Ultrasonic scalpel amplitude-change pole design method and system based on composite structure optimization

Abstract

The invention provides an ultrasonic scalpel amplitude-change pole design method and system based on composite structure optimization. The method comprises the steps that a performance target parameter set set during system initialization is received; dividing the amplitude-change pole into an input section, a middle transition section and an output section to construct a structure design model, and reversely optimizing the structure design model through a performance prediction module to generate a corresponding parameter set; sLM process parameters are collected, the parameter set is combined to be input into a pre-constructed error prediction module, and manufacturing error prediction is generated; real-time amplitude-change pole performance data are collected and combined, the manufacturing error prediction and the performance offset are combined, an individual final performance value is generated through Bayesian weighted fusion, and an individual performance label of each individual amplitude-change pole is constructed; the tissue electrical impedance, the cutter head temperature and the ultrasonic echo signal are collected in real time in the operation, the driving voltage of the transducer is dynamically adjusted by combining the individual performance label, and closed-loop control over the amplitude is carried out. According to the invention, a safer and more intelligent energy output scheme is provided for a high-complexity operation scene.

Description

Technical Field

[0001] This invention belongs to the field of ultrasonic amplitude transformer optimization management, and particularly relates to a design method and system for an ultrasonic surgical scalpel amplitude transformer based on composite structure optimization. Background Technology

[0002] As a key device in modern precision surgery, the performance of the amplitude transformer, a core component of the ultrasonic scalpel, directly determines the instrument's energy transmission efficiency and clinical operational stability. Traditional amplitude transformer designs often rely on empirical formulas and simplified theoretical models, making it difficult to effectively coordinate multiple performance indicators such as amplitude stability, structural fatigue life, and thermal management efficiency. Especially under prolonged high-frequency operation, the amplitude transformer is prone to fatigue damage due to localized stress concentration, or excessive temperature rise affecting resonance accuracy, and even posing a risk of tissue thermal damage. While existing design methods attempt to incorporate finite element analysis for structural verification, most remain at the level of single-objective optimization or local parameter adjustment, lacking the ability to systematically and collaboratively optimize geometric configurations and material distribution. Furthermore, conventional optimization algorithms are prone to getting trapped in local optima when dealing with multi-parameter, strongly coupled composite structural optimization problems, making it difficult to achieve a balanced improvement in global performance. Therefore, there is an urgent need in this field for a design method that can systematically integrate vibration modal analysis, multiphysics simulation, and intelligent optimization algorithms to achieve a comprehensive breakthrough in amplitude transformer stability, structural durability, and thermal management efficiency, meeting the clinical needs of highly reliable ultrasonic surgical instruments. Summary of the Invention

[0003] The purpose of this invention is to propose a design method and system for an ultrasonic scalpel amplitude transformer based on composite structure optimization, which has the advantages of achieving multi-objective collaborative optimization, breaking through the limitations of local optimal solutions, and improving the overall performance of the amplitude transformer.

[0004] To achieve the above objectives, a method for designing an ultrasonic surgical scalpel amplitude transformer based on composite structure optimization is provided in a first aspect of the present invention, characterized by comprising the following steps: Obtain the initial design parameters of the amplitude transformer, which include at least its geometry, dimensions, and material properties; A finite element analysis model of the amplitude transformer is established based on the initial design parameters, and vibration mode analysis and resonant frequency calculation are performed using this model to obtain the dynamic performance indicators of the amplitude transformer. The structural optimization objectives are set as follows: to improve the amplitude stability, fatigue resistance and thermal management capability of the amplitude transformer. A genetic algorithm is used, with the optimization objective as the fitness function, to perform composite structural optimization iterative calculations on the geometry and material distribution of the amplitude rod, generating an optimal set of design parameters that satisfy the optimization objective.

[0005] Furthermore, it also includes: obtaining the initial design parameters of the amplitude transformer specifically includes: calling the basic configuration from the preset amplitude transformer type database, the basic configuration including stepped, conical and exponential shapes; receiving user-input functional constraints, the functional constraints including at least the operating frequency range, installation space size and connection interface specifications; Based on the basic configuration and the functional constraints, the initial geometry and initial dimensions of the amplitude rod are determined.

