3D printing control method and system

Abstract

The invention relates to the technical field of 3D printing control, in particular to a 3D printing control method and system. The method comprises the following steps that a piezoelectric ceramic actuator is used for conducting pulse plastic melting state collection on a 3D printing nozzle, and plastic melt pulse monitoring data are generated; carrying out damping coefficient fitting on the plastic melt pulse monitoring data, and carrying out PID control instruction parameter dynamic adjustment to obtain a plastic melt flow extrusion control strategy; obtaining a target 3D printing model; and intelligent 3D printing control optimization is conducted on the target 3D printing model through a plastic melting flow extrusion control strategy, and an intelligent 3D printing control strategy is obtained. The micro sensor is integrated in the printing nozzle to collect melt flow data in real time, and printing parameters are dynamically adjusted, so that the flow behavior of the high-viscosity plastic melt is accurately controlled, and the surface quality and the size precision of a 3D printing piece are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of 3D printing control technology, and in particular to a 3D printing control method and system. Background Art

[0002] Additive manufacturing technology, also known as 3D printing technology, heats and melts filamentary thermoplastic materials, extrude them through a nozzle, and stacks them layer by layer along a predetermined path to form three-dimensional solid parts. 3D printing can quickly and efficiently manufacture parts with complex structures, especially in the fields of aerospace, medical equipment, industrial manufacturing and consumer electronics. In the 3D printing process, plastic materials need to be heated and melted before being extruded and deposited by a nozzle. For ordinary plastic materials, due to their good fluidity in the molten state, the melt flow behavior is easier to control, which can better meet the requirements of printing accuracy and surface quality. When processing high-viscosity plastic materials, due to their poor fluidity in the molten state, the melt flow behavior is more complicated, and it is difficult to achieve precise control of the melt flow behavior, which leads to a series of printing defects. However, most traditional 3D printing extrusion control methods use constant speed extrusion, which makes it difficult to achieve real-time and precise control of the melt flow behavior. For example, poor melt flow can lead to defects such as rough texture and poor interlayer bonding on the printed part surface; while too fast melt flow can lead to decreased dimensional accuracy and overflow, resulting in problems such as rough surface and low dimensional accuracy of the printed part. Summary of the invention

[0003] Based on this, the present invention provides a 3D printing control method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a 3D printing control method comprises the following steps: Step S1: using a piezoelectric ceramic actuator to integrate the 3D printing nozzle at the outlet of the melting cavity, and performing initial actuator signal modulation to generate an initial pulse drive extrusion signal; performing preliminary molten plastic extrusion according to the initial pulse drive extrusion signal, and performing pulse plastic melting state acquisition to generate plastic melt pulse monitoring data; Step S2: extracting the melt flow dominant frequency from the plastic melt pulse monitoring data to generate melt dominant frequency data; performing damping coefficient fitting on the plastic melt pulse monitoring data to generate a plastic melt extrusion damping coefficient; performing microfluidic control instruction correction on the initial pulse drive extrusion signal based on the melt dominant frequency data and the plastic melt extrusion damping coefficient to obtain corrected microfluidic control instruction data; Step S3: performing real-time pulse extrusion according to the modified microfluidic control instruction data, and monitoring the pulse extrusion process to obtain real-time plastic melt extrusion monitoring data; dynamically adjusting PID control instruction parameters according to the real-time plastic melt extrusion monitoring data to obtain a plastic melt flow extrusion control strategy; Step S4: Perform a plastic melt flow stability analysis according to the plastic melt flow extrusion control strategy to generate melt flow trend status data; obtain a target 3D printing model; mesh the target 3D printing model to obtain 3D model printing mesh data; divide the 3D model printing mesh data into plastic melt flow risk areas according to the melt flow trend status data, and perform intelligent 3D printing control optimization to obtain an intelligent 3D printing control strategy.

[0005] Preferably, step S1 comprises the following steps: Step S11: using a piezoelectric ceramic actuator to integrate the 3D printing nozzle at the outlet of the melting cavity to obtain a micro extrusion actuator; Step S12: performing initial actuator signal modulation on the micro extrusion actuator to generate an initial pulse drive extrusion signal; Step S13: Based on the initial pulse driving extrusion signal, the micro extrusion actuator is controlled to drive the displacement execution and perform preliminary molten plastic extrusion to obtain pulse extrusion plastic melt; Step S14: integrating the nozzle monitoring network using the micro pressure sensor, the temperature sensor and the flow rate sensor to generate a nozzle sensing monitoring network; Step S15: collecting pulse plastic melting state of pulse extrusion plastic melting through the nozzle sensor monitoring network to generate original pulse plastic melting state data; Step S16: Perform time-series synchronization calibration on the original pulse plastic-melt state data to generate plastic-melt pulse monitoring data.

[0006] Preferably, step S2 comprises the following steps: Step S21: performing Kalman filtering noise reduction processing on the plastic melt pulse monitoring data to generate pre-processed plastic melt monitoring data; Step S22: performing time domain window segmentation on the pre-processed plastic melt monitoring data through a preset time window to generate time series plastic melt monitoring data; Step S23: performing fast Fourier transform processing on the time-series melt monitoring data, and extracting the melt flow dominant frequency to generate melt dominant frequency data; Step S24: performing autocorrelation analysis according to the time series plastic melt monitoring data and performing damping coefficient fitting to generate a plastic melt extrusion damping coefficient; Step S25: constructing a second-order oscillation model of melt flow dynamics based on the melt dominant frequency data and the plastic melt extrusion damping coefficient to generate a melt flow dynamics model; Step S26: using the melt flow dynamics model to correct the microfluidic control instruction through the initial pulse driving extrusion signal to obtain corrected microfluidic control instruction data.

[0007] Preferably, step S24 comprises the following steps: Step S241: extracting the state of the plastic melt according to the time-series plastic melt monitoring data, and obtaining melt extrusion pressure time-series data, melt extrusion temperature time-series data, and melt extrusion flow rate time-series data respectively; Step S242: performing autocorrelation operation on the melt extrusion pressure time series data to generate melt pressure autocorrelation series coefficients; Step S243: performing peak detection according to the melt pressure autocorrelation sequence coefficient, and intercepting the first peak decay portion to obtain pressure decay curve data; Step S244: performing exponential function fitting on the pressure decay curve data based on the melt pressure autocorrelation series coefficient, and calculating the pressure decay coefficient to generate the pressure decay coefficient; Step S245: processing the melt temperature period main frequency component according to the melt extrusion temperature time series data to generate melt temperature fluctuation main frequency data; Step S246: calculating the flow rate change rate according to the melt extrusion flow rate time series data, and performing root mean square value processing to generate a root mean square value of the flow rate change rate; Step S247: Calculate the melt flow damping coefficient based on the RMS value of the flow rate change rate, the melt temperature fluctuation main frequency data, and the pressure attenuation coefficient to generate the plastic melt extrusion damping coefficient.

[0008] Preferably, step S26 comprises the following steps: Step S261: obtaining desired plastic melt flow state data; Step S262: using a melt flow dynamics model to simulate the desired plastic melt flow state data, and performing an optimal control waveform parameter combination for the extrusion actuator to obtain a desired pulse extrusion drive control signal; Step S263: generating a driving waveform signal according to the desired pulse extrusion driving control signal, and performing signal discrete processing to generate a discrete driving waveform control signal; Step S264: effectively correcting the discrete drive waveform control signal through the initial pulse drive extrusion signal, thereby obtaining effective discrete control signal sequence data; Step S265: adding a timestamp according to the valid discrete control signal sequence data, and performing instruction code conversion to generate corrected microfluidic control instruction data.

[0009] Preferably, step S3 comprises the following steps: Step S31: loading the corrected microfluidic control instruction data into the microcontroller, and controlling the piezoelectric ceramic actuator to perform real-time pulse extrusion, while using the nozzle sensor monitoring network to perform real-time pulse extrusion process monitoring to obtain real-time plastic melt extrusion monitoring data; Step S32: performing monitoring feature preprocessing according to the real-time plastic melt extrusion monitoring data to generate real-time plastic melt extrusion monitoring feature data; Step S33: calculating the expected plastic melt flow state deviation value of the modified microfluidic control instruction data based on the real-time plastic melt extrusion monitoring characteristic data to generate real-time plastic melt extrusion state deviation data; Step S34: dynamically adjust the PID control instruction parameters of the corrected micro-flow control instruction data according to the real-time plastic melt extrusion state deviation data to obtain the plastic melt flow extrusion control strategy.

[0010] Preferably, step S33 includes the following steps: Step S331: extracting operation timestamps according to real-time plastic melt extrusion monitoring feature data to generate plastic melt extrusion timestamp data; Step S332: mapping the control cycle expected state parameter of the modified microfluidic control instruction data through the plastic melt extrusion timestamp data to obtain the fluid expected parameter value; Step S333: performing real-time control state parameter conversion on the real-time plastic melt extrusion monitoring characteristic data to obtain real-time fluid parameter values; Step S334: using the real-time fluid parameter value to calculate the state deviation value of the fluid expected parameter value, and generating real-time extrusion state deviation data; Step S335: scaling the real-time extrusion state deviation data using a preset scaling factor, and performing parameter limiting processing to obtain real-time plastic melt extrusion state deviation data.

[0011] Preferably, step S4 comprises the following steps: Step S41: performing a plastic melt flow stability analysis according to a plastic melt flow extrusion control strategy to generate melt flow trend state data; Step S42: Meshing the target 3D printing model to obtain 3D model printing mesh data; Step S43: performing 3D printing area identification according to the 3D model printing grid data, and performing flow state-printing area mapping processing according to the melt flow trend state data to generate melt flow state-printing area mapping data; Step S44: dividing the plastic melt flow risk area according to the melt flow state-printing area mapping data to obtain a melt accumulation risk area and a poor flow risk area; Step S45: performing intelligent 3D printing control optimization on the melt accumulation risk area and the poor flow risk area, thereby obtaining an intelligent 3D printing control strategy.

