Intelligent control method and system for 3D printing process based on real-time feedback

Abstract

The invention discloses a 3D printing process intelligent control method and system based on real-time feedback, and relates to the technical field of 3D printing, a data acquisition module collects a printing layer surface image and printing data in real time, constructs an image data set and a printing data set, and performs cleaning and standardization processing on the data in a preprocessing module; the image analysis module analyzes surface defects of a printing layer by using a convolutional neural network, generates a defect score D, compares the defect score D with a preset defect score threshold D1, evaluates the state of the printing layer and triggers early warning, and the comprehensive evaluation module calculates a crack generation probability Z by combining printing data and the defect score; and the intelligent control module compares the crack generation probability Z with a preset crack generation probability threshold value Zl, evaluates the printing state and feeds back and controls printing parameters according to a result, so that crack and structure defects in the printing process are effectively prevented and reduced, and the printing precision and the finished product quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of 3D printing, and in particular to a 3D printing process intelligent control method and system based on real-time feedback. Background Art

[0002] As a manufacturing method of adding materials layer by layer, 3D printing technology is widely used in aerospace, medical, electronics and precision engineering. In recent years, with the development of materials science, 3D printing technology has gradually extended to the manufacturing of ceramic materials. Ceramic materials have excellent properties such as high temperature resistance, corrosion resistance, and insulation. Therefore, they have important applications in the manufacture of high-temperature components, insulating components and wear-resistant structural parts. However, the ceramic 3D printing process is complex and requires high printing accuracy and real-time control. During the printing process, the deposition, cooling rate and temperature distribution of each layer need to be precisely controlled to ensure the dimensional accuracy and structural integrity of the printed parts.

[0003] The current ceramic 3D printing process still has some technical deficiencies. First, due to the brittleness of ceramic materials, they are easily affected by temperature gradients and cooling rates during the printing process, resulting in internal stress, which leads to loose bonding between material layers and even cracks or tiny gaps. Secondly, existing printing systems usually use preset printing paths and printing parameters, lack dynamic adjustment capabilities based on real-time feedback, and cannot optimize the actual printing layer status. Therefore, during the printing process, if the surface quality of the printed layer is poor or the cooling process is uneven, the system cannot respond and adjust the parameters in time, resulting in unstable quality of the printed parts and difficulty in achieving the expected accuracy and strength requirements.

[0004] This defect mainly comes from the complex thermodynamic changes, stress distribution and material properties during the printing process of ceramic materials. When the temperature and cooling rate are not properly controlled during the printing process, the temperature gradient inside the material will induce thermal stress, resulting in weakened adhesion between printed layers, and even abnormal phenomena such as cracks and interlayer separation. These abnormalities will destroy the structural integrity of ceramic parts, affect the mechanical properties and dimensional accuracy of printed parts, and reduce their reliability in high temperature or high load environments. Therefore, it has become an urgent need to achieve high-precision real-time monitoring and intelligent control of the ceramic 3D printing process, which can effectively reduce the probability of defects and improve the overall quality of printed parts by adjusting printing parameters in real time. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a 3D printing process intelligent control method and system based on real-time feedback, which solves the problems in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent control system for a 3D printing process based on real-time feedback, comprising a data acquisition module, a preprocessing module, an image analysis module, a comprehensive evaluation module and an intelligent control module;

[0007] The data acquisition module is used to collect high-definition images and printing data of the printing layer surface during the 3D printing process, and to construct an image data set and a printing data set based on the collected high-definition images and printing data of the printing layer surface;

[0008] The preprocessing module is used to preprocess the high-definition image of the printing layer surface and the printing data in the image data set and the printing data set;

[0009] The image analysis module is used to construct a defect analysis model based on a convolutional neural network, input the preprocessed high-definition image of the printed layer surface into the defect analysis model, obtain the printed layer defect score D, and preset the defect score threshold D1, compare and analyze the preset defect score threshold D1 with the printed layer defect score D, evaluate the printed layer status, and trigger the early warning mechanism to control the printed layer;

[0010] The comprehensive evaluation module is used to obtain the crack generation probability Z according to the preprocessed printing data set and the printing layer defect score D;

[0011] The intelligent control module is used to preset a crack generation probability threshold Zl, and compare and analyze the preset crack generation probability threshold Zl and the crack generation probability Z, evaluate the 3D printing status, and based on the evaluated 3D printing status, feed back data to the 3D printing data center to control the 3D printing process.

[0012] Preferably, the data acquisition module includes an image data acquisition unit and a print data acquisition unit;

[0013] The image data acquisition unit is used to use a high-resolution camera installed above the print head to collect the surface image of the printed layer after each layer printing process is completed, and transmit the collected surface image of the printed layer to the 3D printing data center through wireless network technology to construct an image data set;

[0014] The printing data acquisition unit is used to deploy an intelligent sensor group at the bottom of the 3D printer platform to obtain printing data of the 3D printing process, wherein the printing data includes printing layer temperature, printing layer stress and deposition layer thickness, and obtains the thermal expansion coefficient and effective elastic modulus of the material from the physical property database of the printing material and the material manual;

[0015] The print layer temperature data refers to the temperature of the print layer obtained by real-time monitoring using an infrared temperature sensor;

[0016] The printing layer stress refers to using a laser stress sheet installed at the bottom of the printing platform to monitor the stress changes generated during the printing process in real time and obtain the printing layer stress data;

[0017] The thickness of the deposited layer refers to the use of a deployed optical sensor to detect the height change of the printed layer through the reflected light signal to obtain the height data of the deposited layer;

[0018] The obtained printing layer temperature data, printing layer stress data and deposition layer height data are transmitted to the 3D printing data center through wireless network technology to construct a printing data set.

