Intelligent rice bucket lightweight simulation design method and system and storage medium

Abstract

The present application relates to the technical field of model design, and more particularly to a kind of intelligent rice bucket lightweight simulation design method, system and storage medium.The method comprises the following steps: weight and displacement sensor is arranged on the body of intelligent rice bucket, stress and deformation data under different load conditions are obtained, and three-dimensional simulation model is constructed based on data and structural parameters;The deformation of barrel wall, support structure and base is calculated, and the simulation mechanics result is obtained;According to the result, the three-dimensional model is iteratively adjusted and the lightweight sample is processed;Finally, the performance is verified by loading test, and the lightweight design result meeting the preset index is output.The present application collects multi-condition stress and deformation data on intelligent rice bucket, iteratively optimizes the geometry of barrel wall and support structure combined with three-dimensional simulation model, realizes the optimization design of material lightweight, structural reliability improvement and use durability.

Description

Technical Field

[0001] This invention relates to the field of model design technology, and in particular to a lightweight simulation design method, system and storage medium for an intelligent rice bucket. Background Technology

[0002] Early rice bucket designs primarily relied on simple plastic injection molding, focusing on capacity and sealing while neglecting structural stress characteristics and material utilization efficiency. With advancements in materials science and the introduction of lightweight concepts, designers began experimenting with increasing bucket wall thickness or adding supporting ribs to enhance strength. However, these methods often resulted in material waste and increased overall weight.

[0003] In recent years, some studies have begun to attempt to analyze the stress and deformation of the barrel body using three-dimensional simulation models and to optimize the thickness in local areas. However, existing designs only strengthen the support structure by adding support rods or adjusting the overall shape, failing to accurately identify local stress concentration areas. They also lack methods for adjusting microscopic geometric parameters based on stress concentration areas (such as rod diameter, connection angle, and node spacing correction), making it difficult to balance lightweighting and structural strength. Furthermore, there is a lack of refined methods for adjusting the barrel wall thickness based on stress distribution, making it impossible to maximize material savings while ensuring strength. This can easily lead to localized over-thickness or under-thickness, affecting performance and the lightweighting effect. Summary of the Invention

[0004] Therefore, it is necessary to provide a lightweight simulation design method, system, and storage medium for a smart rice bucket to solve at least one of the aforementioned technical problems.

[0005] To achieve the above objectives, a lightweight simulation design method for a smart rice bucket is provided, the method comprising the following steps:

[0006] Step S1: By installing weight sensors and displacement sensors on the body of the smart rice bucket, force and deformation data of the bucket under empty, half-load and full-load conditions are obtained.

[0007] Step S2: Construct a three-dimensional simulation model based on the force data, deformation data, and structural parameters of the smart rice bucket;

[0008] Step S3: Calculate the wall thickness, deformation of the support structure and base structure in the three-dimensional simulation model respectively to obtain the simulation mechanical results;

[0009] Step S4: Adjust the three-dimensional simulation model according to the simulation mechanics results, and iteratively process the smart rice bucket based on the adjusted three-dimensional simulation model to form a lightweight optimized prototype;

[0010] Step S5: Conduct a loading test on the lightweight optimized sample. When the test results meet the preset performance indicators, output the lightweight design results of the smart rice bucket.

[0011] This specification provides a lightweight simulation design system for a smart rice bucket, used to execute the aforementioned lightweight simulation design method for a smart rice bucket. The lightweight simulation design system for a smart rice bucket includes:

[0012] The data acquisition module is used to acquire force and deformation data of the smart rice bucket under empty, half-load and full-load conditions by deploying weight sensors and displacement sensors on the bucket body.

[0013] The model building module is used to build a three-dimensional simulation model based on force data, deformation data, and structural parameters of the smart rice bucket.

[0014] The stress analysis module is used to calculate the wall thickness, the deformation of the support structure and the base structure in the three-dimensional simulation model, and to obtain the simulation mechanical results.

[0015] The prototype processing module is used to adjust the three-dimensional simulation model based on the simulation mechanics results, and to iteratively process the smart rice bucket based on the adjusted three-dimensional simulation model to form a lightweight optimized prototype.

[0016] The optimization test module is used to conduct loading tests on the lightweight optimized sample. When the test results meet the preset performance indicators, the lightweight design results of the smart rice bucket are output.

[0017] The present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the aforementioned lightweight simulation design method for a smart rice bucket.

[0018] The present invention has the following beneficial effects:

[0019] First, by performing detailed simulation calculations on the barrel wall, supporting structure, and base structure, stress distribution, strain and displacement, and boundary force deformation information are obtained, forming comprehensive simulation mechanical results. This process not only identifies areas of concentrated stress but also quantifies the deformation characteristics of each structural component, which helps to achieve targeted structural optimization, avoid overall redundant design, improve material utilization, and reduce manufacturing costs.

[0020] Second, in the lightweight optimization phase, an iterative correction method based on simulation results is proposed. This method parametrically adjusts the barrel wall thickness and the geometric dimensions of the supporting structure, and combines local geometric reduction, expansion, and curvature smoothing operations to effectively improve the local stress path and reduce stress concentration effects. This approach breaks through the traditional design approach of simply increasing the overall thickness, achieving the goal of "meeting the required strength with the least amount of material," thereby improving the product's lightweight and durability.

