Printing optimization method and system based on UV flatbed digital printer

Abstract

This invention discloses a printing optimization method and system based on a UV flatbed digital printer, relating to the field of printing optimization control technology. It involves modal analysis and frequency response analysis of a fluid-structure interaction finite element model, establishing a hyperbolic function relationship between radial displacement amplitude and fluid viscosity, acquiring self-sensing signals and performing envelope detection processing, extracting the voltage peak value of the self-sensing signal at the first-order resonant frequency of the piezoelectric printhead, converting the voltage peak value into the maximum radial displacement amplitude, calculating the fluid viscosity value based on the hyperbolic function relationship, calculating the equivalent resistance parameter based on the fluid viscosity value, constructing a discrete system state equation, inputting the volume change rate sequence of the piezoelectric printhead's structural deformation cavity into the discrete system state equation, calculating the instantaneous jet flow rate sequence, correcting the volume change rate sequence through an iterative algorithm to obtain the optimal volume change rate sequence, obtaining the optimal driving voltage waveform parameter sequence based on the optimal volume change rate sequence, and controlling the piezoelectric printhead to perform ink jetting.

Description

Technical Field

[0001] This invention relates to the field of print optimization control technology, and in particular to a print optimization method and system based on a UV flatbed digital printer. Background Technology

[0002] UV flatbed digital printers are widely used in the advertising, building materials, and decoration industries. The core component of a UV flatbed digital printer is the piezoelectric inkjet printhead. The piezoelectric printhead uses a driving voltage waveform to control the deformation of piezoelectric ceramics, thereby squeezing ink within microchannels to form droplets that are ejected. In actual printing, the viscosity of UV ink is extremely sensitive to temperature changes. Fluctuations in the printer's operating environment temperature or the self-heating of the printhead during printing can cause the ink viscosity within the microchannels to drift, leading to changes in droplet velocity, ink interruption, or the formation of satellite droplets, severely affecting print quality. Therefore, adjusting the driving waveform in real time according to the ink state is crucial to ensuring print quality.

[0003] Currently, Chinese invention application number 202411352767.0 discloses a method, apparatus, device, and medium for optimizing the waveform of an inkjet printer. The method involves acquiring a target pattern, using a machine vision system to collect ink dot images, extracting target ink dots using an edge contour extraction algorithm, calculating the geometric feature data of the ink dots, comparing the calculated geometric feature data with preset standard feature data, and adjusting the drive waveform parameters of the printhead until the printed ink dots meet the quality requirements. The aforementioned technologies have the following shortcomings: they require high-precision industrial cameras and machine vision systems to photograph and process the printing results, increasing the hardware cost and size of the printing equipment, and are difficult to install and deploy inside compact or multi-head UV printers; the adjustment of waveform parameters is based on the image of ink dots printed on the medium, which is a visual feedback-based lagging adjustment that cannot perceive the fluid physical state inside the microchannel before or at the moment of ink ejection. When the temperature inside the printhead fluctuates drastically, causing a sudden change in viscosity, the system must wait until inferior ink dots appear and are photographed and identified before it can react, and cannot prevent ejection defects in real time from the root cause; they focus on the superficial optimization of droplet morphology and do not establish a physical model between the driving waveform and fluid viscosity and the acoustic characteristics of the flow channel. When faced with the complex and ever-changing rheological characteristics of UV inks, simple image feedback adjustment is difficult to quickly converge to the optimal waveform. Summary of the Invention

[0004] The technical problem solved by this invention is that existing technologies cannot accurately identify the dynamically changing fluid viscosity in the microchannel of a piezoelectric printhead under the influence of temperature fluctuations in real time without introducing external invasive sensors. Furthermore, they cannot adaptively adjust the piezoelectric drive waveform in real time according to the current fluid viscosity state. This results in poor consistency in the ejection speed, volume, and shape of ink droplets under varying temperatures and long-term printing environments, which can easily lead to satellite droplets or ink breaks, affecting the printing accuracy.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a printing optimization method based on a UV flatbed digital printer, comprising the following steps: Step S1: Modal analysis and frequency response analysis are performed on the fluid-structure interaction finite element model of the piezoelectric nozzle to obtain the hyperbolic function relationship between the radial displacement amplitude and the fluid viscosity. The fluid-structure interaction finite element model includes the piezoelectric ceramic tube region, the glass tube region, and the microchannel fluid region. Step S2: Acquire the self-sensing signal, perform envelope detection processing on the self-sensing signal to obtain the voltage peak value, convert the voltage peak value into the maximum radial displacement amplitude, and calculate the fluid viscosity value based on the hyperbolic function relationship and the maximum radial displacement amplitude. Step S3: Calculate the equivalent resistance parameter based on the fluid viscosity value, and combine it with the preset equivalent acoustic sensing parameter and equivalent acoustic capacitance parameter to construct the discrete system state equation reflecting the fluid viscosity state. Step S4: Input the initial volume change rate sequence into the discrete system state equation, calculate the instantaneous injection flow rate sequence, and obtain the optimal volume change rate sequence based on the instantaneous injection flow rate sequence and iterative calculation. Step S5: Calculate the optimal driving voltage waveform parameter sequence based on the optimal volume change rate sequence, and use the optimal driving voltage waveform parameter sequence to control the piezoelectric printhead to eject ink.

[0006] Preferably, step S1 includes the following sub-steps: Step S101: Based on the geometric dimensions and material properties of the piezoelectric printhead of the UV flatbed digital printer, construct a fluid-structure interaction finite element model of the piezoelectric printhead. The fluid-structure interaction finite element model includes a piezoelectric ceramic tube region, a glass tube region, and a microchannel fluid region. The piezoelectric printhead includes a piezoelectric actuator and a structural deformation cavity. Step S102: Perform modal analysis on the fluid-structure interaction finite element model, identify the vibration mode in which the piezoelectric ceramic tube produces radial uniform expansion and contraction deformation without axial bending deformation, and mark it as the first-order vibration mode. Mark the characteristic frequency corresponding to the first-order vibration mode as the first-order resonant frequency. A frequency response analysis is performed on a fluid-structure interaction finite element model by applying a fixed amplitude AC voltage excitation to a predetermined frequency range centered on the first-order resonant frequency. Specifically, this includes: The dynamic viscosity of the microchannel fluid region is set as the scanning variable. Several fluid viscosity values ​​are selected within a preset viscosity variation range. Frequency response analysis is performed for each fluid viscosity value to obtain the maximum radial displacement amplitude of the piezoelectric actuator at the first resonant frequency under different fluid viscosity values.

