A nonlinear coupling control method, device and medium for piezoelectric dispensing

Abstract

The application discloses a nonlinear coupling control method and device for piezoelectric dispensing and a medium, and relates to the technical fields of precise dispensing and motion control. The method comprises the following steps: acquiring structural parameters of a dispensing system, and establishing a nonlinear dynamics model of the dispensing system; performing parameter scanning and sensitivity index calculation on the structural parameters through the nonlinear dynamics model to obtain key parameters whose responses to the dispensing system are greater than a preset threshold; constructing a multi-objective optimization function with the maximum displacement, the maximum speed and the vibration amplitude of a striker as performance targets; building a co-simulation platform, importing the key parameters into the co-simulation platform, and obtaining real-time response energy of the striker; adjusting weight coefficients of the multi-objective optimization function; and performing iterative simulation and iterative adjustment through an optimization algorithm to obtain an optimal parameter combination of the key parameters. The application realizes the fusion of nonlinear dynamics modeling and a co-simulation optimization platform through the above method, and improves the motion performance of the striker and the overall stability of the dispensing system.

Description

Technical Field

[0001] This application relates to the field of precision dispensing and motion control technology, and in particular to a nonlinear coupling control method, device and medium for piezoelectric dispensing. Background Technology

[0002] In fields such as microelectronic packaging, high-end displays, and semiconductor manufacturing, the requirements for precision, speed, and stability in dispensing processes are gradually increasing. Among the many types of dispensing valves, piezoelectric dispensing valves have become one of the mainstream choices due to their advantages of high speed, high precision, and miniaturization. The dispensing pin, as the core actuator in the dispensing system, directly affects the dispensing volume, dispensing consistency, and system reliability due to its motion characteristics.

[0003] Current research often employs linear analysis methods, such as a one-degree-of-freedom mass-spring-damping model, to describe the motion of the firing pin. However, this type of modeling is difficult to characterize the nonlinear features of the system and amplifies factors such as structural coupling, structural damping variations, and nonlinear stiffness.

[0004] Traditional PID control algorithms and preset parameter control strategies have significant shortcomings in terms of response speed and robustness, making it difficult to balance the system's dynamic adaptability and control accuracy.

[0005] In terms of control and optimization, current mainstream methods mostly focus on single-objective or local parameter adjustments, lacking a coordinated control mechanism among multiple parameters. Existing solutions often rely on single-platform analysis, such as using only MATLAB for post-processing or static structural analysis based on finite element platforms like ANSYS, lacking cross-platform dynamic linkage capabilities, especially in areas involving real-time performance feedback and self-updating of control strategies. Current technologies lack a complete solution path for cross-platform linkage and multi-parameter coupled solutions in the dynamic modeling and control optimization of dispensing systems, indicating that there is still room for further improvement in this field.

[0006] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0007] In existing technologies for high-frequency, high-precision dispensing scenarios, inaccurate modeling, control lag, and a lack of multi-parameter collaborative optimization capabilities result in insufficient dispensing accuracy and stability. Summary of the Invention

[0008] This application provides a nonlinear coupling control method, device, and medium for piezoelectric dispensing, which can solve the problems of inaccurate modeling, control lag, and lack of multi-parameter collaborative optimization capabilities in high-frequency and high-precision dispensing scenarios in the prior art, resulting in insufficient dispensing accuracy and stability.

[0009] In a first aspect, embodiments of this application provide a nonlinear coupling control method for piezoelectric dispensing. The method includes: acquiring the structural parameters of the dispensing system and establishing a nonlinear dynamic model of the dispensing system; using the nonlinear dynamic model, performing parameter scanning and sensitivity index calculation on the structural parameters to obtain key parameters whose response to the dispensing system is greater than a preset threshold; constructing a multi-objective optimization function with the maximum displacement, maximum velocity, and vibration amplitude of the ejector pin as performance objectives; building a co-simulation platform, importing the key parameters into the co-simulation platform, and obtaining the real-time response energy of the ejector pin based on the co-simulation platform; adjusting the weight coefficients of the multi-objective optimization function based on the real-time response energy; and performing iterative simulation and iterative adjustment through an optimization algorithm to obtain the optimal parameter combination of the key parameters.

[0010] In one implementation of this application, the structural parameters of the dispensing system are obtained, and a nonlinear dynamic model of the dispensing system is established. Specifically, this includes: obtaining the structural parameters of the piezoelectric dispensing system, including the piezoelectric stack, lever amplification mechanism, striker, preload spring, and striker spring; modeling the system based on the Lagrange equation, according to the piezoelectric stack, lever amplification mechanism, striker, preload spring, and striker spring, and deriving the motion differential equation of the dispensing system through the Lagrange equation; introducing a nonlinear stiffness term into the potential energy term of the motion differential equation to characterize the nonlinear change in the stiffness of the striker; and introducing a nonlinear damping term into the damping term of the motion differential equation to characterize the nonlinear damping effect of the system, thereby obtaining a nonlinear dynamic model.

