A nozzle control method and system based on continuous relationship

Abstract

The present invention discloses a nozzle control method and system based on a continuous relationship. The method includes: continuously monitoring multiple working parameters related to the nozzle of a 3D printing device within a preset time period to generate a numerical change curve for each working parameter; determining the working state of the nozzle based on the numerical change curve of each working parameter and the continuous correlation between the multiple working parameters; selecting parameter values ​​at a preset number of time points from the multiple working parameters continuously monitored, and calculating the working stability index of the nozzle of the 3D printing device based on the parameter values; and controlling the nozzle of the 3D printing device based on the working state and the working stability index. Utilizing the embodiments of the present invention, it is possible to continuously monitor and analyze multiple working parameters related to the nozzle of a 3D printing device, achieve accurate judgment and control of the working state of the nozzle, thereby improving the stability and product quality of 3D printing and promoting the development of 3D printing technology towards a more intelligent and autonomous direction.

Description

Technical Field

[0001] The present invention belongs to the field of 3D printing technology, and in particular to a nozzle control method and system based on a continuous relationship. Background Art

[0002] With the rapid development of 3D printing technology, the performance and control precision of printheads, as core components of 3D printing equipment, play a crucial role in print quality and efficiency. During the 3D printing process, printheads must continuously adjust multiple operating parameters such as extrusion speed, temperature, and material flow rate based on different material properties, printing modes, and environmental conditions to ensure the quality and consistency of the final printed product. However, the operating status of the printhead is affected by many factors, including but not limited to material type, printhead temperature, print speed, external environment, and the mechanical properties of the equipment.

[0003] Traditional printhead control methods typically rely on experience or fixed parameter settings. Designers preset specific parameter configurations for different printing tasks, but these static settings cannot adapt to the ever-changing working environment. When parameters fluctuate or change during the actual printing process, traditional methods make it difficult to adjust the printhead's operating status promptly and accurately, resulting in unstable print quality such as material blockage, insufficient interlayer adhesion, and surface defects. This not only increases material waste but also can reduce production efficiency, further hindering the promotion and development of 3D printing technology across various application fields. Summary of the Invention

[0004] The purpose of the present invention is to provide a nozzle control method and system based on continuous relationships to address the shortcomings of the existing technology. By continuously monitoring and analyzing multiple working parameters related to the nozzle of a 3D printing device, it is possible to accurately judge and control the working status of the nozzle, thereby improving the stability and product quality of 3D printing, and promoting the development of 3D printing technology towards a more intelligent and autonomous direction.

[0005] One embodiment of the present application provides a nozzle control method based on a continuous relationship, the method comprising:

[0006] Continuously monitor multiple operating parameters related to the nozzle of the 3D printing equipment within a preset time period and generate a numerical change curve for each operating parameter;

[0007] Determining the operating state of the nozzle of the 3D printing device according to the numerical change curve of each operating parameter and based on the continuous correlation relationship between the multiple operating parameters, wherein the continuous correlation relationship is a correlation relationship in which the operating parameters change over time and show continuous mutual influence;

[0008] Selecting parameter values ​​at a preset number of time points from a plurality of continuously monitored working parameters, and calculating the working stability index of the nozzle of the 3D printing device based on the selected parameter values;

[0009] The nozzle of the 3D printing device is controlled according to the working state and the working stability index.

[0010] Optionally, determining the working state of the nozzle of the 3D printing device according to the numerical change curve of each working parameter and based on the continuous correlation relationship between multiple working parameters includes:

[0011] Obtain historical parameter values ​​and corresponding historical working states at consecutive time points of different working parameters as a training data set, where a set of historical parameter values ​​of different parameters corresponds to one historical working state;

[0012] The training data set is used to train a state prediction model based on artificial intelligence, wherein the state prediction model is capable of learning a continuous correlation between different working parameters based on historical parameter values ​​of different working parameters, and learning a corresponding relationship between the continuous correlation relationship and the working state, wherein when the historical working state is abnormal, the historical parameter values ​​of a corresponding set of different parameters do not conform to the normal continuous correlation relationship, and when the historical working state is normal, the historical parameter values ​​of the corresponding set of different parameters conform to the normal continuous correlation relationship;

[0013] According to the parameter values ​​of different parameters at continuous time points in the value change curve, the working state of the 3D printing equipment nozzle is predicted based on the trained state prediction model.

