Analysis Method and System for Rotary Ultrasonic Hole Machining Based on Finite Element Simulation Method

Abstract

The present invention provides a method and system for rotary ultrasonic hole machining analysis based on the finite element simulation method, which relates to the technical field of hole machining analysis. The specific steps include: realizing the real-time adjustment of vibration parameters through sensor data acquisition and an adaptive algorithm; adopting microscopic modeling and finite element stress analysis to identify the material wear mechanism and optimize the material; integrating sensor monitoring to adjust the machining speed and chip removal path in real time by setting thresholds; using big data and statistical models to comprehensively evaluate the process and provide optimization suggestions. The present invention uses molecular dynamics and finite element methods to analyze the stress distribution and identify the wear mode, which can scientifically optimize the composition and structure of the drill bit material and extend its service life. In addition, the temperature and pressure on the chip removal path are monitored in real time, and the machining speed and path are automatically adjusted, which helps to avoid thermal damage and improve the machining quality. The comprehensive process evaluation system provides data support for further optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of hole machining analysis, and specifically to a rotary ultrasonic hole machining analysis method and system based on the finite element simulation method. Background Technique

[0002] Currently, rotary ultrasonic hole machining has become one of the key technologies in high-precision manufacturing and is widely used in fields such as aerospace, automotive manufacturing, and electronics. However, traditional machining methods often face a series of technical challenges during operation. First of all, the effective control of vibration is the core factor to ensure machining accuracy. Due to the complex working conditions during machining, it is difficult to monitor and adjust the changes in vibration frequency and amplitude in real time, which leads to unstable machining and affects the dimensional accuracy and surface quality of the machined parts. In addition, the vibration generated during machining may also cause additional wear of the equipment, accelerating equipment aging and failures. There is a lack of a real-time response mechanism for the vibration state in the existing technology, and it is impossible to achieve dynamic optimization of key parameters.

[0003] In terms of wear and chip removal, traditional methods also have obvious deficiencies. The stress distribution at the interface between the abrasive grains and the material is complex, and it is difficult to accurately predict through simple experiments and empirical formulas. This leads to an unclear material wear mechanism, and the selection and design of drill bit materials lack a scientific basis, often relying on the trial-and-error method for adjustment, with low efficiency and high cost. At the same time, the changes in temperature and pressure during the chip removal process are also difficult to monitor and control. When these parameters exceed the tolerance of the material, it is easy to cause thermal damage and defects, affecting the quality and performance of the finished product. In the existing technology, there is a lack of an effective means to adjust the machining speed and chip removal path in real time to adapt to the dynamic changes during the machining process, thereby ensuring the stability and efficiency of the entire machining process. In summary, these deficiencies seriously restrict the further development and application of the rotary ultrasonic hole machining technology.

[0004] The above information disclosed in the background technique section is only used to enhance the understanding of the background of the present disclosure, so it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a rotary ultrasonic hole machining analysis method and system based on the finite element simulation method to solve the problems raised in the above background technique.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A rotary ultrasonic hole machining analysis method based on the finite element simulation method, the specific steps include:

[0008] Step 1: Real-time collect the vibration data of the machining equipment and use an adaptive algorithm to achieve real-time adjustment of the vibration parameters, where the vibration parameters are the vibration frequency and amplitude;

[0009] Step 2: Use molecular dynamics software to construct a microstructure model of the drill bit material, apply the finite element method to calculate the stress distribution at the interface between the abrasive grain and the material, analyze the stress results, identify the wear mode, and adjust the composition and structure of the drill bit material according to the wear mechanism;

[0010] Step 3: Real-time collect the temperature data and pressure data on the chip removal path, set the temperature and pressure thresholds based on the material properties, and when the thresholds are exceeded, the system automatically adjusts the machining speed and the chip removal path;

[0011] Step 4: Conduct process evaluation through data and statistical models and provide optimization suggestions.

