Intelligent auxiliary method for autogenous bone transplantation

Abstract

The invention discloses an intelligent auxiliary method for autologous bone transplantation, which comprises the following steps: constructing a three-dimensional jaw bone model, a three-dimensional tooth model and a three-dimensional defect area model based on CBCT (cone beam computed tomography) image data of a target object; according to the three-dimensional defect area model, obtaining defect area three-dimensional form data, inputting the defect area three-dimensional form data into the trained bone increment prediction model, and outputting to-be-supplemented autologous bone block design parameters; according to the design parameters of the to-be-supplemented bone block, in combination with the three-dimensional tooth model, determining a bone taking position of the to-be-supplemented bone block in the three-dimensional jaw bone model, and automatically marking the bone taking position so as to plan a bone block cutting path; according to the bone block cutting path, a predicted cutting force is obtained by combining a cutting force model; planning the motion track of the mechanical arm according to the predicted cutting force to realize auxiliary cutting of the autogenous bone block; in the cutting process, the actual cutting force is monitored in real time, and the movement track of the mechanical arm is dynamically adjusted according to the actual cutting force. According to the method, the preoperative planning is optimized, the operation precision in the operation is improved, and the operation risk is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-assisted surgical machines, and more particularly to an intelligent assistance method for autologous bone transplantation surgery. Background Art

[0002] During the preoperative planning phase of traditional oral onlay bone grafting (i.e., autologous bone transplantation), the surgeon primarily relies on CBCT imaging experience to determine the location and morphology of the donor bone area. This is subject to significant individual variability and inaccurate planning. During surgery, the autologous bone blocks are cut and trimmed using free-hand manipulation, relying primarily on the surgeon's clinical experience, which lacks objectivity and can easily lead to insufficient bone supply or excessive cutting. Furthermore, the surgery lacks real-time force feedback and dynamic path adjustment, making it difficult to accurately perform within a narrow anatomical space. Existing oral surgical robots primarily focus on three-dimensional navigation and positioning of implants, but in the onlay bone grafting scenario, there is a lack of integrated preoperative modeling, donor bone block morphology prediction, force-position coupling control, and real-time dynamic monitoring. These deficiencies lead to high technical sensitivity, significant surgical trauma, and a high risk of complications for patients.

[0003] Therefore, how to optimize preoperative planning, improve intraoperative operation accuracy and reduce surgical risks for onlay bone grafting is an urgent problem that needs to be solved by technicians in this field. Summary of the Invention

[0004] In view of the above problems, the present invention provides an intelligent auxiliary method for autologous bone transplantation surgery to at least solve some of the technical problems mentioned in the above background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] The present invention provides an intelligent auxiliary method for autologous bone transplantation surgery, comprising the following steps:

[0007] S1. Construct a 3D jaw model, a 3D tooth model, and a 3D defect area model based on the CBCT image data of the target object;

[0008] S2. Acquire three-dimensional morphological data of the defect area according to the three-dimensional defect area model, input the data into a trained bone augmentation prediction model, and output design parameters of the proposed bone augmentation block;

[0009] S3. Determine the bone removal location of the proposed bone block in the three-dimensional jaw model based on the design parameters of the proposed bone block and in combination with the three-dimensional tooth model, and automatically mark the location to plan the bone block cutting path;

[0010] S4. Obtaining a predicted cutting force based on the bone cutting path and a cutting force model;

[0011] S5. Planning the motion trajectory of the robotic arm according to the predicted cutting force to assist in cutting the autologous bone block;

[0012] S6. During the cutting process, the actual cutting force is monitored in real time, and the motion trajectory of the robot arm is dynamically adjusted according to the actual cutting force.

[0013] Furthermore, the step S1 specifically includes:

[0014] S11, acquiring CBCT image data of the target object;

[0015] S12, preprocessing the CBCT image data;

[0016] S13, using a deep learning segmentation model to segment the pre-processed CBCT image data according to alveolar bone, teeth, and defect areas, and output corresponding binary mask images;

[0017] S14, performing three-dimensional surface reconstruction using the Marching Cubes algorithm based on the binary mask images corresponding to the alveolar bone, teeth, and defect area, to obtain corresponding three-dimensional jaw models, three-dimensional tooth models, and three-dimensional defect area models, respectively;

[0018] S15. Apply the ICP method and the TPS method to perform rigid and non-rigid registration to align the three-dimensional jaw model, the three-dimensional tooth model, and the three-dimensional defect area model to the same spatial reference system.

