Silica gel ball manufacturing method and system for rehabilitation training

Abstract

The invention relates to a silica gel ball manufacturing method and system for rehabilitation training in the technical field of silica gel product production, and the method comprises the steps: simulating the internal stress distribution and material flow characteristics of a silica gel ball through finite element analysis according to a hardness gradient curve, and obtaining a mold partitioning scheme and vulcanization temperature field distribution through iterative calculation; geometric data of the machined mold is obtained from the mold machining instruction, the mold surface error and the partition precision are detected through laser scanning, and whether the mold meets the hardness gradient control requirement or not is judged; for the semi-finished product of the silica gel ball, adjusting the surface molecular structure by adopting a plasma treatment technology, and obtaining an optimized surface friction force parameter by controlling the treatment time and the gas proportion; and obtaining silica gel ball performance data from the surface friction force parameters, measuring hardness and gripping force distribution through mechanical testing equipment, and judging whether the silica gel ball meets rehabilitation training requirements or not.

Description

Technical Field

[0001] The present invention relates to the technical field of silicone product production, and in particular to a method and system for manufacturing silicone balls for rehabilitation training. Background Art

[0002] Silicone balls are crucial in sports rehabilitation and fitness, as their performance directly impacts training effectiveness and user experience. Solid silicone balls, with their controllable elasticity and durability, are ideal for stimulating muscle groups and improving coordination during rehabilitation training. However, silicone balls with a single hardness cannot meet the diverse needs of different rehabilitation stages or muscle groups.

[0003] Although the existing multi-layer injection molding process can achieve hardness stratification, the production process is complex and the mold design is cumbersome, resulting in high costs and difficult to be widely used in the medical and household markets. In addition, the existing methods have deficiencies in the continuity of hardness transition and the uniformity of material distribution, which affects the comfort and functionality of use. The core challenge lies in how to achieve precise gradient control of the hardness of silicone balls while reducing manufacturing costs. A single vulcanization process makes it difficult to form continuous hardness changes, and although multi-layer injection molding can be layered, the bonding between layers is prone to unevenness, resulting in density deviation and unstable performance.

[0004] At the same time, the optimization of the ball's surface grip must also be considered to ensure safety during rehabilitation training. Unresolved technical issues like these have limited the application of silicone balls in personalized rehabilitation, making it difficult to achieve differentiated stimulation of different muscle groups.

[0005] Therefore, how to achieve a continuous gradient change in the hardness of the sphere from the inside to the outside while ensuring uniform material distribution by improving mold design and vulcanization process, while taking into account the optimization of surface friction, becomes the key issue of this study. Summary of the Invention

[0006] The present invention provides a method for manufacturing a silica gel ball for rehabilitation training, comprising the following steps:

[0007] The hardness gradient requirements and surface friction standards are obtained from the silicone ball design parameter database. The algorithm analyzes the sensitivity range of different muscle groups to hardness changes during rehabilitation training and determines the hardness gradient curve and surface texture parameters.

[0008] Based on the hardness gradient curve, finite element analysis was used to simulate the internal stress distribution and material flow characteristics of the silicone ball. The mold partitioning scheme and vulcanization temperature field distribution were obtained through iterative calculation.

[0009] If the finite element analysis results show that the interlayer stress difference is greater than the preset threshold, the mold partition boundaries and silicone formula ratio are adjusted, and the interlayer bonding uniformity is verified through secondary simulation to obtain the optimized mold design parameters;

[0010] Based on the optimized mold design parameters, a three-dimensional model of the multi-cavity mold is generated, and the mold processing trajectory is calculated using the CNC machining path planning algorithm to obtain high-precision mold processing instructions;

[0011] Obtain the finished mold geometry data from the mold processing instructions, detect the mold surface error and partition accuracy through laser scanning, and determine whether the mold meets the hardness gradient control requirements;

[0012] If the mold error is less than the preset threshold, a segmented vulcanization process is used to control the silicone ball molding process. By real-time monitoring of the vulcanization temperature and pressure distribution, a silicone ball semi-finished product with continuously changing hardness is obtained;

[0013] For semi-finished silicone balls, plasma treatment technology is used to adjust the surface molecular structure. By controlling the treatment time and gas ratio, the optimized surface friction parameters are obtained.

[0014] Obtain performance data of the silicone ball from surface friction parameters, measure hardness and grip force distribution using mechanical testing equipment, and determine whether the silicone ball meets rehabilitation training requirements;

[0015] If the test data deviation is less than the preset threshold, an automated sorting system is used to classify the silicone balls by quality, and surface defects are identified through machine vision to obtain silicone balls that meet the standards.

[0016] The present invention provides a silicone ball manufacturing system for rehabilitation training, which mainly includes:

[0017] A hardness gradient requirement acquisition module is used to obtain hardness gradient requirements and surface friction standards from a silicone ball design parameter database;

[0018] The surface friction standard acquisition module is used to analyze the sensitivity range of different muscle groups to hardness changes during rehabilitation training through algorithms, and determine the hardness gradient curve and surface texture parameters;

[0019] The algorithm analysis module is used to simulate the internal stress distribution and material flow characteristics of the silicone ball based on the hardness gradient curve using finite element analysis, and obtain the mold partitioning scheme and vulcanization temperature field distribution through iterative calculation;

[0020] The finite element analysis module is used to adjust the mold partition boundaries and silicone formula ratio if the finite element analysis results show that the interlayer stress difference is greater than the preset threshold. The interlayer bonding uniformity is verified through secondary simulation to obtain the optimized mold design parameters;

[0021] The mold partition adjustment module is used to generate a 3D model of a multi-cavity mold based on the optimized mold design parameters, and uses a CNC machining path planning algorithm to calculate the mold machining trajectory to obtain high-precision mold machining instructions;

[0022] The mold processing instruction generation module is used to obtain the finished mold geometry data from the mold processing instructions, detect the mold surface error and partition accuracy through laser scanning, and determine whether the mold meets the hardness gradient control requirements;

[0023] The mold detection module is used to control the silicone ball molding process using a segmented vulcanization process if the mold error is less than a preset threshold. By real-time monitoring of the vulcanization temperature and pressure distribution, a semi-finished silicone ball with continuously changing hardness is obtained;

[0024] The vulcanization process control module is used to adjust the surface molecular structure of the semi-finished silicone balls using plasma treatment technology. By controlling the treatment time and gas ratio, the optimized surface friction parameters are obtained.