[0007] Preferably, step S1 further includes the following sub-steps: Step S103: Using the least squares method, curve fitting is performed on several sets of fluid viscosity values ​​and maximum radial displacement amplitude data pairs to obtain the hyperbolic function relationship between the radial displacement amplitude of the piezoelectric actuator at the first resonant frequency and the fluid viscosity in the microchannel fluid region of the piezoelectric nozzle. The mathematical expression of the hyperbolic function is: ; in, This represents the maximum radial displacement amplitude. This represents the fluid viscosity value. The first fitting coefficient is determined through the curve fitting. The second fitting coefficient is determined by the curve fitting.

[0008] Preferably, step S2 includes the following sub-steps: Step S201 involves controlling a direct digital frequency synthesizer to generate a sinusoidal sweep signal, and simultaneously loading the sinusoidal sweep signal into two paths, specifically including: One path is applied to the drive end of the piezoelectric nozzle, and the other path is applied to the matching capacitor, wherein the capacitance value of the matching capacitor is configured to be equal to the static capacitance value of the piezoelectric nozzle; Step S202: Use a transimpedance operational amplifier to acquire the total current signal flowing through the piezoelectric nozzle and the reference current signal flowing through the matching capacitor, convert the total current signal into a first voltage signal, and convert the reference current signal into a second voltage signal; Step S203: The first voltage signal and the second voltage signal are subtracted using a differential amplifier to eliminate the current component generated by the sinusoidal sweep frequency signal from the first voltage signal, thereby obtaining the self-sensing signal caused by the radial displacement of the piezoelectric actuator.

[0009] Preferably, step S2 includes the following sub-steps: Step S204: Input the self-sensing signal into a linear amplifier and an envelope detector circuit to obtain an envelope voltage signal characterizing the amplitude of the self-sensing signal; Step S205: Extract the voltage peak value of the envelope voltage signal at the first resonant frequency, and convert the voltage peak value into the maximum radial displacement amplitude of the piezoelectric actuator corresponding to the envelope voltage signal according to the preset piezoelectric voltage and displacement proportional coefficient. Step S206: Substitute the maximum radial displacement amplitude of the piezoelectric actuator corresponding to the envelope voltage signal into the hyperbolic function relationship for inversion calculation to obtain the fluid viscosity value. The mathematical expression for the fluid viscosity value is: ; in, This represents the fluid viscosity value. This represents the maximum radial displacement amplitude of the piezoelectric actuator corresponding to the envelope voltage signal. The first fitting coefficient, is the second fitting coefficient.

[0010] Preferably, step S3 includes the following sub-steps: Step S301: Calculate the equivalent acoustic impedance parameter of the microchannel fluid region of the piezoelectric nozzle based on the fluid viscosity value. The mathematical expression for the equivalent acoustic impedance parameter is: ; in, For equivalent acoustic impedance parameters, The effective length of the microchannel in the piezoelectric nozzle. The cross-sectional area of ​​the microchannel of the piezoelectric nozzle; Step S302: Substitute the equivalent acoustic impedance parameter, the preset equivalent acoustic sensing parameter, and the preset equivalent acoustic capacitance parameter into the dynamic equation of the piezoelectric nozzle to construct a continuous-time state-space model. The continuous-time state-space model takes the volume change rate corresponding to the piezoelectric driving waveform as input and the volume flow rate at the nozzle as output. Step S303: Set the sampling period of the system and use a discretization algorithm to convert the continuous-time state-space model into a discrete system state equation in the discrete-time domain.

[0011] Preferably, step S4 includes the following sub-steps: Step S401: The initial volume change rate sequence of the structural deformation cavity of the initial driving waveform is used as the input sequence. The initial volume change rate sequence is substituted into the discrete system state equation to calculate the instantaneous jet flow rate sequence. Step S402: Calculate the difference between the instantaneous jet flow rate sequence and the preset jet flow rate sequence to obtain the flow error sequence, and calculate the norm of the flow error sequence.

[0012] Preferably, step S4 further includes the following sub-steps: Step S403: Determine whether the norm of the flow error sequence is less than a preset convergence threshold. If it is greater than or equal to the preset convergence threshold, then use a P-type iterative learning law to correct the current volume change rate sequence. The mathematical expression of the corrected volume change rate sequence is: ; in, For the revised first The sequence of volume change rates in each iteration. For the first The sequence of volume change rates in each iteration. For learning gain, For the first The flow error sequence obtained from the next iteration is in The value at time; Substitute the corrected volume change rate sequence into the discrete system state equation and repeat steps S401 to S402. If the norm of the flow error sequence is less than the convergence threshold, stop the iteration and obtain the optimal volume change rate sequence.

[0013] Preferably, step S5 includes the following sub-steps: Step S501: Combining the optimal volume change rate sequence with the effective vibration area parameters of the piezoelectric nozzle structure deformation cavity and the electromechanical coupling coefficient of the piezoelectric actuator, the optimal driving voltage waveform parameter sequence is obtained through inversion calculation. The mathematical expression for the optimal driving voltage waveform parameter sequence is: ; in, This is the optimal driving voltage waveform parameter sequence. This is the optimal volume change rate sequence. The effective vibration area parameter of the deformation cavity in the piezoelectric nozzle structure. is the electromechanical coupling coefficient of the piezoelectric actuator; Step S502: The optimal driving voltage waveform parameter sequence is converted into a high-voltage analog driving waveform through a digital-to-analog converter and power amplifier circuit, and the high-voltage analog driving waveform is applied to the piezoelectric nozzle to generate an ink jet flow rate that meets the preset requirements.

[0014] The printing optimization system based on UV flatbed digital printers includes an analysis module, an identification module, a construction module, an optimization module, and a control module. The analysis module is used to perform modal analysis and frequency response analysis on the fluid-structure interaction finite element model of the piezoelectric nozzle, and obtain the hyperbolic function relationship between the radial displacement amplitude and the fluid viscosity. The fluid-structure interaction finite element model includes a piezoelectric ceramic tube region, a glass tube region, and a microchannel fluid region. The identification module is used to collect self-sensing signals, perform envelope detection processing on the self-sensing signals to obtain voltage peak values, convert the voltage peak values ​​into maximum radial displacement amplitude, and calculate fluid viscosity values ​​based on hyperbolic function relationships and maximum radial displacement amplitude. The construction module is used to calculate the equivalent resistance parameter based on the fluid viscosity value, and combine it with the preset equivalent acoustic sensing parameter and equivalent acoustic capacitance parameter to construct a discrete system state equation that reflects the fluid viscosity state. The optimization module is used to input the initial volume change rate sequence into the discrete system state equation, calculate the instantaneous injection flow rate sequence, and obtain the optimal volume change rate sequence based on the instantaneous injection flow rate sequence and iterative calculation. The control module is used to calculate the optimal driving voltage waveform parameter sequence based on the optimal volume change rate sequence, and the optimal driving voltage waveform parameter sequence controls the piezoelectric printhead to perform ink ejection.

