Biological implant robot machining posture planning and error compensation method based on global stiffness model

Abstract

The application discloses a biological implant robot machining posture planning and error compensation method based on a global stiffness model, belongs to the technical field of biological manufacturing and surgical robot auxiliary machining, and comprises the following steps: a biomechanical stiffness atlas covering the whole posture space of the implant is constructed by sparse modal sampling combined with an inverse distance weighting algorithm. The application effectively solves the problem that the cell responses are inconsistent due to the machining quality differences of different regions of the complex curved surface implant through the global surface biological activity consistency, and significantly improves the long-term success rate of implant surgery. The application is easy to deploy on the existing medical robot through hardware pollution. Through micron-level functional molding, the micron-level biological texture depth is accurately controlled, and the manufacturing level of the personalized implant is improved from simple geometric shape customization to a new height of surface biological function customization.

Description

Technical Field

[0001] This invention belongs to the field of biomanufacturing and surgical robot-assisted processing technology, specifically referring to a method for posture planning and error compensation in the processing of bio-implant robots based on a global stiffness model. Background Technology

[0002] Personalized implants, such as pelvic prostheses and mandibular prostheses reconstructed based on the patient's CT / MRI data, typically have extremely complex free-form surfaces; in order to promote osseointegration after implantation, bioactive microstructures with specific depths and morphologies need to be processed on their surfaces.

[0003] Using industrial robots or medical robotic arms to fabricate microstructures on the surface of implants presents significant challenges:

[0004] Non-uniform stiffness fields lead to morphological differences: When the robot moves along the complex curved surface of the implant, its joint posture changes continuously, resulting in drastic fluctuations in end-effector stiffness. This causes areas with high stiffness to have clear textures, while areas with low stiffness have excessively deep textures or vibration marks. This inconsistency in surface quality severely affects the uniformity of cell adhesion and bone ingrowth; cutting errors in difficult-to-machine biomaterials: Materials such as medical titanium alloys have high hardness and are prone to elastic deformation during robot cutting; this results in the actual depth of the machined microstructures often being shallower than the designed depth, reducing the cell adhesion force of the implant.

[0005] Cleanroom environment limitations: Medical device manufacturing workshops have extremely high requirements for environmental cleanliness, making it difficult to install external hardware such as hydraulic dampers or complex force sensors, which limits the application of traditional vibration suppression methods. To address this, a method for posture planning and error compensation for bio-implant robot processing based on a global stiffness model is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a method for posture planning and error compensation in the processing of bio-implant robots based on a global stiffness model, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for posture planning and error compensation in the processing of bio-implant robots based on a global stiffness model, comprising the following steps:

[0008] S1. A biomechanical stiffness map covering the full attitude space of the implant is constructed by combining sparse modal sampling with an inverse distance weighting algorithm.

[0009] S2. With the goal of obtaining the most uniform surface microstructure, redundant angle planning is performed on the robot to determine the optimal machining posture while keeping the tool tip position and tool axis vector unchanged.

[0010] S3. Based on the stiffness map and cutting force model, the tool deflection is predicted, and the CNC machining code is corrected by mirror feedforward compensation to ensure the depth accuracy of the microstructure.

[0011] Preferably, the sparse modal sampling in S1 specifically includes: selecting at least 9 typical spatial points as sampling points within the working area of ​​the robot processing the implant. The typical spatial points cover the key areas of the implant processing path, including the curvature change zone of the surface, the inflection point of the processing path, the tool tip position corresponding to the robot joint motion limit, and the boundary point of the processing range; using a force hammer to perform modal testing on each sampling point, adjusting the robot to more than 3 different joint postures at each sampling point, and collecting dynamic stiffness data in the cutting direction, feed direction, and normal direction under each posture. The dynamic stiffness data includes stiffness amplitude, damping ratio, and natural frequency to ensure that the sampling data can reflect the differences in stiffness characteristics of the robot under different positions and postures in the processing space.

