3D printing extrusion monitoring method and system based on flow sensing correction

Abstract

The application discloses a 3D printing extrusion monitoring method and system based on flow sensing correction, and the method comprises the following steps: when a 3D printing task is executed, real-time acquisition of extrusion flow data and sensing data of a discharge head of a 3D printing platform; according to the sensing data, determination of motion posture parameters of the discharge head based on a posture recognition algorithm; according to the motion posture parameters, determination of flow correction parameters corresponding to the discharge head based on a prediction model corresponding to the discharge head; and according to the flow correction parameters, correction of the extrusion flow data to determine actual flow data corresponding to the discharge head. It can be seen that the application can realize precise flow monitoring based on posture analysis and dynamic correction, improve the stability and printing quality of the 3D printing process, and reduce the risk of flow deviation caused by posture change.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a 3D printing extrusion monitoring method and system based on flow sensing correction. Background Technology

[0002] With the widespread application of 3D printing technology in high-precision manufacturing, enterprises and users are increasingly emphasizing the improvement of print quality and stability through precise flow monitoring. Among these challenges, analyzing and correcting flow deviations to reduce the risk of printing defects has become a key technical issue. Existing technologies typically monitor flow status by collecting extrusion flow data from the 3D printing platform's ejector head and adjusting printing parameters based on standard rules to support the printing process. However, existing solutions lack dynamic recognition of the ejector head's motion and predictive modeling for flow correction, making it difficult to accurately correct flow deviations caused by posture changes. This results in insufficient flow monitoring accuracy, easily leading to printing defects or material waste, and limiting the stability and quality of the 3D printing process. Therefore, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a 3D printing extrusion monitoring method and system based on flow sensing correction, which can realize accurate flow monitoring based on attitude analysis and dynamic correction, improve the stability and printing quality of the 3D printing process, and reduce the risk of flow deviation caused by attitude changes.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a 3D printing extrusion monitoring method based on flow sensing correction, the method comprising: During 3D printing tasks, the extrusion flow rate data and sensor data of the 3D printing platform's output head are acquired in real time. Based on the sensor data and an attitude recognition algorithm, the motion attitude parameters of the discharge head are determined. Based on the motion posture parameters, and using the prediction model corresponding to the discharge head, the flow correction parameters corresponding to the discharge head are determined. The extrusion flow rate data is corrected according to the flow rate correction parameters to determine the actual flow rate data corresponding to the discharge head.

[0005] As an optional implementation, in a first aspect of the invention, the sensing data includes at least one of the following: speed data, height data, acceleration data, angle data, and sound data of the discharge head.

[0006] As an optional implementation, in the first aspect of the present invention, determining the motion posture parameters of the discharge head based on the sensing data and an attitude recognition algorithm includes: Obtain the sensor data set corresponding to multiple preset attitude vectors of the discharge head; Calculate the data similarity between the sensor data and each set of sensor data; The motion posture parameters of the discharge head are determined based on the preset posture vector corresponding to the data similarity.

[0007] As an optional implementation, in the first aspect of the present invention, the sensor data set corresponding to the plurality of preset attitude vectors is pre-established through the following steps: Based on finite element simulation software, a model is created according to the preset physical parameters corresponding to the discharge head to obtain the discharge head model; According to the preset posture angle division rules, the material discharge head is divided into multiple preset postures; For each of the preset postures, a finite element simulation is performed on the discharge head model to obtain the corresponding preset posture vector; the preset posture vector includes the posture angle, the current posture direction, and the posture change direction; A control command corresponding to each preset posture vector is generated and sent to the 3D printing platform for execution. During execution, sensor data is acquired based on the set sensors to obtain a set of sensor data corresponding to each preset posture vector.

[0008] As an optional implementation, in the first aspect of the present invention, determining the motion posture parameters of the discharge head based on the preset posture vector corresponding to the data similarity includes: Select the preset attitude vectors corresponding to the sensor data set whose data similarity is greater than a preset similarity threshold to obtain multiple similar attitude vectors; The motion posture parameters of the discharge head are obtained by calculating the weighted sum of all the similar posture vectors; wherein the calculation weight of each similar posture vector is proportional to the corresponding data similarity.