[0021] Third, by introducing a simulation method combining static and dynamic loads in the loading test, the structural reliability of the smart rice bucket under grain stacking conditions was not only verified, but also the impact and vibration during handling and use were simulated, ensuring that the optimized prototype maintains stable performance during long-term use. The test method has the advantages of detailed data acquisition and comprehensive simulation of working conditions, ultimately ensuring that the output lightweight design results meet the preset standards in terms of strength, stability, and durability, and have high engineering application value. Attached Figure Description

[0022] Figure 1 A flowchart illustrating the steps of a lightweight simulation design method for a smart rice bucket;

[0023] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0024] Figure 3 This is a schematic diagram of a three-dimensional simulation model of a lightweight simulation design method for an intelligent rice bucket according to this application.

[0025] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0026] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0027] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0028] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0029] To achieve the above objectives, please refer to Figures 1 to 3 A lightweight simulation design method for a smart rice bucket, the method comprising the following steps:

[0030] Step S1: By installing weight sensors and displacement sensors on the body of the smart rice bucket, force and deformation data of the bucket under empty, half-load and full-load conditions are obtained.

[0031] In one embodiment, multiple weight sensors and displacement sensors are installed at key stress locations on the outer wall and bottom of the smart rice container. The weight sensors are used to collect stress data of the container under different loads, and the displacement sensors are used to collect deformation data of the container under load.

[0032] The bucket is placed in an unloaded state, with no rice inside. The weight sensor outputs zero-point reference data, and the displacement sensor records the initial deformation baseline of the bucket. Then, rice is added to the bucket to 30%–60% of its rated capacity, creating a half-load state. The weight sensor collects corresponding force distribution data, and the displacement sensor simultaneously detects minute deformations of the bucket under this state. Rice is then added to the bucket until it reaches 90%–100% of its rated capacity, creating a full-load state. At this point, both the weight sensor and displacement sensor output force and deformation data under high load conditions. The weight sensor's measurement range is preferably 0 kg–50 kg, with an accuracy of no less than ±0.05 kg; the displacement sensor's range is 0 mm–10 mm, with a preferred resolution of 0.01 mm, to ensure accurate capture of minute deformations of the bucket.

[0033] By collecting sensor data under three typical conditions, the complete force-deformation relationship of the barrel can be obtained, providing experimental basis for subsequent lightweight simulation design and structural optimization.

[0034] Step S2: Construct a three-dimensional simulation model based on the force data, deformation data, and structural parameters of the smart rice bucket;

[0035] In one embodiment, the force data is obtained from strain sensors and pressure sensors installed on the body, support structure, and connecting parts of the smart rice bucket to acquire the force distribution under no-load, half-load, and full-load conditions. The force data acquisition accuracy is preferably 0.1N to 0.5N. Deformation data is acquired by displacement sensors or a 3D laser scanner, with a resolution preferably 0.01mm to 0.1mm to ensure the accuracy of structural deformation measurement. The structural parameters of the smart rice bucket include the bucket's geometric dimensions (such as height, diameter, and wall thickness), the dimensions of the support base, and the position and shape parameters of the feeding and discharging ports. These parameters can be parametrically modeled using 3D modeling software (such as SolidWorks or ANSYS). The structural parameters of the smart rice bucket are usually directly obtainable from the bucket wall in the form of labels.

[0036] In another embodiment, the 3D simulation model is constructed using the finite element method (FEM). The bucket body, base, and key stress-bearing components are divided into finite element mesh elements, with a mesh size preferably between 2mm and 10mm. Simultaneously, stress data is used as the load boundary conditions, and deformation data is used as the model verification constraint conditions. For example, when modeling a 50kg smart rice bucket in 3D, the stress data shows that under full load, the maximum pressure at the bottom of the bucket is 450N, and the average circumferential strain of the bucket wall is 0.35%. The deformation results obtained through finite element simulation differ from the actual measured deformation data by less than 5%, indicating that the model accuracy meets the requirements.

[0037] Step S3: Calculate the wall thickness, deformation of the support structure and base structure in the three-dimensional simulation model respectively to obtain the simulation mechanical results;

[0038] In one embodiment, stress data acquired under different load conditions are combined with a three-dimensional simulation model, and the finite element method is used to perform mechanical simulation of the barrel. During this simulation, the barrel wall thickness, support structure, and base structure are set as independent analysis units to facilitate the extraction of deformation results for each part separately.

[0039] Specifically, for the barrel wall thickness, the radial deformation of the barrel wall thickness at different height positions is calculated by applying a vertically distributed load to the finite element elements of the barrel wall, thus obtaining the overall deformation curve of the barrel wall thickness; for the support structure, the deflection and strain distribution of the support beam in the vertical and horizontal directions are analyzed by applying uniformly distributed loads and concentrated loads to the support beam elements, thereby obtaining the degree of deformation of the support structure; for the base structure, gravity loads and boundary constraints are applied to the base finite element elements to calculate the compressive deformation and stress concentration areas of the base.

