Additive product production process monitoring system and method based on edge calculation

Abstract

The invention discloses an additive product production process monitoring system and method based on edge calculation, and relates to the technical field of electric digital data processing. The additive product production process monitoring system based on edge calculation comprises a data acquisition module, an abnormal hot spot identification module, a time-frequency analysis module and an edge node optimization module. According to the invention, abnormal hot spot identification is carried out through the acquired thermal imaging data, whether to upload to the cloud server is judged based on the abnormal hot spot identification result, then time-frequency analysis is carried out according to the acquired sound wave signal data, and finally whether to carry out edge node optimization according to the acquired working parameters is judged based on the time-frequency analysis result. Therefore, the recognition accuracy of the abnormal operation state in the additive product production process monitoring process is improved, and the problem that the collaborative recognition accuracy of the abnormal operation state and the sound wave signal state in the additive product production process monitoring process is not high in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and particularly to an additive product production process monitoring system and method based on edge computing. Background Art

[0002] With the rapid development of additive manufacturing (3D printing) technology and its wide application in the fields of aerospace, medical, automotive, mold, etc., higher requirements are put forward for the real-time monitoring and quality control of the production process. Traditional production monitoring methods usually rely on a centralized cloud computing architecture, which has problems such as high data transmission delay, insufficient real-time performance, and large bandwidth pressure, and it is difficult to meet the requirements of high-precision and high-real-time monitoring in the additive manufacturing process. Edge computing, as a distributed computing paradigm, provides an effective way to solve the above problems by sinking computing, storage, and network functions to edge nodes close to the data source.

[0003] Existing technologies use big data and artificial intelligence technologies to deeply analyze the collected production data, and at the same time feedback the results of the deep analysis to the edge nodes to achieve comprehensive monitoring of the additive manufacturing process.

[0004] For example, the intelligent monitoring and management method and system for an exterior wall decorative panel production line disclosed in the invention patent announcement with the publication number CN119167503B includes: before the decorative panel composite process, using UG modeling software to build models of the decorative panel and the composite process equipment to obtain model files, and obtaining reference data thresholds during the composite process through simulation; during the actual composite process, using three-dimensional imaging analysis technology to process the three-dimensional data set obtained from the glue application and bonding process and the edge overflow glue amount image obtained from the pressure curing process, comparing and analyzing the results of the data processing with the reference data thresholds, and displaying the warning information on the human-machine interaction interface; after the composite process is completed, comprehensively detecting the finished decorative panel products, establishing a quality inspection database, and recording the inspection results.

[0005] For example, the production supply chain monitoring and management system and method based on industrial Internet disclosed in the invention patent announcement with the publication number CN116306325B includes: real-time monitoring of the receiving information, determining the customer distribution map according to the receiving information; performing in-map clustering on the customer distribution maps for different time periods and sorting according to the time period to obtain a supply layer group; obtaining a product supply chain containing time information, and establishing a mapping model according to the supply layer group and the product supply chain; receiving the supply chain change information input by the user, and inputting the supply chain change information into the mapping model to obtain a theoretical distribution layer.

[0006] However, in the process of implementing the technical solutions of the present invention in the embodiments of the present application, it is found that the above technologies have at least the following technical problems: In the prior art, there may be a lack of an adaptive adjustment mechanism for dynamic changes in the additive manufacturing process, resulting in the inability to flexibly adjust the recognition strategy when facing different process parameters and environmental conditions, reducing the accuracy of abnormal state recognition, and there is a problem of low accuracy in the collaborative recognition of abnormal operating states and acoustic signal states during the monitoring process of the additive product production process. Summary of the Invention

[0007] By providing a monitoring system and method for the additive product production process based on edge computing, the embodiments of the present application solve the problem of low accuracy in the collaborative recognition of abnormal operating states and acoustic signal states during the monitoring process of the additive product production process in the prior art, and achieve an improvement in the accuracy of abnormal operating state recognition during the monitoring process of the additive product production process.

[0008] The embodiments of the present application provide a monitoring system for the additive product production process based on edge computing, including: a data acquisition module, an abnormal hot spot recognition module, a time-frequency analysis module, and an edge node optimization module; wherein, the data acquisition module is used to collect multi-modal data of a specified additive product in a target monitoring area in real time at the end of the additive manufacturing period, and the multi-modal data is collected in real time through an edge computing node with sensors and actuators deployed in the target monitoring area, and the multi-modal data includes thermal imaging data and acoustic signal data; the abnormal hot spot recognition module is used to perform abnormal hot spot recognition based on the acquired thermal imaging data to obtain an abnormal hot spot recognition result, and the abnormal hot spot recognition is used to analyze the temperature change of the specified additive product during the additive manufacturing process; the time-frequency analysis module is used to determine whether to upload to the cloud server based on the abnormal hot spot recognition result. If so, perform time-frequency analysis based on the acquired acoustic signal data to obtain a time-frequency analysis result, otherwise perform step size optimization, and the step size optimization means to improve the heat dissipation capacity during the 3D printing process by adjusting the step size of the 3D printing path, and the time-frequency analysis is used to analyze the operating state of the corresponding additive manufacturing equipment of the specified additive product during the additive manufacturing process; the edge node optimization module is used to determine whether to send it to the edge node based on the time-frequency analysis result. If so, perform edge node optimization based on the acquired working parameters, otherwise continue to monitor the production process of the specified additive product in the next additive manufacturing period, and the working parameters are used to reflect the fault repair state of the corresponding additive manufacturing equipment of the specified additive product during the additive manufacturing process, and the edge node optimization means to improve the load balancing of the edge node by adjusting the memory allocation and the number of threads.

[0009] The embodiments of the present application provide a method for monitoring the additive product production process based on edge computing, including the following steps: Step 1, collect in real time the multimodal data of a specified additive product in the target monitoring area at the end of the additive manufacturing period. The multimodal data is collected in real time by an edge computing node with sensors and actuators deployed in the target monitoring area, and the multimodal data includes thermal imaging data and acoustic signal data; Step 2, perform abnormal hot spot identification based on the obtained thermal imaging data to obtain the abnormal hot spot identification result. The abnormal hot spot identification is used to analyze the temperature change of the specified additive product during the additive manufacturing process; Step 3, judge whether to upload to the cloud server based on the abnormal hot spot identification result. If so, perform time-frequency analysis on the obtained acoustic signal data to obtain the time-frequency analysis result. Otherwise, perform step size optimization. The step size optimization means adjusting the step size of the 3D printing path to improve the heat dissipation capacity during the 3D printing process. The time-frequency analysis is used to analyze the operating state of the corresponding additive manufacturing equipment of the specified additive product during the additive manufacturing process; Step 4, judge whether to send it to the edge node based on the time-frequency analysis result. If so, perform edge node optimization based on the obtained working parameters. Otherwise, continue to monitor the production process of the specified additive product in the next additive manufacturing period. The working parameters are used to reflect the fault repair state of the corresponding additive manufacturing equipment of the specified additive product during the additive manufacturing process. The edge node optimization means adjusting the memory allocation and the number of threads to improve the load balancing of the edge node.