[0006] Furthermore, the finite element model construction process also includes: meshing the geometry of the amplitude transformer to generate a finite element mesh; applying boundary conditions to the finite element mesh, the boundary conditions including at least the fixed constraints simulating the connection between the amplitude transformer and the ultrasonic transducer; defining material properties and applying load conditions, the load conditions being the application of harmonic excitation corresponding to the target operating frequency at the input end of the amplitude transformer. Furthermore, the specific objectives of the structural optimization include: characterizing the amplitude stability objective as the amplitude fluctuation coefficient of the working end of the amplitude transformer within a preset bandwidth near the target resonant frequency; defining the fatigue resistance objective as the maximum equivalent stress of the amplitude transformer structure under periodic load being lower than the fatigue limit of its material, and meeting a preset safety margin requirement; characterizing the thermal management capability objective as the highest temperature of the key parts of the amplitude transformer not exceeding a preset limit under continuous operation, while its temperature distribution gradient must meet the uniformity condition; and combining the above-characterized target parameters to form the multi-objective fitness function of the optimization algorithm. Furthermore, the use of the genetic algorithm also includes the following steps: encoding the geometry and material distribution parameters of the amplitude rod to generate an initial population; performing finite element analysis on each individual in the initial population to calculate its fitness value corresponding to the fitness function; based on the fitness value, performing selection, crossover, and mutation operations on the individuals in the population to generate the final offspring population; iteratively executing the evaluation process until a preset convergence condition is reached, and decoding the individual with the highest fitness value in the final offspring population as the optimal design parameter set.

[0007] Furthermore, the process includes: decoding each individual chromosome to restore specific amplitude rod geometric and material parameters, and updating the finite element model accordingly; performing modal analysis and harmonic response analysis on the updated finite element model to extract the amplitude, structural stress field distribution, and temperature field distribution at the target resonant frequency; calculating the amplitude fluctuation coefficient, the ratio of maximum equivalent stress to fatigue limit, and the difference between the highest and average temperatures of key components based on the extracted amplitude, stress, and temperature data; and substituting the above calculation results into the multi-objective fitness function to calculate the comprehensive fitness value representing the individual's performance.

[0008] Furthermore, the maximum axial force requirement for the interface needs to be determined in conjunction with the maximum feed force that the ultrasonic surgical instrument may withstand during clinical operation, and the initial deviation range between the target operating frequency and the center frequency of the ultrasonic transducer shall not exceed ±500Hz.

[0009] Furthermore, the multi-objective fitness function is: Where ω1, ω2, and ω3 are weighting coefficients and satisfy ω1 + ω2 + ω3 = 1, and f1 is the amplitude fluctuation coefficient. and These are penalty terms for fatigue resistance and thermal management capability not meeting the constraints, respectively. Multi-objective collaborative optimization is achieved by minimizing F.

[0010] Furthermore, The objective function for amplitude stability is: ; in, This represents the amplitude value of the working end of the amplitude transformer at frequency f. The target resonant frequency; Preset bandwidth; The average amplitude within the bandwidth; the optimization direction is to minimize. This means reducing amplitude fluctuations.

[0011] The objective function for fatigue resistance is: ; This represents the maximum equivalent stress of the amplitude transformer under periodic load. The fatigue limit of the amplitude transformer material is S; the preset safety margin is S; the constraint conditions are as follows: This ensures that the structure meets fatigue resistance requirements.

[0012] Objective function for thermal management capability: ; This represents the highest temperature at a critical component of the amplitude transformer. Preset temperature limit; Temperature at various points in the three-dimensional space of the amplitude transformer; The overall average temperature of the amplitude transformer; the constraint condition is... At the same time, the maximum temperature and the uniformity of temperature distribution are controlled.

