A Customized Seating Optimization Method and System Based on 3D Scanning and Finite Element Analysis

Abstract

This invention relates to the field of product design technology, specifically to a customized seat optimization method and system based on 3D scanning and finite element analysis. The method involves performing 3D scanning on a seated subject, acquiring and preprocessing the scan data; establishing a finite element model based on the preprocessed scan data, and meshing the subject's hip and leg areas within the model; performing finite element analysis based on set material parameters and boundary conditions to obtain pressure distribution data for the subject's hips and legs; optimizing the material density of the seat's surface functional layer based on the pressure distribution data; optimizing the size, distribution density, and geometric parameters of the support units in the seat's support structure layer based on the pressure distribution data; and verifying the optimized seat using finite element analysis until it meets design requirements. By combining personalized data input with precise mechanical analysis and dynamic structural optimization, the seat is upgraded from a "universal fit" to an "individually customized fit," fundamentally improving comfort and biomechanical compatibility.

Description

Technical Field

[0001] This invention relates to the field of product design technology, specifically to a customized seat optimization method and system based on 3D scanning and finite element analysis. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Traditional seat designs are primarily based on standardized models, such as THUMS (Total Human Model for Safety, a high-precision computer-simulated human body model mainly used for biomechanical analysis in vehicle safety and ergonomics). These models use fixed anatomical parameters (typically reflecting only 50th percentile male characteristics) and have limited dynamic adaptability, leading to significant errors in seat pressure distribution prediction. In non-customized seat designs, because they cannot adapt to individual pelvic tilt, soft tissue thickness, hip shape, and other characteristics, they are prone to causing pressure concentration in the ischial tuberosity area, increasing the risk of pressure sores and musculoskeletal disorders, with particularly pronounced effects on individuals with specific body types and obese individuals.

[0004] While existing seat design solutions have shown some improvement, they still struggle to accurately reflect individual anthropometric features and real-time soft tissue deformation. They fail to meet the demands of modern customized seat design for comfort and biomechanical compatibility in terms of pressure distribution prediction accuracy and dynamic posture adaptability. Furthermore, these design methods are inefficient and have lengthy model preparation cycles, hindering the widespread application of large-scale personalized customization. Summary of the Invention

[0005] To address the technical problems mentioned above, this invention provides a customized seat optimization method and system based on 3D scanning and finite element analysis. By combining personalized data input with precise mechanical analysis and dynamic structural optimization, the seat is upgraded from "universal adaptation" to "individual exclusive adaptation", fundamentally improving comfort and biomechanical compatibility.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of this invention provides a customized seat optimization method based on 3D scanning and finite element analysis, comprising the following steps:

[0008] 3D scans were performed on subjects in a seated position to acquire and preprocess the scan data;

[0009] A finite element model was established based on the preprocessed scan data, and the buttocks and legs of the subject in the model were meshed.

[0010] Finite element analysis was performed based on the set material parameters and boundary conditions to obtain pressure distribution data of the subject's buttocks and legs;

[0011] The material density of the seat surface functional layer is optimized based on pressure distribution data. Specifically, the pressure distribution data is classified to determine areas with different pressure sensitivity and the material density of the seat surface functional layer in each area. When a certain area exceeds the set pressure threshold range, the material density of the seat surface functional layer is increased to enhance support. When the pressure is below the set pressure threshold range, the material density of the surface functional layer is reduced to enhance flexibility, thereby optimizing the material properties of the seat surface functional layer.

[0012] Optimize the size, distribution density, and geometric parameters of the support units in the seat support structure layer based on pressure distribution data;

[0013] The optimized seat was verified using finite element analysis until it met the design requirements.

[0014] Furthermore, a 3D scan was performed on the seated subject to obtain scan data, including the following steps:

[0015] Subjects wearing tight-fitting, textureless, matte black clothing sat on the chair to be optimized. Reflective patches were placed at key points on the surface of the clothing, and calibration balls were placed at the set positions of the scanning area.

[0016] Centered on the subject, the scanning head of the structured light 3D scanner moves along a spiral path to cover the subject's whole body or target area. It covers the shoulders to the feet from the top view, captures the spinal curvature and hip contours from the side view, and obtains the details of the ischial tuberosity pressure point from the front view, thus obtaining scanning data.

[0017] Furthermore, the material properties of the functional layer on the seat surface are optimized, specifically as follows:

[0018] Based on the initial pressure distribution data, spatial adaptive adjustment of material properties is achieved through the density-strain correlation function, as shown in the following equation:

[0019] ρ(x,y) = ρ 0 + k·(σ max (x,y) - σ threshold ) / (σ max - σ threshold ) ;

[0020] ρ(x,y) Let (x, y) be the material density at a spatial point (x, y) on the functional layer of the seat surface. ρ 0 represents the initial baseline density for the corresponding region. k The modulation coefficient, σmax (x,y) This represents the maximum pressure value at a point (x, y) on the contact surface between the seat and the human body, obtained from finite element analysis. σ threshold This is the threshold for pressure ulcer risk. σ max This represents the maximum pressure value across the entire area where the seat contacts the human body.

