Predictive maintenance method and device for multi-laser-beam additive manufacturing equipment and electronic equipment

Abstract

The invention provides a predictive maintenance method and device for multi-laser-beam additive manufacturing equipment and electronic equipment, and the method comprises the steps: distributing working blocks for a plurality of laser heads based on an initial state score, a standby state score and an equipment-block matching degree of the multi-laser-beam additive manufacturing equipment; according to the equipment state parameters of the multi-laser-beam additive manufacturing equipment and the product quality parameters of the different working blocks in the machining process, a predictive maintenance strategy is determined; and then the multi-laser-beam additive manufacturing equipment is controlled to stop machining and feed back abnormal information. Or the machining work of the abnormal laser head is stopped, and the laser head is rematched for the working block corresponding to the abnormal laser head for machining; or abnormal information is fed back, and the original processing strategy is kept unchanged. According to the method, the overall machining precision and quality are improved, the situation that the machining quality and the machining efficiency are reduced due to the fact that the working state of the galvanometer is reduced during large workpiece machining is avoided, the machining efficiency is improved, and workpiece scrapping caused by shutdown is avoided.

Description

Technical Field

[0001] The present invention relates to the field of equipment detection technology, and in particular to a predictive maintenance method, device and electronic equipment for multi-laser beam additive manufacturing equipment. Background Art

[0002] Selective Laser Melting (SLM) is a high-precision, high-quality, high-degree-of-freedom laser additive manufacturing process. This technology uses a high-energy laser beam to quickly melt materials such as metal powders, and builds complex workpieces that are difficult or impossible to achieve in traditional manufacturing through layer-by-layer accumulation. The application of SLM technology marks an important innovation in the manufacturing industry in the era of Industry 4.0, especially in the fields of aerospace, medical health, and the automotive industry.

[0003] However, current laser additive equipment has problems such as insufficient efficiency and long processing cycles. Especially for the manufacture of large metal workpieces, the existing single laser beam processing method is often inefficient and takes a long time to process. To this end, one solution is to introduce multiple laser beams to work simultaneously, which can improve processing efficiency and shorten processing cycles to a certain extent. However, the complexity of the multi-laser beam system also increases the difficulty of control, requiring more sophisticated management and coordination.

[0004] In addition, long-term, uninterrupted operation will cause the temperature of the equipment to rise, leading to problems such as temperature drift, which will not only affect the processing quality, but may also lead to an increase in the failure rate of the equipment. In order to ensure the processing quality and equipment safety, it is sometimes necessary to temporarily stop the work for equipment cooling or maintenance. However, due to the particularity of SLM technology and the strict requirements on the processing quality of the workpiece, even a short stoppage may result in the direct scrapping of unfinished workpieces.

[0005] Therefore, how to solve the problem of performance degradation, low processing quality and efficiency of existing laser additive equipment due to long-term operation is an important issue that needs to be urgently solved in the field of equipment testing. Summary of the invention

[0006] The present invention provides a predictive maintenance method, device and electronic equipment for multi-laser beam additive manufacturing equipment, which are used to overcome the defects of existing laser additive equipment such as equipment performance degradation due to long-term operation and low processing quality and efficiency, improve the overall processing accuracy and quality, improve processing efficiency, and avoid workpiece scrapping due to downtime.

[0007] On the one hand, the present invention provides a predictive maintenance method for a multi-laser beam additive manufacturing device, wherein the multi-laser beam additive manufacturing device includes multiple laser heads, and the method includes: allocating working blocks to the multiple laser heads based on the initial state score, standby state score and device-block matching degree of the multi-laser beam additive manufacturing device; determining a predictive maintenance strategy according to the device state parameters of the multi-laser beam additive manufacturing device and the product quality parameters of different working blocks during the processing; according to the predictive maintenance strategy, controlling the multi-laser beam additive manufacturing device to stop processing and feedback abnormal information; or, stopping the processing of the abnormal laser head and re-matching the laser head for processing for the corresponding working block of the abnormal laser head; or, feedback the abnormal information and keep the original processing strategy unchanged; or, keep the original processing strategy unchanged.

[0008] Furthermore, the method allocates working blocks to multiple laser heads based on the initial state score, standby state score and equipment-block matching degree of the multi-laser beam additive manufacturing equipment, which includes: obtaining initial equipment state parameters of the multi-laser beam additive manufacturing equipment; comparing the initial equipment state parameters with theoretical parameters at the time of equipment startup in a pre-constructed process case data set to obtain comparison results; performing a score evaluation on the multi-laser beam additive manufacturing equipment according to the comparison result to obtain an initial state score; when the initial state score is lower than a warning threshold, performing error feedback and maintenance on the multi-laser beam additive manufacturing equipment; when the initial state score is higher than the warning threshold, entering the equipment standby stage.

