A method, device and system for designing an ultrasonic horn based on a genetic algorithm

Abstract

This invention discloses a design method, device, and system for an ultrasonic amplitude transformer based on a genetic algorithm, belonging to the field of manufacturing technology. Considering the strong global search capability and excellent convergence of genetic algorithms, this invention combines the principles of genetic algorithms with a fitness function to construct a fitness optimization algorithm based on genetic algorithms to optimize the contour parameters of the ultrasonic amplitude transformer. This ensures that the expected optimal amplification factor is obtained under different optimization objectives, effectively avoiding the local optimum problem common in traditional design methods and improving design efficiency and performance. By optimizing the contour of the amplitude transformer's geometry, the amplitude transformer can achieve the preset target amplification factor under operating conditions, thereby improving the efficiency of ultrasonic energy transfer and obtaining the expected output displacement amplitude. The final optimization result outputs the optimal geometric contour and parameters of the amplitude transformer, which can then be used for subsequent engineering manufacturing and application according to actual needs.

Description

Technical Field

[0001] This invention belongs to the field of manufacturing technology, and more specifically, relates to a method, device and system for designing ultrasonic amplitude transformers based on genetic algorithms. Background Technology

[0002] The ultrasonic amplitude transformer, a key energy transfer component in ultrasonic equipment, amplifies the minute vibration signals generated by the ultrasonic generator into signals with larger amplitudes, and then transfers energy through the contact surface. It is mainly used to amplify the amplitude or velocity of mechanical vibrations and focus ultrasonic energy onto a small area. Energy transfer efficiency is improved by adjusting the load matching between the transducer and the tool head. Based on vibration type, it can be classified as longitudinal vibration, bending vibration, torsional vibration, etc., and based on amplitude transformer shape, it can be classified as stepped, exponential, catenary, etc. It is widely used in welding, cutting, and cleaning. As a crucial component connecting the transducer and the tool head, its metal material and variable cross-section design directly affect the vibration amplification factor, and a flange is used to achieve system fixation and isolation from operating conditions.

[0003] Traditional ultrasonic amplifier design usually relies on empirical calculations or experiments. In order to accurately find the optimal geometric parameters within a limited design space at higher operating frequencies to achieve the target amplification factor, reduce energy loss, and improve energy transfer efficiency, optimizing the geometry of the ultrasonic amplifier has become a research hotspot.

[0004] Existing optimization methods are relatively simple, usually relying on linear or local optimization strategies, which make it difficult to fully consider the multi-dimensional performance requirements of ultrasonic amplitude transformers in complex working environments, ultimately leading to poor optimization design results. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides an ultrasonic amplitude transformer design method, device and system based on genetic algorithm. Its purpose is to solve the technical problem that the existing optimization methods are relatively simple and rely on linear or local optimization strategies, resulting in poor optimization design effect.

[0006] To achieve the above objectives, according to one aspect of the present invention, a method for designing an ultrasonic amplitude transformer based on a genetic algorithm is provided, comprising:

[0007] S1: The optimization objective is to achieve a preset value for the amplification factor of the ultrasonic amplitude transformer, and decision variables and constraints are set; the profile parameter population and fitness function corresponding to the genetic algorithm are initialized; the decision variables include: the lengths {L1, L2, L3} of the three steps of the ultrasonic amplitude transformer, the radii {R1, R2} of the two equal sections of the ultrasonic amplitude transformer, the material density, and the Young's modulus; the constraints include size constraints and boundary constraints.

[0008] S2: Input the optimization objective, the decision variables, and the constraints into the genetic algorithm and calculate the fitness value of the objective function corresponding to the optimization objective according to the fitness function;

[0009] S3: Determine whether the fitness value meets the stopping condition;

[0010] S4: If not satisfied, perform selection, crossover, and mutation operations based on the fitness value to update the contour parameter population, calculate the fitness value corresponding to the updated contour population, and return to S3; if satisfied, use the current contour population as the optimal contour parameter and output it.

[0011] Furthermore, the dimensional constraints include:

[0012] L min ≤{L1, L2, L3}≤L max ;

[0013] L1 = L3;

[0014] tan(2kL1) = - (k / )tan( L2);

[0015] R1≥R2;

[0016] Among them, L min L is the minimum length threshold. max The maximum length threshold is given by k, where k is the wave number. for = , For the geometric parameters of the amplitude transformer, R1 is the fixed radius of the input terminal, and R2 is the radius of the output terminal.