[0010] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By performing abnormal hot spot identification on the obtained thermal imaging data, and then judging whether to upload to the cloud server based on the abnormal hot spot identification result. If so, perform time-frequency analysis on the obtained acoustic signal data. Otherwise, perform step size optimization. Finally, judge whether to perform edge node optimization based on the time-frequency analysis result and the obtained working parameters, thereby realizing the accurate identification and efficient processing of abnormal operating states in the production process of the specified additive product, so as to better adapt to the complex changes in the additive product production process, and effectively solve the problem of low accuracy in the collaborative identification of abnormal operating states and acoustic signal states in the monitoring process of the additive product production process in the prior art.

[0011] 2. Compensate the degree of difference between the obtained molten pool coverage area and the set value of the molten pool coverage area in the database through the molten pool coverage area compensation factor to obtain the molten pool coverage area index. At the same time, perform coupling processing on the obtained molten pool coverage area index, molten pool average temperature index and thermal gradient value index to obtain the abnormal hot spot determination value, thereby realizing the improvement of the accuracy of obtaining the abnormal hot spot determination value. This step effectively eliminates the measurement errors caused by equipment aging, material differences or environmental factors, thereby effectively improving the accuracy and reliability of abnormal hot spot determination.

[0012] 3. By obtaining the degree of difference between the peak energy frequency and the maximum allowable peak energy frequency in the database, and performing compensation operations in combination with the introduced peak energy frequency compensation factor, the peak energy frequency index is obtained. At the same time, the result of inversely processing the total energy index of the obtained time-frequency diagram is coupled with the obtained peak energy frequency index and the abnormal frequency proportion index to obtain the time-frequency analysis determination value, thereby improving the accuracy of obtaining the time-frequency analysis determination value. This process effectively eliminates measurement errors caused by factors such as equipment performance fluctuations, environmental noise interference, or signal transmission losses.

[0013] 4. Through the optimization of the first edge node and the second edge node, the system can dynamically adjust the memory allocation and the number of threads according to the real-time obtained working parameters, ensuring that the edge nodes can operate efficiently when processing the data of the additive product production process, reducing delays or insufficient processing capabilities caused by improper resource allocation, thereby improving the accuracy of abnormal state recognition, enhancing the stability and reliability of the system, and providing strong support for the intelligent upgrade of the additive manufacturing industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic structural diagram of a monitoring system for an additive product production process based on edge computing provided by an embodiment of the present application; Figure 2 It is a monitoring flow chart of a specified additive product production process provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] In an embodiment of the present application, by providing a monitoring system and method for an additive product production process based on edge computing, the problem of low accuracy in the collaborative recognition of abnormal operating states and acoustic signal states during the monitoring of the additive product production process in the prior art is solved. The data acquisition module collects multi-modal data of a specified additive product in a target monitoring area at the end of the additive manufacturing period in real time. At the same time, the abnormal hot spot recognition module performs abnormal hot spot recognition based on the obtained thermal imaging data to obtain the abnormal hot spot recognition result. Then, the time-frequency analysis module determines whether to upload it to the cloud server based on the abnormal hot spot recognition result. If so, time-frequency analysis is performed on the obtained acoustic signal data to obtain the time-frequency analysis result. Otherwise, step size optimization is performed. Finally, the edge node optimization module determines whether to send it to the edge node based on the time-frequency analysis result. If so, the edge node is optimized according to the obtained working parameters. Otherwise, the production process of the specified additive product in the next additive manufacturing period is continuously monitored, realizing an improvement in the accuracy of abnormal operating state recognition during the monitoring of the additive product production process.

[0016] The technical solution in the embodiment of the present application is to solve the problem of low accuracy in the collaborative recognition of abnormal operating states and acoustic signal states during the monitoring of the additive manufacturing product production process. The general idea is as follows: Abnormal hot spot recognition is performed through the obtained thermal imaging data, and then it is determined whether to upload to the cloud server based on the abnormal hot spot recognition result. If so, time-frequency analysis is performed according to the obtained acoustic signal data. Otherwise, step size optimization is performed. Finally, it is determined whether to perform edge node optimization according to the obtained working parameters based on the time-frequency analysis result, achieving the effect of improving the accuracy of abnormal operating state recognition during the monitoring of the additive manufacturing product production process.

[0017] To better understand the above technical solution, the above technical solution will be described in detail below in combination with the accompanying drawings of the specification and specific implementation manners.

[0018] As Figure 1 shown, it is a schematic structural diagram of an additive manufacturing product production process monitoring system based on edge computing provided by an embodiment of the present application. The additive manufacturing product production process monitoring system based on edge computing provided by an embodiment of the present application includes: a data acquisition module, an abnormal hot spot recognition module, a time-frequency analysis module, and an edge node optimization module.

[0019] Among them, the data acquisition module is used to collect multi-modal data of a specified additive manufacturing product in a target monitoring area in real time at the end of the additive manufacturing period. The multi-modal data includes thermal imaging data and acoustic signal data, which are collected in real time through edge computing nodes deployed in the target monitoring area; the thermal imaging data includes the molten pool coverage area, the average temperature of the molten pool, and the thermal gradient value, which are used to reflect the temperature change of the specified additive manufacturing product in the target monitoring area during the additive manufacturing process; the acoustic signal data includes the peak energy frequency, the total energy of the time-frequency diagram, and the abnormal frequency ratio, which are used to reflect the ultrasonic change of the specified additive manufacturing product in the target monitoring area during the 3D printing process; the peak energy frequency is used to quantify the energy concentration degree of the corresponding acoustic signal of the specified additive manufacturing product in the target monitoring area during the 3D printing period, such as the energy intensity at a specific frequency (set by a preset person according to the actual 3D printing scenario), reflecting whether there is abnormal vibration or resonance during the 3D printing process; the total energy of the time-frequency diagram is used to quantify the overall energy distribution of the corresponding acoustic signal of the specified additive manufacturing product (such as molten polymer material, polylactide) in the target monitoring area during the 3D printing period, such as the sum of the energies of all frequency components in the time-frequency diagram, reflecting the overall acoustic characteristics of the 3D printing process; the abnormal frequency ratio is used to quantify the proportion of abnormal frequency components in the corresponding acoustic signal of the specified additive manufacturing product in the target monitoring area during the 3D printing period, such as the proportion of frequency components exceeding the frequency allowable range, reflecting whether there are defects or faults during the 3D printing process.

[0020] The abnormal hot spot recognition module is used to recognize abnormal hot spots based on the acquired thermal imaging data, and obtain the abnormal hot spot recognition result. The abnormal hot spot recognition is used to analyze the temperature change of the specified additive product during the additive manufacturing process.

[0021] The time-frequency analysis module is used to determine whether to upload to the cloud server based on the abnormal hot spot recognition result. If so, it performs time-frequency analysis based on the acquired acoustic signal data to obtain the time-frequency analysis result. Otherwise, it performs step size optimization, which means adjusting the step size of the 3D printing path to improve the heat dissipation ability during the 3D printing process. The time-frequency analysis is used to analyze the operating state of the corresponding additive manufacturing equipment during the additive manufacturing process of the specified additive product.

