Intelligent fault diagnosis method for metal 3D printing equipment

Abstract

The invention relates to the technical field of metal 3D printing equipment fault diagnosis, in particular to an intelligent fault diagnosis method for metal 3D printing equipment, which comprises the following steps of: calculating a quality value of a molten pool by monitoring state information of the molten pool in real time, judging whether the quality value is abnormal or not, and triggering a fault self-checking instruction if the quality value is abnormal; the method comprises the following steps: analyzing vibration amplitude and positioning error data of a laser scanning galvanometer, calculating a stability evaluation value and judging whether the stability evaluation value is normal or not, if so, monitoring power distribution and a focus drift distance of each height layer of a laser beam, calculating an irradiation evaluation value and judging whether the stability evaluation value is normal or not, and if not, monitoring the powder spraying amount of a powder nozzle, and comparing and analyzing the powder spraying amount with a reference value. And calculating a powder spraying evaluation value, judging whether the powder spraying evaluation value is normal or not, and if all the parts are abnormal, matching a fault grading condition table and determining a fault grade, so that intelligent and precise real-time fault diagnosis and automatic grading are realized, and the printing efficiency and the yield are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of metal 3D printing equipment fault diagnosis, and specifically provides an intelligent fault diagnosis method for metal 3D printing equipment. Background Art

[0002] A metal 3D printing device is an advanced manufacturing device that manufactures three-dimensional entities through a layer-by-layer construction method based on the principles of layered manufacturing and discrete accumulation. This technology is widely applicable to various metal materials such as aluminum alloy, titanium alloy, and stainless steel, and can meet the requirements for the manufacture of complex structures in multiple industrial fields such as aerospace, automotive manufacturing, and biomedical; Currently, the quality inspection of metal 3D printing usually relies on means such as X-ray CT scanning to identify printing defects. However, most of these methods are used for inspection after printing is completed, making it difficult to detect abnormalities generated during the printing process in a timely manner, resulting in a high waste piece rate and increased production costs. Therefore, it is crucial to achieve real-time monitoring and fault diagnosis of the equipment during the printing process; During the metal 3D printing process, the molten pool is the core area where the material melts, and its state directly affects the printing quality and the final forming effect. The state of the molten pool is affected by various factors, among which the states of the laser scanning galvanometer, laser beam, and powder nozzle are the key factors leading to abnormal molten pools. If the abnormalities of these components in the metal 3D printing equipment cannot be detected in a timely manner, it is easy to cause the accumulation of printing defects and affect the quality of the finished product; To solve the above defects, a technical solution is provided now. Summary of the Invention

[0003] The present invention aims to solve the problem that existing metal 3D printing equipment lacks accurate analysis of the molten pool state during the printing process to timely identify abnormalities and real-time monitoring and diagnosis of the fault source, and proposes an intelligent fault diagnosis method for metal 3D printing equipment.

[0004] The object of the present invention can be achieved through the following technical solutions: An intelligent fault diagnosis method for a metal 3D printing device includes the following steps: By monitoring and analyzing the molten pool state information of the printing device, calculate the quality state value of the molten pool, and accordingly determine whether the molten pool is in an abnormal state. If the molten pool is determined to be in an abnormal state, trigger a fault self-check instruction; Based on the triggered fault self-check instruction, obtain the vibration amplitude data and positioning error data of the laser scanning galvanometer, calculate the vibration amplitude change difference and positioning error fluctuation value, determine the stability evaluation value of the laser scanning galvanometer, and determine whether it is in a normal state; If it is in a normal state, obtain the laser power of each height layer of the laser beam and the laser focus drift distance of the laser beam, calculate the power stability value and focus drift value, determine the irradiation evaluation value of the laser beam, and determine whether it is in a normal state; If it is in the normal state, obtain the powder spraying amount of the powder nozzle, compare and analyze it with the reference powder spraying amount, determine the powder spraying evaluation value of the powder nozzle, and determine whether it is in the normal state; If the laser scanning galvanometer, the laser beam, and the powder nozzle are all determined to be in the abnormal state, match the corresponding fault classification situation table to determine the fault level.

