Energy-containing material 3d printing parameter adjusting system and method based on data fusion

Abstract

The invention discloses an energetic material 3d printing parameter adjusting system and method based on data fusion, and relates to the technical field of energetic material additive manufacturing. Transmitting sensor data to a central database, wherein the data transmission adopts encryption and error correction technologies; the received sensor data is preprocessed; analyzing the correlation between the operation parameter group and the corresponding performance group; constructing a screening database based on the operation parameter groups of which the correlation is greater than a preset correlation threshold value and the corresponding performance scores; analyzing a corresponding safety threshold value according to the operation parameter group corresponding to the energetic material grain marked as the defective product; and monitoring the operation parameters in real time, performing system early warning on abnormal conditions, and adjusting the operation parameters based on a preset parameter adjustment rule base. The dynamic conversion of the safety threshold is realized; and the abnormity identification capability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energetic material additive manufacturing, and in particular to an energetic material 3D printing parameter adjustment system and method based on data fusion. Background Art

[0002] Additive manufacturing of energetic materials has extremely high requirements for the stability and precision of the additive manufacturing process due to the special nature of the materials. In the traditional additive manufacturing process, the process operating parameters are usually preset fixed values, but energetic materials may change their physical and chemical properties during the additive manufacturing process due to various factors (chemical reactions of the materials themselves, changes in ambient temperature, etc.), thus affecting the printing quality and even causing safety issues.

[0003] However, in the existing additive manufacturing of energetic materials, the monitoring of operating data often adopts preset thresholds. However, the thresholds are easily biased due to manual preset, thereby increasing false alarms or missing warnings of key events. Moreover, the preset thresholds lack flexibility, which reduces the quality of decision-making.

[0004] Therefore, the present invention discloses a parameter adjustment system and method for 3D printing of energetic materials based on data fusion to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to provide a system and method for adjusting parameters of 3D printing of energetic materials based on data fusion to solve the problems raised in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solution: a method for adjusting parameters of energetic material 3D printing based on data fusion, the method comprising the following steps: S1: Install sensors on the additive manufacturing equipment; transmit sensor data to the central database, using encryption and error correction technology; S2: preprocessing the received sensor data; constructing an operating parameter group from the preprocessed data, and constructing a performance group of the energetic material column, analyzing the correlation between the operating parameter group and the corresponding performance group; constructing a screening database based on the operating parameter group and the corresponding performance score whose correlation is greater than a preset correlation threshold; S3: Analyze the corresponding safety threshold according to the operation parameter group corresponding to the energetic material column marked as a bad product in the screening database; S4: Monitor the operating parameters in real time, issue system warnings for abnormal situations, and adjust the operating parameters based on the preset parameter adjustment rule library.

[0007] According to the above scheme, sensors are installed on the additive manufacturing equipment; the sensors include a flow sensor installed on the material delivery pipeline, a nozzle temperature sensor and a viscosity sensor installed at the printing nozzle, a stress sensor installed on the printing platform, and an ambient temperature sensor and a humidity sensor installed in the printing chamber; The monitoring data collected by the sensor is transmitted to the central database through a high-speed data acquisition card based on Ethernet; during the monitoring data transmission process, the monitoring data is encrypted using the AES encryption algorithm, and the CRC error correction code technology is used to detect and correct errors in the monitoring data transmission process.