[0012] Preferably, the construction of the biomechanical stiffness map in S1 specifically involves: using multiple sets of dynamic stiffness data obtained from sparse modal sampling as a sample dataset, and establishing a data mapping model using inverse distance weighting. The algorithm sets the distance weighting function as follows:

[0013] ,

[0014] In the formula, The spatial Euclidean distance between the point to be interpolated and the i-th sample point is represented by , and p is the distance attenuation coefficient, with a value range of 2-5. By calculating the spatial distance and corresponding weight between any point to be interpolated and all sample points in the processing space, the sample data is weighted and interpolated to map the sparse experimental data to the entire implant processing space, quickly generating a virtual stiffness field covering the entire attitude space of the implant. The virtual stiffness field is stored in the form of a three-dimensional map, which can query the dynamic stiffness characteristic parameters of the cutting direction, feed direction and normal under any tool tip position and any joint attitude in real time, providing data support for subsequent attitude planning and tool deflection prediction.

[0015] Preferably, the objective function setting in S2 specifically includes: at each cutting point in the implant processing path, the optimization objective is clearly defined as obtaining the most uniform surface microstructure, and the quantitative index of the optimization objective is minimizing the texture deviation caused by stiffness fluctuations. Texture deviations include depth deviation, width deviation, and edge contour deviation of the microstructure; a correlation model between texture deviation and stiffness fluctuations is established through a large number of experiments, and the optimization objective is transformed into a calculable mathematical expression, that is, the objective function is:

[0016] ,

[0017] In the formula, For microstructure depth deviation, For microstructure width deviation, For microstructure edge contour deviation, These are the weighting coefficients for the corresponding deviations, and The weighting coefficients are determined based on the bioactivity design requirements of the implant. For microgroove-like microstructures, The value should be no less than 0.5, and priority should be given to ensuring the uniformity of depth.

[0018] Preferably, the posture optimization in S2 specifically includes: determining the search range of robot redundancy angles under the premise that the tool tip position coordinate error does not exceed a preset threshold of ±0.001mm and the tool axis vector direction error does not exceed a preset threshold of ±0.1°. The search range is determined based on the robot's joint motion limits, machining space boundaries, implant geometric contour constraints, and machine tool kinematic constraints. The search step size of each redundant joint is not greater than a preset threshold of 0.5°. A genetic algorithm is used to optimize the redundancy angles within the search range. The population size is initialized, and the number of iterations is set to 30-50. The robot posture corresponding to each candidate redundancy angle is queried for its dynamic stiffness parameters through the generated virtual stiffness field. The texture deviation quantization value under the posture is calculated by substituting it into the texture deviation association model. The redundancy angle corresponding to the smallest texture deviation quantization value is selected as the optimal redundancy angle. If there are multiple redundancy angles with the same texture deviation quantization value, the redundancy angle with the minimum joint motion energy consumption is selected.

[0019] Preferably, the strategy execution in S2 specifically includes: for processing scenarios of bio-implants with complex curved surfaces such as personalized pelvic prostheses, mandibular prostheses, acetabular cups, and femoral stems, the robot's low-stiffness singularity regions are identified in real time through a virtual stiffness field during the posture optimization process. The low-stiffness singularity region is a posture region where the dynamic stiffness parameter is lower than a preset threshold. The preset threshold is determined based on the cutting characteristics of the implant material, the depth of microstructure design, and the processing requirements. For medical titanium alloy TC4 material, the preset threshold for dynamic stiffness in the cutting direction is not less than 5000 N / mm. By selecting the optimal redundancy angle, the robot always avoids the low-stiffness singularity region when processing complex areas such as the spherical surface inside the acetabular cup, the curved surface of the femoral stem, and the large curvature arc surface of the mandibular angle, and performs cutting in the posture with optimal dynamic stiffness. This ensures that the stiffness fluctuation amplitude between different cutting points along the entire processing path is controlled within ±10%, effectively eliminating problems such as uneven texture depth, blurred edges, and vibration marks caused by posture changes.

[0020] Preferably, the tool deflection prediction in S3 specifically includes: obtaining the dynamic stiffness parameters under the optimal posture based on the constructed virtual stiffness field, and calculating the cutting resistance in combination with a preset cutting force model. The cutting force model is established based on the mechanical properties of the implant material, such as tensile strength, hardness, and elastic modulus; the geometric parameters of the tool, such as the rake angle, clearance angle, and cutting edge radius; and the process parameters, such as cutting speed, feed rate, and depth of cut, using empirical formulas.

[0021]

[0022] In the formula, Indicates cutting resistance. This represents the unit cutting force coefficient of the material. Represents the cutting cross-sectional area and is used to calculate the cutting resistance.