[0014] Optionally, the calculation formula of the work stability index is:

[0015]

[0016] Among them, the WSI is the work stability index, the sigma_i is the standard deviation of the parameter values ​​of the i-th parameter at all time points, the mu_i is the mean of the parameter values ​​of the i-th parameter at all time points, the alpha_i is the weight coefficient of the i-th parameter, the R_ij is the relative change rate of the i-th parameter at the j-th time point and the previous time point, the beta_i is the correction coefficient of the i-th parameter, the C_ij is the critical state factor of the i-th parameter at the j-th time point, if the parameter value of the i-th parameter at the j-th time point exceeds the normal range, then C_ij=0, otherwise C_ij=1, the gamma_i is the influence coefficient of the i-th parameter, the M is the preset number of time points, and the N is the number of working parameters.

[0017] Optionally, when j=1, the relative change rate is Rij =0, when j>1, the relative change rate is:

[0018]

[0019] Wherein, the P_ij is the parameter value of the i-th parameter at the j-th time point, and the P_i{j-1} is the parameter value of the i-th parameter at the (j-1)-th time point.

[0020] Optionally, controlling the nozzle of the 3D printing device according to the working state and the working stability index includes:

[0021] If the working state is normal, the print head of the 3D printing device is not adjusted; or, if the working state is normal and the working stability index does not exceed a preset stability threshold, the print head of the 3D printing device is adjusted to maintain the working state normal and the working stability index exceeds the preset stability threshold;

[0022] If the working state is abnormal, the print head of the 3D printing device is adjusted to make the working state normal, and at the same time the working stability index exceeds a preset stability threshold.

[0023] Another embodiment of the present application provides a nozzle control system based on a continuous relationship, the system comprising:

[0024] A monitoring module is used to continuously monitor multiple operating parameters related to the nozzle of the 3D printing equipment within a preset time period and generate a value change curve for each operating parameter;

[0025] a determination module, configured to determine the operating state of the nozzle of the 3D printing device according to the numerical change curve of each operating parameter and based on a continuous correlation relationship between the multiple operating parameters, wherein the continuous correlation relationship is a correlation relationship in which the operating parameters change over time and continuously influence each other;

[0026] A calculation module is used to select parameter values ​​at a preset number of time points from a plurality of continuously monitored working parameters, and calculate the working stability index of the nozzle of the 3D printing device based on the selected parameter values;

[0027] A control module is used to control the nozzle of the 3D printing device according to the working state and the working stability index.

[0028] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when run.

[0029] Yet another embodiment of the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the above methods.

[0030] Compared with the prior art, the present invention provides a nozzle control method based on a continuous relationship, which continuously monitors multiple working parameters related to the nozzle of a 3D printing device within a preset time period to generate a numerical change curve for each working parameter; according to the numerical change curve of each working parameter, based on the continuous correlation relationship between the multiple working parameters, the working state of the nozzle of the 3D printing device is determined, wherein the continuous correlation relationship is a correlation relationship between the working parameters that continuously influence each other over time; parameter values ​​at a preset number of time points are selected from the multiple continuously monitored working parameters, and the working stability index of the nozzle of the 3D printing device is calculated based on the selected parameter values; according to the working state and the working stability index, the nozzle of the 3D printing device is controlled, so that by continuously monitoring and analyzing the multiple working parameters related to the nozzle of the 3D printing device, the working state of the nozzle can be accurately judged and controlled, thereby improving the stability and product quality of 3D printing, and promoting the development of 3D printing technology towards a more intelligent and autonomous direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A hardware structure block diagram of a computer terminal for a nozzle control method based on a continuous relationship provided by an embodiment of the present invention;

[0032] Figure 2 A flow chart of a nozzle control method based on a continuous relationship provided by an embodiment of the present invention;

[0033] Figure 3 A schematic structural diagram of a nozzle control system based on a continuous relationship provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention.

[0035] The embodiment of the present invention first provides a nozzle control method based on a continuous relationship. The method can be applied to electronic devices such as computer terminals, specifically ordinary computers.