[0009] As an optional implementation, in the first aspect of the invention, determining the flow correction parameter corresponding to the discharge head based on the motion posture parameters and a prediction model corresponding to the discharge head includes: The difference between the extrusion flow rate data and the reference flow rate threshold is calculated to obtain the flow rate difference. The flow rate difference and the motion posture parameters are input into the prediction model corresponding to the discharge head to obtain the flow rate correction parameters corresponding to the discharge head.

[0010] As an optional implementation, in the first aspect of the invention, the prediction model is trained using a training dataset that includes multiple training motion posture parameters and corresponding flow difference labels and flow correction parameter labels.

[0011] As an optional implementation, in the first aspect of the invention, the step of correcting the extrusion flow rate data according to the flow rate correction parameter to determine the actual flow rate data corresponding to the discharge head includes: Calculate the correction weight that is proportional to the flow difference; Calculate the product of the correction weight and the flow correction parameter to obtain the optimized correction parameter; The sum of the optimized correction parameters and the extrusion flow data is calculated to obtain the actual flow data corresponding to the discharge head.

[0012] A second aspect of this invention discloses a 3D printing extrusion monitoring system based on flow sensing correction, the system comprising: The acquisition module is used to acquire extrusion flow data and sensor data of the 3D printing platform's output head in real time during the execution of 3D printing tasks; The identification module is used to determine the motion posture parameters of the discharge head based on the sensor data and an attitude recognition algorithm. The determination module is used to determine the flow correction parameters corresponding to the discharge head based on the motion posture parameters and the prediction model corresponding to the discharge head; The calibration module is used to calibrate the extrusion flow rate data according to the flow rate calibration parameters to determine the actual flow rate data corresponding to the discharge head.

[0013] As an optional implementation, in a second aspect of the invention, the sensing data includes at least one of the following: speed data, height data, acceleration data, angle data, and sound data of the discharge head.

[0014] As an optional implementation, in a second aspect of the invention, the specific method by which the identification module determines the motion posture parameters of the discharge head based on the sensing data and an attitude recognition algorithm includes: Obtain the sensor data set corresponding to multiple preset attitude vectors of the discharge head; Calculate the data similarity between the sensor data and each set of sensor data; The motion posture parameters of the discharge head are determined based on the preset posture vector corresponding to the data similarity.

[0015] As an optional implementation, in a second aspect of the invention, the sensor data set corresponding to the plurality of preset attitude vectors is pre-established through the following steps: Based on finite element simulation software, a model is created according to the preset physical parameters corresponding to the discharge head to obtain the discharge head model; According to the preset posture angle division rules, the material discharge head is divided into multiple preset postures; For each of the preset postures, a finite element simulation is performed on the discharge head model to obtain the corresponding preset posture vector; the preset posture vector includes the posture angle, the current posture direction, and the posture change direction; A control command corresponding to each preset posture vector is generated and sent to the 3D printing platform for execution. During execution, sensor data is acquired based on the set sensors to obtain a set of sensor data corresponding to each preset posture vector.

[0016] As an optional implementation, in a second aspect of the invention, the specific method by which the identification module determines the motion posture parameters of the discharge head based on the preset posture vector corresponding to the data similarity includes: Select the preset attitude vectors corresponding to the sensor data set whose data similarity is greater than a preset similarity threshold to obtain multiple similar attitude vectors; The motion posture parameters of the discharge head are obtained by calculating the weighted sum of all the similar posture vectors; wherein the calculation weight of each similar posture vector is proportional to the corresponding data similarity.

[0017] As an optional implementation, in a second aspect of the invention, the determining module determines the flow correction parameters corresponding to the discharge head based on the motion posture parameters and a prediction model corresponding to the discharge head, including: The difference between the extrusion flow rate data and the reference flow rate threshold is calculated to obtain the flow rate difference. The flow rate difference and the motion posture parameters are input into the prediction model corresponding to the discharge head to obtain the flow rate correction parameters corresponding to the discharge head.

[0018] As an optional implementation, in a second aspect of the invention, the prediction model is trained using a training dataset that includes multiple training motion posture parameters and corresponding flow difference labels and flow correction parameter labels.

[0019] As an optional implementation, in a second aspect of the invention, the specific method by which the correction module corrects the extrusion flow rate data according to the flow rate correction parameters to determine the actual flow rate data corresponding to the discharge head includes: Calculate the correction weight that is proportional to the flow difference; Calculate the product of the correction weight and the flow correction parameter to obtain the optimized correction parameter; The sum of the optimized correction parameters and the extrusion flow data is calculated to obtain the actual flow data corresponding to the discharge head.

