexogen, aluminum powder and tri-nitro toluene, Cl-20, aluminum powder and photosensitive resin.

[0013] In an embodiment, the light in step S1 is selected from daylight lamp, ultraviolet lamp, and infrared lamp, and the working parameters of the ultraviolet lamp are 365 nm and 3 W.

[0014] In an embodiment, in steps S2 and S4, the image processing program of Matlab is used for analysis.

[0015] In an embodiment, in step S2, the mass percentage of the tracer in the standard sample is 1% and 5%.

[0016] In an embodiment, in step S2, RGB, HSV, YCbCr, and CIE color gamut are selected, and the color index under the RGB color gamut is selected from gray value, lightness, and brightness.

[0017] In an embodiment, in step S3, the mixing device is selected from static mixer, vertical kneader, and horizontal kneader.

[0018] In an embodiment, in step S4, the average value of the color index is calculated first, and the average value is:

[0019]

[0020] Further, the standard deviation is calculated, and the standard deviation of the color index is:

[0021]

[0022] In the formula, C n is the color index of the nth pixel point, The total average value of the color index of the pixel points in the sampling area, k is the number of sampling points;

[0023] The coefficient of variation of the color index is obtained, and the formula is as follows:

[0024]

[0025] In the formula, CoV is the coefficient of variation, sigma is the standard deviation of different pixel point indexes, The average value of different pixel point indexes.

[0026] In summary, the present application provides a detection method for the mixing uniformity of energetic materials, which can realize real-time monitoring and detection analysis of the mixing process of energetic materials. The method is simple in sample preparation, easy to operate, and efficient in detection. The mixing uniformity of energetic materials can be evaluated by obtaining the image of the energetic materials with the tracer added, which widens the application of online detection in the field of energetic material detection. At the same time, the modified fluorescent agent is selected as the tracer, which not only ensures that the tracer is not easily decomposed after being dissolved in the resin, but also prolongs the existence time of the fluorescent signal, which is beneficial to detection and analysis. In addition, the detection method also reduces the negative influence of the tracer, environment and equipment on the uniformity detection of energetic materials during online monitoring, and improves the accuracy of online detection results.

[0027] In order to make the above features and advantages of the application more obvious and easy to understand, the following examples are given, and the accompanying drawings are described in detail as follows. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The flowchart of the present application.

[0029] Figure 2 The image of the mixed sample under different tracer concentrations in the present application.

[0030] Figure 3 The image of the mixed sample in the present application. Figure 2 The relationship between the brightness average value of the mixed sample and the mass percentage of the tracer.

[0031] Figure 4 The image and brightness distribution of the mixed sample before and after the static mixer in Example 1 of the present application.

[0032] Figure 5 The image and brightness distribution of the mixed sample before and after the vertical kneader in Example 2 of the present application.

[0033] Figure 6 The image and brightness distribution of the mixed sample after the horizontal kneader in Example 3 of the present application. DETAILED DESCRIPTION

[0034] In order to make the technical scheme of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the described embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0035] The present application provides a detection method for the mixing uniformity of energetic materials, as shown in the following steps. Figure 1 The present application provides a detection method for the mixing uniformity of energetic materials, as shown in the following steps.

[0036] Step S1: mixing energetic materials with a tracer at different mass ratios to obtain a mixed sample, illuminating the mixed sample, and taking an image after the tracer develops color to obtain a mixed sample image;

[0037] Step S2: analyzing the mixed sample image and obtaining a color index, analyzing the change of the average of the color index with the mass ratio of the tracer, and selecting a mixed sample with a linear relationship between the concentration of the tracer and the color index as a standard sample;

[0038] Step S3: taking different concentrations of the standard sample into a mixing device for mixing and taking an image to obtain a standard sample mixed image;

[0039] Step S4: sampling the standard sample mixed image and obtaining a color index, calculating the coefficient of variation of the color index, and evaluating the mixing uniformity by the coefficient of variation.

[0040] In the above step S1, the energetic materials can be selected from octogon and photosensitive resin, or octogon, aluminum powder and photosensitive resin, hexogen, aluminum powder and TNT, Cl-20, aluminum powder and photosensitive resin, and preferably octogon and photosensitive resin. The mass percentage of the tracer is 1-15%. The tracer is selected from one or more combinations of rhodamine 6b, modified strontium aluminate, and fluorescein isothiocyanate, and preferably modified strontium aluminate. The modified material is selected from silica, PMMA, alumina, etc., and preferably silica. The illumination can be provided by a daylight lamp, an ultraviolet lamp, or an infrared lamp, and preferably an ultraviolet lamp, and irradiation is performed under the condition of 365 nm and 3 W. Selecting a low-power ultraviolet lamp can reduce the energy stimulation of the energetic materials and prevent decomposition and energy release. The image taken can be a color image or a grayscale image, and preferably a color image.