[0012] Preferably, step S45 includes the following steps: Step S451: Acquire real-time 3D printing point data; Step S452: when the real-time 3D printing point data is located in the melt accumulation risk area, obtaining the preset nozzle residence time in the accumulation area; Step S453: evaluating the accumulation degree according to the melt accumulation risk area, and performing residence time increase coefficient processing to generate the accumulation area residence time increase coefficient; Step S454: adjusting the preset nozzle dwell time of the deposition area by using the deposition area dwell time increase coefficient to generate the deposition area adjusted dwell time; when the deposition area adjusted dwell time is greater than or equal to the preset maximum dwell time threshold, marking the preset maximum dwell time threshold as the deposition area optimized dwell time; when the deposition area adjusted dwell time is less than the preset maximum dwell time threshold, marking the deposition area adjusted dwell time as the deposition area optimized dwell time; Step S455: performing nozzle residence time adjustment control on the modified microfluidic control instruction data based on the optimized residence time of the accumulation area, and obtaining a nozzle residence control strategy for the accumulation area; Step S456: when the real-time 3D printing point data is located in a poor flow risk area, obtaining preset nozzle temperature data of the poor flow area; Step S457: analyzing the melt flow characteristics of the poor flow risk area according to the poor flow risk area, matching the spraying temperature increase coefficient according to the preset nozzle temperature data of the poor flow area, and generating the spraying temperature optimization data of the poor flow area; Step S458: adjusting the spraying temperature according to the optimized data of the spraying temperature in the congested area, and controlling the direction and posture of the nozzle to obtain a spraying control strategy for the congested area; Step S459: Optimize the 3D printing spray control based on the accumulation area spray control strategy and the congested area spray control strategy to obtain an intelligent 3D printing control strategy.

[0013] Preferably, the present invention further provides a 3D printing control system for executing the 3D printing control method as described above, the 3D printing control system comprising: The pulse extrusion and monitoring module is used to integrate the 3D printing nozzle at the outlet of the melting cavity using a piezoelectric ceramic actuator, and perform initial actuator signal modulation to generate an initial pulse drive extrusion signal; extrude the molten plastic according to the initial pulse drive extrusion signal, and collect the pulse plastic melting state to generate plastic melt pulse monitoring data; The microfluidic instruction correction module is used to extract the melt flow dominant frequency from the plastic melt pulse monitoring data to generate the melt dominant frequency data; perform damping coefficient fitting on the plastic melt pulse monitoring data to generate the plastic melt extrusion damping coefficient; perform microfluidic instruction correction on the initial pulse drive extrusion signal based on the melt dominant frequency data and the plastic melt extrusion damping coefficient to obtain the corrected microfluidic control instruction data; A real-time extrusion control module is used to perform real-time pulse extrusion according to the modified microfluidic control instruction data, and to monitor the real-time pulse extrusion process to obtain real-time plastic melt extrusion monitoring data; dynamically adjust PID control instruction parameters according to the real-time plastic melt extrusion monitoring data to obtain a plastic melt flow extrusion control strategy; The intelligent printing optimization module is used to perform plastic melt flow stability analysis according to the plastic melt flow extrusion control strategy and generate melt flow trend status data; mesh the target 3D printing model to obtain 3D model printing mesh data; divide the 3D model printing mesh data into plastic melt flow risk areas through the melt flow trend status data, and perform intelligent 3D printing control optimization to obtain an intelligent 3D printing control strategy.

[0014] The beneficial effect of the present invention is that by pulse monitoring the plastic melt in the initial extrusion process, it is possible to generate plastic melt pulse monitoring data reflecting the melt flow characteristics, extract the dominant frequency of the melt flow, and fit the plastic melt extrusion damping coefficient. The initial pulse drive extrusion signal is corrected by microfluidic instructions using these parameters, which is equivalent to fine-tuning the printing process based on real-time feedback, effectively overcoming the challenge of poor fluidity of high-viscosity materials, and realizing accurate control of the extrusion amount. Compared with the traditional method, it is no longer a simple reliance on empirical parameters, but a real-time adaptive adjustment process, which can effectively solve the differences brought by different materials and different printing environments, and has stronger adaptability. According to the corrected microfluidic control instruction data, real-time pulse extrusion is performed, and real-time plastic melt extrusion monitoring data is obtained through real-time monitoring, which ensures the real-time controllability of the extrusion process. The PID control instruction parameters are dynamically adjusted using real-time monitoring data, which effectively solves the problem of melt flow instability and ensures that the melt is extruded in a more uniform and stable state. This adaptive control mechanism can significantly reduce defects such as surface roughness and poor interlayer bonding caused by uneven melt flow, thereby improving the overall quality of the printed part. By analyzing the 3D model printing grid data through the melt flow trend state data, it is possible to achieve intelligent 3D printing control optimization through melt flow trend prediction and risk area division, thereby improving printing quality and efficiency. Especially for the 3D printing of high-viscosity plastic materials, it is ensured that the prints can maintain excellent quality and precision in complex structure areas. Therefore, a 3D printing control method of the present invention integrates a micro sensor in the print nozzle to monitor the melt flow state in real time, and uses these data to dynamically adjust the printing parameters to achieve precise control of the flow behavior of high-viscosity plastic melts. This method is particularly aimed at the problems of poor melt flow and too fast flow. It optimizes the printing process through real-time data feedback, effectively avoiding defects such as rough surface of printed parts, poor interlayer bonding, and reduced dimensional accuracy, significantly improving the printing quality, and is particularly suitable for 3D printing of high-viscosity and high-performance plastic materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the steps of the 3D printing control method of the present invention; Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart; Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG. The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0016] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0017] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0018] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0019] To achieve this, please refer to Figures 1 to 3 The present invention provides a 3D printing control method, comprising the following steps: Step S1: using a piezoelectric ceramic actuator to integrate the 3D printing nozzle at the outlet of the melting cavity, and performing initial actuator signal modulation to generate an initial pulse drive extrusion signal; performing preliminary molten plastic extrusion according to the initial pulse drive extrusion signal, and performing pulse plastic melting state acquisition to generate plastic melt pulse monitoring data; Step S2: extracting the melt flow dominant frequency from the plastic melt pulse monitoring data to generate melt dominant frequency data; performing damping coefficient fitting on the plastic melt pulse monitoring data to generate a plastic melt extrusion damping coefficient; performing microfluidic control instruction correction on the initial pulse drive extrusion signal based on the melt dominant frequency data and the plastic melt extrusion damping coefficient to obtain corrected microfluidic control instruction data; Step S3: performing real-time pulse extrusion according to the modified microfluidic control instruction data, and monitoring the pulse extrusion process to obtain real-time plastic melt extrusion monitoring data; dynamically adjusting PID control instruction parameters according to the real-time plastic melt extrusion monitoring data to obtain a plastic melt flow extrusion control strategy; Step S4: Perform a plastic melt flow stability analysis according to the plastic melt flow extrusion control strategy to generate melt flow trend status data; obtain a target 3D printing model; mesh the target 3D printing model to obtain 3D model printing mesh data; divide the 3D model printing mesh data into plastic melt flow risk areas according to the melt flow trend status data, and perform intelligent 3D printing control optimization to obtain an intelligent 3D printing control strategy.

[0020] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic diagram of a step flow chart of a 3D printing control method of the present invention. In this embodiment, the 3D printing control method includes the following steps: Step S1: using a piezoelectric ceramic actuator to integrate the 3D printing nozzle at the outlet of the melting cavity, and performing initial actuator signal modulation to generate an initial pulse drive extrusion signal; performing preliminary molten plastic extrusion according to the initial pulse drive extrusion signal, and performing pulse plastic melting state acquisition to generate plastic melt pulse monitoring data; In the embodiment of the present invention, the size is selected to be 10mm 5mm 2mm piezoelectric ceramic stack actuator. Use high-strength and high-temperature resistant epoxy resin glue to fix the piezoelectric ceramic actuator at the outlet of the melting cavity of the 3D printing nozzle to ensure that the actuator and the nozzle are tightly fitted to prevent leakage of molten plastic. Solder the electrode leads of the actuator to the control circuit board and use high-temperature insulating tape for insulation. Use a function generator to generate the initial pulse drive extrusion signal, and set the signal parameters to a square wave signal with a frequency of 100Hz, a duty cycle of 50%, and an amplitude of 5V. Connect the output of the function generator to the signal amplifier circuit on the control circuit board, and use an operational amplifier to build an amplifier circuit with a voltage gain of 10 to amplify the 5V square wave signal to 50V to drive the piezoelectric ceramic actuator. Set the heating temperature of the 3D printing nozzle to the melting temperature of PLA plastic, 200℃. After the plastic filament is completely melted, apply the initial pulse drive extrusion signal to the piezoelectric ceramic actuator. Driven by the electrical signal, the actuator produces periodic telescopic displacement, pushing the molten plastic out of the nozzle. When the micro extrusion actuator performs pulse extrusion according to step S13, the pressure sensor, temperature sensor and flow rate sensor in the nozzle sensor monitoring network will collect data in real time to form the original pulse plastic melting state data.

[0021] Step S2: extracting the melt flow dominant frequency from the plastic melt pulse monitoring data to generate melt dominant frequency data; performing damping coefficient fitting on the plastic melt pulse monitoring data to generate a plastic melt extrusion damping coefficient; performing microfluidic control instruction correction on the initial pulse drive extrusion signal based on the melt dominant frequency data and the plastic melt extrusion damping coefficient to obtain corrected microfluidic control instruction data; In an embodiment of the present invention, the plastic melt pulse monitoring data collected in step S1 is subjected to fast Fourier transform (FFT) analysis to extract the dominant frequency of the melt flow. For example, the curve of the melt diameter changing with time is subjected to FFT analysis by MATLAB software to identify the frequency component with the largest amplitude in the spectrum diagram, which is the dominant frequency, and it is assumed that the dominant frequency is 200Hz. At the same time, the plastic melt pulse monitoring data is fitted with a damped oscillation model by nonlinear least squares method to obtain the plastic melt extrusion damping coefficient. For example, it is assumed that the damping coefficient obtained by fitting is 0.1. According to the extracted melt dominant frequency of 200Hz and the damping coefficient of 0.1, the initial pulse drive extrusion signal is corrected. For example, the frequency of the initial square wave signal is adjusted to the melt dominant frequency of 200Hz, and the duty cycle is adjusted to 60% to compensate for the energy loss caused by damping. The new pulse signal can be realized by changing the frequency and duty cycle of the square wave signal, or by using a more complex waveform (such as a sine wave or a triangle wave). The corrected parameters are converted into digital signals and output to the driving circuit of the piezoelectric ceramic actuator through a digital signal processor (DSP) or a microcontroller (MCU) to achieve precise control of the extrusion process and obtain corrected microfluidic control instruction data.