[0015] The beneficial effects of this invention are as follows: By sensing the ink viscosity drift within the microchannels caused by ambient temperature or printhead self-heating in real time, and adaptively adjusting the drive waveform based on a physical model, the problem of unstable ejection quality caused by temperature fluctuations in UV printing is solved, achieving high consistency printing across the entire temperature range. Utilizing the self-sensing characteristics of the piezoelectric actuator to invert fluid viscosity eliminates the need for expensive online viscometers or bulky machine vision inspection equipment used in existing technologies, reducing hardware costs. It also solves the technical challenge of not being able to install external sensors in compact multi-printhead parallel architectures. By detecting the electrical response inside the printhead, the fluid state can be acquired and waveform compensation completed before or instantaneously before droplet ejection. This rapid response mechanism based on internal mechanisms prevents the generation of ejection defects and greatly improves the yield rate of high-speed industrial printing. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the steps of a printing optimization method based on a UV flatbed digital printer, as provided in an embodiment of the present invention; Figure 2 This is a basic flowchart of a printing optimization system based on a UV flatbed digital printer, provided as an embodiment of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] Example 1, referring to Figure 1 This paper provides a printing optimization method based on UV flatbed digital printers, including the following steps: Step S1: Obtain the pre-established mapping relationship between the vibration characteristics of the piezoelectric actuator and the fluid viscosity. The mapping relationship is based on the fluid-structure interaction finite element model of the piezoelectric nozzle. Through modal analysis and frequency response analysis, the hyperbolic function relationship between the radial displacement amplitude and the fluid viscosity is obtained. Step S2: Acquire the self-sensing signal, perform envelope detection processing on the self-sensing signal to obtain the voltage peak value, convert the voltage peak value into the maximum radial displacement amplitude, and calculate the fluid viscosity value based on the hyperbolic function relationship and the maximum radial displacement amplitude. Step S3: Calculate the equivalent resistance parameter based on the fluid viscosity value, and combine it with the preset equivalent acoustic sensing parameter and equivalent acoustic capacitance parameter to construct the discrete system state equation reflecting the fluid viscosity state. Step S4: Input the initial volume change rate sequence into the discrete system state equation, calculate the instantaneous injection flow rate sequence, and obtain the optimal volume change rate sequence based on the instantaneous injection flow rate sequence and iterative calculation. Step S5: Calculate the optimal driving voltage waveform parameter sequence based on the optimal volume change rate sequence, and use the optimal driving voltage waveform parameter sequence to control the piezoelectric printhead to eject ink.

[0019] This invention solves the problem of unstable ejection quality caused by temperature fluctuations in UV printing by real-time sensing of ink viscosity drift within the microchannels due to ambient temperature or printhead self-heating, and adaptively adjusting the drive waveform based on a physical model. It achieves high-consistency printing across the entire temperature range. Utilizing the self-sensing characteristics of piezoelectric actuators to invert fluid viscosity eliminates the need for expensive online viscometers or bulky machine vision inspection equipment, reducing hardware costs. It also overcomes the technical challenge of not being able to install external sensors in compact multi-printhead parallel architectures. By detecting the electrical response inside the printhead, the fluid state can be acquired and waveform compensation completed before or instantaneously before droplet ejection. This rapid response mechanism based on internal mechanisms prevents ejection defects at their source, significantly improving the yield rate of high-speed industrial printing.

[0020] In a specific embodiment, step S1 includes the following sub-steps: Step S101: Based on the geometric dimensions and material properties of the piezoelectric printhead of the UV flatbed digital printer, a fluid-structure interaction (FSI) finite element model of the piezoelectric printhead is constructed. The FSI finite element model includes a piezoelectric ceramic tube region, a glass tube region, and a microchannel fluid region. Coupled physical fields of solid mechanics, electrostatic field, and acoustic structural boundary are defined in the FSI finite element model, and potential boundary conditions and fluid boundary conditions are applied. The FSI finite element model is then meshed, and the mesh is refined at the junction of the microchannel fluid region and the channel wall.

[0021] UV flatbed digital printers include piezoelectric printheads, which include microchannels for containing fluid and piezoelectric actuators for driving deformation of the microchannels. The regions of the microchannels acted upon by the piezoelectric actuators form structural deformation cavities.

[0022] When discretizing a fluid-structure interaction finite element model, a specific meshing strategy is required to accurately capture the viscous damping effect of the fluid-solid interaction within the microchannel. Specifically, since the influence of fluid viscosity on the vibration of the piezoelectric actuator is mainly concentrated in the boundary layer region where the fluid and solid meet, a uniform mesh cannot be used. In this embodiment, a fluid dynamics-specific mesh calibration strategy is employed for the fluid region of the microchannel.

[0023] The specific encryption processes include: At the interface between the microchannel fluid region and the inner walls of the piezoelectric ceramic tube and the glass tube (i.e., the fluid-structure interaction boundary), a multi-layered dense boundary layer mesh is generated. The number of boundary layers is set to 3 to 5, with the thickness of the first layer set to 1 / 100 to 1 / 50 of the characteristic dimension of the channel, transitioning towards the channel center at a growth rate of 1.1 to 1.2. The mesh at the channel center can be relatively sparse, while the mesh at the channel walls must be sufficiently fine to resolve the velocity gradient changes of the fluid during vibration.

[0024] Step S102: Perform modal analysis on the fluid-structure interaction finite element model to identify the first-order vibration mode of the radial extension vibration generated by the piezoelectric actuator, and the first-order resonant frequency corresponding to the first-order vibration mode. The modal analysis specifically includes: A characteristic frequency solver was used to perform modal calculations on the fluid-structure interaction finite element model to obtain the first few natural frequencies of the piezoelectric nozzle and the vibration mode shape corresponding to each natural frequency.

[0025] The displacement field data in the vibration mode shape are analyzed to identify the vibration mode in which the piezoelectric ceramic tube produces radial uniform expansion and contraction deformation without axial bending deformation, and this mode is marked as the first-order vibration mode. The characteristic frequency corresponding to the first-order vibration mode is marked as the first-order resonant frequency.