[0011] In one implementation of this application, a nonlinear dynamics model is used to perform parameter scanning and sensitivity index calculation on the structural parameters to obtain key parameters whose response to the dispensing system is greater than a preset threshold. Specifically, this includes: assigning values ​​to the structural parameters using the control variable method, calling the nonlinear dynamics model for simulation, and obtaining the corresponding impactor response data; based on the impactor response data, using a contribution allocation algorithm to calculate the marginal contribution value of the structural parameters to the output response; using a global sensitivity analysis algorithm to calculate the sensitivity index of the structural parameters; and selecting key parameters whose overall importance is higher than a preset threshold based on the marginal contribution value and the sensitivity index.

[0012] In one implementation of this application, a co-simulation platform is built, key parameters are imported into the co-simulation platform, and the real-time response energy of the impact pin is obtained based on the co-simulation platform. Specifically, this includes: using the co-simulation platform, which includes a simulation environment and a computing environment; solving a nonlinear dynamic model through the simulation environment to obtain the real-time displacement, velocity data, and vibration amplitude of the impact pin; importing a multi-objective optimization function through the computing environment, executing an optimization algorithm, and calculating the real-time response energy based on the displacement, velocity data, and vibration amplitude obtained from the simulation environment.

[0013] In one implementation of this application, the weight coefficients of the multi-objective optimization function are adjusted according to the real-time response energy. Specifically, this includes: calculating the real-time response energy based on the real-time displacement, real-time velocity, and real-time vibration amplitude of the striker transmitted back from the computing environment; calculating the sum of the real-time response energies and calculating the proportion of the real-time response energy of each objective respectively, so that the weight coefficients are dynamically adjusted.

[0014] In one implementation of this application, after obtaining the optimal parameter combination of key parameters through iterative simulation and adjustment using an optimization algorithm, the method further includes: constructing an experimental verification platform, which includes a high-frequency driver, a high-precision vibration measurement system, and a dispensing system prototype; configuring the optimal parameter combination into the dispensing system prototype, applying a preset driving voltage through the high-frequency driver, and collecting the actual response data of the impact pin using the high-precision vibration measurement system; and comparing the actual response data with the preset target value of the multi-objective optimization function and the response data before optimization to verify the effectiveness of the optimal parameter combination.

[0015] In one implementation of this application, the optimal parameter combination for key parameters is obtained through iterative simulation and adjustment using an optimization algorithm. Specifically, this includes: using a genetic algorithm to initialize and generate candidate parameter combinations for key parameters; executing a co-simulation platform on the candidate parameter combinations and calculating the corresponding multi-objective optimization function values ​​as fitness; performing selection, crossover, and mutation operations on the population based on the fitness to generate a new generation population until the convergence condition is met, and taking the candidate parameter combination with the best fitness as the optimal parameter combination.

[0016] In one implementation of this application, the candidate parameter combination with the best fitness is taken as the optimal parameter combination until the convergence condition is met. Specifically, this includes: calculating the performance evaluation value of the current candidate parameter combination based on the adjusted multi-objective optimization function; recording the optimal performance evaluation value in historical iterations and monitoring the trend of change; and determining that the convergence condition is met if the improvement of the optimal performance evaluation value in multiple consecutive iterations is less than the preset tolerance.

[0017] Secondly, embodiments of this application also provide a nonlinear coupling control device for piezoelectric dispensing, the device including at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: perform any step of a nonlinear coupling control method for piezoelectric dispensing.

[0018] Thirdly, embodiments of this application also provide a non-volatile computer storage medium for nonlinear coupling control of piezoelectric dispensing, storing computer-executable instructions, wherein the computer-executable instructions are configured to execute any one of the steps of a nonlinear coupling control method for piezoelectric dispensing.

[0019] This application provides a nonlinear coupling control method, device, and medium for piezoelectric dispensing. It incorporates a Duffing nonlinear model to simultaneously capture structural stiffness nonlinearity and hysteresis effects. The high-precision dynamic model, including nonlinear stiffness and damping terms, significantly improves the prediction accuracy of the system under high-speed conditions. Parameter sensitivity analysis accurately identifies key optimization variables, narrowing the optimization search space and improving efficiency. A dynamic weight adjustment mechanism based on real-time response energy is introduced, achieving coordinated optimization and dynamic balance of multiple objectives such as displacement, velocity, and vibration amplitude. Finally, by using COMSOL–MATLAB in conjunction with a co-simulation platform for iterative optimization, the optimal parameter combination is obtained and applied to real-time control, effectively improving the control accuracy, response speed, and operational stability of the dispensing system. This method is suitable for high-frequency, low-volume, and high-consistency dispensing conditions and possesses good scalability and engineering applicability. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0021] Figure 1 A flowchart illustrating a nonlinear coupling control method for piezoelectric dispensing provided in this application embodiment;