[0023] A deformation calculation model is established by using dynamic stiffness parameters and cutting resistance, which is implemented as follows:

[0024] ,

[0025] In the formula, To allow for more cutting depth, To determine the normal dynamic stiffness parameter under optimal posture, the steady-state elastic deformation of the tool during the cutting process is calculated, and the calculation accuracy of the tool depth is controlled within 0.1μm. This includes deformation components in the cutting direction, feed direction, and normal direction. The normal deformation component directly affects the depth accuracy of the microstructure and is therefore the main compensation target.

[0026] Preferably, the code correction in S3 specifically includes: using the mirror compensation principle, performing reverse offset correction on the tool position coordinates in the CNC machining G code; for each tool position on the machining path, determining the offset direction and offset amount based on the normal deformation component of the tool relief amount; if it is predicted that the microstructure will be shallower than the design depth due to tool relief, then the corresponding tool position coordinates are offset towards the inside of the implant along the normal direction, and the offset amount is equal to the normal tool relief amount; if it is predicted through the stiffness model that there is an overcutting risk in a certain area, then it is offset towards the outside of the implant along the normal direction, and the offset amount is 1.2 times the overcutting prediction value, ensuring that the overcutting problem is completely avoided; during the offset correction process, the continuity of the machining path must be ensured, and the rate of change of the offset of adjacent tool positions should not exceed 5% to avoid machine tool movement impact.

[0027] Preferably, the final shaping in S3 specifically includes: performing machine tool kinematics simulation verification on the corrected G-code, simulating the robot's machining trajectory using professional simulation software, detecting whether the machining path after tool position offset is consistent with the design path, and whether the depth, width, and contour of the microstructure meet biological design standards; if the microstructure depth error in the simulation results exceeds a preset threshold of ±0.5μm, then readjusting the cutting force coefficient or offset calculation ratio in the tool deflection prediction model until the simulation results meet the requirements; transmitting the verified G-code to the machining robot, controlling the robot to perform machining operations according to the corrected path, monitoring the robot's joint posture and cutting force changes in real time during machining, if the cutting force fluctuation exceeds a preset range of ±15%, then pausing machining and recalibrating the tool deflection to ensure that the final microstructure depth accurately meets biological design standards and has no visible vibration marks.

[0028] Preferably, the bio-implant includes a personalized pelvic prosthesis, mandibular prosthesis, acetabular cup, and femoral stem reconstructed based on the patient's CT / MRI data. The implant material is medical-grade titanium alloy TC4 or other medical-grade difficult-to-process biomaterials. The microstructure includes microgrooves and micropits for promoting cell adhesion and bone ingrowth. The design depth of the microstructure is 1-100 μm, and the depth accuracy error after processing is controlled within the micrometer range of ±1 μm. The entire method does not require the addition of external hardware such as hydraulic dampers and force sensors. It only achieves process upgrades through software algorithms, fully complies with the GMP standards for clean manufacturing of medical devices, and can be directly deployed on existing industrial robots or medical robotic arms.

[0029] Compared with the prior art, the beneficial effects of the present invention are:

[0030] 1. This invention effectively solves the problem of inconsistent cell response caused by differences in processing quality in different areas of complex curved implants by achieving full-surface bioactivity consistency, and significantly improves the long-term success rate of implantation surgery.

[0031] 2. This invention complies with GMP standards by eliminating hardware contamination and adopts a pure software algorithm solution. It does not require the introduction of hydraulic oil, air pipes or additional sensors, fully meets the high standards of clean manufacturing of medical devices, and is easy to deploy on existing medical robots.

[0032] 3. This invention achieves precise control over the depth of micron-level biological texture through micron-level functional molding, raising the level of personalized implant manufacturing from simple geometric shape customization to a new height of surface biological function customization. Attached Figure Description

[0033] Figure 1 This is the operational flow of the bio-implant robot processing posture planning and error compensation method based on the global stiffness model of this invention. Figure 1 ;

[0034] Figure 2 This is the operational flow of the bio-implant robot processing posture planning and error compensation method based on the global stiffness model of this invention. Figure 2 ;

[0035] Figure 3 This is the operational flow of the bio-implant robot processing posture planning and error compensation method based on the global stiffness model of this invention. Figure 3 ;

[0036] Figure 4 This is the operational flow of the bio-implant robot processing posture planning and error compensation method based on the global stiffness model of this invention. Figure 4 . Detailed Implementation

[0037] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] Example

[0039] Please see Figures 1-4 As shown, the present invention provides a technical solution comprising the following steps:

[0040] S1. A biomechanical stiffness map covering the full attitude space of the implant is constructed by combining sparse modal sampling with an inverse distance weighting algorithm.