[0022] The edge node optimization module is used to determine whether to send it to the edge node based on the time-frequency analysis result. If so, it performs edge node optimization according to the acquired working parameters. Otherwise, it continues to monitor the production process of the specified additive product in the next additive manufacturing period. The working parameters are used to reflect the fault repair status of the corresponding additive manufacturing equipment during the additive manufacturing process of the specified additive product. The edge node optimization means adjusting the memory allocation and the number of threads to improve the load balancing of the edge node.

[0023] Specifically, the edge computing node is equipped with a variety of sensors, including but not limited to visual sensors, infrared thermal imagers, acceleration sensors, and time sensors. Among them, the visual sensor is used to monitor the area of the molten pool coverage, the infrared thermal imager is used to monitor the average temperature and thermal gradient value of the molten pool, the acceleration sensor is used to monitor the energy frequency peak value, the total energy of the time-frequency diagram, and the abnormal frequency ratio, and the time sensor is used to monitor the average failure working duration and the average failure repair duration.

[0024] Before designing the additive product production process monitoring system based on edge computing, a database for storing various setting data is established. The database includes but not limited to preset abnormal hot spot determination values, preset time-frequency analysis determination values, additive manufacturing periods, and 3D printing periods. Various values therein are directly set by technicians. Among them, the setting basis of the preset abnormal hot spot determination value can be determined according to the actual production scenario of the additive product. For example, the preset abnormal hot spot determination value is represented by the result of summing and averaging the historical abnormal hot spot determination values of the specified additive product in the target monitoring area in the database at the end of the historical additive manufacturing period. In addition, various values in the database can be set and fine-tuned by technicians according to actual debugging.

[0025] In this embodiment, the edge computing nodes are directly deployed in the target monitoring area, without the need to transmit data to the cloud or remote servers, thus significantly reducing the latency of multi-modal data acquisition. Secondly, the edge computing nodes can simultaneously collect and process thermal imaging data and acoustic signal data, realizing the real-time fusion and preliminary analysis of multi-modal data, forming a distributed monitoring network, which helps to ensure the continuity of the system, improve the accuracy and reliability of anomaly detection. At the same time, combining the advantages in aspects such as accurate identification of anomaly hotspots, optimization of decision-making through time-frequency analysis, improvement of step size and heat dissipation capacity, and enhancement of load balance of edge nodes, the intelligent monitoring and management of the additive manufacturing product production process are realized, providing strong support for the intelligent upgrading of the additive manufacturing industry.

[0026] Furthermore, the specific process for obtaining thermal imaging data is as follows: E1, obtain the area of the molten pool coverage region of the specified additive manufacturing product in the target monitoring area at the end of the additive manufacturing period. If the obtained area of the molten pool coverage region is greater than the set value of the molten pool coverage region area in the database and does not exceed the maximum allowable molten pool coverage region area in the database, it indicates that the acquisition of the molten pool coverage region is effective and E2 is executed. Otherwise, the deviation of the obtained molten pool coverage region area is input into the spot size algorithm to output the increased amplitude of the spot size until the obtained area of the molten pool coverage region is greater than the set value of the molten pool coverage region area in the database and then E2 is executed. The area of the molten pool coverage region is used to quantify the fusion effect of the specified additive manufacturing product in the target monitoring area during the additive manufacturing period. The set value of the molten pool coverage region area is represented by the result of summing and averaging the historical molten pool coverage region areas of the specified additive manufacturing product in the target monitoring area at the end of the historical additive manufacturing periods in the database. The maximum allowable molten pool coverage region area is represented by the result of summing and averaging the maximum values of the historical molten pool coverage region areas of the specified additive manufacturing product in the target monitoring area at the end of each historical additive manufacturing period in the database; E2, obtain the average temperature of the molten pool (not exceeding the set value of the average temperature of the molten pool, such as +100 °C) and the thermal gradient value (not exceeding the set value of the thermal gradient value, such as 5 °C / mm) of the specified additive manufacturing product in the molten pool coverage region at the end of the additive manufacturing period. The average temperature of the molten pool is used to quantify the stability of energy input of the specified additive manufacturing product in the target monitoring area during the additive manufacturing period, and the thermal gradient value is used to quantify the thermal stress distribution of the specified additive manufacturing product in the target monitoring area during the additive manufacturing period.

[0027] In this embodiment, by accurately obtaining and judging the area of the molten pool coverage region, the fusion effect of a specified additive product during additive manufacturing can be accurately evaluated. Based on the quantitative analysis of the average temperature of the molten pool, the stability of the energy input during additive manufacturing can be understood in real time. The acquisition and quantification of the thermal gradient value enable the operator to clearly master the distribution of thermal stress during additive manufacturing. This acquisition process can ensure the unified and accurate evaluation of the fusion effect, energy input stability, and thermal stress distribution of additive products under different batches and production conditions, which helps to improve the consistency and reliability of products and meet the high requirements of customers for product quality.

[0028] Further, abnormal hot spot identification is performed based on the obtained thermal imaging data. The specific process is as follows: First, the difference degree between the obtained area of the molten pool coverage region and the set value of the area of the molten pool coverage region in the database is compensated by the area compensation factor of the molten pool coverage region to obtain the area index of the molten pool coverage region. The area index of the molten pool coverage region The specific limiting expression is: , where represents the area index of the molten pool coverage region of the specified additive product in the target monitoring area at the end of the additive manufacturing period, represents the area compensation factor of the molten pool coverage region, represents the area of the molten pool coverage region of the specified additive product in the target monitoring area at the end of the additive manufacturing period, represents the maximum allowable area of the molten pool coverage region, represents the set value of the area of the molten pool coverage region. The units of the area of the molten pool coverage region, the maximum allowable area of the molten pool coverage region, and the set value of the area of the molten pool coverage region are the same, all in square millimeters (mm²).

[0029] Then, the difference degree between the obtained average temperature of the molten pool and the set value of the average temperature of the molten pool in the database is compensated by the average temperature compensation factor of the molten pool to obtain the average temperature index of the molten pool. The average temperature index of the molten pool The specific limiting expression is: , where represents the average temperature of the molten pool of the specified additive product in the molten pool coverage region in the target monitoring area at the end of the additive manufacturing period, represents the average temperature compensation factor of the molten pool, represents the average temperature of the molten pool of the specified additive product in the target monitoring area at the end of the additive manufacturing period, Represents the set value of the average temperature of the molten pool. The unit of the average temperature of the molten pool is the same as that of the set value of the average temperature of the molten pool, both being degrees Celsius (°C). The set value of the average temperature of the molten pool is represented by the result of summing and averaging the historical average temperatures of the molten pool of the specified additive product in the target monitoring area in the database at the end of the historical additive manufacturing period.