[0022] Figure 2 A schematic diagram of a dispensing system for a nonlinear coupling control method for piezoelectric dispensing, provided in an embodiment of this application;

[0023] Figure 3(a) is a simplified side view of a nonlinear coupling control method for piezoelectric dispensing provided in an embodiment of this application;

[0024] Figure 3(b) is a simplified front view of a nonlinear coupling control method for piezoelectric dispensing provided in an embodiment of this application;

[0025] Figure 4 A schematic diagram of a nonlinear dynamic model for a nonlinear coupling control method for piezoelectric dispensing provided in an embodiment of this application;

[0026] Figure 5 A schematic diagram of a lever-type displacement amplification mechanism for a nonlinear coupling control method for piezoelectric dispensing provided in an embodiment of this application;

[0027] Figure 6 A flowchart illustrating the parameter identification and sensitivity analysis process of a nonlinear coupling control method for piezoelectric dispensing provided in this application embodiment;

[0028] Figure 7 A schematic diagram of a co-simulation platform for a nonlinear coupling control method for piezoelectric dispensing provided in an embodiment of this application;

[0029] Figure 8 A diagram showing the relationship between MATLAB and COMSOL for a nonlinear coupling control method for piezoelectric dispensing provided in an embodiment of this application;

[0030] Figure 9 A comparison diagram of the firing pin motion before and after optimization of a nonlinear coupling control method for piezoelectric dispensing provided in an embodiment of this application;

[0031] Figure 10 A schematic diagram of the overall framework of a nonlinear coupling control method for piezoelectric dispensing provided in this application embodiment;

[0032] Figure 11 A schematic diagram of the internal structure of a nonlinear coupling control method device for piezoelectric dispensing provided in an embodiment of this application;

[0033] In the diagram: 1. Support; 2. Piezoelectric stack; 3. Displacement amplification mechanism; 4. Glue inlet pipe; 5. Strike pin; 6. Strike pin spring; 7. Nozzle. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0035] This application provides a nonlinear coupling control method, device, and medium for piezoelectric dispensing, which solves the problems of inaccurate modeling, control lag, and lack of multi-parameter collaborative optimization capabilities in the prior art for high-frequency and high-precision dispensing scenarios, resulting in insufficient dispensing accuracy and stability.

[0036] The technical solutions proposed in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0037] Figure 1 This is a flowchart illustrating a nonlinear coupling control method for piezoelectric dispensing, provided as an embodiment of this application. Figure 1 As shown in the figure, the nonlinear coupling control method for piezoelectric dispensing provided in this application embodiment specifically includes the following steps:

[0038] Step 10: Obtain the structural parameters of the dispensing system and establish a nonlinear dynamic model of the dispensing system.

[0039] In this step, such as Figure 2 As shown, the dispensing valve system mainly consists of a bracket 1, a piezoelectric stack 2, a displacement amplification mechanism 3, a dispensing channel 4, a striker 5, a pre-tension spring and a striker spring 6, and a nozzle 7. The piezoelectric stack 2 serves as the system's drive source, generating micro-displacement under external voltage excitation. The displacement amplification mechanism 3 outputs angular displacement, which in turn drives the striker 5 to move at high speed along the axial direction, completing the precise dispensing operation. The pre-tension spring and striker spring 6 act as reset mechanisms, ensuring the striker returns to its initial position after the dispensing operation. Each component in the system works in concert, with the driving force provided by the piezoelectric actuator and the elastic restoring force of the springs working together to achieve a precise dispensing process. Specifically: the piezoelectric drive structure has a mass of... With piezoelectric stacks as the core, it applies a driving voltage Under the action, a small expansion and contraction displacement is generated. It provides the driving energy source for the entire dispensing process; the displacement amplification mechanism has a mass of The moment of inertia is A lever-type structure is used to amplify the displacement of the piezoelectric stack. Magnified to the displacement of the firing pin end This achieves dispensing strokes from nanometers to micrometers; the impact pin actuator moves along a linear trajectory under the drive of the amplification mechanism, and its end completes targeted dispensing through the nozzle, with a mass of... Elastic reset system: includes a preload spring and a striker spring, which provide initial preload force in the initial state and restore force after the striker moves to ensure the striker returns to its original position; Guide and support structure: consists of guide rails and brackets, which provide constraint and support for the striker along the set direction to ensure the striker moves in the specified direction; Auxiliary structure: structures with a weak impact on the system dynamics, such as glue inlet pipes and encapsulation shells, are not included in the modeling scope to improve modeling efficiency and solution speed.

