Adaptive detection method and device for polyenergetic cutting cable profile size and bulk density

Abstract

An adaptive detection method and apparatus for the dimensions and bulk density of shaped charge cutting cables are disclosed, relating to the field of precision pyrotechnics inspection technology. This adaptive detection method, targeting the composite surface structure of shaped charge cutting cables with "irregular thin walls + arc contours," proposes a novel detection framework through vision-laser fusion, specialized calibration materials, and a three-level optimization algorithm, demonstrating high specificity. This adaptive detection method achieves fully automated, integrated, and high-precision detection of key dimensions and bulk density of shaped charge cutting cable surfaces. It successfully integrates previously separate operational steps into a coherent process, replacing the outdated, manual, step-by-step approach. The adaptive detection apparatus, used in conjunction with the aforementioned adaptive detection method, can quickly and efficiently complete the detection of shaped charge cutting cable surfaces through high-precision position and height adjustment, providing a new technical path for precision pyrotechnics inspection.

Description

Technical Field

[0001] This invention relates to the field of precision testing technology for pyrotechnic products, and more specifically, to an adaptive testing method and apparatus for the dimensions and bulk density of shaped charge cutting cables. Background Technology

[0002] shaped charge cutting cables are linear explosive separation pyrotechnic devices that utilize the focusing effect to generate a high-speed metal jet under explosive action to rapidly cut targets. Due to their focusing penetration principle, the profile dimensions and volumetric density of the shaped charge cutting cable directly determine the formation quality and cutting efficiency of the focused jet. When the profile dimensions and volumetric density deviate significantly from the design values, the jet energy focusing efficiency will decrease significantly, and in severe cases, it will lead to cutting failure. Therefore, the detection of the profile dimensions and volumetric density of the shaped charge cutting cable is a key aspect of quality control during the production process.

[0003] In current actual production sites, the dimensions and volume density of ductile cutting cables are still mainly measured manually, which has inherent defects such as low efficiency, high labor intensity, poor consistency and easy omissions. This has become a bottleneck restricting the improvement of the production quality and intelligent production of ductile cutting cables. Machine vision inspection technology, with its high precision, high efficiency, and automation capabilities, has found some application in dimensional inspection in the industrial field. However, when directly applied to the surface dimensions and volume density detection of shaped charge cutting cables, the following problems arise: 1) The surface of the shaped charge cutting cable is a composite surface coupled with a metal shell area and a non-metallic agent area. Different areas have different reflective characteristics, and existing standard vision algorithms often encounter problems such as incomplete contour extraction and edge positioning jumps when extracting this type of composite surface; 2) The surface of the shaped charge cutting cable is a "non-standard thin-walled + arc-shaped contour" structure, and general geometric algorithms are difficult to calculate the key dimensions; 3) The overall size of the shaped charge cutting cable is small, with the length and width of the surface usually only a few millimeters. The accuracy detection requirement reaches the micrometer level, and vision methods without accuracy compensation are difficult to meet the standards; 4) The production environment of pyrotechnic products is extremely complex, and traditional vision calibration methods with specific positioning and posture requirements and cumbersome operation cannot meet the actual production needs; 5) Volume density detection also involves the integration of multiple technologies such as weight detection and three-dimensional height detection, and there is currently no specific adaptive integrated processing solution.

[0004] With the rapid development of my country's aerospace and engineering blasting industries, the requirements for the detection of the dimensions and bulk density of shaped charge cutting cables are becoming increasingly stringent. Traditional manual measurement methods can no longer meet the actual production requirements of shaped charge cutting cables. Therefore, a more intelligent and efficient method and device are needed to accurately measure the key dimensions and bulk density of shaped charge cutting cables. Summary of the Invention

[0005] The purpose of this invention is to provide an adaptive detection method for the surface dimensions and bulk density of shaped charge cutting cables, which effectively solves the key problems in the detection of shaped charge cutting cables, not only improving detection accuracy and efficiency, but also enhancing adaptability to working conditions.

[0006] Another objective of this invention is to provide an adaptive detection device for the surface dimensions and bulk density of a shaped charge cutting cable, which can be used in conjunction with the aforementioned adaptive detection method to achieve micron-level high-precision measurement and integrated intelligent detection.

