CN106777721B - Finite element model-based implant structure optimization method - Google Patents
Finite element model-based implant structure optimization method Download PDFInfo
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- CN106777721B CN106777721B CN201611207432.5A CN201611207432A CN106777721B CN 106777721 B CN106777721 B CN 106777721B CN 201611207432 A CN201611207432 A CN 201611207432A CN 106777721 B CN106777721 B CN 106777721B
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Abstract
The invention discloses a finite element model-based implant structure optimization method, which comprises the following steps: -establishing a full-scale model of the implant structure to be analyzed, defining and creating a finite element model by using the sample points; using a Latin hypercube sampling method to distribute LHD points uniformly in a design space, and maximizing the minimum distance between experimental points; setting a plurality of sampling points by using the LHD as a sampling method, and establishing a support vector machine (SVR); carrying out line-by-line finite analysis on the sampled data; -approximating the stress response of the implant-bone interface by an SVR model and applying a simulated annealing algorithm to search for an optimized result. Establishing a finite element model of a gum bone and an implant of a termination object, establishing an SVR model by using an LHD (design-driven regression) design algorithm, and finally performing model verification and optimization. The manufactured implant can be matched with each planting object, personalized production can be achieved through the 3D printing technology, the implant is fixed more firmly, and planting time is greatly prolonged.
Description
Technical Field
The invention relates to a finite element model-based implant structure optimization method. Relating to the calculation of a patent classification number G06; calculating; the counting G06F electric digital data processing G06F17/00 is particularly suitable for computer-aided design of a function-specific digital computing device or data processing method G06F 17/50.
Background
With the cooperative development of dentistry and other subjects, the implant structure which is not taken into consideration before, especially the thread structure, is taken into consideration, the gum of implant patients with different ages, sexes and constitutions reflects the constitution of the implant, especially the visual reflection of bone, and some young or better bone crowds can adapt to implants with higher thread density to obtain more durable implant effect, and can be used for a lifetime after being implanted.
And the implant object with larger grade or loose bone can not adapt to the implant with higher thread density due to the poor bone density of the implant object, which is very easy to cause the collapse of gum quality and causes more pain to patients in the process of implantation.
The cementum mainly comprises a cortical layer and a cancellous layer, the relative thicknesses of the cortical layer and the cancellous layer of different planting objects are greatly different, the cortical layer thickness is relatively thicker for young or good-physique patients, and if firm planting is needed, longer-scale planting objects are needed. In contrast, in older plants, the thickness of the cortex is relatively thin, and the length of the plant needs to be relatively short.
The finite element model analysis-based method can perform stress and structure analysis according to tooth data acquired by detection means such as CT and the like, and an implant optimized for an individual is arranged for each implant object, so that the obtained implant can be perfectly matched with each patient.
Disclosure of Invention
In view of the above problems of the prior art, the present invention is directed to a finite element model-based method for optimizing a structure of an implant, comprising the steps of:
establishing a full-size model of the implant structure to be analyzed, and defining and establishing a finite element model by using sample points;
using a Latin hypercube sampling method to distribute LHD points uniformly in a design space, and maximizing the minimum distance between experimental points;
setting a plurality of sampling points by using the LHD as a sampling method, and establishing a support vector machine (SVR); carrying out line-by-line finite analysis on the sampled data;
-approximating the stress response of the implant-bone interface by an SVR model and applying a simulated annealing algorithm to search for an optimized result.
As a preferred embodiment, the implant completes the conversion from the two-dimensional plane coordinate of the section of the propeller blade to the three-dimensional space coordinate according to the propeller projection principle and the coordinate conversion formula, and further completes the drawing of the section contour curve of the propeller blade;
wherein: phi is a pitch angle; theta is a pitch angle; l is the distance between the maximum thickness line and the reference line; r is the radius of the cut surface of the leaf; x1、Y1、Z1The coordinate values are under a local coordinate system; x, Y and Z are coordinate values under the global coordinate system.
In a preferred embodiment, the SVR model is as follows:
wherein: rho is bone density; t is time; u. ofx、uy、uzThe components of the speed in the x, y and z axes of a space rectangular coordinate system are respectively; p is pressure; x, Y, Z are the components of the external force in the x, y, z directions, respectively; μ is the bone viscosity coefficient; Δ is the laplacian operator.
In a preferred embodiment, the SVR model is solved by an annealing algorithm.
Due to the adoption of the technical scheme, the finite element model of the gum bone and the implant of the implant is established, the LHD design algorithm is utilized to establish the SVR model, and finally, the model verification and optimization are carried out. The manufactured implant can be matched with each planting object, personalized production can be achieved through the 3D printing technology, the implant is fixed more firmly, and planting time is greatly prolonged.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a basic structure diagram of an implant according to the present invention;
FIG. 2 is a schematic view showing the shape of an exemplary implant according to an embodiment of the present invention;
FIG. 3 is a reference diagram for finite element modeling analysis of the present invention, FIG. 3a is a schematic diagram of a finite element structure of an implant, FIG. 3b is a schematic diagram of a position model of the implant and cementum, and FIG. 3c is a schematic diagram of a result of finite element analysis after the cementum and the implant are combined;
FIG. 4 is a flow chart of the present invention.