[0040] In this embodiment, some structures in the system have been appropriately simplified to simplify the model and improve simulation efficiency. The simplified model is shown in Figures 3(a) and 3(b), which includes the core components and transmission path, and can accurately reflect the main dynamic behavior of the actual dispensing valve.

[0041] As an optional embodiment, the structural parameters of the dispensing system are obtained, and a nonlinear dynamic model of the dispensing system is established, specifically including: Step 101: Obtain the structural parameters of the piezoelectric dispensing system, including the piezoelectric stack, lever amplification mechanism, striker, preload spring, and striker spring; Step 102: Based on the Lagrange equation, model the piezoelectric stack, lever amplification mechanism, striker, preload spring, and striker spring, and derive the motion differential equation of the dispensing system through the Lagrange equation.

[0042] In this step, to accurately describe the dynamic behavior of the piezoelectrically driven striking pin motion system, a nonlinear dynamic model of the system is established from an energy perspective using the Lagrange equation. For example... Figure 4 As shown (Duffing specifically refers to a mathematical model type that describes nonlinear stiffness characteristics), the system includes structural components such as piezoelectric stacks, lever amplification mechanisms, strikers, and springs, exhibiting multi-degree-of-freedom coupling characteristics and significant nonlinear behavior.

[0043] Establish the generalized kinetic and potential energy expressions for the system, and construct the Lagrange function:

[0044] ;

[0045] Given the Lagrange equation:

[0046]

[0047] The Lagrange equation applicable to this system is:

[0048]

[0049] like Figure 5 As shown, based on the transmission properties of levers:

[0050]

[0051] Among them, the total kinetic energy of the system This includes the kinetic energy of the piezoelectric ceramic, the rotational kinetic energy of the amplification mechanism, and the kinetic energy of the striker:

[0052]

[0053]

[0054] Step 103: Introduce a nonlinear stiffness term into the potential energy term of the motion differential equation to characterize the nonlinear change in the stiffness of the firing pin;

[0055] In this step, potential energy The system comprises the elastic potential energy of the piezoelectric ceramic, the nonlinear Duffing-type elastic potential energy of the striker spring, and the elastic potential energy of the preload spring. Considering the large displacement amplitude of the striker spring during operation, which easily exhibits nonlinear characteristics such as stiffness softening, a Duffing-type nonlinear stiffness is introduced in the modeling. The preload spring, with a much higher stiffness than the striker spring, a smaller working displacement, and a stable elastic range, can be approximated as a linear spring to simplify the model calculation. The total potential energy of the system is as follows:

[0056]

[0057] in , , These represent the initial compression amounts of the piezoelectric ceramic, the preload spring, and the striker spring, respectively. , , These are the linear stiffness coefficients of the piezoelectric ceramic, the preload spring, and the striker spring, respectively. denoted as the nonlinear stiffness coefficient of the striking pin spring.

[0058] Step 104: Introduce a nonlinear damping term (cubic polynomial type) into the damping term of the motion differential equation to characterize the nonlinear damping effect of the system and obtain a nonlinear dynamic model.

[0059] In this step, the dissipation function This includes the nonlinear viscous damping of the piezoelectric structure and the resistance experienced by the firing pin, the effects of plastic friction and fluid damping, and introduces the Rayleigh dissipation function:

[0060]

[0061] in, and These are the equivalent linear damping coefficient and the nonlinear damping coefficient of the piezoelectric ceramic, respectively. and These are the equivalent linear damping coefficient and the nonlinear damping coefficient, respectively, for the motion of the firing pin.

[0062] For dissipation function about Differentiate:

[0063]

[0064] in , ;

[0065] Total external driving force on the system It mainly consists of a piezoelectric voltage driving term and a Gaussian pulse perturbation term, used to simulate voltage driving force and environmental disturbances:

[0066]

[0067] in, The piezoelectric drive coefficient, For the driving voltage function, The amplitude coefficient, These are times when the environment is most susceptible to disturbance. This is the pulse width parameter.

[0068] Introducing each part of the energy into the Lagrange equations:

[0069]

[0070] in:

[0071]

[0072] In the model of this application embodiment, the motion coordinates of the firing pin It is established with its static equilibrium position, reached under the combined action of piezoelectric ceramic and spring preload, as the origin. At this equilibrium position, the initial voltage... Initial compression and The resulting resultant force is in equilibrium. Therefore, the initial condition for this model is the initial displacement. First Impression of Speed First Impression of Acceleration .

[0073] Step 20: Using a nonlinear dynamic model, perform parameter scanning and sensitivity index calculation on the structural parameters to obtain key parameters that respond to the dispensing system with a value greater than a preset threshold.