[0020] A third aspect of the present invention discloses another 3D printing extrusion monitoring system based on flow sensing correction, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the 3D printing extrusion monitoring method based on flow sensing correction disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the 3D printing extrusion monitoring method based on flow sensing correction disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention acquires real-time extrusion flow data and sensor data from the 3D printing platform's discharge head, determines motion posture parameters based on a posture recognition algorithm, and determines flow correction parameters based on motion posture parameters and a prediction model to correct the extrusion flow data and obtain actual flow data. This enables precise flow monitoring based on posture analysis and dynamic correction, improving the stability and printing quality of the 3D printing process and reducing the risk of flow deviation caused by posture changes. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of a 3D printing extrusion monitoring method based on flow sensing correction disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of the structure of a 3D printing extrusion monitoring system based on flow sensing correction disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of another 3D printing extrusion monitoring system based on flow sensing correction disclosed in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] This invention discloses a 3D printing extrusion monitoring method and system based on flow sensing correction. It acquires real-time extrusion flow data and sensor data from the 3D printing platform's ejector head, determines motion posture parameters based on a posture recognition algorithm, and determines flow correction parameters based on these parameters and a prediction model to correct the extrusion flow data and obtain the actual flow data. This enables precise flow monitoring based on posture analysis and dynamic correction, improving the stability and printing quality of the 3D printing process and reducing the risk of flow deviation caused by posture changes. Detailed explanations follow.

[0031] Example 1 Please see Figure 1 , Figure 1 This is a schematic flowchart of a 3D printing extrusion monitoring method based on flow sensor correction disclosed in an embodiment of the present invention. Figure 1 The described 3D printing extrusion monitoring method based on flow sensor correction can be applied to data processing systems / data processing equipment / data processing servers (wherein, the server includes local processing servers or cloud processing servers). Figure 1 As shown, this 3D printing extrusion monitoring method based on flow sensor correction may include the following operations: 101. When performing 3D printing tasks, acquire the extrusion flow data and sensor data of the 3D printing platform's output head in real time.

[0032] Optionally, the extrusion flow data can be a real-time flow value, an average flow value, or a flow curve; this invention does not impose any limitations.

[0033] Optionally, the sensing data may include attitude data, vibration data, temperature data, or pressure data, and the present invention does not limit it.

[0034] Optionally, this real-time acquisition process can be implemented based on sensor acquisition, data interface transmission, or wireless communication, and the present invention does not limit it.

[0035] 102. Based on the sensor data and the attitude recognition algorithm, determine the motion attitude parameters of the discharge head. Optionally, the pose recognition algorithm can be a feature matching algorithm, a neural network algorithm, or a time series analysis algorithm; this invention does not limit the specific algorithm.

[0036] Optionally, the motion posture parameters may include posture angle, posture direction, posture change rate, or posture vector, which are not limited in this invention.

[0037] Optionally, this determination process can be optimized by incorporating sensor data quality, equipment calibration, or environmental interference; this invention does not limit this process.

[0038] 103. Based on the motion posture parameters and the prediction model corresponding to the discharge head, determine the flow correction parameters corresponding to the discharge head. Optionally, the prediction model can be a regression model, a neural network model, or a machine learning model, and this invention does not limit it.

[0039] Optionally, the flow correction parameter can be a correction coefficient, an offset value, or an adjustment range; this invention does not impose any limitations on it.

[0040] Optionally, this determination process can be optimized by combining the posture parameter distribution or model training data, and this invention does not limit it.

[0041] 104. Based on the flow correction parameters, correct the extrusion flow data to determine the actual flow data corresponding to the discharge head.

[0042] Optionally, the correction process can be based on linear correction, nonlinear correction, or dynamic adjustment, and the present invention does not limit it.

[0043] Optionally, the actual flow data can be a corrected flow value, flow range, or flow trend; this invention does not impose any limitations on this.

[0044] Optionally, this correction process can be optimized in conjunction with the stability of traffic data or the requirements of printing tasks; this invention does not limit this.