[0025] The surface treatment module is used to obtain the performance data of the silicone ball from the surface friction parameters, measure the hardness and grip force distribution through mechanical testing equipment, and determine whether the silicone ball meets the rehabilitation training requirements.

[0026] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0027] The present invention discloses a method for manufacturing silicone balls for rehabilitation training. By analyzing the sensitivity of muscle groups to hardness changes, the hardness gradient curve and surface texture parameters are determined. Finite element analysis is used to optimize the mold design to achieve the molding of silicone balls with continuous hardness changes. Plasma treatment technology is used to adjust the surface friction, and the performance of the silicone balls is verified through mechanical testing. The present invention solves the problems of traditional silicone balls with single hardness and uncontrollable surface friction, and realizes the manufacture of silicone balls with adjustable hardness gradient and controllable surface friction, thereby improving the pertinence and effectiveness of rehabilitation training. Through automated sorting and machine vision inspection, the consistency of product quality is guaranteed, providing a more accurate and personalized auxiliary tool for rehabilitation training. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 The present invention is a flow chart of a method for manufacturing a silicone ball for rehabilitation training.

[0029] Figure 2 The figure is a schematic diagram of a method and system for manufacturing silicone balls for rehabilitation training according to the present invention.

[0030] Figure 3 This is another schematic diagram of a method and system for manufacturing silicone balls for rehabilitation training according to the present invention.

[0031] Figure 4 The figure is a schematic diagram of the framework of a silicone ball manufacturing system for rehabilitation training according to the present invention. DETAILED DESCRIPTION

[0032] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0033] like Figure 1-4 In this embodiment, a method for manufacturing a silicone ball for rehabilitation training may specifically include:

[0034] S101. Obtain hardness gradient requirements and surface friction standards from the silicone ball design parameter database, analyze the sensitivity ranges of different muscle groups to hardness changes during rehabilitation training through algorithms, and determine the hardness gradient curve and surface texture parameters.

[0035] Hardness gradient data and surface friction standard data are obtained from a silicone ball design parameter database to generate a structured dataset. A linear regression algorithm is used to analyze the correlation between hardness gradient and surface friction in the structured dataset, generating a mapping relationship between hardness gradient and friction. Based on this mapping relationship, training data for different muscle groups in rehabilitation training is obtained. The impact of changes in hardness gradient on muscle group stimulation intensity is analyzed, and the sensitivity range of hardness changes for each muscle group is determined. If the sensitivity range covers a preset number of muscle groups, the sensitivity ranges are grouped using a clustering algorithm to generate hardness gradient curves for each muscle group. A texture generation algorithm is used to optimize the texture parameters corresponding to surface friction based on the hardness gradient curve to generate a texture parameter set. By matching and analyzing the texture parameter set with the hardness gradient curve, it is determined whether the parameter combination meets the friction standard for rehabilitation training, and the final hardness gradient and surface texture parameters are determined. If the final hardness gradient and surface texture parameters meet the standard, they are stored in the database to generate a silicone ball design parameter configuration file.

[0036] Specifically, the hardness gradient requirements, such as a hardness range of 20 to 80 Shore A and a surface friction standard of 0.3 to 0.8, were extracted from a database of silicone ball design parameters. Using machine learning algorithms, such as support vector machines (SVM), the sensitivity ranges of different muscle groups to hardness changes during rehabilitation training were analyzed.

[0037] For example, the sensitivity range of upper limb muscles to stiffness changes is 30 to 50 Shore A, while that of lower limb muscles is 40 to 60 Shore A. Based on these data, a polynomial regression algorithm is used to determine the stiffness gradient curve. For example, the stiffness gradient curve of upper limb muscles is y = 0.02x 2 +0.5x+20, for lower limb muscles: y=0.03x 2+0.4x+30. Surface texture analysis algorithms, such as Fourier transforms, are used to determine surface texture parameters, such as 0.5 to 0.7 for upper limb muscles and 0.6 to 0.8 for lower limb muscles. These parameters and curves are used to optimize the design of the silicone balls to meet the rehabilitation training needs of different muscle groups.

[0038] S102. Based on the hardness gradient curve, finite element analysis is used to simulate the internal stress distribution and material flow characteristics of the silicone ball, and the mold partitioning scheme and vulcanization temperature field distribution are obtained through iterative calculation.

[0039] Acquire the three-dimensional model data and hardness gradient curve data of the silicone ball to generate an initial silicone ball model. Use the finite element analysis method to mesh the initial silicone ball model, calculate the internal stress distribution, and obtain stress distribution data. Analyze the material flow characteristics based on the stress distribution data and extract the flow characteristic parameters. If the flow characteristic parameters exceed the preset threshold, adjust the hardness gradient curve, re-perform the finite element analysis, and iteratively optimize to obtain optimized stress distribution data and flow characteristic parameters. Determine the mold partitioning scheme based on the optimized stress distribution data. Simulate the vulcanization process for the mold partitioning scheme, calculate the temperature field distribution, and obtain the vulcanization temperature field distribution. Use a genetic algorithm to optimize the vulcanization temperature field distribution, adjust the mold partitioning scheme, and determine the final mold partitioning scheme and vulcanization temperature field distribution.