[0074] As an optional embodiment, a nonlinear dynamics model is used to perform parameter scanning and sensitivity index calculation on the structural parameters to obtain key parameters whose response to the dispensing system is greater than a preset threshold. Specifically, this may include: Step 201: Assigning values ​​to the structural parameters using the control variable method, calling the nonlinear dynamics model for simulation, and obtaining the corresponding impactor response data; Step 202: Based on the impactor response data, using a contribution allocation algorithm to calculate the marginal contribution value of the structural parameters to the output response; Step 203: Using a global sensitivity analysis algorithm to calculate the sensitivity index of the structural parameters; Step 204: Based on the marginal contribution value and the sensitivity index, selecting key parameters whose overall importance is higher than a preset threshold.

[0075] In this step, based on the established nonlinear disturbance dynamics model, this application starts from the actual controllable parameters, identifies the key variables involved in the system, and systematically analyzes the influence of each parameter on the dynamic performance of the firing pin to screen out the variables most sensitive to the system response, providing a basis for variable selection for subsequent optimization algorithms. The threshold is determined through orthogonal experiments and variance analysis, and parameters with an importance greater than 80% can be selected as key parameters.

[0076] By analyzing the parameters involved in the dynamic model, the stiffness of the piezoelectric stack is determined. Preload spring stiffness Stiffness of the firing pin spring Magnification Piezoresistive damping coefficient , ... The controlled variable method was used to scan the data, changing the values ​​of individual parameters and recording the changes in output indicators such as pin displacement, velocity, and vibration amplitude to preliminarily assess the influence of each parameter.

[0077] Based on parameter scanning, Shapley values ​​and the Sobol method are introduced for multivariate sensitivity analysis to quantitatively characterize the marginal contribution and interaction effect of each parameter on the system output response. The first- and second-order sensitivity indices for each parameter are also calculated. The detailed process is as follows: Figure 6 As shown.

[0078] Step 30: Construct a multi-objective optimization function with the maximum displacement, maximum velocity, and vibration amplitude of the firing pin as performance objectives.

[0079] In this step, to improve the overall performance of the dispensing valve system under different operating conditions, a multi-objective optimization function is constructed with the motion indicators of the ejector pin as the performance targets. The optimization objectives include: maximum displacement X, maximum velocity V, and vibration amplitude A, which correspond to the accuracy, response speed, and motion stability requirements in the dispensing process, respectively. Given that the three performance indicators—displacement, velocity, and vibration amplitude—have different dimensions and significantly different orders of magnitude, normalization is employed to ensure the comparability and numerical balance of each indicator within the unified optimization function. The maximum displacement, maximum velocity, and maximum acceleration of the ejector pin are divided by their corresponding reference values. , , The performance target values, pre-set according to specific dispensing process requirements, are converted into dimensionless performance indicators within a unit interval. The normalized form is as follows:

[0080]

[0081] Based on the weighted summation strategy, the multi-objective optimization function is constructed as follows:

[0082]

[0083] Step 40: Build a co-simulation platform, import the key parameters into the co-simulation platform, and obtain the real-time response energy of the firing pin based on the co-simulation platform.

[0084] As an optional embodiment, a co-simulation platform is built, key parameters are imported into the co-simulation platform, and the real-time response energy of the impact pin is obtained based on the co-simulation platform. Specifically, it may include: Step 401: Based on the co-simulation platform, which includes a simulation environment and a computing environment; solving the nonlinear dynamic model through the simulation environment to obtain the real-time displacement, velocity data, and vibration amplitude of the impact pin; Step 402: Importing a multi-objective optimization function through the computing environment, executing the optimization algorithm, and calculating the real-time response energy based on the displacement, velocity data, and vibration amplitude obtained from the simulation environment.

[0085] In this step, to obtain the optimal combination of structural parameters, this application designs an integrated parameter optimization and performance simulation system based on the COMSOL Multiphysics and MATLAB co-simulation platform. This system can read parameters in real time and provide feedback on simulation results for dynamic optimization. During the optimization process, the dynamic weights in the objective function are adjusted in conjunction with the simulation data to ultimately achieve the optimal balance of comprehensive performance. The specific process is as follows: Figure 7 As shown.

[0086] First, a simplified multibody dynamics model of the dispensing system was constructed in the COMSOL Multiphysics platform, taking into account factors such as nonlinear springs, piezoelectric ceramic drive, and friction. Using the COMSOL solver, key information such as the maximum displacement, maximum velocity, and vibration amplitude of the ejector pin within one cycle was calculated and output in real time.

[0087] Then, multi-objective optimization was performed using the MATLAB platform. Data transfer and result feedback between COMSOL and MATLAB were achieved through the COMSOL and MATLAB LiveLink interface. In each round of optimization, MATLAB automatically inputs the optimization parameters into the COMSOL model and calls COMSOL to run the simulation. The simulation results are extracted and returned to MATLAB. MATLAB updates the objective function value based on the obtained performance indicators and drives the optimization algorithm to continue iterating until the convergence condition is met. The specific connection between MATLAB and COMSOL is as follows: Figure 8 As shown.