[0019] Furthermore, the preprocessing specifically includes grayscale normalization processing, resampling processing and artifact suppression processing.

[0020] Furthermore, in step S2, the design parameters of the bone block to be repaired include: the shape, size and position of the fixing nail hole of the bone block to be repaired.

[0021] Furthermore, the step S3 further includes: generating a three-dimensional model of the autologous bone block that can be directly cut through Boolean operations and morphological algorithms according to the annotated content.

[0022] Furthermore, in step S4, the cutting force model is a cutting force model that considers force-position coupling, which is expressed as:

[0023]

[0024] Among them, F r Indicates radial cutting force; F t Indicates the tangential cutting force; F a represents the axial cutting force; N represents the number of cutting edges in the tool; j represents the jth cutting edge; K r Indicates the radial cutting force coefficient; K t Indicates the tangential cutting force coefficient; Ka Indicates the axial cutting force coefficient; h j It represents the instantaneous undeformed chip thickness of the cutting edge element; db(z) represents the undeformed cutting width at the axial position z.

[0025] Furthermore, the robotic arm is an arc-track telecentric structure.

[0026] Furthermore, the holding device at the end of the robotic arm fixes the arc turning needle.

[0027] Furthermore, the improved parameter method is used to perform kinematic modeling of the robotic arm.

[0028] It can be seen from the above technical solutions that, compared with the prior art, the present invention provides an intelligent auxiliary method for autologous bone transplantation surgery, which has the following beneficial effects:

[0029] The present invention automatically imports the CBCT data of the target object, segments the bone tissue and defect area based on a deep learning model, generates a three-dimensional jaw model in real time, and predicts the required bone block morphology, thereby optimizing preoperative planning, shortening the bone grafting operation time, and helping to reduce surgical trauma and alleviate postoperative complications.

[0030] The present invention avoids cutting errors and damage to important anatomical structures through real-time monitoring of cutting forces and precise path tracking, thus helping to reduce surgical risks.

[0031] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0033] Figure 1 A schematic flow chart of an intelligent assisted method for autologous bone transplantation surgery provided in an embodiment of the present invention.

[0034] Figure 2 A schematic diagram of arc knife parameters provided in an embodiment of the present invention.

[0035] Figure 3 Schematic diagram of the relationship between joints and coordinate systems of the improved parameter method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] The embodiment of the present invention discloses an intelligent auxiliary method for autologous bone transplantation surgery, see Figure 1 As shown, the following steps are included:

[0038] S1. Construct a 3D jaw model, a 3D tooth model, and a 3D defect area model based on the CBCT image data of the target object;

[0039] S2. Based on the three-dimensional defect area model, three-dimensional morphological data of the defect area is obtained and input into the trained bone augmentation prediction model to output the design parameters of the proposed bone block (i.e., the design parameters of the proposed autologous bone block);

[0040] S3. Based on the design parameters of the proposed bone block and in combination with the 3D tooth model, the bone removal position of the proposed bone block is determined in the 3D jaw model and automatically marked to plan the bone block cutting path;

[0041] S4, obtaining the predicted cutting force according to the bone cutting path and the cutting force model;

[0042] S5. Planning the motion trajectory of the robotic arm based on the predicted cutting force to assist in cutting the autologous bone block;

[0043] S6. During the cutting process, the actual cutting force is monitored in real time, and the motion trajectory of the robot arm is dynamically adjusted according to the actual cutting force.

[0044] The intelligent auxiliary method for autologous bone transplantation surgery provided by the embodiment of the present invention can effectively solve the problems of inaccurate preoperative planning, lack of force feedback and dynamic path adjustment during cutting, low surgical efficiency and high surgical risk in the existing technology.

[0045] Next, each of the above steps will be described in detail.