[0007] The embodiments of the present invention are implemented as follows:

[0008] An adaptive detection method for the surface dimensions and volume density of a shaped charge cutting cable includes the following steps:

[0009] S1. Obtain the mass of the shaped charge cutting cable sample;

[0010] S2. Use a circular calibration object to calibrate the conversion coefficient between pixel size and actual size;

[0011] S3. Detect the height difference between the shaped charge cutting cable sample and the circular calibration object, adjust their heights to be on the same imaging plane, and acquire the surface image of the shaped charge cutting cable sample.

[0012] S4. Preprocess, extract and optimize the profile image of the shaped cutting cable sample to obtain a complete profile.

[0013] S5. Extract feature curves based on the optimized surface profile and calculate key dimensions of the surface;

[0014] S6. Calculate the bulk density by combining the mass, height, and cross-sectional area of ​​the shaped cutting cable sample.

[0015] Furthermore, in other preferred embodiments of the present invention, in step S2, the conversion coefficient calibration includes:

[0016] An image of a circular calibration object is acquired, its pixel diameter is measured, and the conversion factor is calculated using the actual diameter of the circular calibration object. The formula for calculating the conversion factor is: k con = D / d In the formula, D The actual size of the circular calibration object. d The pixel size of the circular calibration object.

[0017] Furthermore, in other preferred embodiments of the present invention, a calibration step is included before step S2:

[0018] Pre-acquire calibration images of the shaped charge cutting cable sample and ensure the clarity and size of the calibration images are appropriate by adaptively adjusting the imaging parameters.

[0019] Furthermore, in other preferred embodiments of the present invention, adaptive adjustment of imaging parameters includes:

[0020] Calculate the ratio of the area of ​​the minimum bounding rectangle of the profile in the calibration image to the area of ​​the whole image, and adjust the distance between the camera and the shaped charge cutting cable sample to make the ratio close to the preset threshold.

[0021] The image sharpness is evaluated using a gray-level gradient squared sum algorithm, and the lens focal length, aperture and exposure time are automatically adjusted until the sharpness meets the standard.

[0022] Furthermore, in other preferred embodiments of the present invention, step S4 includes three levels of processing:

[0023] S41. The image is reduced in dimension by gray-scale weighting and smoothed by bilateral filtering. Then, the initial contour is extracted by Canny algorithm.

[0024] S42. Use morphological dilation, erosion and area thresholding to remove false contours;

[0025] S43. The continuity of the fracture profile is achieved through morphological closing operations, opening operations, and hole filling.

[0026] Furthermore, in other preferred embodiments of the present invention, step S42 includes:

[0027] Morphological dilation of the contour image:

[0028] Repeat n times;

[0029] After median filtering and smoothing, morphological erosion is performed:

[0030] Repeat n times;

[0031] Calculate the area of ​​all contours, and mark contours with an area less than 1 / 15 of the largest contour area as pseudo contours and remove them.

[0032] The value of n ranges from 8 to 15.

[0033] Furthermore, in other preferred embodiments of the present invention, step S43 includes:

[0034] Perform morphological closing operation on the contour image:

[0035] Repeat n times;

[0036] Fill the holes that do not correspond to the maximum contour;

[0037] Then perform morphological opening operations:

[0038] Repeat n times to smooth the outline;

[0039] The value of n ranges from 8 to 15.

[0040] Furthermore, in other preferred embodiments of the present invention, in step S5, the critical dimension calculation employs a piecewise smoothing algorithm based on random sampling, including:

[0041] S51. Randomly select points from the characteristic curve to calculate the linear parameters, and iteratively filter the set of internal points to divide the linear segment;

[0042] S52. Mark the points between adjacent linear segments as arc segments, and optimize the arc parameters using least squares and Levenberg-Marquardt algorithms.

[0043] S53. Extract the energy-concentrating angle, total width, effective hypotenuse length, total height, back shell thickness, and energy-concentrating apex shell thickness based on linear segments and circular arc segments.

[0044] Furthermore, in other preferred embodiments of the present invention, the optimization objective of the arc segment parameters is:

[0045] .

[0046] An adaptive detection device for the surface dimensions and bulk density of a shaped charge cutting cable, used to perform the above-mentioned adaptive detection method, comprising:

[0047] The precision compensation platform is used to support the shaped charge cutting cable sample and the circular calibration object;

[0048] The detection component includes an image acquisition unit and an illumination unit. The image acquisition unit includes an area array camera and a zoom lens for acquiring images of the shaped charge cutting cable sample and the circular calibration object. The illumination unit uses a ring light source to provide uniform illumination for image acquisition.