[0013] Secondly, the present invention also provides a composite structure-optimized ultrasonic scalpel amplitude transformer design system for implementing the aforementioned composite structure-optimized ultrasonic scalpel amplitude transformer design method, comprising: The parameter acquisition module is configured to acquire the initial design parameters of the amplitude transformer, which include at least the geometry, dimensions and material properties. The finite element analysis module is configured to establish a finite element analysis model of the amplitude transformer based on the initial design parameters, and to perform vibration mode analysis and resonant frequency calculation through the model to obtain the dynamic performance index of the amplitude transformer. The target setting module is configured to set structural optimization targets, which are to improve the amplitude stability, fatigue resistance and thermal management capability of the amplitude transformer. The optimization calculation module is configured to use a genetic algorithm, with the optimization objective as the fitness function, to perform composite structural optimization iterative calculations on the geometry and material distribution of the amplitude rod, and generate an optimal set of design parameters that satisfy the optimization objective.

[0014] Furthermore, the optimization calculation module is configured to: encode the geometry and material distribution parameters of the amplitude transformer into genetic algorithm chromosomes and generate an initial population; for each individual in the population, call the finite element analysis module to perform dynamic performance simulation and calculate the corresponding fitness value according to the structural optimization objective; based on the fitness value, perform selection, crossover, and mutation operations on the individuals in the population to generate a new generation population; iteratively execute the above processing until the preset convergence condition is met, and decode and output the individual with the highest fitness value in the final population as the optimal design parameter set.

[0015] The beneficial technical effects of the present invention are at least as follows: This invention provides a design method and system for an ultrasonic scalpel amplitude transformer based on composite structure optimization, offering significant benefits in multiple dimensions. Regarding amplitude stability, by accurately acquiring initial design parameters and combining them with a finite element analysis model for precise calculation of vibration modes and resonant frequencies, followed by iterative optimization of the geometry and material distribution using a genetic algorithm, the amplitude fluctuation coefficient of the amplitude transformer's working end within a preset bandwidth near the target resonant frequency can be effectively reduced. This ensures stable amplitude output during surgery, improves the accuracy of cutting, coagulation, and other operations, and reduces surgical errors caused by amplitude fluctuations. Regarding fatigue resistance, the optimization objective clearly requires that the maximum equivalent stress of the amplitude transformer under cyclic loads be lower than the material fatigue limit and meet a safety margin. The composite structure optimized by the algorithm can disperse stress concentration, avoiding structural fatigue damage caused by long-term vibration and significantly extending the lifespan of the scalpel. The design extends the lifespan of the amplitude transformer, reducing instrument replacement frequency and medical costs, while also minimizing surgical safety risks associated with amplitude transformer fatigue failure. In terms of thermal management, the design optimizes material distribution and structural morphology to ensure that the highest temperature of key components of the amplitude transformer does not exceed preset limits during continuous operation, and that the temperature distribution gradient meets uniformity requirements. This prevents material degradation or thermal damage to human tissue caused by localized overheating, ensuring stable instrument performance during surgery. Furthermore, the integrated design system incorporates modules for parameter acquisition, finite element analysis, target setting, and optimization calculations, automating and intelligentizing the design process. This significantly shortens the design cycle, improves design efficiency and parameter accuracy, and provides an efficient solution for the standardized and personalized design of ultrasonic surgical scalpel amplitude transformers. This promotes the iterative upgrading of ultrasonic surgical instrument technology and helps improve the quality and safety of clinical surgeries. Attached Figure Description

[0016] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a design method for an ultrasonic scalpel amplitude transformer based on composite structure optimization, as disclosed in an embodiment of the present invention. Figure 2 This is a design framework diagram of an ultrasonic surgical scalpel amplitude transformer based on composite structure optimization, as disclosed in an embodiment of the present invention. Detailed Implementation

[0018] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0019] Example 1 like Figure 1 As shown in the figure, an embodiment of the present invention provides a design method for an ultrasonic surgical scalpel amplitude transformer based on composite structure optimization, the method comprising: S1. Obtain the initial design parameters of the amplitude transformer, which include at least its geometry, dimensions, and material properties.

