Porous biological ceramic artificial bone additive manufacturing method and system

Abstract

The invention discloses a porous biological ceramic artificial bone additive manufacturing method and system, and relates to the technical field of additive manufacturing, the sintering process is monitored, sintering state data is obtained, the deviation value of the sintering process is constructed after deviation analysis, and if the deviation value exceeds the expectation, the sintering process is determined. The trained sintering parameter control model is used for controlling sintering parameters, the sintered materials subjected to annealing treatment are tested to obtain quality data of the sintered materials, the trained quality identification model is used for evaluating the quality of the sintered materials, and the sintered materials are distinguished according to the obtained quality data; printing parameters are monitored in real time, if the printing parameters are abnormal, fault values are generated through continuously obtained abnormal data, and if the obtained fault values exceed expectation, the printing control parameters are optimized; and the cooling speed of the sintered material adapts to the actual environmental condition, the influence of the environmental condition on the sintering process is reduced, and the sintering effect is improved.

Description

Technical Field

[0001] The present invention relates to the field of additive manufacturing technology, and in particular to a method and system for additive manufacturing of porous bioceramic artificial bones. Background Art

[0002] Additive manufacturing of porous bioceramic artificial bones combines the biocompatibility of bioceramic materials with the precision of additive manufacturing. Through 3D printing technology, customizable artificial bones with complex porous structures can be created. These pores not only mimic the microenvironment of natural bone tissue but also promote cell growth and vascular ingrowth, accelerating bone healing. This shortens manufacturing cycles, reduces costs, and offers more precise and effective solutions for the treatment of bone diseases such as fractures and bone defects.

[0003] A Chinese invention patent with authorization publication number CN108437472B discloses an additive manufacturing device and method, wherein the additive manufacturing device includes a ray generating device, the ray generating device comprising: a cathode capable of emitting electrons when heated; a laser for generating laser light, the laser being used to heat the cathode; a gate for converging the electrons to form an electron beam; and an anode located below the cathode and grounded, the anode having a hole in the middle, and a potential difference formed between the anode and the cathode for allowing the electron beam to pass through the hole. The present invention generates laser light through a laser, and the laser heats the cathode, causing the cathode to generate electrons and form an electron beam. Compared to the prior art method of electrically heating the cathode, this method does not require high current heating, avoids the magnetic field generated by the current affecting the distribution of electrons, and increases the life of the cathode. It also improves the quality of the beam spot during additive manufacturing, as well as the quality and efficiency of additive manufacturing.

[0004] Combined with the above application and the contents of the prior art: Additive manufacturing requires a series of processes, such as adjusting the sintering environment, mixing the sintering materials, controlling the sintering process, and controlling the printing process. Failure or abnormality in any of the above processes will have a certain impact on the quality of the final target product.

[0005] Existing additive manufacturing methods for porous bioceramic artificial bone primarily focus on improving the quality of bioceramic products by controlling the quality of raw materials, such as adjusting the composition and ratio of the raw materials. However, simply adjusting the raw material combination cannot fully guarantee the quality of porous bioceramics. For example, if there are certain anomalies in the environmental conditions within the sintering area, especially large fluctuations in temperature and humidity data, and a large number of particles in the air, the entire additive manufacturing process will be affected to a certain extent, affecting the quality of the final bioceramic product.

[0006] To this end, the present invention provides a method and system for manufacturing porous bioceramic artificial bone additives. Summary of the Invention

[0007] (1) Technical problems solved In response to the shortcomings of the existing technology, the present invention provides a method and system for manufacturing porous bioceramic artificial bone additive materials. The sintering parameters are controlled by using a trained sintering parameter control model, the quality data of the sintered material after annealing is obtained after testing, the quality of the sintered material is evaluated using a trained quality recognition model, and the sintered materials are distinguished according to the obtained quality scores; the printing parameters are monitored in real time, and if there are any abnormalities in the printing parameters, a fault value is generated from the continuously obtained abnormal data; if the obtained fault value exceeds the expected value, the printing control parameters are optimized; the cooling rate of the sintered material is adapted to the actual environmental conditions, the influence of the environmental conditions on the sintering process is reduced, and the sintering effect is improved; thereby solving the technical problems described in the background technology.