[0041] The image is processed by using Matlab software in the step S2, and RGB, HSV, YCbCr, CIE color gamut can be selected, and RGB color gamut is preferably selected. The color index in the RGB color gamut is selected from gray value, lightness and brightness, and the color index in the CIE color gamut can be selected as L*a*b, and the lightness in the RGB color gamut is preferably selected.

[0042] The standard sample is selected from a mixed sample with linear change between the average of the color index and the mass percentage of the tracer in the step S3, and the mass percentage of strontium aluminate in the standard sample is preferably 1% and 5%. The mixing device is selected from a static mixer, a vertical kneader and a horizontal kneader. The image is a color image.

[0043] The image is sampled by using Matlab software in the step S4, and all pixel points or multiple regions can be selected, and all pixel points are preferably selected. The color information at the sampling position is converted into a color index by using a color segmenter, a user-defined function or an image batch processor, and the user-defined function is preferably selected.

[0044] In addition, if the tracers in different mixed samples show different colors, the L*a*b values of the image sampling region can be obtained in the CIE color gamut, and the uniformity of mixing can be analyzed by the change trend of the color function in the L*a*b coordinate system; or the gray value, lightness and brightness of the color shown by a certain mixed sample in the sampling region can be analyzed in the RGB color gamut, and the uniformity of mixing can be analyzed by calculating the coefficient of variation.

[0045] If the tracers in different mixed samples show the same color, the interval of the gray value, lightness and brightness of the image sampling region can be analyzed in the RGB color gamut, and the interval boundary value can be used to distinguish different energetic materials, and the coefficient of variation can be calculated to analyze the uniformity of mixing; or the color index can be calibrated in the RGB color gamut, and the uniformity of mixing can be analyzed by the linear change of the color index with the concentration of the tracer.

[0046] The color index obtained by sampling needs to be averaged, and the average value of the color index is calculated by the following formula:

[0047]

[0048] The standard deviation of the color index is calculated by the following formula:

[0049]

[0050] In the formula, C n is the color index of the nth pixel point, is the total average value of the color index of the pixel points in the sampling region, and k is the number of sampling points.

[0051] The coefficient of variation of color index is calculated according to the following formula:

[0052]

[0053] In the formula, CoV is the coefficient of variation, sigma is the standard deviation of different pixel point indexes, and mu is the average value of different pixel point indexes.

[0054] In the technical solution, the silica modified strontium aluminate can be dissolved in the photosensitive resin, has a small influence on the rheological property of the energetic material, and ensures the accuracy of the detection of the mixing uniformity; the fluorescent agent is used as the tracer to reduce the influence of the experimental environment on the detection result, has a low requirement on the device, reduces the detection cost and the difficulty of device assembly. Meanwhile, after the surface coating modification, the strontium aluminate has good stability, is not easy to decompose after being dissolved in the resin, effectively prolongs the time of the existence of the fluorescent signal, and is beneficial to the detection and analysis. In addition, the 365nm, 3W ultraviolet lamp is selected for light irradiation, under which condition, the fluorescent agent can be fully colored, and the photosensitive resin is prevented from being cured, and the mixing effect is affected

[0055] In the application, the change of the color index is used as the evaluation of the mixing uniformity of the energetic material, the standardization calibration experiment is simple, the brightness signal is easy to observe and obviously changes with the concentration, and the collection and analysis are more convenient. In addition, the image is processed by using the Matlab software, and the detection result of the mixing uniformity of the energetic material can be quickly obtained.

[0056] The application will be further described in detail in combination with specific embodiments.

[0057] Embodiment 1:

[0058] Step S1: Selecting the energetic material containing octogon and photosensitive resin for detection, mixing the dried octogon and photosensitive resin in a mass ratio of 7:3, and adding 1-15% of silica modified strontium aluminate as a tracer, and finally obtaining a mixed sample. The mixed samples with different contents of the tracer are irradiated by an ultraviolet lamp, and the color is developed, and then the image is taken.

[0059] Step S2: Selecting the Matlab software to process the image, processing and analyzing the image under the RGB color domain, and obtaining the average blue brightness of different mixed samples. As shown in the formula (1), with the increase of the mass percentage of the silica modified strontium aluminate, the blue brightness of the image of the mixed sample is continuously deepened. When the mass percentage of the silica modified strontium aluminate is 1-5%, as shown in the formula (2), the blue brightness of the image of the mixed sample is 0.2-0.3, which is much higher than that of the sample without the tracer. Figure 2 Figure 3 ​As shown in the figure, the brightness mean value of the image of the mixed sample linearly changes with the mass percentage of silica modified strontium aluminate. Therefore, the mixed sample with 5% silica modified strontium aluminate and the mixed sample with 1% silica modified strontium aluminate are selected as standard samples.