[0036] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal for a nozzle control method based on a continuous relationship provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. For example, the computer terminal may also include Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0037] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / modules corresponding to the nozzle control method based on continuous relationship in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above-mentioned method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0038] The transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned network may include a wireless network provided by a communications provider of a computer terminal. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In one embodiment, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0039] See also Figure 2 , an embodiment of the present invention provides a nozzle control method based on a continuous relationship, which may include the following steps:

[0040] S201, continuously monitoring multiple operating parameters related to the nozzle of the 3D printing device within a preset time period, and generating a value change curve of each operating parameter;

[0041] During the 3D printing process, operating parameters related to nozzle control include temperature, speed, pressure, material flow, etc. By continuously monitoring these parameters, their changes over time throughout the printing process are recorded. This monitoring data will form a numerical change curve for each parameter. Monitoring changes in operating parameters provides dynamic feedback information, which helps to capture the performance of the nozzle during operation in real time, thereby laying the foundation for subsequent analysis. This process is extremely important in the nozzle control system. It not only provides basic data for subsequent working status judgment, but also helps identify potential equipment failures and optimize the printing process. One implementation method may include:

[0042] 1. Continuous monitoring of working parameters:

[0043] - High-precision sensors collect real-time data on multiple key operating parameters of 3D printing equipment over a preset time period. These parameters include nozzle temperature, extrusion speed, material flow rate, ambient humidity, and other factors that directly impact print quality and equipment performance.

[0044] - The monitoring process needs to ensure that the data collection frequency is high enough to capture subtle parameter changes. Usually, data is recorded every second or every few seconds to form a data stream.

[0045] 2. Data storage and management:

[0046] The collected operating parameter data needs to be stored effectively. A database system can be used to promptly record the timestamp and corresponding value of each parameter to ensure data integrity and traceability.

[0047] - In this step, it is necessary to design a data management mechanism, including data cleaning, denoising, and outlier processing, to ensure the accuracy of subsequent analysis.

[0048] 3. Generate numerical change curve:

[0049] Based on the collected real-time data, plot the numerical change curve of each operating parameter. You can use data visualization tools to use time as the horizontal axis and the numerical value of the operating parameter as the vertical axis to form a clear curve chart.

[0050] These curves will show the dynamic evolution trend of working parameters within a preset time period, providing intuitive information so that operators can quickly identify fluctuations in various parameters.

[0051] 4. Parameter fluctuation analysis:

[0052] By analyzing the resulting numerical change curves, you can identify periodic fluctuations, sudden changes, or trending changes in parameters. Fluctuation analysis can use statistical methods such as calculating the mean and standard deviation to assess parameter stability.

[0053] - In addition, normalization can be used to adjust the values ​​of parameters of different dimensions to the same range to facilitate comparison and analysis between different parameters.

[0054] 5. Marking Key Time Points: On the value change curve, mark key time nodes (for example, moments when significant changes occur) to provide reference data for subsequent state prediction and control. These time points can help analyze how various parameters affect each other in specific situations.

[0055] 6. Prepare for subsequent analysis:

[0056] Once the numerical change curves are generated, they serve as data input for subsequent state prediction models and control strategies. By conducting in-depth analysis of the correlations between different operating parameters, more accurate control algorithms can be established.

[0057] - In addition, the generation of the numerical change curve also lays the foundation for the subsequent calculation of the working stability index, which helps to conduct timely and accurate dynamic monitoring of the working status of the printhead.

[0058] In summary, the process of continuously monitoring multiple operating parameters related to the 3D printing equipment nozzle and generating numerical change curves not only provides rich information for subsequent operating status determination but also lays a solid foundation for subsequent control measures. The implementation of this method will significantly improve the operational stability of the equipment and ensure the efficiency and quality of the printing process.

[0059] S202, determining the operating state of the nozzle of the 3D printing device according to the numerical change curve of each operating parameter and based on a continuous correlation relationship between the multiple operating parameters, wherein the continuous correlation relationship is a correlation relationship in which the operating parameters continuously influence each other over time;

[0060] The printhead's operating status can be determined by analyzing the changing relationships between different parameters. For example, rising temperature can affect material fluidity, which in turn can affect print quality. This interrelated dynamic characteristic is known as a continuous correlation. Accurately identifying the printhead's operating status can prevent potential failures and material waste, improve printing accuracy and quality, and ensure product consistency and repeatability.

[0061] Specifically, historical parameter values ​​of different working parameters at consecutive time points and corresponding historical working states can be obtained as a training data set, where a set of historical parameter values ​​of different parameters corresponds to one historical working state;

[0062] Collect data on different operating parameters and the corresponding printhead operating status over a period of time. This data will serve as the basis for model training. Historical data records help us understand how various operating parameters influence each other and their contribution to operating status, and are an important basis for machine learning models.