[0008] (2) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for manufacturing porous bioceramic artificial bone additives, comprising: monitoring environmental conditions within a sintering area; if the current conditions are abnormal, obtaining an abnormal value based on abnormal environmental condition data analysis; and adjusting the environmental conditions within the sintering area if the abnormal value exceeds expectations; Monitor the sintering process and obtain sintering status data. After performing deviation analysis, construct the deviation value of the sintering process. If the deviation value exceeds the expectation, issue parameter optimization instructions to the outside. The trained sintering parameter control model is used to control the sintering parameters and constrain the temperature drop rate when cooling the sintered material; After testing the annealed sintered materials, the quality data of the sintered materials is obtained. The quality of the sintered materials is evaluated using the trained quality recognition model, and the sintered materials are distinguished based on the quality score. Monitor printing parameters in real time. If there are any abnormalities in the printing parameters, generate fault values ​​based on the continuously acquired abnormal data. If the acquired fault values ​​exceed expectations, optimize the printing control parameters.

[0009] Furthermore, the environmental conditions in the sintering area are monitored in real time to obtain corresponding environmental condition data. The environmental condition data is used as input to identify whether the current environmental conditions are abnormal using the trained abnormal environment recognition model. If there is an abnormality, an abnormality reminder instruction is issued to the outside. The time node when the abnormal instruction is received is recorded, and the corresponding time node is used as the abnormal node; if the number of abnormal nodes within the preset monitoring period exceeds expectations, the abnormal value is obtained by analyzing the environmental abnormal data within the monitoring period.

[0010] Furthermore, after receiving the sintering instruction, the target sintering structure is designed through the software. After selecting the sintering material, the sintering process is carried out and monitored. After summarizing the sintering data obtained during several consecutive monitoring cycles, a sintering status data set is obtained; a deviation analysis is performed on the sintering status data obtained through monitoring and the reference data value, and the deviation value of the sintering process is constructed from the deviation analysis data.

[0011] Further, after receiving the optimization instruction, the sintering parameters are controlled using the trained sintering parameter control model; The sintering stage is divided into the pre-sintering stage and the main sintering stage. Reducing the deviation value of the sintering process is taken as the optimization goal. The pre-trained genetic algorithm is used to optimize the time ratio between the pre-sintering stage and the main sintering stage to obtain the optimized sintering time ratio.

[0012] Furthermore, after sintering is completed, the sintered material is cooled to restrict the temperature drop rate; The sintered material is cooled at a cooling rate that meets the constraint conditions until it reaches a preset annealing temperature and is annealed.

[0013] Further, the performance of the sintered material after annealing is tested and corresponding test data is obtained; Scanning electron microscope and transmission electron microscope are used to observe the microstructure and pore distribution of the material to obtain the corresponding material structure data; the obtained mechanical property data and material structure data are summarized to generate a quality data set of the sintered material.

[0014] Furthermore, the quality data of the sintered material is used as input, and the quality of the sintered material is evaluated using the trained quality recognition model to obtain the quality score of the sintered sample; If the quality score obtained is lower than the quality threshold, it will be regarded as unqualified product, otherwise it will be regarded as qualified product; if the proportion of unqualified products exceeds expectations, the sintering process will be optimized.

[0015] Furthermore, qualified sintered materials are used for printing and the printing process is monitored in real time to obtain corresponding printing control parameters; real-time printing status parameters are used as input, and abnormalities are identified using the trained abnormal data recognition model. If an abnormality exists, the abnormal proportion of the corresponding printing status parameters and the corresponding abnormal nodes are obtained; the abnormal proportion of the control parameters and the abnormal nodes are summarized as abnormal data to generate a printing abnormal data set.

[0016] Furthermore, a fault value is generated from the abnormal data in the printing abnormal data set. If the acquired fault value exceeds the fault threshold, the printing control parameters are optimized using the trained multi-objective optimization algorithm with the reduction of the abnormal value of the printing control parameters as the optimization goal.