[0008] According to the above scheme, S2 includes the following contents: S201: Filter the monitoring data using a filtering algorithm; normalize the monitoring data of each sensor based on the detection range of each sensor; obtain the change of the nozzle temperature and viscosity per unit time by performing differential calculation on the monitoring data of the nozzle temperature sensor and the monitoring data of the viscosity sensor, and calculate the ratio of the two to generate the nozzle temperature viscosity change rate characteristic R corresponding to time t TV =△Tem÷(△Vis+ε);wherein, △Tem represents the change of nozzle temperature per unit time at time t, which is equal to the nozzle temperature at time t minus the nozzle temperature at time t-1; △Vis represents the change of viscosity per unit time at time t, which is equal to the viscosity at time t minus the viscosity at time t-1; ε represents the initial value coefficient, which is a preset constant; Obtain stress sensor monitoring data and analyze the standard deviation σ of the stress distribution corresponding to time t S , maximum value S max and the minimum value S min ; Obtain flow sensor monitoring data and printing speed monitoring data, calculate the flow printing speed ratio at the same time, and analyze the standard deviation σ of the flow printing speed ratio corresponding to time t FV ; Combine the real-time ambient temperature PT and the average humidity HA to form the operating parameter group P at time t = [R TV , σ S , S max , S min , σ FV , PT, HA]; Through the data fusion of this application, it is possible to integrate the information of a single sensor and the information provided by sensors of different categories, eliminate redundancy and contradiction, improve the timeliness and reliability of information extraction, and improve the efficiency of data use; S202: Use a high-precision three-dimensional scanner to accurately measure the shape and size of the energetic material column after printing, use an electron microscope to observe the internal microstructure of the energetic material column, and analyze the uniformity index UI and porosity index PI of the material; use energy testing equipment to measure the energy performance index of the energetic column, the energy performance index includes detonation velocity DV, detonation pressure EP and energy release ER; normalize the uniformity index, porosity index and energy performance index; form a performance group [UI, PI, DV, EP, ER] at time t; calculate the performance score PS based on the performance group using the weighted average method; the weight coefficient in the performance score analysis is preset by the system; S203: Analyze the correlation COR between the operating parameter group at time t and the corresponding performance score: ; Among them, P （i，j） represents the jth data between the initial time t0 and the time t of the i-th operating parameter in the operating parameter group P, i∈[1,7]; j∈[1,J]; i and j are both positive integers; J represents the total number of monitoring data collection between the initial time t0 and the time t; △P （i，j） represents the average value of all data of the i-th operating parameter in the operating parameter group P from the initial time t0 to the time t; PS j represents the jth performance score between the initial time t0 and time t, △PS j Represents the average value of the performance score between the initial time t0 and time t; The operating parameter groups with correlation COR greater than the preset correlation threshold and the corresponding performance scores are extracted to build a screening database.

[0009] Through correlation analysis, the operating parameter groups that meet the requirements and the corresponding performance scores are used to build a screening database, which can effectively screen the data, delete the data that does not meet the requirements, and improve the accuracy of subsequent data analysis; According to the above solution, S3 contains the following: S301: Extract the operating parameter group corresponding to the energetic material column marked as a bad product in the screening database; and construct a bad parameter set from the i-th operating parameter data, denoted as BP i M ={BP （i，m） |i∈[1,7],m∈[1,M]}; m is a positive integer, where BP （i，m） The average value of the i-th operating parameter data of the energetic material column of the m-th defective product; M represents the total number of defective products included in the defective parameter set, and M is greater than 2; the defective products are arranged in the production order in the defective parameter set; S302: Analyze bad parameter set BP iM Moving Average EMA M =α×EMA M-1 + (1-α) × BP （i，M） ; Among them, EMA M-1 Denotes the bad parameter set BP i M-1 The moving average index; α represents the moving average coefficient, which is a system preset constant; the safety threshold THR corresponding to the i-th operating parameter data is analyzed based on the moving average index M =β×EMA M + (1-β) × BP i min ; BP i min Denotes the bad parameter set BP i M The minimum value in , β represents the safety threshold coefficient, and the safety threshold coefficient is a system preset constant; S303: When the M+1th energetic material column marked as a defective product appears; if BP （i，M+1） ≤(1-θ)×THR M or BP （i，M+1） ≥(1+θ)×THR M , θ is a preset constant; the bad parameter set is expanded to make BP i M+1 ={BP （i，m） |i∈[1,7],m∈[1,M+1]; Analyze the bad parameter set BP according to S302 i M+1 Corresponding safety threshold THR M+1 ; If (1-θ)×THR M <BP （i，M+1） < (1 + θ) × THR M , then the bad parameter set BP i M+1 Corresponding safety threshold THR M+1 =THR M .

[0010] The present application further analyzes the operating parameter group corresponding to the energetic material column marked as a defective product, realizes the dynamic transformation of the safety threshold, improves the ability of abnormality identification, and improves the efficiency of equipment operation.

[0011] According to the above scheme, in S4, the operating parameters are monitored in real time. If one of the real-time operating parameters exceeds the corresponding safety threshold, a system warning is issued, and the operating parameters are adjusted based on a preset parameter adjustment rule library.

