Calibration method and system for screw extrusion 3D printing flow mechanical error

Abstract

The invention relates to a screw extrusion 3D printing flow mechanical error calibration method and system, and the method comprises the steps: obtaining datum line three-dimensional point cloud data through standard printing in a preparation stage, and generating a datum sequence value through filtering; in the sampling stage, the ratio relation between the screw rotating speed and the printing speed is established, and whether calibration is triggered or not is judged through residual white noise detection; in the calibration stage, periodic characteristics of a reference sequence are extracted by adopting fast Fourier transform, a deviation over-limit region is identified, rotating speed parameters are dynamically adjusted, and a configuration file containing periodic fluctuation correction information is generated; in the verification stage, the calibration waveform and the sampling waveform are matched through a sliding window, and the optimal parameter loading position is determined; and finally, the calibration is completed after the standard is determined through repeated sampling, otherwise, iterative correction is carried out through an optimization algorithm. According to the technology, flow errors caused by screw abrasion are accurately compensated through a three-dimensional scanning measurement and spectrum analysis algorithm, the service life of equipment is prolonged while the printing precision is maintained, and the technology is particularly suitable for high-precision industrial-grade 3D printing scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of 3D printing flow calibration, and in particular to a method and system for calibrating mechanical errors of screw extrusion 3D printing flow. Background Art

[0002] Screw extrusion technology is widely used in the industrial sector and holds great promise in 3D printing. For example, in FGF printing, the screw, a core component, suffers from mechanical errors that significantly impact print quality. Screw mechanical errors, such as uneven pitch, thread shape deviation, and coaxiality deviation, can lead to fluctuations in extrusion flow, resulting in uneven layer thickness and rough surface finishes, severely impacting print quality. Therefore, addressing the impact of screw wear on flow is crucial for improving product quality in 3D printing and other screw extrusion processes.

[0003] Currently, traditional flow calibration methods primarily rely on empirical formulas or simple sensor feedback adjustments, making it difficult to accurately compensate for screw wear errors. On the one hand, empirical formulas cannot accurately reflect the complex relationship between screw wear errors and flow rate. On the other hand, simple sensor feedback adjustments suffer from slow response speeds and limited accuracy, making it impossible to accurately adjust flow rate in real time. This not only hinders product quality but also increases production costs and cycle time. Furthermore, existing technologies are unable to effectively address the impact of screw wear errors on flow accuracy. Therefore, developing a high-precision flow calibration method that can effectively compensate for screw wear errors is a pressing technical challenge for those skilled in the art. Summary of the Invention

[0004] The main purpose of this invention is to overcome the shortcomings and deficiencies of the existing technology and to provide a method and system for calibrating the mechanical error of screw extrusion 3D printing flow. Through precise measurement technology and accurate calibration algorithm, it can effectively compensate for the influence of screw wear error on flow accuracy, extend the service life of the screw, and meet the needs of various industrial scenarios with stringent requirements on flow accuracy.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for calibrating mechanical error of screw extrusion 3D printing flow, comprising the following steps:

[0007] S1. Preparation: Print the baseline using a standard printer, obtain its 3D point cloud coordinates through scanning, calculate the cross-sectional area of ​​each position along the baseline, perform filtering, and generate a baseline value sequence.

[0008] Sampling and calibration determination phase: Adjust the screw motor speed, establish a ratio between motor speed and printing speed, print a calibration line and scan to obtain its 3D point cloud coordinates, calculate the cross-sectional area of ​​the calibration line and perform filtering, and determine whether to initiate calibration based on the error condition and residual sequence white noise.

[0009] S3. Calibration phase: Treating the reference value sequence as a periodic sequence, extracting periodic features through Fast Fourier Transformation, identifying areas of excessive deviation, adjusting the screw motor speed to bring the cross-sectional area of ​​the calibration points closer to the reference value, and generating a calibration profile; the calibration profile includes correction information for the ratio of screw motor speed to printing speed at different positions within a cycle, as the cross-sectional area of ​​the calibration line fluctuates with position.