[0005] Furthermore, the specific process of solving the quality state value of the molten pool is as follows: Obtain the thermal imaging image of the molten pool during the current monitoring period; Extract the morphological parameters of the molten pool from the thermal imaging image, and then compare and analyze the morphological parameters of the molten pool with the corresponding preset reference intervals respectively. If a certain morphological parameter is within the preset reference interval, assign a value of 0, otherwise assign a value of 1, and accumulate all the assigned values to calculate the morphological value of the molten pool; Extract the color temperature of each detection point in the thermal imaging image, and match it with the reference color temperature of the corresponding thermal imaging image of the molten pool to determine the color temperature mismatch points, and integrate the adjacent color temperature mismatch points to form each abnormal area in the thermal imaging image; Extract the numerical values of the color temperature of each detection point in each abnormal area for average calculation to obtain the average color temperature of each abnormal area, which is used as the temperature value of each abnormal area in the thermal imaging image; Compare and analyze the temperature values of each abnormal area in the thermal imaging image with the temperature threshold to determine the overheated area and the overcooled area; Calculate the total areas of the overheated area and the overcooled area, and calculate their ratios with the area of the thermal imaging image respectively to obtain the overheat ratio and the overcool ratio. Multiply the overheat ratio and the overcool ratio by the corresponding weight coefficients respectively and then add them to obtain the temperature distribution value of the molten pool; Identify the defects of the molten pool during the current monitoring period, calculate the total area of each type of defect area, and calculate its ratio with the total area of the molten pool to obtain the defect value of the molten pool; Determine the quality state value of the molten pool based on the morphological value, the temperature distribution value, and the defect value.

[0006] Furthermore, the specific process of solving the vibration amplitude change difference is as follows: Obtain the vibration amplitude data of the laser scanning galvanometer during the current monitoring period, and perform average calculation on it to obtain the average vibration amplitude of the laser scanning galvanometer. Calculate the difference between the vibration amplitude data of the laser scanning galvanometer at each monitoring moment and the average vibration amplitude to obtain the vibration amplitude change value, and extract the maximum vibration amplitude change value and the minimum vibration amplitude change value from it for difference calculation to obtain the vibration amplitude change difference.

[0007] Furthermore, the specific process of solving the positioning error fluctuation value is as follows: Obtain the error between the target position and the actual position of the laser scanning galvanometer under the same instruction during the current monitoring period, and construct a positioning error sequence; Eliminate the maximum positioning error and the minimum positioning error in the positioning error sequence, and then calculate the root mean square error of the error sequence after elimination to obtain the positioning error fluctuation value.

[0008] Furthermore, the specific process of solving the power stability value is as follows: Divide the laser beam into each height layer according to a preset height to obtain each height layer of the laser beam; Obtain the laser power of each height layer of the laser beam at each monitoring moment during the current monitoring period, and arrange them in descending order to obtain a laser power sequence, and extract the mode laser power from it as the laser power of each height layer of the laser beam corresponding to the current monitoring period, and then form a power data set of the laser beam corresponding to the current monitoring period; Thus, analyze the power stability corresponding to the laser beam to obtain the power stability value corresponding to the laser beam.

[0009] Furthermore, the specific process of solving the focus drift value is as follows: Obtain the laser focus position of the laser beam at each monitoring moment during the current monitoring period, calculate the distance difference between it and the corresponding laser focus target position to obtain the laser focus drift distance of the laser beam during the current monitoring period, and sort it in descending order to obtain a laser focus drift distance sequence; Extract the values of the first one-third of the laser focus drift distances in the laser focus drift distance sequence and calculate their average value to obtain the focus drift value corresponding to the laser beam.

[0010] Furthermore, the specific process of solving the powder spraying evaluation value of the powder nozzle is as follows: Obtain the powder spraying amount of the powder nozzle at each monitoring moment during the current monitoring period to obtain the powder spraying amount fpz of the powder nozzle at each monitoring moment during the current monitoring period t ; Extract the standard powder spraying amount of the powder nozzle corresponding to the current monitoring period as the reference powder spraying amount fpz of the powder nozzle corresponding to the current monitoring period * ; According to the formula: , obtain the powder spraying evaluation value FPZ of the powder nozzle, where t represents the number of each monitoring moment in the current monitoring period, n represents the total number of the numbers of each monitoring moment in the current monitoring period, k represents a natural constant greater than zero, Fpp t represents the powder spraying amount change rate of the powder nozzle corresponding to the current monitoring period, Fpp * represents the reference powder spraying amount change rate, and a1, a2, and λ all represent set correction factors.

