A control method and system for an automatic dispensing machine

Abstract

The application discloses a control method and system of an automatic dispensing machine, and relates to the technical field of automatic control, which comprises the following steps: collecting the dielectric loss factor and temperature change rate of glue and preprocessing; calculating the dynamic viscosity, shear temperature rise and phase change rate of the glue by a time-frequency joint analysis method to generate a phase change parameter matrix; inputting the phase change parameter matrix into a soliton dynamics model to obtain a soliton waveform of the glue under the nonlinear action and dissipation effect, optimizing the soliton waveform in combination with real-time viscosity to generate pulse parameters and dispersion compensation coefficients, and forming a soliton driving instruction set; loading the soliton driving instruction set to an FPGA controller of an electric valve to generate a high-voltage pulse waveform and execute high-precision dispensing, and monitoring the jet waveform and performing dynamic calibration; and the application optimizes the waveform by the soliton dynamics model and the step-by-step Fourier method, and realizes the accurate balance of the dissipation effect and nonlinear action of the glue.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic control, and particularly relates to a control method and system of an automatic dispensing machine. BACKGROUND

[0002] In the field of modern industrial manufacturing, automatic dispensing technology, as a high-precision and high-efficiency colloid dispensing method, is widely used in the fields of precision manufacturing such as electronic packaging, semiconductor manufacturing and medical devices. In recent years, with the development of material science and fluid mechanics, automatic dispensing technology has gradually evolved from traditional open-loop control to closed-loop control, and the dispensing precision and stability have been significantly improved. In the prior art, automatic dispensing machines mainly realize colloid dispensing through pressure control, time control and volume control, and realize closed-loop control in combination with sensor feedback. However, the prior art still has many deficiencies in colloid nonlinear characteristic modeling, real-time dynamic calibration and dispensing quality evaluation.

[0003] The main problem of the existing automatic dispensing technology is that the existing technology effectively integrates the dynamic coupling relationship of the dielectric loss factor, temperature change rate and shear temperature rise of the colloid and other multi-dimensional physical parameters, so that the nonlinear dynamic response of the colloid under high pressure and high shear rate cannot be accurately characterized, and then the pulse parameters and dispersion compensation coefficient setting deviate from the actual colloid behavior. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a control method of an automatic dispensing machine to solve the nonlinear dynamic response modeling deviation problem caused by the lack of coupling of multi-dimensional physical parameters in the prior art.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a control method of an automatic dispensing machine, which comprises collecting and preprocessing the dielectric loss factor and temperature change rate of glue, calculating the dynamic viscosity, shear temperature rise and phase change rate of the glue through time-frequency joint analysis method, and generating a phase change parameter matrix; inputting the phase change parameter matrix into a soliton dynamics model to obtain the soliton waveform of the colloid under the nonlinear action and dissipation effect, and optimizing the soliton waveform in combination with the real-time viscosity to generate pulse parameters and dispersion compensation coefficients, and forming a soliton driving instruction set; loading the soliton driving instruction set to the FPGA controller of the electric valve to generate a high-pressure pulse waveform and execute high-precision dispensing, while monitoring the jet waveform and performing dynamic calibration; after dispensing is completed, verifying the dispensing quality score and defining a dispensing score benchmark, and dynamically optimizing the soliton driving instruction set and the soliton dynamics model according to the dispensing quality score and the dispensing score benchmark.

[0008] As a preferred scheme of the control method of the automatic dispensing machine, the dynamic viscosity, the shear temperature rise and the phase change rate of the glue are calculated by the time-frequency joint analysis method to generate a phase change parameter matrix, and the specific steps are as follows,

[0009] The relaxation time τ and the distribution coefficient α of the dielectric loss factor are extracted from the relationship curve of the dielectric loss factor and the frequency component, and the dynamic viscosity is calculated through the Cole-Cole model;

[0010] The shear temperature rise is calculated by the numerical integration method for time integration of the temperature change rate;

[0011] Based on the change trend of the dielectric loss factor, the phase change rate is calculated;

[0012] The dynamic viscosity, the shear temperature rise and the phase change rate are integrated into a phase change parameter matrix M through the time series matrix method.

[0013] As a preferred scheme of the control method of the automatic dispensing machine, the dynamic viscosity, the shear temperature rise and the phase change rate of the glue are calculated by the time-frequency joint analysis method to generate a phase change parameter matrix, and the specific steps are as follows,

[0014] Based on the nonlinear characteristics of the glue, the Ginzburg-Landau equation is selected as the soliton wave function;

[0015] According to the geometric size of the nozzle and the flow characteristics of the glue, the grid division rule is defined;

[0016] According to the initial alternating state, the initial condition of space-time discretization is set;

[0017] According to the grid division rule and the initial condition, the soliton wave function is discretized into space-time grid nodes;

[0018] Based on the space-time grid nodes, the dispersion term is calculated in the frequency domain and the nonlinear term is calculated in the time domain by using the step-by-step Fourier method, and finally the soliton dynamics model is formed;

[0019] The phase change parameter matrix is input into the dynamics model to obtain the soliton waveform of the glue under the nonlinear action and the dissipation effect.

