Intelligent furniture topology design method based on human body pressure distribution data

Abstract

The invention discloses an intelligent furniture topology design method based on human body pressure distribution data, which comprises the following steps: collecting the human body pressure distribution data of a user, including pressure distribution characteristics in a sitting posture state and a prone posture state, and extracting human body biomechanical parameters; s2, inputting the pressure distribution data and the biomechanical parameters into a parameterized modeling system, constructing a furniture initial design domain and the like, personalized furniture design is achieved by collecting the real pressure distribution data of the user in the sitting posture and the prone posture and combining the biomechanical parameters, the comfort and health supporting performance of furniture are remarkably improved, and the method is suitable for popularization and application. According to the method, topological optimization is conducted through a bidirectional progressive structure optimization algorithm, efficient distribution of materials is achieved while the structural rigidity is guaranteed, a complex topological structure can be efficiently and accurately manufactured in combination with an additive manufacturing process, especially a wood flour laser sintering technology, integration from design to manufacturing is achieved, and the production flexibility and the material utilization rate are improved.

Description

Technical Field

[0001] This invention relates to the field of furniture design technology, and in particular to an intelligent furniture topology design method based on human body pressure distribution data. Background Technology

[0002] As people's demands for quality of life increase, furniture design is gradually shifting from standardization to personalization and ergonomics. Traditional furniture design methods rely heavily on experience or general ergonomic data, lacking precise collection and analysis of the actual pressure distribution of individual users. This results in deficiencies in comfort, support, and health aspects. Especially during prolonged sitting or lying down, unreasonable pressure distribution can easily lead to fatigue, discomfort, and even musculoskeletal problems. Traditional furniture structures are mostly solid or homogeneously filled, resulting in low material utilization, high weight, and difficulties in manufacturing complex and personalized structures. Summary of the Invention

[0003] In view of this, in order to solve the problems existing in the technical background, the present invention proposes a smart furniture topology design method based on human body pressure distribution data. Specifically, it includes the following:

[0004] A smart furniture topology design method based on human body pressure distribution data includes the following steps:

[0005] Step S1: Collect user's human body pressure distribution data, including pressure distribution characteristics in sitting and lying positions, and extract human biomechanical parameters.

[0006] Step S2: Input the pressure distribution data and biomechanical parameters into the parametric modeling system to construct the initial design domain for the furniture;

[0007] Step S3: Based on the bidirectional progressive structural optimization algorithm, perform topology optimization on the design domain and generate a lightweight structural model according to the material retention rate and mesh density threshold.

[0008] Step S4: In conjunction with the constraints of additive manufacturing process, the optimized structural model is adjusted for manufacturing adaptability to generate the final furniture design scheme;

[0009] Step S5: The furniture body is manufactured using a wood powder laser sintering process.

[0010] 2. The intelligent furniture topology design method based on human body pressure distribution data according to claim 1, wherein the pressure distribution data collected in step S1 includes dynamic pressure change data, which is used to reflect the pressure distribution state of the user in different usage scenarios.

[0011] 3. The intelligent furniture topology design method based on human body pressure distribution data according to claim 1, characterized in that the dynamic pressure change data is collected in real time by a pressure sensor and bound to user identity information for dynamic optimization of personalized furniture design.

[0012] 4. The intelligent furniture topology design method based on human body pressure distribution data according to claim 1, characterized in that the bidirectional progressive structural optimization algorithm used in step S3 is the BESO algorithm, which realizes progressive optimization of material distribution through forward prediction and reverse elimination, and the additive manufacturing process in step S4 is the wood powder laser sintering process, wherein the wood powder material includes wood powder-based composite materials and environmentally friendly adhesives.

[0013] 5. The intelligent furniture topology design method based on human body pressure distribution data according to claim 4, characterized in that the bidirectional progressive structural optimization algorithm used in step S3 performs iterative optimization of material distribution based on element sensitivity.

[0014] ;

[0015] in, Let C represent the sensitivity of the i-th unit, and C be the structural flexibility. The state of the unit material is represented by 1, which indicates retention and 0, which indicates deletion. The algorithm gradually retains high-sensitivity units through forward iteration and removes low-sensitivity units through reverse iteration. The optimization process is controlled according to the set material retention rate and mesh density threshold. Under the premise of meeting the stiffness and lightweight objectives, a lightweight furniture structure model with efficient material distribution is generated, realizing the unity of mechanical performance and morphological aesthetics.