[0010] S4. Calibration verification and loading phase: Select the sampling length to scan the calibration line, match the waveform in the calibration configuration file with the sampled waveform, determine the optimal loading position using the sliding window method, and load the calibration parameters.

[0011] S5. Qualification judgment stage: Repeat sampling and calibration judgment. If the conditions are met, the calibration is completed. Otherwise, the representative points are calibrated through the optimization algorithm until the error requirements are met.

[0012] As a preferred technical solution, in step S1, generating a reference value sequence is specifically as follows:

[0013] Based on the set step size, calculate the cross-sectional area A along the reference line i , after filtering to remove data noise, the filtered cross-sectional area data is All cross-sectional area data are taken as a baseline value sequence.

[0014] As a preferred technical solution, in step S2, the motor speed is adjusted according to a ratio relationship with the printing speed to cover different flow conditions.

[0015] As a preferred technical solution, in step S2, the different λ(x i ) and the cross-sectional area A of the printed lines at the corresponding position i The data were sorted and the cross-sectional area A was analyzed using the preset data analysis software. i The data is fitted and analyzed, and a variety of different types of fitting functions are selected. By comparing the errors and correlations of the fitting functions, the most appropriate fitting function is selected, and the parameters of the fitting function are automatically calculated through the optimization algorithm of the least squares method, and finally the approximate relationship A is obtained. i =ψ(λ i ).

[0016] As a preferred technical solution, in step S2, whether to start calibration is determined based on the error condition and the residual sequence white noise, specifically:

[0017] Define the calibration decision factor ∈ and calculate and The value of The residual sequence is calculated as the average of the cross-sectional area at each position of the baseline And use statistical analysis methods to determine whether the residual sequence is a white noise sequence. If it satisfies And the residual sequence res i If it is a white noise sequence, it is considered that no calibration is required; otherwise, the calibration process is started.

[0018] As a preferred technical solution, in step S3, when identifying the area where the deviation exceeds the limit, the calibration sensitivity factor α is defined, and the area that meets the The sequence is used as the calibration region, where A i =f(x i ) is a periodic sequence, is the average value of the cross-sectional area at each position of the reference line.

[0019] As a preferred technical solution, in step S4, the waveform in the calibration configuration file is matched with the sampled waveform, specifically:

[0020] A sliding window with the same length as the calibration cross-sectional area waveform is taken from the printed line cross-sectional area waveform, and the mean square error between the waveform in the sliding window and the cross-sectional area calibration waveform is calculated in sequence.

[0021] As a preferred technical solution, in step S4, the length of the sliding window is equal to the length of the calibration waveform cycle, and the sliding step is an integer multiple of the calibration line cross-sectional area calculation step.

[0022] As a preferred technical solution, in step S5, the optimization algorithm is a genetic algorithm or a particle swarm algorithm, and the objective function is to minimize the sum of squares of the errors between the calibrated cross-sectional area and the reference value.

[0023] In a second aspect, the present invention provides a screw extrusion 3D printing flow mechanical error calibration system, which is applied to the aforementioned screw extrusion 3D printing flow mechanical error calibration method, including a reference value sequence generation module, a sampling and calibration determination module, a calibration module, a calibration verification and loading module, and a qualified determination module;

[0024] The reference value sequence generation module uses a standard printer to print the reference line, obtains its three-dimensional point cloud coordinates by scanning, calculates the cross-sectional area of ​​each position of the reference line and performs filtering processing to generate the reference value sequence;

[0025] The sampling and calibration determination module is used to adjust the screw motor speed, establish a ratio between the motor speed and the printing speed, print the calibration line and scan to obtain its three-dimensional point cloud coordinates, calculate the cross-sectional area of ​​the calibration line and perform filtering, and determine whether to start calibration based on the error condition and the residual sequence white noise;