[0022] Step S3: performing real-time pulse extrusion according to the modified microfluidic control instruction data, and monitoring the pulse extrusion process to obtain real-time plastic melt extrusion monitoring data; dynamically adjusting PID control instruction parameters according to the real-time plastic melt extrusion monitoring data to obtain a plastic melt flow extrusion control strategy; In an embodiment of the present invention, the corrected microfluidic control instruction data obtained in step S2 is loaded into a signal generator to drive the piezoelectric ceramic actuator to perform real-time pulse extrusion. At the same time, a laser displacement sensor (e.g., a laser displacement sensor with an accuracy of 1 μm) is used to monitor the extrusion amount of the molten plastic at the nozzle outlet in real time. The collected extrusion amount data is compared with the preset extrusion amount target value, and the deviation value is input into the PID controller. The PID controller calculates the control signal according to the set proportional, integral, and differential parameters, and adjusts the driving voltage of the piezoelectric ceramic actuator to achieve precise control of the extrusion amount. For example, when the actual extrusion amount is less than the target extrusion amount, the PID controller increases the driving voltage and increases the extrusion speed; otherwise, the driving voltage is reduced. The PID parameters are continuously monitored and adjusted until the extrusion process is stable, and real-time plastic melt extrusion monitoring data is obtained. According to the real-time monitoring data, such as the melt extrusion speed, pressure, temperature, etc., the parameters of the PID controller (e.g., proportional coefficient, integral coefficient, differential coefficient) are dynamically adjusted to maintain the stability of the melt flow. By continuously optimizing PID parameters, the plastic melt flow extrusion control strategy is finally obtained. For example, the best PID parameter combination and the corresponding control output range are determined.

[0023] Step S4: Perform a plastic melt flow stability analysis according to the plastic melt flow extrusion control strategy to generate melt flow trend status data; obtain a target 3D printing model; mesh the target 3D printing model to obtain 3D model printing mesh data; divide the 3D model printing mesh data into plastic melt flow risk areas according to the melt flow trend status data, and perform intelligent 3D printing control optimization to obtain an intelligent 3D printing control strategy.

[0024] In an embodiment of the present invention, based on the plastic melt flow extrusion control strategy obtained in step S3, the flow stability of the plastic melt is analyzed. For example, by analyzing the changes in parameters such as melt extrusion speed, pressure, and temperature over time, it is determined whether the melt flow is stable. And the analysis results are converted into melt flow trend state data, for example, 0 represents stable melt flow and 1 represents unstable melt flow. A target 3D printing model, such as a cube model, is obtained using computer-aided design (CAD) software. The target 3D printing model is meshed using finite element analysis software (for example, ANSYS), and the model is discretized into several small units to obtain 3D model printing grid data, such as the size of each unit, node coordinates, etc. According to the melt flow trend state data, the 3D model printing grid data is analyzed to identify the melt flow risk area. For example, the melt flow trend state data is mapped to the grid of the 3D model. If the melt flow trend state corresponding to a grid unit is 1, the unit is marked as a risk area. For the risk area, corresponding control optimization measures are taken, such as reducing the printing speed, increasing the layer height, adjusting the nozzle temperature, etc. By optimizing the control of risk areas, an intelligent 3D printing control strategy is ultimately obtained. For example, different printing parameters are used for different areas to ensure the stability and printing quality of the entire printing process.

[0025] Step S1 includes the following steps: Step S11: using a piezoelectric ceramic actuator to integrate the 3D printing nozzle at the outlet of the melting cavity to obtain a micro extrusion actuator; Step S12: performing initial actuator signal modulation on the micro extrusion actuator to generate an initial pulse drive extrusion signal; Step S13: Based on the initial pulse driving extrusion signal, the micro extrusion actuator is controlled to drive the displacement execution and perform preliminary molten plastic extrusion to obtain pulse extrusion plastic melt; Step S14: integrating the nozzle monitoring network using the micro pressure sensor, the temperature sensor and the flow rate sensor to generate a nozzle sensing monitoring network; Step S15: collecting pulse plastic melting state of pulse extrusion plastic melting through the nozzle sensor monitoring network to generate original pulse plastic melting state data; Step S16: Perform time-series synchronization calibration on the original pulse plastic-melt state data to generate plastic-melt pulse monitoring data.

[0026] In the embodiment of the present invention, for example, a PZT piezoelectric ceramic disc with a diameter of 5 mm and a thickness of 1 mm is selected and firmly fixed to the outside of the nozzle outlet by epoxy resin adhesive. In order to ensure that the piezoelectric ceramic actuator can effectively transmit mechanical force, a metal buffer layer with a thickness of 0.1 mm, such as a brass sheet, is set between the piezoelectric ceramic and the nozzle to prevent the direct force of the piezoelectric ceramic from damaging the nozzle structure. At the same time, the upper and lower surfaces of the piezoelectric ceramic are coated with conductive silver paste, which is respectively connected to the positive and negative electrodes of the driving circuit by welding to form a complete driving circuit. After the assembly is completed, the entire structure forms a highly integrated micro-extrusion actuator, and the slight deformation of the piezoelectric ceramic actuator can be directly converted into a slight extrusion amount change of the plastic melt at the nozzle outlet. Use a function generator to generate an initial pulse drive extrusion signal. Set the signal parameters as: frequency 100Hz, duty cycle 50%, amplitude 5V square wave signal. This signal will be used as the initial drive signal of the micro-extrusion actuator. The output of the function generator is connected to the signal amplification circuit on the control circuit board to amplify the signal amplitude to the voltage value required to drive the actuator. For example, an operational amplifier can be used to construct an amplifier circuit with a voltage gain of 10 to amplify a 5V square wave signal to 50V. The heating temperature of the 3D printing nozzle is set to 200℃, and the PLA plastic filament is fed into the melting cavity. After the plastic filament is completely melted, the initial pulse drive extrusion signal generated in step S12 is applied to the micro extrusion actuator. Driven by the electrical signal, the actuator produces periodic telescopic displacement, thereby pushing the molten plastic out of the nozzle to form a pulsed extrusion plastic melt. The extrusion speed and flow rate can be controlled by adjusting the frequency and duty cycle of the signal. For example, increasing the frequency can increase the extrusion speed, and increasing the duty cycle can increase the extrusion flow rate. A micro pressure sensor with a range of 0-10MPa, a temperature sensor with a measurement range of 0-300℃, and a flow rate sensor with a range of 0-10ml / min are selected. These three sensors are installed near the nozzle respectively, and the sensor signal output is connected to the data acquisition card using wires. The data acquisition card is responsible for converting the analog signal into a digital signal and transmitting it to the computer for subsequent processing. For example, a pressure sensor can be installed at the inlet of the nozzle, a temperature sensor can be installed on the heating block of the nozzle, and a flow rate sensor can be installed at the outlet of the nozzle. The sampling frequency of the data acquisition card is set to 1kHz. When step S13 performs preliminary molten plastic extrusion, the data acquisition card is started at the same time to collect the pressure, temperature and flow rate signals collected by the nozzle sensor monitoring network in real time, and the collected data is stored in the computer to generate the original pulse plastic melting state data.For example, the pressure value fluctuates in a pulsed manner between 0.5MPa and 1.2MPa; the temperature sensor records the temperature of the plastic in the molten cavity, for example, the temperature value is maintained at about 200°C; the flow rate sensor records the extrusion speed of the plastic, for example, the flow rate value fluctuates in a pulsed manner between 5mm / s and 10mm / s. These original sensor data, including timestamp, pressure value, temperature value and flow rate value, constitute the original pulse plastic melt state data. Since there is a time difference in the data acquisition of each sensor, the original pulse plastic melt state data needs to be time-series synchronized and calibrated. Timing synchronization can be performed using software or hardware methods. For example, a synchronization trigger signal can be sent at the beginning of data acquisition, and the time when each sensor receives the trigger signal can be recorded, and then the data can be calibrated according to the time difference. The calibrated data is arranged in chronological order to generate plastic melt pulse monitoring data. For example, the data of the three sensors are unified on the same time axis. Through a linear interpolation algorithm, such as using the interp1d function of the scipy library in Python, the data of the temperature sensor and the flow rate sensor can be interpolated on the same time axis as the pressure sensor. In addition, there is a time offset in the data of different sensors, and the time offset needs to be determined through a cross-correlation algorithm and then corrected. For example, through cross-correlation, it is found that the pressure signal is delayed by 0.01 seconds compared to the temperature signal, so the temperature signal time axis is shifted forward by 0.01 seconds to complete the timing synchronization. The data after timing synchronization calibration, including pressure, temperature and flow rate, constitute the plastic melt pulse monitoring data. These data are synchronized and can reflect the state of the molten plastic at the same time.

[0027] Step S2 includes the following steps: Step S21: performing Kalman filtering noise reduction processing on the plastic melt pulse monitoring data to generate pre-processed plastic melt monitoring data; Step S22: performing time domain window segmentation on the pre-processed plastic melt monitoring data through a preset time window to generate time series plastic melt monitoring data; Step S23: performing fast Fourier transform processing on the time-series melt monitoring data, and extracting the melt flow dominant frequency to generate melt dominant frequency data; Step S24: performing autocorrelation analysis according to the time series plastic melt monitoring data and performing damping coefficient fitting to generate a plastic melt extrusion damping coefficient; Step S25: constructing a second-order oscillation model of melt flow dynamics based on the melt dominant frequency data and the plastic melt extrusion damping coefficient to generate a melt flow dynamics model; Step S26: using the melt flow dynamics model to correct the microfluidic control instruction through the initial pulse driving extrusion signal to obtain corrected microfluidic control instruction data.

[0028] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S2 in the embodiment are shown in the flowchart. In this embodiment, step S2 includes: Step S21: performing Kalman filtering noise reduction processing on the plastic melt pulse monitoring data to generate pre-processed plastic melt monitoring data; In an embodiment of the present invention, the Kalman filter toolbox provided by MATLAB is used to perform Kalman filter noise reduction processing on the data. For example, a linear dynamic model of the flow state of the plastic melt is established, which describes the change trend of the pressure, temperature and flow rate of the plastic melt over time. For example, it is assumed that the pressure change conforms to the first-order linear model: P(k+1)=aP(k)+bU(k)+w(k), where P(k) represents the pressure at time k, U(k) represents the drive signal, w(k) represents the process noise, and a and b are model parameters. The measurement equation describes the relationship between the sensor measurement value and the true state, for example, the observed pressure value is equal to the actual pressure value plus the measurement noise: Z(k)=P(k)+v(k), where Z(k) represents the sensor measurement value and v(k) represents the measurement noise. Then, the Kalman filter algorithm is used, for example, the kalman function is used in MATLAB to iteratively calculate the optimal state estimate, thereby filtering out the noise. For example, the pressure, temperature and flow rate data can be Kalman filtered respectively to obtain the pressure, temperature and flow rate data after noise reduction. The noise-reduced data are merged to generate pre-processed plastic melt monitoring data.