[0026] Frequency response analysis was performed on a fluid-structure interaction finite element model by applying a fixed-amplitude AC voltage excitation to a pre-defined frequency sweep range centered on the first-order resonant frequency. Specifically, this included: The dynamic viscosity of the microchannel fluid region is set as the scanning variable. Several fluid viscosity values ​​are selected within a preset viscosity variation range. Frequency response analysis is performed for each fluid viscosity value to obtain the maximum radial displacement amplitude of the piezoelectric actuator at the first resonant frequency under different fluid viscosity conditions.

[0027] It should be noted that the maximum radial displacement amplitude characterizes the intensity of vibration of the piezoelectric actuator at the first resonant frequency.

[0028] Fluid viscosity is the main energy dissipation factor. The higher the fluid viscosity, the greater the fluid damping and the smaller the amplitude of the piezoelectric actuator. The fluid viscosity and the amplitude of the piezoelectric actuator have a non-linear inverse relationship.

[0029] Step S103: Using the least squares method, curve fitting is performed on several sets of fluid viscosity values ​​and maximum radial displacement amplitude data pairs to obtain the hyperbolic function relationship between the radial displacement amplitude of the piezoelectric actuator at the first resonant frequency and the fluid viscosity in the microchannel fluid region of the piezoelectric nozzle. The mathematical expression of the hyperbolic function is: ; in, This represents the maximum radial displacement amplitude. This represents the fluid viscosity value. These are the first fitting coefficients determined through curve fitting. The second fitting coefficient is determined through curve fitting.

[0030] It should be noted that, The viscosity sensitivity coefficient reflects the sensitivity of a piezoelectric nozzle with a specific structure to changes in viscosity. This is the asymptotic value for high viscosity, reflecting the residual displacement determined by the stiffness of the structure itself as the viscosity approaches infinity. and These two coefficients need to be obtained through finite element simulation, and they are fixed constants for different nozzle models.

[0031] This invention locks the first-order resonant frequency of a piezoelectric nozzle by constructing a fluid-structure interaction finite element model with boundary layer mesh refinement, and uses parametric scanning to fit the hyperbolic function relationship between the maximum radial displacement amplitude and fluid viscosity, revealing the nonlinear suppression mechanism of viscous damping on the resonant amplitude, transforming weak viscosity changes into significant amplitude differences, thus providing a highly sensitive physical benchmark and accurate mathematical model support for subsequent viscosity inversion.

[0032] In a specific embodiment, step S2 includes the following sub-steps: Step S201 involves controlling the direct digital frequency synthesizer to generate a sinusoidal sweep signal, and simultaneously loading the sinusoidal sweep signal into two paths, specifically including: One path is applied to the drive end of the piezoelectric nozzle, and the other path is applied to the matching capacitor, whose capacitance value is configured to be equal to the static capacitance value of the piezoelectric nozzle.

[0033] Step S202: Use a transimpedance operational amplifier to collect the total current signal flowing through the piezoelectric nozzle and the reference current signal flowing through the matching capacitor, convert the total current signal into a first voltage signal, and convert the reference current signal into a second voltage signal.

[0034] It should be noted that the current signal generated by the piezoelectric effect is usually very weak, in the microampere range. Directly measuring the voltage would lead to impedance mismatch and signal attenuation. This step uses a transimpedance amplifier to convert the current signal into a voltage signal without loss, utilizing the virtual ground characteristic.

[0035] First voltage signal The corresponding total current flowing through the piezoelectric nozzle ,in, For the total current, This is an invalid background current. It is a piezoelectric current.

[0036] Second voltage signal The reference current flowing through the matching capacitor .

[0037] Step S203: The first voltage signal and the second voltage signal are subtracted using a differential amplifier to eliminate the current component generated by the sinusoidal sweep frequency signal from the first voltage signal, thereby obtaining the self-sensing signal caused by the radial displacement of the piezoelectric actuator.

[0038] It should be noted that the physical operations performed by the differential amplifier are as follows: ; in, For output voltage, For gain.

[0039] because and All of them contain a large and identical static capacitance component, which is composed of... The subtraction operation can completely cancel out this part of the background signal that is unrelated to viscosity, thus eliminating background noise.

[0040] After the subtraction operation, the output only retains the self-sensing voltage signal generated by the mechanical vibration of the piezoelectric actuator. The amplitude of the self-sensing voltage signal directly reflects the amplitude of the piezoelectric oscillator, and the amplitude is limited by the viscosity damping of the fluid in the microchannel. Therefore, the self-sensing voltage signal is the only physical carrier for subsequent viscosity identification.

[0041] This invention designs a capacitor bridge differential detection circuit based on a matching capacitor. By introducing a reference branch with the same static capacitance value as the piezoelectric nozzle and using a differential amplifier to perform subtraction, it can completely eliminate the static background current component that is independent of viscosity from the complex total current signal, accurately extract the weak piezoelectric self-sensing motional current, significantly improve the signal-to-noise ratio of the self-sensing signal, and solve the technical problem that the effective signal is easily submerged by background noise.

[0042] In a specific embodiment, step S2 includes the following sub-steps: Step S204: Input the self-sensing signal into the linear amplifier and envelope detector circuit to obtain the envelope voltage signal characterizing the amplitude of the self-sensing signal.

[0043] It should be noted that the self-sensing signal acquired in step S2 is a high-frequency sine wave in the time domain. The carrier frequency of the high-frequency sine wave is the first-order resonant frequency, typically between 20kHz and 100kHz. The amplitude of the self-sensing signal contains fluid damping information, but the rapidly changing waveform of the self-sensing signal's amplitude is not convenient for direct calculation. Step S301 uses a linear amplifier to amplify the weak differential signal to a standard level, which is 0 to 3.3V. Subsequently, the high-frequency carrier component is removed by an envelope detector circuit, extracting the slowly changing DC envelope signal. The voltage value of the DC envelope signal directly corresponds to the amplitude intensity of the piezoelectric oscillator at the current moment.

[0044] Step S205: Extract the voltage peak value of the envelope voltage signal at the first resonant frequency, and convert the voltage peak value into the maximum radial displacement amplitude of the piezoelectric actuator corresponding to the envelope voltage signal according to the preset piezoelectric voltage and displacement proportional coefficient.

[0045] It should be noted that although the system ultimately monitors the fluid viscosity within the microchannel, the circuit directly measures the voltage. To utilize the fluid-structure interaction finite element model established in step S1, electrical quantities must be mapped back to mechanical quantities. Based on the reversibility of the inverse and direct piezoelectric effects of piezoelectric materials, the radial displacement amplitude of the piezoelectric actuator under small-signal excitation... Peak voltage of the self-sensing voltage signal It exhibits a highly linear proportional relationship. Radial displacement amplitude This indicates the amount of mechanical deformation of the piezoelectric actuator.