[0040] Specifically, in the finite element analysis, a 3D model of the silicone sphere was first constructed, using a tetrahedral mesh with a mesh size of 0.5 mm to ensure accuracy. Material properties were defined by inputting an elastic modulus of 2.5 MPa and a Poisson's ratio of 0.49. In the boundary condition settings, the outer surface of the silicone sphere was fixed to simulate the constraints encountered in actual use. The stress distribution was calculated using the nonlinear static analysis module, with 100 iterations and a convergence accuracy of 0.001. The analysis revealed that the maximum stress within the silicone sphere, reaching 1.8 MPa, occurred in the center, while the stress at the edges was lower, approximately 0.5 MPa. Based on the stress distribution results, the mold was partitioned using the K-means clustering algorithm, with a cluster number of 3 and 50 iterations, to determine the mold partitioning scheme. For the vulcanization temperature field simulation, the heat conduction analysis module was used, with an initial temperature of 25°C, a vulcanization temperature of 150°C, a heating rate of 5°C / minute, a calculation time step of 1 minute, and a total calculation time of 30 minutes. Through iterative calculations, the temperature distribution of each mold area was determined. The center area had the highest temperature, reaching 145 degrees Celsius, while the edge areas were relatively cool, at approximately 130 degrees Celsius. Based on this temperature distribution, the heating power was adjusted to 500 watts in the center area and 300 watts in the edge areas to achieve uniform vulcanization. Ultimately, through finite element analysis and iterative calculations, the mold partitioning scheme and vulcanization temperature field distribution were optimized, improving the quality and performance of the silicone balls.

[0041] S103. If the finite element analysis results show that the interlayer stress difference is greater than a preset threshold, the mold partition boundary and the silicone formula ratio are adjusted, and the interlayer bonding uniformity is verified through secondary simulation to obtain the optimized mold design parameters.

[0042] Finite element analysis is used to obtain interlayer stress difference data, determine whether the interlayer stress difference exceeds a preset threshold, and obtain stress distribution analysis results. If the interlayer stress difference exceeds the preset threshold, an optimization algorithm is used to adjust the mold partition boundaries and determine the mold partition boundary parameters. Based on the mold partition boundary parameters, the initial silicone formula ratio values are obtained, and the optimized formula ratio is obtained by matching the mold partition boundary parameters with the material property database. A secondary finite element simulation is performed using the mold partition boundary parameters and the optimized formula ratio to obtain interlayer bonding uniformity data. By comparing the interlayer bonding uniformity data with the preset uniformity standard, it is determined whether the requirements are met and a verification result is obtained. If the verification result meets the preset uniformity standard, the optimized mold design parameters are output and the final design parameters are determined. Based on the final design parameters, a digital model of the mold partitions and silicone formula is generated to obtain executable production parameters.

[0043] Specifically, in the finite element analysis, if the interlayer stress difference exceeds the preset threshold (for example, 50MPa), the system will automatically adjust the mold partition boundary, increase the original partitions from 5 to 8, and recalculate the stress distribution of each partition. At the same time, the silicone formula ratio will also be optimized according to the stress distribution, and the original silicone A and silicone B ratio will be adjusted from 7:3 to 6:4 to reduce the interlayer stress difference. After the adjustment, the system will perform a secondary simulation and use the nonlinear contact algorithm in the ANSYS software to calculate the interlayer bonding uniformity. The simulation results show that the interlayer stress difference is reduced to 30MPa, which is lower than the preset threshold, and the interlayer bonding uniformity is improved by 15%. Finally, the system outputs the optimized mold design parameters, including the partition boundary coordinates, silicone formula ratio and interlayer bonding uniformity index, to provide precise guidance for subsequent mold manufacturing.

[0044] S104. Generate a three-dimensional model of the multi-cavity mold based on the optimized mold design parameters, calculate the mold processing trajectory using a CNC machining path planning algorithm, and obtain high-precision mold processing instructions.

[0045] Acquire a mold design parameter set, wherein the mold design parameter set includes geometric parameters and material parameters of the multi-cavity mold; parse the mold design parameter set, determine the structural layout of the multi-cavity mold, and obtain structural description data; adopt a geometric modeling algorithm to construct a three-dimensional model of the multi-cavity mold according to the structural description data to obtain three-dimensional model data; judge whether the three-dimensional model data meets the preset geometric constraints, and if so, adopt a path planning algorithm to generate a processing trajectory to obtain trajectory data; if not, adjust the structural description data and reconstruct the three-dimensional model; according to the trajectory data, combine the precision control rules to optimize the processing trajectory to obtain high-precision trajectory data; extract processing features from the high-precision trajectory data, generate processing instructions, and obtain an instruction sequence; verify the consistency of the processing instructions with the three-dimensional model data through the instruction sequence to obtain the final processing instructions; judge whether the final processing instructions meet the preset precision threshold, and if so, output high-precision mold processing instructions; if not, adjust the optimization parameters and regenerate the processing trajectory.

[0046] Specifically, in the optimized mold design parameters, the stress distribution of the mold was first determined through finite element analysis. The mold was simulated using ANSYS software, with the material set to 45 steel, the elastic modulus to 210GPa, and the Poisson's ratio to 0.3. The analysis results showed that the maximum stress occurred at the corners of the mold, reaching 350MPa. Based on this, the wall thickness of the mold was adjusted to 15mm, and a fillet with a radius of 5mm was added to the corners to reduce stress concentration. Next, UGNX software was used to generate a three-dimensional model of the multi-cavity mold, setting the mold size to 200mm×150mm×50mm, the number of cavities to 4, and the size of each cavity to 50mm×50mm×30mm. The spacing between the cavities was ensured to be 10mm to avoid interference during processing. For CNC machining path planning, an A*-based path optimization method was used. The machining accuracy was set to 0.01mm, the tool diameter to 10mm, the cutting speed to 200mm / min, and the feed rate to 0.1mm / rev. The algorithm calculated the optimal machining trajectory, ensuring the shortest and collision-free tool path. Finally, the generated machining trajectory was converted to G-code, with the spindle speed set to 3000rpm and the coolant flow rate to 5L / min. High-precision mold machining instructions were generated, ensuring a surface roughness of Ra 0.8μm, meeting high-precision requirements.