[0019] Preferably, the preprocessing module includes a print data processing unit and an image data processing unit;

[0020] The print data processing unit is used to perform noise filtering, data cleaning and normalization processing on the print data in the print data set;

[0021] The noise filtering refers to using a Kalman filter to remove random noise from the print data;

[0022] The data cleaning refers to detecting outliers using a box plot, calculating the interquartile range IQR in the box plot, and obtaining a normal data range (Q1-1.5×IQR, Q3+1.5×IQR). Print data that is not within the normal data range is considered abnormal data and outliers are removed.

[0023] The normalization process refers to performing dimensionless processing on the collected printing data, and unifying the units of the collected printing data.

[0024] Preferably, the image data processing unit is configured to perform region cropping, RGB standardization and boundary setting on the image data in the image data set;

[0025] The area cropping refers to using computer vision technology to crop out the printing area and remove background information;

[0026] The RGB standardization refers to performing pixel operations on image data and performing standard version processing on each pixel;

[0027] The boundary setting refers to marking the image boundary points using an edge detection algorithm.

[0028] Preferably, the image analysis module includes a model building unit and a preliminary evaluation unit;

[0029] The model building unit is used to build a defect analysis model based on the convolutional neural network technology, and in combination with the 3D printing database, obtain historical printing image data, input the historical printing image data as training data into the input layer of the defect analysis model, use back propagation and optimization algorithms to adjust the defect analysis model parameters, input the real-time collected printing image data into the trained defect analysis model, and obtain the printing layer defect score D;

[0030] Wherein, the defect analysis model includes an input layer, a convolution layer, a pooling layer, a fully connected layer and an output layer;

[0031] The input layer is used to input the real-time collected printing image data into the defect analysis model;

[0032] The convolution layer is used to extract local features of image data and generate a local feature map;

[0033] The pooling layer is used to reduce the dimension of the local feature map obtained by the convolutional layer;

[0034] The fully connected layer is used to map the features to a print layer defect score D output;

[0035] The output layer is used to output the print layer defect score D, and the print layer defect score D is specifically obtained as follows:

[0036] ;

[0037] In the formula, is the average edge strength, is the texture uniformity, is the color uniformity coefficient, are the weight values ​​of average edge intensity, texture uniformity and color uniformity coefficient, respectively, and c is the bias term;

[0038] The average edge strength The method of obtaining is:

[0039] Perform edge detection on the image, use the Sobel operator to detect the edge strength of all pixels in the image, and obtain the horizontal gradient edge strength and vertical gradient edge strength , and according to the horizontal gradient edge strength and vertical gradient edge strength Get the edge strength G of each pixel:

[0040] ;

[0041] According to the edge strength G of each pixel, the average edge strength of the whole image is obtained. :

[0042] ;

[0043] In the formula, is the total number of pixels in the image, k is the kth pixel of the image, is the edge strength of the kth pixel;

[0044] The texture uniformity The method of obtaining is:

[0045] Convert the image into a grayscale image, obtain the grayscale co-occurrence matrix, and obtain the texture uniformity based on the grayscale co-occurrence matrix:

[0046] ;

[0047] In the formula, a and b represent the two gray values ​​of the gray level co-occurrence matrix, is the relative frequency of the combination of a pixel with gray value a and its adjacent pixel with gray value b in the gray-level co-occurrence matrix;

[0048] The color uniformity coefficient The method of obtaining is:

[0049] Convert the image to HSV color space, including three channels: hue, saturation, and brightness. Calculate the standard deviation of each channel, and obtain the color uniform distribution coefficient based on the standard deviation and mean of each channel. :

[0050] ;

[0051] In the formula, , and are the standard deviations of the hue channel, saturation channel, and brightness channel, respectively.

[0052] Preferably, the preliminary evaluation unit is used to preset a defect score threshold D1, and compare and analyze the obtained print layer defect score D with the defect score threshold D1 to evaluate the 3D printing quality. The specific evaluation content is as follows:

[0053] If the print layer defect score D is less than the defect score threshold Dl, that is, D<Dl, the printing is judged to be in a normal risk state, indicating that the surface quality of the print layer is normal and no adjustment is required, and the printing process is continuously monitored;

[0054] If the print layer defect score D is greater than or equal to the defect score threshold Dl, that is, D≥Dl, the printing is judged to be in an abnormal risk state, indicating that there are defects on the print layer surface, triggering the alarm mechanism, immediately stopping printing and recording the printing data and sending it to the 3D printing controller until the 3D printing controller responds.

[0055] Preferably, the comprehensive evaluation module includes a printing data analysis unit and a crack probability prediction unit;

[0056] The print data analysis unit is used to obtain an average cooling rate according to the preprocessed print data set. and heat stress ;

[0057] The average cooling rate The method of obtaining is:

[0058] ;

[0059] In the formula, It's at the time When the instantaneous temperature of the printed layer is It's at the time The instantaneous temperature of the printed layer at , M is the total number of sampling time points, is the i-th sampling time point, is the i-1th sampling time point, i=1, 2, ..., M, is the time interval between two adjacent data collection time points;

[0060] The thermal stress The method of obtaining is:

[0061] ;

[0062] In the formula, is the temperature difference at different locations within the printed material, is the thermal expansion coefficient of the printing material, is the effective elastic modulus of the printing material.

[0063] Preferably, the crack probability prediction unit is used to construct a crack probability prediction model, and use historical printing data to train the crack probability prediction model;

[0064] The defect score D of the printed layer and the average cooling rate and heat stress The trained crack probability prediction model is input to obtain the crack generation probability Z. The crack generation probability Z is obtained as follows:

[0065] ;

[0066] In the formula, They are the defect score D of the printed layer, the average cooling rate and heat stress The weight coefficient of , q is the bias term.