[0049] A three-dimensional moving system is used to precisely adjust the relative position of the detection components and the accuracy compensation platform;

[0050] The height registration stage is set on the precision compensation platform and includes at least two independently adjustable support platforms for placing shaped charge cutting cable samples or circular calibration objects. The support platforms are equipped with mass sensors that can be used to measure the mass of shaped charge cutting cable samples or circular calibration objects.

[0051] The height detection unit includes a laser altimeter for measuring the height of the shaped charge cutting cable sample and the circular calibration object;

[0052] The control system is used to coordinate the operation of various components and execute adaptive detection methods.

[0053] The beneficial effects of the embodiments of the present invention are:

[0054] This invention provides an adaptive detection method for the dimensions and bulk density of shaped charge cutting cables. Targeting the composite surface structure of shaped charge cutting cables with "irregular thin walls + arc contours," it proposes a novel detection framework through vision-laser fusion, specialized calibration materials, and a three-level optimization algorithm, demonstrating high specificity. This adaptive detection method achieves fully automated, integrated, and high-precision detection of key dimensions and bulk density of shaped charge cutting cable surfaces. It successfully integrates previously separate operational steps into a coherent process, replacing the outdated, manual, step-by-step approach. This invention also provides an adaptive detection device, used in conjunction with the above-mentioned adaptive detection method, which can quickly and efficiently complete the detection of shaped charge cutting cable surfaces through high-precision position and height adjustment, providing a new technical path for the precision detection of pyrotechnic products. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of the preprocessing effect of an adaptive detection method for the surface size and volume density of a shaped cutting cable provided in Embodiment 1 of the present invention, wherein a) is the surface image after dimensionality reduction by gray-scale weighting method, b) is the image after bilateral filtering smoothing, and c) is the image after the initial contour is extracted using the Canny algorithm;

[0057] Figure 2 This is a schematic diagram of the pseudo-contour removal algorithm flow and the final effect of an adaptive detection method for the surface size and volume density of a shaped charge cutting cable provided in Embodiment 1 of the present invention, wherein a) is the pseudo-contour removal algorithm flow and b) is a schematic diagram of the final effect.

[0058] Figure 3 This is a schematic diagram of the fracture profile continuation algorithm flow and final effect of an adaptive detection method for the surface size and volume density of a shaped charge cutting cable provided in Embodiment 1 of the present invention, wherein a) is the fracture profile continuation algorithm flow and b) is a schematic diagram of the final effect.

[0059] Figure 4This is a schematic diagram of the key dimension detection and contour area calculation algorithm processing effect of the adaptive detection method for the surface size and volume density of the shaped energy cutting cable provided in Embodiment 1 of the present invention, wherein a) is the key dimension detection result and b) is the effect diagram after the contour area calculation algorithm processing.

[0060] Figure 5 This is a schematic diagram of an adaptive detection device for the surface dimensions and bulk density of a shaped charge cutting cable provided in Embodiment 2 of the present invention;

[0061] Icons: 100-Adaptive detection device; 110-Precision compensation platform; 111-Height registration stage; 112-Height detection unit; 120-Detection component; 121-Illumination unit; 122-Area array camera; 123-Zoom lens; 130-Three-dimensional movement system; 131-First servo cylinder; 132-Second servo cylinder; 133-Third servo cylinder; 200-Focused cutting cable sample; 300-Circular calibration object. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] The abbreviations and key terms used in the following embodiments are defined as follows:

[0064] Example 1

[0065] This embodiment provides an adaptive detection method for the surface dimensions and bulk density of a shaped charge cutting cable, which includes the following steps:

[0066] S1. Obtain the mass of the shaped charge cutting cable sample.

[0067] S2. Use a circular calibration object to calibrate the conversion coefficient between pixel size and actual size.

[0068] Specifically, the conversion factor calibration includes:

[0069] An image of a circular calibration object is acquired, its pixel diameter is measured, and the conversion factor is calculated using the actual diameter of the circular calibration object. The formula for calculating the conversion factor is: k con = D / d In the formula, D The actual size of the circular calibration object. d The pixel size of the circular calibration object.