[0011] Further, the specific process of determining the fault level is as follows: The stability evaluation value of the laser scanning galvanometer, the irradiation evaluation value of the laser beam, and the powder spraying evaluation value of the powder nozzle are respectively compared and matched with the fault classification situation table stored in the cloud database, and the corresponding fault level is obtained accordingly.

[0012] The technical solution provided by the present invention has the following beneficial effects compared with the known prior art: 1. In the present invention, through continuous monitoring of the molten pool state and combined with the analysis of key parameters of the laser scanning galvanometer, laser beam, and powder nozzle, compared with the traditional ex-post detection method relying on X-ray CT scanning, it can accurately detect abnormalities during the printing process, reduce the accumulation of defects, thereby realizing efficient fault intelligent detection and diagnosis of metal 3D printing equipment, significantly improving the equipment stability and printing quality, effectively reducing the waste piece rate, avoiding the discovery of defects only after printing is completed, thus reducing material waste and production losses, optimizing the production process, and improving the overall production efficiency.

[0013] 2. In the present invention, by adopting the multi-parameter fusion analysis technology, integrating various data such as molten pool thermal imaging analysis, vibration detection, laser power evaluation, and powder spraying amount monitoring, calculating core parameters such as the quality state value of the molten pool, the stability evaluation value of the laser scanning galvanometer, the irradiation evaluation value of the laser beam, and the powder spraying evaluation value of the powder nozzle, thus realizing accurate fault identification, reducing misjudgment and missed judgment, and improving the accuracy of anomaly detection.

[0014] 3. In the present invention, by constructing a cloud database to store the fault classification situation table and automatically matching and analyzing based on the monitoring data, intelligent fault classification is realized, significantly reducing manual intervention, making fault diagnosis more standardized and efficient, and improving the equipment maintenance response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0016] Figure 1 It is a block diagram of the overall process method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0018] As Figure 1 shown, an intelligent fault diagnosis method for a metal 3D printing device includes the following steps: A1: Monitor the molten pool state information of the printing device, and thus perform determination and analysis processing on the molten pool state of the printing device. The specific analysis process is as follows: Obtain the thermal imaging image of the molten pool during the current monitoring period through an infrared thermal imager; Extract the morphological parameters of the molten pool from the thermal imaging image. The morphological parameters include length, width, and depth. Then, compare and analyze the morphological parameters of the molten pool with the corresponding preset reference intervals respectively. If a certain morphological parameter is within the preset reference interval, assign a value of 0; otherwise, assign a value of 1. Accumulate all the assigned values to calculate the morphological value of the molten pool, and mark it as Rxt; Uniformly arrange detection points on the thermal imaging image to obtain each detection point in the thermal imaging image. Extract the color temperature of each detection point in the thermal imaging image and match it with the reference color temperature of the preset thermal imaging image corresponding to the molten pool. If the color temperature of a certain detection point matches the reference color temperature of the preset thermal imaging image corresponding to the molten pool, mark this detection point as a color temperature compliant point; if the color temperature of a certain detection point does not match the reference color temperature of the preset thermal imaging image corresponding to the molten pool, mark this detection point as a color temperature non-compliant point; Integrate each adjacent color temperature non-compliant point in the thermal imaging image to form each abnormal area in the thermal imaging image; Extract the numerical values of the color temperature of each detection point in each abnormal area for average calculation to obtain the average color temperature of each abnormal area, which is used as the temperature value of each abnormal area in the thermal imaging image; Compare and analyze the temperature values of each abnormal area in the thermal imaging image with the preset temperature threshold. If the temperature value of an abnormal area in the thermal imaging image is greater than or equal to the preset temperature threshold, mark this abnormal area in the thermal imaging image as an overheated area; if the temperature value of an abnormal area in the thermal imaging image is less than the preset temperature threshold, mark this abnormal area in the thermal imaging image as a cold area; Calculate the total areas of the overheated area and the cold area, and calculate their ratios to the area of the thermal imaging image respectively to obtain the overheat ratio and the cold ratio. Multiply the overheat ratio and the cold ratio by the corresponding weight coefficients respectively and then add them to obtain the temperature distribution value of the molten pool, and mark it as Rwd; Identify the defects of the molten pool during the current monitoring period through machine vision technology. The defects include pores, cracks, and lack of fusion. Calculate the total area of various defect regions, and calculate the ratio with the total area of the molten pool to obtain the defect value of the molten pool, which is marked as Rqx. According to the formula: , obtain the quality value RCP of the molten pool, where η1, η2, and η3 respectively represent the weight coefficients of the shape value, temperature distribution value, and defect value. Compare and analyze the quality value of the molten pool with the preset reference comparison interval. When the quality value of the molten pool is within the preset reference comparison interval, the molten pool is determined to be in an abnormal state; otherwise, the molten pool is determined to be in a normal state. If the molten pool is determined to be in an abnormal state, trigger a fault self-check instruction and execute A2.