[0088] Step 50: Adjust the weight coefficients of the multi-objective optimization function based on the real-time response energy.

[0089] As an optional embodiment, the weight coefficients of the multi-objective optimization function are adjusted according to the real-time response energy. Specifically, this may include: Step 501: Calculate the real-time response energy based on the real-time displacement, real-time velocity, and real-time vibration amplitude of the striker transmitted back from the computing environment; Step 502: Calculate the sum of the real-time response energy and calculate the proportion of the real-time response energy of each objective respectively, so that the weight coefficients are dynamically adjusted.

[0090] In this step, , , This represents the weighting coefficient of each objective item, corresponding to each performance metric.

[0091] The response energy is defined as:

[0092]

[0093]

[0094]

[0095] The dynamic weights of each objective function are calculated based on the energy percentage:

[0096]

[0097] Step 60: Perform iterative simulation and adjustment using optimization algorithms to obtain the optimal parameter combination for key parameters.

[0098] As an optional embodiment, the optimal parameter combination of key parameters is obtained through iterative simulation and adjustment using an optimization algorithm. Specifically, it may include: Step 601: Using a genetic algorithm, candidate parameter combinations of key parameters are generated; Step 602: For the candidate parameter combinations, a co-simulation platform is executed and the corresponding multi-objective optimization function value is calculated as the fitness; Step 603: Based on the fitness, selection, crossover, and mutation operations are performed on the population to generate a new generation of population until the convergence condition is met, and the candidate parameter combination with the best fitness is taken as the optimal parameter combination.

[0099] As an optional embodiment, until the convergence condition is met, the candidate parameter combination with the best fitness is taken as the optimal parameter combination. Specifically, it may include: Step 6031: Calculate the performance evaluation value of the current candidate parameter combination based on the adjusted multi-objective optimization function; Step 6032: Record the optimal performance evaluation value in the historical iterations and monitor the changing trend; Step 6033: If the improvement of the optimal performance evaluation value in multiple consecutive iterations is less than the preset tolerance, it is determined that the convergence condition is met.

[0100] As an optional embodiment, after obtaining the optimal parameter combination of key parameters through iterative simulation and adjustment using optimization algorithms, the method may further include: Step 70: Constructing an experimental verification platform, which includes a high-frequency driver, a high-precision vibration measurement system, and a dispensing system prototype; Step 80: Configuring the optimal parameter combination into the dispensing system prototype, applying a preset driving voltage through the high-frequency driver, and collecting the actual response data of the impact pin using the high-precision vibration measurement system; Step 90: Comparing the actual response data with the preset target value of the multi-objective optimization function and the response data before optimization, respectively, to verify the effectiveness of the optimal parameter combination.

[0101] In this step, to verify the validity of this application, a piezoelectric dispensing valve system of a certain type is analyzed as an example. The basic parameters of the system are: piezoelectric stack stiffness. Preload spring stiffness Stiffness of the firing pin spring Nonlinear stiffness coefficient Magnification Piezoresistive damping coefficient Strike pin damping coefficient .

[0102] Shapley values ​​and Sobol sensitivity analyses were performed on the key parameters, and the results are shown in Table 1.

[0103] Table 1. Results of parameter sensitivity analysis

[0104]

[0105] The analysis results show that (Preload spring stiffness) (Striking pin spring stiffness) (Nonlinear stiffness coefficient) and The overall importance score of the (firing pin damping coefficient) was greater than the threshold of 0.15, and it was identified as a key optimization variable.

[0106] To meet the requirements of high-precision dispensing applications, the target performance values ​​are set as follows: , , Based on the four determined key parameters, a multi-objective optimization function is established:

[0107]

[0108] The weighting coefficients are updated in real time using a dynamic adjustment mechanism.

[0109] Using the MATLAB-COMSOL co-simulation platform, a genetic algorithm was employed for multi-objective optimization. The population size was set to 50, the number of iterations to 100, the crossover probability to 0.8, and the mutation probability to 0.1. The search space for each key parameter during the optimization process was as follows:

[0110] [64, 96] N / mm (nominal value ±20%) [8.4, 15.6] N / mm (nominal value ±30%) [0.5, 1.5] N / mm (nominal value ±50%) [0.35, 0.65] N·s / mm (nominal value ±30%)

[0111] After 85 iterations, the convergence was achieved, and the optimal parameter combination was obtained as follows: , , , .

[0112] The parameter combinations before and after optimization were input into the COMSOL simulation model, and a comparative analysis was conducted under the same driving conditions (voltage amplitude 100V, frequency 3 kHz). The results are shown in Table 2.

