Synchronous detection system for surface density and wall thickness uniformity of composite bulletproof helmet

Abstract

The invention discloses a synchronous detection system for surface density and wall thickness uniformity of a composite bulletproof helmet, which belongs to the technical field of bulletproof helmet detection and comprises a detector cooperative control module, a two-parameter synchronous acquisition module, a data preprocessing module, a two-parameter cooperative constraint module and an intelligent modeling output module. Through the integrated design that the detection unit integrates the 3D scanning detector, the microwave thickness measuring probe and the beta-ray density detector, and in cooperation with the synchronous triggering and double-channel signal acquisition mode of the double-parameter synchronous acquisition module, synchronous acquisition of the surface density and wall thickness data of the helmet is realized; the problems that traditional step-by-step detection is low in efficiency and poor in data relevance are solved, the detection efficiency and the accuracy of quality judgment are greatly improved, meanwhile, a detection report containing process optimization suggestions is output, the problems that traditional detection results are not visual, and connection with the production process is not smooth are effectively solved, and the product quality is improved. And accurate guidance can be provided for mass production quality control and process adjustment.

Description

Technical Field

[0001] This invention belongs to the field of bulletproof helmet testing technology, specifically relating to a system for synchronously detecting the areal density and wall thickness uniformity of composite bulletproof helmets. Background Technology

[0002] Bulletproof helmets are protective equipment that protects the human head from high-speed projectiles or fragments. Currently, in-depth research has been conducted on bulletproof performance and wearing comfort, including bulletproof materials, helmet structure, composite structure, molding process, etc. Through continuous technological innovation, traditional metal helmets have been gradually replaced by composite bulletproof helmets. Among them, the areal density and wall thickness uniformity of composite bulletproof helmets are the core indicators that determine their bulletproof protection performance, which are directly related to the safety of use and the stability of product quality. It is particularly important to inspect the areal density and wall thickness uniformity after the composite bulletproof helmet is manufactured. Currently, existing technologies mostly employ a step-by-step detection mode for surface density and wall thickness, requiring the use of multiple devices for separate operation. This not only results in low detection efficiency but also easily leads to a lack of correlation between the two parameters in the same area, thus affecting the accuracy of quality judgment. Furthermore, the lack of intuitive visualization after detection makes it difficult to quickly locate abnormal areas and fails to provide precise guidance for optimizing production processes. To avoid the aforementioned technical problems, it is indeed necessary to provide a system for simultaneously detecting the areal density and wall thickness uniformity of composite bulletproof helmets to overcome the deficiencies in the prior art. Summary of the Invention

[0003] The purpose of this invention is to provide a system for simultaneously detecting the areal density and wall thickness uniformity of composite bulletproof helmets, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a system for synchronously detecting the areal density and wall thickness uniformity of composite bulletproof helmets, comprising: A rotating platform is used to support the composite bulletproof helmet and drive it to rotate around a central axis. An arc-shaped track is fixed above the rotating platform by a bracket, and the arc curvature of the arc-shaped track is adapted to the contour of the outer surface of the helmet; The detection unit, slidably connected to the arc-shaped track, is used to detect the uniformity of the surface density and wall thickness of the composite bulletproof helmet.

[0005] As a preferred embodiment, the detection unit includes: A 3D scanning detector is used to scan the outer surface of a helmet and generate a three-dimensional shape model of the helmet. A microwave thickness measurement probe is symmetrically arranged on one side of the 3D scanner and is used to measure the helmet wall thickness; A beta-ray density detector, symmetrically positioned on the other side of the 3D scanner, is used to measure the surface density of the helmet. The probe generates high-frequency microwave pulses, such as 10 GHz, with a wavelength of approximately 30 mm, which can distinguish between different material layers such as anti-penetration layers, energy-absorbing layers, and buffer layers. The interlayer interface is identified by the intensity of the reflected signal, and the dual-parameter measurement reduces misjudgments, thereby improving the detection confidence.