[0070] Alternatively, a circular caliper may be used, which must have known dimensions and be made of the same material as the shaped charge cutting cable.

[0071] A calibration step is included before step S2:

[0072] Calibration images of the shaped charge cutting cable sample are pre-acquired, and imaging parameters are adaptively adjusted to ensure appropriate image sharpness and size. The adjusted imaging parameters are used in the imaging processes of steps S2 and S3.

[0073] Specifically, adaptive adjustment of imaging parameters includes:

[0074] Calculate the ratio of the area of ​​the minimum bounding rectangle of the profile in the calibration image to the area of ​​the whole image, and adjust the distance between the camera and the shaped charge cutting cable sample to make the ratio close to the preset threshold.

[0075] The image sharpness is evaluated using a gray-level gradient squared sum algorithm, and the lens focal length, aperture and exposure time are automatically adjusted until the sharpness meets the standard.

[0076] Furthermore, the area ratio is calculated using the formula... K area = s / S Calculate, where, K area It is the area ratio. s The area of ​​the minimum bounding rectangle of the outline. S The area is the entire image area. The area ratio threshold is set to... T area ,make sure K area and T area The difference should not exceed 5% to generate an image of appropriate size.

[0077] The formula for calculating image sharpness is:

[0078] ,

[0079] In the formula, M , N These are the width and height of the image, respectively. f ( x ,y ) is pixels ( x , y The corresponding grayscale value. Image sharpness optimization is achieved by comparing image sharpness. G With sharpness threshold T cla To complete, ensure G and T cla The difference should not exceed 3% to obtain an image with acceptable clarity.

[0080] Furthermore, the adaptive detection method for the surface dimensions and bulk density of a shaped charge cutting cable provided in this embodiment also includes:

[0081] S3. Detect the height difference between the shaped charge cutting cable sample and the circular calibration object, adjust their heights to be on the same imaging plane, and acquire the surface image of the shaped charge cutting cable sample.

[0082] In this step, the height of the circular calibration object is collected. H 1. Height of the shaped charge cutting cable sample H 2. By adjusting the height, H 1= H 2. The surface image can be acquired, thereby compensating for calibration accuracy by eliminating height differences.

[0083] Furthermore, the adaptive detection method for the surface dimensions and bulk density of a shaped charge cutting cable provided in this embodiment also includes:

[0084] S4. Preprocess, extract and optimize the profile image of the shaped cutting cable sample to obtain a complete profile.

[0085] Optionally, step S4 includes three levels of processing:

[0086] S41. The image is reduced in dimension by gray-scale weighting and smoothed by bilateral filtering. Then, the initial contour is extracted by Canny algorithm.

[0087] Among them, the surface image after dimensionality reduction using the gray-scale weighting method is as follows: Figure 1 As shown in a), the image after bilateral filtering smoothing is as follows: Figure 1 As shown in b), the image after extracting the initial contour using the Canny algorithm is as follows. Figure 1 As shown in c) of the document.

[0088] S42. Use morphological dilation, erosion and area thresholding to remove false contours;

[0089] S43. The continuity of the fracture profile is achieved through morphological closing operations, opening operations, and hole filling.

[0090] Further, step S42 includes:

[0091] 1) Perform morphological dilation on the contour image:

[0092] .

[0093] In the formula, P This is a profile image of the surface. S It is a structural element (the structural element used in this embodiment is a rectangle). h The translation amount is the structural element translation (in this embodiment, the translation amount is set to 2).

[0094] 2) Repeat the above expansion process 10 times.

[0095] 3) Median filtering is used to smooth the surface contour image of the energy-focused cutting.

[0096] 4) Perform morphological etching treatment:

[0097] .

[0098] 5) Repeat the above corrosion treatment 10 times.

[0099] 6) Calculate the area of ​​all contours and find the largest contour area. C max And set an area threshold. C t = C max / 15, filter out all areas smaller than C t The outline is marked as a pseudo-outline.

[0100] 7) Generate a mask of the same size as all pseudo contours and with all pixel grayscale values ​​of 0. Perform a logical AND operation between the mask and the image to remove the pseudo contours.

[0101] The process of step S43 is as follows: Figure 2 As shown in a), the processing effect is as follows: Figure 2 As shown in b) of the diagram.

[0102] Further, step S43 includes:

[0103] 1) Perform morphological closing operation on the contour image:

[0104] .