[0021] Furthermore, a finite element model is established based on the preprocessed scan data. Specifically, referring to the multiple views of the bone shape in the THUMS model, the internal bones and tissues are modeled and combined based on the shell in the preprocessed scan data. The model is then closed and exported as an STP format model file.

[0022] Furthermore, the subject's buttocks and legs in the model are meshed. Specifically, the soft tissues and bones in the model are modeled based on tetrahedral elements of a set size. The mesh is generated by dividing the model into surface meshes and 3D volume meshes, with the mesh gradually becoming denser from the outside to the inside.

[0023] Furthermore, the material parameters during the finite element analysis are as follows: the cortical bone adopts a linear elastic model, the cancellous bone adopts a porous elastoplastic model, and the gluteus maximus fat pad adopts an Ogden hyperelastic model.

[0024] The boundary conditions for the finite element analysis are as follows: restrict the horizontal translation of the bony landmarks of the pelvis and retain the vertical degree of freedom; set the degree of freedom of the thickness region in the seat support structure layer to 0; set the interfacial friction coefficient between the subject's skin and the surface functional layer and the sliding friction coefficient of the soft tissue in the model.

[0025] Furthermore, based on the data provided by the subjects, the vertical load of the seat was determined, and the vertical load was distributed to the ischial tuberosity and posterior thigh region based on the scan data. Pressure distribution data was obtained through stress distribution analysis.

[0026] Furthermore, the pressure distribution data is classified as follows:

[0027] Pressure-sensitive areas are specifically defined as areas selected from pressure distribution data that exceed a set pressure ulcer risk threshold, including the ischial tuberosity projection area.

[0028] The surrounding transition zone is specifically defined as the area that surrounds the pressure-sensitive area and where the pressure value gradually decreases from the pressure-sensitive area outwards to a set threshold, including the periphery of the ischial tuberosity area.

[0029] The thigh contact area, specifically, is the area below the set value.

[0030] Furthermore, the support unit is a honeycomb structure with a negative Poisson's ratio.

[0031] A second aspect of the present invention provides a customized seat optimization system based on 3D scanning and finite element analysis, comprising:

[0032] The 3D data acquisition module is configured to acquire and preprocess 3D scan data of a subject in a seated position.

[0033] The finite element modeling module is configured to: build a finite element model based on the preprocessed scan data, and mesh the subject's buttocks and legs in the model;

[0034] The finite element analysis module is configured to perform finite element analysis based on set material parameters and boundary conditions to obtain pressure distribution data of the subject's buttocks and legs;

[0035] The surface functional layer optimization module is configured to optimize the material density of the seat surface functional layer based on pressure distribution data. Specifically, it classifies the pressure distribution data, determines regions with different pressure sensitivity levels, and sets the material density of the seat surface functional layer in each region. When a region exceeds a set pressure threshold range, it increases the material density of the seat surface functional layer to enhance support. When the pressure is below the set pressure threshold range, it decreases the material density of the surface functional layer to enhance flexibility, thereby optimizing the material properties of the seat surface functional layer.

[0036] The support structure layer optimization module is configured to optimize the size, distribution density, and geometric parameters of the support units in the seat support structure layer based on pressure distribution data.

[0037] The verification module is configured to perform finite element verification on the optimized seat until it meets the design requirements.

[0038] Compared with existing technologies, one or more of the above technical solutions have the following beneficial effects:

[0039] 1. By combining the scanning data of the subjects with mechanical analysis, the dynamic structure of the chair is optimized, upgrading the chair from a "universal fit" to an "individually customized fit," fundamentally improving comfort and biomechanical compatibility. 3D scanning technology can capture human body shape and real-time soft tissue deformation with high precision and personalization. Compared to the traditional THUMS standard model, it can more accurately reflect individual sitting posture details, making the chair design more tailored to individual characteristics and effectively avoiding stress concentration problems caused by parameter errors.

[0040] 2. By using finite element analysis technology, pressure distribution in different areas (such as the high pressure risk in the ischial tuberosity area) can be simulated based on a personalized human body model. This accurately locates pressure ulcer risk points and differences in support requirements, avoiding pressure distribution errors caused by "empirical judgment" in traditional design. It provides quantitative guidance for structural optimization, improves the comfort and biomechanical compatibility of the seat, and reduces the risk of pressure ulcers and musculoskeletal diseases.

[0041] 3. The different zones defined by the pressure distribution data reflect the different pressure conditions on the user's legs and buttocks. By using high-density materials in high-pressure zones and gradient density zoning in transition zones, the material density is adjusted according to local pressure. Combined with the optimization of the support structure layer, the support is strengthened in high-pressure sensitive areas to reduce the risk of pressure sores, while maintaining flexibility in low-pressure areas to improve comfort. At the same time, a balance between lightweight and high performance is achieved through topology optimization.