[0047] S105 , obtaining the processed mold geometry data from the mold processing instruction, detecting the mold surface error and partition accuracy through laser scanning, and determining whether the mold meets the hardness gradient control requirements.

[0048] The three-dimensional geometric data of the completed mold is extracted from the mold processing instructions. The instruction content is decomposed using a preset parsing algorithm to obtain the three-dimensional mold geometric data. Surface point cloud data of the three-dimensional mold geometric data is acquired through laser scanning and detection. Surface error data and partition accuracy data are generated using a point cloud registration algorithm. The mold surface error data is compared with a preset error threshold. If the surface error data exceeds the preset error threshold, the mold surface is determined to be unqualified, thereby obtaining a surface error analysis result. The partition accuracy data is compared with the preset precision partition requirements. If the partition accuracy data meets the preset precision partition requirements, the partition accuracy is determined to be qualified, thereby obtaining a partition accuracy analysis result. The mold surface is scanned using laser hardness testing technology to obtain hardness gradient distribution data. The hardness gradient distribution data is compared with a preset hardness gradient threshold. If the hardness gradient distribution data meets the preset hardness gradient threshold, the mold hardness control is determined to be qualified, thereby obtaining a hardness control analysis result. A logical AND operation is performed based on the surface error analysis result, the partition accuracy analysis result, and the hardness control analysis result. If the surface error analysis result, the partition accuracy analysis result, and the hardness control analysis result all meet the requirements, then the mold is determined to meet the overall requirements, thereby obtaining a final determination result.

[0049] Specifically, the geometric data of the finished mold is extracted from the mold processing instructions. First, the G-code parsing module of the CNC machine tool is used to obtain the mold processing path and cutting parameters, such as a processing depth of 2.5 mm and a cutting speed of 120 meters per minute. Then, the processed mold is geometrically reconstructed using 3D modeling software to generate a model file in STL format, with the model accuracy controlled within 0.01 mm. Subsequently, a laser scanner is used to perform high-precision scanning of the mold surface with a scanning point cloud density of 1,000 points per square millimeter. The scan data is compared with the design model through a point cloud processing algorithm (such as the ICP algorithm) to calculate the surface error distribution, and the error threshold is set to ±0.05 mm. For partition accuracy detection, a regional segmentation algorithm (such as K-means clustering) is used to divide the mold surface into several areas. The size of each area is 10 mm × 10 mm. The average error value of each area is calculated, and the error of each area is required to be no more than ±0.03 mm. Finally, the hardness gradient control algorithm is used to analyze the hardness distribution in different areas of the mold. For example, the surface hardness requirement is HRC58-62, and the internal hardness is HRC45-50. Finite element analysis software is used to simulate the heat treatment process to ensure that the hardness gradient meets the design requirements and the gradient change rate is controlled within the range of 1-2 HRC per millimeter.

[0050] S106. If the mold error is less than a preset threshold, a segmented vulcanization process is used to control the silicone ball molding process, and a silicone ball semi-finished product with continuously changing hardness is obtained by real-time monitoring of the vulcanization temperature and pressure distribution.

[0051] Mold error data is collected via sensors to determine whether the mold error is less than a preset threshold. If the mold error is less than the preset threshold, the error distribution characteristics of the mold error data are determined. Based on the error distribution characteristics, initial control parameters for the vulcanization temperature and pressure distribution are generated using a staged vulcanization process. A real-time monitoring system acquires temperature and pressure data from the vulcanization equipment to determine whether the temperature and pressure data deviate from the initial control parameters. If the temperature or pressure data deviate from the initial control parameters, the vulcanization process parameters are adjusted using a support vector machine algorithm to obtain optimized control parameters. Based on the optimized control parameters, a closed-loop control system is used to adjust the vulcanization equipment to produce a silicone ball semi-finished product with a continuously varying hardness. Hardness distribution data is acquired from the silicone ball semi-finished product using a hardness testing device to determine whether the hardness distribution data meets the continuity requirement. If the hardness distribution data meets the continuity requirement, the optimized control parameters and hardness distribution data are recorded in a data storage system to generate a process control log for the silicone ball semi-finished product.

[0052] Specifically, if the mold error is less than a preset threshold of 0.05 mm, the system automatically initiates a staged vulcanization process to control the silicone ball molding process. First, a high-precision sensor collects real-time vulcanization temperature data, maintaining it between 150°C and 180°C. A PID algorithm dynamically adjusts the heating power based on the temperature change rate, ensuring temperature fluctuations do not exceed ±2°C. Simultaneously, a pressure sensor monitors the vulcanization pressure, set within a range of 10 MPa to 15 MPa. A fuzzy control algorithm adjusts the pressure valve in real time to ensure uniform pressure distribution and a deviation within ±0.5 MPa. During the vulcanization process, the system collects temperature and pressure data every 5 seconds and uses a multivariate regression analysis model to predict the hardness trend of the silicone balls. When the hardness value reaches the preset range of 70 ± 5 Shore A, the system automatically switches to the next vulcanization stage until the entire vulcanization process is complete. Finally, a continuous hardness tester analyzes the hardness distribution of the semi-finished silicone balls to ensure that the hardness gradient meets design requirements and the hardness change rate does not exceed 2 Shore A / mm. All data is uploaded to a cloud database in real time for subsequent quality analysis and process optimization.

[0053] S107. For the semi-finished silica gel balls, plasma treatment technology is used to adjust the surface molecular structure, and the optimized surface friction parameters are obtained by controlling the treatment time and gas ratio.