[0020] As a preferred scheme of the control method of the automatic dispensing machine, the dynamic viscosity, the shear temperature rise and the phase change rate of the glue are calculated by the time-frequency joint analysis method to generate a phase change parameter matrix, and the specific steps are as follows,

[0021] The time width, peak pressure and frequency of the pulse are extracted from the soliton waveform by wavelet transform, and are mapped into pulse parameters by fuzzy logic control algorithm;

[0022] Based on the pulse parameters, the dispersion compensation coefficient is calculated by NLSE, the pulse parameters are integrated with the dispersion compensation signal, and the soliton driving instruction set is generated.

[0023] As a preferred scheme of the control method of the automatic dispensing machine, the step of generating the high-voltage pulse waveform and performing high-precision dispensing includes identifying the pulse period and duty cycle, setting the driving voltage amplitude according to the peak pressure, and generating the high-voltage pulse waveform through the DSP in combination with the dispersion compensation coefficient β2, loading the generated high-voltage pulse waveform to the piezoelectric valve driving circuit, and driving the piezoelectric valve to perform high-precision dispensing operation.

[0024] As a preferred scheme of the control method of the automatic dispensing machine, the step of collecting the jet waveform, performing time-frequency analysis on the jet waveform, extracting the time width, peak pressure and frequency of the actual pulse in the dispensing process, and comparing with the soliton driving instruction set to identify the jet waveform deviation.

[0025] A waveform accuracy threshold is defined, and the jet waveform deviation is compared with the waveform accuracy threshold to trigger dynamic calibration.

[0026] As a preferred scheme of the control method of the automatic dispensing machine, the step of verifying the dispensing quality score and defining the dispensing score benchmark includes the following specific steps,

[0027] The geometric size of the dispensing path and the uniformity of the glue distribution are extracted, and the dispensing quality error is calculated;

[0028] The dispensing score benchmark is defined based on historical dispensing quality data;

[0029] By comparing the dispensing score benchmark and the dispensing quality score, the soliton driving instruction set and the soliton dynamics model are dynamically optimized.

[0030] In a second aspect, the present application provides a control system of an automatic dispensing machine, comprising a matrix generation module, an instruction set generation module, a calibration module and a quality verification module; the matrix generation module is used for collecting and preprocessing the dielectric loss factor and temperature change rate of glue, calculating the dynamic viscosity, shear temperature rise and phase change rate of the glue through time-frequency joint analysis, and generating a phase change parameter matrix; the instruction set generation module is used for inputting the phase change parameter matrix into a soliton dynamics model, obtaining a soliton waveform of the glue under the action of nonlinearity and dissipation effect, optimizing the soliton waveform in combination with real-time viscosity, generating pulse parameters and dispersion compensation coefficients, and forming a soliton driving instruction set; the calibration module is used for loading the soliton driving instruction set to an FPGA controller of an electric valve, generating a high-voltage pulse waveform and performing high-precision dispensing, and simultaneously monitoring the jet waveform and performing dynamic calibration; the quality verification module is used for verifying a dispensing quality score after dispensing is completed, defining a dispensing score benchmark, and dynamically optimizing the soliton driving instruction set and the soliton dynamics model according to the dispensing quality score and the dispensing score benchmark.

[0031] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, any step of the control method of the automatic dispensing machine according to the first aspect of the present application is implemented.

[0032] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, any step of the control method of the automatic dispensing machine according to the first aspect of the present application is implemented.

[0033] The present application has the following beneficial effects: the phase change parameter matrix is generated through time-frequency joint analysis, the dynamic viscosity, shear temperature rise and phase change rate of the glue are dynamically calculated, and high-precision modeling of the dynamic characteristics of the glue is realized; meanwhile, the waveform is optimized through the soliton dynamics model and the step-by-step Fourier method, the calculation divergence problem of the traditional Navier-Stokes equation in a high-pressure nonlinear flow field is overcome, and the accurate balance of the dissipation effect and the nonlinear action of the glue is realized. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0035] Fig. 1 The flowchart of the control method of the automatic dispensing machine in embodiment 1.

[0036] Fig. 2A flow chart of the soliton dynamics model generation process in the control method of the automatic dispensing machine in Embodiment 1.

[0037] Fig. 3 A flow chart of the dispensing quality verification in the control method of the automatic dispensing machine in Embodiment 1.

[0038] Fig. 4 A schematic diagram of the automatic dispensing machine control system module interaction flow chart in Embodiment 1. DETAILED DESCRIPTION

[0039] In order to make the above objectives, characteristics and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0040] In the following description, a large number of specific details are set forth in order to facilitate a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0041] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an embodiment that is separate or alternative to other embodiments.