[0041] S2. With the goal of obtaining the most uniform surface microstructure, redundant angle planning is performed on the robot to determine the optimal machining posture while keeping the tool tip position and tool axis vector unchanged.

[0042] S3. Based on the stiffness map and cutting force model, the tool deflection is predicted, and the CNC machining code is corrected by mirror feedforward compensation to ensure the depth accuracy of the microstructure.

[0043] Preferably, the sparse modal sampling in S1 specifically includes: selecting typical spatial points as sampling points within the working area of ​​the robot processing the implant. The typical spatial points cover the key areas of the implant processing path, including the curvature change zone of the surface, the inflection point of the processing path, the tool tip position corresponding to the robot joint motion limit, and the boundary point of the processing range; using a force hammer to perform modal testing on each sampling point, adjusting different joint postures of the robot at each sampling point, and collecting dynamic stiffness data in the cutting direction, feed direction, and normal direction under each posture. The dynamic stiffness data includes stiffness amplitude, damping ratio, and natural frequency.

[0044] Preferably, the construction of the biomechanical stiffness map in S1 specifically involves: using multiple sets of dynamic stiffness data obtained from sparse modal sampling as a sample dataset, and establishing a data mapping model using inverse distance weighting. The algorithm sets the distance weighting function as follows:

[0045] ,

[0046] In the formula, The spatial Euclidean distance between the point to be interpolated and the i-th sample point is represented by p, which is the distance attenuation coefficient. By calculating the spatial distance and corresponding weight between any point to be interpolated and all sample points in the processing space, the sample data is weighted and interpolated to map the sparse experimental data to the entire implant processing space. This quickly generates a virtual stiffness field covering the entire attitude space of the implant. The virtual stiffness field is stored in the form of a three-dimensional map and can query the dynamic stiffness characteristic parameters of the cutting direction, feed direction and normal direction under any tool tip position and any joint attitude in real time.

[0047] Preferably, the objective function setting in S2 specifically includes: at each cutting point in the implant processing path, the optimization objective is clearly defined as obtaining the most uniform surface microstructure, and the quantitative index of the optimization objective is minimizing the texture deviation caused by stiffness fluctuations. Texture deviations include depth deviation, width deviation, and edge contour deviation of the microstructure; a correlation model between texture deviation and stiffness fluctuations is established through a large number of experiments, and the optimization objective is transformed into a calculable mathematical expression, that is, the objective function is:

[0048] ,

[0049] In the formula, For microstructure depth deviation, For microstructure width deviation, For microstructure edge contour deviation, These are the weighting coefficients for the corresponding deviations, and The weighting coefficients are determined based on the bioactivity design requirements of the implant. For microgroove-like microstructures, The value should be no less than 0.5, and priority should be given to ensuring the uniformity of depth.

[0050] Preferably, the posture optimization in S2 specifically includes: determining the search range of robot redundancy angles under the premise that the tool tip position coordinate error does not exceed a preset threshold and the tool axis vector direction error does not exceed a preset threshold. The search range is determined based on the robot's joint motion limits, machining space boundaries, implant geometric contour constraints, and machine tool kinematic constraints. The search step size of each redundant joint is not greater than a preset threshold. A genetic algorithm is used to optimize the redundancy angles within the search range. The population size is initialized. The robot posture corresponding to each candidate redundancy angle is queried for its dynamic stiffness parameters through the generated virtual stiffness field. The texture deviation quantization value under the posture is calculated by substituting it into the texture deviation association model. The redundancy angle corresponding to the smallest texture deviation quantization value is selected as the optimal redundancy angle. If there are multiple redundancy angles with the same texture deviation quantization value, the redundancy angle with the minimum joint motion energy consumption is selected.

[0051] Preferably, the strategy execution in S2 specifically includes: for processing scenarios of bio-implants with complex curved surfaces such as personalized pelvic prostheses, mandibular prostheses, acetabular cups, and femoral stems, the robot's low-stiffness singularity regions are identified in real time through a virtual stiffness field during the posture optimization process. The low-stiffness singularity regions are posture areas where the dynamic stiffness parameter is lower than a preset threshold. The preset threshold is determined based on the cutting characteristics of the implant material, the depth of microstructure design, and the processing requirements. By selecting the optimal redundancy angle, the robot always avoids the low-stiffness singularity regions when processing complex areas such as the spherical surface inside the acetabular cup, the curved surface of the femoral stem, and the large curvature arc surface of the mandibular angle, and performs cutting in the posture with optimal dynamic stiffness. This ensures that the stiffness fluctuation amplitude between different cutting points along the entire processing path is controlled within ±10%, effectively eliminating problems such as uneven texture depth, blurred edges, and vibration marks caused by posture changes.