[0067] Preferably, the intelligent control module is used to preset a crack generation probability threshold Z1, compare and analyze the crack generation probability threshold Z1 with the crack generation probability Z, and evaluate the 3D printing status. The specific evaluation content is as follows:

[0068] If the crack generation probability Z is greater than or equal to the crack generation probability threshold Zl, that is, Z≥Zl, the printing is judged to be in an abnormal state, the alarm mechanism is triggered, the temperature is immediately reduced to half of the original temperature, and the printing path is replanned, and the printing time interval between layers is adjusted to twice the original time interval;

[0069] If the crack generation probability Z is less than the crack generation probability threshold Zl, that is, Z<Zl, it is determined that the printing is in a normal state, the printing state is relatively stable, the printing state is continuously monitored and a printing report is generated and stored in the 3D printing data center.

[0070] A 3D printing process intelligent control method based on real-time feedback comprises the following steps:

[0071] Step 1: Collect high-definition images and printing data of the printing layer surface during the 3D printing process, and construct an image data set and a printing data set based on the collected high-definition images and printing data of the printing layer surface;

[0072] Step 2: preprocessing the high-definition image of the printing layer surface and the printing data in the image data set and the printing data set;

[0073] Step 3: construct a defect analysis model based on a convolutional neural network, input the preprocessed high-definition image of the printed layer surface into the defect analysis model, obtain the printed layer defect score D, and preset a defect score threshold D1, compare and analyze the preset defect score threshold D1 with the printed layer defect score D, evaluate the printed layer status, and trigger an early warning mechanism to control the printed layer;

[0074] Step 4: Obtain the crack generation probability Z based on the preprocessed printing data set and the printing layer defect score D;

[0075] Step 5: preset a crack generation probability threshold Zl, and compare and analyze the preset crack generation probability threshold Zl and the crack generation probability Z to evaluate the 3D printing status, and based on the evaluated 3D printing status, feedback data to the 3D printing data center to control the 3D printing process.

[0076] The present invention provides a 3D printing process intelligent control method and system based on real-time feedback, which has the following beneficial effects:

[0077] (1) The present invention realizes all-round monitoring and analysis of the surface image and printing data of the printing layer through real-time data acquisition and preprocessing. The data acquisition module obtains the temperature, stress, deposition layer thickness and high-definition image of the printing layer surface through a high-resolution camera and an intelligent sensor group, providing rich data support for subsequent analysis and judgment. The data is transmitted to the 3D printing data center through a wireless network. The system can build and update the image data set and the printing data set in real time to ensure that the data in the printing process can be collected and processed comprehensively and quickly, avoiding the monitoring of printing quality affected by data loss. This real-time data acquisition and transmission method effectively improves the accuracy and timeliness of data acquisition and provides a solid data foundation for intelligent control.

[0078] (2) The image analysis module and comprehensive evaluation module in the system can efficiently analyze the surface defects of the printed layer and evaluate the risk of crack generation based on data such as cooling rate and thermal stress. In the image analysis module, the convolutional neural network model is used to analyze the surface image of the printed layer. It can not only extract features such as edge strength, texture uniformity and color uniformity, but also quantify and generate a defect score D, and preset a defect score threshold Dl for preliminary evaluation, thereby realizing a rapid evaluation of the surface quality of the printed layer. In addition, the comprehensive evaluation module calculates the crack generation probability by combining the defect score, average cooling rate and thermal stress, and compares and analyzes the crack generation probability Z by presetting the crack generation probability threshold Zl, providing a comprehensive printing quality evaluation method. Compared with the traditional static monitoring method, the system can perform dynamic evaluation based on real-time data, so that the system can trigger corresponding control measures at the early stage of identifying anomalies, reducing the uncertainty and potential defects in the printing process.

[0079] (3) The intelligent control module provides timely and effective feedback control during the printing process. When the system detects that the probability of crack generation Z exceeds the preset threshold Zl, it will immediately trigger the alarm mechanism and automatically take adjustment measures, including lowering the printing temperature, replanning the printing path, and increasing the time interval between layers, thereby reducing thermal stress and cooling rate, and reducing the risk of crack generation. At the same time, when the system assesses that the printing status is normal, it will continue to monitor and generate a printing report, which will be stored in the 3D printing data center to provide a basis for subsequent quality tracking and optimization. This automated intelligent control and adjustment function can fine-tune and optimize the printing status without relying on human intervention, which not only improves the overall quality and reliability of the printed parts, but also improves the automation and intelligence level of the 3D printing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 This is a block diagram of an intelligent control system for a 3D printing process based on real-time feedback according to the present invention.

[0081] Figure 2 The present invention is a flow chart of a 3D printing process intelligent control method based on real-time feedback. DETAILED DESCRIPTION

[0082] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0083] Example 1, please refer to Figure 1 , the present invention provides a 3D printing process intelligent control system based on real-time feedback, a data acquisition module, a preprocessing module, an image analysis module, a comprehensive evaluation module and an intelligent control module;

[0084] The data acquisition module is used to collect high-definition images and printing data of the printing layer surface during the 3D printing process, and to construct an image data set and a printing data set based on the collected high-definition images and printing data of the printing layer surface;

[0085] The preprocessing module is used to preprocess the high-definition image of the printing layer surface and the printing data in the image data set and the printing data set;

[0086] The image analysis module is used to construct a defect analysis model based on a convolutional neural network, input the preprocessed high-definition image of the printed layer surface into the defect analysis model, obtain the printed layer defect score D, and preset the defect score threshold D1, compare and analyze the preset defect score threshold D1 with the printed layer defect score D, evaluate the printed layer status, and trigger the early warning mechanism to control the printed layer;

[0087] The comprehensive evaluation module is used to obtain the crack generation probability Z according to the preprocessed printing data set and the printing layer defect score D;

[0088] The intelligent control module is used to preset a crack generation probability threshold Zl, and compare and analyze the preset crack generation probability threshold Zl and the crack generation probability Z, evaluate the 3D printing status, and based on the evaluated 3D printing status, feed back data to the 3D printing data center to control the 3D printing process.