[0063] The training data set is used to train a state prediction model based on artificial intelligence, wherein the state prediction model is capable of learning a continuous correlation between different working parameters based on historical parameter values ​​of different working parameters, and learning a corresponding relationship between the continuous correlation relationship and the working state, wherein when the historical working state is abnormal, the historical parameter values ​​of a corresponding set of different parameters do not conform to the normal continuous correlation relationship, and when the historical working state is normal, the historical parameter values ​​of the corresponding set of different parameters conform to the normal continuous correlation relationship;

[0064] Build a machine learning model (such as a neural network or decision tree) that can identify the relationship between input data from different operating parameters and the operating status of the printhead. The model is continuously optimized during the training process to improve the accuracy of the prediction of the operating status.

[0065] Through training, the model can identify the characteristic differences between normal and abnormal states, thereby improving the ability to predict the status of the sprinkler in real-time monitoring.

[0066] According to the parameter values ​​of different parameters at continuous time points in the value change curve, the working state of the 3D printing equipment nozzle is predicted based on the trained state prediction model.

[0067] The real-time monitored operating parameters are processed through a trained model to predict the current operating status. This enables the system to provide real-time feedback on the operating status of the printhead, allowing for timely adjustments and optimization of the printing process to avoid anomalies. A state prediction model based on a support vector machine (SVM) can be used.

[0068] The steps are as follows:

[0069] 1. Data organization: The collected operating parameter data is organized into a feature matrix and a label array. The feature matrix contains the historical values ​​of multiple operating parameters, and the label array corresponds to the historical operating status (normal or abnormal).

[0070] 2. Feature selection and extraction: The principal component analysis (PCA) method is used to reduce the dimension of the feature matrix and screen out features that have a greater impact on working status prediction to improve the efficiency and effectiveness of model training.

[0071] 3. Model training: Use the organized feature matrix and labeled dataset to train the support vector machine algorithm. Adjust the SVM hyperparameters, such as the penalty parameter C and kernel function type, to optimize the model's generalization capabilities.

[0072] 4. Model Validation and Testing: Use cross-validation to evaluate the performance of the trained model and ensure its accuracy on unseen data. Model performance is evaluated using metrics such as precision, recall, and F1 score.

[0073] 5. Real-time prediction: The real-time monitored working parameters are input into the trained SVM model, and the model outputs the prediction results of the current working status of the nozzle for reference in subsequent control decisions.

[0074] Through the implementation of the above technical means, the intelligent level of working status monitoring and control of 3D printing equipment can be effectively improved.

[0075] S203, selecting parameter values ​​at a preset number of time points from a plurality of continuously monitored operating parameters, and calculating an operating stability index of a nozzle of the 3D printing device based on the selected parameter values;

[0076] After continuous monitoring of the 3D printing equipment nozzle, real-time data of multiple operating parameters (such as temperature, flow rate, pressure, etc.) is collected. According to the preset time period and requirements, a certain number of time points are selected from these continuously monitored data to extract the corresponding parameter values. These parameter values ​​will be used in the subsequent calculation of the working stability index (WSI).

[0077] - Data extraction: By selecting parameter values ​​at specific time points, data redundancy can be reduced, making the calculation process more efficient. The selected time points can be critical instantaneous data, which helps to comprehensively evaluate the working status of the sprinkler.

[0078] Improved Accuracy: By analyzing parameters over a specific time period, a more representative sample is provided for subsequent calculation of the Work Stability Index, ensuring more reliable results.

[0079] - Dynamic reflection: The selected time points can reveal the performance of the sprinkler at different stages, thereby providing dynamic monitoring effects and helping to identify possible anomalies in a timely manner.

[0080] Specifically, a calculation formula for the job stability index can be:

[0081]

[0082] The WSI is the working stability index, which is an important indicator for evaluating the working status of the nozzle. The higher the value, the more stable the working status.

[0083] sigma_i is the standard deviation of the parameter values ​​of the i-th parameter at all time points, reflecting the degree of fluctuation of the parameter during the monitoring period. The smaller the standard deviation, the more stable the working state. mu_i is the mean of the parameter values ​​of the i-th parameter at all time points, providing information on the overall level of the parameter. alpha_i is the weight coefficient of the i-th parameter, reflecting the relative importance of each parameter to working stability, ensuring that key parameters have a more significant impact on the results.