[0046] Preset piezoelectric voltage and displacement proportionality coefficient The piezoelectric voltage constant of piezoelectric ceramics ( The piezoelectric voltage and displacement proportionality coefficient are determined by the wall thickness and polarization direction of the ceramic tube. In practical applications, the preset piezoelectric voltage and displacement proportionality coefficient are usually calibrated once during the factory calibration stage using a laser Doppler vibration meter. .

[0047] Step S206: Substitute the maximum radial displacement amplitude of the piezoelectric actuator corresponding to the envelope voltage signal into the hyperbolic function relationship for inversion calculation to obtain the fluid viscosity value. The mathematical expression for the fluid viscosity value is: ; in, This represents the fluid viscosity value. This represents the maximum radial displacement amplitude of the piezoelectric actuator corresponding to the envelope voltage signal. The first fitting coefficient, is the second fitting coefficient.

[0048] It should be noted that, This represents the portion of displacement attenuation caused by fluid viscous damping, because This represents the theoretical residual displacement when the fluid viscosity approaches infinity. Physically, it is the system's background vibration component determined by the structural stiffness of the piezoelectric nozzle and the non-fluid coupling region. According to the inverse function of the hyperbolic relationship, when the measured maximum radial displacement amplitude... Infinitely approaching the asymptotic value When the denominator approaches zero, the calculated viscosity value tends to infinity, easily leading to numerical overflow. Furthermore, if the displacement is affected by signal noise, the... This will cause the calculation results to lose their physical meaning.

[0049] Therefore, a lower limit threshold for safe displacement is preset in the algorithm implementation. Displacement safety lower limit threshold Set as , It is 0.0001.

[0050] Before calculation, the measurement data should be amplitude-limited. Then a mandatory order This prevents the denominator from being zero or negative, ensuring the numerical stability of the control system.

[0051] This invention employs a signal processing strategy that combines envelope detection with inverse function operation. By using preset piezoelectric voltage and displacement proportionality coefficients, the peak value of the electrical signal is restored to the physical displacement, and then substituted into the inverse function of the hyperbola model for inversion. This successfully quantifies the abstract analog voltage signal into a specific physical viscosity value, realizing quantitative diagnosis of the fluid state in the microchannel. Moreover, the calculation process is efficient, robust, and avoids nonlinear interference of circuit gain fluctuations on the measurement results.

[0052] In a specific embodiment, step S3 includes the following sub-steps: Step S301: Calculate the equivalent acoustic resistance parameter of the microchannel fluid region of the piezoelectric nozzle based on the fluid viscosity value. The mathematical expression for the equivalent acoustic resistance parameter is: ; in, For equivalent acoustic impedance parameters, The effective length of the microchannel in the piezoelectric nozzle. This represents the cross-sectional area of ​​the microchannel in the piezoelectric nozzle.

[0053] Step S302: Substitute the equivalent acoustic impedance parameter, the preset equivalent acoustic sensing parameter, and the preset equivalent acoustic capacitance parameter into the dynamic equation of the piezoelectric nozzle to construct a continuous-time state-space model. The continuous-time state-space model takes the volume change rate corresponding to the piezoelectric driving waveform as input and the volume flow rate at the nozzle as output.

[0054] It should be noted that the equivalent acoustic parameter represents the ink inertia, while the equivalent acoustic capacity parameter represents the flexibility of the ink and structure.

[0055] Equivalent acoustic impedance parameters The dissipation term in the corresponding kinetic equation reflects the energy loss caused by viscous friction when the fluid flows within the microchannel. Since fluid damping is directly related to viscosity, the equivalent acoustic impedance parameter... It is the variable that is dynamically calculated based on the fluid viscosity identified in step S2.

[0056] Equivalent acoustic parameters The inertial term in the corresponding dynamic equation represents the inertial characteristics exhibited by the ink column in the microchannel when it accelerates or decelerates. It is mainly determined by the fluid density and the geometric length of the channel and is regarded as a constant in the continuous-time state-space model.

[0057] Equivalent acoustic volume parameters The elastic term in the corresponding dynamic equation comprehensively reflects the compressibility of the ink itself and the elastic deformation capability of the printhead cavity structure, and is also regarded as a constant in the continuous-time state-space model.

[0058] The constructed dynamic equation is essentially a second-order differential equation describing the volumetric motion of the fluid. Physically, it means that the driving pressure equals the sum of the inertial force, viscous drag, and elastic restoring force. The mathematical expression of the dynamic equation is: ; in, For the spray volume, For volumetric flow rate, The input pressure is used. To facilitate the implementation of the computer control algorithm, the above high-order differential equations are transformed into a system of first-order differential equations, i.e., a continuous-time state-space model. The system state variables are selected as follows: At this point, the calculated equivalent acoustic impedance parameters are directly input into the system matrix, changing the damping elements within the matrix. This means that when the fluid viscosity changes, the matrix changes accordingly, thus accurately simulating the overdamped characteristics at high viscosity or the underdamped characteristics at low viscosity. Overdamped characteristics result in slower flow velocity, while underdamped characteristics result in excessively fast flow velocity and oscillations.

[0059] Step S303: Set the sampling period of the system and use a discretization algorithm to convert the continuous-time state-space model into the discrete system state equation in the discrete-time domain.

[0060] It should be noted that the continuous-time dynamic model is transformed into the state equations of a discrete system. The state equations of the discrete system are as follows: This is to enable the printer's main control chip to predict the next jet state in each discrete clock cycle using digital calculations, thereby providing an accurate mathematical prediction model for subsequent calculations of the optimal drive waveform.

[0061] This invention maps the identified fluid viscosity to the equivalent acoustic impedance parameter of the microchannel in real time, and constructs a discrete system state equation containing inertia, damping and flexibility terms accordingly. This endows the control model with the ability to adapt to changes in the environment, enabling the discrete system state equation to accurately predict the fluid tailing and response characteristics under the current viscosity. It provides a high-precision prediction model for waveform optimization algorithms and solves the problem that fixed models cannot adapt to variable viscosity conditions.

[0062] In a specific embodiment, step S4 includes the following sub-steps: Step S401: The initial volume change rate sequence of the piezoelectric nozzle structure deformation cavity corresponding to the initial driving waveform is used as the input sequence. The initial volume change rate sequence is substituted into the discrete system state equation to calculate the instantaneous jet flow rate sequence.