[0029] Step S22: performing time domain window segmentation on the pre-processed plastic melt monitoring data through a preset time window to generate time series plastic melt monitoring data; In an embodiment of the present invention, the pre-processed plastic melt monitoring data after the Kalman filter noise reduction process is still a continuous time series data. In order to perform time domain analysis, it is necessary to perform time domain window segmentation through a preset time window. For example, a time window is set to 100 milliseconds, which is equivalent to 0.1 seconds. The selection of this time window needs to consider the driving frequency of the piezoelectric actuator and the time scale of the melt flow. For example, if the actuator driving frequency is 50Hz, 0.1 seconds corresponds to 5 driving cycles, which is sufficient to contain the complete information of a driving cycle. Then, the pre-processed plastic melt monitoring data is segmented according to this time window to form a series of time segment data. For example, assuming that the total length of the data is 1 second and the time window is 0.1 seconds, 10 time segment data will be segmented, and each segment contains 0.1 seconds of pressure, temperature and flow rate data. The data of these time segments are called time-series plastic melt monitoring data, and each segment represents the melt flow state within a period of time.

[0030] Step S23: performing fast Fourier transform processing on the time-series melt monitoring data, and extracting the melt flow dominant frequency to generate melt dominant frequency data; In an embodiment of the present invention, each time-series plastic melt monitoring data segment generated in step S22 is processed by fast Fourier transform (FFT). The fft function provided by MATLAB can be used for FFT transformation. The time domain signal is converted into a frequency domain signal to obtain a frequency spectrum of each data segment. In the frequency spectrum, the frequency component with the largest amplitude is found, which is the dominant frequency of the melt flow of the data segment. For example, the dominant frequency of the first data segment is 200Hz, and the dominant frequency of the second data segment is 210Hz. The dominant frequencies of each data segment are arranged in chronological order to generate melt dominant frequency data.

[0031] Step S24: performing autocorrelation analysis according to the time series plastic melt monitoring data and performing damping coefficient fitting to generate a plastic melt extrusion damping coefficient; In an embodiment of the present invention, an autocorrelation analysis is performed on the time series pressure data after each time window segmentation, for example, using the xcorr function in MATLAB. Autocorrelation analysis can measure the similarity between a time series and its own delayed series. The result of the autocorrelation function is expressed as a graph with time delay as the horizontal axis and the correlation coefficient as the vertical axis. The decay rate of the autocorrelation curve reflects the damping characteristics of the system. For example, a fast decay of the autocorrelation curve means that the system damping is large, and a slow decay means that the system damping is small. In order to quantify the damping coefficient, the autocorrelation curve is fitted to an exponential decay function, such as y=A exp(-λt), where A is the initial amplitude, λ is the attenuation coefficient, and t is the time delay. The attenuation coefficient λ can be obtained by curve fitting using the least squares method, for example, using the curve_fit function of the scipy library in Python. The damping coefficient ζ can be calculated by the following formula: ζ=λ / (2πf), where f is the dominant frequency of the melt extracted in step S23. For example, after fitting, λ=10 is obtained. Assuming that the dominant frequency is 50Hz, the damping coefficient ζ=10 / (2π50)≈0.032.

[0032] Step S25: constructing a second-order oscillation model of melt flow dynamics based on the melt dominant frequency data and the plastic melt extrusion damping coefficient to generate a melt flow dynamics model; In the embodiment of the present invention, based on the melt dominant frequency data generated in step S23 and the plastic melt extrusion damping coefficient generated in step S24, a second-order oscillation model of melt flow dynamics is constructed. A typical second-order oscillation model can be expressed as the following differential equation: md²x / dt²+cdx / dt+kx=F(t), where m is the equivalent mass, c is the damping coefficient, k is the equivalent stiffness, x is the displacement of the plastic melt, and F(t) is the external force (i.e., driving force). According to the dominant frequency f extracted in step S23, the equivalent stiffness k can be calculated: k=m(2πf)²; according to the damping coefficient ζ obtained by fitting in step S24, the damping coefficient c can be calculated: c=2ζsqrt(mk). Substituting the dominant frequency f and the damping coefficient ζ into the above formula, the parameters of the specific second-order oscillation model of melt flow dynamics can be obtained. For example, assuming f=50Hz, ζ=0.032, and the equivalent mass m=1, then k=98696 and c=19.98.

[0033] Step S26: using the melt flow dynamics model to correct the microfluidic control instruction through the initial pulse driving extrusion signal to obtain corrected microfluidic control instruction data.

[0034] In an embodiment of the present invention, the initial pulse driving extrusion signal is used as input and input into the melt flow dynamics model. The model calculates the dynamic response of the melt, including the displacement, velocity and acceleration of the melt. In order to achieve more accurate extrusion control, the initial driving signal needs to be compensated to offset the hysteresis and oscillation during the melt flow process. For example, if the model predicts that there is a hysteresis in the melt response, an advance amount can be added to the driving signal; if the model predicts that there is oscillation in the melt response, a damping term can be added to the driving signal. For example, if it is predicted that the melt will start to respond after a delay of 0.01 seconds after the drive signal is applied, the drive signal is advanced by 0.01 seconds; if it is predicted that the melt will oscillate under the action of the drive signal, a signal in the opposite direction of the melt oscillation speed is added to the drive signal. The corrected signal parameters are converted into digital signals, which are output to the drive circuit of the piezoelectric ceramic actuator through a digital signal processor (DSP) or a microcontroller (MCU) to achieve precise control of the extrusion process and obtain corrected microfluidic control instruction data.

[0035] Step S24 includes the following steps: Step S241: extracting the state of the plastic melt according to the time-series plastic melt monitoring data, and obtaining melt extrusion pressure time-series data, melt extrusion temperature time-series data, and melt extrusion flow rate time-series data respectively; Step S242: performing autocorrelation operation on the melt extrusion pressure time series data to generate melt pressure autocorrelation series coefficients; Step S243: performing peak detection according to the melt pressure autocorrelation sequence coefficient, and intercepting the first peak decay portion to obtain pressure decay curve data; Step S244: performing exponential function fitting on the pressure decay curve data based on the melt pressure autocorrelation series coefficient, and calculating the pressure decay coefficient to generate the pressure decay coefficient; Step S245: processing the melt temperature period main frequency component according to the melt extrusion temperature time series data to generate melt temperature fluctuation main frequency data; Step S246: calculating the flow rate change rate according to the melt extrusion flow rate time series data, and performing root mean square value processing to generate a root mean square value of the flow rate change rate; Step S247: Calculate the melt flow damping coefficient based on the RMS value of the flow rate change rate, the melt temperature fluctuation main frequency data, and the pressure attenuation coefficient to generate the plastic melt extrusion damping coefficient.

[0036] In an embodiment of the present invention, the time-series plastic melt monitoring data generated in step S22 is decomposed to extract the time-series data of three key parameters, namely, melt extrusion pressure, melt extrusion temperature and melt extrusion flow rate. For example, the time-series plastic melt monitoring data is stored in a matrix form, with each column representing a parameter and each row representing a time point. Then the first column can be extracted as the melt extrusion pressure time-series data, the second column as the melt extrusion temperature time-series data, and the third column as the melt extrusion flow rate time-series data. The purpose of the autocorrelation operation is to measure the similarity between a time series and its own delayed sequence, which can reveal the periodicity or attenuation characteristics in the time series. In terms of specific operation, assuming that the melt extrusion pressure time-series data is expressed as P(t), where t represents time, then the autocorrelation function R(τ) can be defined as: R(τ)=E[(P(t)-μ)(P(t+τ)-μ)], where E represents the expected operation, μ is the mean of the pressure time-series data, and τ represents the time delay. In fact, when calculating the autocorrelation function, a discrete form is used to calculate the correlation coefficient under different time delays. For example, the autocorrelation operation can be implemented through the xcorr function of MATLAB, which returns an autocorrelation sequence whose horizontal axis is the time delay τ and the vertical axis is the correlation coefficient R(τ). For example, the calculated autocorrelation sequence coefficient is expressed as a decaying oscillation waveform with the delay time as the horizontal axis. The maximum value is obtained when the delay time is 0, and then the correlation gradually decreases as the delay time increases. The autocorrelation sequence coefficient is usually expressed as a decaying oscillation curve. The purpose of peak detection is to find the first significant peak in the autocorrelation sequence, that is, the first local maximum after the time delay is 0. You can use a ready-made peak detection algorithm, such as Python's scipy.signal.find_peaks function, to locate the peak. For example, through the peak detection algorithm, it is found that the first peak of the autocorrelation sequence appears at a delay time of 0.02 seconds, and the corresponding correlation coefficient is 0.8. Then, starting from the first peak, the attenuated part of the autocorrelation curve is intercepted until the value of the autocorrelation coefficient decays to a certain proportion of the initial peak, such as 1 / e of the initial peak, which is about 0.37 times. The intercepted data is the pressure decay curve data, which reflects the decay process of pressure autocorrelation and the decay characteristics caused by damping when the melt flows. The pressure decay curve usually shows exponential decay characteristics, and an exponential function can be used to fit the curve. The form of the exponential function is: y=A exp(-λt), where y is the pressure autocorrelation coefficient, A is the initial amplitude, λ is the attenuation coefficient, and t is the time delay. The pressure decay curve data is fitted to the above exponential function by using a curve fitting algorithm such as the least squares method, for example, using the scipy.optimize.curve_fit function in Python, so as to obtain the fitting parameters A and λ. The temperature time series data contains some periodic fluctuations, which are related to the heating control system in the melting cavity and the flow state of the plastic melt. In order to extract the main frequency components of the temperature fluctuation, it is necessary to perform spectrum analysis on the temperature time series data. The fast Fourier transform (FFT) algorithm can be used, for example, the fft.fft function of the NumPy library can be used in Python. After FFT processing, a spectrum diagram of the temperature signal is obtained, with the horizontal axis being the frequency and the vertical axis being the amplitude. Then, the frequency component with the largest amplitude is found in the spectrum diagram. This frequency is the main frequency of the melt temperature fluctuation. For example, after FFT spectrum analysis, it is found that the amplitude at 5Hz is the largest, so it is considered that the main frequency of the melt temperature fluctuation is 5Hz, which is related to the cycle of heating control. The flow rate change rate refers to how fast the flow rate changes over time, and can reflect the stability of the plastic melt flow. The flow rate change rate can be calculated using a differential algorithm, such as using a first-order difference approximate derivative: dv / dt≈(v(t+Δt)-v(t)) / Δt, where v(t) is the flow rate at time t, and Δt is the time interval. After calculating the flow rate change rate, it is necessary to perform root mean square processing on it, that is, to find the square root of the average value of the square of the flow rate change rate. The root mean square value can effectively reflect the average level of the flow rate change rate and can filter out some noise. The damping coefficient is a parameter that comprehensively considers the characteristics of the plastic melt and the flow conditions. An empirical formula can be used to calculate the damping coefficient, for example: ζ=α RMS+β f_T+γ λ, where ζ is the damping coefficient, RMS is the root mean square value of the flow rate change rate, f_T is the main frequency of melt temperature fluctuation, λ is the pressure attenuation coefficient, and α, β and γ are weight coefficients. These weight coefficients can be obtained by fitting experimental data. For example, through multiple experiments, the damping coefficients under different flow conditions are measured, and the least squares method is used for fitting to obtain the weight coefficients. For example, after fitting, α=0.5, β=0.1, and γ=0.2 are obtained. Substitute the obtained pressure attenuation coefficient, the main frequency data of melt temperature fluctuation and the root mean square value of the flow rate change rate into the above formula to obtain the final plastic melt extrusion damping coefficient.