[0006] A composite bulletproof helmet surface density and wall thickness uniformity synchronous detection system includes a detector coordination control module for controlling the synchronization of the rotation speed of the rotary table and the moving speed of the detection unit to generate a composite scanning trajectory covering the helmet surface. A dual-parameter synchronous acquisition module, connected to the detection unit, is used to synchronously acquire wall thickness data and areal density data at each measuring point of the helmet; The data preprocessing module is used to perform noise filtering and outlier processing on the collected wall thickness and areal density data. The dual-parameter collaborative constraint module, based on the physical correlation model of wall thickness and surface density, performs rationality verification on dual-parameter data for the same region. The intelligent modeling output module is used to generate a 3D model of the helmet from the corrected data and to perform gradient color rendering and annotation according to the preset quality level rules.

[0007] As a preferred embodiment, the composite scanning trajectory generated by the collaborative control module is a spiral trajectory with circumferential rotation and radial arc, used to fully cover the top, sides, edges and chin protection area of ​​the helmet.

[0008] As a preferred implementation, the dual-parameter synchronous acquisition module adopts a synchronous triggering and dual-channel signal acquisition mode, and ensures the timing deviation of wall thickness data and areal density data acquisition in the same area through timestamp calibration.

[0009] As a preferred embodiment, the noise filtering of the data preprocessing module includes Gaussian convolution filtering for microwave signals and sliding window weighted average filtering for beta-ray signals.

[0010] As a preferred implementation, the intelligent modeling output module completes the point cloud data through radial basis function interpolation to generate a complete 3D model of the helmet.

[0011] As a preferred embodiment, the gradient color rendering annotation includes marking deviation types for substandard areas, and the deviation types include being too thick, too thin, too dense, and too sparse.

[0012] As a preferred implementation, the intelligent modeling output module outputs a detection report, which includes a 3D model screenshot, color-coded legends, percentage of abnormal area, distribution location, and measurement error range.

[0013] Compared with the prior art, the beneficial effects of the present invention are: This invention integrates a 3D scanning detector, a microwave thickness probe, and a beta-ray density detector into a single detection unit. Combined with the synchronous triggering and dual-channel signal acquisition mode of the dual-parameter synchronous acquisition module, it achieves synchronous acquisition of helmet surface density and wall thickness data. This solves the problems of low efficiency and poor data correlation in traditional step-by-step detection, and significantly improves detection efficiency and the accuracy of quality judgment.

[0014] This invention generates a spiral scanning trajectory using a collaborative control module, and combines an arc-shaped track with the helmet's contour to achieve full coverage of the helmet's top, sides, edges, and chin protection areas without blind spots. At the same time, through layered correction, noise filtering, and dual-parameter collaborative operation, it can effectively reduce detection errors and improve the reliability of detection data.

[0015] This invention generates a 3D visualization model through an intelligent modeling output module, marking abnormal areas and deviation types with gradient colors, and simultaneously outputs a test report containing process optimization suggestions. This effectively solves the problems of traditional test results being unintuitive and poorly integrated with production processes, and provides precise guidance for mass production quality control and process adjustment. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the three-dimensional structure of the detection device of the present invention. Figure 1 ; Figure 2 This is a schematic diagram of the three-dimensional structure of the detection device of the present invention. Figure 2 ; Figure 3 This is a flowchart of the detection system of the present invention.

[0017] In the diagram: 1. Rotary platform; 2. Arc track; 3. Detection unit. Detailed Implementation

[0018] The present invention will be further described below with reference to embodiments.

[0019] The following embodiments are used to illustrate the present invention, but should not be used to limit the scope of protection of the present invention. The conditions in the embodiments can be further adjusted according to specific conditions, and simple improvements to the method of the present invention under the premise of the concept of the present invention are all within the scope of protection claimed by the present invention.

[0020] Please see Figure 1-3 The present invention provides a device for simultaneously detecting the areal density and wall thickness uniformity of composite bulletproof helmets, comprising: A rotating platform is used to support the composite bulletproof helmet and drive it to rotate around a central axis. Furthermore, the surface of the rotary table is equipped with an adjustable arc-shaped positioning groove and a flexible anti-slip pad for placing bulletproof helmets, or by using a pre-set mold that fits into the inner wall of the bulletproof helmet, the bulletproof helmet can be positioned and placed, ensuring that the bulletproof helmet will not easily shake when the rotary table rotates, ensuring stability during the measurement process and reducing errors generated during the measurement process. An arc-shaped track is fixedly mounted above the rotating platform by a bracket. The arc curvature of the track is adapted to the contour of the outer surface of the helmet, and the track has a built-in dual grating ruler to achieve dual position feedback.