[0042] Embodiment 1, refer to Figs. 1-4 , for the first embodiment of the present application, the embodiment provides a control method of an automatic dispensing machine, comprising the following steps:

[0043] S1, collect the dielectric loss factor and temperature change rate of the glue and pre-process, calculate the dynamic viscosity, shear temperature rise and phase change rate of the glue by time-frequency joint analysis method, and generate a phase change parameter matrix;

[0044] Integrate a micro dielectric spectrum sensor array inside the piezoelectric valve, real-time collect the dielectric loss factor of the glue, at the same time acquire the temperature change rate of the glue through a temperature sensor, and transmit the dielectric loss factor and temperature change rate of the glue to the central processing unit through an SMEMA communication port.

[0045] Filter and denoise the dielectric loss factor and temperature change rate to remove high-frequency interference signals;

[0046] Normalize the dielectric loss factor and temperature change rate to eliminate dimensional differences;

[0047] Fill in the missing data points of the dielectric loss factor and temperature change rate through an interpolation algorithm;

[0048] Further, when filtering and denoising the dielectric loss factor and the temperature change rate, a Butterworth low-pass filter is used to remove high-frequency interference signals by setting an appropriate cutoff frequency to retain effective data characteristics, and the filtering effect is verified through frequency domain analysis to ensure that noise components are effectively suppressed; when normalizing the dielectric loss factor and the temperature change rate, the data is linearly mapped to the [0, 1] interval, and the specific method is as follows: for the dielectric loss factor, take the maximum and minimum values, and convert each data point to a proportional value relative to the maximum and minimum values; for the temperature change rate, the same method is used to eliminate dimensional differences, making the data comparable and ensuring that the normalized data is uniformly distributed; when filling in missing data points through interpolation algorithms, a cubic spline interpolation method is used to construct a smooth curve based on known data points, for example, in a time series, if a data point is missing, the dielectric loss factor and the temperature change rate of the missing point are calculated using the adjacent data points before and after it, ensuring data continuity and integrity, and the accuracy of the interpolation results is verified through curve fitting.

[0049] The pre-processed dielectric loss factor and temperature change rate are subjected to short-time Fourier transform to obtain time-frequency domain signals, extract frequency components at different time points, and generate a frequency relationship curve.

[0050] It should be noted that first, the dielectric loss factor and temperature change rate data are segmented and truncated with a fixed window length and overlap rate to ensure time continuity; then, the data in each window is subjected to fast Fourier transform to calculate the amplitude and phase of the frequency components and generate time-frequency domain signals; then, the frequency components at different time points are extracted to analyze the frequency response characteristics of the glue; finally, the amplitudes and phases of the frequency components are integrated in time sequence to generate a relationship curve between the dielectric loss factor, the temperature change rate, and the frequency.

[0051] The relaxation time τ and the distribution coefficient α of the dielectric loss factor are extracted from the relationship curve between the dielectric loss factor and the frequency component through nonlinear least squares fitting, and the dynamic viscosity is calculated through the Cole-Cole model, with the expression being:

[0052]

[0053] where η(t) is the dynamic viscosity at time t, η ∞ is the high-frequency limit viscosity, ω is the angular frequency of the time-frequency domain signal, η0 is the low-frequency limit viscosity, j is the imaginary unit, and t represents time.

[0054] It should be noted that the relaxation time τ and the distribution coefficient α of the dielectric loss factor are extracted from the curve of the dielectric loss factor versus the frequency component by nonlinear least squares fitting, and the dynamic viscosity is calculated based on the Cole-Cole model. First, the curve of the dielectric loss factor versus the frequency component is taken as the input data, and the mathematical expressions of the relaxation time α and the distribution coefficient α are defined; then, the estimated values of the relaxation time τ and the distribution coefficient α are initialized, for example, the initial value of τ is set to 1×10^-3 seconds, and the initial value of α is set to 0.5, and the Levenberg-Marquardt algorithm is used for nonlinear least squares iteration optimization to minimize the error between the predicted value and the actual data; then, the optimal parameter values are determined by convergence judgment, for example, the relaxation time τ is extracted as 2.5×10^-3 seconds, and the distribution coefficient α is extracted as 0.35; finally, the extracted parameters are substituted into the Cole-Cole model to calculate the dynamic viscosity η(t).

[0055] The shear temperature rise is calculated by numerically integrating the temperature rate of change with respect to time, and the expression is:

[0056]

[0057] where ΔT(t) represents the shear temperature rise from time 0 to t, dT is the temperature rate of change, and dt is the time infinitesimal.

[0058] The trend of the dielectric loss factor is identified by the sliding window analysis method;

[0059] The specific process is as follows: first, set the window length to 10 ms and the overlap rate to 50%, and perform segmented analysis on the dielectric loss factor data; then, calculate the average value and standard deviation of the dielectric loss factor data in each window to extract the trend characteristics; then, fit the slope of the window data by linear regression to determine the change direction (up, down or stable) of the dielectric loss factor; finally, combine the trend characteristics with the time series to generate the change curve of the dielectric loss factor, and further identify the change trend.

[0060] Based on the trend of the dielectric loss factor, the phase change rate is calculated, and the expression is:

[0061]

[0062] where dS is the phase change rate, ε ″ (t) is the dielectric loss factor at time t, ε ″ (t-Δt) is the dielectric loss factor at time t-Δt, and Δt is the sampling interval.