[0012] Another aspect of the present application provides a parameter adjustment system for 3D printing of energetic materials based on data fusion, the system is applied to the above-mentioned parameter adjustment method for 3D printing of energetic materials based on data fusion, the system includes a data acquisition and transmission module, a data screening module, a threshold analysis module and an early warning adjustment module; The data acquisition and transmission module is used to install sensors on the additive manufacturing equipment; the sensor data is transmitted to the central database, and the data transmission adopts encryption and error correction technology; The data screening module pre-processes the received sensor data to construct an operating parameter group, constructs a performance group of the energetic material column, and analyzes the correlation between the operating parameter group and the corresponding performance group; The threshold analysis module analyzes the corresponding safety threshold according to the operation parameter group corresponding to the energetic material column marked as a bad product in the screening database; The early warning adjustment module is used to monitor the operating parameters in real time, issue a system early warning for abnormal situations, and adjust the operating parameters based on a preset parameter adjustment rule base.

[0013] According to the above solution, the data acquisition and transmission module includes a data acquisition unit and a data transmission unit; The data acquisition unit includes a flow sensor installed on the material delivery pipeline, a nozzle temperature sensor and a viscosity sensor installed at the print nozzle, a stress sensor installed on the printing platform, and an ambient temperature sensor and a humidity sensor installed in the printing chamber; The data transmission unit is used to transmit the monitoring data collected by the sensor to the central database based on Ethernet through a high-speed data acquisition card; during the monitoring data transmission process, the monitoring data is encrypted using the AES encryption algorithm, and the CRC error correction code technology is used to detect and correct errors in the monitoring data transmission process.

[0014] According to the above scheme, the data screening module includes a data preprocessing unit and a correlation analysis unit; The data preprocessing unit is used to analyze the monitoring data collected by the sensor and the corresponding performance data to construct an operating parameter group and a performance group; The correlation analysis unit is used to analyze the correlation between the operating parameter group and the performance group, and to construct a screening database based on the correlation analysis results.

[0015] According to the above scheme, the threshold analysis module includes a parameter screening unit and a safety threshold analysis unit; The parameter screening unit is used to extract the operating parameter group corresponding to the energetic material column marked as a defective product in the screening database; and arrange the defective product in the defective parameter set according to the production order of the defective product; The safety threshold analysis unit is used to analyze the moving average index of the bad parameter set, and further analyze the corresponding safety threshold based on the moving average index.

[0016] According to the above scheme, the early warning adjustment module is used to monitor the operating parameters in real time. If any of the real-time operating parameters exceeds the corresponding safety threshold, a system early warning is issued, and the operating parameters are adjusted based on a preset parameter adjustment rule library.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: through the data fusion of the present application, the information of a single sensor and the information provided by sensors of different categories can be integrated, redundancy and contradiction can be eliminated, the timeliness and reliability of information extraction can be improved, and the efficiency of data use can be improved; through the analysis of correlation, the operating parameter groups that meet the requirements and the corresponding performance scores are used to construct a screening database, which can effectively screen the data, delete the data that does not meet the requirements, and improve the accuracy of subsequent data analysis; the present application further analyzes the operating parameter groups corresponding to the energetic material columns marked as defective products, and realizes the dynamic transformation of the safety threshold; improves the ability of abnormality identification, and improves the efficiency of equipment operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a flow chart of a parameter adjustment method for 3D printing of energetic materials based on data fusion according to the present invention; Figure 2 This is a structural schematic diagram of a parameter adjustment system for 3D printing of energetic materials based on data fusion according to the present invention. DETAILED DESCRIPTION

[0019] 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.

[0020] See also Figure 1 The present invention provides a technical solution: a parameter adjustment method for 3D printing of energetic materials based on data fusion, the method comprising the following steps: S1: Install sensors on the additive manufacturing equipment; transmit sensor data to the central database, using encryption and error correction technology; Installing sensors on the additive manufacturing equipment; the sensors include a flow sensor installed on the material delivery pipeline, a nozzle temperature sensor and a viscosity sensor installed at the print nozzle, a stress sensor installed on the printing platform, and an ambient temperature sensor and a humidity sensor installed in the printing chamber; Example 1: A high-precision turbine flow sensor is selected and installed at the inlet and outlet of the material conveying pipeline. The flow sensor at the inlet is used to monitor the initial flow of energetic materials from the storage container into the conveying pipeline, and the flow sensor at the outlet ensures the flow stability of the material before entering the nozzle. The installation position of the sensor ensures that it fits tightly with the inner wall of the pipeline to reduce measurement errors. After installation, the flow sensor is calibrated to achieve a measurement accuracy of ±0.1ml / min, which can meet the requirements of high-precision flow monitoring of energetic materials.

