Helmet pressure self-adaptive manufacturing method based on spiral photonic crystal fiber array

Abstract

The invention discloses a helmet pressure self-adaptive manufacturing method based on a spiral photonic crystal fiber array, and the method comprises the steps: enabling a photonic crystal fiber array to be embedded in a helmet lining, and monitoring the pressure distribution in real time when the helmet is in contact with a head; collecting pressure data, and demodulating phase change by using phase modulation and interference technologies to quantify pressure; converting the collected pressure data into point cloud data in a three-dimensional space, wherein each point comprises position information and a pressure value; combining the generated pressure point cloud data with the head three-dimensional model, and generating a continuous three-dimensional pressure field through a radial basis function interpolation algorithm; aligning the optical fiber pressure point cloud with the head 3D scanning model through an ICP algorithm; based on the generated three-dimensional pressure field, the thickness distribution of the foaming material is calculated in combination with a nonlinear stress-strain curve of the foaming material; the thickness data of the foaming material obtained through calculation is imported into numerical control foaming forming equipment, mold filling parameters are dynamically adjusted, and personalized customization of the helmet is achieved.

Description

Technical Field

[0001] The present invention relates to a helmet pressure adaptive manufacturing method based on a spiral photonic crystal fiber array. Background Art

[0002] Traditional helmet fit detection mostly relies on discrete pressure sensors (such as piezoelectric films), which have problems such as low spatial resolution and poor anti-electromagnetic interference. Although existing fiber optic sensors have high sensitivity, they are limited by linear layout and packaging processes and are difficult to adapt to the requirements of helmet curved surfaces and dynamic pressure monitoring. When manufacturing helmets, the internal foaming materials cannot accurately conform to the shape of the head, resulting in poor comfort or ineffective protection.

[0003] In the field of helmet manufacturing, the existing technologies have the following defects, which severely limit the comfort, protection performance, and production efficiency of helmets.

[0004] 1) Limitations of pressure monitoring technology: Traditional helmet fit detection mainly relies on discrete pressure sensors (such as piezoelectric films). However, these sensors have the problem of low spatial resolution and cannot accurately capture the minute pressure changes in the contact area between the helmet and the head.

[0005] 2) Adaptability problems of fiber optic sensors: Although existing fiber optic sensors have high sensitivity, their linear layout and packaging processes limit their application on helmet curved surfaces. The fibers are difficult to adapt to the complex curved surface structure of helmets, resulting in incomplete pressure monitoring coverage and inability to meet the requirements of dynamic pressure monitoring.

[0006] 3) Static problems in foam material customization: In the existing helmet manufacturing process, the thickness adjustment of foam materials relies on static 3D scanning technology. This static method cannot respond in real time to the head deformation during movement, resulting in the foam materials being unable to accurately conform to the shape of the head. This not only affects the comfort of the helmet but may also lead to ineffective protection, especially in scenarios with dynamic pressure changes.

[0007] 4) Insufficiencies in data processing and visualization: Existing technologies have deficiencies in pressure data processing and visualization and cannot generate an intuitive three-dimensional pressure field model. This makes it difficult for manufacturers to accurately analyze the pressure distribution characteristics in each area of the helmet inner wall, and thus they cannot optimize the thickness distribution of foam materials targeted. Summary of the Invention

[0008] In view of the above defects, the present technical solution proposes a helmet pressure adaptive manufacturing method based on a spiral photonic crystal fiber array. Through high-precision pressure monitoring, real-time data feedback, three-dimensional pressure field generation, and a closed-loop manufacturing process, it solves the problems of poor comfort, ineffective protection, and low production efficiency in the existing technologies, realizes the personalized and rapid customization of helmets, and significantly improves the manufacturing level and user experience of helmets.