[0021] The detection unit, slidably connected to the arc-shaped track, is used to detect the surface density and wall thickness uniformity of the composite bulletproof helmet. A sliding seat for mounting the detection unit is provided on the arc-shaped track. A servo motor is installed on the sliding seat. A drive gear is installed on the output shaft of the servo motor. The gear directly meshes with the teeth on the arc-shaped track. When the motor rotates, the gear rolls on the teeth, realizing the precise movement of the sliding seat along the arc-shaped track. Through the control of the servo motor, the movement speed can be adaptively adjusted within the range of 0.5-5mm / s.

[0022] The detection unit includes: The 3D scanning detector is used to scan the outer surface of the helmet and generate a three-dimensional shape model of the helmet; it scans the outer surface of the helmet and collects global spatial location data, providing a basis for generating the three-dimensional shape model. A microwave thickness measurement probe is symmetrically arranged on one side of the 3D scanner and is used to measure the helmet wall thickness; A beta-ray density detector, symmetrically positioned on the other side of the 3D scanner, is used to measure the surface density of the helmet.

[0023] A system for simultaneously detecting the areal density and wall thickness uniformity of composite bulletproof helmets includes: The collaborative control module is used to synchronize the rotation speed of the rotary table with the moving speed of the detection unit to generate a composite scanning trajectory covering the helmet surface. The collaborative control module is signal-connected to the rotary table and linear drive slide of the detection device and is used to control the servo motors that drive the rotary table and slide to work. By synchronously adjusting the rotation speed (10-30 r / min) and the moving speed of the detection unit, the vertical deviation between the probe and the helmet detection surface can also be dynamically corrected using data from the laser alignment instrument in actual operation to generate a composite scanning trajectory of rotational circumferential and arc-shaped radial directions.

[0024] Furthermore, the collaborative control module incorporates an error correction algorithm to receive real-time position feedback data from the rotary table's dual grating rulers and the arc-shaped track at a frequency of 100Hz. This algorithm dynamically corrects the collaborative deviation between the movement step and the rotation angle, ensuring that the scanning trajectory coverage density is ≥10 points / cm. 2 This ensures that there are no blind spots in the testing process; A dual-parameter synchronous acquisition module is connected to the detection unit and is used to synchronously acquire wall thickness data and areal density data at each measuring point of the helmet. The timing deviation of the acquisition at the same measuring point is ≤0.5ms through timestamp calibration, and wall thickness data and areal density data are acquired synchronously. The data preprocessing module is used to perform noise filtering and outlier processing on the collected wall thickness and areal density data. The microwave signal is filtered using Gaussian convolution, as shown in the following formula: ,in These are the pixel values ​​after convolution. The Gaussian kernel radius is k=3-5. The original pixel value is used to smooth interlayer reflection noise using this formula; The beta-ray signal is filtered using a sliding window weighted average, with the following formula: ,in, The areal density data is the weighted average. The weighting coefficients decrease linearly with respect to the distance from the current data point. The data represents the original data within the window, and N is the window size. For high count rates, N = 3-5, and for low count rates, N = 8-10.

[0025] Outlier handling: Calculate the mean of the dataset. with standard deviation Remove excess For the range of data, the abrupt change value is corrected by neighborhood interpolation for the helmet edge region. The formula is as follows: ,in, For the corrected data, For valid data points in the neighborhood, Number of neighboring data points ≥4; The dual-parameter collaborative constraint module, based on the physical correlation model of wall thickness and surface density, performs rationality verification on dual-parameter data for the same region. The intelligent modeling output module is used to generate a 3D model of the helmet from the corrected data and to perform gradient color rendering and annotation according to the preset quality level rules.

[0026] The detection unit includes a 3D scanning detector and microwave thickness probes and beta-ray density detectors symmetrically arranged on both sides of the 3D scanning detector. The 3D scanning detector is mounted on a sliding base, which is slidably connected to an arc-shaped track. The microwave thickness probes and density detectors are used to simultaneously detect the surface density and wall thickness uniformity of the composite bulletproof helmet. The beta-ray density detector is equipped with an anti-scattering shield and a dose dynamic calibration unit.