[0054] Initial surface molecular structure data and baseline friction parameters of the semi-finished silicone ball are obtained. The initial surface molecular structure data is obtained using a plasma treatment device, and the baseline friction parameters are obtained using a friction testing device. A machine learning model is used to analyze the relationship between the initial surface molecular structure data and the baseline friction parameters to determine a preliminary optimization range for the treatment time and gas ratio. Based on this preliminary optimization range, the treatment time and gas ratio of the plasma treatment device are adjusted to generate a first set of treated surface molecular structure data. The friction parameters of the first set of treated surface molecular structure data are obtained using a friction testing device, and a determination is made as to whether the friction parameters meet a preset threshold. If the friction parameters do not meet the preset threshold, the treatment time and gas ratio are adjusted using an iterative optimization algorithm to generate a second set of treated surface molecular structure data. The friction parameters of the second set of treated surface molecular structure data are obtained using the friction testing device to determine whether the optimization target is met. Based on this determination, data fusion technology is used to integrate the first and second sets of treated surface molecular structure data and the corresponding friction parameters to determine the final treatment time and gas ratio configuration. The final processing time and gas ratio configuration is applied by a plasma processing device to process the silicone ball semi-finished product to obtain optimized surface friction parameters.

[0055] Specifically, during the surface treatment of semi-finished silicone balls, basic parameters are first set using a plasma device. For example, 300 watts of radio frequency power and 50 Pascals of chamber pressure are used as initial conditions. A mixed gas is then introduced for surface activation, with the volume ratio of argon to oxygen controlled at 4:1. Fluctuations in the gas ratio are monitored in real time by a mass spectrometer and fed back to the control system to ensure that the ratio deviation does not exceed plus or minus 2 percent. The treatment time is dynamically adjusted based on the target surface energy value. For example, when the contact angle needs to be reduced from 110 degrees to 70 degrees, a gradient time algorithm is used. A linear model with a rate of 2 degrees per minute is used for the first 5 minutes, followed by an exponential decay model for the next 3 minutes. Finally, the surface roughness is tested using an atomic force microscope. If the measured Ra value is between 0.15 and 0.25 microns, the surface is considered qualified. To optimize friction, Hertz contact theory was used to calculate the contact area. Combined with the Coulomb friction model, an iterative algorithm was used to adjust processing parameters until the measured friction coefficient stabilized within a range of 0.35 ± 0.02 when the normal load was 1 Newton. During the data processing phase, cubic spline interpolation was used to fit the nonlinear relationship between process parameters and friction coefficient. This ultimately generated a process mapping matrix for mass production control. All sensor data during the process was uploaded to the MES system via the OPC-UA protocol, enabling closed-loop optimization of process parameters.

[0056] S108. Obtain performance data of the silicone ball from the surface friction parameters, measure the hardness and grip force distribution using mechanical testing equipment, and determine whether the silicone ball meets the needs of rehabilitation training.

[0057] A friction coefficient meter was used to sample multiple points on the surface of the silicone ball to obtain friction parameter datasets from different regions. Based on this friction parameter dataset, a friction distribution map of the silicone ball surface was generated. Hardness tests were performed using a Shore durometer at evenly distributed locations on the silicone ball surface to obtain a hardness value dataset, and the average hardness value and standard deviation were calculated. A pressure sensor array was used to measure the force distribution on the silicone ball under different grip forces, generating a grip force distribution heatmap. A support vector machine algorithm was used to compare the friction parameter dataset with a pre-established standard set of rehabilitation training parameters to determine whether the friction force met rehabilitation training requirements. A correlation analysis was performed between the hardness value dataset and the grip force distribution heatmap, and a hardness-grip force relationship model was established to quantitatively determine the impact between the two. A decision tree algorithm was used to comprehensively analyze the friction force distribution map, hardness value dataset, and grip force distribution heatmap to generate a multi-dimensional assessment of the silicone ball's performance. A match between the assessment results and the rehabilitation training standard requirements was calculated. If the match exceeded a preset threshold, the silicone ball was deemed to meet rehabilitation training requirements; if it fell below the preset threshold, it was deemed not to meet the requirements.

[0058] Specifically, when obtaining the performance data of the silicone ball through the surface friction parameters, the friction coefficient between the silicone ball and the standard contact surface is first measured using a friction coefficient tester. For example, the friction coefficient is measured to be 0.75, indicating that its surface has good anti-slip properties. Next, the hardness of the silicone ball is measured using mechanical testing equipment such as a hardness tester, and the Shore hardness is measured to be 50A. This value indicates that the silicone ball has moderate elasticity and is suitable for grasping and squeezing movements in rehabilitation training. In order to further evaluate the grip force distribution of the silicone ball, a pressure distribution test system is used to perform a multi-point pressure test on the silicone ball. The analysis results show that when a force of 10N is applied, the pressure distribution on the surface of the silicone ball is uniform, with the maximum pressure point being 12kPa and the minimum pressure point being 8kPa. This indicates that the silicone ball can evenly disperse pressure when subjected to force, reducing local stress concentration. Combining this data, an algorithm was used to analyze the overall performance of the silicone balls. For example, a weighted average method was used to comprehensively score the friction coefficient, hardness, and pressure distribution data. With a weight of 30% for the friction coefficient, 40% for the hardness, and 30% for the pressure distribution, the calculated overall score was 85, indicating the silicone balls' good suitability for rehabilitation training. Finally, by comparing the results to rehabilitation training standards, such as a friction coefficient greater than 0.7, a hardness between 40A and 60A, and a pressure distribution uniformity within ±20%, the silicone balls were confirmed to meet all requirements and are suitable for rehabilitation training.

[0059] S109. If the test data deviation is less than the preset threshold, the quality of the silicone balls is classified by an automated sorting system, and surface defects are identified by machine vision to obtain silicone balls that meet the standards.

[0060] A sensor acquires test data from silicone balls, calculates the deviation between the test data and a preset threshold, and determines whether the deviation is less than the threshold to identify silicone balls with acceptable deviations. A machine vision system scans the surface of the silicone balls with acceptable deviations to obtain a first image. A convolutional neural network algorithm processes the first image to identify surface defects, generating a second image containing defect features. Based on the defect features in the second image, the size and location of the defects are extracted, and a determination is made as to whether the defects exceed a preset threshold to determine the severity of the defects. Using a pre-established quality classification model and the defect severity as input, a decision tree algorithm is used to classify the silicone balls, generating quality grade labels. Based on the quality grade labels, an automated sorting system is driven to perform sorting operations, assigning the silicone balls to collection areas corresponding to the quality grade, resulting in sorted silicone balls. The sorting system then generates feedback on the classification accuracy. If the accuracy falls below a preset standard, a support vector machine algorithm is used to optimize the sorting parameters to generate an optimized sorting configuration. Using this optimized sorting configuration, the automated sorting operation is re-executed to determine whether all silicone balls meet the standards, resulting in the final silicone ball products that meet the standards.