[0052] Preferably, the tool deflection prediction in S3 specifically includes: obtaining the dynamic stiffness parameters under the optimal posture based on the constructed virtual stiffness field, and calculating the cutting resistance in combination with a preset cutting force model. The cutting force model is established based on the mechanical properties of the implant material, such as tensile strength, hardness, and elastic modulus; the geometric parameters of the tool, such as the rake angle, clearance angle, and cutting edge radius; and the process parameters, such as cutting speed, feed rate, and depth of cut, using empirical formulas.

[0053]

[0054] In the formula, Indicates cutting resistance. This represents the unit cutting force coefficient of the material. Represents the cutting cross-sectional area and is used to calculate the cutting resistance.

[0055] A deformation calculation model is established by using dynamic stiffness parameters and cutting resistance, which is implemented as follows:

[0056] ,

[0057] In the formula, To allow for more cutting depth, To determine the normal dynamic stiffness parameter under optimal posture, the steady-state elastic deformation of the tool during the cutting process is calculated, and the calculation accuracy of the tool depth is controlled within 0.1μm. This includes deformation components in the cutting direction, feed direction, and normal direction. The normal deformation component directly affects the depth accuracy of the microstructure and is therefore the main compensation target.

[0058] Preferably, the code correction in S3 specifically includes: using the mirror compensation principle, performing reverse offset correction on the tool position coordinates in the CNC machining G code; for each tool position on the machining path, determining the offset direction and offset amount based on the normal deformation component of the tool relief amount; if it is predicted that the microstructure will be shallower than the design depth due to tool relief, then the corresponding tool position coordinates are offset towards the inside of the implant along the normal direction, and the offset amount is equal to the normal tool relief amount; if it is predicted through the stiffness model that there is an overcutting risk in a certain area, then it is offset towards the outside of the implant along the normal direction, and the offset amount is 1.2 times the overcutting prediction value, ensuring that the overcutting problem is completely avoided; during the offset correction process, the continuity of the machining path must be ensured, and the rate of change of the offset of adjacent tool positions should not exceed 5% to avoid machine tool movement impact.

[0059] Preferably, the final shaping in S3 specifically includes: performing machine tool kinematics simulation verification on the corrected G-code, simulating the robot's machining trajectory using professional simulation software, detecting whether the machining path after tool position offset is consistent with the design path, and whether the depth, width, and contour of the microstructure meet biological design standards; if the microstructure depth error in the simulation results exceeds a preset threshold of ±0.5μm, then readjusting the cutting force coefficient or offset calculation ratio in the tool deflection prediction model until the simulation results meet the requirements; transmitting the verified G-code to the machining robot, controlling the robot to perform machining operations according to the corrected path, monitoring the robot's joint posture and cutting force changes in real time during machining, if the cutting force fluctuation exceeds a preset range of ±15%, then pausing machining and recalibrating the tool deflection to ensure that the final microstructure depth accurately meets biological design standards and has no visible vibration marks.

[0060] Preferably, the bio-implant includes a personalized pelvic prosthesis, mandibular prosthesis, acetabular cup, and femoral stem reconstructed based on the patient's CT / MRI data. The implant material is medical-grade titanium alloy TC4 or other medical-grade difficult-to-process biomaterials. The microstructure includes microgrooves and micropits for promoting cell adhesion and bone ingrowth. The design depth of the microstructure is 1-100 μm, and the depth accuracy error after processing is controlled within the micrometer range of ±1 μm. The entire method does not require the addition of external hardware such as hydraulic dampers and force sensors. It only achieves process upgrades through software algorithms, fully complies with the GMP standards for clean manufacturing of medical devices, and can be directly deployed on existing industrial robots or medical robotic arms.

[0061] In this embodiment, the surface microgrooving of the TC4 titanium alloy personalized mandibular prosthesis is processed as follows:

[0062] Data input:

[0063] Import the STL model of the patient's mandibular bone defect and design microgroove texture paths on its surface to induce gingival soft tissue attachment.