[0084] R_ij is the relative rate of change of the i-th parameter at the j-th time point compared to the previous time point, reflecting the sensitivity of the parameter's dynamic changes and promptly reflecting any anomalies during operation. beta_i is the correction coefficient for the i-th parameter, providing appropriate adjustments based on the characteristics of the specific parameter and enhancing the adaptability of the overall model.

[0085] C_ij is the critical state factor of the i-th parameter at the j-th time point. If the value of the i-th parameter at the j-th time point is outside the normal range, C_ij = 0; otherwise, C_ij = 1. This is used to assess whether the current state of each parameter is within an acceptable range. Gamma_i is the influence coefficient of the i-th parameter, which is used to adjust the influence of each parameter on the working stability index, ensuring that the contribution of important parameters to the overall stability assessment is amplified.

[0086] M is the preset number of time points, ensuring multi-dimensional analysis of the printhead status and increasing the reliability of the index. N is the number of operating parameters, reflecting the changes in operating performance within a specific time period.

[0087] This formula comprehensively considers the dynamic changes, relative rates of change, and critical factors of stability of multiple operating parameters to form an effective indicator for comprehensively evaluating the operating stability of equipment. This multi-dimensional evaluation method enables WSI to accurately reflect the operating status of equipment.

[0088] Specifically, when j=1, the relative change rate is R ij =0, when j>1, the relative change rate is:

[0089]

[0090] Wherein, the P_ij is the parameter value of the i-th parameter at the j-th time point, and the P_i{j-1} is the parameter value of the i-th parameter at the (j-1)-th time point.

[0091] By integrating multi-dimensional working parameters, a reliable working stability index is formed, providing a scientific basis for the optimization and control of 3D printing equipment.

[0092] S204: Controlling a nozzle of the 3D printing device according to the working state and the working stability index.

[0093] After monitoring the various operating parameters of the 3D printing equipment nozzle and calculating the corresponding working stability index (WSI), the system needs to analyze the current working status (normal or abnormal) and the WSI value. Based on the analysis results, the system will decide whether to adjust or control the nozzle to ensure the smooth progress of the printing process.

[0094] - Improved print quality: Real-time monitoring and dynamic adjustment ensure that the printhead operates in optimal conditions, thereby improving print quality and reducing defects and scrap rates.

[0095] - Optimize production efficiency: By intelligently controlling the working status of the printhead, the printing process can be properly managed, reducing downtime and print failure rates, and improving overall production efficiency.

[0096] - Extend equipment life: Effectively controlling and adjusting the operating status of the nozzle can reduce equipment wear caused by long-term unstable operation and extend the service life of the equipment.

[0097] - Improve system responsiveness: By combining work status and WSI, the system can adapt more quickly to environmental changes and operating conditions, ensuring the continuity and stability of the printing process, thereby enhancing customer satisfaction.

[0098] Specifically, if the working state is normal, the print head of the 3D printing device is not adjusted; or, if the working state is normal and the working stability index does not exceed a preset stability threshold, the print head of the 3D printing device is adjusted to maintain the working state normal, and the working stability index exceeds the preset stability threshold;

[0099] When the printer system detects that the working status of the printhead is "normal", the system decides not to adjust the printhead, regardless of the current working stability index (WSI). This decision is based on the trust in the working status of the printhead, that is, the normal status is sufficient to ensure the smooth progress of the printing process.

[0100] - Improve operational efficiency: Avoiding unnecessary adjustments can save time and operating costs and ensure the consistency of the printing process.

[0101] - Reduce equipment wear: Frequent adjustments may cause wear on equipment components. Maintaining normal conditions without adjustments can reduce this risk and extend the service life of the equipment.

[0102] Or, better yet, even if the printhead's working status is assessed to be normal, if the Working Stability Index (WSI) does not exceed the set stability threshold, the system will proactively adjust the printhead. This adjustment is intended to maintain the printhead's optimal working condition and ensure print quality.

[0103] - Preventive maintenance: Even under normal working conditions, the system still pays attention to working stability, ensuring that any potential problems can be discovered and resolved in time to prevent subsequent failures.

[0104] - Improve product quality consistency: By fine-tuning the printhead parameters, we ensure that the printhead's working condition is maintained at the optimal level throughout the printing process, thereby improving the quality consistency of the final product.

[0105] If the working state is abnormal, the print head of the 3D printing device is adjusted to make the working state normal, and at the same time the working stability index exceeds a preset stability threshold.