[0063] It should be noted that first, the dielectric loss factor data of the time sequence is obtained, for example, at time t equal to 1.0 seconds, the dielectric loss factor is 0.45, at time t minus sampling interval Δt equal to 0.9 seconds, the dielectric loss factor is 0.40, and the sampling interval Δt is set to 0.1 seconds; then, the dielectric loss factor of the current time t is subtracted from the dielectric loss factor of the previous time t minus the sampling interval Δt to obtain the change amount of the dielectric loss factor, and then divided by the sampling interval Δt to obtain the phase change rate, for example, at t equal to 1.0 seconds, the phase change rate is 0.5; then, repeat the above calculation for each data point in the time sequence, for example, at t equal to 1.1 seconds, the dielectric loss factor is 0.48, the dielectric loss factor of the previous time t minus the sampling interval Δt is 0.45, and the calculated phase change rate is 0.3;

[0064] By time sequence matrixing method, the dynamic viscosity, shear temperature rise and phase change rate are integrated into a phase change parameter matrix M.

[0065] It should be noted that first, the dynamic viscosity, shear temperature rise and phase change rate are stored as column vectors in time sequence, for example, at time t = 1.0 seconds, the dynamic viscosity is 0.45 Pa·s, the shear temperature rise is 2.5 K, and the phase change rate is 0.3; then, align the three column vectors by time point to form a matrix M, where each row corresponds to a time point, and each column corresponds to dynamic viscosity, shear temperature rise and phase change rate respectively; then, the phase change parameter matrix M is standardized by matrix operation method to ensure consistent data format; finally, the phase change parameter matrix M is used as input for subsequent soliton dynamics model calculation, for example, at time t = 1.0 seconds, the corresponding row data of the phase change parameter matrix M can be directly used to generate soliton waveform.

[0066] S2, input the phase change parameter matrix into the soliton dynamics model, obtain the soliton waveform of the colloid under the action of nonlinearity and dissipation effect, and optimize the soliton waveform combined with real-time viscosity to generate pulse parameters and dispersion compensation coefficients, and form a soliton driving instruction set;

[0067] The dynamic viscosity of the colloid under different shear rates is measured by a dynamic viscosity tester to obtain the nonlinear characteristics of the colloid.

[0068] It should be noted that the dynamic viscosity of the colloid at different shear rates is measured by a dynamic viscosity tester to obtain the nonlinear characteristics of the colloid. The specific process is as follows: first, set the shear rate range of the dynamic viscosity tester, for example, from 0.1 to 100 seconds per minute, and increase step by step; then, load the colloid sample into the test cavity of the dynamic viscosity tester, start the tester, and record the dynamic viscosity values at different shear rates, for example, the dynamic viscosity is 0.5 Pa·s when the shear rate is 10 seconds per minute; then, plot the curve of dynamic viscosity versus shear rate, and identify the nonlinear characteristics of the colloid.

[0069] Based on the nonlinear characteristics of the colloid, the Ginzburg-Landau equation is selected as the soliton wave function, and the phase transition parameter matrix is mapped into the soliton wave function. The nonlinear gain coefficient is controlled by the dynamic viscosity, the dissipation coefficient is controlled by the shear temperature rise, and the dissipation coefficient is modified by the phase transition rate.

[0070] It should be noted that first, the dynamic viscosity in the phase transition parameter matrix is mapped to the nonlinear gain coefficient, for example, when the dynamic viscosity is a certain value, the nonlinear gain coefficient is a certain corresponding value; then, the shear temperature rise in the phase transition parameter matrix is mapped to the dissipation coefficient, for example, when the shear temperature rise is a certain value, the dissipation coefficient is a certain corresponding value; then, the phase transition rate in the phase transition parameter matrix is used to modify the dissipation coefficient, for example, when the phase transition rate is a certain value, the modified dissipation coefficient is a certain corresponding value; finally, the mapped nonlinear gain coefficient and the modified dissipation coefficient are substituted into the soliton wave function to generate the soliton wave function.

[0071] Further, the soliton wave function maps the colloid dielectric loss factor, temperature change rate and dynamic viscosity to the nonlinear gain coefficient β and the dissipation coefficient γ.

[0072] The mapping expression of the nonlinear gain coefficient β is:

[0073]

[0074] Where β0 is the basic nonlinear gain coefficient, and η1 is the critical viscosity.

[0075] The mapping expression of the dissipation coefficient γ is:

[0076]

[0077] Where γ0 is the basic dissipation coefficient, and T1 is the reference temperature.

[0078] The modified expression of the dissipation coefficient is:

[0079]

[0080] wherein, a1 is a correction coefficient, γ' is a corrected dissipation coefficient, z is the spatial coordinate of the nozzle axis of the dispenser, and q represents the gradient change of the glue flow state in z.

[0081] It should be noted that a1 is determined according to the phase transition rate and flow characteristics of the glue, and the value range is usually 0 < a1 ≤ 1; γ0 is determined according to the viscoelasticity characteristics of the glue, and the value range is usually 0 < γ0 ≤ 10; β0 is determined according to the nonlinear response and dynamic viscosity test results of the glue, and the value range is usually 0 < β0 ≤ 5.