[0061] Specifically, when the test data deviation is less than the preset threshold of 0.05, the system automatically starts the automated sorting process. First, the diameter, weight and other physical parameters of the silicone balls are collected by high-precision sensors to ensure that they meet the standard range (diameter 20±0.1mm, weight 5±0.05g). Subsequently, the machine vision system uses a defect detection algorithm based on convolutional neural networks (CNN) to perform a full-scale scan of the surface of the silicone balls to identify defects such as scratches and bubbles, and defects with an area of more than 0.1mm 2 Silicone balls that do not meet the standards will be marked as unqualified. The system uses image processing technology to shoot each silicone ball from multiple angles to generate high-resolution images, and uses edge detection algorithms (such as the Canny algorithm) to accurately extract the defect outline and calculate the defect area. For silicone balls that meet the standards, the system automatically classifies them as finished products and transports them to the packaging area via a conveyor belt. At the same time, it records the quality data of each silicone ball and generates a quality report to facilitate subsequent traceability and analysis. The entire sorting process uses closed-loop control to ensure a sorting accuracy of more than 99.9%. The sorting efficiency and quality are dynamically adjusted through a real-time monitoring system to cope with changes in the characteristics of different batches of silicone balls.

[0062] The present invention provides a silicone ball manufacturing system for rehabilitation training, which mainly includes:

[0063] A hardness gradient requirement acquisition module is used to obtain hardness gradient requirements and surface friction standards from a silicone ball design parameter database;

[0064] The surface friction standard acquisition module is used to analyze the sensitivity range of different muscle groups to hardness changes during rehabilitation training through algorithms, and determine the hardness gradient curve and surface texture parameters;

[0065] The algorithm analysis module is used to simulate the internal stress distribution and material flow characteristics of the silicone ball based on the hardness gradient curve using finite element analysis, and obtain the mold partitioning scheme and vulcanization temperature field distribution through iterative calculation;

[0066] The finite element analysis module is used to adjust the mold partition boundaries and silicone formula ratio if the finite element analysis results show that the interlayer stress difference is greater than the preset threshold. The interlayer bonding uniformity is verified through secondary simulation to obtain the optimized mold design parameters;

[0067] The mold partition adjustment module is used to generate a 3D model of a multi-cavity mold based on the optimized mold design parameters, and uses a CNC machining path planning algorithm to calculate the mold machining trajectory to obtain high-precision mold machining instructions;

[0068] The mold processing instruction generation module is used to obtain the finished mold geometry data from the mold processing instructions, detect the mold surface error and partition accuracy through laser scanning, and determine whether the mold meets the hardness gradient control requirements;

[0069] The mold detection module is used to control the silicone ball molding process using a segmented vulcanization process if the mold error is less than a preset threshold. By real-time monitoring of the vulcanization temperature and pressure distribution, a semi-finished silicone ball with continuously changing hardness is obtained;

[0070] The vulcanization process control module is used to adjust the surface molecular structure of the semi-finished silicone balls using plasma treatment technology. By controlling the treatment time and gas ratio, the optimized surface friction parameters are obtained.

[0071] The surface treatment module is used to obtain silicone ball performance data from surface friction parameters, measure hardness and grip force distribution through mechanical testing equipment, and determine whether the silicone ball meets rehabilitation training requirements;

[0072] The above embodiment is only one of the preferred implementation methods of the present invention and should not be used to limit the scope of protection of the present invention. Any changes or modifications that have no substantive meaning made to the main design concept and spirit of the present invention, as long as the technical problems they solve are still consistent with the present invention, should be included in the scope of protection of the present invention.

Claims

1. A method for manufacturing a silica gel ball for rehabilitation training, characterized in that: The method comprises the following steps: S101. Obtain hardness gradient requirements and surface friction standards from a silicone ball design parameter database, analyze the sensitivity ranges of different muscle groups to hardness changes during rehabilitation training through an algorithm, and determine a hardness gradient curve and surface texture parameters. S102. Based on the hardness gradient curve, finite element analysis is used to simulate the internal stress distribution and material flow characteristics of the silicone ball. The mold partitioning scheme and vulcanization temperature field distribution are obtained through iterative calculation; S103. If the finite element analysis results show that the interlayer stress difference is greater than a preset threshold, the mold partition boundaries and the silicone formula ratio are adjusted, and the interlayer bonding uniformity is verified through a secondary simulation to obtain optimized mold design parameters; S104, generating a three-dimensional model of the multi-cavity mold based on the optimized mold design parameters, calculating the mold machining trajectory using a CNC machining path planning algorithm, and obtaining high-precision mold machining instructions; S105, obtaining the processed mold geometry data from the mold processing instructions, detecting the mold surface error and partition accuracy through laser scanning, and determining whether the mold meets the hardness gradient control requirements; S106. If the mold error is less than a preset threshold, a segmented vulcanization process is used to control the silicone ball molding process, and a semi-finished silicone ball with continuously varying hardness is obtained by real-time monitoring of the vulcanization temperature and pressure distribution. S107. For the semi-finished silica gel balls, plasma treatment technology is used to adjust the surface molecular structure, and the optimized surface friction parameters are obtained by controlling the treatment time and gas ratio; S108. Obtain performance data of the silicone ball from the surface friction parameters, measure hardness and grip force distribution using mechanical testing equipment, and determine whether the silicone ball meets the rehabilitation training requirements; S109. If the test data deviation is less than the preset threshold, the quality of the silicone balls is classified by an automated sorting system, and surface defects are identified by machine vision to obtain silicone balls that meet the standards.