[0045] As can be seen, the above-mentioned embodiments of the invention acquire the extrusion flow data and sensor data of the 3D printing platform in real time, determine the motion posture parameters based on the posture recognition algorithm, determine the flow correction parameters based on the motion posture parameters and the prediction model, and correct the extrusion flow data to obtain the actual flow data. This enables accurate flow monitoring based on posture analysis and dynamic correction, improves the stability and printing quality of the 3D printing process, and reduces the risk of flow deviation caused by posture changes.

[0046] As an optional embodiment, the sensing data in the above steps includes at least one of the following: speed data, height data, acceleration data, angle data, and sound data of the discharge head.

[0047] As can be seen, the above optional embodiments limit the type of sensing data to comprehensively characterize the movement characteristics of the material head, assist in achieving accurate flow monitoring based on attitude analysis and dynamic correction, improve the stability and printing quality of the 3D printing process, and reduce the risk of flow deviation caused by attitude changes.

[0048] As an optional embodiment, the step above, determining the motion posture parameters of the discharge head based on sensor data and an attitude recognition algorithm, includes: Acquire the sensor data set corresponding to multiple preset attitude vectors of the discharge head; Calculate the data similarity between the sensor data and each set of sensor data; Based on the preset posture vector corresponding to the data similarity, the motion posture parameters of the discharge head are determined.

[0049] Optionally, the preset attitude vector may include an attitude angle vector, a direction vector, or a change vector, and the present invention does not limit it.

[0050] Optionally, the sensor dataset can be historical data, simulation data, or training data; this invention does not impose any limitations.

[0051] Optionally, the acquisition process can be implemented based on database queries, simulation generation, or real-time acquisition; this invention does not impose any limitations.

[0052] Optionally, the data similarity can be cosine similarity, Euclidean distance, Jaccard coefficient, or dynamic time warping distance; this invention does not impose any limitations.

[0053] Optionally, the calculation of data similarity can be based on vector comparison, statistical analysis, or feature matching, and this invention does not limit it.

[0054] Optionally, the calculation of data similarity can be optimized by combining data dimensions or sensor accuracy, and this invention does not limit it.

[0055] Optionally, the determination of the motion posture parameters can be achieved based on similarity threshold screening, weighted fusion, or cluster analysis, and this invention does not limit the scope of the determination.

[0056] As can be seen, through the above optional embodiments, by calculating the data similarity between the sensor data and the preset attitude vector sensor data set, and determining the motion attitude parameters of the discharge head according to the preset attitude vector corresponding to the similarity, the accuracy of motion attitude recognition is improved by similarity matching on the basis of accurate flow monitoring, providing reliable parameter support for flow correction and reducing the risk of attitude misjudgment.

[0057] As an optional embodiment, the sensor data set corresponding to the multiple preset attitude vectors in the above steps is pre-established through the following steps: Based on finite element simulation software, a model is created according to the preset physical parameters corresponding to the discharge head to obtain the discharge head model; According to the preset posture angle division rules, multiple preset postures corresponding to the discharge head are divided. For each preset posture, a finite element simulation is performed on the discharge head model to obtain the corresponding preset posture vector; optionally, the preset posture vector includes the posture angle, the current posture direction, and the posture change direction. The system generates control commands corresponding to each preset attitude vector and sends them to the 3D printing platform for execution. During execution, the system acquires sensor data based on the set sensors to obtain the sensor data set corresponding to each preset attitude vector.

[0058] Optionally, the finite element simulation software can be ANSYS, COMSOL, or Abaqus software; this invention does not impose any limitation.

[0059] Optionally, the physical parameters may include material properties, geometric dimensions, mechanical parameters, or thermal parameters, and the present invention does not limit them.

[0060] Optionally, this modeling process can be optimized by combining mesh generation, boundary condition settings, or material model selection; this invention does not impose any limitations on this.

[0061] Optionally, the attitude angle division rule can be an equal-interval division, a dynamic division, or an experience-based division rule; this invention does not impose any limitations.

[0062] Optionally, the preset posture may include a static posture, a dynamic posture, or a transitional posture, and the present invention does not limit it.

[0063] Optionally, this division process can be optimized by combining attitude range, angle accuracy or printing scenario, and the present invention does not limit it.

[0064] Optionally, the finite element simulation can be a static simulation, a dynamic simulation, or a multiphysics simulation; this invention does not impose any limitations.

[0065] Optionally, the preset attitude vector may include additional dimensions, such as attitude acceleration or attitude torque, which is not limited in this invention.