[0082] The nozzle of the dispenser is measured by three-dimensional scanning and high-speed photography to obtain the geometric size of the nozzle and the flow characteristics of the glue;

[0083] It should be noted that the geometric size of the nozzle includes the diameter, length and internal profile shape of the nozzle; the flow characteristics of the glue refer to the velocity distribution, viscosity variation and phase transition behavior of the glue in the nozzle.

[0084] According to the geometric size of the nozzle and the flow characteristics of the glue, a grid division rule is defined;

[0085] It should be noted that based on the geometric size (such as diameter, length and internal profile shape) of the nozzle and the flow characteristics (such as velocity distribution, viscosity variation and phase transition behavior) of the glue, the internal space of the nozzle is divided into regular grid cells. For example, evenly distributed grid nodes are arranged in the axial and radial directions respectively to ensure that each grid cell can accurately describe the physical boundary conditions of the glue flow. Then, according to the flow characteristics of the glue, the grid density is adjusted, for example, the number of grid nodes is increased in the area where the flow rate changes greatly to improve the calculation accuracy. Finally, the grid division rule is applied to the internal space of the nozzle to generate a space-time grid for the discretization of the soliton wave function.

[0086] According to the initial alternating state, the initial condition of space-time discretization is set;

[0087] It should be noted that the initial alternating state refers to the initial flow state of the glue at the entrance of the nozzle, including the velocity distribution, viscosity distribution and phase transition rate of the glue. First, at the entrance of the nozzle, the velocity value of each grid node is set according to the initial velocity distribution of the glue, for example, the velocity is higher in the central area and lower in the edge area. Then, the viscosity value of each grid node is set according to the initial viscosity distribution of the glue, for example, the viscosity is uniformly distributed at the entrance. Then, the phase transition rate value of each grid node is set according to the initial phase transition rate of the glue, for example, the phase transition rate is zero at the entrance. Finally, the initial velocity, viscosity and phase transition rate values are used as the initial conditions of space-time discretization.

[0088] The initial alternating state refers to the initial flow state of the glue at the entrance of the nozzle, including the velocity distribution, viscosity distribution and phase transition rate of the glue.

[0089] According to the meshing rule and the initial condition, the soliton wave function is discretized into space-time mesh nodes;

[0090] It should be noted that based on the meshing rule, the internal space of the nozzle is divided into space-time mesh nodes, each node corresponds to a specific spatial position and time point. Then, according to the initial condition, the initial amplitude and phase value of the soliton wave function is set at each space-time mesh node, for example, the amplitude is a certain specific value at the entrance, and the phase is zero. Then, the soliton wave function is discretized into numerical form on the space-time mesh nodes, ensuring that the soliton wave function value of each node can accurately describe the nonlinearity and dissipation effect of colloidal flow.

[0091] Based on the space-time mesh nodes, the split-step Fourier method is used to calculate the dispersion term in the frequency domain and the nonlinear term in the time domain, and finally the soliton dynamics model is constructed.

[0092] Based on the space-time mesh nodes, the split-step Fourier method is used to decompose the soliton wave function into two parts in the frequency domain and the time domain. Then, the dispersion term is calculated in the frequency domain, for example, the soliton wave function is converted into a frequency domain signal through Fourier transform, and the influence of dispersion effect on the waveform is calculated. Then, the nonlinear term is calculated in the time domain, for example, the frequency domain signal is converted back to the time domain through inverse Fourier transform, and the influence of nonlinear effect on the waveform is calculated. Finally, the dispersion term and the nonlinear term are combined to form the soliton dynamics model.

[0093] The phase transition parameter matrix is input into the soliton dynamics model to obtain the soliton waveform of the colloid under the action of nonlinearity and dissipation effect, expressed as:

[0094]

[0095] Where ψ is the soliton waveform of the colloid under the action of nonlinearity and dissipation effect, κ represents the wave number of the soliton function, ω1 is the angular frequency of the soliton function, sech is the hyperbolic secant function, and z represents the spatial coordinate of the colloid in the axial direction of the nozzle.

[0096] The time width, peak pressure and frequency of the pulse are extracted from the soliton waveform through wavelet transform, and are mapped to the pulse parameters through fuzzy logic control algorithm;

[0097] It should be noted that the soliton waveform is subjected to wavelet transform to decompose into different scale and frequency components, for example, by selecting a specific wavelet basis function, local features in the waveform are extracted; then, the time width, peak pressure and frequency of the pulse are identified from the wavelet transform result, for example, by detecting the extreme points of the wavelet coefficients, the time width and peak pressure of the pulse are determined, and by analyzing the frequency distribution of the wavelet coefficients, the frequency of the pulse is determined; then, the time width, peak pressure and frequency extracted are mapped into pulse parameters using a fuzzy logic control algorithm, for example, by defining a fuzzy rule base, the time width is mapped into a pulse width parameter, the peak pressure is mapped into a pulse pressure parameter, and the frequency is mapped into a pulse frequency parameter.