[0027] The composite scanning trajectory generated by the collaborative control module is a spiral trajectory with circumferential rotation and radial arc, used to fully cover the top, sides, edges, and chin protection area of ​​the helmet.

[0028] The dual-parameter synchronous acquisition module adopts synchronous triggering and dual-channel signal acquisition mode, and ensures the timing deviation of wall thickness data and areal density data acquisition in the same area through timestamp calibration.

[0029] The dual-parameter synchronous acquisition module has a pre-stored database of the helmet's multi-layer structure, microwave dielectric constant, and β-ray attenuation coefficient. The database is generated based on the calibration of a standard composite test block. During testing, layered compensation correction is performed using a calibration formula.

[0030] Wall thickness correction formula: ,in To correct the wall thickness unit to mm, For the actual measured wall thickness, The standard dielectric constant of the target layer, The dielectric constant is the measured value.

[0031] Areal density correction formula: ,in, To correct for the density unit g / cm³ 2 , The intensity of the incident beta rays, The intensity of the transmitted rays. The standard attenuation coefficient of the target layer. To correct the wall thickness.

[0032] The dual-parameter synchronous acquisition module constructs a correlation model between wall thickness and areal density, generated based on a linear regression algorithm, with the following formula: ,in, This is the theoretical value of surface density. This is the measured wall thickness. For material correlation coefficient, R0 is the intercept, obtained by fitting standard sample data. 2 When the measured value is ≥0.98, a local rescan is triggered. The local rescan is used to perform accurate supplementary measurements, thereby addressing data anomalies and missing data issues in a targeted manner. This ensures the reliability of the final test report and the accuracy of process optimization suggestions.

[0033] The intelligent modeling output module completes the point cloud data through radial basis function interpolation to generate a complete 3D model of the helmet; After receiving the corrected data, the 3D point cloud is completed using radial basis function interpolation, as shown in the formula: Where ƒ(x,y,z) represents the interpolated three-dimensional data. These are the interpolation coefficients. A Gaussian kernel is used for the radial basis functions. The coordinates of the interpolation point. Given the coordinates of known data points, where K is the number of known neighboring points, the effective 3D point cloud data is obtained after a full-process optimization involving dual-parameter synchronous acquisition, data preprocessing, composite layer hierarchical correction, and dual-parameter collaborative constraints. This includes the (x, y, z) spatial coordinates of each measurement point and the corresponding corrected wall thickness and surface density data. When the point cloud density is below 0.1 points / mm... 2 When a local rescan is triggered, a complete 3D model of the helmet is generated, and then gradient color rendering and annotation are performed. The gradient color rendering annotation includes marking the deviation type of the non-compliant area. The deviation type includes too thick, too thin, too dense, and too sparse. Specifically, the corrected measured data is compared with the standard theoretical benchmark value, and the deviation direction and type are determined by combining the error threshold. This is achieved through data preprocessing, layer correction, benchmark comparison, and threshold determination. For details, please refer to the following: Too thick: If the standard thickness is 5mm and the actual thickness is 5.04mm, it is judged to be 0.04mm too thick; Too thin: If the standard thickness is 5mm, and the actual measured thickness is 4.95mm, it is judged to be 0.05mm too thin; Deviation threshold corresponding to 3D model color annotation: If there is a slight deviation, mark it in yellow: ±0.02-±0.04mm, and automatically generate text annotation, indicating a thickness deviation of 0.03mm; If the deviation exceeds the standard, mark it in red: >±0.04mm, and automatically generate text annotations, for example, the red area on the rear edge is 0.042mm thinner.