[0066] Optionally, the simulation process can be optimized by combining solver settings, iterative convergence, or result verification, and this invention does not limit it.

[0067] Optionally, the control command can be a motion control command, an attitude adjustment command, or a simulation execution command; the present invention does not impose any limitation on this.

[0068] Optionally, the generation process of the control command can be based on the command template, path planning or attitude mapping, and the present invention does not limit it.

[0069] Optionally, the execution process of the control command can be optimized by combining platform calibration, sensor synchronization, or real-time feedback, and this invention does not limit it.

[0070] As can be seen, through the above optional embodiments, by modeling and simulating preset postures based on finite element simulation software to generate preset posture vectors and corresponding sensor data sets, the authenticity and coverage of preset data are improved through simulation modeling on the basis of accurate posture parameter determination, providing a high-quality reference for motion posture recognition and reducing the risk of recognition errors caused by simulation deviations.

[0071] As an optional embodiment, the step above, determining the motion posture parameters of the discharge head based on the preset posture vector corresponding to the data similarity, includes: Select the preset attitude vectors corresponding to the sensor data set whose data similarity is greater than the preset similarity threshold, and obtain multiple similar attitude vectors; The motion posture parameters of the discharge head are obtained by calculating the weighted sum of all similar posture vectors; the calculation weight of each similar posture vector is proportional to the corresponding data similarity.

[0072] Optionally, the similarity threshold can be a fixed threshold, a dynamic threshold, or a threshold adjusted based on pose type; this invention does not impose any limitations.

[0073] Optionally, the weighted summation can be calculated using linear weighting, dynamic weighting, or adaptive weighting; this invention does not impose any limitations on this method.

[0074] Optionally, the weighted summation of the similar pose vectors can be implemented based on vector operations, matrix fusion, or feature weighting, and this invention does not limit the scope of the calculation.

[0075] As can be seen, through the above optional embodiments, by filtering similar posture vectors whose data similarity exceeds the threshold and weighting and summing them to determine the motion posture parameters, the accuracy and robustness of parameter calculation are improved on the basis of accurate posture recognition through threshold filtering and similarity weighting, thus providing optimized posture data for traffic correction and reducing the risk of noise interference.

[0076] As an optional embodiment, the step described above, determining the flow correction parameters corresponding to the discharge head based on the motion posture parameters and the prediction model corresponding to the discharge head, includes: Calculate the difference between the extrusion flow rate data and the reference flow rate threshold to obtain the flow rate difference; The flow rate difference and motion attitude parameters are input into the prediction model corresponding to the discharge head to obtain the flow rate correction parameters corresponding to the discharge head.

[0077] Optionally, the referenced traffic threshold can be a standard traffic value, a dynamic threshold, or a threshold adjusted based on the printing task; this invention does not limit this.

[0078] Optionally, the difference can be an absolute difference, a relative difference, or a standardized difference; this invention does not impose any limitation on this.

[0079] Optionally, the prediction model can be a trained neural network model, regression model, or classification model; this invention does not limit the model.

[0080] As can be seen, through the above optional embodiments, the flow correction parameters are determined by calculating the difference between the extrusion flow rate and the reference threshold and combining the motion posture parameters into the prediction model. Thus, based on the accurate determination of the actual flow rate, the difference analysis is used to determine whether the flow rate is too high or too low. Furthermore, the model prediction based on the flow rate and posture improves the pertinence and accuracy of the correction parameters, providing precise guidance for flow data correction and reducing the risk of uncorrected abnormal flow rates.

[0081] As an optional embodiment, in the above steps, the prediction model is trained using a training dataset that includes multiple training motion posture parameters and corresponding flow difference labels and flow correction parameter labels.

[0082] As can be seen, the training details of the prediction model are defined through the above optional embodiments, so as to accurately predict the correction parameters based on flow conditions and attitude, assist in realizing accurate flow monitoring based on attitude analysis and dynamic correction, improve the stability and printing quality of the 3D printing process, and reduce the risk of flow deviation caused by attitude changes.

[0083] As an optional embodiment, the step above, correcting the extrusion flow rate data according to the flow rate correction parameters to determine the actual flow rate data corresponding to the discharge head, includes: Calculate the correction weight that is proportional to the flow difference; Calculate the product of the correction weight and the flow correction parameter to obtain the optimized correction parameter; The sum of the optimized correction parameters and the extrusion flow data is calculated to obtain the actual flow data corresponding to the discharge head.