[0040] During the above calculation process, the system extracts and compares the simulation results to obtain the deformation data of each part under different loads, and finally generates the overall simulation mechanical results of the smart rice bucket under different working conditions, providing a basis for subsequent structural optimization and lightweight design.

[0041] Step S4: Adjust the three-dimensional simulation model according to the simulation mechanics results, and iteratively process the smart rice bucket based on the adjusted three-dimensional simulation model to form a lightweight optimized prototype;

[0042] In one embodiment, finite element analysis software is first used to perform a simulation mechanical analysis of the stress on the smart rice bucket under loading conditions (empty, half-loaded, and fully loaded) to obtain the stress distribution, displacement deformation, and safety margin of the bucket under different working conditions. If the simulation results show that there is stress concentration, redundant wall thickness, or insufficient stiffness in a local area of ​​the bucket, the geometric parameters of that area are adjusted in the three-dimensional simulation model. For example, the wall thickness of the redundant part is reduced by 0.5mm to 1.5mm, or ribs, rounded corners, or other structures are added to the high-stress area.

[0043] In another embodiment, to address the problem of uneven pressure on the bottom of the barrel that appears in the simulation calculation, the load-bearing structure is optimized by adjusting the number and distribution of the bottom support ribs, so that the maximum deformation at the bottom is kept below 2mm, thereby improving load-bearing stability and reducing the amount of material used.

[0044] After the local structural adjustments are completed in the 3D simulation model, the model data is imported into 3D printing equipment or CNC machining equipment for rapid prototyping to form iteratively processed samples. Physical loading tests are then conducted on the iterative samples to verify their stress performance and weight changes. If the test results show that the sample meets the requirements for lightweighting and strength, it is identified as a lightweight optimized sample; if performance deviations still exist, the model parameters are adjusted based on the new simulation mechanical results, and the next round of iterative processing is carried out.

[0045] For example, in one iteration, by reducing the thickness of the barrel sidewall from 3.0mm to 2.2mm and adding three longitudinal ribs in the barrel opening area, the weight of the sample was reduced by about 15%, and the maximum deformation under full load was reduced from the original 3.8mm to 1.9mm, meeting the usage requirements.

[0046] Step S5: Conduct a loading test on the lightweight optimized sample. When the test results meet the preset performance indicators, output the lightweight design results of the smart rice bucket.

[0047] In one embodiment, the lightweight optimized prototype is a smart rice bucket body prototype optimized based on finite element simulation. The loading test is carried out using a hydraulic loading device or a universal testing machine, gradually increasing the applied load during the test until the preset design load limit is reached.

[0048] In another embodiment, the test conditions may include vertical static load, lateral impact load, and drop impact test. The vertical static load is preferably set to 30kg to 50kg to simulate the load-bearing capacity of the rice bucket when fully filled with grain; the impact energy range of the lateral impact test can be set to 5J to 15J to verify the impact resistance of the bucket under handling or collision conditions; the drop impact test can select a drop height of 0.5m to 1.0m to examine the structural integrity of the bucket during an accidental drop.

[0049] In a specific test, the lightweight optimized prototype maintained no significant deformation of the bucket body under a static load of 40 kg; under a lateral impact load of 10 J, the surface of the bucket body only showed minor indentations without cracking; and after a drop test from a height of 0.8 m, the overall structure of the bucket body remained stable. All of the above results meet the design specifications, therefore the design results of the lightweight optimized smart rice bucket are output. Loading tests verify the structural reliability and performance stability of the lightweight optimized prototype, ensuring that while reducing material weight, it still meets the load-bearing and impact resistance requirements of actual use, thus achieving a balance between lightweight design and safety.

[0050] As an example of the present invention, reference is made to... Figure 2 As shown, step S3 in this example includes:

[0051] Step S31: Determine the corresponding simulation units for the barrel wall, support structure, and base structure from the 3D simulation model;

[0052] Step S32: Perform stress calculation on the simulation unit corresponding to the barrel wall to obtain the stress distribution information of the barrel wall under the preset load conditions; determine the thickness of the barrel wall based on the stress distribution information.

[0053] Step S33: Perform mechanical analysis on the simulation unit corresponding to the support structure to obtain the strain and displacement information of the support structure under the preset load conditions; determine the deformation of the support structure based on the strain and displacement information.

[0054] Step S34: Perform force calculations on the simulation unit corresponding to the base structure under boundary condition constraints to obtain the deformation distribution information of the base structure under stress; determine the deformation of the base structure based on the deformation distribution information.

[0055] Step S35: Integrate thickness and deformation to obtain simulation mechanical results.

[0056] In one embodiment, the three-dimensional simulation model can be generated by CAD modeling and importing into finite element analysis software. The barrel wall, supporting structure, and base structure are respectively divided into shell elements, beam elements, and solid elements for mechanical calculations. Specifically, the barrel wall region is divided into two layers of shell elements in the thickness direction, the supporting structure is divided into beam elements, and the base structure is divided into solid elements. Preset load conditions include static load under full material load and external horizontal impact force. Calculation results show that the maximum principal stress of the barrel wall under stress is concentrated in the lower middle region of the barrel. By setting an allowable stress range (e.g., not exceeding 60% of the material's yield strength), the thickness in this region is adjusted accordingly, for example, increasing from 2.0 mm to 3.0 mm, to ensure the safety of the barrel wall under long-term loads.