[0089] In this embodiment, through precise data acquisition, preprocessing, image analysis, comprehensive evaluation and intelligent control modules, the defects and crack generation risks in the printing process are monitored in real time to ensure the stability and consistency of printing quality. The system uses dual acquisition of high-definition images and printing data, combined with convolutional neural networks for defect analysis, to effectively identify tiny defects on the surface of the printing layer, and optimizes the printing process by dynamically adjusting printing parameters to reduce the occurrence of quality problems. In addition, through a comprehensive evaluation of the probability of crack generation, the system can predict and avoid potential structural defects in advance, improve production efficiency, reduce material waste and reduce the rate of defective products. On the whole, the system realizes a fully automated intelligent control and feedback mechanism, which not only improves printing quality and accuracy, but also optimizes the production process, reduces human intervention and operational errors, and promotes the application of 3D printing technology in efficient and refined production.

[0090] Example 2, please refer to Figure 1 , specifically:

[0091] The data acquisition module includes an image data acquisition unit and a print data acquisition unit;

[0092] The image data acquisition unit is used to use a high-resolution camera installed above the print head to collect the surface image of the printed layer after each layer printing process is completed, and transmit the collected surface image of the printed layer to the 3D printing data center through wireless network technology to construct an image data set;

[0093] The printing data acquisition unit is used to deploy an intelligent sensor group at the bottom of the 3D printer platform to obtain printing data of the 3D printing process, wherein the printing data includes printing layer temperature, printing layer stress and deposition layer thickness, and obtains the thermal expansion coefficient and effective elastic modulus of the material from the physical property database of the printing material and the material manual;

[0094] The print layer temperature data refers to the use of infrared temperature sensors to monitor the temperature of the print layer in real time. Temperature acquisition helps monitor and control the heating and cooling rates of the material during the printing process to avoid changes in material properties or poor interlayer bonding due to temperature fluctuations.

[0095] The printing layer stress refers to using a laser stress sheet installed at the bottom of the printing platform to monitor the stress changes generated during the printing process in real time and obtain the printing layer stress data;

[0096] The thickness of the deposited layer refers to the use of a deployed optical sensor to detect the height change of the printed layer by reflecting light signals to obtain the height data of the deposited layer. The deposited layer refers to each layer of material formed by the nozzle depositing the molten material layer by layer on the printing platform or the printed layer during the 3D printing process;

[0097] The obtained printing layer temperature data, printing layer stress data and deposition layer height data are transmitted to the 3D printing data center through wireless network technology to construct a printing data set.

[0098] In this embodiment, through the real-time collection of 3D printing process data, strong data support is provided for subsequent printing process control and quality analysis. Through the image data acquisition unit using a high-resolution camera installed above the print head, a high-precision surface image of the printed layer can be acquired after each layer is printed. This image acquisition method can accurately capture the detailed structure of the printed layer to form a high-definition image data set, which provides basic data for subsequent defect detection and printing quality evaluation. The application of wireless network technology enables image data to be quickly transmitted to the 3D printing data center, ensuring efficient and delay-free transmission of image data, and realizing real-time monitoring of the printing process. The printing data set stored in the data center helps to analyze abnormal changes in the printing process and establish a long-term management system for historical data, which provides an important reference for subsequent intelligent control and optimization. By integrating real-time monitoring and data aggregation of multiple sensors, the system can promptly detect and respond to any abnormal situation that occurs during the printing process, ensuring the smooth progress of the 3D printing process and the quality of the final product.

[0099] Example 3, please refer to Figure 1 , specifically:

[0100] The pre-processing module includes a print data processing unit and an image data processing unit;

[0101] The print data processing unit is used to perform noise filtering, data cleaning and normalization processing on the print data in the print data set;

[0102] The noise filtering refers to using a Kalman filter to remove random noise from the print data;

[0103] The data cleaning refers to detecting outliers using a box plot, calculating the interquartile range IQR in the box plot, and obtaining a normal data range (Q1-1.5×IQR, Q3+1.5×IQR). Print data that is not within the normal data range is considered abnormal data and outliers are removed.

[0104] The above Q1 and Q3 are the 1st quantile and the 3rd quantile respectively. By arranging the data from small to large, the data at the 25% and 75% positions in the data set are obtained as the 1st quantile and the 3rd quantile;

[0105] The normalization process refers to performing dimensionless processing on the collected printing data, and unifying the units of the collected printing data.

[0106] The image data processing unit is to perform region cropping, RGB standardization and boundary setting on the image data in the image data set;

[0107] The area cropping refers to using computer vision technology to crop out the printing area and remove background information;

[0108] The RGB standardization refers to performing pixel operations on image data, processing each pixel as a standard version to reduce the impact of lighting changes;

[0109] The boundary setting refers to marking the image boundary points using an edge detection algorithm.