[0046] In the above step S1, a three-dimensional jaw model, a three-dimensional tooth model, and a three-dimensional defect area model are constructed based on the CBCT image data of the target object. Specifically, the steps include:

[0047] S11, acquiring CBCT image data of the target object;

[0048] S12, preprocessing the CBCT image data; the preprocessing specifically includes grayscale normalization, resampling, and artifact suppression (such as histogram equalization, wavelet enhancement, etc.);

[0049] S13. Segment the preprocessed CBCT image data according to alveolar bone, teeth, soft tissue, and defect area using a deep learning segmentation model (improved nnU-Net model), and output the corresponding binary mask image;

[0050] The above deep learning segmentation model can adopt the improved nnU-Net model. For details, please refer to the document "nnU-Net for Brain Tumor Segmentation";

[0051] The binary mask image of the soft tissue is an independent segmentation result, which identifies soft tissues such as mucosa and nerves. When constructing 3D jaw models, 3D tooth models, and 3D defect area models, the binary mask image corresponding to the soft tissue is usually regarded as background or part that needs to be excluded.

[0052] S14. Based on the binary mask images corresponding to the alveolar bone, teeth, and defect area, a Marching Cubes algorithm is used to perform 3D surface reconstruction to obtain the corresponding 3D jaw model, 3D tooth model, and 3D defect area model, respectively. Voxel fusion and mesh optimization are then used to improve the construction accuracy and rendering efficiency of the 3D geometric model.

[0053] S15. Apply the ICP (Iterative Closest Point) method and the TPS (Thin Plate Spline) method for rigid and non-rigid registration to align the 3D jaw model, 3D tooth model, and 3D defect area model to the same spatial reference system. This ensures that subsequent operations (such as bone block increment prediction, bone donor area annotation, path planning, etc.) can be accurately positioned in the same space.

[0054] In step S2, three-dimensional morphological data of the defect area is obtained based on the three-dimensional defect area model and input into the trained bone augmentation prediction model to output design parameters of the proposed bone block; the proposed bone block design parameters include: the shape, size and location of the fixed nail hole of the proposed bone block;

[0055] During the training process, this bone augmentation prediction model can obtain a large amount of three-dimensional morphological data of the defect area, namely the corresponding labels, as a porous bone defect dataset; the labels include the shape, size and fixed nail hole positions of the bone block to be filled. Using the three-dimensional morphological data of the defect area as input and the corresponding labels as output, a bone augmentation prediction model based on a combination of convolutional neural networks (such as improved U-Net) and random forests is trained.

[0056] In step S3, based on the design parameters of the proposed bone block and in combination with the three-dimensional tooth model, the bone removal location of the proposed bone block is determined in the three-dimensional jaw model and automatically annotated. The annotation content specifically includes: the bone donor area of ​​the proposed bone block (such as the chin and mandibular ramus), the contact surface between the proposed bone block and the jawbone, and the location and direction of the fixed nail hole; this provides input data for subsequent robot gripper path planning; and based on the annotation content, a three-dimensional model of the autologous bone block that can be directly cut can also be generated through Boolean operations and morphological algorithms.

[0057] Among them, the three-dimensional tooth model provides a key spatial reference when positioning and designing bone blocks in the mandibular defect area; specifically, the anatomical position of the tooth determines the implantation space of the bone block to be filled, the direction of the fixed nail hole, and the path planning to avoid damaging the tooth root or periodontal tissue; in preoperative simulation and intraoperative navigation, the three-dimensional tooth model also helps doctors quickly identify anatomical landmarks and improve the accuracy of alignment.

[0058] In the above step S4, the predicted cutting force is obtained based on the bone cutting path in combination with the cutting force model. Specifically, the spatial motion trajectory of the tool relative to the mandibular tissue can be obtained through the bone cutting path, including geometric and kinematic parameters such as feed rate, cutting depth, and tool posture angle. These spatial motion parameters are used as inputs of the cutting force model. The cutting force model uses these spatial motion parameters to analyze the spatial position changes of the tool as it moves along the planned path, calculates the material removal state of the blade at different axial positions and rotational phases, and then integrates and solves the predicted radial, tangential, and axial cutting force components.