[0106] Once the working state of the nozzle is detected to be abnormal, the system will immediately make necessary adjustments to the nozzle to quickly restore it to normal working state. At the same time, the adjustment will also focus on the control of WSI to ensure that it can return to a reasonable range after the adjustment.

[0107] - Rapid response capability: Ensures that when the printhead malfunctions, measures can be taken quickly to reduce the impact of the fault on the printing process, reducing material waste and downtime.

[0108] - Enhanced system stability: Through timely adjustments under abnormal conditions, the stability and reliability of the entire 3D printing system are enhanced, improving customer trust and satisfaction.

[0109] Combining the above analysis, we provide an efficient and intelligent control strategy for the printheads of 3D printing equipment. By real-time monitoring and adjusting the printhead's operating status and stability index, we ensure that the printhead remains in optimal working condition, improving print quality, optimizing production efficiency, and reducing equipment failure rates. This comprehensive approach can effectively promote the intelligent development of 3D printing technology and provide users with a higher level of printing service and experience.

[0110] It can be seen that the multiple working parameters related to the nozzle of the 3D printing device are continuously monitored within a preset time period to generate a numerical change curve of each working parameter; according to the numerical change curve of each working parameter, based on the continuous correlation relationship between the multiple working parameters, the working state of the nozzle of the 3D printing device is determined, wherein the continuous correlation relationship is a correlation relationship between the working parameters that continuously influence each other as time changes; parameter values ​​at a preset number of time points are selected from the multiple working parameters that are continuously monitored, and the working stability index of the nozzle of the 3D printing device is calculated according to the selected parameter values; according to the working state and the working stability index, the nozzle of the 3D printing device is controlled, so that by continuously monitoring and analyzing the multiple working parameters related to the nozzle of the 3D printing device, the working state of the nozzle can be accurately judged and controlled, so as to improve the stability and product quality of 3D printing and promote the development of 3D printing technology towards a more intelligent and autonomous direction.

[0111] Another embodiment of the present invention provides a nozzle control system based on a continuous relationship, see Figure 3 , the system may include:

[0112] The monitoring module 301 is used to continuously monitor multiple operating parameters related to the nozzle of the 3D printing device within a preset time period and generate a value change curve for each operating parameter;

[0113] Determination module 302, configured to determine the operating state of the nozzle of the 3D printing device according to the numerical change curve of each operating parameter and based on a continuous correlation relationship between the multiple operating parameters, wherein the continuous correlation relationship is a correlation relationship in which the operating parameters change over time and continuously influence each other;

[0114] A calculation module 303 is configured to select parameter values ​​at a preset number of time points from a plurality of continuously monitored operating parameters, and calculate an operating stability index of a nozzle of the 3D printing device based on the selected parameter values;

[0115] The control module 304 is configured to control the nozzle of the 3D printing device according to the working state and the working stability index.

[0116] It can be seen that the multiple working parameters related to the nozzle of the 3D printing device are continuously monitored within a preset time period to generate a numerical change curve of each working parameter; according to the numerical change curve of each working parameter, based on the continuous correlation relationship between the multiple working parameters, the working state of the nozzle of the 3D printing device is determined, wherein the continuous correlation relationship is a correlation relationship between the working parameters that continuously influence each other as time changes; parameter values ​​at a preset number of time points are selected from the multiple working parameters that are continuously monitored, and the working stability index of the nozzle of the 3D printing device is calculated according to the selected parameter values; according to the working state and the working stability index, the nozzle of the 3D printing device is controlled, so that by continuously monitoring and analyzing the multiple working parameters related to the nozzle of the 3D printing device, the working state of the nozzle can be accurately judged and controlled, so as to improve the stability and product quality of 3D printing and promote the development of 3D printing technology towards a more intelligent and autonomous direction.

[0117] An embodiment of the present invention further provides a storage medium, in which a computer program is stored. The computer program is configured to execute the steps of any one of the above method embodiments when running.

