A calibration method for flow rate of FDM 3D printer

Abstract

The application provides a kind of FDM3D printer flow calibration method, it is related to 3D printing forming technical field, the method is in real time by extrusion pressure sensor Monitoring extrusion resistance value, and based on the resistance value Dynamic adjustment of the rotational speed of extruder screw;Temperature gradient distribution is obtained in nozzle area using infrared thermal imaging module, temperature-viscosity-flow mapping relationship is established and dynamic flow model is constructed to correct material thermal expansion and cold shrinkage effect;The thickness of printing layer is measured by laser displacement sensor, and the preset layer thickness threshold is combined to trigger flow compensation;Flow calibration is carried out through multi-sensor feedback and dynamic parameter adjustment, automatic calibration process is realized, manual intervention is reduced, real-time compensation mechanical resonance, material shrinkage and other interference factors are improved Calibration accuracy and shorten calibration time.

Description

Technical Field

[0001] This invention relates to the field of 3D printing technology, and more specifically to a method for calibrating the flow rate of an FDM 3D printer. Background Technology

[0002] FDM (Fused Deposition Modeling) 3D printing technology builds 3D models by heating and extruding thermoplastic materials layer by layer. Its printing accuracy, surface quality, and mechanical properties are directly affected by the stability of the extrusion flow rate. Flow rate deviations can lead to uncontrolled material extrusion and malfunctioning process parameters, resulting in economic and reliability losses. Specifically, excessive flow rate can cause material buildup, surface roughness, and even structural collapse; insufficient flow rate leads to incomplete model filling and weakened interlayer bonding. Flow rate must be strictly matched with parameters such as printing speed and temperature. Flow rate errors can disrupt the process window, causing warping or uneven fiber distribution. Flow rate calibration errors require repeated parameter corrections or scrapped parts, increasing material consumption and production costs.

[0003] To address the aforementioned issues, current mainstream calibration methods rely on manually adjusting the extrusion ratio or printing test models, but the following technical bottlenecks still exist: 1. Low efficiency: The test model needs to be printed multiple times (e.g., 5-10 iterations), and a single calibration takes several hours. In addition, there is a serious waste of consumables (e.g., 50-100 grams of PLA material are consumed in a single test).

[0004] 2. High dependence on experience: Operators need to observe vague indicators such as surface smoothness and interlayer bonding force with the naked eye. Subjective errors lead to unstable calibration results (if the same operator repeats the calibration, the error is ±5%, and the difference between different operators can reach ±15%).

[0005] 3. Lack of dynamic response: Traditional methods cannot compensate for material properties (such as diameter fluctuations of ±0.2mm due to humidity changes) or mechanical vibrations (such as extrusion pressure pulsations caused by belt loosening) in real time, and are only suitable for static ideal environments.

[0006] Traditional flow calibration methods are limited by low efficiency, reliance on experience, and poor dynamic adaptability, making it difficult to meet the high-precision and high-stability production requirements of industrial applications. Therefore, breaking through the limitations of traditional technologies to effectively calibrate the flow of FDM 3D printing has become a key issue facing current technology. Summary of the Invention

[0007] In view of this, in order to solve the problems of low efficiency, reliance on experience and poor dynamic adaptability of traditional flow calibration methods, the purpose of this invention is to propose a flow calibration method for FDM3D printers. The flow calibration is performed through multi-sensor feedback and dynamic parameter adjustment, realizing an automated calibration process, reducing manual intervention, compensating for interference factors such as mechanical resonance and material shrinkage in real time, improving calibration accuracy and shortening calibration time.

[0008] To achieve the above objectives, the present invention provides the following technical solution: To achieve the above objectives, in a first aspect, the present invention provides a method for calibrating the flow rate of an FDM 3D printer, comprising the following steps: The extrusion resistance value is monitored in real time by an extrusion pressure sensor, and the speed of the extruder screw is dynamically adjusted based on the resistance value. The temperature gradient distribution in the nozzle area is obtained by using an infrared thermal imaging module, a temperature-viscosity-flow mapping relationship is established, and a dynamic flow model is constructed to correct for the thermal expansion and contraction effect of the material. The thickness of the printed layer is measured by a laser displacement sensor, and flow compensation is triggered by combining the preset layer thickness threshold.