[0059] Autologous bone transplant surgery is a highly precise procedure, requiring the robotic arm to cut the donor bone and reshape it using a high-speed rotating bur according to preoperatively planned motion paths. To meet these requirements, the robotic arm must undergo integrated force-position control.

[0060] As an intelligent auxiliary tool for autologous bone transplantation surgery, the robotic arm must meet the following requirements: 1) Flexibility: The robotic arm must adapt to the complex bone changes during surgery while maintaining force feedback response under external force interference; 2) Force control accuracy: The cutting force must be kept within a safe and efficient range to avoid excessive bone damage or insufficient cutting; 3) Trajectory control accuracy: The end of the robotic arm must strictly follow the preoperatively planned trajectory to ensure that the donor bone is reshaped accurately to meet surgical requirements;

[0061] In order to meet the above requirements, it is necessary to consider the cutting force prediction of force-position coupling. The construction process of the cutting force model considering force-position coupling is as follows:

[0062] (1) Geometric modeling of cutting tools:

[0063] The tool used in the cutting force modeling is an arc tool because it can represent many types of tools, such as flat-end tools and ball-end tools. For example, when the fillet radius is 0, the arc tool can be transformed into a flat-end tool; when the fillet radius is equal to the tool radius, the arc tool can be transformed into a ball-end tool. Figure 2 As shown;

[0064] 1) Radial lag angle of the blade element at the axial position z Affected by tool diameter, tool corner radius, and tool helix angle; radial lag angle Expressed as:

[0065]

[0066] Among them, β t Indicates the helix angle of the tool. When the direction of rotation of the tool blade is right-handed, β t Take a negative value; r represents the radius of the arc cutter corner; D represents the tool diameter;

[0067] 2) Radial position angle Ψ of the jth blade at axial position z j (z), expressed as:

[0068]

[0069] Wherein, Φ represents the spindle rotation angle. At time t, the spindle rotation angle is Φ=ωt; ω represents the spindle rotation speed; N represents the total number of cutting edges of the tool;

[0070] 3) The position of the blade element P in the tool coordinate system is expressed as:

[0071]

[0072] Among them, x p ,y p ,z p are the x, y, and z coordinates of the blade element P in the tool coordinate system; R(z) represents the distance between the blade element P and the tool axis; R r Indicates the radius of the center circle of the arc cutter;

[0073] The above-mentioned cutting tool geometric modeling provides the robot arm with the geometric space information of the cutting edge, which provides the basis for the geometric transformation of the cutting force model;

[0074] (2) Based on the above-mentioned cutting tool geometry modeling, a cutting force model considering force-position coupling is constructed:

[0075] 1) Discrete the blade into blade elements along the blade axis; the cutting force of the tool element refers to the product of the cutting force on the tool and the cutting force; the tangential force F of the tool element t , radial force Fr and axial cutting force F a The mechanical model is as follows:

[0076]

[0077] Among them, K r Indicates the radial cutting force coefficient; K t Indicates the tangential cutting force coefficient; K a Indicates the axial cutting force coefficient; h j represents the instantaneous undeformed chip thickness of the blade element; db(z) represents the undeformed cutting width at the axial position z; and db(z) = dz / sinκ, κ = arccos((rz) / r), κ represents the axial contact angle;

[0078] Integrate the discrete blade elements along the tool axis and add the cutting forces of each discrete blade element to obtain the total cutting force model of all blade elements in radial, tangential and axial directions:

[0079]

[0080] Among them, F r Indicates radial cutting force; F t Indicates the tangential cutting force; F a represents the axial cutting force; N represents the number of cutting edges in the tool; j represents the jth cutting edge;

[0081] In order to facilitate the calculation, the radial, tangential and axial cutting forces are transformed by rotating coordinates (i.e. the radial position angle Ψ j (z)) The cutting forces in the X, Y and Z directions are specifically expressed as:

[0082]

[0083] Based on the cutting force calculation of force-position coupling, the cutting force of the robotic arm can be accurately controlled, tool orientation planning can be achieved, the surgical process can be optimized, and the flexibility of the robotic arm can be improved.

[0084] In the above step S5, the motion trajectory of the robot arm is planned according to the predicted cutting force to achieve auxiliary cutting of the autologous bone block; wherein:

[0085] (1) The robotic arm adopts an arc track telecentric structure. The bone cutting holder is designed with an arc track telecentric structure, which not only ensures a simple structure but also improves the degree of freedom and flexibility of the end of the robotic arm.