[0017] Furthermore, the boundary constraints include: free output and fixed input constraints.

[0018] Further, the optimization function corresponding to the optimization objective is: min{f = 1 / (r+c)}; where r is the amplification factor and c is a non-zero constant; or the optimization function corresponding to the optimization objective is: min{f = |r 预设 -r |};r is the magnification factor, r 预设 This is the preset value corresponding to the amplification factor r.

[0019] Furthermore, r = q|cos( L2)-tga2sin( L2)|, where q is the area coefficient with a value of R1 / R2, and k is the wave number. for = , , For the geometric parameters of the amplitude transformer, .

[0020] Furthermore, the selection operation employs a roulette wheel selection method, the crossover operation employs a single-point crossover method, and the mutation operation employs a Gaussian mutation method.

[0021] According to another aspect of the present invention, an ultrasonic amplitude transformer design device based on a genetic algorithm is provided, comprising:

[0022] The initialization module is used to set the amplification factor of the ultrasonic amplitude transformer to a preset value as the optimization objective, and to set decision variables and constraints; it also initializes the contour parameter population and fitness function corresponding to the genetic algorithm.

[0023] The decision variables include: the lengths {L1, L2, L3} of the three stepped sections of the ultrasonic amplitude transformer, the radii {R1, R2} of the two equal cross sections of the ultrasonic amplitude transformer, the material density, and Young's modulus; the constraints include dimensional constraints and boundary constraints.

[0024] The input module is used to input the optimization objective, the decision variables, and the constraints into the genetic algorithm and calculate the fitness value of the objective function corresponding to the optimization objective according to the fitness function;

[0025] The judgment module is used to determine whether the fitness value meets the stopping condition;

[0026] The loop module is used to perform selection, crossover, and mutation operations based on the fitness value to update the contour parameter population if the condition is not met. It calculates the fitness value corresponding to the updated contour population and inputs the current fitness value into the judgment module for judgment again. If the condition is met, the current contour population is taken as the optimal contour parameter and output.

[0027] According to another aspect of the present invention, an ultrasonic amplitude transformer design system based on a genetic algorithm is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the ultrasonic amplitude transformer design method based on the genetic algorithm.

[0028] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the ultrasonic amplitude transformer design method based on a genetic algorithm.

[0029] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0030] (1) This invention provides a design method for an ultrasonic amplitude transformer based on a genetic algorithm. Considering the strong global search capability and excellent convergence of the genetic algorithm, the idea of ​​the genetic algorithm is combined with the fitness function to construct a fitness optimization algorithm based on the genetic algorithm. This algorithm is used to optimize the contour parameters of the ultrasonic amplitude transformer to ensure that the expected optimal amplification factor is obtained under different optimization objectives. This effectively avoids the local optimum problem common in traditional design methods and improves design efficiency and performance. By optimizing the contour of the amplitude transformer's geometry, the amplitude transformer can achieve the preset target amplification factor under working conditions, thereby improving the efficiency of ultrasonic energy transfer and obtaining the expected output displacement amplitude. The final optimization result will output the optimal geometric contour and parameters of the amplitude transformer, which can also be used for subsequent engineering manufacturing and application according to actual needs.

[0031] (2) The optimization problem of the amplitude rod magnification factor in this scheme is related to the optimization of the amplitude rod's geometric parameters. The geometric parameters of the amplitude rod include length, cross-sectional radius, etc. The goal is to optimize these parameters to achieve the preset (e.g., maximize) amplitude rod magnification factor. Genetic algorithms can be deeply combined with amplitude rod contour parameter optimization to improve the probability of outputting the optimal geometric contour of the amplitude rod.

[0032] (3) This scheme uses a genetic algorithm for selection, crossover, and mutation operations to design the amplitude rod profile. The selection operation adopts the roulette wheel selection method to ensure that individuals with high fitness have a greater probability of being selected. The crossover operation adopts the single-point crossover method to ensure that offspring individuals can inherit the superior characteristics of their parents. The mutation operation adopts the Gaussian mutation method to ensure population diversity and avoid premature convergence. Attached Figure Description

[0033] Figure 1 This is a flowchart of an ultrasonic amplitude transformer design method based on a genetic algorithm provided in Embodiment 1 of the present invention;

[0034] Figure 2 This is a schematic diagram of the ultrasonic amplitude transformer provided in Embodiment 1 of the present invention;

[0035] Figure 3 This is a flowchart of another ultrasonic amplitude transformer design method based on genetic algorithm provided in Embodiment 1 of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0037] Example 1

[0038] like Figure 1 As shown, this embodiment provides a design method for an ultrasonic amplitude transformer based on a genetic algorithm, including: S1-S4.