[0009] As a further aspect of the present invention, the dynamic adjustment of the extruder screw speed includes the following steps: When the detected extrusion resistance fluctuation exceeds the preset resistance value (±15%), PID closed-loop control is activated to fine-tune the screw speed in steps of a preset speed (0.1 mm / s). Based on the time-domain signal from the pressure sensor, wavelet transform is used to eliminate noise interference caused by mechanical vibration.

[0010] As a further aspect of the present invention, the establishment of the temperature-viscosity-flow rate mapping relationship includes the following steps: A database of viscosity-temperature profiles for at least three materials (PLA, PETG, TPU) is pre-stored. The nozzle temperature is collected in real time by an infrared thermal imaging module, and the viscosity correction factor at the current temperature is calculated by interpolation.

[0011] As a further aspect of the present invention, when constructing the dynamic flow model, the pressure difference between the inlet and outlet of the extruder gearbox measured by the extrusion pressure sensor is set to be... The pressure difference value is used to characterize the extrusion resistance; the average temperature of the nozzle area calculated by the infrared thermal imaging module is... The established dynamic flow model is then expressed as:

[0012] in, To squeeze out flow in real time, For PLA, k = 1.2 × 10⁻⁶, which is a material property constant. - ³ mm³ / (MPa·s), The pressure difference between the inlet and outlet of the extruder gearbox is measured by an extrusion pressure sensor. The average temperature of the nozzle region calculated by the infrared thermal imaging module. The viscosity-temperature correction factor (dimensionless) for PLA. The value is 0.03, which is the TPU value. It is 0.05; The vibration compensation coefficient is dimensionless. The mechanical resonance frequency, The vibration phase difference is used to calculate the reverse displacement of the piezoelectric ceramic actuator.

[0013] As a further aspect of the present invention, the material viscosity-temperature correction coefficient in the dynamic flow model... The acquisition includes the following steps: A database of pre-stored material viscosity-temperature curves, containing at least Arrhenius equation parameters for PLA, PETG, and TPU; Local thermal conductivity is calculated in real time using infrared thermal imaging data. Correct the temperature term:

[0014] in, This is the base viscosity-temperature correction factor at the reference temperature (25℃). The coefficient of thermal expansion is used to characterize the sensitivity of a material to changes in volume / thermal conductivity caused by temperature variations. This refers to the local thermal conductivity (nozzle area) measured in real time. Thermal conductivity at a reference temperature (25℃).

[0015] As a further aspect of the present invention, the triggering of traffic compensation includes the following steps: Layer thickness deviation measured by laser displacement sensor The compensation flow rate is calculated as follows:

[0016] in, To compensate for the subsequent traffic, For the initial target traffic, For preset layer thickness, The actual layer thickness measured by the laser displacement sensor. For the elastic modulus of the material Functions: ; The thickness of the printed layer was measured using a laser displacement sensor. ,when When the value exceeds 5%, a secondary calibration process is triggered to recalibrate the pressure sensor reference value.

[0017] As a further aspect of the present invention, the elastic modulus of the material Real-time acquisition includes: Strain measurement of extruded filaments based on strain gauges Calculated using Hooke's Law ,in For stress; when When the change exceeds ±15%, a secondary calibration process is triggered to update the n value.

[0018] As a further aspect of the present invention, the FDM 3D printer flow calibration method further includes: When printing test strips, a Helmholtz coil is used to generate a magnetic field of 0.1-1T to detect the eddy current loss of the metal nozzle and correct the extruder motor efficiency η. When η decreases by more than 10%, motor drive parameter compensation is triggered.

[0019] As a further aspect of the present invention, the excitation frequency of the Helmholtz coil is synchronized with the screw speed of the extruder, and the eddy current signal is extracted by lock-in amplification technology.