[0110] In this embodiment, high-quality and accurate data support is provided for the subsequent intelligent control of the 3D printing process through multi-level cleaning and standardization operations on the printing data and image data. First, the printing data processing unit uses a Kalman filter to remove random noise in the data to ensure the stability and accuracy of temperature, stress and deposition layer thickness. At the same time, the abnormal data that may affect the analysis results are effectively filtered out through box plot detection and elimination of outliers, and the dimensional differences of various types of data are eliminated through normalization processing, so that the comparison and calculation between data are more consistent and scientific. These steps ensure the stability and reliability of the printing data and lay the foundation for the accurate prediction of the probability of crack generation. Secondly, the image data processing unit uses computer vision technology to perform regional cropping on the printed image, remove background information, and make the image more focused on the printing area. Through RGB standardization, the influence of light changes on the consistency of image color is reduced. The image boundary is set through edge detection technology to clarify the effective area of ​​image analysis. These preprocessing measures improve the quality of image data, provide standardized data input for subsequent convolutional neural network defect analysis, ensure the accuracy and consistency of defect scoring results, improve the precision and consistency of data, enable intelligent control systems to make accurate judgments based on high-quality data, and effectively reduce the risk of error accumulation and misjudgment, thereby improving the stability and reliability of the printing process and ensuring the quality and structural integrity of the prints.

[0111] Example 4, please refer to Figure 1 , specifically:

[0112] The image analysis module includes a model building unit and a preliminary evaluation unit;

[0113] The model building unit is used to build a defect analysis model based on the convolutional neural network technology, and in combination with the 3D printing database, obtain historical printing image data, input the historical printing image data as training data into the input layer of the defect analysis model, use back propagation and optimization algorithms to adjust the defect analysis model parameters, input the real-time collected printing image data into the trained defect analysis model, and obtain the printing layer defect score D;

[0114] Wherein, the defect analysis model includes an input layer, a convolution layer, a pooling layer, a fully connected layer and an output layer;

[0115] The input layer is used to input the real-time collected printing image data into the defect analysis model;

[0116] The convolution layer is used to extract local features of image data and generate a local feature map;

[0117] The pooling layer is used to reduce the dimension of the local feature map obtained by the convolutional layer;

[0118] The fully connected layer is used to map the features to a print layer defect score D output;

[0119] The output layer is used to output the print layer defect score D, and the print layer defect score D is specifically obtained as follows:

[0120] ;

[0121] In the formula, is the average edge strength, is the texture uniformity, is the color uniformity coefficient, are the weight values ​​of average edge intensity, texture uniformity and color uniformity coefficient, respectively, and c is the bias term;

[0122] The average edge strength The method of obtaining is:

[0123] Perform edge detection on the image, use the Sobel operator to detect the edge strength of all pixels in the image, and obtain the horizontal gradient edge strength and vertical gradient edge strength , and according to the horizontal gradient edge strength and vertical gradient edge strength Get the edge strength G of each pixel:

[0124] ;

[0125] According to the edge strength G of each pixel, the average edge strength of the whole image is obtained. :

[0126] ;

[0127] Where N is the total number of pixels in the image, k is the kth pixel in the image, is the edge strength of the kth pixel;

[0128] The Sobel operator is an edge detection operator used in image processing to extract edge information in an image. It estimates the gradient of an image by calculating the grayscale change rate of a pixel in the horizontal and vertical directions, thereby highlighting areas in the image where the grayscale changes dramatically.

[0129] The texture uniformity The method of obtaining is:

[0130] Convert the image into a grayscale image, obtain the grayscale co-occurrence matrix, and obtain the texture uniformity based on the grayscale co-occurrence matrix:

[0131] ;

[0132] In the formula, a and b represent the two gray values ​​of the gray level co-occurrence matrix, is the relative frequency of the combination of a pixel with gray value a and its adjacent pixel with gray value b in the gray-level co-occurrence matrix;

[0133] The color uniformity coefficient The method of obtaining is:

[0134] Convert the image to HSV color space, including three channels: hue, saturation, and brightness. Calculate the standard deviation of each channel, and obtain the color uniform distribution coefficient based on the standard deviation and mean of each channel. :

[0135] ;

[0136] In the formula, are the standard deviations of the hue channel, saturation channel, and brightness channel, respectively.

[0137] The standard deviations of the hue channel, saturation channel and brightness channel are obtained by calculating the standard deviations of the pixel values ​​of the hue channel, saturation channel and brightness channel;

[0138] The preliminary evaluation unit is used to preset a defect score threshold D1, and compare and analyze the obtained print layer defect score D with the defect score threshold D1 to evaluate the 3D printing quality. The specific evaluation content is as follows:

[0139] If the print layer defect score D is less than the defect score threshold Dl, that is, D<Dl, the printing is judged to be in a normal risk state, indicating that the surface quality of the print layer is normal and no adjustment is required, and the printing process is continuously monitored;

[0140] If the print layer defect score D is greater than or equal to the defect score threshold Dl, that is, D≥Dl, the printing is judged to be in an abnormal risk state, indicating that there are defects on the print layer surface, triggering the alarm mechanism, immediately stopping printing and recording the printing data and sending it to the 3D printing controller until the 3D printing controller responds.

[0141] In this embodiment, by constructing a defect analysis model based on a convolutional neural network (CNN), efficient recognition and evaluation of surface defects of the printed layer in the 3D printing process is achieved, which has significant beneficial effects. First, through the model construction unit, the system uses historical printing image data to train the defect analysis model, and uses back propagation and optimization algorithms to continuously adjust the model parameters, so that the model can accurately extract features such as the edge, texture, and color of the printed layer surface. In the real-time printing process, the system inputs the collected images into the trained model and automatically calculates the defect score D. This score combines the average edge strength, texture uniformity, and color uniformity coefficient of the printed layer, so that the defect detection result is accurate and reliable. Secondly, the preliminary evaluation unit evaluates the printing quality by comparing and analyzing the preset defect score threshold D1 with the defect score D in real time. When the defect score D is lower than the defect score threshold D1, the system determines that the printing process is in a normal state and continuously monitors it without additional intervention. When the defect score D is greater than or equal to the defect score threshold D1, the system determines that it is in an abnormal risk state and immediately triggers the alarm mechanism to stop printing and send the data to the control personnel for further processing. The real-time feedback and intelligent control functions of this module improve the automation and intelligence level of the 3D printing process, which can effectively reduce the time and energy required for human intervention and avoid subsequent printing quality problems caused by surface defects of the printing layer.