[0098] Based on the pulse parameters, the dispersion compensation coefficient is calculated by NLSE, and the expression is:

[0099]

[0100] Wherein, E0 is the time width of the pulse, L is the total distance of the pulse propagating in the optical fiber, Δλ is the wavelength offset of the pulse, and D is the dispersion compensation coefficient;

[0101] The pulse parameters and the dispersion compensation signal are integrated to generate a soliton driving instruction set.

[0102] S3, the soliton driving instruction set is loaded to the FPGA controller of the electric valve to generate a high-voltage pulse waveform and perform high-precision dispensing, while monitoring the jet waveform and performing dynamic calibration;

[0103] The soliton driving instruction set is transmitted to the FPGA controller through the SMEMA communication port;

[0104] The FPGA controller identifies the pulse period and duty cycle according to the time width E0 and the frequency, sets the driving voltage amplitude according to the peak pressure, and generates a high-voltage pulse waveform through DSP combined with the dispersion compensation coefficient β2;

[0105] It should be noted that the FPGA controller calculates the pulse period and duty cycle according to the time width E0 and the frequency, for example, by determining the duration of the pulse through the time width E0, determining the repetition period of the pulse through the frequency, and calculating the duty cycle; then, the driving voltage amplitude is set according to the peak pressure, for example, by determining the specific value of the driving voltage through the mapping relationship between the peak pressure and the driving voltage; then, combined with the dispersion compensation coefficient β2, a high-voltage pulse waveform is generated through DSP, for example, by integrating the time width E0, the frequency, the driving voltage amplitude and the dispersion compensation coefficient β2 into a high-voltage pulse waveform through a digital signal processing algorithm.

[0106] The generated high-voltage pulse waveform is loaded to the piezoelectric valve driving circuit to drive the piezoelectric valve to perform high-precision dispensing operation;

[0107] In the dispensing process, the jet waveform is collected in real time by a high-speed camera and a micro dielectric spectrum sensor array, time-frequency analysis is performed on the jet waveform, the time width, peak pressure and frequency of the actual pulse in the dispensing process are extracted, and the jet waveform deviation Δψ is identified by comparing with the soliton driving instruction set;

[0108] It should be noted that the specific process is as follows: first, the jet waveform data is collected in real time by a high-speed camera and a micro dielectric spectrum sensor array, for example, at time t = 1.0 seconds, the amplitude and phase information of the jet waveform is collected; then, time-frequency analysis is performed on the jet waveform, for example, the time width, peak pressure and frequency are extracted by short-time Fourier transform, for example, at time t = 1.0 seconds, the time width is extracted to be a certain specific value, the peak pressure is extracted to be a certain specific value, and the frequency is extracted to be a certain specific value; then, the extracted time width, peak pressure and frequency are compared with the target values in the soliton driving instruction set, for example, by calculating the difference between the measured values and the target values, the jet waveform deviation Δψ is identified.

[0109] Based on the historical jet waveform deviation and the dispensing quality statistics, a waveform accuracy threshold Y is defined;

[0110] When Δψ≥Y, dynamic calibration is triggered;

[0111] When Δψ<Y, dynamic calibration is not triggered;

[0112] The dynamic calibration specifically refers to: according to the deviation value, the pulse parameters and the dispersion compensation coefficient are recalculated, and the calibrated high-voltage pulse waveform is generated and loaded to the piezoelectric valve driving circuit.

[0113] It should be noted that based on the historical jet waveform deviation and the dispensing quality statistics, a waveform accuracy threshold Y is defined, and the value range is 0<Y≤0.1.

[0114] According to the jet waveform deviation Δψ, the pulse parameters (time width, peak pressure, frequency) and the dispersion compensation coefficient are recalculated, for example, the time width and the peak pressure are adjusted by the deviation value, and the waveform propagation characteristics are optimized by the dispersion compensation coefficient; then, based on the recalculated pulse parameters and the dispersion compensation coefficient, the calibrated high-voltage pulse waveform is generated, for example, a new high-voltage pulse signal is generated by the FPGA controller and the DSP module; then, the calibrated high-voltage pulse waveform is loaded to the piezoelectric valve driving circuit to drive the piezoelectric valve to perform dispensing operation; finally, the jet waveform data is collected in real time, the jet waveform deviation Δψ is recalculated, and if Δψ<Y, the calibration is completed.

[0115] S4, after dispensing, verify the dispensing quality score and define the dispensing score benchmark, dynamically optimize the soliton driving instruction set and the soliton dynamics model according to the dispensing quality score and the dispensing score benchmark.

[0116] After dispensing, the dispensing path geometry and glue distribution uniformity are extracted by three-dimensional scanning measurement and edge detection and gray scale analysis, and the dispensing quality error is calculated, and the expression is:

[0117]

[0118] Wherein, Q is the dispensing quality score, the range is [0, Q max ], Q max is the maximum value of the dispensing quality score, n is the total number of dispensing ports of the dispensing machine, P k represents the measured glue line width of the kth dispensing port, A is the target glue line width, b k represents the measured glue line height of the kth dispensing port, B is the target glue line height, c k represents the standard deviation of the glue distribution of the kth dispensing port, C is the target glue distribution uniformity, and H is the scaling coefficient of the dispensing quality score.