[0034] Simultaneously, the measured density value after correction can be compared with the standard value of areal density; if there is a dispute, the theoretical benchmark value can be used for cross-verification to ensure that it is not a data error; If the density is too high, the measured value will be greater than the standard value, for example, the standard surface density is 2.8 g / cm³. 2 The actual measured value was 3.0 g / cm³. 2 This indicates a density of 0.2 g / cm³. 2 ; If the surface density is too sparse, the measured value will be less than the standard value, for example, the standard surface density is 2.8 g / cm³. 2 The actual measured value was 2.3 g / cm³. 2 This indicates a density of 0.5 g / cm. 2 ; Deviation threshold corresponding to 3D model color annotation: Slight deviation from yellow, ±0.5-±0.8 g / cm³ 2 It also automatically generates text labels, such as "sparse area 0.6g / cm". 2 ; Excessive levels (red): >±0.8g / cm³ 2 It also automatically generates text labels, such as surface density deviation of 0.9 g / cm³. 2 .

[0035] The gradient color rendering annotation includes using different colors to render areas where the helmet's protective performance meets the standards, areas where the helmet's protective performance is basically up to standard but requires attention, and areas where the helmet's protective performance does not meet the standards. In this model, the areas that meet the helmet's protective performance standards are rendered in pure green to visually indicate qualified parts without any manufacturing defects. Areas where the helmet's protective performance is basically up to standard but require attention are highlighted in light yellow, with the average deviation value of that area indicated. Furthermore, areas with substandard protective performance are rendered in dark red, and the spatial coordinates (x, y, z) of the area, the maximum deviation value, and the type of deviation (too thick, too thin, too dense, or too sparse) are marked.

[0036] The intelligent modeling output module outputs a detection report, which includes a 3D model screenshot, color-coded legends, percentage of abnormal area, distribution location, and measurement error range.

[0037] Screenshot of the 3D model: By presenting a complete 3D visualization model of the helmet, it clearly displays the green qualified area, the yellow slightly deviated area and the red excessive area, and supports multi-angle screenshots such as top, side and bottom, so as to further intuitively grasp the overall uniformity of the helmet. The color-coded illustrations are as follows: The error standards corresponding to the gradient colors are indicated, with green representing a wall thickness error ≤ ±0.02mm and a surface density error ≤ ±0.5g / cm³. 2 Yellow indicates a wall thickness error of ±0.02-±0.04 mm or an areal density error of ±0.5-±0.8 g / cm³. 2 Red indicates a wall thickness error > ±0.04 mm or a surface density error > ±0.8 g / cm³. 2 ; The percentage of abnormal areas: By statistically analyzing the percentage of yellow areas with slight deviations and red areas exceeding standards in the total area of ​​the helmet inspection, the overall quality of the helmet's manufacturing process can be quantitatively reflected. For example, "yellow areas account for 1.3% and red areas account for 0.2%", thus enabling a rapid assessment of the quality stability of mass-produced helmets. The locations of the abnormal areas are as follows: By accurately marking the spatial coordinate range of each abnormal area, such as the red area located at X:110-130mm, Y:70-90mm, Z:45-55mm and its corresponding helmet part, such as the transition area between the side and the top, and the right rear edge, the specific location of the helmet defect can be clearly identified. Without professional knowledge, one can clearly know the specific situation of the helmet defect. Measurement error range: The error boundaries for the tests are: wall thickness measurement error ≤ ±0.02 mm, and areal density measurement error ≤ ±0.5 g / cm³. 2 To verify the reliability and accuracy of the test data; By statistically analyzing the above content, the final test report can provide suggestions for process optimization. Combined with the deviation types of abnormal areas (thickness, thinness, density, and sparseness) and their distribution characteristics, it can provide accurate suggestions and guidance for adjusting the ply compaction degree and optimizing the molding pressure distribution in production processes, which will facilitate subsequent optimization of the production and manufacturing of composite bulletproof helmets.

[0038] By repeating the test on the same helmet at least three times, the repeatability error of wall thickness measurement can be ≤ ±0.005 mm, and the repeatability error of areal density measurement can be ≤ ±0.1 g / cm³. 2 .

[0039] During use, the helmet is positioned by placing the composite bulletproof helmet to be tested on the rotating platform. The collaborative control module generates a composite scanning trajectory. The rotating platform rotates at a constant speed, with a selectable speed of 20 r / min. The 3D scanning detector moves along an arc-shaped track, with a selectable moving speed of 2 mm / s. At this time, the wall thickness and surface density data of at least 12,000 measuring points across the entire helmet area are collected through synchronous triggering of dual probes.