[0084] Optionally, the correction weight can be a linear weight, an exponential weight, or a logarithmic weight; this invention does not limit the type of weight.

[0085] Optionally, the calculation process of the correction weight can be based on proportional adjustment, numerical mapping or dynamic calculation, and the present invention does not limit it.

[0086] As can be seen, through the above optional embodiments, by calculating a correction weight proportional to the flow difference and optimizing it by multiplying it with the flow correction parameter, and then adding it to the extrusion flow data to obtain the actual flow data, the adaptability and accuracy of the correction process are improved through weight optimization on the basis of accurate flow correction, providing reliable actual flow for 3D printing monitoring and reducing the risk of over- or under-correction.

[0087] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a 3D printing extrusion monitoring system based on flow sensor correction disclosed in an embodiment of the present invention. Figure 2 The described flow sensor-based 3D printing extrusion monitoring system can be applied to data processing systems / data processing equipment / data processing servers (including local processing servers or cloud processing servers). Figure 2 As shown, the 3D printing extrusion monitoring system based on flow sensor correction may include: The acquisition module 201 is used to acquire the extrusion flow data and sensor data of the 3D printing platform's output head in real time when performing a 3D printing task.

[0088] The identification module 202 is used to determine the motion posture parameters of the discharge head based on the sensor data and the posture recognition algorithm. The determination module 203 is used to determine the flow correction parameters corresponding to the discharge head based on the motion posture parameters and the prediction model corresponding to the discharge head. The calibration module 204 is used to calibrate the extrusion flow rate data according to the flow rate calibration parameters to determine the actual flow rate data corresponding to the discharge head.

[0089] As can be seen, the above-mentioned embodiments of the invention acquire the extrusion flow data and sensor data of the 3D printing platform in real time, determine the motion posture parameters based on the posture recognition algorithm, determine the flow correction parameters based on the motion posture parameters and the prediction model, and correct the extrusion flow data to obtain the actual flow data. This enables accurate flow monitoring based on posture analysis and dynamic correction, improves the stability and printing quality of the 3D printing process, and reduces the risk of flow deviation caused by posture changes.

[0090] As an optional embodiment, the sensing data includes at least one of the following: speed data, height data, acceleration data, angle data, and sound data of the discharge head.

[0091] As can be seen, the above optional embodiments limit the type of sensing data to comprehensively characterize the movement characteristics of the material head, assist in achieving accurate flow monitoring based on attitude analysis and dynamic correction, improve the stability and printing quality of the 3D printing process, and reduce the risk of flow deviation caused by attitude changes.

[0092] As an optional embodiment, the identification module determines the specific method of the motion posture parameters of the discharge head based on the sensor data and the posture recognition algorithm, including: Acquire the sensor data set corresponding to multiple preset attitude vectors of the discharge head; Calculate the data similarity between the sensor data and each set of sensor data; Based on the preset posture vector corresponding to the data similarity, the motion posture parameters of the discharge head are determined.

[0093] As can be seen, through the above optional embodiments, by calculating the data similarity between the sensor data and the preset attitude vector sensor data set, and determining the motion attitude parameters of the discharge head according to the preset attitude vector corresponding to the similarity, the accuracy of motion attitude recognition is improved by similarity matching on the basis of accurate flow monitoring, providing reliable parameter support for flow correction and reducing the risk of attitude misjudgment.

[0094] As an optional embodiment, the sensor data set corresponding to multiple preset attitude vectors is pre-established through the following steps: Based on finite element simulation software, a model is created according to the preset physical parameters corresponding to the discharge head to obtain the discharge head model; According to the preset posture angle division rules, multiple preset postures corresponding to the discharge head are divided. For each preset posture, a finite element simulation is performed on the discharge head model to obtain the corresponding preset posture vector; optionally, the preset posture vector includes the posture angle, the current posture direction, and the posture change direction. The system generates control commands corresponding to each preset attitude vector and sends them to the 3D printing platform for execution. During execution, the system acquires sensor data based on the set sensors to obtain the sensor data set corresponding to each preset attitude vector.