[0021] Miniature thermocouple temperature sensors and vibration-based viscosity sensors are installed at key locations inside the print head. The probe of the temperature sensor is inserted deep into the nozzle, close to the flow path of the energetic material, so as to accurately measure the actual temperature of the material in the nozzle. The viscosity sensor is installed near the discharge port of the nozzle, which can sense the viscosity change of the energetic material when it is about to be extruded in real time. The measurement range of the temperature sensor is set to -20℃ to 200℃, with a resolution of 0.1℃; the measurement range of the viscosity sensor is 1-10000mPa・s, with an accuracy of ±2% of the measurement value, which can effectively cover the possible viscosity range of the energetic material during the printing process.

[0022] Multiple piezoelectric stress sensors are installed under the printing platform in a uniformly distributed manner. The sensors are firmly connected to the bottom of the platform through a special adhesive to ensure that the vertical and horizontal stresses borne by the platform during the printing process can be accurately measured. The stress measurement range is set to 0-500N and the sensitivity is 0.1N, which can accurately capture small changes in stress.

[0023] Temperature and humidity integrated sensors are installed at the four corners and the center of the printing chamber. The sensors are installed at a moderate height to avoid being affected by the heat of the printing equipment itself or local air flow. The measurement accuracy of the ambient temperature sensor is ±0.2°C, and the measurement accuracy of the humidity sensor is ±3%RH. The measurement ranges are -10°C to 50°C and 10%-90%RH, respectively, which are sufficient to monitor the temperature and humidity changes in the printing chamber.

[0024] The monitoring data collected by the sensor is transmitted to the central database through a high-speed data acquisition card based on Ethernet; during the monitoring data transmission process, the monitoring data is encrypted using the AES encryption algorithm, and the CRC error correction code technology is used to detect and correct errors in the monitoring data transmission process.

[0025] S2: preprocessing the received sensor data; constructing an operating parameter group from the preprocessed data, and constructing a performance group of the energetic material column, analyzing the correlation between the operating parameter group and the corresponding performance group; constructing a screening database based on the operating parameter group and the corresponding performance score whose correlation is greater than a preset correlation threshold; In S2, the following contents are included: S201: filtering the monitoring data using a filtering algorithm; normalizing the monitoring data of each sensor based on the detection range of each sensor; Example 2: The present invention uses a linear normalization method to normalize the data of all sensors. Take the flow sensor data as an example, assuming that its measurement range is 0-10 ml / min, and the actual flow value collected is x, then the normalized flow value y=(x-0) / (10-0).

[0026] By performing differential calculation on the nozzle temperature sensor monitoring data and the viscosity sensor monitoring data, the change of nozzle temperature and viscosity per unit time is obtained, and the ratio of the two is calculated to generate the nozzle temperature viscosity change rate characteristic R corresponding to time t TV =△Tem÷(△Vis+ε);△Tem represents the change of nozzle temperature per unit time at time t, which is equal to the nozzle temperature at time t minus the nozzle temperature at time t-1;△Vis represents the change of viscosity per unit time at time t, which is equal to the viscosity at time t minus the viscosity at time t-1;ε represents the initial value coefficient, which is a preset constant; Obtain stress sensor monitoring data and analyze the standard deviation σ of the stress distribution corresponding to time t S , maximum value S max and the minimum value S min ; Obtain flow sensor monitoring data and printing speed monitoring data, calculate the flow printing speed ratio at the same time, and analyze the standard deviation σ of the flow printing speed ratio corresponding to time t FV ; Combine the real-time ambient temperature PT and the average humidity HA to form the operating parameter group P at time t = [R TV , σ S , S max , S min , σ FV , PT, HA]; S202: Use a high-precision 3D scanner to accurately measure the shape and size of the energetic material grain after printing, use an electron microscope to observe the internal microstructure of the energetic material grain, and analyze the uniformity index UI and porosity index PI of the material; use energy testing equipment to measure the energy performance index of the energetic grain, the energy performance index includes detonation velocity DV, detonation pressure EP and energy release ER; normalize the uniformity index, porosity index and energy performance index; form a performance group [UI, PI, DV, EP, ER] at time t; calculate the performance score PS based on the performance group using the weighted average method; the weight coefficient in the performance score analysis is preset by the system; S203: Analyze the correlation COR between the operating parameter group at time t and the corresponding performance score: ; Among them, P （i，j） represents the jth data between the initial time t0 and the time t of the i-th operating parameter in the operating parameter group P, i∈[1,7]; j∈[1,J]; i and j are both positive integers; J represents the total number of monitoring data collection between the initial time t0 and the time t; △P （i，j） represents the average value of all data of the i-th operating parameter in the operating parameter group P from the initial time t0 to the time t; PS j represents the jth performance score between the initial time t0 and time t, △PS j Represents the average value of the performance score between the initial time t0 and time t; The operating parameter groups with correlation COR greater than the preset correlation threshold and the corresponding performance scores are extracted to build a screening database.