[0040] During data processing, the microwave signal undergoes Gaussian convolution. If k=4 and the signal noise intensity is filtered by 18dB, the signal-to-noise ratio increases from 35dB to 52dB. The β-ray signal is affected by the counting statistics, resulting in significant fluctuations in the original data (±0.12g / cm). 2 This can easily interfere with the accuracy of subsequent corrections. The β-ray signal was weighted using a sliding window with N=5, resulting in a count rate of 1200 counts / s. After processing, the data fluctuation was significantly reduced from ±0.12 g / cm³. 2 Reduced to ±0.04 g / cm 2 This effectively suppressed random noise, and the stable data provided a reliable foundation for subsequent stratified correction.

[0041] Furthermore, the edge area of ​​the helmet is prone to sudden changes in data due to changes in the probe's viewing angle. By interpolating M=6 neighboring data and taking the average of 6 reliable data around the jump point to replace it, the error caused by the viewing angle can be basically eliminated.

[0042] with standard deviation Calculate the areal density dataset =2.75g / cm 2 , =0.08g / cm 2 Remove values ​​exceeding [2.51, 2.99] g / cm³. 2 For abnormal data within the range, the helmet edge area was corrected by interpolating M=6 neighboring data points, resulting in two jump values. Before correction, the jump amplitude was 0.05mm / 0.12g / cm. 2 After correction, 0.01mm / 0.03g / cm 2 ,pass The criteria, which are commonly used industry standards for data screening, are used to calculate the areal density dataset. =2.75g / cm 2 The median average of the density of the detection points =0.08g / cm 2 , represents the average fluctuation range of the data deviating from the mean. The normal data range is [2.51, 2.99] g / cm³. 2 By removing three abnormal data points that exceed the range, which are mostly caused by probe interference or surface impurities, the accuracy of subsequent tests can be avoided. The composite layer at each measuring point is determined based on the spatial coordinates of the detector. Different regions correspond to different composite layers on different parts of the helmet. The measurements are then corrected according to the materials of each composite layer. After correction, the single-layer wall thickness measurement deviation is ≤0.015mm, and the areal density deviation is ≤0.2g / cm³. 2 Composite helmets have a multi-layered structure, and the different materials of different layers can cause deviations in the detection signals. By calling the standard parameters of the corresponding layer according to the layer where the measurement point is located, the error of the correction can be avoided and the data can be made more accurate.

[0043] By verifying the data using the correlation model formula, it was found that the wall thickness deviation at the two measuring points was 0.035 mm and the areal density deviation was 0.9 g / cm³. 2 The threshold was exceeded, triggering a local rescan with a step size of 0.08 mm. After correction, the wall thickness deviation was 0.01 mm, and the areal density deviation was 0.3 g / cm³. 2The data meets the requirements of the correlation interval. Since the wall thickness and surface density of helmets made of the same material are strongly correlated, the data is verified to be reasonable by using a pre-established reliable correlation model. If the deviation of both exceeds the standard, the area is re-detected accurately to eliminate unreliable data and further improve the reliability of the data.

[0044] The point cloud was completed using radial basis function interpolation, achieving a point cloud density of 0.12 points / mm. 2 Meets the requirement of ≥0.1 points / mm 2 To meet the requirements, generate a 3D model of the helmet; The markings are done according to color rendering rules, as detailed below; if the green area accounts for 98.5% of the main area, the yellow area accounts for 1.3% of the side and top transition area, and the red area accounts for 0.2% of the right rear edge, with coordinates X: 120mm, Y: 80mm, Z: 50mm, a test report is output simultaneously. The test report includes multi-angle model screenshots, anomaly area statistics, and suggestions for process optimization, such as increasing the number of layers on the right rear edge by 1, and adjusting the molding pressure to 1.2MPa. The test data is transformed into an intuitive 3D model, using different colors to mark qualified, slightly deviated, and out-of-range areas, thereby quickly identifying problems and directly providing suggestions for production adjustments. This allows the test results to be directly linked to process optimization, solving the problem that traditional test results are difficult to understand intuitively and lack adjustment and directional suggestions.