[0037] Step S26 includes the following steps: Step S261: obtaining desired plastic melt flow state data; Step S262: using a melt flow dynamics model to simulate the desired plastic melt flow state data, and performing an optimal control waveform parameter combination for the extrusion actuator to obtain a desired pulse extrusion drive control signal; Step S263: generating a driving waveform signal according to the desired pulse extrusion driving control signal, and performing signal discrete processing to generate a discrete driving waveform control signal; Step S264: effectively correcting the discrete drive waveform control signal through the initial pulse drive extrusion signal, thereby obtaining effective discrete control signal sequence data; Step S265: adding a timestamp according to the valid discrete control signal sequence data, and performing instruction code conversion to generate corrected microfluidic control instruction data.

[0038] In an embodiment of the present invention, the desired plastic melt flow state data, such as the desired melt extrusion speed, pressure and temperature, are pre-set according to the geometric features and material properties of the 3D printing model. These data can be determined based on empirical values ​​or theoretical models. For example, for a model that requires high-precision printing, a lower extrusion speed and pressure, as well as a more stable temperature can be set. Assume that the desired extrusion speed is 10 mm / s, the pressure is 1 MPa, and the temperature is 200 ° C. By adjusting the input drive signal parameters, such as frequency, duty cycle and amplitude, the melt flow state output by the model is observed, and the optimal control waveform parameter combination is found. The desired pressure, temperature and flow rate data are input into the melt flow dynamics model, and the model is reversely solved to determine what kind of drive signal the piezoelectric ceramic actuator should apply in order to achieve this state. For example, an optimization algorithm, such as a gradient descent method, can be used to find the optimal drive waveform parameters, including the frequency, amplitude and phase of the drive signal. The goal of the algorithm is to minimize the error between the actual melt flow state and the desired melt flow state. For example, after calculation by the optimization algorithm, it is found that in order to make the pressure at the nozzle outlet reach the expected value, the driving signal of the piezoelectric ceramic should be a sine wave signal with a frequency of 60Hz and an amplitude of 40V. Use a function signal generator to generate the desired waveform signal, and then discretize the signal. The purpose of discretization is to convert the continuous time signal into a digital signal so that the digital controller can execute it. For example, the sine wave signal is discretized at a sampling frequency of 10kHz, that is, the continuous sine wave waveform is sampled at a time interval of 10kHz to obtain a series of discrete data points, each of which represents the driving voltage value at a moment. According to the melt flow dynamics model and the desired melt state, the discrete driving waveform control signal is adjusted. For example, according to the model prediction, it is found that the initial 50Hz driving signal will cause the melt pressure to overshoot. In order to avoid pressure overshoot, in the discrete driving waveform control signal, an attenuation factor needs to be added to the rising edge of the driving signal. For example, when the initial signal is 50V, the value of the corrected signal becomes 40V at the rising edge. In addition, if the expected extrusion amount is inconsistent with the extrusion amount generated by the initial drive signal, the discrete drive waveform control signal also needs to be scaled. For example, if the expected extrusion amount is 1.2 times the initial extrusion amount, the amplitude of the drive signal needs to be amplified by 1.2 times. The sequence after the discrete drive waveform control signal is adjusted constitutes the effective discrete control signal sequence data, which represents the corrected drive signal. A timestamp is added to each data point in the effective discrete control signal sequence data, and the timestamp corresponds to the position of the data point on the time axis. Then, the discrete drive signal with a timestamp is converted into an instruction code that can be recognized and executed by the actuator. For example, the voltage value is converted into the PWM (pulse width modulation) signal duty cycle of the digital controller, and the corresponding execution time is set.For example, if the voltage value of a data point is 40V and the corresponding PWM duty cycle is 80%, then the instruction code is “at timestamp 10.01 seconds, output PWM signal with a duty cycle of 80%”. After all data points are converted into instruction codes, the modified microfluidic control instruction data is generated.

[0039] Step S3 includes the following steps: Step S31: loading the corrected microfluidic control instruction data into the microcontroller, and controlling the piezoelectric ceramic actuator to perform real-time pulse extrusion, while using the nozzle sensor monitoring network to perform real-time pulse extrusion process monitoring to obtain real-time plastic melt extrusion monitoring data; Step S32: performing monitoring feature preprocessing according to the real-time plastic melt extrusion monitoring data to generate real-time plastic melt extrusion monitoring feature data; Step S33: calculating the expected plastic melt flow state deviation value of the modified microfluidic control instruction data based on the real-time plastic melt extrusion monitoring characteristic data to generate real-time plastic melt extrusion state deviation data; Step S34: dynamically adjust the PID control instruction parameters of the corrected micro-flow control instruction data according to the real-time plastic melt extrusion state deviation data to obtain the plastic melt flow extrusion control strategy.

[0040] As an example of the present invention, refer to Figure 3 As shown, Figure 1 Detailed implementation steps of step S3 in the flowchart, in this example, step S3 includes: Step S31: loading the corrected microfluidic control instruction data into the microcontroller, and controlling the piezoelectric ceramic actuator to perform real-time pulse extrusion, while using the nozzle sensor monitoring network to perform real-time pulse extrusion process monitoring to obtain real-time plastic melt extrusion monitoring data; In an embodiment of the present invention, the modified microfluidic control instruction data generated in step S26 is loaded into a microcontroller (e.g., STM32). The piezoelectric ceramic actuator is driven according to the timestamp and control signal in the instruction. For example, if the control instruction includes an instruction "output PWM signal at timestamp 1.02 seconds, duty cycle 70%", the microcontroller will generate a PWM signal with a duty cycle of 70% at 1.02 seconds, which drives the piezoelectric ceramic actuator through the power amplifier, so that the plastic melt at the nozzle outlet is extruded in the desired pulse mode. At the same time, the sensor monitoring network installed on the nozzle also starts working, collecting pressure, temperature and flow rate data during the extrusion process in real time. These data are collected and transmitted to the microcontroller or data acquisition system at a high-frequency sampling rate, forming real-time plastic melt extrusion monitoring data.

[0041] Step S32: performing monitoring feature preprocessing according to the real-time plastic melt extrusion monitoring data to generate real-time plastic melt extrusion monitoring feature data; In an embodiment of the present invention, for example, the pressure data may be subjected to a sliding average filter to reduce high-frequency noise; the mean, variance, and peak value of the pressure data may be calculated as pressure characteristic quantities; the mean and fluctuation range of the temperature data may be calculated as temperature characteristic quantities; and the mean and standard deviation of the flow rate data may be calculated as flow rate characteristic quantities. For example, by means of a sliding average filter, the fluctuation of the pressure signal may be made smoother; by calculating the peak value, the intensity of the pulse extrusion may be quantified; and by calculating the mean, the average state of the melt extrusion may be reflected. The extracted characteristic data are combined into a characteristic vector to generate real-time plastic melt extrusion monitoring characteristic data. For example, a characteristic vector may contain three characteristics: the pressure mean, the temperature mean, and the flow rate mean.

[0042] Step S33: calculating the expected plastic melt flow state deviation value of the modified microfluidic control instruction data based on the real-time plastic melt extrusion monitoring characteristic data to generate real-time plastic melt extrusion state deviation data; In an embodiment of the present invention, the real-time plastic melt extrusion monitoring characteristic data generated in step S32 is compared with the expected plastic melt flow state data obtained in step S261, and the deviation value between the two is calculated. For example, the distance between the feature vectors can be calculated using the Euclidean distance or the Manhattan distance as the state deviation value. The deviation value can also be calculated for each feature separately, for example, pressure deviation = real-time pressure mean - expected pressure value. The calculated deviation values ​​are combined into a deviation vector to generate real-time plastic melt extrusion state deviation data. For example, the monitoring characteristic data is aligned with the expected state data, for example, the actual pressure mean is matched with the expected pressure mean according to the timestamp. Then, the difference between the actual value and the expected value is calculated, for example, the difference between the actual pressure mean and the expected pressure mean, and the difference between the actual flow rate standard deviation and the expected flow rate standard deviation. This difference value is the state deviation, which reflects the degree of deviation between the current melt flow state and the expected state. For example, if the actual pressure mean is 0.78MPa and the expected pressure mean is 0.8MPa, the pressure deviation is -0.02MPa; if the actual flow rate standard deviation is 0.1mm / s and the expected flow rate standard deviation is 0.05mm / s, the flow rate deviation is 0.05mm / s. These deviation values ​​constitute the real-time plastic melt extrusion state deviation data, which reflects the gap between the current control effect and the expected control target.

[0043] Step S34: dynamically adjust the PID control instruction parameters of the corrected micro-flow control instruction data according to the real-time plastic melt extrusion state deviation data to obtain the plastic melt flow extrusion control strategy.

[0044] In the embodiment of the present invention, the PID control parameters of the modified microfluidic control instruction data are dynamically adjusted according to the real-time plastic melt extrusion state deviation data generated in step S33. The PID controller calculates the control amount according to the deviation value, which is used to adjust the drive signal parameters of the piezoelectric ceramic actuator, such as frequency, duty cycle and amplitude. The parameters of the PID controller include a proportional coefficient (Kp), an integral coefficient (Ki) and a differential coefficient (Kd). Appropriate PID parameters can be selected according to the dynamic characteristics and control objectives of the system. For example, for a system with high response speed requirements, a larger Kp value can be selected; for a system with high steady-state accuracy requirements, a larger Ki value can be selected; for a system with high overshoot requirements, a larger Kd value can be selected. The PID controller calculates the control amount according to the deviation value and the PID parameters, and superimposes it on the modified microfluidic control instruction data to generate new control instruction data. The PID control parameters are continuously adjusted according to the real-time plastic melt extrusion state deviation data, and finally a plastic melt flow extrusion control strategy is obtained, that is, a set of optimal PID parameters and control instruction data, which can keep the melt flow state stably near the expected value. For example, PID parameters can be automatically adjusted to obtain the best control performance through optimization algorithms such as the Ziegler-Nichols method or genetic algorithms.