[0030] Next, the difference degree between the obtained thermal gradient value and the set value of the thermal gradient in the database is compensated by the thermal gradient value compensation factor to obtain the thermal gradient value index. The thermal gradient value index The specific limiting expression is: , where Represents the thermal gradient value index of the specified additive product in the molten pool coverage area in the target monitoring area at the end of the additive manufacturing period. Represents the thermal gradient value compensation factor. Represents the thermal gradient value of the specified additive product in the target monitoring area at the end of the additive manufacturing period. Represents the set value of the thermal gradient. The unit of the thermal gradient is the same as that of the set value of the thermal gradient, both being degrees Celsius per millimeter (°C / mm). The set value of the thermal gradient is represented by the result of summing and averaging the historical thermal gradient values of the specified additive product in the target monitoring area in the database at the end of the historical additive manufacturing period.

[0031] Finally, the obtained molten pool coverage area index, molten pool average temperature index, and thermal gradient value index are coupled to obtain the abnormal hot spot determination value. The abnormal hot spot determination value is used to reflect the abnormal degree of the working temperature of the corresponding additive production equipment for the specified additive product during the additive manufacturing process. The thermal imaging data includes the molten pool coverage area, molten pool average temperature, and thermal gradient value. The specific limiting expression of the abnormal hot spot determination value is: , where Represents the abnormal hot spot determination value of the specified additive product in the target monitoring area at the end of the additive manufacturing period.

[0032] The database stores preset compensation factors that are closely related to the abnormal hotspot judgment values. A pre-defined mapping relationship is established between these compensation factors and the corresponding melt pool coverage area, melt pool average temperature, and thermal gradient value. It is worth noting that this mapping is not set arbitrarily. It can be one-to-one or many-to-one. For example, in actual applications, when it is necessary to analyze the temperature changes of a specified additive product during the additive manufacturing process, the real-time melt pool coverage area, melt pool average temperature, and thermal gradient value can be directly input into this preset mapping relationship, so that the melt pool coverage area compensation factor, melt pool average temperature compensation factor, and thermal gradient value compensation factor that match the melt pool coverage area, melt pool average temperature, and thermal gradient value can be quickly and accurately obtained.

[0033] It is particularly important that in order to ensure the consistency and comparability of the evaluation, the value ranges of the melt pool coverage area compensation factor, the melt pool average temperature compensation factor and the thermal gradient value compensation factor in this example are all limited to between 0 and 1, and the sum of the three is 1.

[0034] In this embodiment, the abnormal hotspot judgment value increases with the increase of the area covered by the molten pool, the average temperature of the molten pool and the thermal gradient value. Among them, the increase in the area covered by the molten pool may require higher energy input to maintain the melting state, thereby causing an increase in the average temperature of the molten pool. Conversely, the increase in the average temperature of the molten pool may also promote the expansion of the area covered by the molten pool, because high temperature helps the melting and flow of materials.

[0035] Secondly, the increase in the area covered by the molten pool may lead to more uneven heat distribution, especially in the edge area, where the temperature changes may be more drastic, thereby increasing the thermal gradient value; the increase in the average temperature of the molten pool may lead to changes in heat conduction and heat convection, thereby affecting the distribution of the thermal gradient value.

[0036] By considering the mutual influence mechanism between the area covered by the molten pool, the average temperature of the molten pool and the thermal gradient value, it is helpful to more comprehensively evaluate the thermal state in the additive manufacturing process, so as to more accurately identify the location and cause of abnormal hot spots, optimize the formation and stability of the molten pool, reduce the generation of abnormal hot spots, thereby improving the accuracy and reliability of monitoring, and effectively solving the problem of low accuracy in the coordinated identification of abnormal operating conditions and acoustic signal conditions in the monitoring process of additive product production processes in the prior art.

[0037] Further, it is determined whether to upload to the cloud server based on the abnormal hot spot recognition result. The specific process is as follows: If the obtained abnormal hot spot determination value is not greater than the preset abnormal hot spot determination value in the database, the abnormal hot spot recognition result is recorded as no abnormal temperature component in the molten pool coverage area, and at the same time, the corresponding thermal imaging data is uploaded to the cloud server; otherwise, the abnormal hot spot recognition result is recorded as having an abnormal temperature component in the molten pool coverage area and the step size is adjusted.

[0038] Among them, the specific process of step size adjustment is as follows: The obtained hot spot abnormal determination value deviation and step size deviation are input into the step size PID (Proportional-Integral-Derivative) control algorithm of the additive manufacturing equipment to output the actual step size adjustment amplitude. The hot spot abnormal determination value deviation is used to quantify the difference degree between the obtained abnormal hot spot determination value and the preset abnormal hot spot determination value, that is, the difference between the obtained abnormal hot spot determination value and the preset abnormal hot spot determination value. The step size deviation is used to quantify the difference degree between the actual step size and the target step size of the corresponding 3D printing path at the end of the additive manufacturing period, that is, the absolute value of the difference between the target step size and the actual step size; When the re-obtained hot spot abnormal determination value after step size adjustment is not greater than the preset hot spot abnormal determination value in the database and the number of adjustments does not exceed the preset number of times, the step size adjustment is completed and the re-obtained thermal imaging data is uploaded to the cloud server; otherwise, a shutdown instruction is sent and the preset personnel are prompted to perform maintenance.

[0039] In this embodiment, through the precise comparison and quantitative analysis of the abnormal hot spot determination value, the abnormal temperature components in the molten pool coverage area can be identified in a timely and accurate manner, avoiding quality problems of additive manufacturing products caused by temperature abnormalities, such as internal defects of materials and insufficient structural strength. At the same time, using the step size PID control algorithm, the step size is dynamically adjusted according to the hot spot abnormal determination value deviation and step size deviation, making the step size in the additive manufacturing process more accurate and meeting the requirements of the target step size, further improving the dimensional accuracy and shape accuracy of the product. When the step size adjustment fails to meet the requirements, a shutdown instruction is sent in a timely manner and the preset personnel are prompted to perform maintenance, effectively avoiding the expansion of faults and equipment damage that may be caused by the equipment continuing to operate in an abnormal state, and ensuring the safe operation of the equipment.

[0040] Further, time-frequency analysis is performed on the obtained acoustic signal data. The specific process is as follows: First, the difference degree between the energy frequency peak value and the maximum allowable energy frequency peak value in the database is obtained, and combined with the introduced energy frequency peak value compensation factor for compensation operation, the energy frequency peak value index is obtained. The energy frequency peak value index The specific limit expression is: , where represents the energy frequency peak value index of the specified additive product in the molten pool coverage area of the target monitoring area at the end of the 3D printing period. represents the energy frequency peak compensation factor, represents the energy frequency peak of the specified additive product within the molten pool coverage area in the target monitoring region at the end of the 3D printing period, represents the maximum allowable energy frequency peak. The energy frequency peak and the maximum allowable energy frequency peak have the same unit, both in joules per hertz (J / Hz). The maximum allowable energy frequency peak is represented by the result of summing and averaging the maximum values of the historical energy frequency peaks of the specified additive product within the molten pool coverage area in the target monitoring region at the end of each historical 3D printing period in the database.