[0113] Table 2 Performance Comparison Before and After Optimization

[0114]

[0115] The results show that, Figure 9 As shown, the optimized parameter combination meets the design requirements in all key performance indicators. Furthermore, all performance indicators are improved compared to the original combination, demonstrating that the new parameter combination can optimize the firing pin motion.

[0116] To verify the applicability of this application, tests were conducted under different driving frequencies (1 kHz, 3 kHz, 5 kHz) and voltage amplitudes (50V, 100V, 150V) using the same optimized parameter combination. Test results show that the system performance meets the design requirements under all operating conditions, with performance fluctuations less than ±5%, indicating that this application has good robustness and engineering applicability.

[0117] In summary, as Figure 10As shown, this application's embodiment constructs a nonlinear disturbance dynamics model: the transmission system composed of piezoelectric ceramics, lever-type amplification mechanisms, strikers, and springs is considered as a multi-degree-of-freedom nonlinear vibration model. Taking into account factors such as mass distribution, nonlinear stiffness (duffing term), nonlinear viscous damping, and voltage input disturbances, a coupled dynamics model is established using the Lagrange equation. Based on this, actual disturbance sources such as periodic voltage fluctuations, elastic modulus drift, and preload deviations are extracted to construct a disturbance dynamics model that combines theoretical support with engineering effectiveness.

[0118] Parameter Identification and Sensitivity Analysis: Based on the aforementioned perturbation model, parameter scanning experiments were conducted on the MATLAB platform. The control variable method was used to identify the core structural parameters affecting the striker's response performance. Sensitivity analysis methods, such as Shapley values, were used to quantify the influence of each parameter on the system's dynamic response under different operating conditions. These results provide a data foundation for constructing a weighting adjustment mechanism and optimizing variable selection, and clarify the boundaries of the parameter search space.

[0119] Constructing a multi-objective optimization function and state weighting mechanism: By integrating the three performance indicators of the striker's maximum displacement, response speed, and vibration amplitude, a normalized multi-objective optimization function is established, and a weight adaptive mechanism based on state energy feedback is introduced. According to the energy ratio of "displacement-velocity-amplitude" in the current response state of the system, the weights of each objective are adaptively adjusted to achieve dynamic balance adjustment of response speed, stability, and control accuracy.

[0120] A co-simulation optimization platform was built: using the COMSOL and MATLAB internal interface, a control platform was constructed that integrates parameter input, performance response, result feedback, weight update, and parameter re-optimization. The platform integrates genetic algorithms and gradient descent strategies to automatically execute multiple rounds of simulation iterations and parameter tuning. It also integrates modules for dynamic adjustment of the search window and visualization of the optimization path, significantly improving simulation efficiency and parameter search quality.

[0121] Experimental Verification and System Robustness Evaluation: To further verify the effectiveness of the control strategy of this invention, an experimental platform including a high-frequency driver and a high-precision vibration measurement system will be constructed to simulate the dynamic response behavior of the dispensing system under different parameter conditions. The experimental plan will test the performance indicators and compare them with the control scheme before optimization.

[0122] The above are embodiments of the method proposed in this application. Based on the same inventive concept, embodiments of this application also provide a nonlinear coupling control device for piezoelectric dispensing, the structure of which is as follows: Figure 11 As shown.

[0123] Figure 11 This is a schematic diagram of the internal structure of a nonlinear coupling control device for piezoelectric dispensing, provided as an embodiment of this application. Figure 11 As shown, the device includes:

[0124] At least one processor 1101;

[0125] And a memory 1102 that is communicatively connected to at least one processor;

[0126] The memory 1102 stores instructions that can be executed by at least one processor, which is executed by at least one processor 1101 to enable at least one processor 1101 to: any one of the steps of a nonlinear coupling control method for piezoelectric dispensing.

[0127] Some embodiments of this application provide corresponding to Figure 1 A non-volatile computer storage medium for nonlinear coupling control of piezoelectric dispensing is provided, storing computer-executable instructions, wherein the computer-executable instructions are configured to include any one of the steps of a nonlinear coupling control method for piezoelectric dispensing.