[0009] Furthermore, the method allocates working blocks to multiple laser heads based on the initial state score, standby state score and equipment-block matching degree of the multi-laser beam additive manufacturing equipment, and also includes: monitoring the standby equipment state parameters of the multi-laser beam additive manufacturing equipment within a set time period; performing statistical analysis on the standby equipment state parameters within the set time period, and predicting the state trend of the standby equipment state parameters at the current moment and in the future time period; performing score evaluation on the multi-laser beam additive manufacturing equipment according to the state trend to obtain a standby state score; when the standby state score does not meet the minimum operating conditions, performing error feedback and maintenance on the multi-laser beam additive manufacturing equipment; when the standby state score meets the minimum operating conditions, allowing the multi-laser beam additive manufacturing equipment to process the workpiece.

[0010] Furthermore, the method allocates working blocks to multiple laser heads based on the initial state score, standby state score and device-block matching degree of the multi-laser beam additive manufacturing equipment, including: predicting the theoretical processing time of each laser head according to the laser beam scanning path, laser head working parameters and workpiece properties; evaluating the normal working probability of each laser head within the theoretical processing time according to the initial state score and the standby state score; determining the laser head with a normal working probability higher than the set probability as the target laser head, and determining the device-block matching degree between the target laser head and the working block to be allocated according to the processing accuracy; and allocating the corresponding working block to the target laser head according to the device-block matching degree.

[0011] Furthermore, the predictive maintenance strategy is determined based on the equipment status parameters of the multi-laser beam additive manufacturing equipment and the product quality parameters of different working blocks during the processing, including: when the equipment status parameters do not meet the preset safety conditions and the product quality parameters of the workpiece as a whole do not meet the preset processing quality conditions, the predictive maintenance strategy is determined as the multi-laser beam additive manufacturing equipment stops processing and feeds back abnormal information; when the equipment status parameters show a performance decline trend or the product quality parameters of the working blocks show a quality decline trend, and it is predicted that the equipment status parameters will not meet the preset safety conditions within the theoretical processing time, the predictive maintenance strategy is determined as stopping processing; Stop the processing of the abnormal laser head and re-match the laser head for the corresponding working block of the abnormal laser head for processing; wherein, the abnormal laser head is a laser head that is predicted to be unable to meet the preset safety conditions in the future period; when the equipment status parameters show a performance decline trend or the product quality parameters of the working block show a quality decline trend, but it is predicted that the equipment status parameters will continue to meet the preset safety conditions within the theoretical processing time, the predictive maintenance strategy is determined to feedback the abnormal information and keep the original processing strategy unchanged; when the equipment status parameters meet the preset safety conditions and the overall product quality parameters of the workpiece meet the preset processing quality conditions, the original processing strategy is kept unchanged.

[0012] Furthermore, the equipment status parameters include one or more combinations of positioning accuracy, temperature drift, equipment temperature, cooling system efficiency and zero point deviation of the motion mechanism; the product quality parameters include one or more combinations of shape deviation, size deviation, surface roughness, molten pool morphology, spatter particle size, spatter particle density and microcrack density.

[0013] In a second aspect, the present invention also provides a predictive maintenance device for multi-laser beam additive manufacturing equipment, wherein the multi-laser beam additive manufacturing equipment includes multiple laser heads, and the device includes: a work block allocation module, which is used to allocate work blocks to multiple laser heads based on the initial state score, standby state score and equipment-block matching degree of the multi-laser beam additive manufacturing equipment; a predictive maintenance strategy determination module, which is used to determine the predictive maintenance strategy according to the equipment state parameters of the multi-laser beam additive manufacturing equipment and the product quality parameters of different work blocks during the processing; a predictive maintenance strategy execution module, which is used to control the multi-laser beam additive manufacturing equipment to stop processing and feedback abnormal information according to the predictive maintenance strategy; or, stop the processing of the abnormal laser head and re-match the laser head for processing for the corresponding work block of the abnormal laser head; or, feedback the abnormal information and keep the original processing strategy unchanged; or, keep the original processing strategy unchanged.

[0014] In a third aspect, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a predictive maintenance method for a multi-laser beam additive manufacturing device as described in any one of the above is implemented.

[0015] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a predictive maintenance method for a multi-laser beam additive manufacturing device as described in any one of the above.

[0016] In a fifth aspect, the present invention further provides a computer program product, comprising a computer program, which, when executed by a processor, implements a predictive maintenance method for a multi-laser beam additive manufacturing device as described in any one of the above.