[0027] S3: Analyze the corresponding safety threshold according to the operation parameter group corresponding to the energetic material column marked as a bad product in the screening database; In S3, the following are included: S301: Extract the operating parameter group corresponding to the energetic material column marked as a bad product in the screening database; and construct a bad parameter set from the i-th operating parameter data, denoted as BP i M ={BP （i，m） |i∈[1,7],m∈[1,M]}; m is a positive integer, where BP （i，m） The average value of the i-th operating parameter data of the energetic material column of the m-th defective product; M represents the total number of defective products included in the defective parameter set, and M is greater than 2; the defective products are arranged in the production order in the defective parameter set; S302: Analyze bad parameter set BP i M Moving Average EMA M=α×EMA M-1 + (1-α) × BP （i，M） ; Among them, EMA M-1 Denotes the bad parameter set BP i M-1 The moving average index; α represents the moving average coefficient, which is a preset constant of the system; the safety threshold THR corresponding to the i-th operating parameter data is analyzed based on the moving average index M =β×EMA M + (1-β) × BP i min ; BP i min Denotes the bad parameter set BP i M The minimum value in , β represents the safety threshold coefficient, and the safety threshold coefficient is a system preset constant; S303: When the M+1th energetic material column marked as a defective product appears; if BP （i，M+1） ≤(1-θ)×THR M or BP （i，M+1） ≥(1+θ)×THR M , θ is a preset constant; the bad parameter set is expanded to make BP i M+1 ={BP （i，m） |i∈[1,7],m∈[1,M+1]; Analyze the bad parameter set BP according to S302 i M+1 Corresponding safety threshold THR M+1 ; If (1-θ)×THR M <BP （i，M+1） < (1 + θ) × THR M , then the bad parameter set BP i M+1 Corresponding safety threshold THR M+1 =THR M .

[0028] Example 3: In this example, BP1 5 ={5, 5, 5, 5, 6}; α=0.2; solid EMA4=5; Moving average index EMA5 = α × EMA4 + (1-α) × BP （1，5） =0.2×5+0.8×6=5.8; In this embodiment, β is 0.5; Solid THR5 = β × EMA5 + (1-β) × BP1 min =0.5×5.8+0.5×5=5.4; If the sixth energetic material column BP marked as a defective product appears （1，6） =7, in this embodiment, θ is 0.2; Then (1+θ)×THR5=(1+0.2)×5.4=6.48<7=BP （1，6） ; Thus, BP1 is formed 6 ={5, 5, 5, 5, 6, 7}; according to BP1 6 Analyze the corresponding THR6; S4: Monitor the operating parameters in real time, issue system warnings for abnormal situations, and adjust the operating parameters based on the preset parameter adjustment rule library.

[0029] In S4, the operating parameters are monitored in real time. If one of the real-time operating parameters exceeds the corresponding safety threshold, a system warning is issued, and the operating parameters are adjusted based on a preset parameter adjustment rule library.