[0017] The porous bioceramic artificial bone additive manufacturing system includes an environmental adjustment unit that monitors the environmental conditions in the sintering area. If the current conditions are abnormal, an abnormal value is obtained based on abnormal environmental condition data analysis. If the abnormal value exceeds expectations, the environmental conditions in the sintering area are adjusted. Deviation analysis unit: monitors the sintering process and obtains sintering status data. After performing deviation analysis, it constructs the deviation value of the sintering process. If the deviation value exceeds the expectation, it issues parameter optimization instructions to the outside. The temperature control unit uses the trained sintering parameter control model to control the sintering parameters and constrain the temperature drop rate when cooling the sintered material; The quality recognition unit tests the sintered materials after annealing to obtain the quality data of the sintered materials, uses the trained quality recognition model to evaluate the quality of the sintered materials, and distinguishes the sintered materials based on the quality score; The fault analysis unit monitors the printing parameters in real time. If there is an abnormality in the printing parameters, a fault value is generated from the continuously acquired abnormal data. If the acquired fault value exceeds the expectation, the printing control parameters are optimized.

[0018] (3) Beneficial effects The present invention provides a method and system for manufacturing porous bioceramic artificial bone additives, which has the following beneficial effects: 1. Judge and evaluate the abnormality of the environmental conditions in the sintering area based on the abnormal values. If the current abnormality is large, adaptive control and adjustment of the environmental conditions can reduce the impact of the environmental conditions on the sintering process and ensure the sintering quality.

[0019] 2. The deviation value can be used to judge the abnormal value of the sintering process and determine whether there is any abnormality in the current sintering state. If the sintering state is inconsistent with the expectation and the gap is large, the sintering can be interrupted or adjusted in time to ensure the sintering quality.

[0020] 3. Achieve rapid response and response, reduce the impact of manual hysteresis on the sintering effect, avoid frequent misjudgments and erroneous operations, and improve sintering efficiency; constrain the cooling rate with abnormal values ​​of environmental conditions, so that the cooling rate of the sintering material is adapted to the actual environmental conditions, reduce the impact of environmental conditions on the sintering process, and improve the sintering effect.

[0021] 4. Based on the acquired test data, the quality of the sintered materials is evaluated using the trained quality recognition model, which is more objective and comprehensive, thus avoiding incorrect evaluation and material waste. When the current sintering quality fails to meet expectations, the current sintering process is optimized to ensure the sintering quality in the subsequent sintering process.

[0022] 5. Based on the fault value, it is possible to determine whether there is a fault in the current printing process, maintain the printing equipment, or optimize the control parameters in real time to ensure the quality of additive manufacturing and avoid the failure to handle the fault of the printing equipment in a timely manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of the process for manufacturing porous bioceramic artificial bone additives of the present invention; Figure 2 This is a schematic diagram of the structure of the porous bioceramic artificial bone additive manufacturing system of the present invention. DETAILED DESCRIPTION

[0024] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0025] See also Figure 1 The present invention provides a method for manufacturing porous bioceramic artificial bone additives, comprising: Step 1: Monitor the environmental conditions in the sintering area. If the current conditions are abnormal, obtain abnormal values ​​based on abnormal environmental condition data analysis. If the abnormal values ​​exceed expectations, adjust the environmental conditions in the sintering area. The step 1 includes the following: Step 101: After determining the sintering area for the printed material, monitor the environmental conditions in the sintering area in real time to obtain corresponding environmental condition data, such as temperature, humidity, and particle concentration, and generate a sintering environment data set after aggregation; The convolutional neural network is trained with the labeled sample data to obtain a trained abnormal environment recognition model. The trained abnormal environment recognition model is used to identify whether the current environmental conditions are abnormal using the environmental condition data as input. If abnormal, an abnormal warning instruction is issued to the outside world. During use, considering that environmental conditions will have a certain impact and interference on the printing process, by real-time monitoring of environmental conditions and identification of abnormalities, any abnormalities in the environmental conditions within the sintering area can be sensed and handled in a timely manner; Step 102: Record the time when the abnormal instruction is received, and use the corresponding time as the abnormal node; If the number of abnormal nodes within the preset monitoring period exceeds expectations, under dimensionless conditions, the abnormal value is obtained by analyzing the environmental abnormal data within the monitoring period in the following way: Where: is the abnormal degree of the i-th abnormal parameter, is an outlier, For parameters mean, is a parameter The standard deviation of is the time node at which the abnormality of the i-th abnormal parameter occurs, is the time weighting function, , is the attenuation coefficient, is the total time length, the time span from the first exception to the last exception. is the Dirac delta function, which is used to represent the time abnormalities; Pre-set abnormal thresholds based on historical data and management expectations of environmental conditions; If the abnormal value exceeds the abnormal threshold, it means that the abnormality of the environmental conditions in the sintering area exceeds expectations, which may affect the sintering process and sintering quality. In this case, the environmental conditions in the sintering area should be adaptively adjusted, for example, by filtering the air. When using, combine the contents in steps 101 and 102: When real-time monitoring of environmental conditions is carried out, abnormal values ​​are constructed from the monitoring data based on the acquired monitoring data. The abnormal degree of the environmental conditions in the sintering area is judged and evaluated based on the abnormal values. If the current abnormal degree is large, adaptive control and adjustment of the environmental conditions can be carried out to reduce the impact of the environmental conditions on the sintering process and ensure the sintering quality.