[0009] The technical solutions adopted in the present invention are as follows:

[0010] A helmet pressure self-adaptive manufacturing method based on a spiral photonic crystal fiber array, comprising the following steps:

[0011] 1) Embedding a photonic crystal fiber array that meets the bending loss resistance into the helmet lining, and real-time monitoring of the pressure distribution when the helmet contacts the head;

[0012] 2) Collecting pressure data through a flexible spiral photonic crystal fiber, and demodulating the phase change by using phase modulation and interference techniques to quantify the pressure;

[0013] 3) Converting the collected pressure data into point cloud data in three-dimensional space, and each point contains position information and a pressure value;

[0014] 4) Combining the generated pressure point cloud data with the three-dimensional model of the head, and generating a continuous three-dimensional pressure field through a radial basis function interpolation algorithm;

[0015] 5) Aligning the fiber optic pressure point cloud with the 3D scan model of the head through the ICP algorithm;

[0016] 6) Based on the generated three-dimensional pressure field, combining with the non-linear stress-strain curve of the foaming material, calculating the thickness distribution of the foaming material;

[0017] 7) Importing the calculated thickness data of the foaming material into a numerically controlled foaming forming device, dynamically adjusting the mold filling parameters, realizing an integrated process of measurement - calculation - manufacturing, and completing the personalized customization of the helmet.

[0018] Furthermore, a flexible substrate is arranged inside the helmet, a bionic wrinkled structure is designed on the surface of the flexible substrate, each photonic crystal fiber is wound in a spiral manner to form a sensing unit, and a plurality of sensing units are embedded in the flexible substrate in an array arrangement.

[0019] Furthermore, in step 1), the bending loss resistance of the photonic crystal fiber is calculated by the following formula:

[0020]

[0021] Where: Lbend represents the bending loss, λ represents the optical wave wavelength, R represents the fiber bending radius, and n1 and n2 respectively represent the refractive indices of the fiber core and the cladding.

[0022] Furthermore, in step 1), when monitoring the pressure distribution when the helmet contacts the head, the phase change is converted into an optical intensity signal through a Mach-Zehnder interferometer, and the output optical intensity formula is as follows:

[0023]

[0024] Among them, I represents the output light intensity, I0 represents the initial light intensity, and ΔΦ represents the phase change.

[0025] Furthermore, in step 2), the formula for the phase change caused by each turn of the helical structure of the photonic crystal fiber is as follows:

[0026]

[0027] Among them, ΔΦ represents the phase change, λ represents the optical wave wavelength, n represents the refractive index of the fiber, ΔL represents the change in fiber length, and Δn represents the change in refractive index, which is caused by the photoelastic effect and satisfies:

[0028]

[0029] Among them, p 11 is the radial photoelastic coefficient, p 12 is the transverse photoelastic coefficient, ∈ x is the axial strain component, ∈ y and ∈ z are the radial strain components; considering that ∈ x and ∈ y are 0, so ∈ z is:

[0030]

[0031] Therefore, Δn is expressed as:

[0032]

[0033] Substituting the refractive index change into the phase change formula, we get

[0034]

[0035] After organizing the formula, we get

[0036]

[0037] Among them: the length of the fiber is L, its cross-sectional area is regarded as an ellipse, the length of the major axis of the ellipse is set as a, and the length of the minor axis is b; Δb is the compression amount of the minor axis b in the z direction, and ΔΦ is the phase change measured at the measurement point.

[0038] Furthermore, in step 3), the formula for generating the pressure point cloud is as follows:

[0039] P cloud ={(xi, y i , z i , P i )∣i = 1, 2, …, N}

[0040] p i = △bk

[0041] Among them, (xi, yi, zi) represents the three-dimensional coordinates of the i-th measurement point, and P i represents the pressure value at this point, k is the elastic coefficient of the silicone substrate, following Hooke's law, and N is the total number of measurement points.

[0042] Furthermore, in step 4), the pressure field interpolation formula is as follows:

[0043]

[0044] Among them, P(x, y, z) represents the three-dimensional pressure field, and w i represents the weight coefficient, and P i represents the pressure value of the i-th fiber optic measurement point, x i represents the coordinates of the i-th fiber optic measurement point, and σ represents the width parameter of the radial basis function.