[0042] 4. During material property optimization, based on the initial pressure distribution data, spatial adaptive adjustment of material properties is achieved through the density-strain correlation function. The magnitude of density adjustment is determined according to the modulation coefficient. and Normalized proportion of pressure difference (σ max (x,y) - σ threshold ) / (σ max - σ threshold ) By fully considering the proportion of "the degree to which the actual pressure at a certain point exceeds the threshold" to "the degree to which the global maximum pressure exceeds the threshold", the density adjustment range can be limited to a reasonable range, avoiding excessively high costs caused by unlimited increases in local density.

[0043] 5. By using the density-strain correlation function, the density at each grid location can be dynamically adjusted according to the actual pressure value. The pressure-tissue damage relationship in biomechanics, the density-modulus correlation in materials science, and the balance between lightweighting and performance in engineering optimization are deeply integrated through a mathematical model. This enables the material density to form a precise correspondence with local pressure, ultimately presenting a gradient distribution characteristic on a macroscopic scale of "high density in pressure-sensitive areas - gradient attenuation in transition areas - flexibility maintained in thigh areas," which meets actual product requirements. Attached Figure Description

[0044] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0045] Figure 1This is a schematic diagram of a customized seat optimization process based on 3D scanning and finite element analysis provided by one or more embodiments of the present invention;

[0046] Figure 2 This is a schematic diagram illustrating the processing of 3D data during optimization according to one or more embodiments of the present invention;

[0047] Figure 3 This is a schematic diagram of the pressure distribution during optimization provided by one or more embodiments of the present invention. Detailed Implementation

[0048] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0049] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0050] Example 1:

[0051] like Figure 1 As shown, the customized seat optimization method based on 3D scanning and finite element analysis includes the following steps:

[0052] 3D scans were performed on subjects in a seated position to acquire and preprocess the scan data;

[0053] A finite element model was established based on the preprocessed scan data, and the buttocks and legs of the subject in the model were meshed.

[0054] Finite element analysis was performed based on the set material parameters and boundary conditions to obtain pressure distribution data of the subject's buttocks and legs;

[0055] The material density of the seat surface functional layer is optimized based on pressure distribution data. Specifically, the pressure distribution data is classified to determine areas with different pressure sensitivity and the material density of the seat surface functional layer in each area. When a certain area exceeds the set pressure threshold range, the material density of the seat surface functional layer is increased to enhance support. When the pressure is below the set pressure threshold range, the material density of the surface functional layer is reduced to enhance flexibility, thereby optimizing the material properties of the seat surface functional layer.

[0056] Optimize the size, distribution density, and geometric parameters of the support units in the seat support structure layer based on pressure distribution data;

[0057] The optimized seat was verified using finite element analysis until it met the design requirements.

[0058] like Figure 2As shown, the optimization process is as follows: acquiring 3D scanning data, preprocessing the scanning data, establishing a finite element model, setting material parameters and boundary conditions, adjusting material properties, and verification and optimization.

[0059] Obtaining 3D data of the human body through scanning includes the following steps:

[0060] (1) Select healthy subjects. After informing them of the safety risks and obtaining their consent, the subjects should wear tight-fitting, textureless black matte clothing to avoid geometric distortion caused by reflective or loose clothing.

[0061] (2) Use low-reflectivity patches to mark key points, such as the anterior superior iliac spine and greater trochanter, to assist in subsequent model alignment;

[0062] (3) A structured light 3D scanner was used to scan the subject in a seated position. During the scanning process, the subject maintained a static posture to reduce motion artifacts caused by breathing or muscle micro-movements.

[0063] (4) During scanning, a calibration ball is placed next to the scanning area for automatic alignment of multi-angle data; with the subject as the center, the scanning head moves along a spiral path (distance 0.4-1.2m) to cover the whole body or target area (such as pelvis-thigh), and performs partitioned scanning from the top view (covering the shoulder to the foot), the side view (capturing the spinal curvature and hip contour), and the front view (acquiring details of pressure points such as the ischial tuberosity).

[0064] Data preprocessing includes the following steps:

[0065] (1) Denoise the scanned data to eliminate the influence of instrument noise;

[0066] (2) Eliminate rough surfaces, repair unqualified surfaces, and perform surface fitting. Export the processed model as STP format.

[0067] Establishing a finite element model includes the following steps:

[0068] (1) Construct an internal skeleton and tissue model based on the shell in the scanned data; In this embodiment, the STP format model is imported into the software (e.g., Rhino6), and the internal skeleton and tissue model is constructed based on the scanned shell. Referring to the multiple views of the bone shape of the pelvis and other parts in the THUMS model, the internal skeleton and tissue are modeled and combined in the software according to the scanned shell.

[0069] (2) Use software (such as Solidworks) to close the model and check for intersecting parts, then export it as an STP format file;

[0070] (3) Use software (e.g. HyperMesh) to mesh the model. Use 10mm tetrahedral elements to model the soft tissue and bones in the model. First, perform surface meshing and optimization, then perform 3D volume meshing to generate a mesh that is gradually densified from the outside to the inside.