[0064] Stiffness modeling:

[0065] A force hammer was used to test the robot at nine typical locations in the processing area, and the IDW algorithm was used to generate a complete stiffness map of the area.

[0066] Pose optimization (S2):

[0067] The system automatically calculated that when machining the large curvature arc surface of the mandibular angle, the end effector's dynamic stiffness is insufficient if the robot's default posture is used. This can cause severe vibrations; the algorithm automatically searches and suggests rotating the robot's fourth axis. Adjust the redundancy angle; at this point, the stiffness is increased to... This avoids the low stiffness region.

[0068] Error compensation (S3):

[0069] Simulation predictions show that, due to the high cutting resistance of titanium alloy, even with optimized posture, the microgroove depth will still decrease by 0.02 mm due to tool deflection; the system automatically modifies the machining G-code to increase the depth of cut for this path by 0.02 mm.

[0070] Processing results:

[0071] The robot executes the optimized program; the final mandibular prosthesis has uniform microgroove depth, with errors controlled within a certain range. The edges are clear and sharp, with no visible vibration marks, fully meeting the requirements for clinical implantation.

[0072] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their likenesses.

[0073] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A method for posture planning and error compensation in the processing of bio-implant robots based on a global stiffness model, characterized in that, Includes the following steps: S1. A biomechanical stiffness map covering the full attitude space of the implant is constructed by combining sparse modal sampling with an inverse distance weighting algorithm. S2. With the goal of obtaining the most uniform surface microstructure, redundant angle planning is performed on the robot to determine the optimal machining posture while keeping the tool tip position and tool axis vector unchanged. S3. Based on the stiffness map and cutting force model, the tool deflection is predicted, and the CNC machining code is corrected by mirror feedforward compensation to ensure the depth accuracy of the microstructure.

2. The method for posture planning and error compensation in bio-implant robot processing based on a global stiffness model according to claim 1, characterized in that: In S1, sparse modal sampling specifically includes: selecting typical spatial points as sampling points within the working area of ​​the robot processing the implant. These typical spatial points cover key areas of the implant processing path, including curvature change zones, inflection points of the processing path, tool tip positions corresponding to the robot joint motion limits, and processing range boundary points. A force hammer is used to perform modal testing on each sampling point. Different joint postures of the robot are adjusted at each sampling point, and dynamic stiffness data in the cutting direction, feed direction, and normal direction are collected for each posture. The dynamic stiffness data includes stiffness amplitude, damping ratio, and natural frequency.

3. The method for posture planning and error compensation in bio-implant robot processing based on a global stiffness model according to claim 2, characterized in that: The construction of the biomechanical stiffness map in S1 specifically involves: using multiple sets of dynamic stiffness data obtained from sparse modal sampling as a sample dataset, and establishing a data mapping model using inverse distance weighting. The algorithm sets the distance weighting function as follows: ， In the formula, The spatial Euclidean distance between the point to be interpolated and the i-th sample point is represented by p, which is the distance attenuation coefficient. By calculating the spatial distance and corresponding weight between any point to be interpolated and all sample points in the processing space, the sample data is weighted and interpolated to map the sparse experimental data to the entire implant processing space. This quickly generates a virtual stiffness field covering the entire attitude space of the implant. The virtual stiffness field is stored in the form of a three-dimensional map and can query the dynamic stiffness characteristic parameters of the cutting direction, feed direction and normal direction under any tool tip position and any joint attitude in real time.

4. The method for posture planning and error compensation in bio-implant robot processing based on a global stiffness model according to claim 3, characterized in that: The objective function setting in S2 specifically includes: at each cutting point in the implant processing path, the quantitative index of the optimization objective is to minimize the texture deviation caused by stiffness fluctuations, where texture deviations include depth deviation, width deviation, and edge contour deviation of the microstructure; through extensive experiments, a correlation model between texture deviation and stiffness fluctuations is established, transforming the optimization objective into a calculable mathematical expression, i.e., the objective function is: ， In the formula, For microstructure depth deviation, For microstructure width deviation, For microstructure edge contour deviation, These are the weighting coefficients for the corresponding deviations.