[0045] Step S33 includes the following steps: Step S331: extracting operation timestamps according to real-time plastic melt extrusion monitoring feature data to generate plastic melt extrusion timestamp data; Step S332: mapping the control cycle expected state parameter of the modified microfluidic control instruction data through the plastic melt extrusion timestamp data to obtain the fluid expected parameter value; Step S333: performing real-time control state parameter conversion on the real-time plastic melt extrusion monitoring characteristic data to obtain real-time fluid parameter values; Step S334: using the real-time fluid parameter value to calculate the state deviation value of the fluid expected parameter value, and generating real-time extrusion state deviation data; Step S335: scaling the real-time extrusion state deviation data using a preset scaling factor, and performing parameter limiting processing to obtain real-time plastic melt extrusion state deviation data.

[0046] In an embodiment of the present invention, timestamp information is extracted from the real-time plastic melt extrusion monitoring characteristic data generated in step S32. Real-time plastic melt extrusion monitoring characteristic data usually includes a timestamp for recording the time when each data point is collected. The method for extracting timestamp information depends on the format of data storage. For example, if the data is stored in a table, the timestamp is located in the first column; if the data is stored in a structure array, the timestamp is a separate field. The extracted timestamp information is stored in an array to generate plastic melt extrusion timestamp data. According to the plastic melt extrusion timestamp data generated in step S331, the corresponding control cycle expected state parameters are searched from the modified microfluidic control instruction data generated in step S26. The modified microfluidic control instruction data includes the expected melt flow state parameters in each control cycle, such as the expected extrusion speed, pressure and temperature. By matching the plastic melt extrusion timestamp data with the time information in the modified microfluidic control instruction data, the expected state parameters corresponding to each time point can be found. For example, if the timestamp is 0.05 seconds, the expected state parameters corresponding to the control cycle with the time closest to 0.05 seconds in the modified microfluidic control instruction data are searched. The expected state parameters found are stored in an array to obtain the expected parameter value of the fluid. For example, if the expected parameter value is the mean pressure value, the mean pressure value in the monitoring characteristic data needs to be directly extracted; if the expected parameter value is the flow rate, the mean flow rate value in the monitoring characteristic data needs to be extracted, or the standard deviation of the flow rate needs to be converted into the flow rate value. For example, if the mean pressure value in the monitoring characteristic data is 0.78MPa, the pressure value in the real-time fluid parameter value is 0.78MPa; if the standard deviation of the flow rate in the monitoring characteristic data is 0.1mm / s, and the expected value is the mean flow rate value, it is necessary to convert the standard deviation of the flow rate into the mean flow rate value according to the actual situation. For example, the standard deviation of the flow rate can be mapped to the mean flow rate value through a certain conversion coefficient. Assuming that the coefficient is 2, the flow rate in the real-time fluid parameter value is 0.2mm / s. These converted real-time parameter values ​​constitute the real-time fluid parameter value, and its meaning and unit correspond to the expected parameter value of the fluid. The parameters such as pressure, temperature and flow rate in the real-time fluid parameter value are subtracted from the corresponding expected parameter value of the fluid. For example, if the pressure in the real-time fluid parameter value is 0.78MPa, and the pressure in the expected fluid parameter value is 0.8MPa, the pressure deviation value is -0.02MPa; if the real-time temperature is 198°C and the expected temperature is 200°C, the temperature deviation value is -2°C. These deviation values ​​constitute the real-time extrusion state deviation data, which reflects the degree of deviation between the current actual extrusion state and the expected state. For example, a negative deviation indicates that the actual value is less than the expected value, and a positive deviation indicates that the actual value is greater than the expected value. Multiply the real-time extrusion state deviation data by a preset scaling factor. For example, if the scaling factor of the pressure deviation is 0.5, multiply the pressure deviation value by 0.5.Then, the scaled deviation value is limited to a preset range. For example, the range of the pressure deviation value is limited to between -0.01MPa and 0.01MPa. If the scaled deviation value exceeds this range, its boundary value is taken. For example, if the scaled pressure deviation value is 0.02MPa, then after the limit, its value becomes 0.01MPa. The deviation value after proportional scaling and parameter limit processing constitutes the final real-time plastic melt extrusion state deviation data.

[0047] Step S4 includes the following steps: Step S41: performing a plastic melt flow stability analysis according to a plastic melt flow extrusion control strategy to generate melt flow trend state data; Step S42: Meshing the target 3D printing model to obtain 3D model printing mesh data; Step S43: performing 3D printing area identification according to the 3D model printing grid data, and performing flow state-printing area mapping processing according to the melt flow trend state data to generate melt flow state-printing area mapping data; Step S44: dividing the plastic melt flow risk area according to the melt flow state-printing area mapping data to obtain a melt accumulation risk area and a poor flow risk area; Step S45: performing intelligent 3D printing control optimization on the melt accumulation risk area and the poor flow risk area, thereby obtaining an intelligent 3D printing control strategy.

[0048] In the embodiment of the present invention, the real-time plastic melt extrusion monitoring data obtained in step S31, especially the pressure and flow rate data, are used for stability analysis. For example, the mean, variance and peak value of the pressure data can be calculated to evaluate the degree of pressure fluctuation; the mean and standard deviation of the flow rate data can be calculated to evaluate the uniformity of the flow rate; the autocorrelation coefficient of the pressure and flow rate data can also be calculated to evaluate the periodicity of the flow process; the number of fluctuations of the pressure and flow rate values ​​over a period of time can also be counted to evaluate whether there are frequent sudden changes in pressure or flow rate during the flow process. For example, if the variance value of the pressure data remains at a low level for a long time, it means that the pressure fluctuation is small and the flow is stable; if the peak value of the pressure data is large and the number of fluctuations is large, it means that the flow is unstable. These calculated statistics, such as the pressure mean of 0.8MPa, the pressure variance of 0.005MPa², the flow rate mean of 6mm / s, the flow rate standard deviation of 0.2mm / s, and the number of pressure and flow rate fluctuations, constitute the melt flow trend state data, which can be used to evaluate whether the flow state of the plastic melt is stable or unstable, and the degree of instability. Import the target 3D printing model into the slicing software (e.g., Cura, Simplify3D). The slicing software discretizes the 3D model, generates a series of layers, and converts each layer into a printing path. The printing path consists of a series of line segments or curves, which describes the movement trajectory of the nozzle. Convert the printing path into mesh data, which can be represented by a triangular or quadrilateral mesh. The mesh data contains the coordinates and connection relationship of each mesh node. For example, a cube model can be divided into several small cube units, each unit consisting of 8 vertices and 12 edges. Store the generated mesh data in a file to obtain the 3D model printing mesh data. Divide the model into different areas according to the geometric features or printing parameters of the model, such as the shell, internal filling, support structure, etc. This can be achieved using geometric analysis algorithms, such as curvature analysis, neighborhood analysis, or manual annotation. For example, for the suspended part, the normal vector of the mesh unit can be analyzed to find out the unsupported areas; for small features, the size of the mesh unit can be analyzed to find out the smaller areas. Then, the melt flow trend state data obtained in step S41 is mapped to the identified different 3D printing areas. This mapping process can be performed according to the location and characteristics of the area. For example, if the pressure fluctuation in a certain area is large and the standard deviation of the flow rate is also high, the area is marked as an unstable flow area, and these marks and related data are stored to form melt flow state-printing area mapping data. Define the judgment criteria for melt accumulation risk areas and poor flow risk areas.For example, if the pressure value in the melt flow trend state data of a certain area is continuously high and the flow rate is obviously low, the area is divided into a melt accumulation risk area; if the pressure value in the melt flow trend state data of a certain area fluctuates violently and the standard deviation of the flow rate is high, the area is divided into a poor flow risk area. Then, according to these judgment criteria, the melt flow state-printing area mapping data is analyzed to classify the various areas of the model. For example, assuming that the pressure value of a suspended area is continuously high and the flow rate is obviously low, the area is divided into a melt accumulation risk area; assuming that the pressure value of a small feature area fluctuates violently and the standard deviation of the flow rate is high, the area is divided into a poor flow risk area. After classification, a list of markers for melt accumulation risk areas and poor flow risk areas is formed, which contains the mesh information of the area and the risk level information. For the melt accumulation risk area, measures such as reducing the extrusion amount, slowing down the printing speed, and adding support structures can be taken. For example, the printing speed of the melt accumulation risk area is reduced to 80% of the normal speed, the extrusion amount is also reduced to 80% of the normal value, and the density of the support structure is increased to prevent melt accumulation. For the risk area of ​​poor flow, measures such as increasing the extrusion amount, slowing down the printing speed, and optimizing the printing path can be taken. For example, the printing speed of the risk area of ​​poor flow is reduced to 90% of the normal speed, the extrusion amount is appropriately increased to 110% of the normal value, and the printing path is adjusted to make the plastic melt flow more easily. These different control measures taken for different risk areas constitute the intelligent 3D printing control strategy, including a series of parameter adjustment schemes such as printing speed, extrusion amount, support structure, and printing path. For example, for the melt accumulation risk area of ​​the suspended structure, the printing speed is reduced and the support structure is added; for the risk area of ​​poor flow with small features, the extrusion amount is increased and the printing path is adjusted to ensure that the plastic melt can flow stably. The intelligent 3D printing control strategy can improve printing quality and reduce printing defects.