[0071] Finite element analysis includes the following steps:

[0072] (1) Import the meshed model into finite element analysis software (e.g., Abaqus), and set the material parameters. For cortical bone, a linear elastic model is used (E: 16 GPa, ν: 0.3, ...). ρ 1.9g / cm 3 The cancellous bone was modeled using a porous elastoplastic model (E: 100 MPa, ν: 0.25). ρ 0.8g / cm 3 The gluteus maximus fat pad was modeled using the Ogden hyperelastic model (μ1≈0.03MPa, α1≈10).

[0073] (2) Set boundary conditions to restrict the translation of pelvic bony landmarks (anterior superior iliac spine, ischial tuberosity) in the X / Y directions, while retaining the degree of freedom in the Z direction; completely fix the 3cm thick aluminum alloy layer at the bottom of the cushion (all degrees of freedom = 0); use the penalty function contact algorithm for the skin-foam interface and set the friction coefficient μ = 0.5; bind the muscle and fat tissue to the bone through *TIE connection to allow soft tissue sliding (friction coefficient μ = 0.1);

[0074] (3) Apply load and calculate the vertical force based on the subject's weight. For example, when the 3D scan model has a weight of 75kg, the corresponding vertical force Fz=735.75N. Based on the scan data, the force is dynamically distributed to the ischial tuberosity (contact area 203±28cm). 2 and the back of the thighs;

[0075] (4) Perform stress distribution analysis, for example, by running the Abaqus / Explicit dynamic implicit solver to obtain pressure distribution data.

[0076] Customized seat design and optimization includes the following steps.

[0077] (1) Based on the pressure distribution data obtained from the finite element analysis, a gradient density zoning design strategy was adopted, and high-density support modules (70 kg / m²) were set in the ischial node projection area (pressure-sensitive area). 3 The PU foam uses a medium-density gradient structure (50→30kg / m³) in the surrounding transition zone. 3 The original flexible material (50kg / m²) is retained in the thigh contact area. 3 ).

[0078] (2) Based on the initial pressure distribution data, a dynamic density mapping algorithm is established to achieve spatial adaptive adjustment of material properties through the density-strain correlation function. The function formula is as follows:

[0079] ρ(x,y) = ρ 0 + k·(σ max (x,y) - σ threshold ) / (σ max - σ threshold ) ;

[0080] ρ(x,y) Material density (unit: kg / m³) at a spatial point (x, y) on the functional layer of the seat surface. 3 This refers to the final optimized material density value at that location.

[0081] ρ 0: Initial reference density of the corresponding area (unit: kg / m³) 3 According to the pressure zoning settings (e.g., the initial reference density of the pressure-sensitive area is 70 kg / m³), 3 The surrounding transition zone is 50 kg / m². 3 wait);

[0082] k Modulation coefficient (unit: kg / (m)) 3 •kPa) is used to adjust the sensitivity of density to pressure changes; in this embodiment, the value is taken as 20kg / (m³). 3 •kPa);

[0083] σ max (x,y) The maximum pressure value (unit: kPa) at a point (x, y) on the contact surface between the seat and the human body obtained from finite element analysis reflects the actual pressure situation at that local location.

[0084] σ threshold Pressure ulcer risk threshold (unit: kPa), which is set to 7 kPa in this embodiment. It is the critical value for determining whether pressure may lead to pressure ulcers.

[0085] σ max The maximum pressure value (unit: kPa) within the entire analysis area (the entire area where the seat contacts the human body) serves as a global reference benchmark for normalization calculations, ensuring the rationality of density adjustment ratios.

[0086] (3) The aluminum alloy support layer of the seat cushion adopts a honeycomb skeleton design and introduces a negative Poisson's ratio structure to carry out topology optimization support structure design.

[0087] During the customized seat design and optimization process, after obtaining the pressure distribution data, the specific application of the "gradient density zoning design strategy" needs to be combined with the pressure distribution characteristics and implemented through "region division - density matching - algorithm optimization" to ultimately form a gradient density structure that meets the pressure requirements. The specific process is as follows:

[0088] (1) Based on the pressure distribution results output by the finite element analysis (such as the pressure values ​​of each area), the key pressure areas in contact between the seat and the human body are classified to determine the pressure sensitivity of different areas;

[0089] (2) Based on the pressure characteristics of each region, an initial density benchmark is set for each region to form an "initial zoning framework";

[0090] (3) Based on the initial partitioning framework, a dynamic density mapping algorithm is introduced to achieve spatial fine-tuning of material properties through the functional relationship between pressure value and density.