[0020] Specifically, when obtaining the initial design parameters of the amplitude transformer, it is necessary to comprehensively consider its application scenario and overall compatibility with the ultrasonic scalpel. The geometry needs to be selected according to the vibration transmission requirements. Common shapes include stepped, conical, or exponential shapes. Among them, the stepped shape facilitates amplitude amplification through abrupt changes in cross-section, the conical shape helps reduce stress concentration, and the exponential shape can achieve a smoother vibration transition. The dimensional parameters cover the overall length, input diameter, output diameter, and transition dimensions of each segment. They must meet the installation space constraints and vibration amplification ratio requirements. The vibration amplification ratio η can be expressed as the square root of the ratio of the cross-sectional area of ​​the input end to the cross-sectional area of ​​the output end. ,in The cross-sectional area of ​​the input terminal. (This refers to the cross-sectional area of ​​the output end). The material properties need to match the high-frequency vibration characteristics. Typically, high-strength, high-toughness metallic materials with good acoustic impedance characteristics are selected. Key parameters include elastic modulus E, density ρ, Poisson's ratio μ, and fatigue limit. The longitudinal wave velocity c of the material can be obtained from the formula The calculations need to match the sound velocity of the ultrasonic transducer to reduce energy loss, while the fatigue limit of the material must meet the strength requirements under long-term periodic vibration. The determination of these initial parameters needs to be combined with the clinical surgical requirements for amplitude output, structural strength, and instrument lightweighting, providing a basis for subsequent finite element analysis and optimization calculations.

[0021] S2. Based on the initial design parameters, establish a finite element analysis model of the amplitude transformer, and use the model to perform vibration mode analysis and resonant frequency calculation to obtain the dynamic performance index of the amplitude transformer.

[0022] Specifically, a fixed constraint (restricting translational and rotational degrees of freedom) is set at the connection end between the amplitude transformer and the ultrasonic transducer, and a harmonic excitation force corresponding to the target operating frequency is applied at the input end. ,in Let f be the excitation amplitude and f be the excitation frequency. Vibration modal analysis is performed by solving the dynamic equations. Implementation, in which For the quality matrix, Here is the damping matrix. Given the stiffness matrix and u as the displacement vector, the natural frequencies of the amplitude transformer can be obtained. The mode shape is determined, and the natural frequency of the first-order longitudinal vibration mode must be close to the target resonant frequency. The resonant frequency is calculated through harmonic response analysis. A scan is performed within a preset frequency range to obtain the amplitude-frequency response curve; the frequency corresponding to the peak value of the curve is the actual resonant frequency. Simultaneously, the amplitude A and amplitude amplification factor at the working end can be extracted. ,in This provides benchmark data for dynamic performance indicators such as input amplitude and structural stress distribution, for subsequent optimization.

[0023] Furthermore, the finite element model construction process also includes: The geometry of the amplitude transformer is meshed to generate a finite element mesh; Boundary conditions are applied to the finite element mesh, and the boundary conditions include at least a fixed constraint simulating the connection between the amplitude transformer and the ultrasonic transducer. Define material properties and apply load conditions, wherein the load conditions are to apply harmonic excitation corresponding to the target operating frequency at the input end of the amplitude transformer.

[0024] In some embodiments, during the finite element model construction process, the geometric structure is first meshed. The appropriate element type must be selected based on the structural complexity of the amplitude transformer, and the mesh size must meet the accuracy requirements of vibration analysis. Typically, a finer mesh is used in vibration node regions and stress concentration regions to ensure the accuracy of modal analysis and stress calculation. After meshing, boundary conditions are applied to the finite element mesh: for the connection between the amplitude transformer and the ultrasonic transducer, full constraints are applied (restricting translational degrees of freedom along the x, y, and z axes and rotational degrees of freedom around each axis) to simulate the rigid connection in actual assembly; for the free surface of the amplitude transformer, no constraints are applied to release its vibration degrees of freedom. Subsequently, material properties are defined, inputting parameters such as the elastic modulus E, density ρ, and Poisson's ratio μ. These parameters directly affect the dynamic characteristics of the model. The stiffness matrix [K] of the material can be derived from the elastic modulus and element geometry information, while the mass matrix [M] is constructed based on density and element volume. The application of load conditions must match the ultrasonic vibration characteristics. A harmonic excitation force is applied at the input end of the amplitude transformer, and its expression is: ,in For the amplitude of the excitation force, Angular frequency ( , (where i is the target operating frequency and i is the imaginary unit). This excitation simulates the periodic vibration energy output by the ultrasonic transducer. The finite element model constructed through the above steps can accurately reflect the vibration response of the amplitude transformer under actual working conditions.