[0057] In practical implementation, the maximum displacement of the support structure under full load is 0.8mm to 1.5mm, and the maximum strain is located at the connection between the support rod and the barrel wall. If this displacement exceeds a set threshold (e.g., 2.0mm), the cross-section of the support rod needs to be increased or replaced with a high-strength alloy material to ensure the overall structural stability. The boundary conditions of the base structure are set as fixed constraints, and the loading condition is the full load weight of the barrel plus dynamic impact. Calculation results show that the maximum deformation of the base is distributed at the edge in contact with the ground, with a value range of 0.3mm to 0.9mm. If this value exceeds 1.0mm, it may cause the equipment to tilt or become unstable, which needs to be improved by thickening the base plate or optimizing the layout of the support ribs. The results of the barrel wall thickness adjustment, the deformation data of the support structure, and the deformation distribution data of the base structure are integrated to form a complete simulation mechanical result. This result can not only intuitively reflect the stress situation of different parts, but also provide a quantitative basis for subsequent lightweight design and structural optimization.

[0058] Preferably, the force calculation for the simulation unit corresponding to the base structure under boundary condition constraints includes:

[0059] Obtain the assembly parameters, material properties, and preset boundary conditions of the simulation unit corresponding to the base structure; the preset boundary conditions include the location of the base support point, fixed constraints, and the point of force application.

[0060] Preset loads are applied to the simulation units corresponding to the base structure, and displacement and rotation information of each key node of the base are recorded under boundary condition constraints.

[0061] The local deformation of the base structure under stress is determined based on the displacement and rotation information of each key node.

[0062] By integrating the local deformations of each key node, the deformation distribution information of the base structure under stress is obtained.

[0063] In one embodiment, the assembly parameters, material properties, and preset boundary conditions of the simulation unit corresponding to the base structure are obtained. The material properties include elastic modulus, Poisson's ratio, and density; the assembly parameters include the node connection method and the stiffness coefficient between nodes; and the preset boundary conditions include the location of the base support points, fixed constraints, and the location and direction of the force application points.

[0064] Preset loads, including static loads, dynamic loads, or combined loads, are applied to the simulation elements corresponding to the base structure. Simultaneously, the simulation elements are constrained according to preset boundary conditions, such as applying fixed or hinged constraints at support points to limit the displacement and rotational degrees of freedom of the nodes. The displacement vectors and rotation angles of each key node of the base under load are recorded.

[0065] Based on the displacement and rotation information of each key node, the local deformation of the base structure under stress is calculated using finite element analysis or other structural analysis methods. Local deformation may include nodal displacement, local bending, torsion, and shear deformation.

[0066] By integrating the local deformations of each key node, information on the overall deformation distribution of the base structure under stress is generated, including the deformation amplitude distribution, the location of maximum stress, and possible stress concentration areas. This information can be used for subsequent structural optimization or lightweight design.

[0067] For example, the elastic modulus of the material of the base simulation unit can be taken as 210 GPa and the Poisson's ratio as 0.3; the support point fixed constraint is applied to the four corners of the base, and the load is the static load of 5000 N transmitted by the upper structure; through simulation calculation, the maximum displacement of each key node is about 2.5 mm and the maximum rotation is about 0.8°, thereby determining the overall deformation distribution of the base and providing a basis for optimizing the support structure.

[0068] Preferred methods for selecting key nodes include:

[0069] The initial nodes are determined based on the meshing of the simulation elements corresponding to the base structure;

[0070] Potential critical nodes are identified based on the displacement and rotation changes of each initial node under preset load conditions.

[0071] Based on potential critical nodes, the interface nodes connecting the base and the support structure, as well as the nodes located in areas of concentrated stress, are selected as core critical nodes.

[0072] In one embodiment, the simulation elements corresponding to the base structure are meshed to generate an initial node set. The simulation elements can be finite element types, such as tetrahedral or hexahedral elements, and the mesh size can be set according to the geometric complexity of the base and the required computational accuracy, for example, 5mm to 20mm. The initial nodes are the nodes of each element after meshing.

[0073] For each initial node, displacement and rotation simulation analysis is performed under preset load conditions to obtain the displacement change value and rotation angle change value of each node. Preset load conditions can include static loads, dynamic loads, and local forces, such as a concentrated force or uniformly distributed load of 500N to 2000N on the base under static load. The displacement threshold can be set to 0.1mm to 1mm, and the rotation threshold can be set to 0.05° to 0.5°. Potential critical nodes, i.e., nodes whose displacement or rotation exceeds the threshold under load, are selected based on these indicators.

[0074] Further screening of core critical nodes from potential critical nodes: identifying interface nodes connecting the base and the support structure, which are usually located at the support connection points or around bolt holes; identifying nodes in areas of concentrated stress, such as areas with large bending moments or shear forces, through stress distribution analysis; and finally, selecting interface nodes and nodes in areas of concentrated stress as core critical nodes for subsequent support optimization, structural verification, and improvement of simulation accuracy.