[0086] (2) The end-holder of the robotic arm fixes the arc needle, which is used to assist in cutting the autologous bone block according to the bone block cutting path; the high-speed arc needle tool is integrated at the end of the holder, which can support a variety of tool diameters and shapes to meet personalized cutting needs;

[0087] (3) In the embodiment of the present invention, an improved parameter method is used to perform kinematic modeling on the robot arm; wherein, the improved parameter method is an improved DH parameter method, which avoids singularity by modifying DH (Denavit-Hartenberg) parameters or adopting other geometric description methods, establishes a coordinate system on each link of the robot, realizes coordinate transformation on two links through homogeneous coordinate transformation, and establishes the transformation relationship between the first and last coordinate systems in the multi-link series system;

[0088] Several key points of improving the parameter method are as follows:

[0089] (1) Determine four parameters α, a, d, and υ; specifically:

[0090] In the modeling method, each link is described by four parameters: α, a, d, and ν. Two parameters describe the link itself, and the other two describe the position (connection or geometric relationship) with the adjacent links. For revolute joints, ν is the joint variable, and the other three parameters are fixed and are link parameters. For mobile joints, d is the joint variable, and the other three are joint parameters.

[0091] Improved parameter method joint and coordinate system relationship diagram see Figure 3 shown; in Figure 3 middle,

[0092] α i-1 Indicates: Z i-1 to Z i Around X i-1 Angle of rotation;

[0093] a i-1 Indicates: Z i-1 to Z i Along X i-1 Distance of direction;

[0094] v i Indicates: X i-1 to X i Around Z i Angle of rotation;

[0095] d i Indicates: X i-1 to X i Along Z i Distance of direction;

[0096] The above Z i-1 represents the Z axis of joint i-1; Z i represents the Z axis of joint i; X i-1 represents the X axis of joint i-1; X i represents the X axis of joint i;

[0097] Coordinate system Oi-1 Aligned with joint i-1, its improved parameter matrix is:

[0098]

[0099] (2) Establish the robot arm link coordinate system; specifically:

[0100] 1) Determine each joint axis and link, with the Z axis of the coordinate system along the joint axis;

[0101] 2) Find the intersection point or common perpendicular line of two adjacent joint axes to determine the origin of the robot arm link coordinate system {i}: the intersection point of joint axes i and i+1 or the intersection point of the common perpendicular line and joint axis i is taken as the origin;

[0102] 3) Determine the X axis: When the two axes intersect, Represents the X-axis vector; Represents the Z-axis vector; when the two axes do not intersect, X i The axis coincides with the common perpendicular, and the direction is from i to i+1;

[0103] 4) Determine Y using the right-hand rule i axis;

[0104] 5) Determine the base coordinate system {0}: To simplify the problem, Z0 usually coincides with the axis direction of joint 1, and when joint variable 1 is 0, the coordinate system {0} coincides with {1};

[0105] 6) Determine the end coordinate system {n}: For the revolute joint, u n =0, X n With X n-1 The direction is the same, select the origin so that d n =0; for moving joints, take X n Direction n =0, when d n =0, take X n-1 With X n The intersection point of is the origin;

[0106] (3) List the improved parameter table:

[0107] According to the relationship between the coordinate systems of the various links of the robot arm, the parameter table of the improved parameter method is obtained;

[0108] (4) Obtain the transformation matrix from the parameter table:

[0109] Substituting the parameter table into the parameter matrix of the improved parameter method, we can obtain the homogeneous transformation matrix between each coordinate system. Multiplying the corresponding homogeneous transformation matrices can obtain the homogeneous transformation matrix from the base coordinate system to the terminal coordinate system:

[0110]

[0111] in, Represents the homogeneous transformation matrix from joint n-1 to joint n; [p x p y p z ] T Indicates the position of the end of the manipulator in the base coordinate system; [n x n y n z ] T Represents the direction vector of the X-axis of the robot end coordinate system in the base coordinate system; [n x n y n z ] T Represents the direction vector of the Y-axis of the robot end coordinate system in the base coordinate system; [a x a y a z ] T Represents the direction vector of the Z-axis of the robot's end-of-arm coordinate system in the base coordinate system. Substituting this into the parameter table yields the corresponding positional parameter representation. This modeling approach effectively resolves singularities caused by parallelism between adjacent axes.