[0045] Furthermore, in step 5), when aligning the fiber optic pressure point cloud with the head 3D scan model, the minimum registration error satisfies the following formula:

[0046]

[0047] Among them, R represents the rotation matrix, which is used to rotate the fiber optic pressure point cloud from its own coordinate system to the coordinate system of the head 3D scan model; T represents the translation vector, which is used to translate the fiber optic pressure point cloud in space to a position aligned with the head 3D scan model; P fiber and P head respectively represent the corresponding points on the fiber optic point cloud and the head geometric model.

[0048] Furthermore, in step 6), the calculation formula for the thickness distribution of the foaming material is:

[0049] σ(ε) = E·ε + β·ε n

[0050] Among them, σ represents the stress, that is, the pressure P generated by the foaming material when subjected to an external force, with the unit kPa;

[0051] ε represents the strain, t0 is the initial thickness, and t i is the thickness after compression

[0052] E represents the elastic modulus;

[0053] β and n are the characteristic parameters of the foaming material;

[0054] The formula for inversely calculating the thickness of the foaming material is as follows:

[0055]

[0056] The present invention has the following beneficial effects:

[0057] 1. High-precision pressure monitoring: By using a flexible spiral photonic crystal fiber array, it can monitor the pressure distribution in real time when the helmet contacts the head, providing high-precision pressure data.

[0058] 2. Bending loss resistance: The fiber array design meets the requirements of bending loss resistance, ensuring data accuracy in helmet bending and dynamic pressure monitoring.

[0059] 3. Three-dimensional pressure field generation: Through the radial basis function interpolation algorithm, the discrete pressure point cloud data is converted into a continuous three-dimensional pressure field, providing intuitive visualization data.

[0060] 4. Personalized customization: Based on the generated three-dimensional pressure field, the thickness distribution of the foaming material is inversely calculated to achieve personalized customization of the foaming material of the helmet lining, improving the fit and comfort of the helmet.

[0061] 5. Closed-loop manufacturing process: It realizes an integrated process from pressure monitoring to foaming molding. By real-time data feedback, the manufacturing process is optimized, shortening the production cycle and improving the manufacturing efficiency.

[0062] 6. High sensitivity and anti-interference ability: By using phase modulation and interference technologies, the sensitivity of pressure monitoring and the anti-electromagnetic interference ability are improved.

[0063] 7. Quick response: It can respond in real time to the head deformation during movement, ensuring accurate pressure monitoring and foaming material adjustment in a dynamic environment.

[0064] 8. Cost-effectiveness: By optimizing the manufacturing process and reducing material waste, the production cost is reduced and the economic benefit is improved.

[0065] 9. Innovative design: Combining fiber optic sensing technology and intelligent manufacturing technology, it fills the gap of real-time feedback and closed-loop manufacturing in the existing technology, with significant technological breakthroughs and market potential.

[0066] 10. Wide application scenarios: It is applicable to multiple fields such as sports, medical, and military, and can significantly improve the comfort and protection effect of helmets.

[0067] 11. Data visualization: The generated three-dimensional pressure topology map can intuitively reflect the pressure magnitude and distribution characteristics of each area on the inner wall of the helmet on the head, providing a quantitative basis for dynamically adjusting the thickness of the foaming material. Description of the Drawings

[0068] Figure 1 It is a flow chart of the present invention.

[0069] Figure 2Schematic diagram of the layout of the spiral PCF array (cross-section of the helmet liner).

[0070] Figure 3 Schematic diagram of a sensing unit (a point for measuring the pressure point cloud).

[0071] Figure 4 Schematic diagram of the compression deformation in the z direction (i.e., calculating Δb). Detailed implementation manners

[0072] The present invention will be further described below in conjunction with the accompanying drawings.