[0012] Furthermore, in the vibration system, the core parameters include the amplitude , the frequency and the phase difference :

[0013] Among them, the maximum displacement during system vibration is obtained through a vibration sensor, which is the amplitude ;

[0014] Apply the fast Fourier transform to extract the frequency characteristics from the vibration data: perform the fast Fourier transform on each time-series vibration signal segment to obtain the frequency characteristics corresponding to each time-series vibration signal segment. The frequency characteristics include time, fundamental frequency, and harmonic waves; form a data chain with the frequency characteristics corresponding to the time-series vibration signal segments, that is, a data chain composed of time, fundamental frequency, and harmonic waves. In the data chain, the pointer between time and the fundamental frequency is the timestamp index, which points to the fundamental frequency value at a specific moment, and the pointer between the fundamental frequency and the harmonic waves is the fundamental frequency index, which points to the harmonic wave value at a specific fundamental frequency;

[0015] Determine the phase difference through the experimental calibration method .

[0016] Furthermore, adopt an adaptive algorithm with the goal of minimizing vibration:

[0017] ;

[0018] Among them, is the objective function, representing the deviation of vibration, represents the amplitude at moment, is the desired amplitude, which is determined through experiments and experience;

[0019] Apply the gradient descent method to adjust the vibration parameters:

[0020] ;

[0021] Among them, represents the amplitude at the +1 moment, represents the amplitude at the moment, is the learning rate, selected through debugging, used to ensure the balance between the convergence speed and stability, is the objective function, representing the deviation of vibration;

[0022] The system dynamically adjusts the vibration parameters according to the feedback to ensure that the operation is in the optimal state. The adjustment formula is:

[0023] ;

[0024] where, is the adjusted amplitude, is the current amplitude, is the damping coefficient, calibrated through experiments, used to affect the vibration attenuation speed, is the change rate of the amplitude with time.

[0025] Furthermore, the drill bit material composition and structure are adjusted according to the wear mechanism. The specific logic is as follows: Use molecular dynamics software to construct a microscopic structure model of the drill bit material, define the interatomic interaction potential energy function to simulate the physical and chemical properties of the material, obtain a stable microscopic structure by simulating the behavior of the material at different temperatures and pressures, use the finite element software ABAQUS to import the microscopic structure model, set the boundary conditions and load conditions, and apply the acting force according to the actual use situation , apply the finite element method to calculate the stress distribution at the abrasive grain and material interface:

[0026] ;

[0027] where, is the stress, is the applied acting force, is the contact area, automatically calculated by the finite element software;

[0028] Identify the stress concentration area through the stress distribution result, identify the wear mode, and adjust the drill bit material composition and structure according to the wear mechanism:

[0029] If adhesive wear is identified, increase the lubricant coating;

[0030] If abrasive wear is identified, improve it by adjusting the hardness and toughness of the material;

[0031] If fatigue wear is identified, optimize the grain size and grain boundary structure of the material.

[0032] Furthermore, the system automatically adjusts the processing speed and chip removal path, and the specific logic is as follows: Use a thermocouple to monitor the temperature on the chip removal path in real time, use a pressure sensor to monitor the pressure in the processing area in real time, set the temperature and pressure thresholds based on the heat resistance and compressive strength of the material, and establish the following temperature model and pressure model:

[0033] ;

[0034] where, is the temperature change, is the current temperature, represents the temperature threshold, is the pressure change, is the current pressure, represents the pressure threshold;

[0035] If , then reduce the processing speed and change the cooling strategy;

[0036] If , then adjust the processing speed and change the chip removal path.

[0037] Furthermore, the process evaluation is carried out through data and statistical models and optimization suggestions are provided. The specific logic is as follows: Collect vibration, temperature, pressure and processing speed data, preprocess the data to remove noise and outliers, and use multiple linear regression to analyze the influence of each parameter on the processing quality:

[0038] ;

[0039] where, is the processing quality index, is the vibration frequency, is the amplitude, is the temperature, is the pressure, is the regression coefficient, is the error term.