[0060] Step S3: The two standard samples are added into the static mixer, and the mixed sample in each static mixing element is imaged.

[0061] Step S4: The color segmenter of the Matlab software is used to obtain the blue brightness distribution of all pixel points in the image, as shown in the figure. Figure 4 As shown in the figure, the initial distribution of the blue light index is mainly around 50 and 170, and after being added into the static mixer, the distribution of the blue light index changes. When the number of static mixing elements participating in the mixing increases, the distribution of the blue light index begins to move towards the middle, and after passing through the last static mixing element, the blue light index is mainly distributed around 70-80. By comparing the blue light index distribution of the samples before and after the static mixer, it can be preliminarily judged that the mixing uniformity is improved.

[0062] Further sampling of all pixel points in the image using a custom function in Matlab obtains the blue brightness value, and the coefficient of variation of the blue brightness of the sample after being subjected to different numbers of static mixers is solved, and the results are as follows:

[0063]

[0064] Since the final coefficient of variation is less than 0.05, the mixing uniformity is good.

[0065] Example 2:

[0066] Step S1: Select an energetic material containing octogen and photosensitive resin for detection, mix dry octogen and photosensitive resin in a mass ratio of 7:3, and add 1-15% silica modified strontium aluminate as a tracer, and finally obtain a mixed sample. The mixed samples with different tracer contents are irradiated with an ultraviolet lamp, and the color is developed for image shooting.

[0067] Step S2: The Matlab software is used to process the image, and the blue brightness mean value of different mixed samples is obtained by processing and analyzing the image under the RGB color domain. As shown in the figure, Figure 2 As shown in the figure, with the increase of the mass percentage of silica modified strontium aluminate, the blue brightness of the image of the mixed sample deepens. When the mass percentage of silica modified strontium aluminate is 1-5%, as shown in the figure, Figure 3As shown in the figure, the average brightness of the mixed sample image changes linearly with the mass percentage of silica modified strontium aluminate. Therefore, the mixed sample with 5% silica modified strontium aluminate and the mixed sample with 1% silica modified strontium aluminate are selected as standard samples.

[0068] Step S3: Add the two standard samples into the feeding bin of the vertical kneader and mix. Take pictures of the samples in the vertical kneader every 2 min.

[0069] Step S4: Use the color segmenter of Matlab software to obtain the blue brightness distribution of all pixel points in the image, as shown in the figure. Figure 5 As shown in the figure, the initial distribution of blue light index is mainly around 50 and 170. After adding into the vertical kneader, the distribution of blue light index begins to move towards the middle, indicating that the mixing effect is continuously improved.

[0070] Further sample all pixel points in the image using the custom function in Matlab, obtain the blue brightness value, and solve the coefficient of variation of blue brightness of the samples at different mixing times, as follows:

[0071]

[0072] Since the final coefficient of variation is greater than 0.05, the mixing uniformity is poor.

[0073] Example 3:

[0074] Step S1: Select energetic materials containing octogen and photosensitive resin for detection. Mix dry octogen and photosensitive resin in a mass ratio of 7:3, and add 1-15% silica modified strontium aluminate as a tracer. Finally, obtain the mixed sample. Irradiate the mixed sample with different contents of silica modified strontium aluminate with an ultraviolet lamp, and take pictures after color development.

[0075] Step S2: Use Matlab software to process the image, process and analyze the image under RGB color domain, and obtain the average blue brightness of different mixed samples. As shown in the figure. Figure 2 As shown in the figure, with the increase of the mass percentage of silica modified strontium aluminate, the blue brightness of the mixed sample image deepens continuously. When the mass percentage of silica modified strontium aluminate is 1-5%, as shown in the figure. Figure 3 As shown in the figure, the average brightness of the mixed sample image changes linearly with the mass percentage of silica modified strontium aluminate. Therefore, the mixed sample with 5% silica modified strontium aluminate and the mixed sample with 1% silica modified strontium aluminate are selected as standard samples.

[0076] Step S3: two standard samples are added into a horizontal kneader and mixed for 5 min, and the mixed samples in multiple regions in the horizontal kneading bin are sampled and photographed.