[0026] The calibration module is configured to treat the reference value sequence as a periodic sequence, extract periodic features using a fast Fourier transform, identify areas where deviations exceed the limit, adjust the screw motor speed so that the cross-sectional area of ​​the calibration point approaches the reference value, and generate a calibration configuration file; the calibration configuration file includes correction information for the ratio of the screw motor speed to the printing speed at different positions within a cycle of periodic fluctuations in the cross-sectional area of ​​the calibration line with position;

[0027] The calibration verification and loading module is used to select a sampling length scan calibration line, match the waveform in the calibration configuration file with the sampling waveform, determine the optimal loading position through a sliding window method, and load the calibration parameters;

[0028] The qualified judgment module is used for repeated sampling and calibration judgment. If the conditions are met, the calibration is completed. Otherwise, the representative points are calibrated through the optimization algorithm until the error requirements are met.

[0029] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0030] The present invention provides a method and system for calibrating mechanical errors in screw extrusion 3D printing flow. This method uses three-dimensional point cloud measurement to obtain the cross-sectional area of ​​the baseline and generate a precise sequence of baseline values. It then combines error conditions with residual white noise detection to achieve scientific calibration decisions. Fast Fourier transforms are used to extract periodic features and dynamically adjust the screw motor speed to compensate for periodic flow fluctuations. A sliding window method is used to match waveforms and an optimization algorithm is used to determine optimal calibration parameters, forming a fully automated system encompassing baseline value generation, sampling determination, calibration optimization, verification loading, and qualification determination. Compared to traditional methods that rely on empirical formulas or simple sensor feedback, the present invention can accurately identify systematic deviations caused by mechanical errors such as screw wear, effectively distinguish random noise, and dynamically compensate for periodic flow fluctuations, significantly improving flow control accuracy and printing quality. Furthermore, through adaptive parameter optimization and full-process closed-loop control, the system extends the service life of core equipment components and reduces the defective rate of industrial-grade 3D printing. This system combines technological advancement with engineering practicality, providing a reliable error calibration solution for high-precision printing scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0032] Figure 1 is a flow chart of a flow calibration method according to an embodiment of the present invention;

[0033] Figure 2 Schematic diagram of the structure of a measuring device according to an embodiment of the present invention;

[0034] Figure 3 Schematic diagram of the measuring device according to an embodiment of the present invention;

[0035] Figure 4 The screw plane diagrams of the embodiments of the present invention respectively show a normal screw device and an abnormal screw device with large mechanical errors such as coaxiality, parallelism, and pitch;

[0036] Figure 5 Schematic diagram of the structure of the flow calibration system according to an embodiment of the present invention.

[0037] Explanation of the accompanying figures: 1-screw extrusion nozzle; 2-first measuring device; 3-second measuring device; 4-printing example of the device running; 5-printing platform. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0039] References to "embodiments" in this application mean that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described in this application may be combined with other embodiments.

[0040] like Figure 1 As shown, a method for calibrating mechanical error of screw extrusion 3D printing flow rate in this embodiment includes the following steps:

[0041] S1. Preparation stage: Use a standard printer to print the reference line, and obtain its 3D point cloud coordinates by scanning. The scanning measurement device and principle are as follows: Figure 2 、 Figure 3 shown. Figure 2 The middle screw extruder nozzle 1 is set on the printer frame through a slide rail. The first measuring device 2 and the second measuring device 3 are fixed on the printer frame. The first measuring device 2 and the second measuring device 3 are staggered to collect contour information in different directions. The device runs the printing instance 4 and is set on the printing platform 5. The first measuring device 2 and the second measuring device 3 comprehensively collect the contour point cloud of the printing line and integrate it for processing. Figure 3 The cross-sectional area of ​​each position of the baseline is calculated through the collected point cloud information and filtered to generate a baseline value sequence; the details are as follows:

[0042] S11. Print the calibration baseline. Select a standard printer that has been rigorously calibrated, features high screw precision, and high printing accuracy. Determine the baseline's length, width, and printing material parameters based on the application scenario and calibration requirements. For example, in 3D printing applications, use the same consumables as for subsequent printed products for baseline printing.