[0040] Furthermore, the determination process of the regression coefficient and the error term specifically includes:

[0041] Collect a set of sample data with known processing quality indicators, and the process parameters in these sample data: vibration frequency, amplitude, temperature, and pressure values are different from each other, forming a data set;

[0042] Combine the sample data with known processing quality indicators with the corresponding process parameters to form a data matrix; rows represent samples and columns represent process parameters;

[0043] Set the target variable as the processing quality index of the sample, and the process parameters as the independent variables;

[0044] Select a linear regression model for modeling, and the regression model is expressed as:

[0045] ;

[0046] Among them, is the target variable, is the vibration frequency, is the amplitude, is the temperature, is the pressure, , , , are the regression coefficients to be obtained, is the error term;

[0047] Fit the model by the least squares method, minimize the squared error between the predicted value and the actual value, and the objective function to be minimized is as follows:

[0048] ;

[0049] Among them, is the processing quality index of the th sample, is the processing quality index predicted by the model, is the total number of samples;

[0050] Through linear regression analysis, calculate the regression coefficients of each process parameter and the error term , directly execute this process using python and return these parameters. According to the output of the regression model, evaluate the influence of process parameters: vibration frequency, amplitude, temperature, and pressure on the processing quality, and select the best combination that can maximize the processing quality index according to the model.

[0051] The present invention also further provides a rotary ultrasonic hole machining analysis system based on the finite element simulation method. The rotary ultrasonic hole machining analysis system based on the finite element simulation method is used to execute the above-mentioned rotary ultrasonic hole machining analysis method based on the finite element simulation method, including:

[0052] An intelligent vibration parameter optimization module, which is used to collect the vibration data of the processing equipment in real time and adopt an adaptive algorithm to realize the real-time adjustment of the vibration parameters. The vibration parameters are the vibration frequency and the amplitude;

[0053] A multi-scale wear analysis module, which is used to construct a microscopic structure model of the drill bit material using molecular dynamics software, apply the finite element method to calculate the stress distribution at the interface between the abrasive particle and the material, analyze the stress results, identify the wear mode, and adjust the composition and structure of the drill bit material according to the wear mechanism;

[0054] An intelligent chip removal optimization module, which is used to collect the temperature data and pressure data on the chip removal path in real time, set the temperature and pressure thresholds based on the material properties, and when the thresholds are exceeded, the system automatically adjusts the processing speed and the chip removal path;

[0055] A comprehensive process evaluation module, which is used to conduct process evaluation through data and statistical models and provide optimization suggestions.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] By collecting the vibration, temperature and pressure data of the processing equipment in real time and applying an adaptive algorithm to adjust the vibration parameters, the processing stability and accuracy can be effectively improved. Using molecular dynamics and finite element methods to analyze the stress distribution and identify the wear mode can scientifically optimize the composition and structure of the drill bit material and extend its service life. In addition, real-time monitoring of the temperature and pressure on the chip removal path and automatically adjusting the processing speed and path helps to avoid thermal damage and improve the processing quality. The comprehensive process evaluation system provides data support for further optimization and overall improves the processing efficiency and the finished product quality. Description of the Drawings

[0058] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0059] Figure 2 It is a schematic diagram of the overall system module of the present invention. Specific Embodiments

[0060] In order to make the purpose, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with specific embodiments.

[0061] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the field to which the present invention pertains. The "first", "second" and similar terms used in the present invention do not denote any order, quantity or importance, but are only used to distinguish different components. Words such as "comprising" or "including" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to indicate relative position relationships, and when the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0062] Embodiment:

[0063] Please refer to Figure 1 , the present invention provides a technical solution:

[0064] A method for analyzing rotary ultrasonic hole machining based on the finite element simulation method, the specific steps include:

[0065] Step 1: Real-time collect the vibration data of the processing equipment, and use an adaptive algorithm to achieve real-time adjustment of the vibration parameters, where the vibration parameters are the vibration frequency and amplitude;

[0066] In this embodiment, in the vibration system, the core parameters include the amplitude , the frequency and the phase difference :

[0067] Among them, the maximum displacement during system vibration is obtained through a vibration sensor, that is, the amplitude ;

[0068] Apply the fast Fourier transform to extract frequency features from the vibration data: perform the fast Fourier transform on each time-series vibration signal segment to obtain the frequency features corresponding to each time-series vibration signal segment, where the frequency features include time, fundamental frequency and harmonics; form a data chain with the frequency features corresponding to the time-series vibration signal segments, that is, a data chain composed of time, fundamental frequency and harmonics. In the data chain, the pointer between time and fundamental frequency is the time stamp index, which points to the fundamental frequency value at a specific moment, and the pointer between fundamental frequency and harmonics is the fundamental frequency index, which points to the harmonic value at a specific fundamental frequency;

[0069] Determine the phase difference .