5. The method for posture planning and error compensation in bio-implant robot processing based on a global stiffness model according to claim 4, characterized in that: The posture optimization in S2 specifically includes: determining the search range of robot redundancy angles under the premise that the tool tip position coordinate error does not exceed a preset threshold and the tool axis vector direction error does not exceed a preset threshold. The search range is determined based on the robot's joint motion limits, machining space boundaries, implant geometric contour constraints, and machine tool kinematic constraints. The search step size of each redundant joint is not greater than a preset threshold. A genetic algorithm is used to optimize the redundancy angles within the search range. The population size is initialized, and the robot posture corresponding to each candidate redundancy angle is queried for its dynamic stiffness parameters through the generated virtual stiffness field. The texture deviation quantization value under the posture is calculated by substituting it into the texture deviation association model. The redundancy angle corresponding to the smallest texture deviation quantization value is selected as the optimal redundancy angle. If there are multiple redundancy angles with the same texture deviation quantization value, the redundancy angle with the minimum joint motion energy consumption is selected.

6. The method for posture planning and error compensation in bio-implant robot processing based on a global stiffness model according to claim 5, characterized in that: The strategy execution in S2 specifically includes: for complex curved surface biological implant processing scenarios, during the posture optimization process, the low stiffness singularity region of the robot is identified in real time through a virtual stiffness field. The low stiffness singularity region is a posture region where the dynamic stiffness parameter is lower than a preset threshold. The preset threshold is determined based on the cutting characteristics of the implant material, the depth of microstructure design, and the processing requirements. By selecting the optimal redundancy angle, the robot always avoids the low stiffness singularity region when processing complex areas, and performs cutting in the posture with optimal dynamic stiffness.

7. The method for posture planning and error compensation in bio-implant robot processing based on a global stiffness model according to claim 6, characterized in that: The tool deflection prediction in S3 specifically includes: obtaining the dynamic stiffness parameters under the optimal posture based on the constructed virtual stiffness field, and calculating the cutting resistance in combination with the preset cutting force model. The cutting force model is established based on the mechanical properties of the implant material, such as tensile strength, hardness, and elastic modulus; the geometric parameters of the tool, such as the rake angle, clearance angle, and cutting edge radius; and the process parameters, such as cutting speed, feed rate, and depth of cut, using empirical formulas. ， In the formula, Indicates cutting resistance. This represents the unit cutting force coefficient of the material. Represents the cutting cross-sectional area and is used to calculate the cutting resistance. A deformation calculation model is established by using dynamic stiffness parameters and cutting resistance, which is implemented as follows: ， In the formula, Indicates the amount of cutting allowance. This represents the normal dynamic stiffness parameter under the optimal attitude.

8. The method for posture planning and error compensation in bio-implant robot processing based on a global stiffness model according to claim 7, characterized in that: The code correction in S3 specifically includes: using the mirror compensation principle, performing reverse offset correction on the tool position coordinates in the CNC machining G code; for each tool position on the machining path, determining the offset direction and offset amount based on the normal deformation component of the tool yield; if it is predicted that the microstructure will be shallower than the design depth due to tool yield, then the corresponding tool position coordinates will be offset towards the inside of the implant along the normal direction, with the offset amount being equal to the normal tool yield amount; if it is predicted through the stiffness model that there is an overcutting risk in a certain area, then the coordinates will be offset towards the outside of the implant along the normal direction.

9. The method for posture planning and error compensation in bio-implant robot processing based on a global stiffness model according to claim 8, characterized in that: The final shaping in S3 specifically includes: performing machine tool kinematics simulation verification on the corrected G-code, simulating the robot's machining trajectory using professional simulation software, detecting whether the machining path after tool position offset is consistent with the design path, and whether the depth, width, and contour of the microstructure meet biological design standards; if the microstructure depth error in the simulation results exceeds a preset threshold, the cutting force coefficient or offset calculation ratio in the tool deflection prediction model is readjusted until the simulation results meet the requirements; the verified G-code is transmitted to the machining robot, controlling the robot to perform machining operations according to the corrected path, and the robot's joint posture and cutting force changes are monitored in real time during machining; if the cutting force fluctuation exceeds a preset range, machining is paused and the tool deflection is recalibrated.

10. The method according to any one of claims 1-9, characterized in that: The bio-implants include personalized pelvic prostheses, mandibular prostheses, acetabular cups, and femoral stems reconstructed based on the patient's CT / MRI data. The implant materials are medical biomaterials. The process is upgraded through software algorithms, and the implants can be directly deployed on existing industrial robots.

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