[0016] The above technical solution has the following beneficial effects:

[0017] This invention collects real pressure distribution data of users in sitting and lying positions and combines it with biomechanical parameters to achieve personalized furniture design, significantly improving the comfort and health support performance of furniture. A two-way progressive structural optimization algorithm performs topology optimization, achieving efficient material distribution while ensuring structural stiffness, generating lightweight, high-strength, and aesthetically pleasing furniture structural models that combine mechanical performance and aesthetic value. Combined with additive manufacturing processes, especially wood powder laser sintering technology, it can efficiently and accurately manufacture complex topological structures, realizing the integration of design and manufacturing, and improving production flexibility and material utilization. Attached Figure Description

[0018] Figure 1 This is a flowchart of an intelligent furniture topology design method based on human body pressure distribution data according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: A smart furniture topology design method based on human body pressure distribution data, comprising the following steps:

[0021] Step S1: Collect user's human body pressure distribution data, including pressure distribution characteristics in sitting and lying positions, and extract human biomechanical parameters.

[0022] Step S2: Input the pressure distribution data and biomechanical parameters into the parametric modeling system to construct the initial design domain for the furniture;

[0023] Step S3: Based on the bidirectional progressive structural optimization algorithm, perform topology optimization on the design domain and generate a lightweight structural model according to the material retention rate and mesh density threshold.

[0024] Step S4: In conjunction with the constraints of additive manufacturing process, the optimized structural model is adjusted for manufacturing adaptability to generate the final furniture design scheme;

[0025] Step S5: The furniture body is manufactured using a wood powder laser sintering process.

[0026] Example 2, based on Example 1, in this example, step S1: Human body pressure distribution data acquisition and parameter extraction. In this step, pressure distribution data of the user in different postures is acquired by an array of pressure sensors arranged on the surface of the seat or mattress. The data includes pressure distribution maps of static sitting and lying postures, as well as pressure change sequences during dynamic use. The sensors can be piezoresistive or capacitive pressure sensing units, arranged in a matrix to achieve high-resolution data acquisition. After preprocessing, the acquired data extracts human biomechanical parameters, such as pressure center, pressure gradient, and support area distribution. These parameters reflect the mechanical characteristics and comfort requirements of the user's body shape, providing a basis for subsequent personalized design.

[0027] Step S2: Parametric Modeling and Initial Design Domain Construction. The pressure distribution data and biomechanical parameters extracted in Step S1 are input into the parametric modeling system. Based on user body type, usage scenario, and furniture functional requirements, this system constructs the initial design domain for the furniture. The design domain is a three-dimensional geometric model that defines the basic shape, size range, and load-bearing area of ​​the furniture. Through parametric driving, the system can quickly generate multiple initial structural schemes and provide initial material distribution and boundary conditions for subsequent topology optimization.

[0028] Example 3: In this example, step S3 is a two-way progressive structural topology optimization. This step uses a two-way progressive structural optimization algorithm to optimize the topology of the design domain. This algorithm is based on finite element analysis and guides the retention and deletion of materials by calculating the sensitivity of each element under load. The sensitivity calculation formula is:

[0029]

[0030] in, Let C represent the sensitivity of the i-th unit, and C be the structural flexibility. The state of the unit material is represented by 1 indicating retention and 0 indicating deletion. The algorithm optimizes the material distribution by retaining high-sensitivity units through forward iteration and deleting low-sensitivity units through reverse iteration. During the optimization process, the system controls the iteration process according to the preset material retention rate and mesh density threshold, ultimately generating a lightweight furniture structure model that meets stiffness requirements. This model ensures mechanical performance while presenting a natural and smooth topological form, achieving a unity of structural performance and visual aesthetics. Step S4: Additive Manufacturing Adaptive Adjustment and Scheme Generation. The optimized structural model may have characteristics unfavorable to manufacturing, such as excessively small overhang angles and uneven wall thickness. This step incorporates the manufacturing constraints of wood powder laser sintering technology to adaptively adjust the model. The adjustments include adding support structures, optimizing wall thickness distribution, and smoothing surface transitions to ensure that the model can be directly used for additive manufacturing. The adjusted model is the final furniture design scheme, which can be directly output as a 3D printing file format.