Wherein, in fig. 1, 1 is a bolt, 2 is a connecting piece, 3 is an implant, 4 is cortical bone, and 5 is cancellous bone
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the following describes the technical solutions of the embodiments of the present invention clearly and completely with reference to the accompanying drawings in the embodiments of the present invention:
as shown in fig. 1-4: a finite element model-based implant structure optimization method comprises the following steps:
-establishing a full-scale model of the implant structure to be analyzed, creating a finite element model using the sample point definitions.
Using a Latin hypercube sampling method to distribute LHD points uniformly in a design space, and maximizing the minimum distance between experimental points;
setting a plurality of sampling points by using the LHD as a sampling method, and establishing a support vector machine (SVR); and (4) approximating the stress response of the implant-bone interface through an SVR model, and solving to obtain the optimal parameters.
As a preferred embodiment, the implant completes the conversion from the two-dimensional plane coordinate of the section of the propeller blade to the three-dimensional space coordinate according to the propeller projection principle and the coordinate conversion formula, and further completes the drawing of the section contour curve of the propeller blade;
wherein: phi is a pitch angle; theta is a pitch angle; l is the distance between the maximum thickness line and the reference line; r is the radius of the cut surface of the leaf; x1、Y1、Z1The coordinate values are under a local coordinate system; x, Y and Z are coordinate values under the global coordinate system.
In a preferred embodiment, the SVR model is as follows:
wherein: rho is bone density; t is time; u. ofx、uy、uzThe components of the speed in the x, y and z axes of a space rectangular coordinate system are respectively; p is pressure; x, Y, Z are the components of the external force in the x, y, z directions, respectively; μ is the bone viscosity coefficient; Δ is the laplacian operator. In a preferred embodiment, the SVR model is solved by an annealing algorithm.
Design of experiments (DOE) was performed using the Latin Hypercube Design (LHD). First, a meta-model is created using sample point definitions. Sampling points are uniformly distributed in a design space by using a Latin hypercube sampling method, so that the minimum distance between experimental points is maximized. LHD is used as a sampling method, and a plurality of sampling points are set. The sampled data is subjected to a finite analysis line by line. Genetic Algorithms (GA) are used to find optimal parameters of the SVR model to improve prediction accuracy.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (3)
1. A finite element model-based implant structure optimization method is characterized by comprising the following steps:
establishing a full-size model of the implant structure to be analyzed, and establishing a finite element model by using sampling point definition;
uniformly distributing sampling points in a design space by using a Latin hypercube sampling method LHD, and maximizing the minimum distance between the sampling points;
establishing a support vector machine (SVR) regression model according to the sampling process; approximating the stress response of an implant-bone interface through an SVR regression model, and solving to obtain optimal parameters;
the SVR regression model is as follows:
wherein: rho is bone density; t is time; u. ofx、uy、uzThe components of the speed in the x, y and z axes of a space rectangular coordinate system are respectively; p is pressure; fx、Fy、FzThe components of the external force in the x, y and z directions respectively; μ is the bone viscosity coefficient; Δ is the laplacian operator.
2. The finite element model-based implant structure optimization method of claim 1, further characterized in that the implant completes the conversion from the two-dimensional plane coordinates of the propeller blade section to the three-dimensional space coordinates according to the propeller projection principle and the coordinate conversion formula, thereby completing the drawing of the profile curve of the propeller blade section;
wherein: phi is a pitch angle; theta is a pitch angle; l is the distance between the maximum thickness line and the reference line; r is the radius of the cut surface of the leaf; x1、Y1、Z1The coordinate values are under a local coordinate system; x, Y and Z are coordinate values under the global coordinate system.
3. The finite element model-based implant structure optimization method of claim 1, further characterized by solving the SVR regression model by an annealing algorithm.
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| CN108052693A (en) * | 2017-11-13 | 2018-05-18 | 广州市健齿生物科技有限公司 | A kind of tooth implant automates finite element analysing system |
| CN109977590A (en) * | 2019-04-09 | 2019-07-05 | 哈尔滨理工大学 | A kind of titanium implant structural optimization method based on age factor |
| CN110502784B (en) * | 2019-07-09 | 2023-04-07 | 韶关学院 | Product simulation optimization method |
| CN113786256B (en) * | 2021-11-17 | 2022-02-08 | 极限人工智能有限公司 | Implant safety zone generation method and device, electronic equipment and storage medium |
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| CN105631166A (en) * | 2016-02-26 | 2016-06-01 | 北京朗视仪器有限公司 | Computer aided design method of one-piece threaded anatomic root-form dental implant |
| CN105787177A (en) * | 2016-02-26 | 2016-07-20 | 北京朗视仪器有限公司 | Computer aided design method for dissecting root-shaped dental implant in one-part form mode |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105631166A (en) * | 2016-02-26 | 2016-06-01 | 北京朗视仪器有限公司 | Computer aided design method of one-piece threaded anatomic root-form dental implant |
| CN105787177A (en) * | 2016-02-26 | 2016-07-20 | 北京朗视仪器有限公司 | Computer aided design method for dissecting root-shaped dental implant in one-part form mode |
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| 二段式钛合金种植牙不同弹性模量组件及其组合对骨界面应力分布的影响;石茂林等;《华侨大学学报（自然科学版）》;20140731;第361-366页 * |
| 影响人工种植牙一骨界面应力分布规律因素的多元逐步回归分析;邹敬才等;《医用生物力学》;20001231;第216-221页 * |
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