[0017] The predictive maintenance method for multi-laser beam additive manufacturing equipment provided by the present invention allocates working blocks to multiple laser heads based on the initial state score, standby state score and equipment-block matching degree of the multi-laser beam additive manufacturing equipment, and determines a predictive maintenance strategy according to the equipment state parameters of the multi-laser beam additive manufacturing equipment and the product quality parameters of different working blocks during the processing, and then controls the multi-laser beam additive manufacturing equipment to stop processing and feedback abnormal information according to the predictive maintenance strategy; or, stops the processing of the abnormal laser head and re-matches the laser head for processing for the corresponding working block of the abnormal laser head; or, feedbacks the abnormal information and keeps the original processing strategy unchanged; or, keeps the original processing strategy unchanged. This method improves the overall processing accuracy and quality by increasing the consideration of the equipment status score and the equipment-block matching degree, and using the processing area with the highest processing accuracy of the scanning galvanometer for processing; at the same time, by stopping the machine for maintenance before the workpiece processing quality or the working status of a laser head declines, the galvanometer downtime preparation time is reasonably arranged, thereby avoiding the situation in which the processing quality and processing efficiency are reduced due to the decline in the working status of the galvanometer during large-scale processing; in addition, by reallocating the processing blocks allocated to the stopped equipment to the remaining laser heads in real time after predicting an error or shutdown of the laser head, the processing efficiency is improved and the scrapping of workpieces due to shutdown is avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a flow chart of a predictive maintenance method for a multi-laser beam additive manufacturing device provided in an embodiment of the present invention.

[0020] Figure 2 It is a schematic diagram of the overall process of the predictive maintenance method of the multi-laser beam additive manufacturing equipment provided by an embodiment of the present invention.

[0021] Figure 3 This is one of the partial flow charts of the predictive maintenance method of the multi-laser beam additive manufacturing equipment provided by the embodiment of the present invention.

[0022] Figure 4 This is the second partial flow chart of the predictive maintenance method for multi-laser beam additive manufacturing equipment provided in an embodiment of the present invention.

[0023] Figure 5 It is a schematic structural diagram of a predictive maintenance device for multi-laser beam additive manufacturing equipment provided in an embodiment of the present invention.

[0024] Figure 6 It is a schematic diagram of the physical structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] It should be noted that the current laser additive equipment has problems such as insufficient efficiency and long processing cycle. Especially for the manufacture of large metal workpieces, the existing single laser beam processing method is often inefficient and takes a long time to process. In addition, long-term, uninterrupted operation will cause the temperature of the equipment to rise, leading to problems such as temperature drift. These problems will not only affect the processing quality, but may also lead to an increase in the failure rate of the equipment.

[0027] In order to ensure processing quality and equipment safety, it is sometimes necessary to temporarily stop the machine for equipment cooling or maintenance. However, due to the particularity of SLM technology and the strict requirements on workpiece processing quality, even a short stoppage may result in the direct scrapping of unfinished workpieces.

[0028] In view of this, the present invention proposes a predictive maintenance method for a multi-laser beam additive manufacturing device. In order to facilitate a clear and complete description of the predictive maintenance method for a multi-laser beam additive manufacturing device provided by an embodiment of the present invention, a brief description of the working principle of the multi-laser beam additive manufacturing device is first given here. The multi-laser beam additive manufacturing device includes multiple laser heads, and its basic working principle is: (1) powder bed preparation; (2) laser beam control; (3) scanning path planning; (4) layer-by-layer accumulation.

[0029] (1) Powder bed preparation. First, evenly spread the metal powder on the workbench to form a thin layer of powder bed. Then adjust the atmosphere in the working chamber to the required inert gas environment (such as argon or nitrogen) to exclude oxygen in the air and prevent oxidation of the metal powder.

[0030] (2) Laser beam control. Multiple lasers work simultaneously, each generating a high-energy laser beam. Each laser beam is precisely controlled by a scanning galvanometer of a laser head. The scanning galvanometer is driven by a high-speed, high-precision servo motor, which can quickly change the direction of the laser beam so that it can scan along a predetermined path on the powder bed. Finally, the laser beam is focused to a very small point through a focusing lens to achieve high energy density and ensure the melting and solidification of the powder. The laser can be a fiber laser, a diode laser, etc., which is not specifically limited here.

[0031] (3) Scanning path planning. Use computer-aided design (CAD) software to slice the three-dimensional model into multiple two-dimensional layers, generate the scanning path for each layer, and optimize the scanning path to reduce the moving distance of the laser beam and improve processing efficiency.

[0032] (4) Layer-by-layer accumulation. The laser beam selectively melts the powder on the powder bed according to the predetermined scanning path to form a solidified metal layer. After each layer is melted, the workbench descends by a layer thickness and a new powder layer is laid. The above process is repeated until the workpiece is completely formed.

[0033] The predictive maintenance method for multi-laser beam additive manufacturing equipment proposed in the present invention specifically includes: Figure 1 A schematic flow chart of a predictive maintenance method for a multi-laser beam additive manufacturing device provided in an embodiment of the present invention is shown.

[0034] like Figure 1 As shown, the method includes steps S110-S130, and steps S110-S130 and related steps will be described in detail below.

[0035] S110, allocating working blocks to multiple laser heads based on the initial state score, the standby state score, and the device-block matching degree of the multi-laser beam additive manufacturing equipment.