[0075] For example, for a certain base structure, the initial number of nodes is 1200. After displacement and rotation screening, about 180 potential key nodes are obtained, including 30 interface nodes and 50 nodes in the stress concentration area. Finally, 80 core key nodes are obtained for subsequent support structure optimization analysis.

[0076] Preferably, step S4 includes the following steps:

[0077] Step S41: Correct the barrel wall region of the three-dimensional simulation model based on the simulation mechanics results to obtain the corrected barrel wall thickness model;

[0078] Step S42: Perform stress calculation on the modified barrel wall thickness model and compare the stress calculation results with the preset strength threshold to determine whether the barrel wall thickness modification meets the lightweight design requirements;

[0079] Step S43: When the barrel wall thickness correction meets the lightweight design requirements, adjust the geometric dimensions of the support structure of the three-dimensional simulation model and regenerate the simulation data of the support structure.

[0080] Step S44: Confirm the updated 3D simulation model using the simulation data of the supporting structure and the corrected barrel wall thickness model;

[0081] Step S45: Process the material based on the updated 3D simulation model to obtain a lightweight prototype.

[0082] In one embodiment, the barrel wall region of the three-dimensional simulation model is modified based on the results of simulation mechanical analysis. Specifically, the stress distribution of each barrel wall region under different loads is calculated through finite element analysis (FEA), and the thickness of regions with stress below the allowable value of the material is appropriately reduced, thereby obtaining a modified barrel wall thickness model. The thickness modification range is preferably set to 0.5mm to 2.0mm to ensure the lightweight effect without affecting the structural strength.

[0083] The modified barrel wall thickness model is subjected to stress calculations, determining the stress and deformation under operating and ultimate load conditions. The calculated results are then compared with preset strength thresholds, such as allowable material stress of 150MPa to 250MPa and deformation limits of 0.5mm to 2.0mm. If the calculation results meet the preset strength thresholds, the modified barrel wall thickness is considered to meet the lightweight design requirements.

[0084] When the barrel wall thickness correction meets the lightweight design requirements, the supporting structure of the 3D simulation model undergoes local geometric adjustments. Specifically, this includes re-optimizing the width, thickness, and layout of the support ribs based on the change in barrel wall thickness, and regenerating the simulation data of the supporting structure to ensure that the overall structural stiffness and stability meet the requirements. The simulation data of the supporting structure is compared and verified with the corrected barrel wall thickness model to confirm that the updated 3D simulation model meets the strength, stiffness, and lightweight objectives. This step can utilize 3D model comparison software or automated verification algorithms to confirm structural integrity.

[0085] The updated 3D simulation model is used to fabricate lightweight prototypes. Fabrication methods may include 3D printing rapid prototyping, CNC machining, or laser cutting. The machining accuracy is preferably controlled within ±0.1mm to ensure the consistency between the prototype and the simulation model and to provide reliable prototypes for subsequent performance testing.

[0086] Preferably, adjusting the geometric dimensions of the support structure of the 3D simulation model includes:

[0087] Obtain the stress concentration region of the supporting structure under simulated stress conditions;

[0088] The geometric adjustment parameters of the support structure are determined based on the stress concentration area. These parameters include correction values ​​for the support rod diameter, connection angle, and node spacing.

[0089] The local geometric dimensions of the support structure in the 3D simulation model are adjusted based on geometric adjustment parameters.

[0090] In one embodiment, stress distribution data of the support structure under simulated stress is acquired, and stress concentration areas in the support structure are identified through finite element analysis or other mechanical simulation methods. Stress concentration areas preferably include nodes and members where the stress exceeds 80% to 100% of the material's allowable stress. Based on the identified stress concentration areas, geometric adjustment parameters of the support structure are determined, including correction values ​​for support rod diameter, connection angle, and node spacing. Specifically: the support rod diameter correction value can be adjusted according to the magnitude of local stress, preferably within ±0.5mm to 2.0mm; the connection angle correction value is used to optimize the force direction of the members, preferably within ±3° to ±10°; the node spacing correction value is used to increase or decrease the spacing between adjacent members to improve local stiffness distribution, preferably within ±2mm to ±10mm.

[0091] Based on the aforementioned geometric adjustment parameters, local geometric dimensions of the support structure in the 3D simulation model are adjusted. Specific operations include adjusting the support rod diameter, optimizing rod connection angles, and rearranging node spacing to reduce stress concentration, improve local stiffness, and ensure a balance between the overall structural lightweight requirements and strength objectives. After adjustment, the updated support structure can be simulated again to confirm that the stress distribution uniformity and local strength meet design requirements. If the design objectives are not met, the geometric parameters can be iteratively adjusted until the requirements are met.

[0092] Preferably, adjusting the local geometric dimensions of the support structure in the 3D simulation model based on geometric adjustment parameters includes:

[0093] Identify the areas of the supporting structure in the 3D simulation model that require adjustment.

[0094] The cross-sectional thickness of the region to be adjusted is reduced or increased based on geometric adjustment parameters.

[0095] Slight scaling or expansion of the length or height of the area to be adjusted is performed based on geometric adjustment parameters;

[0096] Curvature smoothing is performed on the transition surface of the region to be adjusted based on geometric adjustment parameters.