[0073] As Figures 1 to 4 shown, a helmet pressure adaptive manufacturing method based on a spiral photonic crystal fiber array of the present invention realizes an integrated process from pressure monitoring to foaming molding, ensures the personalized customization of the helmet, improves the comfort and protection effect of the helmet, optimizes the manufacturing process, and reduces the production cost. The specific steps are as follows:

[0074] I. Pressure monitoring step

[0075] As Figure 2 , each flexible spiral photonic crystal fiber (PCF) is wound in a spiral manner to form a sensing unit, and several such sensing units are embedded in a flexible substrate (such as a polyimide film or a silicone thin layer) of the helmet liner in an array form. The surface of the flexible substrate is designed with a bionic wrinkled structure, and several sensing units are embedded in the flexible substrate in an array arrangement, covering key areas such as the temporal region and the occipital bone. These optical fibers can real-time monitor the pressure distribution when the helmet contacts the head and provide high-precision pressure data. The anti-bending loss design of the optical fibers ensures the data accuracy in the helmet bending and dynamic pressure monitoring.

[0076] Assume the length of the optical fiber is L, and the spiral cross-sectional area formed by it is regarded as an ellipse (as Figure 4 ), assume the major axis length of the ellipse is a, and the minor axis length is b. The light propagates along the x direction, and the spiral extends along the y direction with a negligible distance. The pressure mainly causes compression in the z direction. Therefore, it can be considered that the length L and the major axis a are basically unchanged, and the minor axis b will be compressed, resulting in a change in the light intensity.

[0077] The anti-bending loss of the photonic crystal fiber is calculated by the following formula:

[0078]

[0079] where: Lbend represents the bending loss, λ represents the optical wave wavelength, R represents the optical fiber bending radius, and n1 and n2 respectively represent the refractive indices of the optical fiber core and the cladding.

[0080] When monitoring the pressure distribution during the contact between the monitoring helmet and the head, the phase change is converted into an optical intensity signal through a Mach-Zehnder interferometer, and the output optical intensity formula is as follows:

[0081]

[0082] Among them, I represents the output optical intensity, I0 represents the initial optical intensity, and ΔΦ represents the phase change.

[0083] II. Pressure Data Acquisition and Processing

[0084] Combined with Figure 3 and Figure 4 , pressure data is collected through a flexible spiral photonic crystal fiber, and phase modulation and interference techniques are used to demodulate the phase change to quantify the pressure;

[0085] The phase change formula caused by each turn of the helix is as follows:

[0086]

[0087] Among them, ΔΦ represents the phase change, λ represents the light wave wavelength, n represents the refractive index of the optical fiber, ΔL represents the change in the optical fiber length, Δn represents the change in the refractive index. Since ΔL = 0, the formula can be simplified. Δn is caused by the photoelastic effect and satisfies:

[0088]

[0089] Among them, p 11 is the radial photoelastic coefficient, p 12 is the transverse photoelastic coefficient, ∈ x is the axial strain component, ∈ y and ∈ z radial strain component; considering that ∈ x and ∈ y are 0, so ∈ z is:

[0090]

[0091] Therefore, Δn is expressed as

[0092]

[0093] Furthermore, substituting the refractive index change into the phase change formula, we get

[0094]

[0095] After organizing the formula, we get

[0096]

[0097] where Δb is the compression of the minor axis b in the z direction.

[0098] III. Pressure Point Cloud Generation

[0099] Convert the collected pressure data into point cloud data in three-dimensional space, where each point contains position information and a pressure value. The formula for generating the pressure point cloud is as follows:

[0100] P cloud = {(xi, y i , z i , P i ) | i = 1, 2, …, N}

[0101] p i = △bk

[0102] where (xi, yi, zi) represents the three-dimensional coordinates of the i-th measurement point, P i represents the pressure value at this point, k is the elastic coefficient of the silicone substrate, following Hooke's law, and N is the total number of measurement points. This data conversion provides a basis for the subsequent generation of the three-dimensional pressure field.