[0095] As can be seen, through the above optional embodiments, by modeling and simulating preset postures based on finite element simulation software to generate preset posture vectors and corresponding sensor data sets, the authenticity and coverage of preset data are improved through simulation modeling on the basis of accurate posture parameter determination, providing a high-quality reference for motion posture recognition and reducing the risk of recognition errors caused by simulation deviations.

[0096] As an optional embodiment, the identification module determines the specific method of the motion posture parameters of the discharge head based on the preset posture vector corresponding to the data similarity, including: Select the preset attitude vectors corresponding to the sensor data set whose data similarity is greater than the preset similarity threshold, and obtain multiple similar attitude vectors; The motion posture parameters of the discharge head are obtained by calculating the weighted sum of all similar posture vectors; the calculation weight of each similar posture vector is proportional to the corresponding data similarity.

[0097] As can be seen, through the above optional embodiments, by filtering similar posture vectors whose data similarity exceeds the threshold and weighting and summing them to determine the motion posture parameters, the accuracy and robustness of parameter calculation are improved on the basis of accurate posture recognition through threshold filtering and similarity weighting, thus providing optimized posture data for traffic correction and reducing the risk of noise interference.

[0098] As an optional embodiment, the determining module determines the specific method of the flow correction parameters corresponding to the discharge head based on the motion posture parameters and the prediction model corresponding to the discharge head, including: Calculate the difference between the extrusion flow rate data and the reference flow rate threshold to obtain the flow rate difference; The flow rate difference and motion attitude parameters are input into the prediction model corresponding to the discharge head to obtain the flow rate correction parameters corresponding to the discharge head.

[0099] As can be seen, through the above optional embodiments, the flow correction parameters are determined by calculating the difference between the extrusion flow rate and the reference threshold and combining the motion posture parameters into the prediction model. Thus, based on the accurate determination of the actual flow rate, the difference analysis is used to determine whether the flow rate is too high or too low. Furthermore, the model prediction based on the flow rate and posture improves the pertinence and accuracy of the correction parameters, providing precise guidance for flow data correction and reducing the risk of uncorrected abnormal flow rates.

[0100] As an optional implementation, the prediction model is trained on a training dataset that includes multiple training motion posture parameters and corresponding flow difference labels and flow correction parameter labels.

[0101] As can be seen, the training details of the prediction model are defined through the above optional embodiments, so as to accurately predict the correction parameters based on flow conditions and attitude, assist in realizing accurate flow monitoring based on attitude analysis and dynamic correction, improve the stability and printing quality of the 3D printing process, and reduce the risk of flow deviation caused by attitude changes.

[0102] As an optional embodiment, the calibration module calibrates the extrusion flow rate data according to the flow rate calibration parameters to determine the specific method of the actual flow rate data corresponding to the discharge head, including: Calculate the correction weight that is proportional to the flow difference; Calculate the product of the correction weight and the flow correction parameter to obtain the optimized correction parameter; The sum of the optimized correction parameters and the extrusion flow data is calculated to obtain the actual flow data corresponding to the discharge head.

[0103] As can be seen, through the above optional embodiments, by calculating a correction weight proportional to the flow difference and optimizing it by multiplying it with the flow correction parameter, and then adding it to the extrusion flow data to obtain the actual flow data, the adaptability and accuracy of the correction process are improved through weight optimization on the basis of accurate flow correction, providing reliable actual flow for 3D printing monitoring and reducing the risk of over- or under-correction.

[0104] Example 3 Please see Figure 3 , Figure 3 This is another 3D printing extrusion monitoring system based on flow sensing correction disclosed in the embodiments of the present invention. Figure 3 The described flow sensor-based 3D printing extrusion monitoring system is applied in a data processing system / data processing equipment / data processing server (wherein, the server includes a local processing server or a cloud processing server). Figure 3 As shown, the 3D printing extrusion monitoring system based on flow sensor correction may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the 3D printing extrusion monitoring method based on flow sensing correction described in Embodiment 1.

[0105] Example 4 This invention discloses a computer read storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps of the 3D printing extrusion monitoring method based on flow sensor correction described in Embodiment 1.

[0106] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the 3D printing extrusion monitoring method based on flow sensing correction described in Embodiment 1.