[0025] S3. Set structural optimization goals, which are to improve the amplitude stability, fatigue resistance and thermal management capability of the amplitude transformer.

[0026] Preferably, the set structural optimization objectives specifically include: characterizing the amplitude stability objective as the amplitude fluctuation coefficient of the working end of the amplitude transformer within a preset bandwidth near the target resonant frequency; defining the fatigue resistance objective as the maximum equivalent stress of the amplitude transformer structure under periodic load being lower than the fatigue limit of its material, and meeting a preset safety margin requirement; characterizing the thermal management capability objective as the highest temperature of the key parts of the amplitude transformer not exceeding a preset limit under continuous working conditions, while its temperature distribution gradient must meet the uniformity condition; and combining the above-characterized target parameters to form the multi-objective fitness function of the optimization algorithm.

[0027] Among them, the amplitude fluctuation coefficient refers to the degree of amplitude variation at the working end of the amplitude transformer within a preset bandwidth near the target resonant frequency. Specifically, it can be obtained by extracting the ratio of the amplitude standard deviation to the average value through frequency domain response analysis, and is used to quantify the stability of amplitude output. The ratio of maximum equivalent stress to fatigue limit refers to the multiple relationship between the stress level of the amplitude transformer under cyclic load and the material durability limit. Specifically, it can be obtained by using finite element simulation to obtain the stress peak value and combining it with the material's SN curve for fatigue life assessment, to ensure that the structure operates within the safety margin. The temperature distribution gradient uniformity condition refers to the smoothness of temperature change in the critical area of ​​the amplitude transformer. Specifically, it can be obtained by extracting temperature field data through thermo-mechanical coupling simulation and calculating the maximum temperature difference between adjacent nodes, to avoid performance degradation caused by local overheating.

[0028] Specifically, in the optimization process, the target resonant frequency range is first determined through vibration modal analysis, and harmonic response analysis is performed within this frequency bandwidth, collecting amplitude data at the working end to calculate the fluctuation coefficient. Subsequently, structural stress analysis is performed under periodic load conditions, extracting the maximum equivalent stress value and comparing it with the material fatigue limit, while setting constraints based on a safety factor. Simultaneously, in the thermal management capability assessment, a continuous working load is applied to the amplitude transformer, and heat conduction simulation is performed to monitor the temperature peaks and distribution gradients of key components. These three target parameters are integrated into a fitness function input to the optimization algorithm, adjusting the priority of each objective through weighting coefficients to drive iterative optimization of the geometry and material distribution.

[0029] In some embodiments, the multi-objective fitness function is: ; Where ω1, ω2, and ω3 are weighting coefficients and satisfy ω1 + ω2 + ω3 = 1, and f1 is the amplitude fluctuation coefficient. and These are penalty terms for fatigue resistance and thermal management capability not meeting the constraints, respectively. Multi-objective collaborative optimization is achieved by minimizing F.

[0030] Preferred, The objective function for amplitude stability is: ;

[0031] in, This represents the amplitude value of the working end of the amplitude transformer at frequency f. The target resonant frequency; Preset bandwidth; The average amplitude within the bandwidth; the optimization direction is to minimize. This means reducing amplitude fluctuations.

[0032] The objective function for fatigue resistance is: ; This represents the maximum equivalent stress of the amplitude transformer under periodic load. The fatigue limit of the amplitude transformer material is S; the preset safety margin is S; the constraint conditions are as follows: This ensures that the structure meets fatigue resistance requirements.

[0033] Objective function for thermal management capability: ;

[0034] This represents the highest temperature at a critical component of the amplitude transformer. Preset temperature limit; Temperature at various points in the three-dimensional space of the amplitude transformer; The overall average temperature of the amplitude transformer; the constraint condition is... At the same time, the maximum temperature and the uniformity of temperature distribution are controlled.

[0035] S4. Using a genetic algorithm with the optimization objective as the fitness function, perform composite structure optimization iterative calculations on the geometry and material distribution of the amplitude rod to generate an optimal set of design parameters that satisfy the optimization objective.