[0030] Embodiment 4: Parameter adjustment rules are formulated based on the physicochemical properties of energetic materials, the basic principles of additive manufacturing, and a large number of experimental research results. For the relationship between the temperature-viscosity change rate of energetic materials and the nozzle temperature adjustment, it is determined through thermodynamic analysis and rheological experiments that when the temperature-viscosity change rate exceeds a certain threshold (0.1°C / (mPa・s)), the nozzle temperature is adjusted within a certain range according to the magnitude of the change rate. If the change rate is between 0.1-0.2°C / (mPa・s), reduce the nozzle temperature by 1-5°C; if the change rate is greater than 0.2°C / (mPa・s), reduce the nozzle temperature by 5-10°C. For the relationship between stress concentration and printing speed, according to the principle of material mechanics and the stability analysis of the printed structure, when the standard deviation of the stress distribution exceeds a certain threshold (10N), the printing speed is appropriately reduced. The specific reduction is determined by factors such as the size of the standard deviation and the current height of the drug column. Generally, the printing speed is reduced by 5-15mm / s each time.

[0031] See also Figure 2 , the present invention provides a technical solution: a parameter adjustment system for energetic material 3D printing based on data fusion, the system comprising a data acquisition and transmission module, a data screening module, a threshold analysis module and an early warning adjustment module; The data acquisition and transmission module is used to install sensors on the additive manufacturing equipment; the sensor data is transmitted to the central database, and the data transmission adopts encryption and error correction technology; The data screening module pre-processes the received sensor data to construct an operating parameter group, constructs a performance group of energetic material columns, and analyzes the correlation between the operating parameter group and the corresponding performance group; The threshold analysis module analyzes the corresponding safety threshold according to the operation parameter group corresponding to the energetic material column marked as a bad product in the screening database; The early warning adjustment module is used to monitor the operating parameters in real time, issue system early warnings for abnormal situations, and adjust the operating parameters based on the preset parameter adjustment rule library.

[0032] The data acquisition and transmission module includes a data acquisition unit and a data transmission unit; The data acquisition unit includes a flow sensor installed on the material delivery pipeline, a nozzle temperature sensor and a viscosity sensor installed at the print nozzle, a stress sensor installed on the printing platform, and an ambient temperature sensor and a humidity sensor installed in the printing chamber; The data transmission unit is used to transmit the monitoring data collected by the sensor to the central database through a high-speed data acquisition card based on Ethernet; during the monitoring data transmission process, the monitoring data is encrypted using the AES encryption algorithm, and the CRC error correction code technology is used to detect and correct errors in the monitoring data transmission process.

[0033] The data screening module includes a data preprocessing unit and a correlation analysis unit; The data preprocessing unit is used to analyze the monitoring data collected by the sensor and the corresponding performance data to construct the operation parameter group and the performance group; The correlation analysis unit is used to analyze the correlation between the operating parameter group and the performance group, and to build a screening database based on the correlation analysis results.

[0034] The threshold analysis module includes a parameter screening unit and a safety threshold analysis unit; The parameter screening unit is used to extract the operation parameter group corresponding to the energetic material column marked as a defective product in the screening database; and arrange the defective product in the defective parameter set according to the production order of the defective product; The safety threshold analysis unit is used to analyze the moving average index of the bad parameter set, and further analyze the corresponding safety threshold based on the moving average index.

[0035] The early warning adjustment module is used to monitor the operating parameters in real time. If any of the real-time operating parameters exceeds the corresponding safety threshold, a system early warning will be issued and the operating parameters will be adjusted based on the preset parameter adjustment rule library.

[0036] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0037] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A parameter adjustment method for energetic material 3D printing based on data fusion, characterized in that: The method comprises the following steps: S1: Install sensors on the additive manufacturing equipment; transmit sensor data to the central database, using encryption and error correction technology; S2: preprocessing the received sensor data; constructing an operation parameter group from the preprocessed data, constructing a performance group of the energetic material column, and analyzing the correlation between the operation parameter group and the corresponding performance group; Building a screening database based on the operating parameter groups with correlations greater than a preset correlation threshold and the corresponding performance scores; S3: Analyze the corresponding safety threshold according to the operation parameter group corresponding to the energetic material column marked as a bad product in the screening database; S4: Monitor the operating parameters in real time, issue system warnings for abnormal situations, and adjust the operating parameters based on the preset parameter adjustment rule library.