[0020] As a further aspect of the present invention, the FDM 3D printer flow rate calibration method performs calibration steps based on an FDM 3D printer flow rate calibration system, wherein the FDM 3D printer flow rate calibration system includes: Multimodal sensor array: integrates pressure sensor, infrared thermal imager and laser displacement sensor, used to collect pressure, temperature and displacement data, with a sampling frequency ≥1kHz; Adaptive damping device: includes piezoelectric ceramic actuator and MEMS inertial measurement unit (IMU) to cancel mechanical resonance in the frequency range of 10-200Hz; The edge computing module runs an LSTM neural network model to predict changes in material flow characteristics.

[0021] As a further aspect of the present invention, when the adaptive vibration damping device cancels out mechanical resonance, the vibration frequency f and amplitude A are detected by a MEMS inertial measurement unit; the operating modes of the adaptive vibration damping device include: Passive mode: When the vibration frequency f < 20Hz, energy is absorbed through a mechanical spring damper; Active mode: When the vibration frequency falls within the resonant frequency band f ∈ [20,200]Hz and the amplitude A > threshold amplitude, the piezoelectric ceramic actuator applies a reverse displacement Δx = K·A·sin(2πft), where K is the damping coefficient, K = 0.5-2.0.

[0022] As a further aspect of the present invention, the FDM 3D printer flow calibration method further includes: An environmental parameter compensation model was established to correct the volume change caused by the moisture absorption and expansion of the material based on temperature and humidity sensor data. When the ambient humidity is >50%, the extrusion pressure compensation value ΔP = 0.02 MPa·RH is automatically increased.

[0023] As a further aspect of the present invention, the calibration process of the FDM3D printer flow calibration method includes: Pre-calibration stage: Print a 100mm×100mm reference cube, use a 3D white light scanner to measure flatness and measure flatness error using the three-point contact method; Dynamic calibration phase: Print variable cross-section spiral test strips with stepped flow rate, print 5 sets of test strips, each set containing a flow rate gradient of 100% to 150%; Closed-loop optimization stage: Adjust PID parameters based on BP neural network to make the standard deviation of flow fluctuation <0.8%.

[0024] As a further aspect of the present invention, the three-point contact method for measuring flatness error includes the following steps: Laser displacement sensors are placed at the three corner points of the reference square; The plane equation is fitted using the least squares method, and the flatness error Δz_max is calculated.

[0025] Compared with existing technologies, the FDM 3D printer flow calibration method proposed in this invention has the following advantages: This invention significantly improves printing accuracy, stability, and adaptability through multi-sensor fusion, dynamic model compensation, and intelligent closed-loop control. Multi-dimensional dynamic flow compensation significantly enhances accuracy. By combining extrusion pressure, temperature gradient, and vibration phase difference data, a dynamic flow model is established, overcoming the limitations of traditional single-factor calibration. A dual-mode vibration damping system is employed: in passive mode, a mechanical spring damper absorbs low-frequency vibrations, reducing structural fatigue; in active mode, a piezoelectric ceramic actuator applies reverse displacement, with dynamically adjustable damping coefficients, reducing vibration amplitude and avoiding the hysteresis of traditional passive damping. This achieves a high-precision calibration process, reduces manual intervention, and solves the core challenge of flow control in FDM 3D printing.

[0026] These or other aspects of this application will become more apparent from the following description of embodiments. It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the application. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the accompanying drawings used in the description of the exemplary embodiments or related technologies will be briefly introduced below. The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a method for calibrating the flow rate of an FDM 3D printer according to an embodiment of the present invention.

[0028] Figure 2 This is a flowchart illustrating the dynamic adjustment of the extruder screw speed in a calibration method for FDM 3D printer flow rate according to an embodiment of the present invention.

[0029] Figure 3 This is a flowchart illustrating the establishment of a temperature-viscosity-flow mapping relationship in a method for calibrating the flow rate of an FDM 3D printer according to an embodiment of the present invention.

[0030] Figure 4 This is a flowchart illustrating a calibration method for the flow rate of an FDM 3D printer according to an embodiment of the present invention. Detailed Implementation

[0031] The present application will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0032] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to specific examples and the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and are not intended to limit the scope of this application.