[0036] It should be noted that the geometry and material distribution parameters of the amplitude transformer are encoded to generate an initial population; finite element analysis is performed on each individual in the initial population to calculate its fitness value corresponding to the fitness function; based on the fitness value, selection, crossover, and mutation operations are performed on the individuals in the population to generate the final offspring population; the evaluation process is iteratively executed until the preset convergence condition is reached, and the individual with the highest fitness value in the final offspring population is decoded as the optimal design parameter set.

[0037] In this context, encoding refers to converting continuous design parameters into discrete gene sequences that can be processed by a genetic algorithm. This can be achieved using binary or real-number encoding, and data interaction between the physical model and the optimization algorithm is realized through establishing parameter mapping relationships. The initial population refers to a set containing a predetermined number of candidate solutions, which can be constructed using random generation or Latin hypercube sampling methods, ensuring population diversity by covering different regions of the design space. Fitness values ​​are evaluation indicators that quantify the performance of individuals. This can be achieved by using weighted summation to transform the multi-objective optimization problem into a single-objective function, and normalization to balance the contributions of parameters with different dimensions. Selection, crossover, and mutation operations are operators that simulate biological evolutionary mechanisms, specifically implemented using roulette wheel selection, arithmetic crossover, and Gaussian mutation methods, maintaining population evolutionary momentum by retaining superior genes and introducing new genes. Convergence conditions are the criteria for terminating optimization iterations, which can be set using a maximum iteration threshold or a fitness value change rate threshold, dynamically controlling computational resource allocation by monitoring the population's evolutionary state.

[0038] Preferably, when using a genetic algorithm for composite structure optimization, the geometric parameters of the amplitude transformer (such as the radius of curvature of the transition section and the length ratio of each section) and material distribution parameters (such as the volume fraction gradient of the composite reinforcement phase) must first be encoded into chromosomes to form an initial population (population size N). Each chromosome corresponds to a set of design parameter combinations. Based on the multi-objective fitness function, the fitness value of each individual is calculated through finite element analysis. During the iteration process, a roulette wheel selection method is used to select parent individuals based on the fitness value, with a selection probability... individual fitness value Positive correlation

[0039] ; Individuals with higher fitness values ​​have a greater probability of being selected. A crossover operation is performed on the selected parent individuals, using arithmetic crossover for geometric and material parameters separately, such as offspring parameters: ; Interval random numbers, , For the parent parameters, single-point crossover is used for discrete material distribution types; at the same time, a mutation operation is introduced to randomly perturb the parameters (e.g., , (Based on random bias within the parameter range), the mutation probability is dynamically adjusted with the number of iterations to balance local search and global exploration. After each iteration, the individual with the best fitness value is retained to form a new population. The selection, crossover, and mutation processes are repeated until the convergence condition is met (e.g., the rate of change of fitness value is less than the threshold ε for multiple consecutive generations). Finally, the individual with the smallest fitness value in the population is decoded to obtain the optimal set of design parameters that satisfies the synergistic optimization of amplitude stability, fatigue resistance, and thermal management capability.

[0040] In some embodiments, during the evaluation of each generation of individuals in the genetic algorithm, chromosome decoding and model updating are first performed: the real-number encoded sequence of the individual chromosome is read and restored to the specific parameters of the amplitude transformer, such as the input diameter D1, the output diameter D2, and the axial distribution gradient k of the reinforcing phase. These parameters are then imported into the finite element preprocessing module to automatically update the geometric model and material property assignments. Subsequently, modal analysis and harmonic response analysis are performed on the updated model: the modal analysis uses the BlockLanczos method to solve for the first 5 natural frequencies and determine the mode shape corresponding to the target resonant frequency; the harmonic response analysis scans within the target frequency ±1kHz range to extract the working end amplitude A(f) and the maximum equivalent stress of the structure. And temperature field data T(x,y,z) of key components. Based on the extracted data, performance indicators were calculated: amplitude fluctuation coefficient, ratio of maximum equivalent stress to fatigue limit, and the difference between the highest and average temperatures of key components. Finally, , , (correspond (Calculation basis) Substitute into the multi-objective fitness function, such as when hour =1, otherwise = Finally, the overall fitness value of the individual is calculated, providing an evaluation basis for subsequent genetic operations.