[0041] Then, obtain the degree of difference between the total energy of the time-frequency diagram and the total energy of the reference time-frequency diagram in the database, and perform a compensation operation in combination with the introduced total energy compensation factor of the time-frequency diagram to obtain the total energy index of the time-frequency diagram. The total energy index of the time-frequency diagram The specific limiting expression is: , where represents the total energy index of the time-frequency diagram of the specified additive product within the molten pool coverage area in the target monitoring region at the end of the 3D printing period, represents the total energy compensation factor of the time-frequency diagram, represents the total energy of the time-frequency diagram of the specified additive product within the molten pool coverage area in the target monitoring region at the end of the 3D printing period, represents the total energy of the reference time-frequency diagram. The total energy of the time-frequency diagram and the total energy of the reference time-frequency diagram have the same unit, both in joules (J). The total energy of the reference time-frequency diagram is represented by the result of summing and averaging the historical total energies of the time-frequency diagrams of the specified additive product within the molten pool coverage area in the target monitoring region at the end of the historical 3D printing periods in the database.

[0042] Next, obtain the degree of difference between the abnormal frequency proportion and the maximum allowable abnormal frequency proportion in the database, and perform a compensation operation in combination with the introduced abnormal frequency proportion compensation factor to obtain the abnormal frequency proportion index. The abnormal frequency proportion index The specific limiting expression is: , where represents the abnormal frequency proportion index of the specified additive product within the molten pool coverage area in the target monitoring region at the end of the 3D printing period, represents the abnormal frequency proportion compensation factor, represents the abnormal frequency proportion of the specified additive product within the molten pool coverage area in the target monitoring region at the end of the 3D printing period, It represents the maximum allowable proportion of abnormal frequencies. The unit of the proportion of abnormal frequencies is the same as that of the maximum allowable proportion of abnormal frequencies, both being percentages (%). The maximum allowable proportion of abnormal frequencies is represented by the result of summing and averaging the maximum values of the historical proportions of abnormal frequencies of the specified additive product within the molten pool coverage area in the target monitoring area in the database at the end of each historical 3D printing period.

[0043] Finally, the result of the inverse proportion processing of the total energy index of the obtained time-frequency diagram is coupled with the obtained energy frequency peak index and the proportion of abnormal frequencies index to obtain the time-frequency analysis determination value. The time-frequency analysis determination value is used to reflect the abnormal degree of the acoustic signal data corresponding to the specified additive product during 3D printing. The time-frequency analysis determination value The specific limit expression is: , where represents the time-frequency analysis determination value of the specified additive product within the molten pool coverage area in the target monitoring area at the end of the 3D printing period.

[0044] The database stores preset compensation factors closely related to the time-frequency analysis determination value. A predefined mapping relationship is established between these compensation factors and the corresponding energy frequency peaks, the total energy of the time-frequency diagram, and the proportion of abnormal frequencies. It should be noted that this mapping is not arbitrarily set. It can be a one-to-one correspondence or a many-to-one relationship. For example, in practical applications, when analyzing the abnormal situation of the acoustic signal of a specified additive product during 3D printing, the energy frequency peak, the total energy of the time-frequency diagram, and the proportion of abnormal frequencies obtained in real time can be directly input into this preset mapping relationship, and the energy frequency peak compensation factor, the total energy compensation factor of the time-frequency diagram, and the proportion of abnormal frequencies compensation factor that match the energy frequency peak, the total energy of the time-frequency diagram, and the proportion of abnormal frequencies can be obtained quickly and accurately.

[0045] Especially importantly, in order to ensure the consistency and comparability of the evaluation, the value ranges of the energy frequency peak compensation factor, the total energy compensation factor of the time-frequency diagram, and the proportion of abnormal frequencies compensation factor in this example are all limited to between 0 and 1, and the sum of the three is 1.

[0046] In this embodiment, the time-frequency analysis determination value increases with the increase of the energy frequency peak, the total energy of the time-frequency diagram, and the proportion of abnormal frequencies. Among them, when the total energy of the time-frequency diagram increases, it may be due to the increase of normal workload, and there is no obvious change in the energy frequency peak and the proportion of abnormal frequencies, then it can be judged that the node is operating normally. On the contrary, if all three show abnormal changes at the same time, it is more likely that the node has problems.

[0047] When the total energy of the time-frequency diagram increases, the threshold of the abnormal frequency proportion can be appropriately increased to avoid misjudgment caused by the increase in total energy. When it is found that both the energy frequency peak value and the abnormal frequency proportion increase simultaneously, the possible location of the fault can be further determined. If the energy frequency peak value continues to increase and the abnormal frequency proportion gradually rises, then it can be predicted that the fault may deteriorate further, and measures can be taken in advance for repair and maintenance.

[0048] According to the analysis of the mutual influence mechanism, the increase in the energy frequency peak value leads to an increase in the total energy of the time-frequency diagram, and since the energy is concentrated in the abnormal frequency range, the abnormal frequency proportion increases significantly. Combining the changes in these indicators, it is judged that there may be a hardware fault or software anomaly in the edge computing node. After further inspection, it is found that the temperature of a certain chip of the node is too high, resulting in abnormal signal processing, thus verifying the accuracy of identifying the abnormal operating state by considering the mutual influence mechanism, avoiding the further expansion of the node fault, and effectively solving the problem of low accuracy in the collaborative identification of the abnormal operating state and the acoustic signal state during the monitoring process of the additive product production process in the prior art.

[0049] Further, based on the time-frequency analysis result, it is determined whether to send it to the edge node. The specific process is as follows: If the obtained time-frequency analysis determination value is not greater than the preset time-frequency analysis determination value in the database, the time-frequency analysis result is recorded as no abnormal frequency components in the molten pool coverage area and the production process monitoring instruction for the next additive manufacturing period is sent; otherwise, the time-frequency analysis recognition result is recorded as having abnormal frequency components in the molten pool coverage area and the FFT (Fast Fourier Transform) point number is adjusted. The preset time-frequency analysis determination value is represented by the result of summing and averaging the historical time-frequency analysis determination values of the specified additive product in the molten pool coverage area of the target monitoring area in the database at the end of the historical 3D printing period. The FFT point number adjustment means optimizing the balance relationship between time and frequency resolution by the obtained FFT point number adjustment amplitude. The FFT point number adjustment amplitude is the result obtained by inputting the obtained time-frequency analysis determination value deviation and frequency resolution deviation into the LMS (Least Mean Square) algorithm. The time-frequency analysis determination value deviation is used to quantify the difference degree between the obtained time-frequency analysis determination value and the preset time-frequency analysis determination value, that is, the difference between the obtained time-frequency analysis determination value and the preset time-frequency analysis determination value. The frequency resolution deviation is used to quantify the difference degree between the actual frequency resolution and the target frequency resolution of the adaptive adjustment filter corresponding to the time-frequency diagram of the molten pool coverage area at the end of the 3D printing period, that is, the absolute value of the difference between the actual frequency resolution and the target frequency resolution.