[0033] It should be noted that all uses of "first" and "second" in the embodiments of the present invention are for the purpose of distinguishing two different entities or different parameters with the same name. Therefore, "first" and "second" are merely for convenience of expression and should not be construed as limiting the embodiments of the present invention. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, such as other steps or units inherent in a process, method, system, product, or device that includes a series of steps or units.

[0034] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0035] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0036] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0037] Traditional flow calibration methods are limited by low efficiency, reliance on experience, and poor dynamic adaptability, making it difficult to meet the high precision and high stability requirements of industrial production. This invention proposes a flow calibration method for FDM3D printers. Flow calibration is performed through multi-sensor feedback and dynamic parameter adjustment, realizing an automated calibration process, reducing manual intervention, and compensating for interference factors such as mechanical resonance and material shrinkage in real time, thereby improving calibration accuracy and shortening calibration time.

[0038] See Figure 1 As shown, an embodiment of the present invention provides a method for calibrating the flow rate of an FDM 3D printer, the method comprising the following steps: Step S10: Monitor the extrusion resistance value in real time using an extrusion pressure sensor, and dynamically adjust the speed of the extruder screw based on the resistance value.

[0039] In this step, see Figure 2 As shown, the dynamic adjustment of the extruder screw speed includes the following steps: Step S101: When the detected extrusion resistance value fluctuation exceeds the preset resistance value (±15%), start PID closed-loop control and fine-tune the screw speed in steps of preset speed (0.1mm / s). Step S102: Based on the time-domain signal of the pressure sensor, eliminate noise interference caused by mechanical vibration through wavelet transform.

[0040] In the PID closed-loop control loop, parameters are set based on PID tuning rules. The parameters are set as follows: Kp = 0.6 × critical gain, Ki = 0.05 × critical gain / Ts, Kd = 0.1 × critical gain × Ts, Ts = 0.1s. Therefore, Kp = 0.8, Ki = 0.2, and Kd = 0.1. When adjusting the step size, if Δresistance = +18%, the PID output increment ΔP = Kp·e + Ki·∫e dt + Kd·de / dt = 0.8 × 0.18 + 0.2 × (0.18 × 0.1) + 0.1 × (0.018) = 0.144 + 0.0036 + 0.0018 = 0.1494. The speed adjustment amount Δn = ΔP × 0.1 mm / s = 0.01494 mm / s (the actual speed is adjusted to the original speed + 0.01494 mm / s).

[0041] When eliminating noise interference caused by mechanical vibration through wavelet transform, the db4 wavelet basis is used, the number of decomposition layers is 5, and mechanical vibration noise with a frequency >50Hz is eliminated; the threshold λ=median(|d_j|) / 0.6745 (d_j is the wavelet coefficient); after denoising, the signal-to-noise ratio (SNR) is improved to >30dB, and the root mean square error (RMSE) is <0.5%.

[0042] Step S20: Use an infrared thermal imaging module to obtain the temperature gradient distribution in the nozzle area, establish a temperature-viscosity-flow mapping relationship, and construct a dynamic flow model to correct for the thermal expansion and contraction effect of the material.

[0043] In this step, see Figure 2 As shown, establishing the temperature-viscosity-flow rate mapping relationship includes the following steps: Step S201: Pre-store a database of viscosity-temperature profiles for at least three materials (PLA, PETG, TPU); Step S202: The nozzle temperature is collected in real time by the infrared thermal imaging module, and the viscosity correction coefficient at the current temperature is calculated by interpolation.

[0044] In this embodiment, when constructing the dynamic flow model, the pressure difference between the inlet and outlet of the extruder gearbox measured by the extrusion pressure sensor is assumed to be... The pressure difference value is used to characterize the extrusion resistance; the average temperature of the nozzle area calculated by the infrared thermal imaging module is... The established dynamic flow model is then expressed as:

[0045] in, To squeeze out flow in real time, For PLA, k = 1.2 × 10⁻⁶, which is a material property constant. -³ mm³ / (MPa·s), The pressure difference between the inlet and outlet of the extruder gearbox is measured by an extrusion pressure sensor. The average temperature of the nozzle region calculated by the infrared thermal imaging module. The viscosity-temperature correction factor (dimensionless) for PLA. The value is 0.03, which is the TPU value. It is 0.05; The vibration compensation coefficient is dimensionless. The mechanical resonance frequency, The vibration phase difference is used to calculate the reverse displacement of the piezoelectric ceramic actuator.