[0119] The dispensing score benchmark Q1 is defined based on historical dispensing quality data;

[0120] When Q≤Q1, the dispensing quality is considered to be qualified;

[0121] When Q>Q1, the soliton driving instruction set and soliton dynamics model are optimized.

[0122] The soliton driving instruction set is optimized, specifically: first, adjust the pulse parameters (time width E0, peak pressure, frequency) and dispersion compensation coefficient β2 in the soliton driving instruction set according to the dispensing error. For example, if the measured glue line width P k is too large, reduce the time width E0 or reduce the peak pressure; if the glue distribution uniformity c k is not up to standard, adjust the dispersion compensation coefficient β2 to optimize the waveform propagation characteristics. Then, a new high-voltage pulse waveform is generated through the FPGA controller, and the soliton driving instruction set is updated.

[0123] It should be noted that the dispensing score benchmark Q1 is defined according to historical dispensing quality data, which is used to measure the qualified standard of dispensing quality, and the value range is 0<Q1≤100.

[0124] The dynamics model optimization is optimized, specifically: first, adjust the dynamic viscosity η(t), shear temperature rise ΔT(t) and phase change rate dS in the phase change parameter matrix M according to the dispensing error. For example, if the measured glue line height b k is too low, correct the dynamic viscosity η(t) to improve the flowability of the glue; if the glue distribution uniformity c k is not up to standard, adjust the phase change rate dS to optimize the glue phase change process. Then, the optimized phase change parameter matrix M is re-input into the soliton dynamics model to generate a new soliton waveform.

[0125] The embodiment also provides a control system of an automatic dispensing machine, comprising a matrix generation module, an instruction set generation module, a calibration module and a quality verification module; the matrix generation module is used for collecting and preprocessing the dielectric loss factor and temperature change rate of glue, calculating the dynamic viscosity, shear temperature rise and phase change rate of the glue through a time-frequency joint analysis method, and generating a phase change parameter matrix; the instruction set generation module is used for inputting the phase change parameter matrix into a soliton dynamics model, obtaining a soliton waveform of the glue under nonlinear action and dissipation effect, optimizing the soliton waveform in combination with real-time viscosity, generating pulse parameters and dispersion compensation coefficients, and constituting a soliton driving instruction set; the calibration module is used for loading the soliton driving instruction set to an FPGA controller of an electric valve, generating a high-voltage pulse waveform and performing high-precision dispensing, and simultaneously monitoring a jetting waveform and performing dynamic calibration; and the quality verification module is used for verifying a dispensing quality score and defining a dispensing score benchmark after dispensing is completed, dynamically optimizing the soliton driving instruction set and the soliton dynamics model according to the dispensing quality score and the dispensing score benchmark.

[0126] The embodiment also provides a computer device suitable for the control method of the automatic dispensing machine, comprising a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to implement the control method of the automatic dispensing machine proposed in the above embodiment.

[0127] The computer device can be a terminal, and the computer device comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is used for providing computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used for wired or wireless communication with external terminals. The wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0128] The embodiment also provides a storage medium, which stores a computer program, and the computer program is executed by a processor to implement the control method of the automatic dispensing machine proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk, or an optical disk.

[0129] In summary, the present application achieves high-precision modeling of the dynamic characteristics of the colloid by generating a phase transition parameter matrix through time-frequency joint analysis, dynamically calculating the dynamic viscosity, shear temperature rise, and phase transition rate of the colloid; at the same time, the present application overcomes the calculation divergence problem of the traditional Navier-Stokes equation in a high-pressure nonlinear flow field by optimizing the waveform through a soliton dynamics model and a split Fourier method, and achieves accurate balance of the dissipation effect and nonlinear action of the colloid.

[0130] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, and all of them should be covered in the scope of the claims of the present application.

Claims

1. A control method of an automatic dispensing machine, characterized by: The application relates to a high-precision dispensing method and device based on soliton dynamics. The dielectric loss factor and temperature change rate of glue are collected and pretreated, the dynamic viscosity, shear temperature rise and phase change rate of the glue are calculated through time-frequency joint analysis, and a phase change parameter matrix is generated; The phase change parameter matrix is input into a soliton dynamics model, the soliton waveform of the glue under the action of nonlinearity and dissipation effect is obtained, the soliton waveform is optimized in combination with real-time viscosity, pulse parameters and dispersion compensation coefficients are generated, and a soliton driving instruction set is formed; The soliton driving instruction set is loaded into an FPGA controller of an electric valve, a high-voltage pulse waveform is generated, high-precision dispensing is performed, the jet waveform is monitored and dynamic calibration is performed; After dispensing is completed, dispensing quality scores are verified and dispensing score benchmarks are defined, and the soliton driving instruction set and the soliton dynamics model are dynamically optimized according to the dispensing quality scores and the dispensing score benchmarks.