[0026] Combined with the above application and the contents of the prior art: Additive manufacturing requires a series of processes, such as adjusting the sintering environment, mixing the sintering materials, controlling the sintering process, and controlling the printing process. Failure or abnormality in any of the above processes will have a certain impact on the quality of the final target product.

[0027] Existing additive manufacturing methods for porous bioceramic artificial bone primarily focus on improving the quality of bioceramic products by controlling the quality of raw materials, such as adjusting the composition and ratio of the raw materials. However, simply adjusting the raw material combination cannot fully guarantee the quality of porous bioceramics. For example, if there are certain anomalies in the environmental conditions within the sintering area, especially large fluctuations in temperature and humidity data, and a large number of particles in the air, the entire additive manufacturing process will be affected to a certain extent, affecting the quality of the final bioceramic product.

[0028] Step 2: Monitor the sintering process and obtain sintering status data. After performing deviation analysis, construct the deviation value of the sintering process. If the deviation value exceeds the expectation, issue a parameter optimization instruction to the outside. The second step includes the following: Step 201: After receiving the sintering instruction, the porous structure is designed by software, the behavior of the porous structure under mechanical load is simulated using FEA, and the target sintered structure is obtained after optimizing the pore size and porosity distribution; Select sintering materials, such as high-purity ceramic powder with uniform particle size distribution, and after surface modification, slurry preparation, addition of binder and solvent, and high-shear mixing of the prepared materials, issue sintering instructions to the outside; Step 202: After receiving the sintering instruction, the sintering process is started and monitored, including using differential thermal analysis and thermogravimetric analysis to monitor thermal behavior data and mass change data during the sintering process, and using an optical microscope or infrared thermal imager to monitor temperature distribution and material change data during the sintering process; after aggregating the sintering data over several consecutive monitoring cycles, a sintering status data set is obtained; When in use, the sintering process is detected and monitored to understand the current sintering status of the material in real time, and timely intervention can be made when abnormalities occur in the sintering status.

[0029] Step 203: After setting corresponding reference data values ​​for each stage and each parameter of the sintering process, deviation analysis is performed on the monitored sintering state data and the reference data values, and the deviation value of the sintering process is constructed from the deviation analysis data in the following manner: Where: is the diagonal matrix of standard deviations; For each time node , the deviation value vector of the monitoring parameters, where It's in time The actual value vector of ; is the reference vector value; ,in, Represents the norm of the standardized deviation value vector, usually chosen Norm (i.e., Euclidean norm); is the total number of monitoring parameters, is the total length of time; , is the attenuation coefficient, is the first in the standardized deviation value vector Quantity Based on historical data and management expectations for sintering progress and sintering status, a deviation threshold is pre-set. If the deviation value exceeds the pre-set deviation threshold, it indicates that the sintering status at the current stage may be poor, and the control parameters of the sintering process need to be adjusted and optimized in a timely manner. At this time, a parameter optimization instruction is sent to the outside. When using, combine the contents in steps 201 to 203: After continuously acquiring several sets of sintering status data, the qualified values ​​of various parameters are pre-set. On this basis, the data are compared and the deviation value is constructed from the comparison data. The deviation value can be used to judge the abnormal value of the sintering process and determine whether the current sintering status is abnormal. If the sintering status is inconsistent with the expectation and the gap is large, the sintering can be interrupted or adjusted in time to ensure the sintering quality.