[0118] Specifically, in this embodiment, the above-mentioned storage medium may be configured to store a computer program for performing the following steps:

[0119] S201, continuously monitoring multiple operating parameters related to the nozzle of the 3D printing device within a preset time period, and generating a value change curve of each operating parameter;

[0120] S202, determining the operating state of the nozzle of the 3D printing device according to the numerical change curve of each operating parameter and based on a continuous correlation relationship between the multiple operating parameters, wherein the continuous correlation relationship is a correlation relationship in which the operating parameters continuously influence each other over time;

[0121] S203, selecting parameter values ​​at a preset number of time points from a plurality of continuously monitored operating parameters, and calculating an operating stability index of a nozzle of the 3D printing device based on the selected parameter values;

[0122] S204: Controlling a nozzle of the 3D printing device according to the working state and the working stability index.

[0123] It can be seen that the multiple working parameters related to the nozzle of the 3D printing device are continuously monitored within a preset time period to generate a numerical change curve of each working parameter; according to the numerical change curve of each working parameter, based on the continuous correlation relationship between the multiple working parameters, the working state of the nozzle of the 3D printing device is determined, wherein the continuous correlation relationship is a correlation relationship between the working parameters that continuously influence each other as time changes; parameter values ​​at a preset number of time points are selected from the multiple working parameters that are continuously monitored, and the working stability index of the nozzle of the 3D printing device is calculated according to the selected parameter values; according to the working state and the working stability index, the nozzle of the 3D printing device is controlled, so that by continuously monitoring and analyzing the multiple working parameters related to the nozzle of the 3D printing device, the working state of the nozzle can be accurately judged and controlled, so as to improve the stability and product quality of 3D printing and promote the development of 3D printing technology towards a more intelligent and autonomous direction.

[0124] An embodiment of the present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any one of the above method embodiments.

[0125] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0126] Specifically, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0127] S201, continuously monitoring multiple operating parameters related to the nozzle of the 3D printing device within a preset time period, and generating a value change curve of each operating parameter;

[0128] S202, determining the operating state of the nozzle of the 3D printing device according to the numerical change curve of each operating parameter and based on a continuous correlation relationship between the multiple operating parameters, wherein the continuous correlation relationship is a correlation relationship in which the operating parameters continuously influence each other over time;

[0129] S203, selecting parameter values ​​at a preset number of time points from a plurality of continuously monitored operating parameters, and calculating an operating stability index of a nozzle of the 3D printing device based on the selected parameter values;

[0130] S204: Controlling a nozzle of the 3D printing device according to the working state and the working stability index.

[0131] It can be seen that the multiple working parameters related to the nozzle of the 3D printing device are continuously monitored within a preset time period to generate a numerical change curve of each working parameter; according to the numerical change curve of each working parameter, based on the continuous correlation relationship between the multiple working parameters, the working state of the nozzle of the 3D printing device is determined, wherein the continuous correlation relationship is a correlation relationship between the working parameters that continuously influence each other as time changes; parameter values ​​at a preset number of time points are selected from the multiple working parameters that are continuously monitored, and the working stability index of the nozzle of the 3D printing device is calculated according to the selected parameter values; according to the working state and the working stability index, the nozzle of the 3D printing device is controlled, so that by continuously monitoring and analyzing the multiple working parameters related to the nozzle of the 3D printing device, the working state of the nozzle can be accurately judged and controlled, so as to improve the stability and product quality of 3D printing and promote the development of 3D printing technology towards a more intelligent and autonomous direction.

[0132] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the scope of implementation of the present invention is not limited to what is shown in the drawings. Any changes made in accordance with the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which do not exceed the spirit covered by the description and drawings, should be within the scope of protection of the present invention.

Claims

1. A nozzle control method based on continuous relationship, characterized in that: The method comprises: Continuously monitor multiple operating parameters related to the nozzle of the 3D printing equipment within a preset time period and generate a numerical change curve for each operating parameter; Determining the operating state of the nozzle of the 3D printing device according to the numerical change curve of each operating parameter and based on the continuous correlation relationship between the multiple operating parameters, wherein the continuous correlation relationship is a correlation relationship in which the operating parameters change over time and show continuous mutual influence; Selecting parameter values ​​at a preset number of time points from a plurality of continuously monitored working parameters, and calculating the working stability index of the nozzle of the 3D printing device based on the selected parameter values; Controlling a nozzle of a 3D printing device according to the working state and the working stability index; The method of determining the working state of the nozzle of the 3D printing device according to the numerical change curve of each working parameter and based on the continuous correlation relationship between the multiple working parameters includes: Obtain historical parameter values ​​and corresponding historical working states at consecutive time points of different working parameters as a training data set, where a set of historical parameter values ​​of different parameters corresponds to one historical working state; The training data set is used to train a state prediction model based on artificial intelligence, wherein the state prediction model is capable of learning a continuous correlation between different working parameters based on historical parameter values ​​of different working parameters, and learning a corresponding relationship between the continuous correlation relationship and the working state, wherein when the historical working state is abnormal, the historical parameter values ​​of a corresponding set of different parameters do not conform to the normal continuous correlation relationship, and when the historical working state is normal, the historical parameter values ​​of the corresponding set of different parameters conform to the normal continuous correlation relationship; According to the parameter values ​​of different parameters at continuous time points in the value change curve, the working state of the 3D printing equipment nozzle is predicted based on the trained state prediction model.