2. The method for manufacturing a silicone ball for rehabilitation training according to claim 1, characterized in that: The S101 includes: Obtain hardness gradient data and surface friction standard data from the silicone ball design parameter database to generate a structured data set; A linear regression algorithm is used to analyze the correlation between the hardness gradient and the surface friction in the structured data set to obtain a mapping relationship between the hardness gradient and the friction; Acquiring training data of different muscle groups in rehabilitation training according to the mapping relationship, analyzing the effect of hardness gradient changes on muscle group stimulation intensity, and determining the hardness change sensitivity range of each muscle group; If the sensitivity interval covers a preset number of muscle groups, the sensitivity intervals are grouped using a clustering algorithm to obtain hardness gradient curves for different muscle groups; Using a texture generation algorithm to optimize texture parameters corresponding to surface friction according to the hardness gradient curve to generate a texture parameter set; By matching and analyzing the texture parameter set with the hardness gradient curve, it is determined whether the parameter combination meets the friction standard of rehabilitation training, and the final hardness gradient and surface texture parameters are determined; If the final hardness gradient and surface texture parameters meet the standards, the final hardness gradient and surface texture parameters are stored in a database to generate a silicone ball design parameter configuration file.

3. The method for manufacturing a silicone ball for rehabilitation training according to claim 1, characterized in that: The S102 includes: Obtaining the three-dimensional model data and hardness gradient curve data of the silicone ball to generate an initial silicone ball model; Using a finite element analysis method to mesh the initial silica gel ball model, calculate the internal stress distribution, and obtain stress distribution data; Analyze the material flow characteristics according to the stress distribution data and extract flow characteristic parameters; If the flow characteristic parameters exceed a preset threshold, the hardness gradient curve is adjusted, finite element analysis is re-performed, and iterative optimization is performed to obtain optimized stress distribution data and flow characteristic parameters; Determining a mold partitioning scheme based on the optimized stress distribution data; Simulating the vulcanization process according to the mold partitioning scheme, calculating the temperature field distribution, and obtaining the vulcanization temperature field distribution; A genetic algorithm is used to optimize the vulcanization temperature field distribution, adjust the mold partitioning scheme, and determine a final mold partitioning scheme and vulcanization temperature field distribution.

4. A method for manufacturing a silica gel ball for rehabilitation training according to any one of claims 1 to 3, characterized in that: The S103 includes: Obtain interlayer stress difference data through finite element analysis, determine whether the interlayer stress difference exceeds a preset threshold, and obtain stress distribution analysis results; If the interlayer stress difference exceeds a preset threshold, an optimization algorithm is used to adjust the mold partition boundary to determine the mold partition boundary parameters; According to the mold partition boundary parameters, the initial value of the silicone formula ratio is obtained, and the optimized formula ratio is obtained by matching the material property database; Using the mold partition boundary parameters and the optimized formula ratio, a secondary finite element simulation is performed to obtain interlayer bonding uniformity data; By comparing the inter-layer bonding uniformity data with a preset uniformity standard, it is determined whether the requirements are met and a verification result is obtained; If the verification result meets the preset uniformity standard, the optimized mold design parameters are output and the final design parameters are determined; Based on the final design parameters, a digital model of the mold partitions and silicone formula is generated to obtain executable production parameters.

5. A method for manufacturing a silica gel ball for rehabilitation training according to any one of claims 1 to 3, characterized in that: The S104 includes: Acquire a mold design parameter set, wherein the mold design parameter set includes geometric parameters and material parameters of the multi-cavity mold; Analyzing the mold design parameter set, determining the structural layout of the multi-cavity mold, and obtaining structural description data; Using a geometric modeling algorithm, a three-dimensional model of the multi-cavity mold is constructed according to the structural description data to obtain three-dimensional model data; Determining whether the three-dimensional model data satisfies preset geometric constraints, and if so, generating a machining trajectory using a path planning algorithm to obtain trajectory data; If not, adjusting the structural description data and rebuilding the three-dimensional model; According to the trajectory data, combined with the precision control rules, the processing trajectory is optimized to obtain high-precision trajectory data; Extracting machining features from the high-precision trajectory data, generating machining instructions, and obtaining an instruction sequence; Verifying the consistency of the processing instructions with the three-dimensional model data through the instruction sequence to obtain the final processing instructions; Determining whether the final processing instruction meets a preset accuracy threshold, and if so, outputting a high-precision mold processing instruction; If not satisfied, adjust the optimization parameters and regenerate the processing trajectory.

6. A method for manufacturing a silica gel ball for rehabilitation training according to any one of claims 1 to 3, characterized in that: The S105 includes: Extracting the three-dimensional geometric data of the mold after machining from the mold machining instruction, and decomposing the instruction content using a preset parsing algorithm to obtain the three-dimensional geometric data of the mold; Acquire surface point cloud data of the three-dimensional geometric data of the mold by laser scanning detection, and generate mold surface error data and partition accuracy data by using a point cloud registration algorithm; Comparing the mold surface error data with a preset error threshold, if the surface error data exceeds the preset error threshold, determining that the mold surface is unqualified, and obtaining a surface error analysis result; Comparing the partition accuracy data with the preset partition accuracy requirement, if the partition accuracy data meets the preset partition accuracy requirement, determining that the partition accuracy is qualified, and obtaining a partition accuracy analysis result; Scan the mold surface using laser hardness testing technology to obtain hardness gradient distribution data; Comparing the hardness gradient distribution data with a preset hardness gradient threshold, if the hardness gradient distribution data meets the preset hardness gradient threshold, determining that the mold hardness control is qualified, and obtaining a hardness control analysis result; Based on the surface error analysis results, the partition accuracy analysis results and the hardness control analysis results, a logical AND operation is performed. If the surface error analysis results, the partition accuracy analysis results and the hardness control analysis results are all qualified, it is determined that the mold meets the overall requirements and a final judgment result is obtained.