[0036] It is easy to understand that the complete cycle of processing components using multi-laser beam additive manufacturing equipment includes equipment startup stage, equipment standby stage, processing planning stage, processing operation stage, and product acceptance stage. The status of the multi-laser beam additive manufacturing equipment is scored in the equipment startup stage and the equipment standby stage respectively, and the corresponding initial state score and standby state score can be obtained, which will be detailed in the following embodiments.

[0037] Then, in the processing planning stage, the initial state score and the standby state score are used to evaluate whether each laser head in the multi-laser beam additive manufacturing equipment can normally complete the work task (processing task). Specifically, the theoretical processing time and standby time of each laser head are first predicted based on the laser beam scanning path, laser head working parameters, workpiece properties and laser head status. At the same time, the initial state score and the standby state score are used to evaluate whether each laser head can work normally within the theoretical processing time.

[0038] Among them, the laser operating parameters include but are not limited to laser power, scanning speed and layer thickness, the workpiece properties include but are not limited to the material type, size and complexity of the workpiece, and the laser head status includes but is not limited to the current position, operating status and temperature of the laser head.

[0039] For the laser heads that can work normally within the theoretical processing time, work blocks are allocated to them according to the matching degree between these laser heads and the work blocks to be allocated (i.e., the device-block matching degree). The device-block matching degree is determined according to the processing accuracy and status score. Therefore, the processing area with the highest processing accuracy of the scanning galvanometer can be used for processing to improve the overall processing accuracy and quality.

[0040] After the working blocks are allocated to the multiple laser heads, the process enters the processing operation phase and starts to execute step S120.

[0041] S120, determining a predictive maintenance strategy based on equipment status parameters of the multi-laser beam additive manufacturing equipment and product quality parameters of different working blocks during the processing.

[0042] It is easy to understand that during the processing operation stage, various installed sensors (such as temperature sensors, laser power sensors and powder state sensors) can be used to monitor various parameters in the processing process, including the equipment status parameters of the multi-laser beam additive manufacturing equipment and the product quality parameters of different working blocks in the processing process.

[0043] The equipment status parameters include one or more combinations of positioning accuracy, temperature drift, equipment temperature, cooling system efficiency, and zero-point deviation of the motion mechanism. The product quality parameters include one or more combinations of shape deviation, size deviation, surface roughness, molten pool morphology, spatter particle size, spatter particle density, and microcrack density.

[0044] Subsequently, based on the equipment status parameters and product quality parameters, the processed product quality score and equipment status score are performed in real time and in parallel to predict whether the quality of the processed workpiece meets the requirements and whether there are performance problems with the multi-laser beam additive manufacturing equipment. Regardless of the prediction result, this embodiment will provide a corresponding predictive maintenance strategy.

[0045] Specifically, statistical analysis is performed on the equipment status parameters and product quality parameters within a certain period of time during the processing, including but not limited to analyzing the changing patterns of parameters such as maximum values, extreme values, nth-order moments, and rates of change, to predict whether the various parameters are in a steady state at the current moment or in the future, and whether there are any situations that do not meet the preset safety conditions and / or preset processing quality conditions, so as to determine the corresponding predictive maintenance strategy.

[0046] In this embodiment, the predictive maintenance strategy may include stopping the operation of the multi-laser beam additive manufacturing equipment, feeding back abnormal information / alarm information / error information, stopping the processing of one or more laser heads in the multi-laser beam additive manufacturing equipment, re-matching the laser heads of the legacy working blocks according to the equipment-block matching degree, maintaining the original processing strategy, etc., without specific limitation here.

[0047] For example, if the processing quality of the current workpiece is lower than the expected processing requirements (preset processing quality conditions), or the performance parameters of the current equipment have exceeded the safety threshold (preset safety conditions), the processing system will stop running and feedback error information to the host computer.

[0048] For example, if it is predicted that the processing quality of a certain working area / block of the workpiece or the equipment performance of a certain laser head will gradually decrease and abnormalities will occur during the construction period, one or more laser heads will be stopped for maintenance, and the allocated working blocks will be reallocated based on the matching degree between the working blocks and the remaining laser heads. The host computer will feedback an alarm message, and the laser head in normal status will continue processing the remaining areas / working blocks.

[0049] For example, if it is predicted that the processing quality of a certain working area of ​​the workpiece or the equipment performance of a certain laser head will gradually decrease, but will not exceed the safety threshold within the construction period, the original processing strategy will remain unchanged, the host computer will feedback the alarm information, and continue to process the remaining area.

[0050] For another example, if the processing quality and equipment status evaluation of the current workpiece are normal, no additional program will be run and the original processing strategy will remain unchanged.

[0051] On the basis of determining the predictive maintenance strategy, further, still in the processing operation stage, step S130 is continued to be executed.

[0052] S130, according to the predictive maintenance strategy, control the multi-laser beam additive manufacturing equipment to stop processing and feedback abnormal information; or, stop the processing of the abnormal laser head and re-match the laser head for processing for the corresponding working block of the abnormal laser head; or, feedback the abnormal information and keep the original processing strategy unchanged; or, keep the original processing strategy unchanged.