[0046] Among them, the material viscosity-temperature correction coefficient in the dynamic flow model The acquisition includes the following steps: A database of pre-stored material viscosity-temperature curves, containing at least Arrhenius equation parameters for PLA, PETG, and TPU; Local thermal conductivity is calculated in real time using infrared thermal imaging data. Correct the temperature term:

[0047] in, This is the base viscosity-temperature correction factor at the reference temperature (25℃). The coefficient of thermal expansion is used to characterize the sensitivity of a material to changes in volume / thermal conductivity caused by temperature variations. This refers to the local thermal conductivity (nozzle area) measured in real time. Thermal conductivity at a reference temperature (25℃).

[0048] For example, if the input conditions are: =0.8MPa, T=180℃, =0.18W / (m·K), f=45Hz, then the model calculation result is: 1.2×10 3 ×0.8×e 0.03018 ×180×[1+0.018×sin(2π×45t+0.00045)], the calculated result Q=0.0096 mm³ / s (theoretical value), and the measured error is <±0.5%.

[0049] Step S30: Measure the thickness of the printed layer using a laser displacement sensor, and trigger flow compensation in combination with a preset layer thickness threshold.

[0050] In this step, triggering traffic compensation includes the following steps: Layer thickness deviation measured by laser displacement sensor The compensation flow rate is calculated as follows:

[0051] in, To compensate for the subsequent traffic, For the initial target traffic, For preset layer thickness, The actual layer thickness measured by the laser displacement sensor. For the elastic modulus of the material Functions: ; The thickness of the printed layer was measured using a laser displacement sensor. ,when When the value exceeds 5%, a secondary calibration process is triggered to recalibrate the pressure sensor reference value.

[0052] Wherein, the elastic modulus of the material Real-time acquisition includes: Strain measurement of extruded filaments based on strain gauges Calculated using Hooke's Law ,in For stress; when When the change exceeds ±15%, a secondary calibration process is triggered to update the n value.

[0053] In some embodiments, the FDM 3D printer flow calibration method further includes: When printing test strips, a Helmholtz coil is used to generate a magnetic field of 0.1-1T to detect the eddy current loss of the metal nozzle and correct the extruder motor efficiency η. When η decreases by more than 10%, motor drive parameter compensation is triggered.

[0054] The excitation frequency of the Helmholtz coil is synchronized with the screw speed of the extruder, and the eddy current signal is extracted through lock-in amplification technology.

[0055] In some embodiments, the FDM 3D printer flow rate calibration method performs calibration steps based on an FDM 3D printer flow rate calibration system, the FDM 3D printer flow rate calibration system comprising: Multimodal sensor array: integrates pressure sensor, infrared thermal imager and laser displacement sensor, used to collect pressure, temperature and displacement data, with a sampling frequency ≥1kHz; Adaptive damping device: includes piezoelectric ceramic actuator and MEMS inertial measurement unit (IMU) to cancel mechanical resonance in the frequency range of 10-200Hz; The edge computing module runs an LSTM neural network model to predict changes in material flow characteristics.

[0056] The adaptive vibration damping device, when counteracting mechanical resonance, detects the vibration frequency f and amplitude A using a MEMS inertial measurement unit; the operating modes of the adaptive vibration damping device include: Passive mode: When the vibration frequency f < 20Hz, energy is absorbed through a mechanical spring damper; Active mode: When the vibration frequency falls within the resonant frequency band f ∈ [20,200]Hz and the amplitude A > threshold amplitude, the piezoelectric ceramic actuator applies a reverse displacement Δx = K·A·sin(2πft), where K is the damping coefficient, K = 0.5-2.0.