Claims

1. A nonlinear coupling control method for piezoelectric dispensing, characterized in that, The method includes: Obtain the structural parameters of the dispensing system and establish a nonlinear dynamic model of the dispensing system; By using the nonlinear dynamic model, parameter scanning and sensitivity index calculation are performed on the structural parameters to obtain key parameters that result in a response of the dispensing system greater than a preset threshold. Construct a multi-objective optimization function with the maximum displacement, maximum velocity, and vibration amplitude of the firing pin as performance objectives; A co-simulation platform is built, the key parameters are imported into the co-simulation platform, and the real-time response energy of the firing pin is obtained based on the co-simulation platform, specifically including: The co-simulation platform includes a simulation environment and a computing environment; The nonlinear dynamic model is solved using the simulation environment to obtain the real-time displacement, velocity data, and vibration amplitude of the firing pin. The multi-objective optimization function is imported into the computing environment, the optimization algorithm is executed, and the results are based on the simulation environment. The displacement, velocity data, and vibration amplitude obtained from the environment are used to calculate the real-time response energy. Based on the real-time response energy, the weight coefficients of the multi-objective optimization function are adjusted, specifically including: The real-time response energy is calculated based on the real-time displacement, real-time velocity, and real-time vibration amplitude of the striker transmitted back from the computing environment. The total real-time response energy is calculated, and the proportion of real-time response energy for each target is calculated separately, so that the weighting coefficients are dynamically adjusted. The optimal parameter combination of the key parameters is obtained by iterative simulation and adjustment through optimization algorithms.

2. The nonlinear coupling control method for piezoelectric dispensing according to claim 1, characterized in that, The acquisition of structural parameters of the dispensing system and the establishment of a nonlinear dynamic model of the dispensing system specifically include: Obtain the structural parameters of the piezoelectric dispensing system, including the piezoelectric stack, lever amplification mechanism, impact pin, preload spring, and impact pin spring; Based on the Lagrange equation, the piezoelectric stack, lever amplification mechanism, striker, preload spring and striker spring are modeled, and the motion differential equation of the dispensing system is derived through the Lagrange equation. A nonlinear stiffness term is introduced into the potential energy term of the equation of motion to characterize the nonlinear change in the stiffness of the firing pin. A nonlinear damping term is introduced into the damping term of the equation of motion to characterize the nonlinear damping effect of the system, thus obtaining the nonlinear dynamic model.

3. The nonlinear coupling control method for piezoelectric dispensing according to claim 1, characterized in that, The nonlinear dynamic model is used to perform parameter scanning and sensitivity index calculation on the structural parameters to obtain key parameters whose response to the dispensing system is greater than a preset threshold. Specifically, these parameters include: The structural parameters were assigned values ​​using the controlled variable method, and the nonlinear dynamic model was called to perform simulation to obtain the corresponding striker response data. Based on the striker response data, a contribution allocation algorithm is used to calculate the marginal contribution value of the structural parameters to the output response; The sensitivity index of the structural parameters is calculated using a global sensitivity analysis algorithm. Based on the marginal contribution value and the sensitivity index, the key parameters with a comprehensive importance higher than the preset threshold are selected.

4. The nonlinear coupling control method for piezoelectric dispensing according to claim 1, characterized in that, After obtaining the optimal parameter combination of the key parameters through iterative simulation and adjustment using an optimization algorithm, the method further includes: An experimental verification platform is constructed, which includes a high-frequency driver, a high-precision vibration measurement system, and a prototype dispensing system. The optimal parameter combination is configured into the dispensing system prototype, a preset driving voltage is applied through the high-frequency driver, and the actual response data of the impact pin is collected using the high-precision vibration measurement system; The actual response data is compared with the preset target value of the multi-objective optimization function and the response data before optimization to verify the effectiveness of the optimal parameter combination.

5. The nonlinear coupling control method for piezoelectric dispensing according to claim 1, characterized in that, The step of obtaining the optimal parameter combination of the key parameters through iterative simulation and adjustment using optimization algorithms specifically includes: A genetic algorithm is used to initialize and generate candidate parameter combinations for the key parameters; For the candidate parameter combinations, the co-simulation platform is executed and the corresponding multi-objective optimization function value is calculated as the fitness. Based on the fitness, the population is subjected to selection, crossover, and mutation operations to generate a new generation of population until the convergence condition is met. The candidate parameter combination with the best fitness is then taken as the optimal parameter combination.

6. The nonlinear coupling control method for piezoelectric dispensing according to claim 5, characterized in that, The process of selecting the candidate parameter combination with the best fitness as the optimal parameter combination until the convergence condition is met specifically includes: Based on the multi-objective optimization function after adjusting the weight coefficients, calculate the performance evaluation value of the current candidate parameter combination; Record the best performance evaluation values ​​in historical iterations and monitor the trend of change; If the improvement of the optimal performance evaluation value in multiple consecutive iterations is less than the preset tolerance, the convergence condition is determined to be met.

7. A nonlinear coupling control device for piezoelectric dispensing, characterized in that, The device includes: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to: Perform the steps of the nonlinear coupling control method for piezoelectric dispensing as described in any one of claims 1-6.

8. A non-volatile computer storage medium for non-linear coupling control of piezoelectric dispensing, storing computer-executable instructions, characterized in that, The computer-executable instructions are set as follows: Perform the steps of the nonlinear coupling control method for piezoelectric dispensing as described in any one of claims 1-6.

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