[0112] In the above step S6, a micro-force / torque sensor is also fixed to the end-holding device of the robotic arm; during the cutting process, the actual cutting force is monitored in real time by the torque sensor, and the motion trajectory of the robotic arm is dynamically adjusted according to the actual cutting force to achieve intraoperative force feedback and flexible control.

[0113] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0114] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent assisted method for autologous bone transplantation surgery, characterized in that: The steps include: S1. Construct a 3D jaw model, a 3D tooth model, and a 3D defect area model based on the CBCT image data of the target object; S2. Acquire three-dimensional morphological data of the defect area according to the three-dimensional defect area model, input the data into a trained bone augmentation prediction model, and output design parameters of the proposed bone augmentation block; S3. Determine the bone removal location of the proposed bone block in the three-dimensional jaw model based on the design parameters of the proposed bone block and in combination with the three-dimensional tooth model, and automatically mark the location to plan the bone block cutting path; S4. Obtaining a predicted cutting force based on the bone cutting path and a cutting force model; S5. Planning the motion trajectory of the robotic arm according to the predicted cutting force to assist in cutting the autologous bone block; S6. During the cutting process, the actual cutting force is monitored in real time, and the motion trajectory of the robot arm is dynamically adjusted according to the actual cutting force.

2. The intelligent assisted method for autologous bone transplantation surgery according to claim 1, characterized in that: The step S1 specifically includes: S11, acquiring CBCT image data of the target object; S12, preprocessing the CBCT image data; S13, using a deep learning segmentation model to segment the pre-processed CBCT image data according to alveolar bone, teeth, and defect areas, and output corresponding binary mask images; S14, performing three-dimensional surface reconstruction using the Marching Cubes algorithm based on the binary mask images corresponding to the alveolar bone, teeth, and defect area, to obtain corresponding three-dimensional jaw models, three-dimensional tooth models, and three-dimensional defect area models, respectively; S15. Apply the ICP method and the TPS method to perform rigid and non-rigid registration to align the three-dimensional jaw model, the three-dimensional tooth model, and the three-dimensional defect area model to the same spatial reference system.

3. The intelligent assisted method for autologous bone transplantation surgery according to claim 2, characterized in that: The preprocessing specifically includes grayscale normalization processing, resampling processing and artifact suppression processing.

4. The intelligent assisted method for autologous bone transplantation surgery according to claim 1, characterized in that: In step S2, the design parameters of the proposed bone repair block include: the shape, size and position of the fixing nail hole of the proposed bone repair block.

5. The intelligent assisted method for autologous bone transplantation surgery according to claim 1, characterized in that: Said step S3 further includes: generating a three-dimensional model of the autologous bone block that can be directly cut according to the marked content through Boolean operations and morphological algorithms.

6. The intelligent assisted method for autologous bone transplantation surgery according to claim 1, characterized in that: In step S4, the cutting force model is a cutting force model that considers force-position coupling, which is expressed as: Among them, F r Indicates radial cutting force; F t Indicates the tangential cutting force; F a represents the axial cutting force; N represents the number of cutting edges in the tool; j represents the jth cutting edge; K r Indicates the radial cutting force coefficient; K t Indicates the tangential cutting force coefficient; K a Indicates the axial cutting force coefficient; h j It represents the instantaneous undeformed chip thickness of the cutting edge element; db(z) represents the undeformed cutting width at the axial position z.

7. The intelligent assisted method for autologous bone transplantation surgery according to claim 1, characterized in that: The robotic arm is an arc track telecentric structure.

8. The intelligent assisted method for autologous bone transplantation surgery according to claim 1, characterized in that: The holding device at the end of the robotic arm fixes the arc turning needle.

9. The intelligent assisted method for autologous bone transplantation surgery according to claim 1, characterized in that: The improved parameter method is used to carry out kinematic modeling of the robotic arm.

Images