[0039] S1: The optimization objective is to achieve a preset amplification factor for the ultrasonic amplitude transformer, and decision variables and constraints are set. The profile parameter population and fitness function corresponding to the genetic algorithm are initialized. The decision variables include: the lengths {L1, L2, L3} of each of the three stepped segments of the ultrasonic amplitude transformer, the radii {R1, R2} of each of the two equal cross-sections of the ultrasonic amplitude transformer, the material density, and Young's modulus. Figure 2 As shown; the constraints include size constraints and boundary constraints. S2: Input the optimization objective, the decision variables, and the constraints into the genetic algorithm and calculate the fitness value of the objective function corresponding to the optimization objective according to the fitness function. S3: Determine whether the fitness value meets the stopping condition. S4: If not, perform selection, crossover, and mutation operations based on the fitness value to update the contour parameter population, calculate the fitness value corresponding to the updated contour population, and return to S3; if satisfied, output the current contour population as the optimal contour parameters. Figure 3 This is a flowchart of another ultrasonic amplitude transformer design method based on genetic algorithm provided in Embodiment 1 of the present invention.

[0040] Furthermore, the dimensional constraints include:

[0041] L min ≤{L1, L2, L3}≤L max ;

[0042] The optimization process includes engineering constraints, such as dimensional constraints. One requirement is that the stepped amplitude transformer has a specific shape.

[0043] L1 = L3;

[0044] tan(2kL1) = - (k / )tan( L2);

[0045] R1≥R2;

[0046] Among them, L min L is the minimum length threshold. max The maximum length threshold is given by k, where k is the wave number. for = , For the geometric parameters of the amplitude transformer, R1 is the fixed radius of the input terminal, and R2 is the radius of the output terminal.

[0047] Furthermore, the boundary constraints include: free output and fixed input constraints.

[0048] Furthermore, the optimization function corresponding to the optimization objective is: min{f =1 / (r+c)};

[0049] Where r is the amplification factor and c is a non-zero constant; or the optimization function corresponding to the optimization objective is: min{f = |r 预设 -r |};r is the magnification factor, r 预设 This is the preset value corresponding to the amplification factor r.

[0050] Furthermore, r = q|cos( L2)-tga2sin( L2)|, where q is the area coefficient, and its magnitude is k is the circular wave number. for = , , For the geometric parameters of the amplitude transformer, .

[0051] In addition, it should be added that k is the circular wave number. Here, c is the angular frequency, c is the propagation speed of the longitudinal wave in the amplitude transformer, and E is the Young's modulus of the material. Since the material density is given, the formulas for calculating each parameter are as follows:

[0052] = ;

[0053] ; ; ;

[0054] A genetic algorithm is used to optimize the objective function. The amplification factor at the output end is maximized or reaches a preset amplification factor by dynamically adjusting the lengths L1, L2, and L3 of the amplitude transformer. During the optimization process, the radius of the input end R1 and the radius of the output end R2 are fixed, and the radius of the transition section R... L The amplitude transformer lengths L1, L2, and L3 change freely with L2, while L1 and L3 change simultaneously. The output shows the optimized amplitude transformer lengths L1, L2, and L3, as well as the optimized amplification factor.

[0055] Furthermore, the selection operation employs a roulette wheel selection method, the crossover operation employs a single-point crossover method, and the mutation operation employs a Gaussian mutation method.

[0056] Example 2

[0057] This embodiment provides an ultrasonic amplitude transformer design device based on a genetic algorithm, including: an initialization module, an input module, a judgment module, and a loop module.

[0058] The initialization module is used to set the amplification factor of the ultrasonic amplitude transformer to a preset value as the optimization objective, and to set decision variables and constraints; it also initializes the profile parameter population and fitness function corresponding to the genetic algorithm. The decision variables include: the lengths {L1, L2, L3} of the three stepped sections of the ultrasonic amplitude transformer, the radii {R1, R2} of the two equal cross-sections of the ultrasonic amplitude transformer, the material density, and the Young's modulus; the constraints include dimensional constraints and boundary constraints.

[0059] The input module is used to input the optimization objective, the decision variables, and the constraints into the genetic algorithm and calculate the fitness value of the objective function corresponding to the optimization objective according to the fitness function.