[0142] Example 5, please refer to Figure 1 , specifically:

[0143] The comprehensive evaluation module includes a printing data analysis unit and a crack probability prediction unit;

[0144] The print data analysis unit is used to obtain an average cooling rate according to the preprocessed print data set. and heat stress ;

[0145] The average cooling rate The method of obtaining is:

[0146] ;

[0147] In the formula, It's at the time When the instantaneous temperature of the printed layer is It's at the time The instantaneous temperature of the printed layer at , M is the total number of sampling time points, is the i-th sampling time point, is the i-1th sampling time point, i=1, 2, ..., M, is the time interval between two adjacent data collection time points;

[0148] The thermal stress The method of obtaining is:

[0149] ;

[0150] In the formula, is the temperature difference at different locations within the printed material, is the coefficient of thermal expansion of the printing material, is the effective elastic modulus of the printing material.

[0151] The crack probability prediction unit is used to construct a crack probability prediction model, wherein the crack probability prediction model is constructed by using a random forest algorithm, and the crack probability prediction model is trained using historical printing data, and the crack probability prediction model parameters are optimized using a k-fold cross validation method;

[0152] The defect score D of the printed layer and the average cooling rate and heat stress The trained crack probability prediction model is input to obtain the crack generation probability Z. The crack generation probability Z is obtained as follows:

[0153] ;

[0154] In the formula, They are the defect score D of the printed layer, the average cooling rate and heat stress The weight coefficient of , q is the bias term.

[0155] In this embodiment, the printing data analysis unit and the crack probability prediction unit are used to achieve efficient evaluation and accurate prediction of the printing layer quality and crack risk, which has significant beneficial effects. The printing data analysis unit first calculates the average cooling rate and thermal stress based on the preprocessed printing data set, and analyzes the changing trend of the cooling rate by real-time monitoring and calculating the instantaneous temperature at different time points during the printing process. It can timely discover the thermal stress concentration problem caused by the excessive cooling rate and avoid the influence of drastic temperature changes on the printing layer quality. The thermal stress is obtained based on the temperature difference, thermal expansion coefficient and effective elastic modulus of the material, which effectively reflects the internal stress generated by the uneven temperature inside the material. These data not only provide important information for the subsequent crack probability prediction, but also provide reference for the development of the industry. The key parameters also improve the comprehensiveness and accuracy of data analysis. The crack probability prediction unit is based on the acquired print layer defect score, average cooling rate and thermal stress. By constructing a crack probability prediction model and training the model with historical printing data, the accurate prediction of the probability of crack generation in the print layer is achieved. The three parameters of defect score, cooling rate and thermal stress are weighted and integrated to evaluate the influence of micro defects, temperature control and stress distribution on the surface of the print layer on crack generation, and finally the crack generation probability Z is obtained. The crack generation probability Z can reduce the risk of crack generation in the printing process, improve the overall quality and structural integrity of the printed parts, reduce rework and material waste caused by crack defects, and realize high-precision intelligent control in the 3D printing process.

[0156] Example 6, please refer to Figure 1 , specifically:

[0157] The intelligent control module is used to preset a crack generation probability threshold Zl, compare and analyze the crack generation probability threshold Zl with the crack generation probability Z, and evaluate the 3D printing status. The specific evaluation content is as follows:

[0158] If the crack generation probability Z is greater than or equal to the crack generation probability threshold Zl, that is, Z≥Zl, the printing is judged to be in an abnormal state, the alarm mechanism is triggered, the temperature is immediately reduced to half of the original temperature, and the printing path is replanned, and the printing time interval between layers is adjusted to twice the original time interval;

[0159] If the crack generation probability Z is less than the crack generation probability threshold Zl, that is, Z<Zl, it is determined that the printing is in a normal state, the printing state is relatively stable, the printing state is continuously monitored and a printing report is generated and stored in the 3D printing data center.

[0160] In this embodiment, by comparing and analyzing the crack generation probability threshold Zl with the crack generation probability Z, when the crack generation probability Z is greater than or equal to the crack generation probability threshold Zl, the printing state is automatically determined to be abnormal, and the alarm mechanism is triggered, which can effectively reduce the thermal stress and cooling rate inside the material, alleviate the structural instability caused by temperature fluctuations, and thus greatly reduce the risk of crack generation. On the contrary, when the crack generation probability Z is less than the crack generation probability threshold Zl, the system determines that the printing state is normal, keeps the printing parameters unchanged, and continuously monitors the printing state. At this time, the system will generate a real-time printing status report and store it in the 3D printing data The center provides data support for subsequent quality analysis and historical tracing. This automated real-time feedback and control function enables the system to respond immediately when risks are detected without human intervention, ensuring the molding quality and precision of the printed parts. At the same time, the dynamic adjustment and data storage functions can not only optimize the printing process, but also provide valuable experience data for future printing tasks, thereby gradually improving the stability and reliability of the printing process and the intelligence level of the system. Through this intelligent control module, the system realizes high-precision quality control and autonomous optimization of the printing process, greatly reducing the incidence of cracks and improving the performance and durability of printed parts.