[0050] In this embodiment, by adjusting the number of FFT points, the balance between time resolution and frequency resolution is optimized. During the additive manufacturing process, different application scenarios have different requirements for time resolution and frequency resolution. For example, for rapidly changing signals, higher time resolution is required; for signals with rich frequency components, higher frequency resolution is needed. By adjusting the number of FFT points, different analysis requirements can be met according to the actual situation, enabling real-time monitoring and feedback of the production process and improving the intelligent level of additive product production management.

[0051] Furthermore, the edge node optimization includes the first edge node optimization and the second edge node optimization; the first edge node optimization refers to the optimization corresponding to when the obtained harmonic mean of the working parameters is greater than the maximum value of the historical harmonic mean of the working parameters in the database. The maximum value of the historical harmonic mean of the working parameters represents the maximum value of the historical harmonic mean of the working parameters of the corresponding edge node in the target monitoring area in the database during the historical first edge node optimization process.

[0052] Specifically, the specific process of the first edge node optimization is as follows: F11, input the obtained working parameter deviation and working parameter average value into the memory allocation adjustment algorithm to output the memory allocation increase amplitude. After one memory allocation increase, determine whether the corresponding decrease amplitude of the obtained first harmonic mean deviation of the working parameters is greater than the preset decrease amplitude in the database (set by the preset personnel according to the actual situation). The harmonic mean of the working parameters represents the result of performing harmonic mean processing on the obtained working parameters, and the working parameters include the average failure working duration and the average failure repair duration; F12, if the corresponding decrease amplitude of the obtained harmonic mean deviation of the working parameters is greater than the preset decrease amplitude in the database, it indicates that the memory allocation increase is effective and continue to increase the memory allocation by the preset amplitude until the obtained harmonic mean of the working parameters is within the allowable range of the harmonic mean of the working parameters in the database; F13, if the corresponding decrease amplitude of the obtained harmonic mean deviation of the working parameters is not greater than the preset decrease amplitude in the database, input the re-obtained working parameter deviation and working parameter average value after one memory allocation increase into the thread number adjustment algorithm to output the thread number decrease amplitude and return to F11. The allowable range of the harmonic mean of the working parameters represents the range corresponding to the maximum and minimum values of the historical harmonic mean of the working parameters of the corresponding edge node in the target monitoring area in the database during the historical second edge node optimization process, usually including the cases equal to the maximum and minimum values of the historical harmonic mean of the working parameters. The working parameter deviation represents the absolute value of the difference between the obtained harmonic mean of the working parameters and the working parameter average value, and the working parameter average value represents the result of summing and averaging the obtained average failure working duration and average repair duration. The first harmonic mean deviation of the working parameters represents the difference between the obtained harmonic mean of the working parameters and the maximum value of the historical harmonic mean of the working parameters.

[0053] The optimization of the second edge node represents the optimization corresponding to the case where the harmonic mean of the obtained working parameters is less than the minimum value of the harmonic mean of the historical working parameters in the database. The minimum value of the harmonic mean of the historical working parameters represents the minimum value of the harmonic mean of the working parameters of the corresponding edge node in the target monitoring area in the database during the historical optimization of the second edge node.

[0054] Specifically, the specific process of optimizing the second edge node is as follows: F21, input the obtained working parameter deviation and working parameter average value into the memory allocation adjustment algorithm to output the memory allocation reduction amplitude. After one memory allocation reduction, determine whether the reduction amplitude corresponding to the obtained deviation of the second working parameter harmonic mean is greater than the preset reduction amplitude in the database. The deviation of the second working parameter harmonic mean represents the difference between the minimum value of the harmonic mean of the historical working parameters and the obtained harmonic mean of the working parameters; F22, if the reduction amplitude corresponding to the obtained deviation of the working parameter harmonic mean is greater than the preset reduction amplitude in the database, it indicates that the memory allocation reduction is effective and continue to reduce the memory allocation by the preset amplitude until the obtained harmonic mean of the working parameters is within the allowable range of the harmonic mean of the working parameters in the database; F23, if the reduction amplitude corresponding to the obtained deviation of the working parameter harmonic mean is not greater than the preset reduction amplitude in the database, input the re-obtained working parameter deviation and working parameter average value after one memory allocation reduction into the thread number adjustment algorithm to output the thread number increase amplitude and return to F21.

[0055] In this embodiment, by dynamically adjusting the memory allocation and the number of threads, resources can be reasonably allocated according to the actual working conditions of the edge nodes, avoiding resource waste or shortage. By monitoring and optimizing the working parameters (average failure working duration and average failure repair duration), potential system problems can be discovered in time and measures can be taken for adjustment. When new edge nodes need to be added or existing nodes need to be upgraded, reasonable allocation and scheduling of resources can be achieved through a unified resource management mechanism, improving the overall scalability of the system.

[0056] Such as Figure 2As shown in the figure, it is a monitoring flowchart of the production process of the specified additive product provided by the embodiment of the present application. The monitoring method for the additive product production process based on edge computing provided by the embodiment of the present application includes the following steps: Step 1, collect multi-modal data of the specified additive product in the target monitoring area in real time at the end of the additive manufacturing period. The multi-modal data is collected in real time by an edge computing node with sensors and actuators deployed in the target monitoring area. The multi-modal data includes thermal imaging data and acoustic signal data; Step 2, perform abnormal hot spot identification based on the obtained thermal imaging data to obtain an abnormal hot spot identification result. The abnormal hot spot identification is used to analyze the temperature change of the specified additive product during the additive manufacturing process; Step 3, based on the abnormal hot spot identification result, determine whether to upload it to the cloud server. If so, perform time-frequency analysis on the obtained acoustic signal data to obtain a time-frequency analysis result. Otherwise, perform step size optimization. The step size optimization means adjusting the step size of the 3D printing path to improve the heat dissipation ability during the 3D printing process. The time-frequency analysis is used to analyze the operating state of the corresponding additive manufacturing equipment of the specified additive product during the additive manufacturing process; Step 4, based on the time-frequency analysis result, determine whether to send it to the edge node. If so, perform edge node optimization according to the obtained working parameters. Otherwise, continue to monitor the production process of the specified additive product in the next additive manufacturing period. The working parameters are used to reflect the fault repair state of the corresponding additive manufacturing equipment of the specified additive product during the additive manufacturing process. The edge node optimization means adjusting the memory allocation and the number of threads to improve the load balancing of the edge node.

[0057] At the start of the process, the multi-modal data is collected in real time through the data acquisition module, and the thermal imaging data is processed using the abnormal hot spot identification module to detect whether there are abnormal hot spots. If abnormal hot spots are detected, the process enters the time-frequency analysis module to analyze the acoustic signals; if no abnormalities are detected, step size optimization is performed, and then the data in the next period is continuously monitored. After being processed by the time-frequency analysis module, it is judged whether to upload the data to the cloud. If uploading is selected, it enters the edge node optimization module for optimization processing; if uploading is not selected, step size optimization is performed, and the next period is continuously monitored. After being processed by the edge node optimization module, the process ends.

[0058] The design logic of the entire flowchart is to realize the intelligent monitoring and optimization of the system through the collection and analysis of multi-modal data, combined with anomaly detection and signal processing technologies.