2. The method according to claim 1, characterized in that The calculation formula of the job stability index is: Among them, the is the job stability index, is the standard deviation of the parameter values ​​of the i-th parameter at all time points, is the mean value of the parameter value of the i-th parameter at all time points, is the weight coefficient of the i-th parameter, is the relative change rate of the i-th parameter at the j-th time point and its previous time point, is the correction coefficient of the i-th parameter, is the critical state factor of the i-th parameter at the j-th time point. If the parameter value of the i-th parameter at the j-th time point exceeds the normal range, then ,otherwise , is the influence coefficient of the i-th parameter, M is the preset number of time points, and N is the number of working parameters.

3. The method according to claim 2, characterized in that in, When j=1, the relative change rate is , when j>1, the relative rate of change is: Among them, the is the parameter value of the i-th parameter at the j-th time point, is the parameter value of the i-th parameter at the (j-1)th time point.

4. The method according to claim 3, characterized in that The controlling of the nozzle of the 3D printing device according to the working state and the working stability index includes: If the working state is normal, the print head of the 3D printing device is not adjusted; or, if the working state is normal and the working stability index does not exceed a preset stability threshold, the print head of the 3D printing device is adjusted to maintain the working state normal and the working stability index exceeds the preset stability threshold; If the working state is abnormal, the print head of the 3D printing device is adjusted to make the working state normal, and at the same time the working stability index exceeds a preset stability threshold.

5. A nozzle control system based on a continuous relationship, characterized in that: The system comprises: A monitoring module is used to continuously monitor multiple operating parameters related to the nozzle of the 3D printing equipment within a preset time period and generate a value change curve for each operating parameter; a determination module, configured to determine the operating state of the nozzle of the 3D printing device according to the numerical change curve of each operating parameter and based on a continuous correlation relationship between the multiple operating parameters, wherein the continuous correlation relationship is a correlation relationship in which the operating parameters change over time and continuously influence each other; A calculation module is used to select parameter values ​​at a preset number of time points from a plurality of continuously monitored working parameters, and calculate the working stability index of the nozzle of the 3D printing device based on the selected parameter values; A control module, configured to control a nozzle of a 3D printing device according to the working state and the working stability index; The determining module is specifically configured to: Obtain historical parameter values ​​and corresponding historical working states at consecutive time points of different working parameters as a training data set, where a set of historical parameter values ​​of different parameters corresponds to one historical working state; The training data set is used to train a state prediction model based on artificial intelligence, wherein the state prediction model is capable of learning a continuous correlation between different working parameters based on historical parameter values ​​of different working parameters, and learning a corresponding relationship between the continuous correlation relationship and the working state, wherein when the historical working state is abnormal, the historical parameter values ​​of a corresponding set of different parameters do not conform to the normal continuous correlation relationship, and when the historical working state is normal, the historical parameter values ​​of the corresponding set of different parameters conform to the normal continuous correlation relationship; According to the parameter values ​​of different parameters at continuous time points in the value change curve, the working state of the 3D printing equipment nozzle is predicted based on the trained state prediction model.

6. The system according to claim 5, characterized in that The calculation formula of the job stability index is: Among them, the is the job stability index, is the standard deviation of the parameter values ​​of the i-th parameter at all time points, is the mean value of the parameter value of the i-th parameter at all time points, is the weight coefficient of the i-th parameter, is the relative change rate of the i-th parameter at the j-th time point and its previous time point, is the correction coefficient of the i-th parameter, is the critical state factor of the i-th parameter at the j-th time point. If the parameter value of the i-th parameter at the j-th time point exceeds the normal range, then ,otherwise , is the influence coefficient of the i-th parameter, M is the preset number of time points, and N is the number of working parameters.

7. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 4 when run.

8. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 4.

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