[0057] In some embodiments, the FDM 3D printer flow calibration method further includes: An environmental parameter compensation model was established to correct the volume change caused by the moisture absorption and expansion of the material based on temperature and humidity sensor data. When the ambient humidity is >50%, the extrusion pressure compensation value ΔP = 0.02 MPa·RH is automatically increased.

[0058] In this invention, see Figure 4 As shown, the calibration process for this FDM 3D printer flow rate calibration method includes: Step S1, Pre-calibration stage: Print a 100mm×100mm reference block, use a 3D white light scanner to measure the flatness and measure the flatness error using the three-point contact method; Step S2, Dynamic Calibration Stage: Print variable cross-section spiral test strips with stepped flow rate, print 5 sets of test strips, each set containing a flow rate gradient of 100% to 150%; Step S3, Closed-loop optimization stage: Adjust PID parameters based on BP neural network to make the standard deviation of flow fluctuation <0.8%.

[0059] The three-point contact method for measuring flatness error includes the following steps: Laser displacement sensors are placed at the three corner points of the reference square; The plane equation is fitted using the least squares method, and the flatness error Δz_max is calculated.

[0060] This invention significantly improves printing accuracy, stability, and adaptability through multi-sensor fusion, dynamic model compensation, and intelligent closed-loop control. Multi-dimensional dynamic flow compensation significantly enhances accuracy. By combining extrusion pressure, temperature gradient, and vibration phase difference data, a dynamic flow model is established, overcoming the limitations of traditional single-factor calibration. A dual-mode vibration damping system is employed: in passive mode, a mechanical spring damper absorbs low-frequency vibrations, reducing structural fatigue; in active mode, a piezoelectric ceramic actuator applies reverse displacement, with dynamically adjustable damping coefficients, reducing vibration amplitude and avoiding the hysteresis of traditional passive damping. This achieves a high-precision calibration process, reduces manual intervention, and solves the core challenge of flow control in FDM 3D printing.

[0061] The above are exemplary embodiments disclosed in this invention. However, it should be noted that various changes and modifications can be made without departing from the scope of the embodiments of this invention as defined by the claims. The functions, steps, and / or actions of the methods according to the disclosed embodiments described herein do not need to be performed in any particular order. Furthermore, although the elements disclosed in the embodiments of this invention may be described or claimed individually, they may be understood as multiple unless explicitly limited to a singular number.

[0062] It should be understood that, as used herein, the singular form "a" is intended to include the plural form as well, unless the context clearly supports an exception. It should also be understood that, as used herein, "and / or" refers to any and all possible combinations of one or more of the associatedly listed items. The embodiment numbers disclosed above are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0063] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples. Within the framework of the invention, technical features of the above embodiments or different embodiments can be combined, and many other variations of different aspects of the invention exist, which are not provided in the details for the sake of brevity. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the invention should be included within the protection scope of the invention.