[0059] In this embodiment, through steps such as real-time collection of multi-modal data, abnormal hot spot identification and time-frequency analysis, as well as step size optimization and edge node optimization, the comprehensive monitoring and optimization of the additive product production process are realized. Compared with the prior art, this method has higher monitoring accuracy, system stability, data processing efficiency, intelligent production level and lower operation and maintenance costs.

[0060] In summary, the embodiments of the present application identify abnormal hotspots through the acquired thermal imaging data, and then determine whether to upload them to the cloud server based on the identification results of the abnormal hotspots. If so, time-frequency analysis is performed according to the acquired acoustic signal data; otherwise, the step size is optimized. Finally, based on the time-frequency analysis results, it is determined whether to perform edge node optimization according to the acquired working parameters, thereby realizing the accurate identification and efficient processing of abnormal operating states in the production process of specified additive products, better adapting to the complex changes in the production process of additive products, and effectively solving the problem of low accuracy in the collaborative identification of abnormal operating states and acoustic signal states in the monitoring process of the production process of additive products in the prior art.

[0061] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0062] The present invention is described with reference to the flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0063] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device realizes the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Therefore, the instructions executed on the computer or other programmable device provide for realizing the specified functions in Figure 1Steps of the functions specified in one process or multiple processes and / or boxes Figure 1 Steps of the functions specified in one box or multiple boxes.

[0065] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they know the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0066] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. The additive product production process monitoring system based on edge computing is characterized by: include: Data acquisition module, abnormal hotspot identification module, time-frequency analysis module and edge node optimization module; The data acquisition module is used to collect multimodal data of a specified additive product in a target monitoring area at the end of an additive manufacturing period in real time. The multimodal data is collected in real time by edge computing nodes with sensors and actuators deployed in the target monitoring area. The multimodal data includes thermal imaging data and acoustic wave signal data. The abnormal hot spot identification module is used to identify abnormal hot spots according to the acquired thermal imaging data to obtain abnormal hot spot identification results, and the abnormal hot spot identification is used to analyze the temperature change of the specified additive product during the additive manufacturing process; The time-frequency analysis module is used to determine whether to upload to the cloud server based on the abnormal hotspot identification result. If so, the time-frequency analysis is performed according to the acquired acoustic signal data to obtain the time-frequency analysis result. Otherwise, the step length optimization is performed. The step length optimization means adjusting the step length of the 3D printing path to improve the heat dissipation capacity during the 3D printing process. The time-frequency analysis is used to analyze the operating status of the corresponding additive manufacturing equipment of the specified additive product during the additive manufacturing process; The edge node optimization module is used to determine whether to send it to the edge node based on the time-frequency analysis results. If so, the edge node is optimized according to the obtained working parameters. Otherwise, the production process of the specified additive product in the next additive manufacturing period continues to be monitored. The working parameters are used to reflect the fault repair status of the corresponding additive manufacturing equipment of the specified additive product during the additive manufacturing process. The edge node optimization means improving the load balancing of the edge node by adjusting the memory allocation and the number of threads.

2. The additive product production process monitoring system based on edge computing as claimed in claim 1, characterized in that: The thermal imaging data is used to reflect the temperature change of the specified additive product in the target monitoring area during the additive manufacturing process; The acoustic wave signal data is used to reflect the ultrasonic changes of the specified additive product in the target monitoring area during the 3D printing process, and the acoustic wave signal data includes energy frequency peak, total energy of the time-frequency graph and abnormal frequency ratio; The energy frequency peak is used to quantify the energy concentration of the corresponding acoustic wave signal of the specified additive product in the target monitoring area during the 3D printing period; The total energy of the time-frequency graph is used to quantify the overall energy distribution of the corresponding acoustic wave signal of the specified additive product in the target monitoring area during the 3D printing period; The abnormal frequency ratio is used to quantify the proportion of abnormal frequency components in the corresponding sound wave signal of a specified additive product in the target monitoring area during the 3D printing period.

3. The additive product production process monitoring system based on edge computing as claimed in claim 2, characterized in that: The specific process of obtaining the thermal imaging data is as follows: E1, obtaining the molten pool coverage area of ​​the specified additive product in the target monitoring area at the end of the additive manufacturing period. If the obtained molten pool coverage area is larger than the set value of the molten pool coverage area in the database, it indicates that the molten pool coverage area is effectively obtained and E2 is executed. Otherwise, the obtained molten pool coverage area deviation is input into the spot size algorithm to output the spot size increase amplitude, until the obtained molten pool coverage area is larger than the set value of the molten pool coverage area in the database, then E2 is executed. The molten pool coverage area is used to quantify the fusion effect of the specified additive product in the target monitoring area during the additive manufacturing period; E2, obtain the average melt pool temperature and thermal gradient value of the specified additive product in the melt pool coverage area at the end of the additive manufacturing period, the average melt pool temperature is used to quantify the energy input stability of the specified additive product in the target monitoring area during the additive manufacturing period, and the thermal gradient value is used to quantify the thermal stress distribution of the specified additive product in the target monitoring area during the additive manufacturing period.

4. The additive product production process monitoring system based on edge computing as claimed in claim 3, characterized in that: The specific process of identifying abnormal hot spots based on the acquired thermal imaging data is as follows: The difference between the obtained molten pool coverage area and the set value of the molten pool coverage area in the database is compensated by the molten pool coverage area compensation factor to obtain the molten pool coverage area index; The difference between the obtained average temperature of the molten pool and the average temperature setting value of the molten pool in the database is compensated by the average temperature compensation factor of the molten pool to obtain the average temperature index of the molten pool; The difference between the obtained thermal gradient value and the set value of the thermal gradient value in the database is compensated by the thermal gradient value compensation factor to obtain the thermal gradient value index; The acquired melt pool coverage area index, melt pool average temperature index and thermal gradient value index are coupled and processed to obtain an abnormal hotspot judgment value, which is used to reflect the abnormal degree of the working temperature of the corresponding additive production equipment in the additive manufacturing process of the specified additive product according to the thermal imaging data, and the thermal imaging data includes the melt pool coverage area, the melt pool average temperature and the thermal gradient value.

5. The additive product production process monitoring system based on edge computing as claimed in claim 4, characterized in that: The specific process of determining whether to upload the abnormal hotspot identification result to the cloud server is as follows: If the abnormal hot spot determination value obtained is not greater than the abnormal hot spot determination value preset in the database, the abnormal hot spot identification result is recorded as no abnormal temperature component in the molten pool coverage area, and the corresponding thermal imaging data is uploaded to the cloud server. Otherwise, the abnormal hot spot identification result is recorded as abnormal temperature component in the molten pool coverage area and the step size is adjusted; The specific process of step size adjustment is as follows: The obtained hot spot abnormality judgment value deviation and step length deviation are input into the step length PID control algorithm of the additive manufacturing equipment to output the actual step length adjustment amplitude; When the hot spot anomaly judgment value reacquired after the step length adjustment is not greater than the hot spot anomaly judgment value preset in the database and the number of adjustments does not exceed the preset number, the step length adjustment is completed and the reacquired thermal imaging data is uploaded to the cloud server, otherwise a shutdown command is sent and the preset personnel are prompted to perform maintenance; The hotspot abnormality determination value deviation is used to quantify the degree of difference between the acquired abnormal hotspot determination value and the preset abnormal hotspot determination value; The step size deviation is used to quantify the degree of difference between the actual step size and the target step size of the corresponding 3D printing path at the end of the additive manufacturing period.