[0049] Step S45 includes the following steps: Step S451: Acquire real-time 3D printing point data; Step S452: when the real-time 3D printing point data is located in the melt accumulation risk area, obtaining the preset nozzle residence time in the accumulation area; Step S453: evaluating the accumulation degree according to the melt accumulation risk area, and performing residence time increase coefficient processing to generate the accumulation area residence time increase coefficient; Step S454: adjusting the preset nozzle dwell time of the deposition area by using the deposition area dwell time increase coefficient to generate the deposition area adjusted dwell time; when the deposition area adjusted dwell time is greater than or equal to the preset maximum dwell time threshold, marking the preset maximum dwell time threshold as the deposition area optimized dwell time; when the deposition area adjusted dwell time is less than the preset maximum dwell time threshold, marking the deposition area adjusted dwell time as the deposition area optimized dwell time; Step S455: performing nozzle residence time adjustment control on the modified microfluidic control instruction data based on the optimized residence time of the accumulation area, and obtaining a nozzle residence control strategy for the accumulation area; Step S456: when the real-time 3D printing point data is located in a poor flow risk area, obtaining preset nozzle temperature data of the poor flow area; Step S457: analyzing the melt flow characteristics of the poor flow risk area according to the poor flow risk area, matching the spraying temperature increase coefficient according to the preset nozzle temperature data of the poor flow area, and generating the spraying temperature optimization data of the poor flow area; Step S458: adjusting the spraying temperature according to the optimized data of the spraying temperature in the congested area, and controlling the direction and posture of the nozzle to obtain a spraying control strategy for the congested area; Step S459: Optimize the 3D printing spray control based on the accumulation area spray control strategy and the congested area spray control strategy to obtain an intelligent 3D printing control strategy.

[0050] In an embodiment of the present invention, the microcontroller or printing control system records the position of the nozzle in real time, and this position is usually represented by three coordinate values ​​of X, Y, and Z. For example, assuming that the print head is printing the top surface of a cube, the coordinates of its current position are (100, 50, 10), and the unit is millimeter. At the same time, the system also records the extrusion speed at the current moment, such as 6mm / s, and the extrusion amount, such as 0.1mm³. These real-time printing point data are continuously updated as the printing process proceeds, and each new printing point data reflects the current position and state of the nozzle. The preset nozzle dwell time refers to the length of time the nozzle stays in the area during normal printing, and this time is usually pre-set according to the geometry of the printing model and the printing parameters. For example, for an area that needs to be filled, the preset nozzle dwell time is shorter, such as 0.1 seconds; and for a suspended area, in order to give the plastic melt sufficient cooling time, the preset dwell time is slightly longer, such as 0.2 seconds. This preset time is usually stored in the configuration file or database of the printing control system, and each area corresponds to a preset dwell time value. When the real-time printing point data is identified as being located in a certain melt accumulation risk area, the system will query the configuration file or database to find the preset nozzle dwell time value corresponding to the area and extract the value. For example, if the current printing point is located at the starting layer of a suspended structure, the system will retrieve the preset dwell time of the area as 0.2 seconds. For example, it can be judged by monitoring the pressure value in the real-time extrusion status data. The higher the pressure value, the higher the degree of accumulation; it can be judged by analyzing the printing path length of the area. The longer the path length, the higher the degree of accumulation; it can also be evaluated based on the support structure density of the area. The greater the support structure density, the higher the degree of accumulation. For example, if the pressure value of the area is continuously high and exceeds the normal range by 10%, it is considered that the area has a high degree of accumulation. Then, according to the degree of accumulation, the dwell time increase coefficient is calculated. The larger the increase coefficient, the greater the increase in dwell time. The relationship between the degree of accumulation and the increase coefficient can be established using a linear function, an exponential function, or other functions. For example, if the pressure value exceeds the normal range by 10%, the increase coefficient is set to 1.1; if the pressure value exceeds the normal range by 20%, the increase coefficient is set to 1.2, and so on. After evaluating the accumulation degree and calculating the increase coefficient, an accumulation area residence time increase coefficient is obtained, which is used to adjust the preset nozzle residence time. For example, if the preset nozzle residence time is 0.2 seconds and the increase coefficient is 1.1, the adjusted residence time is 0.2 seconds. 1.1=0.22 seconds. Next, the adjusted residence time needs to be limited, and a preset maximum residence time threshold is set, such as 0.3 seconds. If the adjusted residence time is greater than or equal to the threshold, the threshold is marked as the optimized residence time for the accumulation area; if the adjusted residence time is less than the threshold, the adjusted residence time is directly marked as the optimized residence time for the accumulation area. For example, if the adjusted residence time is 0.22 seconds, which is less than 0.3 seconds, 0.22 seconds is directly marked as the optimized residence time for the accumulation area; if the adjusted residence time is 0.35 seconds, which is greater than 0.3 seconds, 0.3 seconds is marked as the optimized residence time for the accumulation area. In the modified microfluidic control instruction data, find the control instruction corresponding to the current printing point, modify the residence time parameter of the instruction, and set it as the optimized residence time for the accumulation area. The modified modified microfluidic control instruction data is the accumulation area nozzle residence control strategy, which is used to control the residence time of the nozzle in the melt accumulation risk area, thereby improving the printing quality. For example, if the current printing point is located in the melt accumulation risk area, and the optimized dwell time in the accumulation area is 0.2 seconds, the dwell time parameter corresponding to the printing point in the modified microfluidic control instruction data will be modified to 0.2 seconds. This will cause the nozzle to stay at this point for 0.2 seconds to ensure that the melt is fully accumulated. The preset nozzle temperature data refers to the temperature value set by the nozzle in this area during normal printing. This temperature value is usually preset according to the characteristics of the printing material and the printing parameters. For example, for PLA material, the preset nozzle temperature is 210℃, and for ABS material, the preset nozzle temperature is 240℃. This preset temperature is usually stored in the configuration file or database of the printing control system, and each material or printing area corresponds to a preset temperature value. When the real-time printing point data is identified as being located in a certain flow-impeded risk area, the system will query the configuration file or database to find the preset nozzle temperature value corresponding to the area and extract the value. For example, the length and direction of the printing path in the area can be analyzed. The more tortuous the printing path, the higher the degree of poor flow. The size of the grid unit in the area can also be analyzed. The smaller the grid unit, the greater the flow resistance. The melt flow trend state data in step S41 can also be used, such as the flow rate value and pressure value of the area. For example, if the printing path in the area is relatively tortuous, and the flow rate in the area in step S41 is relatively low, it can be considered that the risk of poor flow is relatively high. Then, according to the flow characteristics, the temperature increase coefficient of the spraying material is calculated. The larger the coefficient, the more the temperature needs to be increased. The relationship between the flow characteristics and the increase coefficient can be established by using linear functions, exponential functions, or table lookups. For example, if the printing path in the area is highly tortuous and the flow rate is relatively low, the temperature increase coefficient of the spraying material can be set to 1.05, which means that it needs to be increased by 5% based on the preset temperature.After calculating the temperature increase coefficient, it is necessary to match it with the preset nozzle temperature data obtained in step S456, calculate the final nozzle optimization temperature, and constitute the optimization data of the spraying temperature in the unsmooth area. The microcontroller or printing control system will read the optimization data of the spraying temperature in the unsmooth area, which contains the nozzle temperature value that needs to be adjusted, and adjust the power of the nozzle heater according to the temperature value so that the nozzle temperature reaches the optimized temperature value as soon as possible. For example, if the spraying temperature optimization data is 231°C and the current nozzle temperature is 220°C, the microcontroller will control the nozzle heater to increase the power so that the nozzle temperature gradually approaches 231°C. While adjusting the temperature, the direction and posture of the nozzle also need to be controlled. For example, for small features or suspended structures, the inclination angle of the nozzle can be adjusted to make the molten plastic flow more easily and avoid problems such as broken wires. This part can be controlled by using a motor to drive the nozzle to rotate. The distance between the nozzle and the printed layer can be controlled by adjusting the Z-axis height of the nozzle to make the flow of the plastic melt more uniform. For example, for some complex structures, the nozzle needs to be tilted 5 degrees or raised 0.05mm to print. All these adjustments to the nozzle temperature and posture constitute the poor area spraying control strategy and are sent to the printer's control system in real time. The accumulation area nozzle dwell control strategy generated in step S455 and the poor area spraying control strategy generated in step S458 are integrated to form an intelligent 3D printing control strategy. The intelligent 3D printing control strategy can determine the area where the current printing point is located based on the real-time 3D printing point data and apply the corresponding control strategy. For example, if the current printing point is located in the melt accumulation risk area, the accumulation area nozzle dwell control strategy is applied; if the current printing point is located in the poor flow risk area, the poor area spraying control strategy is applied; if the current printing point is located in other areas, the default printing parameters are applied. The intelligent 3D printing control strategy can dynamically adjust the control strategy based on the real-time feedback information during the printing process to achieve the best printing effect.

[0051] Preferably, the present invention further provides a 3D printing control system for executing the 3D printing control method as described above, the 3D printing control system comprising: The pulse extrusion and monitoring module is used to integrate the 3D printing nozzle at the outlet of the melting cavity using a piezoelectric ceramic actuator, and perform initial actuator signal modulation to generate an initial pulse drive extrusion signal; extrude the molten plastic according to the initial pulse drive extrusion signal, and collect the pulse plastic melting state to generate plastic melt pulse monitoring data; The microfluidic instruction correction module is used to extract the melt flow dominant frequency from the plastic melt pulse monitoring data to generate the melt dominant frequency data; perform damping coefficient fitting on the plastic melt pulse monitoring data to generate the plastic melt extrusion damping coefficient; perform microfluidic instruction correction on the initial pulse drive extrusion signal based on the melt dominant frequency data and the plastic melt extrusion damping coefficient to obtain the corrected microfluidic control instruction data; A real-time extrusion control module is used to perform real-time pulse extrusion according to the modified microfluidic control instruction data, and to monitor the real-time pulse extrusion process to obtain real-time plastic melt extrusion monitoring data; dynamically adjust PID control instruction parameters according to the real-time plastic melt extrusion monitoring data to obtain a plastic melt flow extrusion control strategy; The intelligent printing optimization module is used to perform plastic melt flow stability analysis according to the plastic melt flow extrusion control strategy and generate melt flow trend status data; mesh the target 3D printing model to obtain 3D model printing mesh data; divide the 3D model printing mesh data into plastic melt flow risk areas through the melt flow trend status data, and perform intelligent 3D printing control optimization to obtain an intelligent 3D printing control strategy.