[0103] IV. Pressure Field Interpolation

[0104] Combine the generated pressure point cloud data with the three-dimensional head model and generate a continuous three-dimensional pressure field through the radial basis function interpolation algorithm. The formula for pressure field interpolation is as follows:

[0105]

[0106] where P(x, y, z) represents the three-dimensional pressure field, w i represents the weight coefficient, P i represents the pressure value of the i-th fiber optic measurement point, x i represents the coordinates of the i-th fiber optic measurement point, and σ represents the width parameter of the radial basis function. The interpolation algorithm of the present invention can generate intuitive visualization data to help analyze the pressure distribution characteristics.

[0107] V. Alignment of Pressure Point Clouds

[0108] Align the fiber optic pressure point cloud with the head 3D scan model through the ICP algorithm. When aligning the fiber optic pressure point cloud with the head 3D scan model, the minimum registration error satisfies the following formula:

[0109]

[0110] where R represents the rotation matrix used to rotate the fiber optic pressure point cloud from its own coordinate system to the coordinate system of the head 3D scan model; T represents the translation vector used to translate the fiber optic pressure point cloud in space to a position aligned with the head 3D scan model; P fiber and Phead respectively represent the corresponding points on the optical fiber point cloud and the head geometry model.

[0111] VI. Calculation of the thickness of the foaming material

[0112] Based on the generated three-dimensional pressure field and combined with the non-linear stress-strain curve of the foaming material, calculate the thickness distribution of the foaming material. The calculation formula for the thickness distribution of the foaming material is:

[0113] σ(ε) = E·ε + β·ε n

[0114] Among them, σ represents stress, that is, the pressure P generated when the foaming material is subjected to an external force, with the unit kPa;

[0115] ε represents strain, t0 is the initial thickness, and t i is the thickness after compression

[0116] E represents the elastic modulus;

[0117] β and n are the characteristic parameters of the foaming material;

[0118] The formula for inversely calculating the thickness of the foaming material is as follows:

[0119]

[0120] The above calculation method can accurately control the filling of the foaming material, improving the fit and comfort of the helmet.

[0121] VII. Foaming molding

[0122] Import the calculated thickness data of the foaming material into the numerical control foaming molding equipment, dynamically adjust the mold filling parameters, realize the integrated process of measurement - calculation - manufacturing, and complete the personalized customization of the helmet. This closed-loop manufacturing process optimizes the manufacturing process through real-time data feedback, shortens the production cycle, and improves the manufacturing efficiency.

[0123] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, several improvements can be made without departing from the principle of the present invention, and these improvements should also be regarded as the protection scope of the present invention.

Claims

1. A helmet pressure self - adaptive manufacturing method based on a spiral photonic crystal fiber array, characterized in that: It includes the following steps: 1) Embed a photonic crystal fiber array that meets the bending loss resistance in the helmet lining to monitor the pressure distribution in real time when the helmet contacts the head; 2) Collect pressure data through a flexible spiral photonic crystal fiber, and use phase modulation and interference technology to demodulate the phase change to quantify the pressure; 3) Convert the collected pressure data into point cloud data in three-dimensional space, and each point contains position information and pressure value; 4) Combine the generated pressure point cloud data with the three-dimensional model of the head, and generate a continuous three-dimensional pressure field through a radial basis function interpolation algorithm; 5) Align the fiber optic pressure point cloud with the 3D scan model of the head through the ICP algorithm; 6) Based on the generated three-dimensional pressure field, combine the non-linear stress-strain curve of the foaming material to calculate the thickness distribution of the foaming material; 7) Import the calculated thickness data of the foaming material into a numerically controlled foaming molding device, dynamically adjust the mold filling parameters, and realize the integrated process of measurement - calculation - manufacturing to complete the personalized customization of the helmet.

2. The helmet pressure adaptive manufacturing method based on a spiral photonic crystal fiber array according to claim 1, wherein: A flexible substrate is set inside the helmet. The surface of the flexible substrate is designed with a bionic wrinkled structure. Each photonic crystal fiber is wound in a spiral manner to form a sensing unit, and several sensing units are embedded in the flexible substrate in an array arrangement.