[0107] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0109] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0110] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0111] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0112] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0114] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0115] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0116] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0117] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0118] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0119] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0120] Finally, it should be noted that the 3D printing extrusion monitoring method and system based on flow sensing correction disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A 3D printing extrusion monitoring method based on flow sensor correction, characterized in that, The method includes: During 3D printing tasks, the extrusion flow rate data and sensor data of the 3D printing platform's output head are acquired in real time. Based on the sensor data and an attitude recognition algorithm, the motion attitude parameters of the discharge head are determined, including: Obtain the sensor data set corresponding to multiple preset attitude vectors of the discharge head. The sensor data set corresponding to the multiple preset attitude vectors is pre-established through the following steps: Based on finite element simulation software, a model is created according to the preset physical parameters corresponding to the discharge head to obtain the discharge head model; According to the preset posture angle division rules, the material discharge head is divided into multiple preset postures; For each of the preset postures, a finite element simulation is performed on the discharge head model to obtain the corresponding preset posture vector; the preset posture vector includes the posture angle, the current posture direction, and the posture change direction; A control command corresponding to each preset posture vector is generated and sent to the 3D printing platform for execution. During execution, sensor data is acquired based on the set sensors to obtain a set of sensor data corresponding to each preset posture vector. Calculate the data similarity between the sensor data and each set of sensor data; The motion posture parameters of the discharge head are determined based on the preset posture vector corresponding to the data similarity. Based on the motion posture parameters, and using the prediction model corresponding to the discharge head, the flow correction parameters corresponding to the discharge head are determined. The extrusion flow rate data is corrected according to the flow rate correction parameters to determine the actual flow rate data corresponding to the discharge head.

2. The 3D printing extrusion monitoring method based on flow sensor correction according to claim 1, characterized in that, The sensing data includes at least one of the following: speed data, height data, acceleration data, angle data, and sound data of the discharge head.

3. The 3D printing extrusion monitoring method based on flow sensor correction according to claim 1, characterized in that, The step of determining the motion posture parameters of the discharge head based on the preset posture vector corresponding to the data similarity includes: Select the preset attitude vectors corresponding to the sensor data set whose data similarity is greater than a preset similarity threshold to obtain multiple similar attitude vectors; The motion posture parameters of the discharge head are obtained by calculating the weighted sum of all the similar posture vectors; wherein the calculation weight of each similar posture vector is proportional to the corresponding data similarity.

4. The 3D printing extrusion monitoring method based on flow sensor correction according to claim 1, characterized in that, The step of determining the flow correction parameters corresponding to the discharge head based on the motion posture parameters and the prediction model corresponding to the discharge head includes: The difference between the extrusion flow rate data and the reference flow rate threshold is calculated to obtain the flow rate difference. The flow rate difference and the motion posture parameters are input into the prediction model corresponding to the discharge head to obtain the flow rate correction parameters corresponding to the discharge head.

5. The 3D printing extrusion monitoring method based on flow sensing correction according to claim 4, characterized in that, The prediction model is trained using a training dataset that includes multiple training motion posture parameters and corresponding flow difference and flow correction parameter labels.

6. The 3D printing extrusion monitoring method based on flow sensor correction according to claim 4, characterized in that, The step of correcting the extrusion flow rate data according to the flow rate correction parameters to determine the actual flow rate data corresponding to the discharge head includes: Calculate the correction weight that is proportional to the flow difference; Calculate the product of the correction weight and the flow correction parameter to obtain the optimized correction parameter; The sum of the optimized correction parameters and the extrusion flow data is calculated to obtain the actual flow data corresponding to the discharge head.

7. A 3D printing extrusion monitoring system based on flow sensor correction, characterized in that, The system executes the 3D printing extrusion monitoring method based on flow sensor correction as described in any one of claims 1-6, the system comprising: The acquisition module is used to acquire extrusion flow data and sensor data of the 3D printing platform's output head in real time during the execution of 3D printing tasks; The identification module is used to determine the motion posture parameters of the discharge head based on the sensor data and an attitude recognition algorithm. The determination module is used to determine the flow correction parameters corresponding to the discharge head based on the motion posture parameters and the prediction model corresponding to the discharge head; The calibration module is used to calibrate the extrusion flow rate data according to the flow rate calibration parameters to determine the actual flow rate data corresponding to the discharge head.

8. A 3D printing extrusion monitoring system based on flow sensor correction, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the 3D printing extrusion monitoring method based on flow sensor correction as described in any one of claims 1-6.

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