[0070] Use an adaptive algorithm, the goal is to minimize vibration:

[0071] ;

[0072] Wherein, is the objective function, representing the deviation of vibration, represents the amplitude at moment, is the desired amplitude, determined by experiments and experience;

[0073] Apply the gradient descent method to adjust the vibration parameters:

[0074] ;

[0075] Wherein, represents the amplitude at +1 moment, represents the amplitude at moment, is the learning rate, selected through debugging, used to ensure the balance between the convergence speed and stability, is the objective function, representing the deviation of vibration;

[0076] The system dynamically adjusts the vibration parameters according to the feedback to ensure the operation is in the optimal state. The adjustment formula is:

[0077] ;

[0078] Wherein, is the adjusted amplitude, is the current amplitude, is the damping coefficient, calibrated through experiments, used to affect the vibration attenuation speed, is the change rate of the amplitude with time.

[0079] Adopting real-time acquisition of the vibration data of the processing equipment and using an adaptive algorithm for real-time adjustment can ensure that the vibration parameters during the processing are always in the optimal state. This real-time adjustment can effectively reduce the negative impact of vibration on the processing accuracy and surface quality, and improve the stability and consistency of processing. Compared with the traditional processing method with fixed parameters, real-time adjustment of vibration parameters can more flexibly adapt to different processing conditions and material changes, reducing damage to the equipment and products. This can extend the service life of the equipment, reduce the maintenance cost, and improve the processing efficiency and quality; in the patent solution, real-time adjustment of vibration parameters is the basis for ensuring the effective implementation of other steps. By optimizing the vibration parameters, not only can the stability of the contact between the drill bit and the material be improved, but also an ideal operating environment is provided for subsequent stress analysis, wear identification, and temperature and pressure adjustment, overall improving the accuracy and efficiency of the processing technology.

[0080] Step 2: Use molecular dynamics software to construct a microscopic structure model of the drill bit material, apply the finite element method to calculate the stress distribution at the interface between the abrasive grain and the material, analyze the stress results, identify the wear mode, and adjust the composition and structure of the drill bit material according to the wear mechanism;

[0081] In this embodiment, the specific logic for adjusting the composition and structure of the drill bit material according to the wear mechanism is as follows: Use molecular dynamics software to construct a microscopic structure model of the drill bit material, define the interatomic interaction potential energy function to simulate the physical and chemical properties of the material, obtain a stable microscopic structure by simulating the behavior of the material at different temperatures and pressures, import the microscopic structure model into the finite element software ABAQUS, set the boundary conditions and load conditions, and apply the acting force according to the actual use situation , apply the finite element method to calculate the stress distribution at the interface between the abrasive grain and the material:

[0082] ;

[0083] Among them, is the stress, is the applied acting force, is the contact area, which is automatically calculated by the finite element software;

[0084] Identify the stress concentration area through the stress distribution result, identify the wear mode, and adjust the composition and structure of the drill bit material according to the wear mechanism:

[0085] If adhesive wear is identified, increase the lubricant coating;

[0086] If abrasive wear is identified, improve it by adjusting the hardness and toughness of the material;

[0087] If fatigue wear is identified, optimize the grain size and grain boundary structure of the material.

[0088] Using molecular dynamics software to construct a microscopic structure model of the drill bit material and applying the finite element method to analyze the stress distribution can deeply understand the interaction at the interface between the abrasive grain and the material. This microscopic-level analysis can accurately identify the wear mode and guide the targeted adjustment of the composition and structure of the drill bit material to improve durability. Different from traditional macroscopic analysis methods, this method reveals the internal stress state and wear mechanism of the material in more detail through microscopic structure modeling. In this way, a more precise material improvement plan can be formulated, improving the wear resistance and service life of the drill bit, reducing frequent replacement and processing downtime; in the overall plan, this step ensures the optimal configuration of the drill bit material, directly affecting the efficiency and quality of processing. Through precise material adjustment, wear is reduced, processing stability is improved, providing a solid foundation for the temperature and pressure control in the subsequent steps and the final process evaluation, and overall improving the processing performance.