[0043] S12. Use a standard printer to accurately print out the reference line according to the set parameters, such as Figure 3 During the printing process, ensure that the printing environment is stable and avoid external factors (such as vibration, temperature changes, etc.) that may affect the printing accuracy.

[0044] S13. Obtain the three-dimensional point cloud coordinates of the printed reference line. Install the measuring device in a suitable location and set the resolution to meet the requirements for accurately capturing the contour details of the reference line, thereby obtaining data of sufficient accuracy.

[0045] S14. Start the measuring device to measure the baseline and obtain three-dimensional point cloud coordinate data of the baseline contour. During the measurement process, the relative position between the measuring device and the baseline must be stable to avoid data errors caused by position changes.

[0046] S15. Select an appropriate step size, calculate the cross-sectional area of ​​each position of the printed baseline along the direction of the baseline and perform filtering. Process the acquired 3D point cloud coordinate data according to the pre-specified step size. Use a professional 3D point cloud coordinate data processing algorithm. The specific method is to start from the starting point of the baseline, move according to the step size, define the range at each cross-sectional position, select a point cloud subset, fit the plane, connect the point cloud on the plane into a polygon, calculate its area as the cross-sectional area of ​​the current position, and repeat until the cross-sectional area A of each position of the baseline is calculated. i .

[0047] S16. Since some minor factors in the printing and scanning processes may introduce noise, the calculated cross-sectional area data A i Perform filtering. Select a suitable filter for filtering. Take Savitzky-Golay filter as an example, set the appropriate filter parameters, and i And its adjacent data points are processed to weaken or remove noise interference and obtain filtered cross-sectional area data This data will serve as the reference value sequence for subsequent calibration.

[0048] S2, sampling and calibration determination stage: adjust the screw motor speed, establish the ratio relationship between the motor speed and the printing speed, print the calibration line and scan to obtain its 3D point cloud coordinates, calculate the cross-sectional area of ​​the calibration line and filter it, and determine whether to start calibration based on the error condition and the residual sequence white noise; specifically:

[0049] S21, set the printing speed and adjust the screw motor speed. Set a certain printing speed, and adjust the screw motor speed n(x i ) to adjust. You can use programming to change the motor speed according to a certain rule (such as arithmetic progression, geometric progression, etc.). For example, start from a low speed and gradually increase the speed in fixed increments, and record the position x corresponding to each speed. i .

[0050] S22, print multiple groups of lines respectively, calculate the ratio of the screw motor speed to the printing speed and measure the cross-sectional area of ​​the printed line at the corresponding position. According to the measured screw motor speed and the set printing speed, calculate the ratio of the motor speed to the printing speed at each position λ (x i ).

[0051] It is understandable that under different λ(x i ), use a measuring device to measure the cross-sectional area of ​​each group of printed lines. To improve the accuracy of the measurement, each group of lines can be scanned and measured multiple times, and the average of the average values ​​of each group of lines in each scan is taken as the final result.

[0052] S23. Analyze the measured data and obtain an approximate relationship. i ) and the cross-sectional area A of the printed lines at the corresponding position i The data is sorted. Use data analysis software (such as MATLAB, Python, etc.) to fit and analyze these data. You can try different fitting function forms, such as linear function, polynomial function, etc., and select the most appropriate fitting function by comparing the fitting error and correlation, and finally get the approximate relationship A i=ψ(λ i ).

[0053] S24. Print the calibration line using the printer that needs to be calibrated and obtain data. Print the calibration line using the same printing parameter settings and environmental conditions as those used for printing the baseline line.

[0054] S25. Measure the calibration line using a measuring device to obtain its three-dimensional point cloud coordinates. Select an appropriate calibration length l on the calibration line. This length should be determined based on the actual application and calibration requirements. Generally, it should contain enough characteristic information to accurately reflect the flow rate changes.