[0077] Step S4: the color divider of Matlab software is used to obtain the blue lightness distribution of all pixel points in the image, as shown in Figure 6 As shown in the figure, the blue light index distribution of the standard sample after being added into the vertical kneader and mixed is mainly around 75, indicating that the mixing effect is good.

[0078] Further, the custom function in Matlab is used to sample all pixel points in the image, obtain the blue lightness value, and solve the coefficient of variation of the blue lightness of the samples in different regions in the horizontal kneader, and the obtained coefficients of variation are 0.0094, 0.0103, 0.0086 and 0.0079.

[0079] Since the coefficients of variation are all less than 0.05, it indicates that the mixing uniformity is good.

[0080] In summary, the application provides a detection method for the mixing uniformity of energetic materials, which can realize real-time monitoring and detection analysis of the mixing process of energetic materials, and the method is simple in sample preparation, convenient in operation, efficient in detection, and can evaluate the mixing uniformity of energetic materials by obtaining the image of the energetic material with the tracer, and the application of online detection in the field of energetic material detection is widened. At the same time, the modified fluorescent agent is selected as the tracer, which can not only ensure that the tracer is not easy to decompose after being dissolved in the resin, but also can prolong the existence time of the fluorescent signal, which is conducive to detection and analysis. In addition, the detection method also reduces the negative influence of the tracer, environment and equipment on the uniformity detection of energetic materials in the online monitoring process, and improves the accuracy of the online detection result.

[0081] Although the application has been disclosed as above, it is not intended to limit the application, and anyone with ordinary knowledge in the art can make some changes and modifications without departing from the spirit and scope of the application, so the protection scope of the application shall be subject to the appended patent claim scope.

Claims

1. A method for detecting the homogeneity of an energetic material, characterized in that, The application relates to a method for evaluating the mixing uniformity of energetic materials. Step S1: mixing energetic materials and tracers in different mass ratios to obtain mixed samples, illuminating the mixed samples, taking images after the tracers develop color, and obtaining mixed sample images; Step S2: analyzing the mixed sample images and obtaining color indexes, analyzing the change of the average color indexes with the mass ratios of the tracers, and selecting mixed samples with linear relationships between the concentrations of the tracers and the color indexes as standard samples; Step S3: mixing the standard samples with different concentrations in a mixing device, taking images, and obtaining standard sample mixed images; Step S4: sampling the standard sample mixed images and obtaining color indexes, calculating the variation coefficients of the color indexes, and evaluating the mixing uniformity according to the variation coefficients. In step S1, the tracers are selected from modified strontium aluminates, and the modifiers are selected from silicon dioxide, PMMA or aluminum oxide; the illumination is selected from ultraviolet lamps, and the working parameters of the ultraviolet lamps are 365nm and 3W.

2. The method of claim 1, wherein the energetic material is selected from the group consisting of: a solid fuel, a liquid fuel, a gas fuel, a propellant, an explosive, and combinations thereof. The mass percentage of the tracers in step S1 is 1-15%.

3. The method for detecting the mixing uniformity of energetic materials as described in claim 2, characterized in that, The energetic materials in step S1 are selected from octogen and photosensitive resin, octogen, aluminum powder and photosensitive resin, hexogen, aluminum powder and TNT, Cl-20, aluminum powder and photosensitive resin.

4. The method of claim 1, wherein the energetic material is selected from the group consisting of: a solid fuel, a liquid fuel, a gas fuel, a propellant, an explosive, and combinations thereof. In steps S2 and S4, image processing programs of Matlab are adopted for analysis.

5. The method for detecting the mixing uniformity of energetic materials as described in claim 1, characterized in that, In step S2, the mass percentages of the tracers in the standard samples are 1% and 5%.

6. The method for detecting the mixing uniformity of energetic materials as described in claim 5, characterized in that, In step S2, RGB, HSV, YCbCr or CIE color domains are selected, and the color indexes in the RGB color domain are selected from gray values, brightness or luminance.

7. The method for detecting the mixing uniformity of energetic materials as described in claim 1, characterized in that, In step S3, the mixing device is selected from static mixers, vertical kneaders or horizontal kneaders.

8. The method for detecting the mixing uniformity of energetic materials as described in claim 1, characterized in that, In step S4, the average value of the color indexes is calculated first, and the average value is: （1） Then, the standard deviation is calculated, and the standard deviation of the color indexes is: （2） wherein the color index of the nth pixel point, the total average of the color index of the pixel points in the sampling region, k is the number of sampling points; Thus, the variation coefficient of the color indexes can be obtained, and the formula is as follows: （3） wherein is the coefficient of variation, is the standard deviation of different pixel indices, is the mean of different pixel indices.

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