[0089] Step 3: Real-time collect the temperature data and pressure data on the chip removal path, set temperature and pressure thresholds based on material properties, and when the thresholds are exceeded, the system automatically adjusts the processing speed and chip removal path;

[0090] In this embodiment, the specific logic for the system to automatically adjust the processing speed and chip removal path is as follows: Use a thermocouple to monitor the temperature on the chip removal path in real time, use a pressure sensor to monitor the pressure in the processing area in real time, set the thresholds of temperature and pressure based on the heat resistance and compressive strength of the material, and establish the following temperature model and pressure model:

[0091] ;

[0092] Wherein, is the temperature change, is the current temperature, represents the temperature threshold, is the pressure change, is the current pressure, represents the pressure threshold;

[0093] If , then reduce the processing speed and change the cooling strategy;

[0094] If , then adjust the processing speed and change the chip removal path.

[0095] By real-time collecting the temperature and pressure data on the chip removal path and automatically adjusting according to the thresholds set based on material properties, the thermal effect and pressure change in the processing can be effectively controlled. This dynamic adjustment helps to maintain the optimal processing conditions, reduce thermal damage and material deformation. Compared with the traditional method, this step can quickly respond to the fluctuations of temperature and pressure through real-time monitoring and adjustment, preventing exceeding the safe range. This improves the safety and accuracy of processing, reduces the scrap rate, and extends the service life of the equipment. In the overall solution, this step ensures the stability of the physical conditions in the processing, directly affecting the quality and efficiency of processing. By optimizing the temperature and pressure, unnecessary energy consumption and material damage are reduced, providing accurate data support for the final process evaluation and improving the overall processing performance.

[0096] Step 4: Conduct process evaluation through data and statistical models and provide optimization suggestions;

[0097] In this embodiment, the specific logic for conducting process evaluation through data and statistical models and providing optimization suggestions is as follows: Collect vibration, temperature, pressure and processing speed data, preprocess the data to remove noise and outliers, and use multiple linear regression to analyze the influence of each parameter on the processing quality:

[0098] ;

[0099] Among them, is the processing quality index, is the vibration frequency, is the amplitude, is the temperature, is the pressure, is the regression coefficient, is the error term.

[0100] Regression coefficient and error term The determination process specifically includes:

[0101] Collect a set of sample data with known processing quality indicators, and the process parameters in these sample data: vibration frequency, amplitude, temperature, and pressure values are different from each other, forming a data set;

[0102] Combine the sample data with known processing quality indicators with the corresponding process parameters to form a data matrix; rows represent samples, and columns represent process parameters;

[0103] Set the target variable as the processing quality indicator of the sample, and the process parameters as independent variables;

[0104] Select a linear regression model for modeling, and the regression model is expressed as:

[0105] ;

[0106] Among them, is the target variable, is the vibration frequency, is the amplitude, is the temperature, is the pressure, , , , are the regression coefficients to be obtained, is the error term;

[0107] Fit the model by the least squares method, minimize the squared error between the predicted value and the actual value, and the objective function to be minimized is as follows:

[0108] ;

[0109] Among them, is the processing quality index of the th sample, is the processing quality index predicted by the model, is the total number of samples;

[0110] Through linear regression analysis, the regression coefficients of each process parameter are calculated and the error term , directly execute this process using Python and return these parameters. According to the output of the regression model, evaluate the influence of process parameters: vibration frequency, amplitude, temperature, and pressure on the machining quality, and select the best combination that can maximize the machining quality index according to the model.

[0111] Process evaluation is carried out through data and statistical models and optimization suggestions are provided, which can systematically analyze and summarize the data of the machining process and identify potential improvement spaces. This step can accurately evaluate the influence of each process parameter on the machining quality, so as to provide specific optimization strategies. Compared with the traditional optimization method mainly based on experience, this step uses a data-driven analysis method to ensure the scientificity and reliability of the evaluation results. This not only improves the accuracy of the optimization suggestions, but also can adapt to different machining conditions faster and improve the overall process efficiency. In the overall solution, step 4 provides a summary and feedback mechanism for the implementation effects of the previous steps. Through systematic process evaluation and optimization suggestions, the stability and quality of machining can be further improved, providing data support and direction guidance for continuous improvement and innovation.