[0063] It should be noted that the structural deformation cavity is the core region of the microchannel inside the piezoelectric printhead, which is used to contain ink and generate pressure waves. The instantaneous jet flow rate sequence is the set of nozzle ink output corresponding to each sampling moment in the discrete system state equation output in the discrete time domain. It intuitively reflects the velocity distribution and fracture characteristics during the droplet formation process and is the most direct physical quantity for evaluating jet quality.

[0064] Step S402: Calculate the difference between the instantaneous jet flow rate sequence and the preset jet flow rate sequence to obtain the flow error sequence, and calculate the norm of the flow error sequence.

[0065] It should be noted that the mathematical expression for the flow error sequence is: ; in, This is a preset jet flow rate sequence, i.e., an ideal satellite-free droplet jet flow rate curve. For the first The instantaneous jet flow rate sequence is calculated in the next iteration. In this embodiment, the infinite norm is preferably used, that is, the value of the point with the largest absolute value in the error sequence is taken. As long as the maximum error is less than the convergence threshold, it is considered that the accuracy requirement is met at every moment in the entire jet process.

[0066] Step S403: Determine whether the norm of the flow error sequence is less than a preset convergence threshold. If it is greater than or equal to the preset convergence threshold, then use a P-type iterative learning law to correct the current volume change rate sequence. The mathematical expression of the corrected volume change rate sequence is: ; in, For the revised first The sequence of volume change rates in each iteration. For the first The sequence of volume change rates in each iteration. For learning gain, For the first The flow error sequence obtained from the next iteration is in The value at any given moment.

[0067] Substitute the corrected volume change rate sequence into the discrete system state equation and repeat steps S401 to S402. If the norm of the flow error sequence is less than the convergence threshold, stop the iteration and obtain the optimal volume change rate sequence.

[0068] It should be noted that a P-type iterative learning law was employed to implement a time-lead correction strategy to compensate for the inertial lag of the fluid system. In discrete fluid systems, The driving input at any given moment often needs to be transmitted through fluid inertia before it can affect the system. The flow rate output at any given moment. In other words, the current error. In fact, it is caused by the input from the previous moment. This is caused by inaccuracies. Therefore, in order to eliminate... The time error we need to correct is... The input at any given moment. The P-type learning law utilizes this causal relationship to feed future error information back into the current input correction, thereby achieving precise phase compensation.

[0069] Learning gain It is a proportional coefficient for adjusting the iteration step size. The larger the value, the faster the convergence, but the more prone it is to oscillation. Smaller size results in greater stability but also longer processing time. In this invention... The value of is determined through offline simulation.

[0070] Through the above iterative process, the driving waveform can be automatically reshaped. In practical applications, excessive flow rate can easily generate satellite droplets. If the flow rate is found to be excessive in the later stages of the jet, the algorithm will automatically reduce the volume change rate sequence input at the corresponding moment. After several iterations, the calculated jet flow rate curve will closely approximate the ideal curve, thereby obtaining the optimal volume change rate sequence for the current viscosity state.

[0071] This invention introduces a P-type iterative learning control algorithm, using the volume change rate sequence of the structural deformation cavity as the control variable, and employs a method based on... The timing error correction strategy effectively compensates for the inherent inertial lag of the fluid system by adjusting the input sequence point by point. It can quickly eliminate satellite droplets and stringing during the jetting process, ensuring that the actual jetting flow curve is very close to the ideal curve, thereby significantly improving the clarity and edge sharpness of the print.

[0072] In a specific embodiment, step S5 includes the following sub-steps: Step S501: Combining the optimal volume change rate sequence with the effective vibration area parameters of the deformation cavity of the piezoelectric nozzle structure and the electromechanical coupling coefficient of the piezoelectric actuator, the optimal driving voltage waveform parameter sequence is obtained through inversion calculation. The mathematical expression for the optimal driving voltage waveform parameter sequence is: ; in, This is the optimal driving voltage waveform parameter sequence. This is the optimal volume change rate sequence. The effective vibration area parameter of the deformation cavity in the piezoelectric nozzle structure. The electromechanical coupling coefficient of the piezoelectric actuator is given in units of 1000 ppm. .

[0073] It should be noted that the effective vibration area parameter is the surface area projection of the effective displacement generated by the piezoelectric actuator on the inner wall of the structural deformation cavity. Due to boundary constraint effects, the vibration of the flow channel wall is not uniform throughout; therefore, the effective vibration area parameter is usually taken as an equivalent coefficient of the geometric surface area, which is 0.6 to 0.8 times the physical inner surface area. The electromechanical coupling coefficient characterizes the efficiency of the piezoelectric actuator in converting electrical energy into mechanical energy, and its unit is... The electromechanical coupling coefficient is directly related to the piezoelectric constant of the piezoelectric material, the wall thickness of the piezoelectric tube, and the polarization process.

[0074] In step S502, the optimal driving voltage waveform parameter sequence is converted into a high-voltage analog driving waveform through a digital-to-analog converter and a power amplifier circuit, and the high-voltage analog driving waveform is applied to the piezoelectric nozzle to generate an ink jet flow rate that meets the preset requirements.

[0075] It should be noted that digital-to-analog conversion specifically includes: A high-speed DAC chip is used to convert discrete digital sequences into continuous low-voltage analog signals. To ensure that high-frequency details of the waveform are not lost, the DAC's sampling rate should be at least 10 times that of the highest frequency component of the waveform.

[0076] The power amplifier circuit specifically includes: Since piezoelectric nozzles are capacitive loads and their driving voltage is typically as high as 20V to 40V, the low-voltage analog signal needs to be amplified and current-expanded by a high-voltage linear power amplifier to ultimately form a high-voltage analog driving waveform with sufficient driving capability, which is then applied to the two ends of the piezoelectric ceramic electrode, thereby exciting a pulse within the microchannel. Precise pressure waves are used to achieve the preset ink jetting effect.

[0077] Based on the principle of inverse piezoelectric effect, this invention combines the effective vibration area parameter and electromechanical coupling coefficient of the piezoelectric nozzle to perform physical inversion calculation of the optimal volume change rate sequence, solving the problem that theoretical fluid control quantities cannot be directly executed by hardware. It accurately restores the mathematically optimal volume change curve to a physically outputtable drive voltage waveform, maximizing the energy utilization efficiency of piezoelectric drive while ensuring the jetting effect.

[0078] Example 2, refer to Figure 2 It provides a printing optimization system based on UV flatbed digital printers, including analysis, identification, construction, optimization and control modules.