[0031] Step S5: Wood Powder Laser Sintering Manufacturing. The furniture body is manufactured using a wood powder laser sintering process. The wood powder material is composed of wood powder-based composite materials and environmentally friendly adhesives, possessing excellent sinterability and molding strength. During the manufacturing process, the laser sintersects the wood powder material layer by layer according to the 3D model data, gradually building up the shape. This process eliminates the need for molds, allowing direct fabrication of complex internal structures and curved surfaces, achieving high-precision and high-efficiency personalized furniture production.

[0032] Example 4, based on Example 1, in this example, in step S2, the parametric modeling system converts the user pressure distribution data collected from pressure sensors and the extracted biomechanical parameters into an initial three-dimensional design domain that can be used for topology optimization. This process is the core link connecting user physiological data and furniture structural design, and its specific implementation is as follows: The system first normalizes and meshes the collected pressure distribution data. The data collected by the pressure sensor array is usually a discrete point matrix. The system generates a continuous pressure distribution surface through an interpolation algorithm and maps it onto the furniture's surface. The biomechanical parameters extracted from the data—such as pressure center coordinates, maximum pressure area, pressure gradient distribution, and support area ratio—are quantified as design constraints.

[0033] The system, based on a parametric modeling platform, constructs a basic design space that matches the user's body shape and usage scenario. This space is a three-dimensional solid region whose outer contour is defined by the functional dimensions of the furniture. The system applies processed pressure distribution data as load conditions to the corresponding surfaces of this space.

[0034] For example, in seat design, the high-pressure area of ​​the ischial tuberosity is mapped to the area in the design domain that requires enhanced support; while low-pressure areas such as the back of the thigh correspond to areas where material can be reduced or the structure can be hollowed out. The center of pressure in biomechanical parameters is used to determine the main load-bearing path, ensuring that the optimized structure can effectively transfer loads and maintain stability.

[0035] The system allows input of various design variables and constraints, such as the overall dimensions of the furniture, the location of reserved connection points, material properties, and the maximum allowable displacement. Using these parameters, the system automatically generates a three-dimensional finite element model that includes initial material distribution, boundary conditions, and load distribution.

[0036] The basic principles and main features of the present invention have been described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are only illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from the spirit and scope of the present invention. All such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the invention is defined by the appended claims and their equivalents.

Claims

1. A smart furniture topology design method based on human body pressure distribution data, characterized in that, Includes the following steps: Step S1: Collect user's human body pressure distribution data, including pressure distribution characteristics in sitting and lying positions, and extract human biomechanical parameters. Step S2: Input the pressure distribution data and biomechanical parameters into the parametric modeling system to construct the initial design domain for the furniture; Step S3: Based on the bidirectional progressive structural optimization algorithm, perform topology optimization on the design domain and generate a lightweight structural model according to the material retention rate and mesh density threshold. Step S4: In conjunction with the constraints of additive manufacturing process, the optimized structural model is adjusted for manufacturing adaptability to generate the final furniture design scheme; Step S5: The furniture body is manufactured using a wood powder laser sintering process.

2. The intelligent furniture topology design method based on human body pressure distribution data according to claim 1, characterized in that, The pressure distribution data collected in step S1 includes dynamic pressure change data, which is used to reflect the pressure distribution status of users in different usage scenarios.

3. The intelligent furniture topology design method based on human body pressure distribution data according to claim 1, characterized in that, The dynamic pressure change data is collected in real time by a pressure sensor and linked to user identity information for dynamic optimization of personalized furniture design.

4. The intelligent furniture topology design method based on human body pressure distribution data according to claim 1, characterized in that, The bidirectional progressive structural optimization algorithm used in step S3 is the BESO algorithm, which achieves progressive optimization of material distribution through forward prediction and reverse elimination. The additive manufacturing process in step S4 is the wood powder laser sintering process, and the wood powder material includes wood powder-based composite materials and environmentally friendly adhesives.

5. The intelligent furniture topology design method based on human body pressure distribution data according to claim 4, characterized in that, The bidirectional progressive structure optimization algorithm used in step S3 performs iterative optimization of material distribution based on element sensitivity. ； in, Let C represent the sensitivity of the i-th unit, and C be the structural flexibility. The state of the unit material is represented by 1, which indicates retention and 0, which indicates deletion. The algorithm gradually retains high-sensitivity units through forward iteration and removes low-sensitivity units through reverse iteration. The optimization process is controlled according to the set material retention rate and mesh density threshold. Under the premise of meeting the stiffness and lightweight objectives, a lightweight furniture structure model with efficient material distribution is generated, realizing the unity of mechanical performance and morphological aesthetics.

Images