[0060] The judgment module is used to determine whether the fitness value meets the stopping condition.

[0061] The loop module is used to perform selection, crossover, and mutation operations based on the fitness value to update the contour parameter population if the condition is not met. It calculates the fitness value corresponding to the updated contour population and inputs the current fitness value into the judgment module for further judgment. If the condition is met, the current contour population is taken as the optimal contour parameters and output.

[0062] Example 3

[0063] This embodiment provides an ultrasonic amplitude transformer design system based on a genetic algorithm, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the ultrasonic amplitude transformer design method based on the genetic algorithm.

[0064] Example 4

[0065] This embodiment provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the ultrasonic amplitude transformer design method based on a genetic algorithm.

[0066] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A design method for an ultrasonic amplitude transformer based on a genetic algorithm, characterized in that, include: S1: Set the amplification factor of the ultrasonic amplitude transformer to a preset value as the optimization objective, and set decision variables and constraints. Initialize the contour parameter population and fitness function corresponding to the genetic algorithm; the decision variables include: the lengths {L1, L2, L3} of the three steps of the ultrasonic amplitude transformer, the radii {R1, R2} of the two equal sections of the ultrasonic amplitude transformer, the material density and Young's modulus; the constraints include size constraints and boundary constraints. S2: Input the optimization objective, the decision variables, and the constraints into the genetic algorithm and calculate the fitness value of the objective function corresponding to the optimization objective according to the fitness function; S3: Determine whether the fitness value meets the stopping condition; S4: If not satisfied, perform selection, crossover, and mutation operations based on the fitness value to update the contour parameter population, calculate the fitness value corresponding to the updated contour population, and return to S3; if satisfied, use the current contour population as the optimal contour parameter and output it. The dimensional constraints include: L min ≤{L1, L2, L3}≤L max ; L1 = L3; tan(2kL1) = - (k / k')tan(k'L2); R1≥R2; Among them, L min L is the minimum length threshold. max The maximum length threshold is given by k, where k is the wave number. for , For the geometric parameters of the amplitude transformer, R1 is the fixed radius of the input terminal, and R2 is the radius of the output terminal; Amplification factor r = q|cos( L2)- sin( L2)|, where q is the area coefficient, and its value is R1 / R2. .

2. The ultrasonic amplitude transformer design method based on genetic algorithm as described in claim 1, characterized in that, The boundary constraints include: free output and fixed input.

3. The ultrasonic amplitude transformer design method based on genetic algorithm as described in claim 1, characterized in that, The optimization function corresponding to the optimization objective is: min{f =1 / (r+c)}; Where r is the amplification factor and c is a non-zero constant.

4. The ultrasonic amplitude transformer design method based on genetic algorithm as described in claim 1, characterized in that, The optimization function corresponding to the optimization objective is: min{f = |r} 预设 -r |};r is the magnification factor, r 预设 This is the preset value corresponding to the amplification factor r.

5. The ultrasonic amplitude transformer design method based on genetic algorithm as described in claim 1, characterized in that, The selection operation uses the roulette wheel selection method, the crossover operation uses the single-point crossover method, and the mutation operation uses the Gaussian mutation method.

6. A device for designing an ultrasonic amplitude transformer based on a genetic algorithm, characterized in that, The method for implementing the ultrasonic amplitude transformer design method based on genetic algorithm as described in any one of claims 1-5 includes: An initialization module is used to set the amplification factor of the ultrasonic amplitude transformer to a preset value as the optimization objective, and to set decision variables and constraints; it initializes the profile parameter population and fitness function corresponding to the genetic algorithm; wherein, the decision variables include: the lengths {L1, L2, L3} of the three steps of the ultrasonic amplitude transformer, the radii {R1, R2} of the two equal sections of the ultrasonic amplitude transformer, the material density, and the Young's modulus; the constraints include size constraints and boundary constraints; The input module is used to input the optimization objective, the decision variables, and the constraints into the genetic algorithm and calculate the fitness value of the objective function corresponding to the optimization objective according to the fitness function; The judgment module is used to determine whether the fitness value meets the stopping condition; The loop module is used to perform selection, crossover, and mutation operations based on the fitness value to update the contour parameter population if the condition is not met. It calculates the fitness value corresponding to the updated contour population and inputs the current fitness value into the judgment module for judgment again. If the condition is met, the current contour population is taken as the optimal contour parameter and output.

7. An ultrasonic amplitude transformer design system based on a genetic algorithm, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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