2. According to claim 1, a method for adjusting parameters of energetic material 3D printing based on data fusion, characterized in that: Installing sensors on the additive manufacturing equipment; the sensors include a flow sensor installed on the material delivery pipeline, a nozzle temperature sensor and a viscosity sensor installed at the print nozzle, a stress sensor installed on the printing platform, and an ambient temperature sensor and a humidity sensor installed in the printing chamber; The monitoring data collected by the sensor is transmitted to the central database through a high-speed data acquisition card based on Ethernet; during the monitoring data transmission process, the monitoring data is encrypted using the AES encryption algorithm, and the CRC error correction code technology is used to detect and correct errors in the monitoring data transmission process.

3. According to claim 2, a method for adjusting parameters of energetic material 3D printing based on data fusion, characterized in that: In S2, the following contents are included: S201: Filter the monitoring data using a filtering algorithm; normalize the monitoring data of each sensor based on the detection range of each sensor; obtain the change of the nozzle temperature and viscosity per unit time by performing differential calculation on the monitoring data of the nozzle temperature sensor and the monitoring data of the viscosity sensor, and calculate the ratio of the two to generate the nozzle temperature viscosity change rate characteristic R corresponding to time t TV =△Tem÷(△Vis+ε);wherein, △Tem represents the change of nozzle temperature per unit time at time t, which is equal to the nozzle temperature at time t minus the nozzle temperature at time t-1; △Vis represents the change of viscosity per unit time at time t, which is equal to the viscosity at time t minus the viscosity at time t-1; ε represents the initial value coefficient, which is a preset constant; Obtain stress sensor monitoring data and analyze the standard deviation σ of the stress distribution corresponding to time t S , maximum value S max and the minimum value S min ; Obtain flow sensor monitoring data and printing speed monitoring data, calculate the flow printing speed ratio at the same time, and analyze the standard deviation σ of the flow printing speed ratio corresponding to time t FV ; Combine the real-time ambient temperature PT and the average humidity HA to form the operating parameter group P at time t = [R TV , σ S , S max , S min , σ FV , PT, HA]; S202: Use a high-precision three-dimensional scanner to accurately measure the shape and size of the energetic material column after printing, use an electron microscope to observe the internal microstructure of the energetic material column, and analyze the uniformity index UI and porosity index PI of the material; use energy testing equipment to measure the energy performance index of the energetic column, the energy performance index includes detonation velocity DV, detonation pressure EP and energy release ER; normalize the uniformity index, porosity index and energy performance index; form a performance group [UI, PI, DV, EP, ER] at time t; calculate the performance score PS based on the performance group using the weighted average method; the weight coefficient in the performance score analysis is preset by the system; S203: Analyze the correlation COR between the operating parameter group at time t and the corresponding performance score: ； Among them, P （i，j） represents the jth data between the initial time t0 and the time t of the i-th operating parameter in the operating parameter group P, i∈[1,7]; j∈[1,J]; i and j are both positive integers; J represents the total number of monitoring data collection between the initial time t0 and the time t; △P （i，j） represents the average value of all data of the i-th operating parameter in the operating parameter group P from the initial time t0 to the time t; PS j represents the jth performance score between the initial time t0 and time t, △PS j Represents the average value of the performance score between the initial time t0 and time t; The operating parameter groups with correlation COR greater than the preset correlation threshold and the corresponding performance scores are extracted to build a screening database.