[0161] Example 7, please refer to Figure 2 , specifically:

[0162] A 3D printing process intelligent control method based on real-time feedback comprises the following steps:

[0163] Step 1: Collect high-definition images and printing data of the printing layer surface during the 3D printing process, and construct an image data set and a printing data set based on the collected high-definition images and printing data of the printing layer surface;

[0164] Step 2: preprocessing the high-definition image of the printing layer surface and the printing data in the image data set and the printing data set;

[0165] Step 3: construct a defect analysis model based on a convolutional neural network, input the preprocessed high-definition image of the printed layer surface into the defect analysis model, obtain the printed layer defect score D, and preset a defect score threshold D1, compare and analyze the preset defect score threshold D1 with the printed layer defect score D, evaluate the printed layer status, and trigger an early warning mechanism to control the printed layer;

[0166] Step 4: Obtain the crack generation probability Z based on the preprocessed printing data set and the printing layer defect score D;

[0167] Step 5: preset a crack generation probability threshold Zl, and compare and analyze the preset crack generation probability threshold Zl and the crack generation probability Z to evaluate the 3D printing status, and based on the evaluated 3D printing status, feedback data to the 3D printing data center to control the 3D printing process.

[0168] In this embodiment, by real-time acquisition and preprocessing of printing data, combined with a convolutional neural network model for defect identification and status assessment, and intelligent feedback control through crack generation probability prediction, the system can identify potential defects in real time during the printing process and assess the risk of crack generation. By automatically adjusting printing parameters, cracks and structural defects in the printing process can be effectively prevented and reduced, which not only improves printing accuracy and finished product quality, but also achieves efficient and intelligent management of the printing process, thereby improving the stability of 3D printing.

[0169] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A 3D printing process intelligent control system based on real-time feedback, characterized in that: It includes data acquisition module, preprocessing module, image analysis module, comprehensive evaluation module and intelligent control module; The data acquisition module is used to collect high-definition images and printing data of the printing layer surface during the 3D printing process, and to construct an image data set and a printing data set based on the collected high-definition images and printing data of the printing layer surface; The preprocessing module is used to preprocess the high-definition image of the printing layer surface and the printing data in the image data set and the printing data set; The image analysis module is used to construct a defect analysis model based on a convolutional neural network, input the preprocessed high-definition image of the printed layer surface into the defect analysis model, obtain the printed layer defect score D, and preset the defect score threshold D1, compare and analyze the preset defect score threshold D1 with the printed layer defect score D, evaluate the printed layer status, and trigger the early warning mechanism to control the printed layer; The comprehensive evaluation module is used to obtain the crack generation probability Z according to the preprocessed printing data set and the printing layer defect score D; The intelligent control module is used to preset a crack generation probability threshold Zl, and compare and analyze the preset crack generation probability threshold Zl and the crack generation probability Z, evaluate the 3D printing status, and based on the evaluated 3D printing status, feed back data to the 3D printing data center to control the 3D printing process.

2. According to claim 1, a 3D printing process intelligent control system based on real-time feedback is characterized in that: The data acquisition module includes an image data acquisition unit and a print data acquisition unit; The image data acquisition unit is used to use a high-resolution camera installed above the print head to collect the surface image of the printed layer after each layer printing process is completed, and transmit the collected surface image of the printed layer to the 3D printing data center through wireless network technology to construct an image data set; The printing data acquisition unit is used to deploy an intelligent sensor group at the bottom of the 3D printer platform to obtain printing data of the 3D printing process, wherein the printing data includes printing layer temperature, printing layer stress and deposition layer thickness, and obtains the thermal expansion coefficient and effective elastic modulus of the material from the physical property database of the printing material and the material manual; The print layer temperature data refers to the temperature of the print layer obtained by real-time monitoring using an infrared temperature sensor; The printing layer stress refers to using a laser stress sheet installed at the bottom of the printing platform to monitor the stress changes generated during the printing process in real time and obtain the printing layer stress data; The thickness of the deposited layer refers to the use of a deployed optical sensor to detect the height change of the printed layer through the reflected light signal to obtain the height data of the deposited layer; The obtained printing layer temperature data, printing layer stress data and deposition layer height data are transmitted to the 3D printing data center through wireless network technology to construct a printing data set.

3. The 3D printing process intelligent control system based on real-time feedback according to claim 2, characterized in that: The pre-processing module includes a print data processing unit and an image data processing unit; The print data processing unit is used to perform noise filtering, data cleaning and normalization processing on the print data in the print data set; The noise filtering refers to using a Kalman filter to remove random noise from the print data; The data cleaning refers to detecting outliers using a box plot, calculating the interquartile range IQR in the box plot, and obtaining a normal data range (Q1-1.5×IQR, Q3+1.5×IQR). Print data that is not within the normal data range is considered abnormal data and outliers are removed. The normalization process refers to performing dimensionless processing on the collected printing data, and unifying the units of the collected printing data.

4. The 3D printing process intelligent control system based on real-time feedback according to claim 3, characterized in that: The image data processing unit is to perform region cropping, RGB standardization and boundary setting on the image data in the image data set; The area cropping refers to using computer vision technology to crop out the printing area and remove background information; The RGB standardization refers to performing pixel operations on image data and performing standard version processing on each pixel; The boundary setting refers to marking the image boundary points using an edge detection algorithm.