3. The helmet pressure adaptive manufacturing method based on a spiral photonic crystal fiber array according to claim 1, characterized in that: In step 1), the bending loss resistance of the photonic crystal fiber is calculated by the following formula: Where: Lbend represents the bending loss, λ represents the optical wave wavelength, R represents the fiber bending radius, and n1 and n2 respectively represent the refractive indices of the fiber core and the cladding.

4. The helmet pressure self-adaptive manufacturing method based on a spiral photonic crystal fiber array according to claim 1, wherein: In step 1), when monitoring the pressure distribution when the helmet contacts the head, the phase change is converted into an optical intensity signal through a Mach-Zehnder interferometer, and the output optical intensity formula is as follows: Where, I represents the output optical intensity, I0 represents the initial optical intensity, and ΔΦ represents the phase change.

5. The helmet pressure self - adaptive manufacturing method based on a spiral photonic crystal fiber array according to claim 1, characterized in that: In step 2), the phase change formula caused by each turn of the spiral of the photonic crystal fiber is as follows: Where, ΔΦ represents the phase change, λ represents the optical wave wavelength, n represents the refractive index of the fiber, ΔL represents the fiber length change, Δn represents the refractive index change, which is caused by the photoelastic effect and satisfies: where p 11 is the radial photoelastic coefficient, p 12 is the transverse photoelastic coefficient, ∈ x is the axial strain component, ∈ y and ∈ z are the radial strain components; considering that ∈ x and ∈ y are 0, so ∈ z is: Therefore, Δn is expressed as: Substitute the refractive index change into the phase change formula to get Rearrange the formula to get Where: the length of the fiber is L, and its cross-sectional area is regarded as an ellipse. Let the long axis length of the ellipse be a and the short axis length be b; Δb is the compression amount of the short axis b in the z direction, and ΔΦ is the phase change measured at the measuring point.

6. The helmet pressure adaptive manufacturing method based on a spiral photonic crystal fiber array according to claim 5, characterized in that: In step 3), the pressure point cloud generation formula is as follows: P cloud = {(xi, y i , z i , P i ) | i = 1, 2, …, N} p i = Δbk Among them, (xi, yi, zi) represents the three-dimensional coordinates of the i-th measurement point, and P i represents the pressure value at this point, k is the elastic coefficient of the silicone substrate, following Hooke's law, and N is the total number of measurement points.

7. The helmet pressure self-adaptive manufacturing method based on a spiral photonic crystal fiber array according to claim 1, characterized in that: In step 4), the pressure field interpolation formula is as follows: Among them, P(x, y, z) represents the three-dimensional pressure field, w i represents the weight coefficient, P i represents the pressure value of the i-th optical fiber measurement point, x i represents the coordinates of the i-th optical fiber measurement point, and σ represents the width parameter of the radial basis function.

8. The helmet pressure adaptive manufacturing method based on a spiral photonic crystal fiber array according to claim 1, wherein: In step 5), when aligning the fiber optic pressure point cloud with the 3D scan model of the head, the minimum registration error satisfies the following formula: Among them, R represents the rotation matrix, which is used to rotate the fiber optic pressure point cloud from its own coordinate system to the coordinate system of the head 3D scan model; T represents the translation vector, which is used to translate the fiber optic pressure point cloud in space to a position aligned with the head 3D scan model; P fiber and P head respectively represent the corresponding points on the fiber optic point cloud and the head geometric model.

9. The helmet pressure adaptive manufacturing method based on a spiral photonic crystal fiber array according to claim 1, wherein: In step 6), the calculation formula for the thickness distribution of the foaming material is: σ(ε) = E·ε + β·ε n Where, σ represents the stress, that is, the pressure P generated by the foaming material when subjected to an external force, with the unit kPa; ε represents strain, t0 is the initial thickness, and t i is the thickness after compression E represents the elastic modulus; β and n are foaming material characteristic parameters; The formula for inversely calculating the thickness of the foaming material is as follows:

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