[0045] The same helmet was tested three times, and the repeatability error for wall thickness measurement was ≤ ±0.005 mm, and the repeatability error for areal density measurement was ≤ ±0.1 g / cm³. 2 ; Compared with industry standard testing equipment, the wall thickness deviation is ≤0.01mm and the areal density deviation is ≤0.2g / cm³. 2 This fully verifies the detection accuracy of the present invention; The color markings on the 3D model perfectly match the abnormal areas detected by actual sampling and dissection, with a positioning deviation of ≤1mm, verifying the accuracy of the results visualization.

[0046] By batch testing 100 helmets, 99% of the batches with an abnormal area ratio of ≤2% were qualified, further demonstrating that the present invention is applicable to mass production quality control.

[0047] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A device for simultaneously detecting the areal density and wall thickness uniformity of composite bulletproof helmets, characterized in that, include: A rotating platform is used to support the composite bulletproof helmet and drive it to rotate around a central axis. An arc-shaped track is fixed above the rotating platform by a bracket, and the curvature of the arc-shaped track is adapted to the contour of the outer surface of the helmet. The detection unit, slidably connected to the arc-shaped track, is used to detect the uniformity of the surface density and wall thickness of the composite bulletproof helmet.

2. The composite bulletproof helmet surface density and wall thickness uniformity synchronous detection device according to claim 1, characterized in that, The detection unit includes: A 3D scanning probe is used to scan the outer surface of a helmet and generate a three-dimensional shape model of the helmet. A microwave thickness measurement probe is symmetrically arranged on one side of the 3D scanner and is used to measure the helmet wall thickness; A beta-ray density detector, symmetrically positioned on the other side of the 3D scanner, is used to measure the surface density of the helmet.

3. A system for synchronously detecting the areal density and wall thickness uniformity of a composite bulletproof helmet, using the synchronous detection device for the areal density and wall thickness uniformity of a composite bulletproof helmet as described in any one of claims 1-2, characterized in that, include: The detector collaborative control module is used to control the synchronization of the rotation speed of the rotary table and the moving speed of the detection unit to generate a composite scanning trajectory covering the surface of the helmet. A dual-parameter synchronous acquisition module, connected to the detection unit, is used to synchronously acquire wall thickness data and areal density data at each measuring point of the helmet; The data preprocessing module is used to perform noise filtering and outlier processing on the collected wall thickness and areal density data. The dual-parameter collaborative constraint module, based on the physical correlation model of wall thickness and surface density, performs rationality verification on dual-parameter data for the same region. The intelligent modeling output module is used to generate a 3D model of the helmet from the corrected data and to perform gradient color rendering and annotation according to the preset quality level rules.

4. The composite bulletproof helmet surface density and wall thickness uniformity synchronous detection system according to claim 3, characterized in that: The composite scanning trajectory generated by the collaborative control module is a spiral trajectory with circumferential rotation and radial arc, used to fully cover the top, sides, edges, and chin protection area of ​​the helmet.

5. The composite bulletproof helmet surface density and wall thickness uniformity synchronous detection system according to claim 3, characterized in that: The dual-parameter synchronous acquisition module adopts synchronous triggering and dual-channel signal acquisition mode, and ensures the timing deviation of wall thickness data and areal density data acquisition in the same area through timestamp calibration.

6. The composite bulletproof helmet surface density and wall thickness uniformity synchronous detection system according to claim 3, characterized in that: The noise filtering in the data preprocessing module includes Gaussian convolution filtering for microwave signals and sliding window weighted average filtering for beta-ray signals.

7. The composite bulletproof helmet surface density and wall thickness uniformity synchronous detection system according to claim 3, characterized in that: The intelligent modeling output module completes the point cloud data through radial basis function interpolation to generate a complete 3D model of the helmet.

8. The composite bulletproof helmet surface density and wall thickness uniformity synchronous detection system according to claim 3, characterized in that: The gradient color rendering annotation includes marking deviation types for substandard areas, and the deviation types include too thick, too thin, too dense, and too sparse.

9. The composite bulletproof helmet surface density and wall thickness uniformity synchronous detection system according to claim 3, characterized in that: The intelligent modeling output module outputs a detection report, which includes a 3D model screenshot, color-coded legends, percentage of abnormal area, distribution location, and measurement error range.

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