6. The additive product production process monitoring system based on edge computing as claimed in claim 2, characterized in that: The time-frequency analysis is performed based on the acquired sound wave signal data, and the specific process is as follows: Obtain the difference between the energy frequency peak value and the maximum allowable energy frequency peak value in the database, perform compensation operation in combination with the introduced energy frequency peak compensation factor, and obtain the energy frequency peak index; Obtain the difference between the total energy of the time-frequency graph and the total energy of the reference time-frequency graph in the database, perform compensation calculation based on the total energy compensation factor of the time-frequency graph introduced, and obtain the total energy index of the time-frequency graph; The difference between the abnormal frequency ratio and the maximum allowable abnormal frequency ratio in the database is obtained, and a compensation operation is performed in combination with the introduced abnormal frequency ratio compensation factor to obtain the abnormal frequency ratio index; The result of inverse proportional processing of the total energy index of the acquired time-frequency graph is coupled with the acquired energy frequency peak index and abnormal frequency proportion index to obtain a time-frequency analysis judgment value, which is used to reflect the abnormality of the acoustic wave signal data for the corresponding acoustic wave signal of the specified additive product during the 3D printing process.

7. The additive product production process monitoring system based on edge computing as claimed in claim 6, characterized in that: The specific process of judging whether to send the message to the edge node based on the time-frequency analysis result is as follows: If the obtained time-frequency analysis judgment value is not greater than the time-frequency analysis judgment value preset in the database, the time-frequency analysis result is recorded as no abnormal frequency component in the molten pool coverage area and the production process monitoring instruction for the next additive manufacturing period is sent; otherwise, the time-frequency analysis identification result is recorded as abnormal frequency component in the molten pool coverage area and the FFT point number is adjusted; The FFT point adjustment means adjusting the amplitude of the acquired FFT points to optimize the balance between time and frequency resolution; The FFT point adjustment amplitude represents the result obtained by inputting the obtained time-frequency analysis determination value deviation and frequency resolution deviation into the LMS algorithm and outputting the result; The time-frequency analysis determination value deviation is used to quantify the degree of difference between the acquired time-frequency analysis determination value and the preset time-frequency analysis determination value; The frequency resolution deviation is used to quantify the degree of difference between the actual frequency resolution and the target frequency resolution of the adaptive adjustment filter of the time-frequency diagram corresponding to the molten pool coverage area at the end of the 3D printing period.

8. The additive product production process monitoring system based on edge computing as claimed in claim 1, characterized in that: The edge node optimization represents a first edge node optimization and a second edge node optimization; The first edge node optimization represents the optimization corresponding to when the obtained harmonic mean value of the working parameters is greater than the maximum value of the harmonic mean values ​​of the historical working parameters in the database; The specific process of the first edge node optimization is as follows: F11, inputting the obtained working parameter deviation and working parameter average value into the memory allocation adjustment algorithm to output the memory allocation increase amplitude, and after one memory allocation increase, determining whether the decrease amplitude corresponding to the obtained first working parameter harmonic average value deviation is greater than the decrease amplitude preset in the database; F12, if the reduction amplitude corresponding to the deviation of the harmonic mean value of the working parameters obtained is greater than the reduction amplitude preset in the database, then continue to increase the memory allocation of the preset amplitude until the harmonic mean value of the working parameters obtained is within the allowable range of the harmonic mean value of the working parameters in the database; F13, if the reduction range corresponding to the obtained harmonic mean deviation of the working parameters is not greater than the reduction range preset in the database, the working parameter deviation and the working parameter average value re-obtained after the memory allocation is increased once are input into the thread quantity adjustment algorithm to output the thread quantity reduction range and return to F11; The working parameter harmonic mean value represents the result of harmonic mean processing of the acquired working parameters; The working parameters include average fault working time and average fault repair time; The first working parameter harmonic mean deviation represents the difference between the obtained working parameter harmonic mean and the maximum value of the historical working parameter harmonic mean.

9. The additive product production process monitoring system based on edge computing as claimed in claim 8, characterized in that: The second edge node optimization represents the optimization corresponding to when the obtained harmonic mean value of the working parameters is less than the minimum harmonic mean value of the historical working parameters in the database; The specific process of the second edge node optimization is as follows: F21, inputting the obtained working parameter deviation and working parameter average value into the memory allocation adjustment algorithm to output the memory allocation reduction amplitude, and after a memory allocation reduction, determining whether the reduction amplitude corresponding to the obtained second working parameter harmonic average value deviation is greater than the reduction amplitude preset in the database; F22, if the reduction amplitude corresponding to the obtained working parameter harmonic mean deviation is greater than the reduction amplitude preset in the database, continue to reduce the memory allocation of the preset amplitude until the obtained working parameter harmonic mean is within the allowable range of the working parameter harmonic mean in the database; F23, if the reduction range corresponding to the obtained harmonic mean deviation of the working parameters is not greater than the reduction range preset in the database, the working parameter deviation and the working parameter mean value re-obtained after the memory allocation is reduced once are input into the thread quantity adjustment algorithm to output the thread quantity increase range and return to F21; The second working parameter harmonic mean deviation represents the difference between the minimum value of the historical working parameter harmonic mean and the obtained working parameter harmonic mean.

10. The method for monitoring the production process of additive products based on edge computing is characterized in that: The following steps are involved: Step 1: real-time acquisition of multimodal data of a specified additive product in a target monitoring area at the end of an additive manufacturing period, wherein the multimodal data is acquired in real time by edge computing nodes with sensors and actuators deployed in the target monitoring area, and the multimodal data includes thermal imaging data and acoustic wave signal data; Step 2: performing abnormal hot spot identification based on the acquired thermal imaging data to obtain abnormal hot spot identification results, wherein the abnormal hot spot identification is used to analyze the temperature change of the specified additive product during the additive manufacturing process; Step 3: Based on the abnormal hotspot identification results, determine whether to upload to the cloud server. If yes, perform time-frequency analysis based on the acquired acoustic signal data to obtain the time-frequency analysis results. Otherwise, perform step length optimization. The step length optimization means adjusting the step length of the 3D printing path to improve the heat dissipation capacity during the 3D printing process. The time-frequency analysis is used to analyze the operating status of the corresponding additive manufacturing equipment of the specified additive product during the additive manufacturing process. Step 4: Based on the time-frequency analysis results, determine whether to send it to the edge node. If so, optimize the edge node according to the obtained working parameters. Otherwise, continue to monitor the production process of the specified additive product in the next additive manufacturing period. The working parameters are used to reflect the fault repair status of the corresponding additive manufacturing equipment of the specified additive product during the additive manufacturing process. The edge node optimization means improving the load balancing of the edge node by adjusting the memory allocation and the number of threads.

Images