[0019] In a specific embodiment, the present invention can obtain the thermal imaging image of the molten pool by using an infrared thermal imager, analyze the shape parameters and color temperature distribution in the image, and can detect the possible overheated or overcooled areas in the molten pool in real time, so as to evaluate the temperature distribution state of the molten pool. At the same time, machine vision technology is used to identify the defects of the molten pool, which further enhances the accuracy of fault detection. By comprehensively calculating the quality value of the molten pool, it can efficiently determine whether the molten pool is in an abnormal state. If an abnormal state is detected, a fault self-check instruction will be automatically triggered, thus preventing defects from being discovered only after printing is completed, providing strong support for timely analyzing and diagnosing the fault state of the key components of the equipment in the future.

[0020] A2: Monitor the stable state information of the laser scanning galvanometer, and perform self-check analysis and processing on the stable state of the laser scanning galvanometer. The specific analysis process is as follows: Obtain the vibration amplitude data of the laser scanning galvanometer during the current monitoring period through a vibration sensor, and calculate its mean value to obtain the mean vibration amplitude of the laser scanning galvanometer. Calculate the difference between the vibration amplitude data of the laser scanning galvanometer at each monitoring moment during the current monitoring period and the mean vibration amplitude to obtain the vibration amplitude change value of the laser scanning galvanometer, and extract the maximum vibration amplitude change value and the minimum vibration amplitude change value from it for difference calculation to obtain the vibration amplitude change difference Xzd. Obtain the error between the target position and the actual position of the laser scanning galvanometer under the same instruction during the current monitoring period through a high-precision position sensor, and construct a positioning error sequence. Eliminate the maximum positioning error and the minimum positioning error in the positioning error sequence to reduce the influence of extreme values on the calculation results, and then calculate the root mean square error of the eliminated error sequence. The calculation formula is: , obtain the positioning error fluctuation value Xwz j, where N represents the total number of sampling points, i represents the sampling point number, and Epos(i) represents the positioning error of the i-th sampling point; According to the formula: , the stability evaluation value JXP of the laser scanning galvanometer is obtained, where η4 and η5 respectively represent the weight coefficients of the change difference of the vibration amplitude and the fluctuation value of the positioning error; Compare and analyze the stability evaluation value of the laser scanning galvanometer with the preset stability evaluation threshold. When the stability evaluation value of the laser scanning galvanometer is greater than the preset stability evaluation threshold, the laser scanning galvanometer is determined to be in a normal state, and A3 is executed; When the stability evaluation value of the laser scanning galvanometer is less than or equal to the preset stability evaluation threshold, the laser scanning galvanometer is determined to be in an abnormal state, and the stability evaluation value of the laser scanning galvanometer is compared and matched with the fault classification table stored in the cloud database to obtain the fault level of the laser scanning galvanometer; Among them, each stability evaluation value of the laser scanning galvanometer corresponds to a fault level, and the fault levels include the first-level fault level, the second-level fault level, and the third-level fault level; And send the fault level of the laser scanning galvanometer to the management terminal, and thus display and notify the fault level of the laser scanning galvanometer through the display terminal; In a specific embodiment, the present invention calculates the change difference of the vibration amplitude and the fluctuation value of the positioning error, obtains the stability evaluation value of the laser scanning galvanometer, and compares it with the preset threshold to determine whether it is normal. If the stability of the laser scanning galvanometer does not meet the standard, according to the stability evaluation value of the laser scanning galvanometer and the fault classification table, the fault level is automatically matched, and the fault level information is sent to the management terminal for display and notification. Through this real-time monitoring, automatic fault diagnosis and fault level analysis, the abnormal state of the laser scanning galvanometer can be accurately identified, thereby providing accurate fault information for maintenance personnel, improving the fault diagnosis efficiency, and reducing the equipment downtime.