[0112] Please refer to Figure 2 , a rotary ultrasonic hole machining analysis system based on the finite element simulation method, including:

[0113] An intelligent vibration parameter optimization module, used to collect the vibration data of the machining equipment in real time and adopt an adaptive algorithm to realize the real-time adjustment of vibration parameters, where the vibration parameters are vibration frequency and amplitude;

[0114] A multi-scale wear analysis module, used to construct a microscopic structure model of the drill bit material using molecular dynamics software, calculate the stress distribution at the interface between the abrasive and the material using the finite element method, analyze the stress results, identify the wear mode, and adjust the composition and structure of the drill bit material according to the wear mechanism;

[0115] An intelligent chip removal optimization module, used to collect the temperature data and pressure data on the chip removal path in real time, set temperature and pressure thresholds based on material properties, and when the thresholds are exceeded, the system automatically adjusts the machining speed and chip removal path;

[0116] A comprehensive process evaluation module, used to carry out process evaluation through data and statistical models and provide optimization suggestions.

[0117] The above formulas are all dimensionless and take their numerical values for calculation. The formula is a formula obtained by software simulation of collecting a large amount of data to get the closest to the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0118] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art will realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They may be located in one place or distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] As described above, only the specific embodiments of the present application are provided, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application.

Claims

1. A rotary ultrasonic hole machining analysis method based on finite element simulation method, characterized in that: The specific steps include: Step 1: collect vibration data of processing equipment in real time, and use adaptive algorithm to achieve real-time adjustment of vibration parameters, wherein the vibration parameters are vibration frequency and amplitude; In a vibration system, the core parameters include amplitude A, frequency f and phase difference Φ: Among them, the maximum displacement of the system during vibration, that is, the amplitude A, is obtained through the vibration sensor; Apply fast Fourier transform to extract frequency features from vibration data: perform fast Fourier transform on each time-series vibration signal segment to obtain frequency features corresponding to each time-series vibration signal segment, wherein the frequency features include time, fundamental frequency and harmonics; construct a data chain with the frequency features corresponding to the time-series vibration signal segment, i.e., a data chain composed of time, fundamental frequency and harmonics, wherein the pointer between time and fundamental frequency is a timestamp index, which points to the fundamental frequency value at a specific moment, and the pointer between fundamental frequency and harmonics is a fundamental frequency index, which points to the harmonic value at a specific fundamental frequency; The phase difference Φ is determined by experimental calibration method; Adaptive algorithm is used with the goal of minimizing vibration: Among them, J is the objective function, representing the deviation of vibration, A(t) represents the amplitude at time t, and A desired is the expected amplitude, determined experimentally and empirically; Apply the gradient descent method to adjust the vibration parameters: Where A(t+1) represents the amplitude at time t+1, A(t) represents the amplitude at time t, α is the learning rate, which is selected through debugging to ensure the balance between convergence speed and stability, and J is the objective function, which represents the deviation of the vibration; The system dynamically adjusts the vibration parameters based on feedback to ensure optimal operation. The adjustment formula is: Among them, A new is the adjusted amplitude, A current is the current amplitude, K d is the damping coefficient, calibrated by experiment, and is used to influence the speed at which vibrations are attenuated. is the rate of change of amplitude with time; Step 2: Use molecular dynamics software to build a microstructure model of the drill bit material, apply the finite element method to calculate the stress distribution at the interface between the abrasive particles and the material, analyze the stress results, identify the wear mode, and adjust the drill bit material composition and structure according to the wear mechanism; Step 3: Collect temperature and pressure data on the chip removal path in real time, set temperature and pressure thresholds based on material properties, and when the thresholds are exceeded, the system automatically adjusts the processing speed and chip removal path; Step 4: Evaluate the process and provide optimization recommendations through data and statistical models.

2. The rotary ultrasonic hole machining analysis method based on finite element simulation method according to claim 1 is characterized in that: The specific logic of adjusting the composition and structure of the drill bit material according to the wear mechanism is as follows: using molecular dynamics software to construct a microstructure model of the drill bit material, defining the potential energy function of the interaction between atoms to simulate the physical and chemical properties of the material, and simulating the behavior of the material under different temperatures and pressures to obtain a stable microstructure. The microstructure model is imported using finite element software ABAQUS, boundary conditions and load conditions are set, and a force F is applied according to actual use. The stress distribution of the interface between the abrasive particles and the material is calculated using the finite element method: Where σ is stress, F is the applied force, and A is the contact area, which is automatically calculated by the finite element software; Identify stress concentration areas through stress distribution results, identify wear patterns, and adjust the drill bit material composition and structure according to the wear mechanism: If adhesive wear is detected, add lubricant coating; If abrasive wear is identified, it can be improved by adjusting the hardness and toughness of the material; If fatigue wear is identified, the grain size and grain boundary structure of the material are optimized.