[0097] In one embodiment, the areas to be adjusted in the support structure of the 3D simulation model are identified. These areas preferably include nodes with significant stress concentrations in the stress analysis, support rod connections, and key areas for local load transfer. The cross-sectional thickness of these areas is reduced or increased based on geometric adjustment parameters to optimize material distribution. The adjustment range of the cross-sectional thickness can be set according to the simulation stress data, preferably within ±0.5mm to 2.0mm, to ensure that local strength is not reduced while reducing weight.

[0098] Based on geometric adjustment parameters, slight scaling or extension of the length or height of the area to be adjusted is performed to improve the local force transmission path. The length or height adjustment is preferably within the range of ±2mm to ±10mm. Fine-tuning makes the force path more uniform and reduces local stress concentration. Curvature smoothing is then applied to the transition surface of the area to be adjusted based on the geometric adjustment parameters to optimize the local geometric transition. Curvature smoothing reduces stress concentration in sharp corners or abrupt changes; surface treatment methods with a curvature continuity level of C1 or higher are preferred.

[0099] After adjustments are completed, secondary simulations can be performed on the adjusted areas to verify the improvement in stress distribution and material utilization. If necessary, geometric parameters can be iteratively fine-tuned until the lightweight design requirements and structural strength targets are met.

[0100] Preferably, the loading test on the lightweight optimized sample in step S5 includes:

[0101] The lightweight optimized prototype of the smart rice bucket was fixed on the support platform, and a bottom force sensing plate and a bucket wall displacement sensor were set on the platform. The sensor measurement range was 0-60 kg, and the displacement measurement range was 0-10 mm.

[0102] The pressure of grain accumulation on the barrel is simulated by applying a progressively equivalent load to the upper edge of the barrel using a ring loading fixture. The loading range is 0 kg to 60 kg, with an incremental growth rate of 2 kg per minute. Strain and displacement data are collected after each loading level has been stable for 30 seconds.

[0103] After the barrel reaches full load, a periodic dynamic load is applied to simulate minor impacts during handling and use. The dynamic load frequency is set to 1-5 Hz, the dynamic load amplitude is 0.5 kg-5 kg, and it is applied continuously for 10 minutes. The nonlinear deformation response of the barrel in the radial and axial directions is recorded in real time.

[0104] In one embodiment, firstly, a lightweight optimized prototype of the smart rice bucket is fixed on a support platform, and a bottom force sensing plate and a bucket wall displacement sensor are arranged on the platform. The force sensing plate is set to a measurement range of 0–60 kg, and the displacement sensor is set to a measurement range of 0–10 mm to ensure that the mechanical response of the prototype during loading can be fully captured.

[0105] A ring-shaped loading fixture was used to apply progressively equivalent loads to the upper edge of the barrel to simulate the pressure exerted by grain accumulation. The loading range was set to 0 kg to 60 kg, with an increment rate of 2 kg per minute. Each loading level was held for 30 seconds to ensure that the force and displacement reached a stable state, while strain and displacement data of the barrel were collected simultaneously.

[0106] Once the barrel reaches full load, a periodic dynamic load is applied to simulate minor impacts during handling and use. The dynamic load frequency is set to 1–5 Hz, and the dynamic load amplitude is set to 0.5 kg–5 kg, applied continuously for 10 minutes. During the test, the radial and axial nonlinear deformation responses of the barrel are recorded in real time to analyze the mechanical properties of the lightweight prototype under dynamic load.

[0107] In a preferred embodiment, to improve the reliability of the test, the loading and dynamic loading process can be repeated multiple times, and the data from each experiment can be statistically analyzed to obtain the average strain, maximum displacement, and stiffness variation curves of the sample under different load conditions, providing a reference for subsequent lightweight optimization design.

[0108] It is important to note that the loading rate, dynamic load amplitude, and duration can be adjusted according to the actual usage scenario of the rice bucket. For example, in environments with frequent handling, the dynamic load amplitude and frequency can be appropriately increased to more accurately reflect the actual stress conditions.

[0109] Of particular importance, after adjusting the local geometric dimensions of the support structure in the 3D simulation model based on geometric adjustment parameters, the following is also included:

[0110] For the support structure after local geometric adjustment, micro-offset adjustment is performed based on the imaginary line of structural stress path to generate stress-optimized local geometric data;

[0111] Based on the local geometric data of stress optimization, micro-marking points are automatically generated to mark the fine-tuning boundaries of the transition surface and the section thickness, thus obtaining fine-tuning marking point data;

[0112] By using fine-tuning marker point data to correct the tilt of the transition surface, the geometric curvature is kept continuous in the key force direction, generating continuous optimized geometric data.

[0113] Stiffness sensitivity verification is performed based on continuous optimization of geometric data.

[0114] In one embodiment, micro-offset adjustments are performed based on an imaginary line of structural stress path to optimize the stress distribution of the supporting structure in key stress directions, generating stress-optimized local geometry data. The micro-offset adjustment amount is typically controlled within 0.1 mm to 2 mm to avoid excessive impact on the overall structural morphology while improving local stress concentration.

[0115] Based on the generated stress-optimized local geometry data, the system automatically generates micro-marker points to identify the fine-tuning boundaries of the transition surface and cross-sectional thickness. The fine-tuning marker point data may include the three-dimensional coordinates, direction vector, and corresponding adjustment range of the marker point, providing a positioning basis for subsequent fine-tuning surface correction.