[0041] refer to Figure 2 Secondly, the present invention also provides a composite structure-optimized ultrasonic scalpel amplitude transformer design system for implementing the aforementioned composite structure-optimized ultrasonic scalpel amplitude transformer design method, comprising: The parameter acquisition module is configured to acquire the initial design parameters of the amplitude transformer, which include at least the geometry, dimensions and material properties. The finite element analysis module is configured to establish a finite element analysis model of the amplitude transformer based on the initial design parameters, and to perform vibration mode analysis and resonant frequency calculation through the model to obtain the dynamic performance index of the amplitude transformer. The target setting module is configured to set structural optimization targets, which are to improve the amplitude stability, fatigue resistance and thermal management capability of the amplitude transformer. The optimization calculation module is configured to use a genetic algorithm, with the optimization objective as the fitness function, to perform composite structural optimization iterative calculations on the geometry and material distribution of the amplitude rod, and generate an optimal set of design parameters that satisfy the optimization objective.

[0042] Preferably, the optimization calculation module is configured to: encode the geometry and material distribution parameters of the amplitude transformer into genetic algorithm chromosomes and generate an initial population; for each individual in the population, call the finite element analysis module to perform dynamic performance simulation and calculate the corresponding fitness value according to the structural optimization objective; based on the fitness value, perform selection, crossover, and mutation operations on the individuals in the population to generate a new generation population; iteratively execute the above processing until the preset convergence condition is met, and decode and output the individual with the highest fitness value in the final population as the optimal design parameter set.

[0043] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0044] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0045] For ease of description, the above devices are described in terms of function, with each unit described separately. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0046] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of computer program products implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0047] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0048] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0049] These computer program instructions can also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0050] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0051] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0052] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0053] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0054] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0055] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0056] Finally, it should be noted that the ultrasonic amplitude transformer optimization management platform disclosed in the embodiments of the present invention is only a preferred embodiment of the present invention and is only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A design method for an ultrasonic surgical scalpel amplitude transformer based on composite structure optimization, characterized in that, Includes the following steps: Obtain the initial design parameters of the amplitude transformer, which include at least its geometry, dimensions, and material properties; A finite element analysis model of the amplitude transformer is established based on the initial design parameters, and vibration mode analysis and resonant frequency calculation are performed using this model to obtain the dynamic performance indicators of the amplitude transformer. The structural optimization objectives are set as follows: to improve the amplitude stability, fatigue resistance and thermal management capability of the amplitude transformer. A genetic algorithm is used, with the optimization objective as the fitness function, to perform composite structural optimization iterative calculations on the geometry and material distribution of the amplitude rod, generating an optimal set of design parameters that satisfy the optimization objective.

2. The ultrasonic scalpel amplitude transformer design method based on composite structure optimization according to claim 1, characterized in that, Also includes: The specific steps for obtaining the initial design parameters of the amplitude transformer include: The basic configuration is retrieved from the preset amplitude lever type database, and the basic configuration includes stepped, conical and exponential shapes; The system receives functional constraints input by the user, which include at least the operating frequency range, installation space dimensions, and connection interface specifications. Based on the basic configuration and the functional constraints, the initial geometry and initial dimensions of the amplitude rod are determined.

3. The ultrasonic scalpel amplitude transformer design method based on composite structure optimization according to claim 1, characterized in that, The finite element model construction process also includes: The geometry of the amplitude transformer is meshed to generate a finite element mesh; Boundary conditions are applied to the finite element mesh, including fixed constraints simulating the connection between the amplitude transformer and the ultrasonic transducer. Define material properties and apply load conditions, wherein the load conditions are to apply harmonic excitation corresponding to the target operating frequency at the input end of the amplitude transformer.

4. The ultrasonic scalpel amplitude transformer design method based on composite structure optimization according to claim 1, characterized in that, The specific objectives for setting the structure optimization include: The amplitude stability target is characterized as the amplitude fluctuation coefficient of the working end of the amplitude transformer within a preset bandwidth near the target resonant frequency. The fatigue resistance target is defined as the maximum equivalent stress of the amplitude rod structure under periodic load being lower than the fatigue limit of its material, and it must meet the preset safety margin requirements. The thermal management capability target is characterized as follows: under continuous working conditions, the highest temperature of the key parts of the amplitude rod does not exceed a preset limit, and its temperature distribution gradient must meet the uniformity condition. The above-described target parameters are combined to form the multi-objective fitness function of the optimization algorithm.