[0055] S26, calculate the cross-sectional area of ​​the calibration line at different positions along the calibration line direction and perform filtering processing. Within the calibration length l, according to the same specified step size as the calculation of the cross-sectional area of ​​the baseline, the three-dimensional point cloud coordinate data of the calibration line is processed to calculate the cross-sectional area A at different positions along the calibration line direction. i .

[0056] S27, similarly, the calculated calibration line cross-sectional area data A i Perform filtering processing and use the same filtering algorithm and parameters as those used to process the baseline data to ensure data consistency and comparability.

[0057] S28. Define the calibration decision factor ∈, which can be determined based on the actual calibration accuracy requirements and experimental data. and The value of is the average value of the cross-sectional area at each position of the reference line.

[0058] At the same time, calculate the residual sequence And use statistical analysis methods (such as autocorrelation analysis, power spectrum analysis, etc.) to determine whether the residual sequence is a white noise sequence. If it satisfies And the residual sequence res i If it is a white noise sequence, it is considered that no calibration is required; otherwise, the calibration process is started.

[0059] S3. Calibration phase: Figure 4 Part (a) and part (b) show the normal screw device and the abnormal screw device with large mechanical errors (such as coaxiality, parallelism, pitch, etc.). Figure 4 It can be seen that due to the large mechanical errors (such as coaxiality and parallelism) of the abnormal screw, its extrusion flow rate has periodic characteristics. Therefore, the measured calibration value sequence can be regarded as a periodic sequence. The periodic characteristics are extracted through fast Fourier transform, the area with excessive deviation is identified, and the screw motor speed is adjusted to make the cross-sectional area of ​​the calibration point close to the reference value. The calibration configuration file is generated; specifically:

[0060] S31, extract the period of the calibration line cross-sectional area sequence. Consider A i =f(x i ) is a periodic sequence, and A i With motor angle Correspondingly, the periods are approximately equal. A period of the cross-sectional area sequence is extracted using the period identification algorithm.

[0061] S32. Intercept the sequence that needs to be calibrated. Define the calibration sensitivity factor α, which is used to eliminate the impact of small fluctuations in the cross-sectional area of ​​the baseline on the calibration and distinguish the calibration area. Calculate The value that satisfies A i sequence, which is considered to be the sequence that needs to be calibrated.

[0062] S33, adjust the screw motor speed and perform calibration. According to the established relationship between the ratio of motor speed to printing speed and the cross-sectional area of ​​printed lines, i =ψ(λ i ), at each calibration point in the calibration sequence, the motor speed is changed by adjusting the control parameters of the screw motor (such as voltage, current, etc.) so that the A of each calibration point is i Approaching the reference value of the corresponding position

[0063] It is understandable that during the calibration process, the motor speed and the cross-sectional area of ​​the printed lines are constantly monitored to ensure the accuracy and stability of the calibration. Through multiple iterative adjustments, the A at the calibration point is adjusted to i As close to the baseline as possible.

[0064] S34, calculate the correction value and generate a calibration configuration file. According to the adjusted motor speed and printing speed, calculate the correction value λ of the ratio of the screw motor speed to the printing speed at different positions i =φ(x i ).

[0065] S35, the calculated correction value λ i =φ(x i ) is recorded and saved to generate a calibration profile. This calibration profile contains correction information for the ratio of the screw motor speed to the printing speed at different positions within a cycle, as the cross-sectional area of ​​the calibration line fluctuates with position, and can be used for subsequent calibration operations.

[0066] S4. Calibration verification and loading phase: Select the sampling length to scan the calibration line, match the waveform in the calibration configuration file with the sampled waveform, determine the optimal loading position using the sliding window method, and load the calibration parameters; specifically:

[0067] S41. Load the calibration and perform calibration verification. Select a certain sampling length, which should be determined according to the specific requirements of the calibration and the actual situation. Use the measuring device to measure the calibration line within the sampling length, obtain the three-dimensional point cloud coordinates, and calculate the cross-sectional area A according to the previous method. i .