[0021] A3: Monitor the irradiation state information of the laser beam, and thus perform self-check analysis and processing on the irradiation state of the laser beam. The specific analysis process is as follows: Divide the laser beam into each height layer according to the preset height to obtain each height layer of the laser beam; Obtain the laser power of each height layer of the laser beam at each monitoring moment within the current monitoring time period through a laser power sensor, and arrange the laser power of each height layer of the laser beam at each monitoring moment within the current monitoring time period in descending order to obtain the laser power sequence of each height layer of the laser beam corresponding to the current monitoring time period. Then extract the mode laser power as the laser power of each height layer of the laser beam corresponding to the current monitoring time period. Furthermore, form the power data set of the laser beam corresponding to the current monitoring time period from the laser power of each height layer of the laser beam corresponding to the current monitoring time period; Analyze the power stability corresponding to the laser beam to obtain the power stability value corresponding to the laser beam. The specific analysis is as follows: Extract the numerical values of the laser power of each height layer of the laser beam corresponding to the current monitoring time period from the power data set of the laser beam corresponding to the current monitoring time period and denote it as jgz d , where d represents the number of each height layer, and d = 1, 2, 3…g, and g represents the total number of height layer numbers; According to the formula: , obtain the power stability value Zgz of the laser beam. Among them, e represents the set natural constant, and u d represents the influence factor corresponding to the set d-th height layer, and jgz d * represents the reference laser power corresponding to the set d-th height layer; Obtain the laser focus position of the laser beam at each monitoring moment corresponding to the current monitoring time period through a focus measuring instrument, and calculate the distance difference between it and the corresponding laser focus target position to obtain the laser focus drift distance of the laser beam within the current monitoring time period; Sort all the laser focus drift distance data of the laser beam within the current monitoring time period from large to small to obtain the laser focus drift distance sequence. The specific expression is: H 1 ≥H 2 ≥H 3 ≥…≥H M , where H 1 is the maximum laser focus drift distance, H M is the minimum laser focus drift distance, and M represents the total number of laser focus drift distances; Extract the numerical values of the first one-third of the laser focus drift distances in the laser focus drift distance sequence to calculate the average value to obtain the focus drift value corresponding to the laser beam and mark it as Zjd; According to the formula: , obtain the irradiation evaluation value ZSP of the laser beam, where η6 and η7 respectively represent the weight coefficients of the power stability value and the focus drift value; Compare and analyze the irradiation evaluation value of the laser beam with a preset irradiation evaluation threshold value. When the irradiation evaluation value of the laser beam is greater than the preset irradiation evaluation threshold value, the laser beam is determined to be in a normal state, and A4 is executed; When the irradiation evaluation value of the laser beam is less than or equal to the preset irradiation evaluation threshold value, the laser beam is determined to be in an abnormal state, and the irradiation evaluation value of the laser beam is compared and matched with the fault classification table stored in the cloud database to obtain the fault level of the laser beam; Among them, each irradiation evaluation value of the laser beam corresponds to a fault level, and the fault levels include a first-level fault level, a second-level fault level, and a third-level fault level; And send the fault level of the laser beam to the management terminal, and then display and notify the fault level of the laser beam through the display terminal; In a specific embodiment, the present invention determines the irradiation evaluation value of the laser beam by calculating the power stability value and the focus drift value of the laser beam, and compares it with a preset threshold value to determine whether the laser beam is in a normal state. If the irradiation state of the laser beam does not meet the standard, it will match the fault classification table based on the irradiation evaluation value, automatically determine the fault level, and send the information to the management terminal for display and notification. Thus, through the above multi-level data analysis and intelligent evaluation, the accuracy of laser beam anomaly detection is significantly improved, manual intervention is reduced, the stability of the printing process is ensured, and the working efficiency and finished product quality of the metal 3D printing equipment are optimized.