[0091] Based on the pressure distribution results output by the finite element analysis (such as pressure values ​​in each area), the key pressure areas in contact between the seat and the human body are classified, and the pressure sensitivity of different areas is determined, including the following steps:

[0092] Pressure-sensitive area (high-pressure area): such as Figure 3 The red area in the image primarily represents the ischial tuberosity projection region. Finite element analysis shows that this area has the highest pressure values ​​(easily approaching or exceeding the pressure ulcer risk threshold). σ threshold =7kPa), is the core area with the highest risk of pressure ulcers and requires strong support;

[0093] Surrounding transition zone (medium pressure zone): such as Figure 3 The yellow area surrounding the red zone is located on the periphery of the ischial tuberosity region, with pressure values ​​gradually decreasing from the core outwards (between...). σ threshold Between high and low pressure, a balance needs to be struck between support and comfort to achieve a smooth transition from high to low support.

[0094] Thigh contact area (low pressure area): such as Figure 3 The green area in the image indicates a lower pressure value (usually below). σ threshold It has low requirements for support strength and focuses more on a flexible contact experience;

[0095] Figure 3In the diagram, the obtained pressure distribution is represented as Mises stress (S), with units of MPa. The data (in scientific notation) corresponding to each color block represents a certain range of stress values, for example: +1.186 - e02 = +1.186 × 10⁻¹⁰. -2 = +0.01186MPa.

[0096] Based on the pressure characteristics of each region, an initial density baseline is set for each region to form an "initial zoning framework," which includes the following steps:

[0097] Pressure-sensitive area: Due to the need to withstand the highest pressure, high-density support material (70kg / m²) is directly used. 3 PU foam, through the dispersion of pressure by high-rigidity materials, reduces local peak pressure;

[0098] Surrounding transition zone: adopts a medium-density gradient structure (50→30kg / m²) 3 That is, from 50 kg / m near the pressure-sensitive area 3 Gradually transition to the outer perimeter of 30kg / m 3 By matching the continuous change in density with the attenuation trend of pressure, secondary pressure concentration caused by abrupt changes in density can be avoided.

[0099] Thigh contact area: Retain the original flexible material (50kg / m 3 While providing basic support, it ensures contact comfort through medium-flexibility materials.

[0100] Based on the initial partitioning framework, a dynamic density mapping algorithm is introduced to achieve spatial fine-tuning of material properties through the functional relationship between pressure and density, including the following steps:

[0101] Function formula: ρ(x,y) = ρ 0 + k(σ max (x,y) - σ threshold ) / (σ max - σ threshold ) ;

[0102] in, σ max (x,y) This represents the maximum pressure value at a point (x, y) obtained from finite element analysis. σ max This represents the maximum pressure value across the entire area where the seat contacts the human body. σ threshold =7kPa is the pressure ulcer risk threshold; k =20kg / (m 3 •kPa) is the modulation coefficient;ρ 0 represents the initial baseline density for this region (e.g., pressure-sensitive areas). ρ 0 = 70 kg / m 3 transition zone ρ 0 = 50 kg / m 3 wait);

[0103] When a certain point of pressure σ max (x,y) Higher than σ threshold When, in the formula ( σ max (x,y) - σ threshold ) is a positive value, ρ(x,y) Will ρ The density increases from 0, meaning the higher the pressure, the greater the density (enhancing support); when the pressure is lower than 0... ​ threshold At that time, the density will be ​ Reduce (enhance flexibility) from 0.

[0104] The density adjustment range is directly determined by the numerical relationship between the parameters in the formula. When the pressure difference is positive, the increase in density is determined by the modulation coefficient. k It is determined together with the normalized ratio of the pressure difference.

[0105] Modulation coefficient k =20kg / (m 3 •kPa), the normalized ratio of the pressure difference is ​ max ​ threshold ​ max - ​ threshold ) This ratio reflects the proportion of "the degree to which the actual pressure at a certain point exceeds the threshold" to "the degree to which the global maximum pressure exceeds the threshold," and is used to limit the density adjustment range within a reasonable range (to avoid unlimited increase in local density).

[0106] Taking a positive pressure difference as an example, let's assume:

[0107] Pressure ulcer risk threshold ​ threshold =7kPa (fixed value);

[0108] Global maximum pressure ​ max =15kPa (maximum global pressure in a certain scenario);

[0109] Actual pressure at a certain point ​max ​ =10kPa (the pressure at this point exceeds the threshold).

[0110] Initial reference density of the region ​ 0 = 70 kg / m 3 (e.g., the initial density of the pressure-sensitive area);

[0111] Modulation coefficient k = 20 kg / (m 3 •kPa).

[0112] but:

[0113] The normalized ratio of the pressure difference = (10-7) / (15-7) = 3 / 8 = 0.375;

[0114] Density increase = k × normalization ratio = 20 × 0.375 = 7.5 kg / m³ 3 ;

[0115] The final density at this point ​ =70 + 7.5 = 77.5 kg / m 3 .

[0116] The pressure distribution on the human-seat contact surface is not a discrete, block-like distribution, but rather exhibits a continuous gradient change. A simple "partitioning + fixed density" method can lead to abrupt density changes, causing secondary stress concentration. However, with this algorithm, the density at each grid location can be dynamically adjusted according to the actual pressure value, achieving a continuous and gradual spatial change in density, matching the natural attenuation law of the pressure field, and avoiding rigid boundary effects.