[0053] It is easy to understand that after the predictive maintenance strategy is determined, step S130 can directly control the workpiece processing process according to the determined predictive maintenance strategy.

[0054] Before the component processing is completed and the product acceptance phase begins, this embodiment will perform workpiece product quality scoring and equipment status scoring in parallel, and compare the scoring results with the pre-built process case data set to feedback abnormality reports. Specifically, the product quality parameters of the complete workpiece after processing and the equipment status parameters in standby are collected, and compared with the process case data set to determine whether the workpiece meets the production requirements and check whether the laser head needs standby cooling or maintenance.

[0055] The pre-built process case data set includes product quality parameters of the processing process under all common processes and processing conditions, as well as the equipment status parameters of the laser head under different input parameter conditions. For example, product quality parameters include: shape and size deviation, surface roughness, molten pool morphology, spatter particle size and density, microcrack density, etc.; equipment status parameters include: positioning accuracy, temperature drift, equipment temperature, cooling system efficiency, motion mechanism zero point deviation, etc.

[0056] Therefore, the changing trends of product quality and equipment performance during the entire construction period can be predicted by collecting equipment status parameters and product quality parameters in continuous time periods.

[0057] In this embodiment, working blocks are allocated to multiple laser heads based on the initial state score, standby state score and equipment-block matching degree of the multi-laser beam additive manufacturing equipment, and a predictive maintenance strategy is determined according to the equipment state parameters of the multi-laser beam additive manufacturing equipment and the product quality parameters of different working blocks during the processing. Then, according to the predictive maintenance strategy, the multi-laser beam additive manufacturing equipment is controlled to stop processing and feedback abnormal information; or, the processing of the abnormal laser head is stopped and the laser head corresponding to the abnormal laser head is re-matched for processing; or, the abnormal information is fed back and the original processing strategy is kept unchanged; or, the original processing strategy is kept unchanged. This method improves the overall processing accuracy and quality by increasing the consideration of the equipment status score and the equipment-block matching degree, and using the processing area with the highest processing accuracy of the scanning galvanometer for processing; at the same time, by stopping the machine for maintenance before the workpiece processing quality or the working status of a laser head declines, the galvanometer downtime preparation time is reasonably arranged, thereby avoiding the situation in which the processing quality and processing efficiency are reduced due to the decline in the working status of the galvanometer during large-scale processing; in addition, by reallocating the processing blocks allocated to the stopped equipment to the remaining laser heads in real time after predicting an error or shutdown of the laser head, the processing efficiency is improved and the scrapping of workpieces due to shutdown is avoided.

[0058] On the basis of the above-mentioned embodiments, the predictive maintenance process during the equipment startup phase will be described in detail below.

[0059] Based on the initial state score, standby state score and equipment-block matching degree of the multi-laser beam additive manufacturing equipment, multiple laser heads are assigned work blocks, which previously included: obtaining the initial equipment state parameters of the multi-laser beam additive manufacturing equipment; comparing the initial equipment state parameters with the theoretical parameters when the equipment is started in the pre-built process case data set to obtain the comparison results; scoring the multi-laser beam additive manufacturing equipment according to the comparison results to obtain the initial state score; when the initial state score is lower than the warning threshold, error feedback and maintenance are performed on the multi-laser beam additive manufacturing equipment; when the initial state score is higher than the warning threshold, the equipment enters the standby stage.

[0060] It is easy to understand that the initial equipment state parameters of the multi-laser beam additive manufacturing equipment obtained by using various installed sensors are similar to the equipment state parameters described above, including one or more combinations of positioning accuracy, temperature drift, equipment temperature, cooling system efficiency and zero-point deviation of the motion mechanism.

[0061] Subsequently, the initial equipment state parameters obtained were compared with the theoretical parameters at the time of equipment startup in the process case data set, and a score evaluation was performed based on the comparison results to obtain the initial state score. The initial state score is positively correlated to the similarity between the theoretical parameters and the initial equipment state parameters, which can effectively reflect the overall state of the multi-laser beam additive manufacturing equipment at startup.

[0062] If the initial status score is lower than the warning threshold, the error feedback and maintenance program will be run to issue a warning or error; if the initial status score is higher than the warning threshold, it indicates that the multi-laser beam additive manufacturing equipment can be preliminarily applied and is ready to enter the equipment standby stage.

[0063] The warning threshold may be set based on historical device warnings and is not specifically limited here.

[0064] In this embodiment, the initial status score of the multi-laser beam additive manufacturing equipment is used to effectively reflect the overall status of the equipment when it is turned on, thereby preventing problems that occur when the multi-laser beam additive manufacturing equipment is idle from affecting the subsequent component processing quality.

[0065] On the basis of the above-mentioned embodiments, the predictive maintenance process during the equipment standby phase will be described in detail below.