[0105] 2) Repeat the above closing operation 9 times.

[0106] 3) Calculate the area of ​​all contours in the image and mark all contours with non-maximum area as holes.

[0107] 4) Generate a mask with the same size as all holes and all pixel grayscale values ​​of 255. Perform a logical AND operation between the mask and the image to fill the holes.

[0108] 5) Perform morphological opening operations on the surface contour image of the shaped energy cutting:

[0109] .

[0110] 6) Repeat the above opening operation 9 times.

[0111] The process of step S43 is as follows: Figure 3 As shown in a), the processing effect is as follows: Figure 3 As shown in b) of the diagram.

[0112] Furthermore, the adaptive detection method for the surface dimensions and bulk density of a shaped charge cutting cable provided in this embodiment also includes:

[0113] S5. Extract feature curves based on the optimized surface profile and calculate the key dimensions of the surface.

[0114] In step S5, the critical dimension calculation employs a piecewise smoothing algorithm based on random sampling, including:

[0115] S51. Randomly select points from the characteristic curve to calculate the linear parameters, and iteratively filter the set of interior points to divide the linear segment.

[0116] Specifically, two points are randomly selected from the characteristic curve. p r1 ( x r1 , y r1 )and p r2 ( x r2 ,y r2 Calculate the linear parameters between two points using the following formula. k r and b r :

[0117] .

[0118] Then calculate the remaining points in the characteristic curve according to the following formula. p re ( x re , y re Linear deviation from these two points d re :

[0119] .

[0120] The deviation is less than d T Data points are placed into the interior point set p N .

[0121] Repeat the above two calculation steps 100 times, recording the set of interior points during the iteration. p N The point set with the largest number p max After the iteration is complete, the point set will be... p max All points in the curve are marked as points belonging to the same linear segment. After removing points belonging to the same linear segment from the characteristic curve, all linear segments are divided according to the target number of linear segments.

[0122] S52. Mark the points between adjacent linear segments as arc segments, and optimize the arc parameters using least squares and the Levenberg-Marquardt algorithm.

[0123] Specifically, if there are a total of n circle There are points, and the coordinates of each point are ( ). x i ,y i Therefore, based on the least squares principle, the nonlinear optimization objectives can be established as shown in the following equations:

[0124] ,

[0125] In the formula, ( x 0 ,y 0) and R 0 represents the coordinates of the center and radius of the arc segment calculated using the Levenberg-Marquardts optimization algorithm.

[0126] Mark linear segments and circular segments as critical dimension segments, such as Figure 4 As shown in a), all data points between each key dimension segment are traversed. The deviation distance of each data point is calculated based on the linear or circular parameter of each key dimension segment, and data points with deviation distances greater than a threshold are filtered out. T dist Outlier data points are identified to smooth the feature curve, resulting in a smoothed image as shown below. Figure 4 As shown in b) of the diagram.

[0127] S53. Based on linear segments and arc segments, extract the pixel values ​​of the energy-concentrating angle, total width, effective hypotenuse length, total height, back shell thickness, and energy-concentrating apex shell thickness on the surface image, and then use conversion coefficients. k con Convert to actual value.

[0128] Based on the feature curves of the smoothed and optimized shaped charge cutting cable profile, the cross-sectional area of ​​the shaped charge cutting cable profile can be obtained by gray-scale counting. s section And through conversion coefficients k con Obtain the actual cross-sectional area S section .

[0129] Furthermore, the adaptive detection method for the surface dimensions and bulk density of a shaped charge cutting cable provided in this embodiment also includes:

[0130] S6. Calculate the bulk density by combining the mass, height, and cross-sectional area of ​​the shaped cutting cable sample.

[0131] The mass of the shaped charge cutting cable sample M The height of the shaped charge cutting cable sample was obtained from step S1. H 2. The actual cross-sectional area of ​​the shaped charge cutting cable sample obtained from step S3. S section Calculated from step S5. The formula for calculating volume density is:

[0132] .

[0133] Example 2

[0134] This embodiment provides an adaptive detection device 100 for the surface dimensions and bulk density of a shaped charge cutting cable, used to execute the aforementioned adaptive detection method, referring to... Figure 5 As shown, it includes a precision compensation platform 110, a detection component 120, a three-dimensional movement system 130, and a control system.