[0117] Secondly, pressure distribution varies significantly among individuals (e.g., pressure in the thigh region of an obese person may be close to that in the ischial tuberosity region), and pressure distribution also changes in different sitting postures for the same person (e.g., ischial pressure increases when leaning forward). Therefore, the algorithm described above uses normalization to correlate local pressure with the global pressure range, achieving adaptive adjustment. For example, if the pressure in the thigh region of an obese person reaches a threshold, the algorithm will automatically increase the density in that region, even if the thigh is traditionally classified as a "low-pressure area," it will still perform density enhancement optimization.

[0118] Furthermore, the elastic modulus of polyurethane (PU) foam exhibits a power-law relationship with its density; even small changes in density can significantly affect its mechanical properties, and a fixed-density design may not accurately match local support requirements. The algorithm described above, however, modulates the coefficient... k Quantify the sensitivity of density response to pressure to ensure the engineering feasibility of density adjustment.

[0119] Furthermore, simply using the highest density material throughout the high-pressure area would increase seat weight and cost, and might excessively compress soft tissues (such as the gluteus maximus fat pad), thus reducing comfort. The algorithm described above increases density only in necessary locations (areas where pressure exceeds the threshold), while maintaining low density in other areas, achieving the optimal solution for material distribution.

[0120] A dynamic density mapping algorithm is employed to deeply integrate biomechanical principles (pressure-tissue damage relationship), materials science (density-modulus correlation), and engineering optimization (lightweighting and performance balance) through a mathematical model. This enables a precise correspondence between material density and local pressure, ultimately presenting a gradient distribution characteristic on a macroscopic scale: "high density in pressure-sensitive areas - gradient attenuation in transition areas - maintenance of flexibility in the thigh area." The pressure data from finite element analysis is transformed into a feasible material density distribution scheme. This scheme addresses the high-pressure risk in the core area through high-density materials while ensuring overall comfort through gradient transitions and flexible materials, achieving a balanced optimization of "support strength - pressure distribution - comfort."

[0121] The seat in this embodiment adopts a layered composite structure, with the surface functional layer in contact with the human body and the bottom supporting structural layer.

[0122] The surface functional layer is composed of PU foam of different densities, designed according to pressure distribution zones. The elastic modulus E of the polyurethane (PU) foam is related to its density. ​ It approximately satisfies a power-law relationship.

[0123] For example: density from 30kg / m³ 3 Increased to 70kg / m 3 At this time, the stiffness can be increased by 3-5 times, effectively reducing compressive strain, and the ischial tuberosity projection area (pressure-sensitive area) will be 70 kg / m. 3 High-density PU foam, with a transition zone of 50→30 kg / m² 3 Medium-density gradient PU foam, with a thigh contact area of ​​50 kg / m². 3 Flexible PU foam.

[0124] The supporting structure layer, namely the aluminum alloy supporting layer, adopts a honeycomb skeleton design and introduces a negative Poisson's ratio structure to serve as the load-bearing base of the entire seat cushion, supporting the functional material layer above. In this embodiment, the aluminum alloy supporting layer is 3cm thick.

[0125] The negative Poisson's ratio structure in honeycomb skeleton design is a material structure with a special geometric topology. Its characteristics are: when stretched, the material expands laterally; when compressed, it contracts laterally (the opposite of the conventional material's "thinning when stretched and thickening when compressed").

[0126] By using a honeycomb structure, material usage is reduced (lightweighting), while the mechanical properties of the honeycomb structure are utilized to distribute loads and improve support strength. At the same time, by taking advantage of the negative Poisson's ratio effect, the support layer contracts laterally under pressure, enhancing its adhesion to the upper foam layer and reducing local stress concentration; it also improves the structure's impact resistance and energy dissipation capabilities (such as absorbing vibration energy).

[0127] By combining the pressure distribution data from finite element analysis, the size, distribution density, and geometric parameters of the negative Poisson's ratio structure of the cellular unit are optimized through algorithms. This ensures that the support layer has higher structural strength in critical pressure areas (such as below the ischial tuberosity projection area) and reduces material redundancy in non-critical areas, achieving the optimal match between "material-performance-load".

[0128] In this embodiment, the parameter optimization of the support structure layer is essentially to transform the "regional pressure value difference" of the pressure distribution data into "structural parameter gradient change": by designing the pressure-sensitive area with "small size, high density, and high stiffness", the support requirements of high pressure load are matched; by designing the low pressure area with "large size, low density, and low stiffness", a balance between lightweight and comfort is achieved, and finally the mechanical performance distribution of the support structure layer is fully adapted to the pressure distribution characteristics.

[0129] Verification and adjustment include the following steps:

[0130] (1) The optimized customized seat was verified by finite element analysis to evaluate its pressure distribution, material properties and other indicators;

[0131] (2) If the verification results do not meet the requirements, return to the previous optimization process for adjustment and optimization until the design standards are met.