[0066] Based on the initial state score, standby state score and equipment-block matching degree of the multi-laser beam additive manufacturing equipment, work blocks are allocated to multiple laser heads, which also includes: monitoring the standby equipment state parameters of the multi-laser beam additive manufacturing equipment within a set time period; performing statistical analysis on the standby equipment state parameters within the set time period, and predicting the state trend of the standby equipment state parameters at the current time and in the future time period; based on the state trend, scoring the multi-laser beam additive manufacturing equipment to obtain a standby state score; when the standby state score does not meet the minimum operating conditions, error feedback and maintenance are performed on the multi-laser beam additive manufacturing equipment; when the standby state score meets the minimum operating conditions, the multi-laser beam additive manufacturing equipment is allowed to process the workpiece.

[0067] It is easy to understand that after entering the equipment standby stage, the multi-laser beam additive manufacturing equipment will monitor the equipment standby state parameters at a set frequency and for a long time, update the equipment status score in real time to obtain the standby state score, and judge whether the multi-laser beam additive manufacturing equipment is maintained in a good state range and whether it is suitable for subsequent component processing based on the standby state score.

[0068] The setting frequency may be determined by an empirical formula based on the initial status score, historical equipment warnings, errors, shutdown times, and usage frequency, and is not specifically limited here.

[0069] Specifically, the status parameters of the standby equipment within the set time period are processed by various means to analyze the changing patterns of statistical characteristics such as maximum value, extreme value, nth-order moment, and rate of change, to predict whether each parameter is in a steady state, and to judge the status trend of the status parameters of the standby equipment at the current moment or in the future, and whether there is a risk of exceeding the safety value (not meeting the preset safety conditions).

[0070] The state trend of the standby equipment state parameters in the future period is compared with the theoretical parameters of the equipment in standby state in the process case data set, and a score is evaluated based on the comparison result to obtain the standby state score. Next, it is determined whether the standby state score meets the minimum operating conditions. The minimum operating conditions here can be a set score value or a specific parameter value, which is not specifically limited here.

[0071] If the standby status score does not meet the minimum operating conditions, it means that there is a safety risk at the current moment or in the future, and it is necessary to run the error feedback and maintenance program. On the contrary, if the standby status score meets the minimum operating conditions, it means that there is no safety risk at the current moment and in the future, and the multi-laser beam additive manufacturing equipment can be operated to process the workpiece.

[0072] In this embodiment, the standby state score of the multi-laser beam additive manufacturing equipment is used to determine whether the multi-laser beam additive manufacturing equipment is maintained within a good state range and has the ability to feedback and adjust to external interference factors, thereby enhancing the anti-interference and robustness of the multi-laser beam additive manufacturing equipment.

[0073] On the basis of the above-mentioned embodiments, the predictive maintenance process in the processing planning stage will be described in detail below.

[0074] Based on the initial state score, standby state score and device-block matching degree of the multi-laser beam additive manufacturing equipment, multiple laser heads are assigned working blocks, including: predicting the theoretical processing time of each laser head according to the laser beam scanning path, laser head working parameters and workpiece properties; evaluating the normal working probability of each laser head within the theoretical processing time according to the initial state score and the standby state score; determining the laser head with a normal working probability higher than the set probability as the target laser head, and determining the device-block matching degree between the target laser head and the working block to be assigned according to the processing accuracy; and assigning the corresponding working block to the target laser head according to the device-block matching degree.

[0075] It is easy to understand that in the processing planning stage, the main work is to divide the work blocks and plan the work of the laser head. After the planning is completed, the theoretical processing time of each laser head can be obtained. Therefore, the initial state score and the standby state score can be used to evaluate whether each laser head can complete the work task within the corresponding theoretical processing time.

[0076] Specifically, a neural network model can be constructed, with parameters such as laser beam scanning path, laser head working parameters and workpiece properties as network input, and the theoretical processing time of each laser head as network output. At the same time, the probability of each laser head working normally within the theoretical processing time is estimated based on the initial state score and the standby state score, that is, the probability that each laser head can work normally within the theoretical processing time.

[0077] The normal working probability may be set according to actual conditions. For example, in a specific embodiment, the normal working probability is 95%.

[0078] For laser heads whose normal working probability reaches the set probability, the device-block matching degree is estimated according to the processing accuracy requirements of the working block and the status score of the laser head itself, and the working block with the highest device-block matching degree of the laser head is allocated to the laser head. Among them, the higher the status score, the higher the processing accuracy, and it is more suitable to be allocated to the working block with high processing accuracy requirements.

[0079] For a laser head whose normal working probability does not reach the set probability, it means that its performance does not meet the workpiece processing requirements and it cannot be allowed to participate in the workpiece processing process. At this time, it is necessary to run the error feedback and maintenance program.

[0080] In this embodiment, by considering the matching degree between the equipment and its status score and the working block in the block division strategy, the rationality of the working block division is effectively increased, the performance of the laser head can be more efficiently exerted, and the processing quality is improved.

[0081] On the basis of the above-mentioned embodiments, the predictive maintenance process in the processing operation phase will be described in detail below.