2. The control method of the automatic dispensing machine according to claim 1, wherein: The dynamic viscosity, shear temperature rise and phase change rate of the glue are calculated through time-frequency joint analysis, and a phase change parameter matrix is generated, and the specific steps are as follows, The relaxation time tau and distribution coefficient alpha of the dielectric loss factor are extracted from the relationship curve of the dielectric loss factor and the frequency component, and the dynamic viscosity is calculated through a Cole-Cole model; The shear temperature rise is calculated through time integration of the temperature change rate by using a numerical integration method; The phase change rate is calculated based on the change trend of the dielectric loss factor; The dynamic viscosity, shear temperature rise and phase change rate are integrated into the phase change parameter matrix M through a time series matrix method.

3. The control method of the automatic dispensing machine according to claim 1, wherein: The soliton waveform of the glue under the action of nonlinearity and dissipation effect is obtained, and the specific steps are as follows, Based on the nonlinear characteristics of the glue, the Ginzburg-Landau equation is selected as the soliton wave function; According to the geometric size of the nozzle and the flow characteristics of the glue, a grid division rule is defined; According to the initial alternating state, the initial conditions of space-time discretization are set; According to the grid division rule and the initial conditions, the soliton wave function is discretized into space-time grid nodes; Based on the space-time grid nodes, a step-by-step Fourier method is adopted to calculate the dispersion term in the frequency domain and the nonlinear term in the time domain, and finally a soliton dynamics model is formed; The phase change parameter matrix is input into the soliton dynamics model to obtain the soliton waveform of the glue under the action of nonlinearity and dissipation effect.

4. The control method of the automatic dispensing machine according to claim 3, wherein: The pulse parameters and dispersion compensation coefficients are generated, and a soliton driving instruction set is formed, and the specific steps are as follows, The time width, peak pressure and frequency of the pulse are extracted from the soliton waveform through wavelet transform, and the pulse parameters are mapped into the pulse parameters through a fuzzy logic control algorithm; Based on the pulse parameters, the dispersion compensation coefficients are calculated through NLSE, the pulse parameters and the dispersion compensation signal are integrated, and the soliton driving instruction set is generated.

5. The control method of the automatic dispensing machine according to claim 1, wherein: The step of generating a high-voltage pulse waveform and performing high-precision dispensing includes identifying the pulse period and duty cycle, setting the driving voltage amplitude according to the peak pressure, and generating a high-voltage pulse waveform through a DSP in combination with the dispersion compensation coefficient beta2, loading the generated high-voltage pulse waveform into a piezoelectric valve driving circuit, and driving the piezoelectric valve to perform high-precision dispensing operation.

6. The control method of an automatic dispensing machine according to claim 5, wherein: The specific steps of monitoring the jet waveform and performing dynamic calibration are as follows, Collecting the jet waveform, performing time-frequency analysis on the jet waveform, extracting the time width, peak pressure and frequency of the actual pulse in the dispensing process, and comparing with the soliton driving instruction set to identify the jet waveform deviation; Defining a waveform accuracy threshold and comparing the jet waveform deviation with the waveform accuracy threshold to trigger dynamic calibration.

7. The control method of an automatic dispensing machine according to claim 6, wherein: The dispensing quality score is verified and a dispensing score benchmark is defined, and the specific steps are as follows, Extracting the geometric size of the dispensing path and the uniformity of the glue distribution, and calculating the dispensing quality error; Defining the dispensing score benchmark based on historical dispensing quality data; By comparing the dispensing score benchmark and the dispensing quality score, the soliton driving instruction set and the soliton dynamics model are dynamically optimized.

8. A control system of an automatic dispensing machine, based on the control method of the automatic dispensing machine according to any one of claims 1 to 7, characterized in that: It includes a matrix generation module, an instruction set generation module, a calibration module and a quality verification module; The matrix generation module is used for collecting and preprocessing the dielectric loss factor and temperature change rate of the glue, calculating the dynamic viscosity, shear temperature rise and phase transition rate of the glue by time-frequency joint analysis method, and generating a phase transition parameter matrix; The instruction set generation module is used for inputting the phase transition parameter matrix into the soliton dynamics model, obtaining the soliton waveform of the glue under the action of nonlinearity and dissipation effect, and optimizing the soliton waveform combined with real-time viscosity, generating pulse parameters and dispersion compensation coefficients, and forming a soliton driving instruction set; The calibration module is used for loading the soliton driving instruction set to the FPGA controller of the electric valve, generating high-voltage pulse waveform and performing high-precision dispensing, while monitoring the jet waveform and performing dynamic calibration; The quality verification module is used for verifying the dispensing quality score and defining the dispensing score benchmark after dispensing is completed, and dynamically optimizing the soliton driving instruction set and the soliton dynamics model according to the dispensing quality score and the dispensing score benchmark. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: The processor executes the computer program to realize the steps of the control method of the automatic dispensing machine of any one of claims 1-7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to realize the steps of the control method of the automatic dispensing machine of any one of claims 1-7.

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