5. The 3D printing process intelligent control system based on real-time feedback according to claim 4, characterized in that: The image analysis module includes a model building unit and a preliminary evaluation unit; The model building unit is used to build a defect analysis model based on the convolutional neural network technology, and in combination with the 3D printing database, obtain historical printing image data, input the historical printing image data as training data into the input layer of the defect analysis model, use back propagation and optimization algorithms to adjust the defect analysis model parameters, input the real-time collected printing image data into the trained defect analysis model, and obtain the printing layer defect score D; Wherein, the defect analysis model includes an input layer, a convolution layer, a pooling layer, a fully connected layer and an output layer; The input layer is used to input the real-time collected printing image data into the defect analysis model; The convolution layer is used to extract local features of image data and generate a local feature map; The pooling layer is used to reduce the dimension of the local feature map obtained by the convolutional layer; The fully connected layer is used to map the features to a print layer defect score D output; The output layer is used to output the print layer defect score D, and the print layer defect score D is specifically obtained as follows: ; In the formula, is the average edge strength, is the texture uniformity, is the color uniformity coefficient, are the weight values ​​of average edge intensity, texture uniformity and color uniformity coefficient, respectively, and c is the bias term; The average edge strength The method of obtaining is: Perform edge detection on the image, use the Sobel operator to detect the edge strength of all pixels in the image, and obtain the horizontal gradient edge strength and vertical gradient edge strength , and according to the horizontal gradient edge strength and vertical gradient edge strength Get the edge strength G of each pixel: ; According to the edge strength G of each pixel, the average edge strength of the whole image is obtained. : ; In the formula, is the total number of pixels in the image, k is the kth pixel of the image, is the edge strength of the kth pixel; The texture uniformity The method of obtaining is: Convert the image into a grayscale image, obtain the grayscale co-occurrence matrix, and obtain the texture uniformity based on the grayscale co-occurrence matrix: ; In the formula, a and b represent the two gray values ​​of the gray level co-occurrence matrix, is the relative frequency of the combination of a pixel with gray value a and its adjacent pixel with gray value b in the gray-level co-occurrence matrix; The color uniformity coefficient The method of obtaining is: Convert the image to HSV color space, including three channels: hue, saturation, and brightness. Calculate the standard deviation of each channel, and obtain the color uniform distribution coefficient based on the standard deviation and mean of each channel. : ; In the formula, , and are the standard deviations of the hue channel, saturation channel, and brightness channel, respectively.

6. The 3D printing process intelligent control system based on real-time feedback according to claim 5, characterized in that: The preliminary evaluation unit is used to preset a defect score threshold D1, and compare and analyze the obtained print layer defect score D with the defect score threshold D1 to evaluate the 3D printing quality. The specific evaluation content is as follows: If the print layer defect score D is less than the defect score threshold Dl, that is, D<Dl, the printing is judged to be in a normal risk state, indicating that the surface quality of the print layer is normal and no adjustment is required, and the printing process is continuously monitored; If the print layer defect score D is greater than or equal to the defect score threshold Dl, that is, D≥Dl, the printing is judged to be in an abnormal risk state, indicating that there are defects on the print layer surface, triggering the alarm mechanism, immediately stopping printing and recording the printing data and sending it to the 3D printing controller until the 3D printing controller responds.

7. The 3D printing process intelligent control system based on real-time feedback according to claim 3, characterized in that: The comprehensive evaluation module includes a printing data analysis unit and a crack probability prediction unit; The print data analysis unit is used to obtain an average cooling rate according to the preprocessed print data set. and heat stress ; The average cooling rate The method of obtaining is: ; In the formula, It's at the time When the instantaneous temperature of the printed layer is It's at the time The instantaneous temperature of the printed layer at , M is the total number of sampling time points, is the i-th sampling time point, is the i-1th sampling time point, i=1, 2, ..., M, is the time interval between two adjacent data collection time points; The thermal stress The method of obtaining is: ; In the formula, is the temperature difference at different locations within the printed material, is the thermal expansion coefficient of the printing material, is the effective elastic modulus of the printing material.

8. The 3D printing process intelligent control system based on real-time feedback according to claim 7, characterized in that: The crack probability prediction unit is used to construct a crack probability prediction model and use historical printing data to train the crack probability prediction model; The defect score D of the printed layer and the average cooling rate and heat stress The trained crack probability prediction model is input to obtain the crack generation probability Z. The crack generation probability Z is obtained as follows: ; In the formula, They are the defect score D of the printed layer, the average cooling rate and heat stress The weight coefficient of , q is the bias term.

9. The 3D printing process intelligent control system based on real-time feedback according to claim 8, characterized in that: The intelligent control module is used to preset a crack generation probability threshold Zl, compare and analyze the crack generation probability threshold Zl with the crack generation probability Z, and evaluate the 3D printing status. The specific evaluation content is as follows: If the crack generation probability Z is greater than or equal to the crack generation probability threshold Zl, that is, Z≥Zl, the printing is judged to be in an abnormal state, the alarm mechanism is triggered, the temperature is immediately reduced to half of the original temperature, and the printing path is replanned, and the printing time interval between layers is adjusted to twice the original time interval; If the crack generation probability Z is less than the crack generation probability threshold Zl, that is, Z<Zl, it is determined that the printing is in a normal state, the printing state is relatively stable, the printing state is continuously monitored and a printing report is generated and stored in the 3D printing data center.

10. A 3D printing process intelligent control method based on real-time feedback, used to implement the education management and training system described in any one of claims 1 to 9, characterized in that: The following steps are included: Step 1: Collect high-definition images and printing data of the printing layer surface during the 3D printing process, and construct an image data set and a printing data set based on the collected high-definition images and printing data of the printing layer surface; Step 2: preprocessing the high-definition image of the printing layer surface and the printing data in the image data set and the printing data set; Step 3: construct a defect analysis model based on a convolutional neural network, input the preprocessed high-definition image of the printed layer surface into the defect analysis model, obtain the printed layer defect score D, and preset a defect score threshold D1, compare and analyze the preset defect score threshold D1 with the printed layer defect score D, evaluate the printed layer status, and trigger an early warning mechanism to control the printed layer; Step 4: Obtain the crack generation probability Z based on the preprocessed printing data set and the printing layer defect score D; Step 5: preset a crack generation probability threshold Zl, and compare and analyze the preset crack generation probability threshold Zl and the crack generation probability Z to evaluate the 3D printing status, and based on the evaluated 3D printing status, feedback data to the 3D printing data center to control the 3D printing process.

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