[0030] Step 3: Use the trained sintering parameter control model to control the sintering parameters and constrain the temperature drop rate when cooling the sintered material; The step three includes the following: Step 301: Train a machine learning algorithm using the labeled sample data to obtain a trained sintering parameter control model; set corresponding target ranges for each sintering parameter, and control the sintering parameters using the trained sintering parameter control model after receiving an optimization instruction; The sintering stage is divided into a pre-sintering stage and a main sintering stage. Reducing the deviation value of the sintering process is taken as the optimization goal. A pre-trained genetic algorithm is used to optimize the time ratio between the pre-sintering stage and the main sintering stage to obtain the optimized sintering time ratio. When in use, by automatically controlling the sintering control parameters and automatically identifying the sintering parameters in real time, it can achieve rapid response and response, reduce the impact of manual delay operations on the sintering effect, avoid the frequent occurrence of misjudgments and erroneous operations, and improve the sintering efficiency.

[0031] Step 302: After sintering, the sintered material is cooled down and the temperature drop rate is controlled. Constraints are as follows: Weight coefficient, , n is the number of cooling stages, It isi Cooling stage to j The time interval between the cooling phases, is the average time interval between cooling stages; The sintered material is cooled at a cooling rate that meets the constraint conditions until it reaches a preset annealing temperature and then annealed. When using, combine the contents in steps 301 to 302: Considering that the environmental conditions in the sintering area may be abnormal, on the basis of the environmental condition data in each stage, the cooling rate is constrained by the abnormal values ​​of the environmental conditions, so that the cooling rate of the sintered material is adapted to the actual environmental conditions, reducing the impact of the environmental conditions on the sintering process and improving the sintering effect.

[0032] Step 4: After testing the annealed sintered material, obtain the quality data of the sintered material, use the trained quality recognition model to evaluate the quality of the sintered material, and distinguish the sintered materials based on the quality score; The step 4 includes the following contents: Step 401: Performing a performance test on the sintered material after annealing and obtaining corresponding test data, including mechanical performance data such as compressive strength, bending strength, and fatigue strength; Use scanning electron microscope and transmission electron microscope to observe the microstructure and pore distribution of the material to obtain the corresponding material structure data; summarize the obtained mechanical property data and material structure data to generate a quality data set of the sintered material; Step 402: Train a convolutional neural network using the labeled sample data to obtain a trained quality recognition model; use the quality data of the sintered material as input, use the trained quality recognition model to evaluate the quality of the sintered material, obtain a quality score for the sintered sample, and label each sintered material using the obtained quality score; Based on the management expectations for sintering quality, a quality threshold is pre-set. If the quality score obtained is lower than the quality threshold, it means that the current sintering quality is poor and may not meet the use conditions. In this case, it will be regarded as unqualified product. Otherwise, it will be regarded as qualified product. If the proportion of defective products exceeds expectations, optimize the sintering process, for example, adjust the environmental conditions in the sintering area, the composition ratio of the sintering materials, and the sintering temperature, etc. When using, combine the contents in steps 401 and 402: After the sintering process is completed, the sintered materials are tested and inspected. Based on the obtained test data, the quality of the sintered materials is evaluated using the trained quality recognition model. Compared with manual evaluation, this model is more objective and comprehensive. Compared with manual screening, it can also avoid incorrect evaluation and material waste. As a further content, when the current sintering quality fails to meet expectations, the current sintering process is optimized to ensure the sintering quality in the subsequent sintering process.