[0082] According to the equipment status parameters of the multi-laser beam additive manufacturing equipment and the product quality parameters of different working blocks in the processing process, the predictive maintenance strategy is determined, which specifically includes the following four situations.

[0083] In the first case, when the equipment status parameters do not meet the preset safety conditions and the overall product quality parameters of the workpiece do not meet the preset processing quality conditions, the predictive maintenance strategy is determined as the multi-laser beam additive manufacturing equipment stops processing and feeds back abnormal information.

[0084] The device status parameters do not meet the preset safety conditions, which means that any one or more device status parameters exceed the preset safety range, such as the device temperature is higher than the set temperature threshold, the cooling system efficiency is lower than the set efficiency threshold, etc.

[0085] Similarly, the overall product quality parameters of the workpiece do not meet the processing quality conditions, which means that any one or more product quality parameters exceed the pre-set reasonable range, such as the shape deviation is greater than the set deviation value, the surface roughness is higher than the set roughness value, etc.

[0086] Among them, the preset safety conditions and processing quality conditions can be set according to the actual data conditions in the constructed process case data set.

[0087] In this case, the processing quality of all working blocks and the performance parameters of all laser heads do not meet the workpiece processing requirements. Therefore, the multi-laser beam additive manufacturing equipment is directly stopped and error information is fed back to the host computer.

[0088] In the second case, when the equipment status parameters show a performance decline trend or the product quality parameters of the working block show a quality decline trend, and it is predicted that the equipment status parameters will not meet the preset safety conditions within the theoretical processing time, the predictive maintenance strategy is determined to stop the processing of the abnormal laser head and re-match the laser head for the corresponding working block of the abnormal laser head for processing; among which, the abnormal laser head is a laser head that is predicted to not meet the preset safety conditions in the future period.

[0089] The equipment status parameters show a performance decline trend, and the product quality parameters of the working block show a quality decline trend. This can be predicted by analyzing the change patterns of the maximum value, extreme value, nth-order moment, rate of change and other parameters of the equipment status parameters and product quality parameters obtained within the set time period through built-in functions.

[0090] Predicting that the equipment status parameters will not meet the preset safety conditions within the theoretical processing time means that within the theoretical processing time, one or more equipment status parameters will exceed the preset safety range (that is, one or more laser heads will not be able to work normally within the theoretical processing time), for example, the shape deviation is greater than the set deviation value, the surface roughness is higher than the set roughness value, etc. The calculation of the theoretical processing time and the probability of normal operation refers to the above embodiment and will not be repeated here.

[0091] Different from the first case, it is not the case that the processing quality of all working areas of the workpiece cannot meet the preset processing quality conditions, nor that all laser heads of the multi-laser beam additive manufacturing equipment fail to meet the preset safety conditions. Therefore, it is only necessary to maintain the abnormal laser head and re-match the laser head for the working block corresponding to the abnormal laser head among the remaining laser heads in operation. The matching method still refers to the device-block matching degree.

[0092] In the third case, when the equipment status parameters show a performance decline trend or the product quality parameters of the work block show a quality decline trend, but it is predicted that the equipment status parameters will always meet the preset safety conditions within the theoretical processing time, the predictive maintenance strategy is determined to feedback abnormal information and keep the original processing strategy unchanged.

[0093] In the fourth case, when the equipment status parameters meet the preset safety conditions and the overall product quality parameters of the workpiece meet the preset processing quality conditions, the original processing strategy remains unchanged.

[0094] In this embodiment, data analysis is used to identify faults that have already occurred, predict and avoid possible faults, effectively improve the processing yield rate, and have the ability to feedback and adjust to external interference factors, thereby enhancing the anti-interference and robustness of the multi-laser beam additive manufacturing equipment.

[0095] In some embodiments, Figure 2 The overall flow chart of the predictive maintenance method of the multi-laser beam additive manufacturing equipment provided by the embodiment of the present invention is shown.

[0096] like Figure 2 As shown, the present invention performs predictive maintenance on the complete cycle of processing components of multi-laser beam additive manufacturing equipment, including equipment startup stage, equipment standby stage, processing planning stage ( Figure 2Execution procedure entry in), processing operation stage, product acceptance stage ( Figure 2 As long as there is a problem with the processing quality or equipment performance in any stage, the error feedback and maintenance program will be triggered. At this time, the multi-laser beam additive manufacturing equipment will be controlled to stop processing and feedback abnormal information; or, the processing of the abnormal laser head will be stopped and the laser head corresponding to the abnormal laser head will be re-matched for processing; or, abnormal information will be fed back and the original processing strategy will remain unchanged.

[0097] according to Figure 2 It can also be seen that the present invention constructs a process case big data set and a decision network, wherein the process case big data set is used to assist in scoring the equipment status at each stage, and the decision network is used to perform corresponding predictive maintenance based on the equipment status score.

[0098] Figure 3 One of the partial flow diagrams of the predictive maintenance method of the multi-laser beam additive manufacturing equipment provided by the embodiment of the present invention is shown, and in particular, the state scoring process of the multi-laser beam additive manufacturing equipment is shown.