[0068] S42. Perform waveform matching. Match the printed line cross-sectional area calibration waveform in the calibration configuration file with the printed line cross-sectional area waveform within the sampling length. A sliding window of the same length as the calibration cross-sectional area waveform is taken from the printed line cross-sectional area waveform, and the mean square error (MSE) between the waveform within the sliding window and the cross-sectional area calibration waveform is calculated.

[0069] S43. Move the sliding window backward sequentially according to the sampling step size, and calculate the mean square error after each movement. Record the window position where the mean square error is the smallest. This position is the position that needs to be calibrated.

[0070] S44, loading the calibration configuration file. Load the calibration configuration file at the position that needs to be calibrated, and calculate the correction value λ according to the configuration file. i =φ(x i ) Adjust the ratio of the screw motor speed to the printing speed to complete the calibration verification operation.

[0071] S45, re-sample and perform calibration. After calibration is completed, re-sample and perform calibration. Print the calibration line, obtain the three-dimensional point cloud coordinates, and calculate the cross-sectional area A according to the previous method. i And perform filtering.

[0072] S5. Qualification determination stage: Repeat sampling and calibration determination. If the conditions are met, calibration is completed. Otherwise, the representative points are calibrated through the optimization algorithm until the error requirements are met. Specifically:

[0073] S51. Repeat the calibration determination step to determine whether the calibration conditions are met.

[0074] S52, if it is difficult to meet the calibration conditions, then enable optimized calibration. If it is difficult to meet the calibration conditions, considering the approximate relationship A i =ψ(λ i ) may be different from the actual situation, and k representative points A are selected within a fluctuation period of the cross-sectional area of ​​the calibration line. i .

[0075] Using A i =ψ(λ i) Determine the direction, range, and corresponding constraints of the screw speed change at the representative point, and set an objective function, which can be, for example, minimizing the sum of squared errors between the calibrated cross-sectional area and the cross-sectional area at the same location on the baseline. Use an optimization algorithm (such as a genetic algorithm or particle swarm optimization) to perform multiple calibration iterations and determine the optimal correction value for the screw speed at the representative point.

[0076] S53, based on the obtained screw speed, recalculate the optimal correction value λ of the ratio of the screw motor speed to the printing speed at different positions i =φ(x i ), continue the calibration operation. Perform waveform matching, load calibration, and perform calibration verification until the calibration conditions are met.

[0077] It should be noted that, for the sake of convenience, the aforementioned method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present invention is not limited to the described order of actions, because according to the present invention, certain steps can be performed in other orders or simultaneously.

[0078] Based on the same concept as the method for calibrating the mechanical error of a screw extrusion 3D printing flow in the above-mentioned embodiment, the present invention also provides a system for calibrating the mechanical error of a screw extrusion 3D printing flow, which can be used to perform the above-mentioned method for calibrating the mechanical error of a screw extrusion 3D printing flow. For ease of explanation, the structural schematic diagram of the embodiment of the system for calibrating the mechanical error of a screw extrusion 3D printing flow only shows the parts related to the embodiment of the present invention. Those skilled in the art will understand that the illustrated structure does not constitute a limitation of the device, and it may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0079] See also Figure 5 In another embodiment of the present application, a calibration system 100 for flow rate mechanical error of screw extrusion 3D printing is provided, the system comprising a reference value sequence generation module 101, a sampling and calibration determination module 102, a calibration module 103, a calibration verification and loading module 104, and a qualification determination module 105;

[0080] The reference value sequence generation module 101 prints a reference line using a standard printer, obtains its three-dimensional point cloud coordinates by scanning, calculates the cross-sectional area of ​​each position of the reference line and performs filtering processing to generate a reference value sequence;

[0081] The sampling and calibration determination module 102 is used to adjust the screw motor speed, establish a ratio between the motor speed and the printing speed, print a calibration line and scan to obtain its three-dimensional point cloud coordinates, calculate the cross-sectional area of ​​the calibration line and perform filtering, and determine whether to start calibration based on the error condition and the residual sequence white noise;