[0033] Step 5: Monitor the printing parameters in real time. If there are any abnormalities in the printing parameters, generate a fault value based on the continuously acquired abnormal data. If the acquired fault value exceeds expectations, optimize the printing control parameters. The step five includes the following: Step 501: Use qualified sintered material for printing and monitor the printing process in real time to obtain corresponding printing control parameters, such as printing speed, layer thickness, and laser power; train a convolutional neural network based on the labeled sample data to obtain a trained abnormal data recognition model; Taking the real-time printing status parameters as input, the trained abnormal data recognition model is used to identify abnormalities. If an abnormality exists, the abnormal ratio of the corresponding printing status parameters and the corresponding abnormal nodes are obtained; the abnormal ratio of the control parameters and the abnormal nodes are summarized as abnormal data to generate a printing abnormal data set; Step 502: Under dimensionless conditions, a fault value is generated by printing abnormal data in the abnormal data set in the following manner: Where: is a vector weight, each element of which represents the weight of the i-th parameter, is the number of abnormal nodes, Represents each element Indicates the i-th parameter at time Is it abnormal? Indicates an exception, Indicates normal, is the weight vector The transpose of Pre-set fault thresholds based on historical data and management expectations for print quality; If the acquired fault value exceeds the fault threshold, it indicates that the current printing process may have a fault or anomaly and needs to be adjusted or optimized. In this case, an adjustment instruction is issued to the outside. Step 503: After receiving the adjustment instruction, the trained multi-objective optimization algorithm is used to optimize the printing control parameters, with the reduction of abnormal values ​​of the printing control parameters as the optimization goal. For example, parameters such as printing speed, layer thickness, and laser power are adjusted, and the optimized printing control parameters are executed until the printing process is completed. When using, combine the contents in steps 501 to 503: When entering the additive manufacturing stage and starting printing, the printing process parameters are monitored and fault values ​​are generated based on the changes in the printing parameters and the degree of abnormality. Based on the fault values, it can be judged whether there is a fault in the current printing process. At this time, after troubleshooting, if there is a fault in the printing equipment, the printing equipment can be maintained. If there is no fault in the printing equipment at present, it may be that there are deficiencies in the control parameters and printing process. The control parameters can be optimized in real time to ensure the quality of additive manufacturing and avoid the failure to deal with the failure of the printing equipment in a timely manner.

[0034] See also Figure 2 The present invention provides a porous bioceramic artificial bone additive manufacturing system, comprising: The environmental adjustment unit monitors the environmental conditions in the sintering area. If the current conditions are abnormal, the abnormal value is obtained based on the abnormal environmental condition data analysis. If the abnormal value exceeds the expected value, the environmental conditions in the sintering area are adjusted; Deviation analysis unit: monitors the sintering process and obtains sintering status data. After performing deviation analysis, it constructs the deviation value of the sintering process. If the deviation value exceeds the expectation, it issues parameter optimization instructions to the outside. The temperature control unit uses the trained sintering parameter control model to control the sintering parameters and constrain the temperature drop rate when cooling the sintered material; The quality recognition unit tests the sintered materials after annealing to obtain the quality data of the sintered materials, uses the trained quality recognition model to evaluate the quality of the sintered materials, and distinguishes the sintered materials based on the quality scores; The fault analysis unit monitors the printing parameters in real time. If there is an abnormality in the printing parameters, a fault value is generated from the continuously acquired abnormal data. If the acquired fault value exceeds the expectation, the printing control parameters are optimized.

[0035] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0036] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0037] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0038] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only for some logical functions. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0039] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0040] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0041] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0042] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for manufacturing porous bioceramic artificial bone additives, characterized by: include, Monitor the environmental conditions in the sintering area. If the current conditions are abnormal, obtain the abnormal value based on the abnormal environmental condition data analysis. If the abnormal value exceeds the expected value, adjust the environmental conditions in the sintering area. Monitor the sintering process and obtain sintering status data. After performing deviation analysis, construct the deviation value of the sintering process. If the deviation value exceeds the expectation, issue parameter optimization instructions to the outside. The trained sintering parameter control model is used to control the sintering parameters and constrain the temperature drop rate when cooling the sintered material; After testing the annealed sintered materials, the quality data of the sintered materials is obtained. The quality of the sintered materials is evaluated using the trained quality recognition model, and the sintered materials are distinguished based on the quality score. Monitor printing parameters in real time. If there are any abnormalities in the printing parameters, generate fault values ​​based on the continuously acquired abnormal data. If the acquired fault values ​​exceed expectations, optimize the printing control parameters.

2. The method for manufacturing porous bioceramic artificial bone additives according to claim 1, characterized in that: Monitor the environmental conditions in the sintering area in real time, obtain the corresponding environmental condition data, use the environmental condition data as input, use the trained abnormal environment recognition model to identify whether the current environmental conditions are abnormal, and if so, issue an abnormal warning instruction to the outside world; Record the time node when the abnormal instruction is received and use the corresponding time node as the abnormal node; If the number of abnormal nodes within a preset monitoring period exceeds expectations, the abnormal value is obtained by analyzing the environmental abnormal data within the monitoring period.