3. The rotary ultrasonic hole machining analysis method based on finite element simulation method according to claim 1 is characterized in that: The system automatically adjusts the processing speed and chip removal path based on the following specific logic: using thermocouples to monitor the temperature on the chip removal path in real time, using pressure sensors to monitor the pressure in the processing area in real time, setting the temperature and pressure thresholds based on the heat resistance and compressive strength of the material, and establishing the following temperature model and pressure model: ΔT=T current -T threshold ΔP=P current -P threshold Where ΔT is the temperature change, T current is the current temperature, T threshold represents the temperature threshold, ΔP is the pressure change, P current is the current pressure, P threshold Indicates the pressure threshold; If ΔT>0, reduce the processing speed and change the cooling strategy; If ΔP>0, adjust the processing speed and change the chip removal path.

4. The rotary ultrasonic hole machining analysis method based on finite element simulation method according to claim 1 is characterized in that: The specific logic of using data and statistical models to evaluate the process and provide optimization suggestions is as follows: collect vibration, temperature, pressure and processing speed data, pre-process the data, remove noise and outliers, and use multivariate linear regression to analyze the impact of each parameter on processing quality: Y=β1f+β2A+β3T+β4P+∈ Among them, Y is the processing quality index, f is the vibration frequency, A is the amplitude, T is the temperature, P is the pressure, β i，i＝1，2，3，4 is the regression coefficient, and ∈ is the error term.

5. The rotary ultrasonic hole machining analysis method based on finite element simulation method according to claim 1 is characterized in that: Regression coefficient β i，i＝1，2，3，4 The determination process of and error term ∈ specifically includes: Collect a set of sample data with known processing quality indicators, and the process parameters in these sample data: vibration frequency, amplitude, temperature, and pressure values ​​are different from each other to form a data set; Combine the sample data of known processing quality indicators with the corresponding process parameters to form a data matrix; the rows represent samples and the columns represent process parameters; The target variable is set as the processing quality index of the sample, and the process parameters are used as independent variables; Select the linear regression model for modeling. The regression model is expressed as: Y=β1f+β2A+β3T+β4P+∈ Among them, Y is the target variable, f is the vibration frequency, A is the amplitude, T is the temperature, P is the pressure, β1, β2, β3, β4 are the regression coefficients to be obtained, and ∈ is the error term; The model is fitted by the least squares method to minimize the square error between the predicted value and the actual value. The minimized objective function is as follows: Among them, Y j is the processing quality index of the jth sample, is the processing quality index predicted by the model, and m is the total number of samples; Through linear regression analysis, the regression coefficient β of each process parameter is calculated. i，i＝1，2，3，4 and error term ∈, use python to directly execute this process and return these parameters. According to the output of the regression model, the influence of process parameters: vibration frequency, amplitude, temperature, and pressure on the processing quality is evaluated, and according to the model, the best combination that can maximize the processing quality index is selected.

6. The rotary ultrasonic hole processing analysis system based on finite element simulation method is characterized by: The rotary ultrasonic hole machining analysis system based on the finite element simulation method is used to execute the rotary ultrasonic hole machining analysis method based on the finite element simulation method according to any one of claims 1 to 5, comprising: An intelligent vibration parameter optimization module is used to collect vibration data of processing equipment in real time and use an adaptive algorithm to achieve real-time adjustment of vibration parameters, which are vibration frequency and amplitude; Multi-scale wear analysis module, which is used to build a microstructure model of drill material using molecular dynamics software, calculate the stress distribution of the interface between abrasive particles and materials using finite element method, analyze stress results, identify wear patterns, and adjust the composition and structure of drill material according to the wear mechanism; Intelligent chip removal optimization module, which is used to collect temperature and pressure data on the chip removal path in real time, and set temperature and pressure thresholds based on material properties. When the thresholds are exceeded, the system automatically adjusts the processing speed and chip removal path; Comprehensive process evaluation module, used to evaluate the process and provide optimization suggestions through data and statistical models.

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