[0022] A4: Monitor the powder spraying state information of the powder nozzle, and thus perform self-check analysis and processing on the powder spraying state of the powder nozzle. The specific analysis process is as follows: Obtain the powder spraying amount of the powder nozzle at each monitoring moment corresponding to the current monitoring time period through a laser particle counter, obtain the powder spraying amount of the powder nozzle at each monitoring moment corresponding to the current monitoring time period, and mark it as fpz t ; Extract the standard powder spraying amount of the powder nozzle corresponding to the current monitoring time period from the cloud database as the reference powder spraying amount of the powder nozzle corresponding to the current monitoring time period, and calibrate it as fpz * ; According to the formula: , obtain the powder spraying evaluation value FPZ of the powder nozzle, where t represents the number of each monitoring moment in the current monitoring time period, n represents the total number of the numbers of each monitoring moment in the current monitoring time period, k represents a natural constant greater than zero, Fpp t represents the powder spraying amount change rate of the powder nozzle corresponding to the current monitoring time period, Fpp * represents the reference powder spraying amount change rate, and a1, a2, and λ all represent set correction factors; Compare and analyze the powder spraying evaluation value of the powder nozzle with a preset powder spraying evaluation threshold. When the powder spraying evaluation value of the powder nozzle is less than the preset powder spraying evaluation threshold, the powder nozzle is determined to be in a normal state; When the powder spraying evaluation value of the powder nozzle is greater than or equal to the preset powder spraying evaluation threshold, the powder nozzle is determined to be in an abnormal state, and the powder spraying evaluation value of the powder nozzle is compared and matched with the fault classification table stored in the cloud database to obtain the fault level of the powder nozzle; Among them, each powder spraying evaluation value of the powder nozzle has a corresponding fault level, and the fault levels include a first-level fault level, a second-level fault level, and a third-level fault level; And send the fault level of the powder nozzle to the management terminal, and thus display and notify the fault level of the powder nozzle through the display terminal; In a specific embodiment, the present invention intelligently monitors and diagnoses the powder spraying state of the powder nozzle, obtains the powder spraying amount data through a laser particle counter, compares it with the standard powder spraying amount in the cloud database, calculates the powder spraying evaluation value of the powder nozzle, and compares it with a preset threshold to determine whether the powder nozzle is in a normal state. If the powder spraying state of the powder nozzle does not meet the standard, match it with the fault classification table, determine the fault level, and send the fault information to the management terminal and notify it through the display terminal, thereby improving the accuracy of powder spraying abnormality detection, reducing manual intervention, and ensuring the quality and stability of metal 3D printing.

[0023] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps described in any one of the above are implemented; A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, the steps described in any one of the above are implemented.

[0024] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific implementation manners. Obviously, many modifications and changes can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the art in the technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An intelligent fault diagnosis method for metal 3D printing equipment, characterized in that: The following steps are involved: By monitoring and analyzing the molten pool status information of the printing device, the quality value of the molten pool is calculated, and it is determined whether the molten pool is in an abnormal state. If the molten pool is determined to be in an abnormal state, a fault self-check instruction is triggered; Based on the triggered fault self-check instruction, the vibration amplitude data and positioning error data of the laser scanning galvanometer are obtained, the vibration amplitude change difference and positioning error fluctuation value are calculated, the stability evaluation value of the laser scanning galvanometer is determined, and it is determined whether it is in a normal state; If it is in a normal state, the laser power of each height layer of the laser beam and the laser focus drift distance of the laser beam are obtained, the power stability value and the focus drift value are calculated, the irradiation evaluation value of the laser beam is determined, and it is determined whether it is in a normal state; If it is in a normal state, the powder spraying amount of the powder nozzle is obtained, and compared and analyzed with the reference powder spraying amount to determine the powder spraying evaluation value of the powder nozzle and determine whether it is in a normal state; If the laser scanning galvanometer, laser beam and powder nozzle are all judged to be in abnormal state, the corresponding fault classification table is matched to determine the fault level.

2. The intelligent fault diagnosis method for metal 3D printing equipment according to claim 1, characterized in that: The specific process of solving the quality value of the molten pool is as follows: Obtain a thermal imaging image of the molten pool during the current monitoring period; The morphological parameters of the molten pool are extracted from the thermal imaging image, and then the morphological parameters of the molten pool are compared and analyzed with the corresponding preset reference intervals. If a certain morphological parameter is within the preset reference interval, it is assigned a value of 0, otherwise it is assigned a value of 1, and all the assigned values ​​are accumulated to calculate the morphological value of the molten pool; Extract the color temperature of each detection point in the thermal imaging image, match it with the reference color temperature of the corresponding thermal imaging image of the molten pool, determine the color temperature discrepancy points, integrate the adjacent color temperature discrepancy points, and form the abnormal areas in the thermal imaging image; The color temperature values ​​of each detection point in each abnormal area are extracted and averaged to obtain the color temperature average of each abnormal area as the temperature value of each abnormal area in the thermal imaging image; Compare and analyze the temperature value of each abnormal area in the thermal imaging image with the temperature threshold to determine the overheated area and the overcooled area; The total area of ​​the overheated area and the undercooled area is calculated, and the total area is proportionally calculated with the area of ​​the thermal imaging image to obtain the overheat ratio and the undercooling ratio. The overheat ratio and the undercooling ratio are multiplied by the corresponding weight coefficients and then added to obtain the temperature distribution value of the molten pool. Identify the defects of the molten pool during the current monitoring period, calculate the total area of ​​each defect area, and calculate the ratio of the total area of ​​the molten pool to obtain the defect value of the molten pool; The quality value of the molten pool is determined based on the morphology value, temperature distribution value and defect value.