[0052] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0053] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A 3D printing control method, characterized in that: The following steps are involved: Step S1: using a piezoelectric ceramic actuator to integrate the 3D printing nozzle at the outlet of the melting cavity, and performing initial actuator signal modulation to generate an initial pulse drive extrusion signal; Perform preliminary molten plastic extrusion according to the initial pulse driving extrusion signal, collect pulse plastic melt state, and generate plastic melt pulse monitoring data; Step S2: extracting the melt flow dominant frequency from the plastic melt pulse monitoring data to generate melt dominant frequency data; performing damping coefficient fitting on the plastic melt pulse monitoring data to generate a plastic melt extrusion damping coefficient; performing microfluidic control instruction correction on the initial pulse drive extrusion signal based on the melt dominant frequency data and the plastic melt extrusion damping coefficient to obtain corrected microfluidic control instruction data; Step S3: performing real-time pulse extrusion according to the modified microfluidic control instruction data, and monitoring the pulse extrusion process to obtain real-time plastic melt extrusion monitoring data; dynamically adjusting PID control instruction parameters according to the real-time plastic melt extrusion monitoring data to obtain a plastic melt flow extrusion control strategy; Step S4: Perform a plastic melt flow stability analysis according to the plastic melt flow extrusion control strategy to generate melt flow trend status data; obtain a target 3D printing model; mesh the target 3D printing model to obtain 3D model printing mesh data; divide the 3D model printing mesh data into plastic melt flow risk areas according to the melt flow trend status data, and perform intelligent 3D printing control optimization to obtain an intelligent 3D printing control strategy.

2. The 3D printing control method according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: using a piezoelectric ceramic actuator to integrate the 3D printing nozzle at the outlet of the melting cavity to obtain a micro extrusion actuator; Step S12: performing initial actuator signal modulation on the micro extrusion actuator to generate an initial pulse drive extrusion signal; Step S13: Based on the initial pulse driving extrusion signal, the micro extrusion actuator is controlled to drive the displacement execution and perform preliminary molten plastic extrusion to obtain pulse extrusion plastic melt; Step S14: integrating the nozzle monitoring network using the micro pressure sensor, the temperature sensor and the flow rate sensor to generate a nozzle sensing monitoring network; Step S15: collecting pulse plastic melting state of pulse extrusion plastic melting through the nozzle sensor monitoring network to generate original pulse plastic melting state data; Step S16: Perform time-series synchronization calibration on the original pulse plastic-melt state data to generate plastic-melt pulse monitoring data.

3. The 3D printing control method according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing Kalman filtering noise reduction processing on the plastic melt pulse monitoring data to generate pre-processed plastic melt monitoring data; Step S22: performing time domain window segmentation on the pre-processed plastic melt monitoring data through a preset time window to generate time series plastic melt monitoring data; Step S23: performing fast Fourier transform processing on the time-series melt monitoring data, and extracting the melt flow dominant frequency to generate melt dominant frequency data; Step S24: performing autocorrelation analysis according to the time series plastic melt monitoring data and performing damping coefficient fitting to generate a plastic melt extrusion damping coefficient; Step S25: constructing a second-order oscillation model of melt flow dynamics based on the melt dominant frequency data and the plastic melt extrusion damping coefficient to generate a melt flow dynamics model; Step S26: using the melt flow dynamics model to correct the microfluidic control instruction through the initial pulse driving extrusion signal to obtain corrected microfluidic control instruction data.

4. The 3D printing control method according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: extracting the state of the plastic melt according to the time-series plastic melt monitoring data, and obtaining melt extrusion pressure time-series data, melt extrusion temperature time-series data, and melt extrusion flow rate time-series data respectively; Step S242: performing autocorrelation operation on the melt extrusion pressure time series data to generate melt pressure autocorrelation series coefficients; Step S243: performing peak detection according to the melt pressure autocorrelation sequence coefficient, and intercepting the first peak decay portion to obtain pressure decay curve data; Step S244: performing exponential function fitting on the pressure decay curve data based on the melt pressure autocorrelation series coefficient, and calculating the pressure decay coefficient to generate the pressure decay coefficient; Step S245: Processing the melt temperature period main frequency component according to the melt extrusion temperature time series data to generate melt temperature fluctuation main frequency data; Step S246: calculating the flow rate change rate according to the melt extrusion flow rate time series data, and performing root mean square value processing to generate a root mean square value of the flow rate change rate; Step S247: Calculate the melt flow damping coefficient based on the RMS value of the flow rate change rate, the melt temperature fluctuation main frequency data, and the pressure attenuation coefficient to generate the plastic melt extrusion damping coefficient.

5. The 3D printing control method according to claim 3, characterized in that: Step S26 includes the following steps: Step S261: obtaining desired plastic melt flow state data; Step S262: using a melt flow dynamics model to simulate the desired plastic melt flow state data, and performing an optimal control waveform parameter combination for the extrusion actuator to obtain a desired pulse extrusion drive control signal; Step S263: generating a driving waveform signal according to the desired pulse extrusion driving control signal, and performing signal discrete processing to generate a discrete driving waveform control signal; Step S264: effectively correcting the discrete drive waveform control signal through the initial pulse drive extrusion signal, thereby obtaining effective discrete control signal sequence data; Step S265: adding a timestamp according to the valid discrete control signal sequence data, and performing instruction code conversion to generate corrected microfluidic control instruction data.

6. The 3D printing control method according to claim 2, characterized in that: Step S3 includes the following steps: Step S31: loading the corrected microfluidic control instruction data into the microcontroller, and controlling the piezoelectric ceramic actuator to perform real-time pulse extrusion, while using the nozzle sensor monitoring network to perform real-time pulse extrusion process monitoring to obtain real-time plastic melt extrusion monitoring data; Step S32: performing monitoring feature preprocessing according to the real-time plastic melt extrusion monitoring data to generate real-time plastic melt extrusion monitoring feature data; Step S33: calculating the expected plastic melt flow state deviation value of the modified microfluidic control instruction data based on the real-time plastic melt extrusion monitoring characteristic data to generate real-time plastic melt extrusion state deviation data; Step S34: dynamically adjust the PID control instruction parameters of the corrected micro-flow control instruction data according to the real-time plastic melt extrusion state deviation data to obtain the plastic melt flow extrusion control strategy.

7. The 3D printing control method according to claim 6, characterized in that: Step S33 includes the following steps: Step S331: extracting operation timestamps according to real-time plastic melt extrusion monitoring feature data to generate plastic melt extrusion timestamp data; Step S332: mapping the control cycle expected state parameter of the modified microfluidic control instruction data through the plastic melt extrusion timestamp data to obtain the fluid expected parameter value; Step S333: performing real-time control state parameter conversion on the real-time plastic melt extrusion monitoring characteristic data to obtain real-time fluid parameter values; Step S334: using the real-time fluid parameter value to calculate the state deviation value of the fluid expected parameter value, and generating real-time extrusion state deviation data; Step S335: scaling the real-time extrusion state deviation data using a preset scaling factor, and performing parameter limiting processing to obtain real-time plastic melt extrusion state deviation data.

8. The 3D printing control method according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing a plastic melt flow stability analysis according to a plastic melt flow extrusion control strategy to generate melt flow trend state data; Step S42: Meshing the target 3D printing model to obtain 3D model printing mesh data; Step S43: performing 3D printing area identification according to the 3D model printing grid data, and performing flow state-printing area mapping processing according to the melt flow trend state data to generate melt flow state-printing area mapping data; Step S44: dividing the plastic melt flow risk area according to the melt flow state-printing area mapping data to obtain a melt accumulation risk area and a poor flow risk area; Step S45: performing intelligent 3D printing control optimization on the melt accumulation risk area and the poor flow risk area, thereby obtaining an intelligent 3D printing control strategy.

9. The 3D printing control method according to claim 8, characterized in that: Step S45 includes the following steps: Step S451: Acquire real-time 3D printing point data; Step S452: when the real-time 3D printing point data is located in the melt accumulation risk area, obtaining the preset nozzle residence time in the accumulation area; Step S453: evaluating the accumulation degree according to the melt accumulation risk area, and performing residence time increase coefficient processing to generate the accumulation area residence time increase coefficient; Step S454: adjusting the preset nozzle dwell time of the deposition area by using the deposition area dwell time increase coefficient to generate the deposition area adjusted dwell time; when the deposition area adjusted dwell time is greater than or equal to the preset maximum dwell time threshold, marking the preset maximum dwell time threshold as the deposition area optimized dwell time; when the deposition area adjusted dwell time is less than the preset maximum dwell time threshold, marking the deposition area adjusted dwell time as the deposition area optimized dwell time; Step S455: performing nozzle residence time adjustment control on the modified microfluidic control instruction data based on the optimized residence time of the accumulation area, and obtaining a nozzle residence control strategy for the accumulation area; Step S456: when the real-time 3D printing point data is located in a poor flow risk area, obtaining preset nozzle temperature data of the poor flow area; Step S457: analyzing the melt flow characteristics of the poor flow risk area according to the poor flow risk area, matching the spraying temperature increase coefficient according to the preset nozzle temperature data of the poor flow area, and generating the spraying temperature optimization data of the poor flow area; Step S458: adjusting the spraying temperature according to the optimized data of the spraying temperature in the congested area, and controlling the direction and posture of the nozzle to obtain a spraying control strategy for the congested area; Step S459: Optimize the 3D printing spray control based on the accumulation area spray control strategy and the congested area spray control strategy to obtain an intelligent 3D printing control strategy.

10. A 3D printing control system, characterized in that: Used to execute the 3D printing control method according to claim 1, the 3D printing control system comprises: The pulse extrusion and monitoring module is used to integrate the 3D printing nozzle at the outlet of the melting cavity using a piezoelectric ceramic actuator, and perform initial actuator signal modulation to generate an initial pulse drive extrusion signal; extrude the molten plastic according to the initial pulse drive extrusion signal, and collect the pulse plastic melting state to generate plastic melt pulse monitoring data; The microfluidic instruction correction module is used to extract the melt flow dominant frequency from the plastic melt pulse monitoring data to generate the melt dominant frequency data; perform damping coefficient fitting on the plastic melt pulse monitoring data to generate the plastic melt extrusion damping coefficient; perform microfluidic instruction correction on the initial pulse drive extrusion signal based on the melt dominant frequency data and the plastic melt extrusion damping coefficient to obtain the corrected microfluidic control instruction data; A real-time extrusion control module is used to perform real-time pulse extrusion according to the modified microfluidic control instruction data, and to monitor the real-time pulse extrusion process to obtain real-time plastic melt extrusion monitoring data; dynamically adjust PID control instruction parameters according to the real-time plastic melt extrusion monitoring data to obtain a plastic melt flow extrusion control strategy; The intelligent printing optimization module is used to perform plastic melt flow stability analysis according to the plastic melt flow extrusion control strategy and generate melt flow trend status data; mesh the target 3D printing model to obtain 3D model printing mesh data; divide the 3D model printing mesh data into plastic melt flow risk areas through the melt flow trend status data, and perform intelligent 3D printing control optimization to obtain an intelligent 3D printing control strategy.

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