3. The method for manufacturing porous bioceramic artificial bone additives according to claim 2, characterized in that: After receiving the sintering instruction, the target sintering structure is designed through the software. After selecting the sintering material, the sintering process is carried out and monitored. After summarizing the sintering data obtained during several consecutive monitoring cycles, a sintering status data set is obtained; a deviation analysis is performed on the sintering status data obtained through monitoring and the reference data value, and the deviation value of the sintering process is constructed from the deviation analysis data.

4. The method for manufacturing porous bioceramic artificial bone additives according to claim 3, characterized in that: After receiving the optimization instruction, the sintering parameters are controlled using the trained sintering parameter control model; The sintering stage is divided into the pre-sintering stage and the main sintering stage. Reducing the deviation value of the sintering process is taken as the optimization goal. The pre-trained genetic algorithm is used to optimize the time ratio between the pre-sintering stage and the main sintering stage to obtain the optimized sintering time ratio.

5. The method for manufacturing porous bioceramic artificial bone additives according to claim 4, characterized in that: After sintering is completed, the sintered material is cooled to restrict the temperature drop rate; The sintered material is cooled at a cooling rate that meets the constraint conditions until it reaches a preset annealing temperature and is annealed.

6. The method for manufacturing porous bioceramic artificial bone additives according to claim 5, characterized in that: Perform performance tests on sintered materials after annealing and obtain corresponding test data; Scanning electron microscope and transmission electron microscope are used to observe the microstructure and pore distribution of the material to obtain the corresponding material structure data; the obtained mechanical property data and material structure data are summarized to generate a quality data set of the sintered material.

7. The method for manufacturing porous bioceramic artificial bone additives according to claim 6, characterized in that: Taking the quality data of sintered materials as input, the trained quality recognition model is used to evaluate the quality of the sintered materials and obtain the quality score of the sintered samples; If the quality score obtained is lower than the quality threshold, it will be regarded as unqualified product, otherwise it will be regarded as qualified product; if the proportion of unqualified products exceeds expectations, the sintering process will be optimized.

8. The method for manufacturing porous bioceramic artificial bone additives according to claim 7, characterized in that: Use qualified sintering materials for printing and monitor the printing process in real time to obtain corresponding printing control parameters; Taking the real-time printing status parameters as input, the trained abnormal data recognition model is used to perform abnormality recognition. If an abnormality exists, the abnormal proportion of the corresponding printing status parameters and the corresponding abnormal nodes are obtained; the abnormal proportion of the control parameters and the abnormal nodes are summarized as abnormal data to generate a printing abnormal data set.

9. The method for manufacturing porous bioceramic artificial bone additives according to claim 8, characterized in that: Generate a fault value by printing the abnormal data in the abnormal data set, If the acquired fault value exceeds the fault threshold, the print control parameters are optimized using the trained multi-objective optimization algorithm with the reduction of abnormal values ​​of the print control parameters as the optimization goal.

10. A porous bioceramic artificial bone additive manufacturing system, characterized by: include, The environmental adjustment unit monitors the environmental conditions in the sintering area. If the current conditions are abnormal, the abnormal value is obtained based on the abnormal environmental condition data analysis. If the abnormal value exceeds the expected value, the environmental conditions in the sintering area are adjusted; Deviation analysis unit: monitors the sintering process and obtains sintering status data. After performing deviation analysis, it constructs the deviation value of the sintering process. If the deviation value exceeds the expectation, it issues parameter optimization instructions to the outside. The temperature control unit uses the trained sintering parameter control model to control the sintering parameters and constrain the temperature drop rate when cooling the sintered material; The quality recognition unit tests the sintered materials after annealing to obtain the quality data of the sintered materials, uses the trained quality recognition model to evaluate the quality of the sintered materials, and distinguishes the sintered materials based on the quality score; The fault analysis unit monitors the printing parameters in real time. If there is an abnormality in the printing parameters, a fault value is generated from the continuously acquired abnormal data. If the acquired fault value exceeds the expectation, the printing control parameters are optimized.

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