[0132] 3D scanning technology can capture human body shape and real-time soft tissue deformation with high precision and personalization. Compared with the traditional THUMS standard model, it can more accurately reflect individual anatomical details, making the seat design more in line with individual characteristics and effectively avoiding stress concentration problems caused by anatomical parameter errors.

[0133] By using finite element analysis technology, pressure distribution in different areas (such as the high pressure risk in the ischial tuberosity area) can be simulated based on a personalized human body model. This accurately locates pressure ulcer risk points and differences in support requirements, avoiding pressure distribution errors caused by "empirical judgment" in traditional design. It provides quantitative guidance for structural optimization, improves the comfort and biomechanical compatibility of the seat, and reduces the risk of pressure ulcers and musculoskeletal diseases.

[0134] Through customized strategies such as gradient density zoning design (high-density materials in high-pressure areas and gradient attenuation in transition areas), dynamic density mapping algorithm (material density adaptively adjusted according to local pressure), and negative Poisson's ratio support structure, the seat structure can match the individual's pressure distribution characteristics in real time. It strengthens support in high-pressure sensitive areas to reduce the risk of pressure sores, while maintaining flexibility in low-pressure areas to improve comfort. At the same time, it achieves a balance between lightweight and high performance through topology optimization.

[0135] Example 2:

[0136] A customized seating optimization system based on 3D scanning and finite element analysis includes:

[0137] The 3D data acquisition module is configured to acquire and preprocess 3D scan data of a subject in a seated position.

[0138] The finite element modeling module is configured to: build a finite element model based on the preprocessed scan data, and mesh the subject's buttocks and legs in the model;

[0139] The finite element analysis module is configured to perform finite element analysis based on set material parameters and boundary conditions to obtain pressure distribution data of the subject's buttocks and legs;

[0140] The surface functional layer optimization module is configured to optimize the material density of the seat surface functional layer based on pressure distribution data. Specifically, it classifies the pressure distribution data, determines regions with different pressure sensitivity levels, and sets the material density of the seat surface functional layer in each region. When a region exceeds a set pressure threshold range, it increases the material density of the seat surface functional layer to enhance support. When the pressure is below the set pressure threshold range, it decreases the material density of the surface functional layer to enhance flexibility, thereby optimizing the material properties of the seat surface functional layer.

[0141] The support structure layer optimization module is configured to optimize the size, distribution density, and geometric parameters of the support units in the seat support structure layer based on pressure distribution data.

[0142] The verification module is configured to perform finite element verification on the optimized seat until it meets the design requirements.

[0143] 3D scanning technology can capture human body shape and real-time soft tissue deformation with high precision and personalization. Compared with the traditional THUMS standard model, it can more accurately reflect individual anatomical details, making the seat design more in line with individual characteristics and effectively avoiding stress concentration problems caused by anatomical parameter errors.

[0144] By using finite element analysis technology, pressure distribution in different areas (such as the high pressure risk in the ischial tuberosity area) can be simulated based on a personalized human body model. This accurately locates pressure ulcer risk points and differences in support requirements, avoiding pressure distribution errors caused by "empirical judgment" in traditional design. It provides quantitative guidance for structural optimization, improves the comfort and biomechanical compatibility of the seat, and reduces the risk of pressure ulcers and musculoskeletal diseases.

[0145] Through customized strategies such as gradient density zoning design (high-density materials in high-pressure areas and gradient attenuation in transition areas), dynamic density mapping algorithm (material density adaptively adjusted according to local pressure), and negative Poisson's ratio support structure, the seat structure can match the individual's pressure distribution characteristics in real time. It strengthens support in high-pressure sensitive areas to reduce the risk of pressure sores, while maintaining flexibility in low-pressure areas to improve comfort. At the same time, it achieves a balance between lightweight and high performance through topology optimization.

[0146] The above are merely preferred embodiments of the present invention and are not intended to limit the present 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. A customized seat optimization method based on 3D scanning and finite element analysis, characterized in that, Includes the following steps: 3D scans were performed on subjects in a seated position to acquire and preprocess the scan data; A finite element model was established based on the preprocessed scan data, and the buttocks and legs of the subject in the model were meshed. Finite element analysis was performed based on the set material parameters and boundary conditions to obtain pressure distribution data of the subject's buttocks and legs; The material density of the seat surface functional layer is optimized based on pressure distribution data. Specifically, the pressure distribution data is classified to determine areas with different pressure sensitivity and the material density of the seat surface functional layer in each area. When a certain area exceeds the set pressure threshold range, the material density of the seat surface functional layer is increased to enhance support. When the pressure is below the set pressure threshold range, the material density of the surface functional layer is reduced to enhance flexibility, thereby optimizing the material properties of the seat surface functional layer. Optimize the size, distribution density, and geometric parameters of the support units in the seat support structure layer based on pressure distribution data; The optimized seat was verified using finite element analysis until it met the design requirements. The material properties of the functional layer on the seat surface are optimized, specifically as follows: Based on the initial pressure distribution data, spatial adaptive adjustment of material properties is achieved through the density-strain correlation function, as shown in the following equation: ρ(x,y)=ρ 0 +k·(σ max (x,y) σ threshold ) / (σ max σ threshold ) ； ρ(x,y) Let (x, y) be the material density at a spatial point (x, y) on the functional layer of the seat surface. ρ 0 represents the initial baseline density for the corresponding region. k The modulation coefficient, σ max (x,y) This represents the maximum pressure value at a point (x, y) on the contact surface between the seat and the human body, obtained from finite element analysis. σ threshold The pressure ulcer risk threshold, σ max This represents the maximum pressure value across the entire area where the seat contacts the human body.