[0116] The tilt of the transition surface is corrected using fine-tuning marker data to maintain the continuity of geometric curvature in key stress directions. Specifically, a local surface fitting function is established at each fine-tuning marker point, and the surface tilt angle and rate of curvature change are adjusted to ensure curvature continuity and smooth transition, generating continuous optimized geometric data. This process controls the rate of curvature change to no more than 0.02–0.05 mm / mm to maintain structural mechanical properties and manufacturing feasibility.

[0117] Stiffness sensitivity verification is performed based on continuous optimization of geometric data. The verification includes: loading the design conditions into the finite element analysis model, calculating the stiffness distribution and displacement response of the support structure in the key stress directions, and comparing them with the original design and target stiffness range to verify whether the local geometric optimization meets the stiffness requirements.

[0118] In a preferred embodiment, steps

[0071] to

[0074] can be repeated for different loading conditions to ensure that the support structure after local geometric adjustment can maintain reasonable stress distribution, continuous curvature and sufficient stiffness under various conditions, thereby completing the fine optimization of the three-dimensional simulation model.

[0119] It is important to note that the micro-offset adjustment amount, rate of curvature change, and stiffness sensitivity threshold can be adjusted based on the material properties, manufacturing process, and application scenario of the supporting structure. For example, when using brittle materials, the micro-offset amount can be appropriately reduced to prevent excessive local stress from causing fracture.

[0120] Of particular importance are the methods for generating the imaginary lines of structural stress paths, including:

[0121] The key nodes of the support structure after local geometric dimension adjustment are calibrated to obtain the coordinates of the cross-section centerline and connection endpoints, and to generate preliminary node set data.

[0122] Based on the initial node set data, connect the key nodes according to the shortest curve connection principle to generate an initial path line;

[0123] In the initial path line, auxiliary sampling points are inserted at preset intervals along the curve for micro-offset and local optimization control, generating sampling path line data;

[0124] Based on the principle of approximating the direction of structural stress, the sampling path is slightly offset so that the path roughly matches the main direction of structural stress flow, in order to obtain the imaginary line of structural stress path.

[0125] In one embodiment, key nodes of the support structure after local geometric dimension adjustments are calibrated. Preliminary node set data is generated by acquiring the coordinates of the centerline of each section and the coordinates of adjacent connection endpoints. Key nodes include the center point of the section, the endpoints of the transition surface, and geometric feature points in the stress concentration areas of the support structure. Based on the preliminary node set data, the key nodes are connected sequentially using the shortest curve connectivity principle to generate preliminary path lines. These path lines can be represented using spline curves or polylines to ensure smooth connections and minimize path length, reflecting the optimal force transmission path of the structure.

[0126] In the initial path line, auxiliary sampling points are inserted along the curve at preset intervals to form sampling path line data. The preset interval can be set to 2 mm to 5 mm depending on the size and complexity of the supporting structure, ensuring fine control of micro-offset adjustment and local optimization, while avoiding excessive computation due to overly dense sampling points. Based on the principle of approximating the structural force direction, the sampling path line is slightly offset so that the path line roughly conforms to the main force flow direction of the structure. The slight offset is usually controlled within the range of 0.05 mm to 0.3 mm to ensure that the hypothetical stress path line can reflect the local force characteristics without changing the overall geometry. The finally generated hypothetical structural stress path line can serve as a reference path for subsequent micro-offset adjustment and local geometry optimization, providing a benchmark for stress optimization, curvature continuity correction, and stiffness sensitivity verification of the local supporting structure.

[0127] It is important to note that when the supporting structure is subjected to forces in multiple directions, multiple hypothetical stress paths can be generated and comprehensively considered according to weight or main force direction during the optimization process to ensure the comprehensiveness and safety of structural optimization.

[0128] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0129] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A lightweight simulation design method for a smart rice bucket, characterized in that, Includes the following steps: Step S1: By installing weight sensors and displacement sensors on the body of the smart rice bucket, force and deformation data of the bucket under empty, half-load and full-load conditions are obtained. Step S2: Construct a three-dimensional simulation model based on the force data, deformation data, and structural parameters of the smart rice bucket; Step S3: Calculate the wall thickness, deformation of the support structure and base structure in the 3D simulation model to obtain the simulation mechanical results; Step S3 includes the following steps: Step S31: Determine the corresponding simulation units for the barrel wall, support structure, and base structure from the 3D simulation model; Step S32: Perform stress calculation on the simulation unit corresponding to the barrel wall to obtain the stress distribution information of the barrel wall under the preset load conditions; determine the thickness of the barrel wall based on the stress distribution information. Step S33: Perform mechanical analysis on the simulation unit corresponding to the support structure to obtain the strain and displacement information of the support structure under the preset load conditions; determine the deformation of the support structure based on the strain and displacement information. Step S34: Perform force calculations on the simulation unit corresponding to the base structure under boundary condition constraints to obtain the deformation distribution information of the base structure under stress; determine the deformation of the base structure based on the deformation distribution information. Step S35: Integrate thickness and deformation to obtain simulation mechanical results; among which, the force calculation of the simulation element corresponding to the base structure under boundary condition constraints includes: Obtain the assembly parameters, material properties, and preset boundary conditions of the simulation unit corresponding to the base structure; the preset boundary conditions include the location of the base support point, fixed constraints, and the point of force application. Preset loads are applied to the simulation units corresponding to the base structure, and displacement and rotation information of each key node of the base are recorded under boundary condition constraints. The local deformation of the base structure under stress is determined based on the displacement and rotation information of each key node. The local deformations of each key node are integrated to obtain the deformation distribution information of the base structure under stress; the selection method for key nodes includes: The initial nodes are determined based on the meshing of the simulation elements corresponding to the base structure; Potential critical nodes are identified based on the displacement and rotation changes of each initial node under preset load conditions. Based on potential critical nodes, the interface nodes connecting the base and the support structure, as well as the nodes located in the stress concentration area, are selected as core critical nodes. Step S4: Adjust the three-dimensional simulation model according to the simulation mechanics results, and iteratively process the smart rice bucket based on the adjusted three-dimensional simulation model to form a lightweight optimized prototype; Step S5: Conduct a loading test on the lightweight optimized sample. When the test results meet the preset performance indicators, output the lightweight design results of the smart rice bucket.