[0082] The calibration module 103 is configured to treat the reference value sequence as a periodic sequence, extract periodic features through fast Fourier transform, identify areas where deviations exceed the limit, adjust the screw motor speed so that the cross-sectional area of ​​the calibration point approaches the reference value, and generate a calibration configuration file; the calibration configuration file includes correction information for the ratio of the screw motor speed to the printing speed at different positions within a cycle of the periodic fluctuation of the cross-sectional area of ​​the calibration line with position;

[0083] The calibration verification and loading module 104 is used to select a sampling length scan calibration line, match the waveform in the calibration configuration file with the sampling waveform, determine the optimal loading position by a sliding window method, and load the calibration parameters;

[0084] The qualification determination module 105 is used for repeated sampling and calibration determination. If the conditions are met, the calibration is completed. Otherwise, the representative points are calibrated through an optimization algorithm until the error requirements are met.

[0085] It should be noted that the calibration system of the screw extrusion 3D printing flow mechanical error of the present invention corresponds one to one with the calibration method of the screw extrusion 3D printing flow mechanical error of the present invention. The technical features and beneficial effects described in the embodiment of the above-mentioned calibration method of the screw extrusion 3D printing flow mechanical error are applicable to the embodiment of the calibration of the screw extrusion 3D printing flow mechanical error. For specific contents, please refer to the description in the embodiment of the method of the present invention. No further details will be given here. This is hereby declared.

[0086] In addition, in the implementation of a screw extrusion 3D printing flow mechanical error calibration system in the above embodiment, the logical division of each program module is only an example. In actual applications, the above functions can be assigned to different program modules as needed, for example, for the convenience of software implementation and configuration requirements of the corresponding hardware. That is, the internal structure of the screw extrusion 3D printing flow mechanical error calibration system is divided into different program modules to complete all or part of the functions described above.

[0087] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0088] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications that do not deviate from the spirit and principles of the present invention should be considered as equivalent replacement methods and are included in the scope of protection of the present invention.

Claims

1. A method for calibrating mechanical error of screw extrusion 3D printing flow, characterized in that: The steps include: S1. Preparation: Print the baseline using a standard printer, obtain its 3D point cloud coordinates through scanning, calculate the cross-sectional area of ​​each position along the baseline, perform filtering, and generate a baseline value sequence. Sampling and calibration determination phase: Adjust the screw motor speed, establish a ratio between motor speed and printing speed, print a calibration line and scan to obtain its 3D point cloud coordinates, calculate the cross-sectional area of ​​the calibration line and perform filtering, and determine whether to initiate calibration based on the error condition and residual sequence white noise. S3. Calibration phase: Treating the reference value sequence as a periodic sequence, extracting periodic features through Fast Fourier Transformation, identifying areas of excessive deviation, adjusting the screw motor speed to bring the cross-sectional area of ​​the calibration points closer to the reference value, and generating a calibration profile; the calibration profile includes correction information for the ratio of screw motor speed to printing speed at different positions within a cycle, as the cross-sectional area of ​​the calibration line fluctuates with position. S4. Calibration verification and loading phase: Select the sampling length to scan the calibration line, match the waveform in the calibration configuration file with the sampled waveform, determine the optimal loading position using the sliding window method, and load the calibration parameters. S5. Qualification judgment stage: Repeat sampling and calibration judgment. If the conditions are met, the calibration is completed. Otherwise, the representative points are calibrated through the optimization algorithm until the error requirements are met.

2. A method for calibrating mechanical error of screw extrusion 3D printing flow according to claim 1, characterized in that: In step S1, the reference value sequence is generated as follows: Based on the set step size, calculate the cross-sectional area A along the reference line i , after filtering to remove data noise, the filtered cross-sectional area data is All cross-sectional area data are taken as a baseline value sequence.

3. The method for calibrating mechanical error of screw extrusion 3D printing flow according to claim 1, characterized in that: In step S2, the motor speed is adjusted according to the ratio relationship with the printing speed to cover different flow conditions.