[0079] The analysis module is used to perform modal analysis and frequency response analysis on the fluid-structure interaction finite element model of the piezoelectric nozzle, and obtain the hyperbolic function relationship between the radial displacement amplitude and the fluid viscosity. The fluid-structure interaction finite element model includes the piezoelectric ceramic tube region, the glass tube region, and the microchannel fluid region.

[0080] The identification module is used to collect self-sensing signals, perform envelope detection processing on the self-sensing signals to obtain voltage peak values, convert the voltage peak values ​​into maximum radial displacement amplitude, and calculate the fluid viscosity value based on the hyperbolic function relationship and the maximum radial displacement amplitude.

[0081] The construction module is used to calculate the equivalent resistance parameter based on the fluid viscosity value, and combine it with the preset equivalent acoustic sensing parameter and equivalent acoustic capacitance parameter to construct the discrete system state equation reflecting the fluid viscosity state.

[0082] The optimization module is used to input the initial volume change rate sequence into the discrete system state equation, calculate the instantaneous injection flow rate sequence, and obtain the optimal volume change rate sequence based on the instantaneous injection flow rate sequence and iterative calculation.

[0083] The control module is used to calculate the optimal drive voltage waveform parameter sequence based on the optimal volume change rate sequence, and the optimal drive voltage waveform parameter sequence controls the piezoelectric printhead to perform ink ejection.

[0084] This invention significantly improves the real-time response speed and computational efficiency of the control algorithm through a modular architecture design with deep decoupling of hardware and software. Utilizing pipeline technology, it allows high-frequency signal acquisition and detection tasks to be executed synchronously with state equation iterative optimization tasks in different clock domains, completely eliminating the computational bottleneck of traditional serial control systems. It can dynamically compensate for viscosity drift caused by ambient temperature fluctuations or ink thixotropy, ensuring that the flight state of each ink drop is optimized in real time under high-frequency jetting at tens of thousands of hertz. This fundamentally solves the problems of ink breakage, stringing, and color difference that are common in traditional open-loop equipment, significantly improving the operational stability and yield of industrial-grade UV printing equipment. Through parameterized configuration, it flexibly adapts to different specifications of piezoelectric printheads, greatly reducing R&D costs and maintenance difficulty. It strictly separates the general fluid acoustic control logic from the specific physical properties of the printhead, allowing users to complete the initial adaptation of different brands or models of piezoelectric printheads simply by adjusting key structural parameters such as the effective vibration area parameter, electromechanical coupling coefficient, and flow channel geometry, without needing to redevelop the underlying control code for each new hardware. This feature not only greatly shortens the development cycle of new models for printing equipment manufacturers and reduces hardware inventory and supply chain costs, but also gives the equipment a strong ability to adapt to the field. When end users change inks with different characteristics or replace aging printhead components, they only need to update the software configuration to quickly restore the best printing state, demonstrating extremely high engineering practicality and broad commercial promotion value.

[0085] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A printing optimization method based on a UV flatbed digital printer, characterized in that, Includes the following steps: Step S1: Modal analysis and frequency response analysis are performed on the fluid-structure interaction finite element model of the piezoelectric nozzle to obtain the hyperbolic function relationship between the radial displacement amplitude and the fluid viscosity. The fluid-structure interaction finite element model includes the piezoelectric ceramic tube region, the glass tube region, and the microchannel fluid region. Step S2: Acquire the self-sensing signal, perform envelope detection processing on the self-sensing signal to obtain the voltage peak value, convert the voltage peak value into the maximum radial displacement amplitude, and calculate the fluid viscosity value based on the hyperbolic function relationship and the maximum radial displacement amplitude. Step S3: Calculate the equivalent resistance parameter based on the fluid viscosity value, and combine it with the preset equivalent acoustic sensing parameter and equivalent acoustic capacitance parameter to construct the discrete system state equation reflecting the fluid viscosity state. Step S4: Input the initial volume change rate sequence into the discrete system state equation, calculate the instantaneous injection flow rate sequence, and obtain the optimal volume change rate sequence based on the instantaneous injection flow rate sequence and iterative calculation. Step S5: Calculate the optimal driving voltage waveform parameter sequence based on the optimal volume change rate sequence, and use the optimal driving voltage waveform parameter sequence to control the piezoelectric printhead to eject ink.

2. The printing optimization method based on a UV flatbed digital printer as described in claim 1, characterized in that, Step S1 includes the following sub-steps: Step S101: Based on the geometric dimensions and material properties of the piezoelectric printhead of the UV flatbed digital printer, construct a fluid-structure interaction finite element model of the piezoelectric printhead. The fluid-structure interaction finite element model includes a piezoelectric ceramic tube region, a glass tube region, and a microchannel fluid region. The piezoelectric printhead includes a piezoelectric actuator and a structural deformation cavity. Step S102: Perform modal analysis on the fluid-structure interaction finite element model, identify the vibration mode in which the piezoelectric ceramic tube produces radial uniform expansion and contraction deformation without axial bending deformation, and mark it as the first-order vibration mode. Mark the characteristic frequency corresponding to the first-order vibration mode as the first-order resonant frequency. A frequency response analysis is performed on a fluid-structure interaction finite element model by applying a fixed amplitude AC voltage excitation to a predetermined frequency range centered on the first-order resonant frequency. Specifically, this includes: The dynamic viscosity of the microchannel fluid region is set as the scanning variable. Several fluid viscosity values ​​are selected within a preset viscosity variation range. Frequency response analysis is performed for each fluid viscosity value to obtain the maximum radial displacement amplitude of the piezoelectric actuator at the first resonant frequency under different fluid viscosity values.

3. The printing optimization method based on a UV flatbed digital printer as described in claim 2, characterized in that, Step S1 further includes the following sub-steps: Step S103: Using the least squares method, curve fitting is performed on several sets of fluid viscosity values ​​and maximum radial displacement amplitude data pairs to obtain the hyperbolic function relationship between the radial displacement amplitude of the piezoelectric actuator at the first resonant frequency and the fluid viscosity in the microchannel fluid region of the piezoelectric nozzle. The mathematical expression of the hyperbolic function is: ； in, This represents the maximum radial displacement amplitude. This represents the fluid viscosity value. The first fitting coefficient is determined through the curve fitting. The second fitting coefficient is determined by the curve fitting.