5. The ultrasonic scalpel amplitude transformer design method based on composite structure optimization according to claim 1, characterized in that, The use of a genetic algorithm also includes the following steps: The geometry and material distribution parameters of the amplitude transformer are encoded to generate an initial population; Finite element analysis is performed on each individual in the initial population to calculate its fitness value corresponding to the fitness function; Based on the fitness value, selection, crossover, and mutation operations are performed on individuals in the population to generate the final offspring population; The evaluation process is iteratively executed until the preset convergence condition is reached, and the individual with the highest fitness value in the final offspring population is decoded as the optimal design parameter set.

6. The design method for the ultrasonic scalpel amplitude transformer based on composite structure optimization according to claim 5, characterized in that, Also includes: Each individual chromosome is decoded and restored to its specific amplitude rod geometric and material parameters, and the finite element model is updated accordingly. Modal analysis and harmonic response analysis were performed on the updated finite element model to extract the amplitude, structural stress field distribution and temperature field distribution at the target resonant frequency. Based on the extracted amplitude, stress and temperature data, the amplitude fluctuation coefficient, the ratio of maximum equivalent stress to fatigue limit, and the difference between the highest temperature and the average temperature of key parts are calculated respectively. Substitute the above calculation results into the multi-objective fitness function to calculate the comprehensive fitness value representing the individual's performance.

7. The ultrasonic scalpel amplitude transformer design method based on composite structure optimization according to claim 2, characterized in that, The maximum axial force requirement for the aforementioned interface needs to be determined in conjunction with the maximum feed force that the ultrasonic surgical instrument may withstand during clinical operation. The initial deviation range between the target operating frequency and the center frequency of the ultrasonic transducer shall not exceed ±500Hz.

8. The design method for the ultrasonic scalpel amplitude transformer based on composite structure optimization according to claim 4, characterized in that, The multi-objective fitness function is: Where ω1, ω2, and ω3 are weighting coefficients and satisfy ω1 + ω2 + ω3 = 1, and f1 is the amplitude fluctuation coefficient. and These are penalty terms for fatigue resistance and thermal management capability not meeting the constraints, respectively. Multi-objective collaborative optimization is achieved by minimizing F.

9. A composite structure-optimized ultrasonic scalpel amplitude transformer design system, used to implement the composite structure-optimized ultrasonic scalpel amplitude transformer design method according to claims 1-8, characterized in that, The system includes: The parameter acquisition module is configured to acquire the initial design parameters of the amplitude transformer, which include at least the geometry, dimensions and material properties. The finite element analysis module is configured to establish a finite element analysis model of the amplitude transformer based on the initial design parameters, and to perform vibration mode analysis and resonant frequency calculation through the model to obtain the dynamic performance index of the amplitude transformer. The target setting module is configured to set structural optimization targets, which are to improve the amplitude stability, fatigue resistance and thermal management capability of the amplitude transformer. The optimization calculation module is configured to use a genetic algorithm, with the optimization objective as the fitness function, to perform composite structural optimization iterative calculations on the geometry and material distribution of the amplitude rod, and generate an optimal set of design parameters that satisfy the optimization objective.

10. The ultrasonic scalpel amplitude transformer design system based on composite structure optimization as described in claim 9, characterized in that, The optimization calculation module is configured as follows: The geometry and material distribution parameters of the amplitude transformer are encoded into chromosomes using a genetic algorithm, and an initial population is generated. For each individual in the population, the finite element analysis module is called to perform dynamic performance simulation, and the corresponding fitness value is calculated according to the structural optimization objective. Based on the fitness value, selection, crossover, and mutation operations are performed on individuals in the population to generate a new generation of population; the above processing is iteratively executed until the preset convergence condition is met, and the individual with the highest fitness value in the final population is decoded and output as the optimal design parameter set.

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