2. The customized seat optimization method based on 3D scanning and finite element analysis as described in claim 1, characterized in that, A 3D scan was performed on a seated subject to acquire scan data, including the following steps: Subjects wearing tight-fitting, textureless, matte black clothing sat on the chair to be optimized. Reflective patches were placed at key points on the surface of the clothing, and calibration balls were placed at the set positions of the scanning area. Centered on the subject, the scanning head of the structured light 3D scanner moves along a spiral path to cover the subject's whole body or target area. It covers the shoulders to the feet from the top view, captures the spinal curvature and hip contours from the side view, and obtains the details of the ischial tuberosity pressure point from the front view, thus obtaining scanning data.

3. The customized seat optimization method based on 3D scanning and finite element analysis as described in claim 1, characterized in that, A finite element model is established based on the preprocessed scan data. Specifically, referring to the multiple views of the bone shape in the THUMS model, the internal bones and tissues are modeled and combined based on the shell in the preprocessed scan data. The model is then closed and exported as an STP format model file.

4. The customized seat optimization method based on 3D scanning and finite element analysis as described in claim 1, characterized in that, The subject's buttocks and legs in the model are meshed. Specifically, the soft tissues and bones in the model are modeled based on tetrahedral elements of a set size. The mesh is generated by dividing the model into surface meshes and 3D volume meshes, and the mesh is gradually refined from the outside to the inside.

5. The customized seat optimization method based on 3D scanning and finite element analysis as described in claim 1, characterized in that, The material parameters used in the finite element analysis were as follows: the cortical bone was modeled as linear elastic, the cancellous bone as porous elastoplastic, and the gluteus maximus fat pad as Ogden hyperelastic. The boundary conditions for finite element analysis are: restricting the horizontal translation of pelvic bony landmarks while preserving the vertical degree of freedom; In the seat support structure layer, the degree of freedom of the thickness region is set to 0; the interfacial friction coefficient between the subject's skin and the surface functional layer and the sliding friction coefficient of the soft tissue in the model are set.

6. The customized seat optimization method based on 3D scanning and finite element analysis as described in claim 1, characterized in that, The vertical load on the seat was determined based on the subject's information. The vertical load was then distributed to the ischial tuberosity and posterior thigh region based on the scan data. Pressure distribution data was obtained through stress distribution analysis.

7. The customized seat optimization method based on 3D scanning and finite element analysis as described in claim 1, characterized in that, The pressure distribution data is classified as follows: Pressure-sensitive areas are specifically defined as areas selected from pressure distribution data that exceed a set pressure ulcer risk threshold, including the ischial tuberosity projection area. The surrounding transition zone is specifically defined as the area that surrounds the pressure-sensitive area and where the pressure value gradually decreases from the pressure-sensitive area outwards to a set threshold, including the periphery of the ischial tuberosity area. The thigh contact area, specifically, is the area below the set value.

8. The customized seat optimization method based on 3D scanning and finite element analysis as described in claim 1, characterized in that, The support unit is a honeycomb structure with a negative Poisson's ratio.

9. A customized seat optimization system based on 3D scanning and finite element analysis, used to implement the optimization method as described in any one of claims 1-8, characterized in that, include: The 3D data acquisition module is configured to acquire and preprocess 3D scan data of a subject in a seated position. The finite element modeling module is configured to: build a finite element model based on the preprocessed scan data, and mesh the subject's buttocks and legs in the model; The finite element analysis module is configured to perform finite element analysis based on set material parameters and boundary conditions to obtain pressure distribution data of the subject's buttocks and legs; The surface functional layer optimization module is configured to optimize the material density of the seat surface functional layer based on pressure distribution data. Specifically, it classifies the pressure distribution data, determines regions with different pressure sensitivity levels, and sets the material density of the seat surface functional layer in each region. When a region exceeds a set pressure threshold range, it increases the material density of the seat surface functional layer to enhance support. When the pressure is below the set pressure threshold range, it decreases the material density of the surface functional layer to enhance flexibility, thereby optimizing the material properties of the seat surface functional layer. The support structure layer optimization module is configured to optimize the size, distribution density, and geometric parameters of the support units in the seat support structure layer based on pressure distribution data. The verification module is configured to perform finite element verification on the optimized seat until it meets the design requirements.

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