4. The printing optimization method based on a UV flatbed digital printer as described in claim 3, characterized in that, Step S2 includes the following sub-steps: Step S201 involves controlling a direct digital frequency synthesizer to generate a sinusoidal sweep signal, and simultaneously loading the sinusoidal sweep signal into two paths, specifically including: One path is applied to the drive end of the piezoelectric nozzle, and the other path is applied to the matching capacitor, wherein the capacitance value of the matching capacitor is configured to be equal to the static capacitance value of the piezoelectric nozzle; Step S202: Use a transimpedance operational amplifier to acquire the total current signal flowing through the piezoelectric nozzle and the reference current signal flowing through the matching capacitor, convert the total current signal into a first voltage signal, and convert the reference current signal into a second voltage signal; Step S203: The first voltage signal and the second voltage signal are subtracted using a differential amplifier to eliminate the current component generated by the sinusoidal sweep frequency signal from the first voltage signal, thereby obtaining the self-sensing signal caused by the radial displacement of the piezoelectric actuator.

5. The printing optimization method based on a UV flatbed digital printer as described in claim 4, characterized in that, Step S2 includes the following sub-steps: Step S204: Input the self-sensing signal into a linear amplifier and an envelope detector circuit to obtain an envelope voltage signal characterizing the amplitude of the self-sensing signal; Step S205: Extract the voltage peak value of the envelope voltage signal at the first resonant frequency, and convert the voltage peak value into the maximum radial displacement amplitude of the piezoelectric actuator corresponding to the envelope voltage signal according to the preset piezoelectric voltage and displacement proportional coefficient. Step S206: Substitute the maximum radial displacement amplitude of the piezoelectric actuator corresponding to the envelope voltage signal into the hyperbolic function relationship for inversion calculation to obtain the fluid viscosity value. The mathematical expression for the fluid viscosity value is: ； in, This represents the fluid viscosity value. This represents the maximum radial displacement amplitude of the piezoelectric actuator corresponding to the envelope voltage signal. The first fitting coefficient is... is the second fitting coefficient.

6. The printing optimization method based on a UV flatbed digital printer as described in claim 5, characterized in that, Step S3 includes the following sub-steps: Step S301: Calculate the equivalent acoustic impedance parameter of the microchannel fluid region of the piezoelectric nozzle based on the fluid viscosity value. The mathematical expression for the equivalent acoustic impedance parameter is: ； in, For equivalent acoustic impedance parameters, The effective length of the microchannel in the piezoelectric nozzle. The cross-sectional area of ​​the microchannel of the piezoelectric nozzle; Step S302: Substitute the equivalent acoustic impedance parameter, the preset equivalent acoustic sensing parameter, and the preset equivalent acoustic capacitance parameter into the dynamic equation of the piezoelectric nozzle to construct a continuous-time state-space model. The continuous-time state-space model takes the volume change rate corresponding to the piezoelectric driving waveform as input and the volume flow rate at the nozzle as output. Step S303: Set the sampling period of the system and use a discretization algorithm to convert the continuous-time state-space model into a discrete system state equation in the discrete-time domain.

7. The printing optimization method based on a UV flatbed digital printer as described in claim 6, characterized in that, Step S4 includes the following sub-steps: Step S401: The initial volume change rate sequence of the structural deformation cavity of the initial driving waveform is used as the input sequence. The initial volume change rate sequence is substituted into the discrete system state equation to calculate the instantaneous jet flow rate sequence. Step S402: Calculate the difference between the instantaneous jet flow rate sequence and the preset jet flow rate sequence to obtain the flow error sequence, and calculate the norm of the flow error sequence.

8. The printing optimization method based on a UV flatbed digital printer as described in claim 7, characterized in that, Step S4 further includes the following sub-steps: Step S403: Determine whether the norm of the flow error sequence is less than a preset convergence threshold. If it is greater than or equal to the preset convergence threshold, then use a P-type iterative learning law to correct the current volume change rate sequence. The mathematical expression of the corrected volume change rate sequence is: ； in, For the revised first The sequence of volume change rates in each iteration. For the first The sequence of volume change rates in each iteration. For learning gain, For the first The flow error sequence obtained from the next iteration is in The value at time; Substitute the corrected volume change rate sequence into the discrete system state equation and repeat steps S401 to S402. If the norm of the flow error sequence is less than the convergence threshold, stop the iteration and obtain the optimal volume change rate sequence.

9. The printing optimization method based on a UV flatbed digital printer as described in claim 8, characterized in that, Step S5 includes the following sub-steps: Step S501: Combining the optimal volume change rate sequence with the effective vibration area parameters of the piezoelectric nozzle structure deformation cavity and the electromechanical coupling coefficient of the piezoelectric actuator, the optimal driving voltage waveform parameter sequence is obtained through inversion calculation. The mathematical expression for the optimal driving voltage waveform parameter sequence is: ； in, This is the optimal driving voltage waveform parameter sequence. This is the optimal volume change rate sequence. The effective vibration area parameter of the deformation cavity in the piezoelectric nozzle structure. is the electromechanical coupling coefficient of the piezoelectric actuator; Step S502: The optimal driving voltage waveform parameter sequence is converted into a high-voltage analog driving waveform through a digital-to-analog converter and power amplifier circuit, and the high-voltage analog driving waveform is applied to the piezoelectric nozzle to generate an ink jet flow rate that meets the preset requirements.

10. A print optimization system based on a UV flatbed digital printer, which is applied in the print optimization method based on a UV flatbed digital printer as described in any one of claims 1-9, characterized in that, It includes an analysis module, an identification module, a construction module, an optimization module, and a control module; The analysis module is used to perform modal analysis and frequency response analysis on the fluid-structure interaction finite element model of the piezoelectric nozzle, and obtain the hyperbolic function relationship between the radial displacement amplitude and the fluid viscosity. The fluid-structure interaction finite element model includes a piezoelectric ceramic tube region, a glass tube region, and a microchannel fluid region. The identification module is used to collect self-sensing signals, perform envelope detection processing on the self-sensing signals to obtain voltage peak values, convert the voltage peak values ​​into maximum radial displacement amplitude, and calculate fluid viscosity values ​​based on hyperbolic function relationships and maximum radial displacement amplitude. The construction module is used to calculate the equivalent resistance parameter based on the fluid viscosity value, and combine it with the preset equivalent acoustic sensing parameter and equivalent acoustic capacitance parameter to construct a discrete system state equation that reflects the fluid viscosity state. The optimization module is used to input the initial volume change rate sequence into the discrete system state equation, calculate the instantaneous injection flow rate sequence, and obtain the optimal volume change rate sequence based on the instantaneous injection flow rate sequence and iterative calculation. The control module is used to calculate the optimal driving voltage waveform parameter sequence based on the optimal volume change rate sequence, and the optimal driving voltage waveform parameter sequence controls the piezoelectric printhead to perform ink ejection.

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