3. The intelligent fault diagnosis method for metal 3D printing equipment according to claim 1, characterized in that: The specific process of solving the difference in vibration amplitude change is as follows: The vibration amplitude data of the laser scanning galvanometer during the current monitoring time period is obtained, and the average is calculated to obtain the vibration amplitude average of the laser scanning galvanometer. The vibration amplitude data of the laser scanning galvanometer at each monitoring moment is subtracted from the vibration amplitude average to obtain the vibration amplitude change value, and the maximum vibration amplitude change value and the minimum vibration amplitude change value are extracted therefrom for difference calculation to obtain the vibration amplitude change difference.

4. The intelligent fault diagnosis method for metal 3D printing equipment according to claim 1, characterized in that: The specific process of solving the positioning error fluctuation value is as follows: Obtain the error between the target position and the actual position of the laser scanning galvanometer under the same instruction in the current monitoring time period, and construct a positioning error sequence; The maximum positioning error and the minimum positioning error in the positioning error sequence are eliminated, and then the root mean square error of the error sequence after elimination is calculated to obtain the positioning error fluctuation value.

5. The intelligent fault diagnosis method for metal 3D printing equipment according to claim 1, characterized in that: The specific process of solving the power stability value is as follows: Dividing the laser beam into various height layers according to a preset height to obtain various height layers of the laser beam; The laser power at each monitoring time in the current monitoring time period corresponding to each height layer of the laser beam is obtained, and the laser power sequence is obtained by arranging the laser power sequence in descending order, and the mode laser power is extracted from the sequence as the laser power at each height layer of the laser beam corresponding to the current monitoring time period, thereby forming a power data set of the laser beam corresponding to the current monitoring time period; Thus, the power stability corresponding to the laser beam is analyzed to obtain the power stability value corresponding to the laser beam.

6. The intelligent fault diagnosis method for metal 3D printing equipment according to claim 1, characterized in that: The specific process of solving the focus drift value is as follows: Obtain the laser focus position of the laser beam at each monitoring moment in the current monitoring time period, and calculate the distance difference between it and the corresponding laser focus target position to obtain the laser focus drift distance of the laser beam in the current monitoring time period, and sort them from large to small to obtain a laser focus drift distance sequence; The values ​​of the first one-third of the laser focus drift distance in the laser focus drift distance sequence are extracted and averaged to obtain the focus drift value corresponding to the laser beam.

7. The intelligent fault diagnosis method for metal 3D printing equipment according to claim 1, characterized in that: The specific process of solving the powder spray evaluation of the powder nozzle is as follows: Get the powder spraying amount of the powder nozzle corresponding to each monitoring moment in the current monitoring time period, and get the powder spraying amount fpz of the powder nozzle corresponding to each monitoring moment in the current monitoring time period t ; Extract the standard powder spraying amount of the powder nozzle corresponding to the current monitoring time period as the reference powder spraying amount fpz of the powder nozzle corresponding to the current monitoring time period * ; According to the formula: , the powder spraying evaluation value FPZ of the powder nozzle is obtained, where t represents the number of each monitoring moment in the current monitoring time period, n represents the total number of each monitoring moment in the current monitoring time period, k represents a natural constant greater than zero, and Fpp t Indicates the powder spraying rate change rate of the powder nozzle corresponding to the current monitoring time period, Fpp * It represents the reference powder spraying amount change rate, and a1, a2, and λ all represent the set correction factors.

8. The intelligent fault diagnosis method for metal 3D printing equipment according to claim 1, characterized in that: The specific process of determining the fault level is as follows: The stability evaluation value of the laser scanning galvanometer, the irradiation evaluation value of the laser beam and the powder spraying evaluation value of the powder nozzle are respectively compared and matched with the corresponding fault classification table stored in the cloud database, thereby obtaining the corresponding fault level.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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