7. A method for manufacturing a silica gel ball for rehabilitation training according to any one of claims 1 to 3, characterized in that: The S106 includes: The mold error data is collected through the sensor to determine whether the mold error is less than the preset threshold; If the mold error is less than a preset threshold, determining an error distribution feature of the mold error data; According to the error distribution characteristics, a staged vulcanization process is adopted to generate initial control parameters of vulcanization temperature and pressure distribution; Acquiring temperature and pressure data from the vulcanizing equipment through a real-time monitoring system to determine whether the temperature and pressure data deviate from the initial control parameters; If the temperature or pressure data deviates from the initial control parameters, the vulcanization process parameters are adjusted using a support vector machine algorithm to obtain optimized control parameters; According to the optimized control parameters, a closed-loop control system is used to adjust the vulcanization equipment to produce a semi-finished silicone ball with continuously changing hardness; Obtain hardness distribution data from the semi-finished silicone ball product using a hardness testing device to determine whether the hardness distribution data meets the continuity requirement; If the hardness distribution data meets the continuity requirement, the optimized control parameters and the hardness distribution data are recorded in a data storage system to generate a process control log for the semi-finished silicone ball product.

8. A method for manufacturing a silica gel ball for rehabilitation training according to any one of claims 1 to 3, characterized in that: The S107 includes: Obtaining initial surface molecular structure data and baseline friction parameters of the semi-finished silica gel ball, wherein the initial surface molecular structure data is obtained by a plasma processing device, and the baseline friction parameters are obtained by a friction testing device; Using a machine learning model to analyze the relationship between the initial surface molecular structure data and the baseline friction parameters to obtain a preliminary optimization range of the processing time and gas ratio; According to the preliminary optimization range, adjusting the processing time and gas ratio of the plasma processing equipment to generate a first set of surface molecular structure data after processing; obtaining friction parameters of the first set of processed surface molecular structure data through a friction testing device, and determining whether the friction parameters reach a preset threshold; If the friction force parameter does not reach a preset threshold, the processing time and gas ratio are adjusted through an iterative optimization algorithm to generate a second set of processed surface molecular structure data; obtaining friction parameters of the second set of processed surface molecular structure data through a friction testing device to determine whether the optimization goal is met; According to the judgment result, using data fusion technology to integrate the first set of processed surface molecular structure data and the second set of processed surface molecular structure data and corresponding friction parameters to determine the final processing time and gas ratio configuration; The final processing time and gas ratio configuration is applied by a plasma processing device to process the silicone ball semi-finished product to obtain optimized surface friction parameters.

9. A method for manufacturing a silica gel ball for rehabilitation training according to any one of claims 1 to 3, characterized in that: The S108 includes: The friction coefficient measuring instrument was used to sample multiple points on the surface of the silicone ball to obtain friction parameter data sets in different areas; generating a friction force distribution map of the surface of the silicone ball according to the friction force parameter data set; Use a Shore hardness tester to perform hardness tests on evenly distributed locations on the surface of the silicone ball to obtain a hardness value data set and calculate the average hardness value and standard deviation; The force distribution of the silicone ball under different grip forces was measured using a pressure sensor array to generate a grip force distribution heat map. Using a support vector machine algorithm to compare the friction force parameter data set with a pre-established rehabilitation training standard parameter set to determine whether the friction force meets the rehabilitation training requirements; Performing a correlation analysis on the hardness value dataset and the grip force distribution heat map, establishing a hardness-grip force relationship model, and determining a quantitative influence relationship between the two; A decision tree algorithm is used to comprehensively analyze the friction force distribution map, hardness value dataset, and grip force distribution heat map to generate a multi-dimensional evaluation result of the silicone ball performance; Calculating the degree of matching between the evaluation result and the rehabilitation training standard requirements; if the degree of matching is higher than a preset threshold, determining that the silicone ball meets the rehabilitation training requirements; If it is lower than the preset threshold, it is determined that the requirement is not met.

10. A silicone ball manufacturing system for rehabilitation training, characterized in that: The system is used to implement the method for manufacturing a silicone ball for rehabilitation training according to any one of claims 1 to 9, and the system comprises: A hardness gradient requirement acquisition module is used to obtain hardness gradient requirements and surface friction standards from a silicone ball design parameter database; The surface friction standard acquisition module is used to analyze the sensitivity range of different muscle groups to hardness changes during rehabilitation training through algorithms, and determine the hardness gradient curve and surface texture parameters; The algorithm analysis module is used to simulate the internal stress distribution and material flow characteristics of the silicone ball based on the hardness gradient curve using finite element analysis, and obtain the mold partitioning scheme and vulcanization temperature field distribution through iterative calculation; The finite element analysis module is used to adjust the mold partition boundaries and silicone formula ratio if the finite element analysis results show that the interlayer stress difference is greater than the preset threshold. The interlayer bonding uniformity is verified through secondary simulation to obtain the optimized mold design parameters; The mold partition adjustment module is used to generate a 3D model of a multi-cavity mold based on the optimized mold design parameters, and uses a CNC machining path planning algorithm to calculate the mold machining trajectory to obtain high-precision mold machining instructions; The mold processing instruction generation module is used to obtain the finished mold geometry data from the mold processing instructions, detect the mold surface error and partition accuracy through laser scanning, and determine whether the mold meets the hardness gradient control requirements; The mold detection module is used to control the silicone ball molding process using a segmented vulcanization process if the mold error is less than a preset threshold. By real-time monitoring of the vulcanization temperature and pressure distribution, a semi-finished silicone ball with continuously changing hardness is obtained; The vulcanization process control module is used to adjust the surface molecular structure of the semi-finished silicone balls using plasma treatment technology. By controlling the treatment time and gas ratio, the optimized surface friction parameters are obtained. The surface treatment module is used to obtain the performance data of the silicone ball from the surface friction parameters, measure the hardness and grip force distribution through mechanical testing equipment, and determine whether the silicone ball meets the rehabilitation training requirements.

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