[0135] The precision compensation platform 110 is used to support the shaped charge cutting cable sample 200 and the circular calibration object 300. The precision compensation platform 110 is equipped with a height registration stage 111 and a height detection unit 112.

[0136] like Figure 5As shown, the height registration stage 111 includes at least two independently adjustable support platforms. In this embodiment, two support platforms are used to place the shaped charge cutting cable sample 200 or the circular calibration object 300, respectively. A mass sensor is installed within each support platform to measure the mass of the shaped charge cutting cable sample 200 or the circular calibration object 300, thus completing the mass acquisition in step S1 of Embodiment 1. The height detection unit 112 includes a laser altimeter, which can be used to measure the height of the shaped charge cutting cable sample 200 and the circular calibration object 300, completing the height detection and adjustment in step S3 of Embodiment 1.

[0137] The detection component 120 includes an illumination unit 121 and an image acquisition unit. The image acquisition unit includes an area array camera 122 and a zoom lens 123, which are used to acquire images of the energy-cutting cable sample 200 and the circular calibration object 300. The illumination unit 121 uses a ring light source to provide uniform illumination for image acquisition.

[0138] like Figure 5 As shown, the three-dimensional moving system 130 includes a first driving device for driving the precision compensation platform 110 to move in the horizontal plane; the first driving device includes a first servo cylinder 131 and a second servo cylinder 132 arranged orthogonally; used to adjust the position of the precision compensation platform 110 in the horizontal plane. Further, the three-dimensional moving system 130 also includes a third servo cylinder 133, which drives the detection component 120 to move in the vertical direction to adjust the distance between the detection component 120 and the shaped charge cutting cable sample 200 and the circular calibration object 300.

[0139] The control system coordinates the operation of the various components and executes the adaptive detection method of Example 1. Specifically, it includes:

[0140] In step S1, the mass of the shaped charge cutting cable sample 200 is obtained by the mass sensor on the support platform, and when the mass data is greater than 0, the three-dimensional moving system 130 is controlled to adjust the positional relationship between the shaped charge cutting cable sample 200 and the detection component 120 and to acquire calibration images.

[0141] The control system's calculation module calculates the area ratio and sharpness based on the acquired calibration images, and further performs adaptive fine-tuning through the three-dimensional motion system 130 to obtain an image of appropriate size and satisfactory sharpness.

[0142] In step S2, the control system automatically acquires the pixel diameter of the circular calibration object 300, and calculates and stores the conversion coefficient based on the pre-entered actual diameter.

[0143] In step S3, the control system obtains the height of the shaped charge cutting cable sample 200 and the circular calibration object 300 through the height detection unit 112, and drives the height registration stage 111 to fine-tune the height of the two support stages so that the shaped charge cutting cable sample 200 and the circular calibration object 300 are on the same imaging plane, and then controls the detection component 120 to perform image acquisition.

[0144] In steps S4 to S6, the control system executes the corresponding algorithm through its calculation module, and finally outputs key dimensions such as the energy-concentrating angle, total width, effective hypotenuse length, total height, back shell thickness and energy-concentrating apex shell thickness, as well as the actual cross-sectional area and bulk density of the 200-shaped surface of the energy-concentrating cutting cable sample obtained by calculating the key dimensions.

[0145] In summary, this invention provides an adaptive detection method for the dimensions and bulk density of shaped charge cutting cables. Targeting the composite surface structure of shaped charge cutting cables with "irregular thin walls + arc contours," it proposes a novel detection framework through vision-laser fusion, specialized calibration materials, and a three-level optimization algorithm, demonstrating high specificity. This adaptive detection method achieves fully automated, integrated, and high-precision detection of key dimensions and bulk density of shaped charge cutting cable surfaces. It successfully integrates previously separate operational steps into a coherent process, replacing the outdated, manual, step-by-step approach. This invention also provides an adaptive detection device, used in conjunction with the aforementioned adaptive detection method, which can quickly and efficiently complete the detection of shaped charge cutting cable surfaces through high-precision position and height adjustment, providing a new technical path for the precision detection of pyrotechnic products.