2. The lightweight simulation design method for a smart rice bucket according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Correct the barrel wall region of the three-dimensional simulation model based on the simulation mechanics results to obtain the corrected barrel wall thickness model; Step S42: Perform stress calculation on the modified barrel wall thickness model and compare the stress calculation results with the preset strength threshold to determine whether the barrel wall thickness modification meets the lightweight design requirements; Step S43: When the barrel wall thickness correction meets the lightweight design requirements, adjust the geometric dimensions of the support structure of the three-dimensional simulation model and regenerate the simulation data of the support structure. Step S44: Confirm the updated 3D simulation model using the simulation data of the supporting structure and the corrected barrel wall thickness model; Step S45: Process the material based on the updated 3D simulation model to obtain a lightweight sample.

3. The lightweight simulation design method for a smart rice bucket according to claim 2, characterized in that, Adjusting the geometric dimensions of the support structure of the 3D simulation model includes: Obtain the stress concentration region of the supporting structure under simulated stress conditions; The geometric adjustment parameters of the support structure are determined based on the stress concentration area. These parameters include correction values ​​for the support rod diameter, connection angle, and node spacing. The local geometric dimensions of the support structure in the 3D simulation model are adjusted based on geometric adjustment parameters.

4. The lightweight simulation design method for a smart rice bucket according to claim 3, characterized in that, Local geometric dimension adjustment of the support structure in the 3D simulation model based on geometric adjustment parameters includes: Identify the areas of the supporting structure in the 3D simulation model that require adjustment. The cross-sectional thickness of the region to be adjusted is reduced or increased based on geometric adjustment parameters. Slight scaling or expansion of the length or height of the area to be adjusted is performed based on geometric adjustment parameters; Curvature smoothing is performed on the transition surface of the region to be adjusted based on geometric adjustment parameters.

5. The lightweight simulation design method for a smart rice bucket according to claim 1, characterized in that, Step S5 involves performing loading tests on the lightweight optimized sample, including: The lightweight optimized prototype of the smart rice bucket was fixed on the support platform, and a bottom force sensing plate and a bucket wall displacement sensor were set on the platform. The sensor measurement range was 0-60 kg, and the displacement measurement range was 0-10 mm. The pressure of grain accumulation on the barrel is simulated by applying a progressively equivalent load to the upper edge of the barrel using a ring loading fixture. The loading range is 0 kg to 60 kg, with an incremental growth rate of 2 kg per minute. Strain and displacement data are collected after each loading level has been stable for 30 seconds. After the barrel reaches full load, a periodic dynamic load is applied to simulate minor impacts during handling and use. The dynamic load frequency is set to 1-5 Hz, the dynamic load amplitude is 0.5 kg-5 kg, and it is applied continuously for 10 minutes. The nonlinear deformation response of the barrel in the radial and axial directions is recorded in real time.

6. A lightweight simulation design system for an intelligent rice bucket, characterized in that, The lightweight simulation design system for a smart rice bucket, used to execute the lightweight simulation design method for a smart rice bucket as described in claim 1, comprises: The data acquisition module is used to acquire force and deformation data of the smart rice bucket under empty, half-load and full-load conditions by deploying weight sensors and displacement sensors on the bucket body. The model building module is used to build a three-dimensional simulation model based on force data, deformation data, and structural parameters of the smart rice bucket. The stress analysis module is used to calculate the wall thickness, the deformation of the support structure and the base structure in the three-dimensional simulation model, and to obtain the simulation mechanical results. The prototype processing module is used to adjust the three-dimensional simulation model based on the simulation mechanics results, and to iteratively process the smart rice bucket based on the adjusted three-dimensional simulation model to form a lightweight optimized prototype. The optimization test module is used to conduct loading tests on the lightweight optimized sample. When the test results meet the preset performance indicators, the lightweight design results of the smart rice bucket are output.

7. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the lightweight simulation design method for the smart rice bucket as described in any one of claims 1-5.

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