4. The method for adjusting parameters of energetic material 3D printing based on data fusion according to claim 3 is characterized in that: In S3, the following are included: S301: Extract the operating parameter group corresponding to the energetic material column marked as a bad product in the screening database; and construct a bad parameter set from the i-th operating parameter data, denoted as BP i M ={BP （i，m） |i∈[1,7],m∈[1,M]}; m is a positive integer, where BP （i，m） The average value of the i-th operating parameter data of the energetic material column of the m-th defective product; M represents the total number of defective products included in the defective parameter set, and M is greater than 2; the defective products are arranged in the production order in the defective parameter set; S302: Analyze bad parameter set BP i M Moving Average EMA M =α×EMA M-1 + (1-α) × BP （i，M） ; Among them, EMA M-1 Denotes the bad parameter set BP i M-1 The moving average index; α represents the moving average coefficient, and the moving average coefficient is a system preset constant; Analyze the safety threshold THR corresponding to the i-th operating parameter data based on the moving average index M =β×EMA M + (1-β) × BP i min ; BP i min Denotes the bad parameter set BP i M The minimum value in , β represents the safety threshold coefficient, and the safety threshold coefficient is a system preset constant; S303: When the M+1th energetic material column marked as a defective product appears; if BP （i，M+1） ≤(1-θ)×THR M or BP （i，M+1） ≥(1+θ)×THR M , θ is a preset constant; the bad parameter set is expanded to make BP i M+1 ={BP （i，m） |i∈[1,7],m∈[1,M+1]; Analyze the bad parameter set BP according to S302 i M+1 Corresponding safety threshold THR M+1 ; If (1-θ)×THR M <BP （i，M+1） < (1 + θ) × THR M , then the bad parameter set BP i M+1 Corresponding safety threshold THR M+1 =THR M .

5. The method for adjusting parameters of energetic material 3D printing based on data fusion according to claim 4, characterized in that: In S4, the operating parameters are monitored in real time. If one of the real-time operating parameters exceeds the corresponding safety threshold, a system warning is issued, and the operating parameters are adjusted based on a preset parameter adjustment rule library.

6. A parameter adjustment system for 3D printing of energetic materials based on data fusion, the system is applied to the parameter adjustment method for 3D printing of energetic materials based on data fusion as described in any one of claims 1 to 5, characterized in that: The system includes a data acquisition and transmission module, a data screening module, a threshold analysis module and an early warning adjustment module; The data acquisition and transmission module is used to install sensors on the additive manufacturing equipment; the sensor data is transmitted to the central database, and the data transmission adopts encryption and error correction technology; The data screening module pre-processes the received sensor data to construct an operating parameter group, constructs a performance group of the energetic material column, and analyzes the correlation between the operating parameter group and the corresponding performance group; The threshold analysis module analyzes the corresponding safety threshold according to the operation parameter group corresponding to the energetic material column marked as a bad product in the screening database; The early warning adjustment module is used to monitor the operating parameters in real time, issue a system early warning for abnormal situations, and adjust the operating parameters based on a preset parameter adjustment rule base.

7. The parameter adjustment system for 3D printing of energetic materials based on data fusion according to claim 6, characterized in that: The data acquisition and transmission module includes a data acquisition unit and a data transmission unit; The data acquisition unit includes a flow sensor installed on the material delivery pipeline, a nozzle temperature sensor and a viscosity sensor installed at the print nozzle, a stress sensor installed on the printing platform, and an ambient temperature sensor and a humidity sensor installed in the printing chamber; The data transmission unit is used to transmit the monitoring data collected by the sensor to the central database based on Ethernet through a high-speed data acquisition card; during the monitoring data transmission process, the monitoring data is encrypted using the AES encryption algorithm, and the CRC error correction code technology is used to detect and correct errors in the monitoring data transmission process.

8. The parameter adjustment system for 3D printing of energetic materials based on data fusion according to claim 6, characterized in that: The data screening module includes a data preprocessing unit and a correlation analysis unit; The data preprocessing unit is used to analyze the monitoring data collected by the sensor and the corresponding performance data to construct an operating parameter group and a performance group; The correlation analysis unit is used to analyze the correlation between the operating parameter group and the performance group, and to construct a screening database based on the correlation analysis results.

9. The parameter adjustment system for 3D printing of energetic materials based on data fusion according to claim 6, characterized in that: The threshold analysis module includes a parameter screening unit and a safety threshold analysis unit; The parameter screening unit is used to extract the operating parameter group corresponding to the energetic material column marked as a defective product in the screening database; and arrange the defective product in the defective parameter set according to the production order of the defective product; The safety threshold analysis unit is used to analyze the moving average index of the bad parameter set, and further analyze the corresponding safety threshold based on the moving average index.

10. The parameter adjustment system for 3D printing of energetic materials based on data fusion according to claim 6, characterized in that: The early warning adjustment module is used to monitor the operating parameters in real time. If any of the real-time operating parameters exceeds the corresponding safety threshold, a system early warning is issued, and the operating parameters are adjusted based on a preset parameter adjustment rule library.

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