Claims

1. A method for calibrating the flow rate of an FDM 3D printer, characterized in that, The method includes the following steps: The extrusion resistance value is monitored in real time by an extrusion pressure sensor, and the speed of the extruder screw is dynamically adjusted based on the resistance value. The temperature gradient distribution in the nozzle area is obtained by using an infrared thermal imaging module, a temperature-viscosity-flow mapping relationship is established, and a dynamic flow model is constructed to correct for the thermal expansion and contraction effect of the material. The thickness of the printed layer is measured using a laser displacement sensor, and flow compensation is triggered based on a preset layer thickness threshold. The establishment of the temperature-viscosity-flow rate mapping relationship includes the following steps: Pre-store a database of viscosity-temperature profiles for at least three materials; The nozzle temperature is collected in real time by an infrared thermal imaging module, and the viscosity correction coefficient at the current temperature is calculated by interpolation. When constructing the dynamic flow model, the pressure difference between the inlet and outlet of the extruder gearbox measured by the extrusion pressure sensor is assumed to be... The pressure difference value is used to characterize the extrusion resistance; the average temperature of the nozzle area calculated by the infrared thermal imaging module is... The established dynamic flow model is then expressed as: in, To squeeze out flow in real time, These are material property constants. The pressure difference between the inlet and outlet of the extruder gearbox is measured by an extrusion pressure sensor. The average temperature of the nozzle region calculated by the infrared thermal imaging module. This is the viscosity-temperature correction factor for the material. The vibration compensation coefficient is... The mechanical resonance frequency, The vibration phase difference is used to calculate the reverse displacement of the piezoelectric ceramic actuator. Material viscosity-temperature correction factor in dynamic flow model The acquisition includes the following steps: A database of pre-stored material viscosity-temperature curves, containing at least Arrhenius equation parameters for PLA, PETG, and TPU; Local thermal conductivity is calculated in real time using infrared thermal imaging data. Correct the temperature term: in, This is the base viscosity-temperature correction factor at the reference temperature. The coefficient of thermal expansion is used to characterize the sensitivity of a material to changes in volume / thermal conductivity caused by temperature variations. The local thermal conductivity is measured in real time. Thermal conductivity at the reference temperature; The triggered traffic compensation includes the following steps: Layer thickness deviation measured by laser displacement sensor The compensation flow rate is calculated as follows: in, To compensate for the subsequent traffic, For the initial target traffic, For preset layer thickness, The actual layer thickness measured by the laser displacement sensor. For the elastic modulus of the material Functions: ; The thickness of the printed layer was measured using a laser displacement sensor. ,when When the value exceeds 5%, a secondary calibration process is triggered to recalibrate the pressure sensor reference value.

2. The calibration method for FDM 3D printer flow rate as described in claim 1, characterized in that, The dynamic adjustment of the extruder screw speed includes the following steps: When the detected extrusion resistance value fluctuation exceeds the preset resistance value, PID closed-loop control is activated to fine-tune the screw speed in steps of the preset speed. Based on the time-domain signal from the pressure sensor, wavelet transform is used to eliminate noise interference caused by mechanical vibration.

3. The calibration method for FDM 3D printer flow rate as described in claim 2, characterized in that, The elastic modulus of the material Real-time acquisition includes: Strain measurement of extruded filaments based on strain gauges Calculated using Hooke's Law ,in For stress; when When the change exceeds ±15%, a secondary calibration process is triggered to update the n value.

4. The calibration method for FDM 3D printer flow rate as described in claim 1, characterized in that, The calibration method for the FDM 3D printer flow rate also includes: When printing test strips, a Helmholtz coil is used to generate a magnetic field of 0.1-1T to detect the eddy current loss of the metal nozzle and correct the extruder motor efficiency η. When η drops by more than 10%, motor drive parameter compensation is triggered; the excitation frequency of the Helmholtz coil is synchronized with the screw speed of the extruder, and the eddy current signal is extracted through lock-in amplification technology.

5. The calibration method for FDM 3D printer flow rate as described in claim 1, characterized in that, The FDM 3D printer flow rate calibration method is based on an FDM 3D printer flow rate calibration system, which includes: Multimodal sensor array: integrates pressure sensor, infrared thermal imager and laser displacement sensor, used to collect pressure, temperature and displacement data, with a sampling frequency ≥1kHz; Adaptive damping device: includes piezoelectric ceramic actuator and MEMS inertial measurement unit, used to cancel mechanical resonance in the frequency range of 10-200Hz; The edge computing module runs an LSTM neural network model to predict changes in material flow characteristics.

6. The calibration method for FDM 3D printer flow rate as described in claim 5, characterized in that, When the adaptive vibration damping device cancels out mechanical resonance, it detects the vibration frequency f and amplitude A through a MEMS inertial measurement unit. The operating modes of the adaptive damping device include: Passive mode: When the vibration frequency f < 20Hz, energy is absorbed by a mechanical spring damper; Active mode: When the vibration frequency falls within the resonant frequency band f ∈ [20,200]Hz and the amplitude A > threshold amplitude, the piezoelectric ceramic actuator applies a reverse displacement Δx = K·A·sin(2πft), where K is the damping coefficient, K = 0.5-2.0.

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