[0146] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An adaptive detection method for the surface dimensions and bulk density of a shaped charge cutting cable, characterized in that, Includes the following steps: S1. Obtain the mass of the shaped charge cutting cable sample; S2. Use a circular calibration object to calibrate the conversion coefficient between pixel size and actual size; S3. Detect the height difference between the shaped charge cutting cable sample and the circular calibration object, adjust their heights to be on the same imaging plane, and acquire the surface image of the shaped charge cutting cable sample. S4. Preprocess, extract and optimize the profile image of the shaped cutting cable sample to obtain a complete profile. S5. Extract feature curves based on the optimized surface contour and calculate key dimensions of the surface; S6. Calculate the bulk density by combining the mass, height, and cross-sectional area of ​​the shaped surface profile of the shaped cutting cable sample; Step S4 includes three levels of processing: S41. The image is reduced in dimension by gray-scale weighting and smoothed by bilateral filtering. Then, the initial contour is extracted by Canny algorithm. S42. Use morphological dilation, erosion and area thresholding to remove false contours; S43. The continuity of the fracture contour is achieved through morphological closing operations, opening operations, and hole filling. In step S5, the critical dimension calculation employs a piecewise smoothing algorithm based on random sampling, including: S51. Randomly select points from the characteristic curve to calculate the linear parameters, and iteratively filter the set of internal points to divide the linear segment; S52. Mark the points between adjacent linear segments as arc segments, and optimize the arc parameters using least squares and Levenberg-Marquardt algorithms. S53. Extract the energy-concentrating angle, total width, effective hypotenuse length, total height, back shell thickness, and energy-concentrating apex shell thickness based on linear segments and circular arc segments.

2. The adaptive detection method according to claim 1, characterized in that, In step S2, the conversion coefficient calibration includes: An image of the circular calibration object is acquired, its pixel diameter is measured, and the conversion coefficient is calculated using the actual diameter of the circular calibration object. The formula for calculating the conversion coefficient is as follows: k con = D / d In the formula, D The actual size of the circular calibration object. d The pixel size of the circular calibrator.

3. The adaptive detection method according to claim 1, characterized in that, A calibration step is included before step S2: The calibration image of the shaped charge cutting cable sample is pre-acquired, and the clarity and size of the calibration image are ensured by adaptively adjusting the imaging parameters.

4. The adaptive detection method according to claim 3, characterized in that, Adaptive adjustment of imaging parameters includes: Calculate the ratio of the area of ​​the minimum bounding rectangle of the profile in the calibration image to the area of ​​the whole image, and adjust the distance between the camera and the shaped charge cutting cable sample to make the ratio close to a preset threshold. The image sharpness is evaluated using a gray-level gradient squared sum algorithm, and the lens focal length, aperture and exposure time are automatically adjusted until the sharpness meets the standard.

5. The adaptive detection method according to claim 4, characterized in that, Step S42 includes: Morphological dilation of the contour image: Repeat n times; After median filtering and smoothing, morphological erosion is performed: Repeat n times; Calculate the area of ​​all contours, and mark contours with an area less than 1 / 15 of the largest contour area as pseudo contours and remove them. The value of n ranges from 8 to 15.

6. The adaptive detection method according to claim 5, characterized in that, Step S43 includes: Perform morphological closing operation on the contour image: Repeat n times; Fill the holes that do not correspond to the maximum contour; Then perform morphological opening operations: Repeat n times to smooth the outline; The value of n ranges from 8 to 15.

7. The adaptive detection method according to claim 6, characterized in that, The optimization objective of the circular arc segment parameters is 。 8. An adaptive detection device for the surface dimensions and bulk density of a shaped charge cutting cable, used to execute the adaptive detection method as described in any one of claims 1 to 7, characterized in that, include: A precision compensation platform is used to support the shaped charge cutting cable sample and the circular calibration object; The detection component includes an image acquisition unit and an illumination unit. The image acquisition unit includes an area array camera and a zoom lens for acquiring images of the shaped charge cutting cable sample and the circular calibration object. The illumination unit uses a ring light source to provide uniform illumination for image acquisition. A three-dimensional moving system is used to precisely adjust the relative position of the detection component and the accuracy compensation platform; A height registration stage is provided on the precision compensation platform. It includes at least two independently adjustable support platforms for placing the shaped charge cutting cable sample or the circular calibration object. A mass sensor is provided in the support platform for measuring the mass of the shaped charge cutting cable sample or the circular calibration object. The height detection unit includes a laser altimeter for measuring the height of the shaped charge cutting cable sample and the circular calibration object; A control system is used to coordinate the operation of the components and execute the adaptive detection method.

Images