4. The method for calibrating mechanical error of screw extrusion 3D printing flow according to claim 1, characterized in that: In step S2, the different λ(x i ) and the cross-sectional area A of the printed lines at the corresponding position i The data were sorted and the cross-sectional area A was analyzed using the preset data analysis software. i The data is fitted and analyzed, and a variety of different types of fitting functions are selected. By comparing the errors and correlations of the fitting functions, the most appropriate fitting function is selected, and the parameters of the fitting function are automatically calculated through the optimization algorithm of the least squares method, and finally the approximate relationship A is obtained. i =ψ(λ i ).

5. The method for calibrating mechanical error of screw extrusion 3D printing flow according to claim 1, characterized in that: In step S2, whether to start calibration is determined based on the error condition and the residual sequence white noise, specifically: Define the calibration decision factor ∈ and calculate and The value of The residual sequence is calculated as the average of the cross-sectional area at each position of the baseline And use statistical analysis methods to determine whether the residual sequence is a white noise sequence. If it satisfies And the residual sequence res i If it is a white noise sequence, it is considered that no calibration is required; otherwise, the calibration process is started.

6. The method for calibrating mechanical error of screw extrusion 3D printing flow according to claim 1, characterized in that: In step S3, when the area with excessive deviation is identified, the calibration sensitivity factor α is defined and the area that satisfies the The sequence is used as the calibration region, where A i =f(x i ) is a periodic sequence, is the average value of the cross-sectional area at each position of the reference line.

7. The method for calibrating mechanical error of screw extrusion 3D printing flow according to claim 1, characterized in that: In step S4, the waveform in the calibration configuration file is matched with the sampled waveform, specifically: A sliding window with the same length as the calibration cross-sectional area waveform is taken from the printed line cross-sectional area waveform, and the mean square error between the waveform in the sliding window and the cross-sectional area calibration waveform is calculated in sequence.

8. The method for calibrating mechanical error of screw extrusion 3D printing flow according to claim 1, characterized in that: In step S4, the length of the sliding window is equal to the period length of the calibration waveform, and the sliding step length is an integer multiple of the step length for calculating the cross-sectional area of ​​the calibration line.

9. The method for calibrating mechanical error of screw extrusion 3D printing flow according to claim 1, characterized in that: In step S5, the optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm, and the objective function is to minimize the sum of squares of the errors between the calibrated cross-sectional area and the reference value.

10. A calibration system for mechanical error of screw extrusion 3D printing flow, characterized in that: A method for calibrating flow mechanical errors of screw extrusion 3D printing, as applied to any one of claims 1-9, comprising a reference value sequence generation module, a sampling and calibration determination module, a calibration module, a calibration verification and loading module, and a qualification determination module; The reference value sequence generation module uses a standard printer to print the reference line, obtains its three-dimensional point cloud coordinates by scanning, calculates the cross-sectional area of ​​each position of the reference line and performs filtering processing to generate the reference value sequence; The sampling and calibration determination module is used to adjust the screw motor speed, establish a ratio between the motor speed and the printing speed, print the calibration line and scan to obtain its three-dimensional point cloud coordinates, calculate the cross-sectional area of ​​the calibration line and perform filtering, and determine whether to start calibration based on the error condition and the residual sequence white noise; The calibration module is configured to treat the reference value sequence as a periodic sequence, extract periodic features using a fast Fourier transform, identify areas where deviations exceed the limit, adjust the screw motor speed so that the cross-sectional area of ​​the calibration point approaches the reference value, and generate a calibration configuration file; the calibration configuration file includes correction information for the ratio of the screw motor speed to the printing speed at different positions within a cycle of periodic fluctuations in the cross-sectional area of ​​the calibration line with position; The calibration verification and loading module is used to select a sampling length scan calibration line, match the waveform in the calibration configuration file with the sampling waveform, determine the optimal loading position through a sliding window method, and load the calibration parameters; The qualified judgment module is used for repeated sampling and calibration judgment. If the conditions are met, the calibration is completed. Otherwise, the representative points are calibrated through the optimization algorithm until the error requirements are met.

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