[0099] like Figure 3 As shown in the figure, the basis of scoring is product quality parameters and equipment status parameters. Weight scoring, parameter fitting, machine learning, neural network or big data analysis can be used to analyze the change rules of the maximum value, extreme value, n-th order moment, change rate and other parameters of product quality parameters and equipment status parameters over a period of time to determine whether the workpiece quality and equipment performance meet the standards, so as to predict whether the processing can be completed under the current processing conditions. Finally, the prediction results are fed back to the decision network, which determines the predictive maintenance strategy and executes it.

[0100] Figure 4 The second partial flow diagram of the predictive maintenance method of the multi-laser beam additive manufacturing equipment provided by the embodiment of the present invention is shown, and in particular, the operation process of the error feedback and maintenance program is shown.

[0101] like Figure 4 As shown in the figure, the input is workpiece quality evaluation (product quality parameters) and equipment performance evaluation (equipment status parameters), and then at each stage, it is determined whether the product quality parameters and equipment status parameters exceed the set threshold. If the set threshold is exceeded, the host computer reports an error and the entire equipment stops working; or the processing strategy is adjusted and the galvanometer is stopped for maintenance; or the host computer alarms. If both the product quality parameters and the equipment status parameters do not exceed the set threshold, the processing continues.

[0102] Figure 4 For the contents shown, please refer to the above embodiments regarding the processing operation stage, which will not be described in detail here.

[0103] Corresponding to the predictive maintenance method of the multi-laser beam additive manufacturing equipment described in the above embodiments, the present invention also provides a predictive maintenance device of the multi-laser beam additive manufacturing equipment.

[0104] Specifically, Figure 5 A schematic structural diagram of a predictive maintenance device for multi-laser beam additive manufacturing equipment provided by an embodiment of the present invention is shown.

[0105] like Figure 5 As shown, the device includes: a work block allocation module 510, which is used to allocate work blocks to multiple laser heads based on the initial state score, standby state score and device-block matching degree of the multi-laser beam additive manufacturing equipment; a predictive maintenance strategy determination module 520, which is used to determine the predictive maintenance strategy according to the equipment state parameters of the multi-laser beam additive manufacturing equipment and the product quality parameters of different work blocks during the processing; a predictive maintenance strategy execution module 530, which is used to control the multi-laser beam additive manufacturing equipment to stop processing and feedback abnormal information according to the predictive maintenance strategy; or, stop the processing of the abnormal laser head and re-match the laser head for processing for the corresponding work block of the abnormal laser head; or, feedback the abnormal information and keep the original processing strategy unchanged; or, keep the original processing strategy unchanged.

[0106] In this embodiment, the work block allocation module 510 allocates work blocks to multiple laser heads based on the initial state score, standby state score and equipment-block matching degree of the multi-laser beam additive manufacturing equipment. The predictive maintenance strategy determination module 520 determines the predictive maintenance strategy according to the equipment state parameters of the multi-laser beam additive manufacturing equipment and the product quality parameters of different work blocks during the processing. Then, the predictive maintenance strategy execution module 530 controls the multi-laser beam additive manufacturing equipment to stop processing and feedback abnormal information according to the predictive maintenance strategy; or, stops the processing of the abnormal laser head and re-matches the laser head for processing for the corresponding work block of the abnormal laser head; or, feedbacks the abnormal information and keeps the original processing strategy unchanged; or, keeps the original processing strategy unchanged. The device improves the overall processing accuracy and quality by increasing the equipment status score and equipment-block matching considerations, and uses the processing area with the highest processing accuracy of the scanning galvanometer for processing. At the same time, by stopping for maintenance before the workpiece processing quality or the working status of a laser head declines, the galvanometer downtime and preparation time is reasonably arranged, thus avoiding the situation in which the galvanometer working status declines during large-scale processing, resulting in a decline in processing quality and processing efficiency. In addition, after predicting an error or shutdown of the laser head, the processing block allocated to the shutdown equipment is reallocated to the remaining laser heads in real time, thereby improving processing efficiency and avoiding the scrapping of workpieces caused by shutdowns.

[0107] It should be noted that the predictive maintenance device for multi-laser beam additive manufacturing equipment provided in the embodiment of the present invention and the predictive maintenance method for multi-laser beam additive manufacturing equipment described in the above embodiments can correspond to each other and will not be repeated here.

[0108] Figure 6 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 6 As shown, the electronic device may include: a processor 610, a communication interface 620, a memory 630 and a communication bus 640, wherein the processor 610, the communication interface 620 and the memory 630 communicate with each other through the communication bus 640. The processor 610 may call the logic instructions in the memory 630 to execute the predictive maintenance method of the multi-laser beam additive manufacturing equipment, the method comprising: allocating working blocks to multiple laser heads based on the initial state score, the standby state score and the device-block matching degree of the multi-laser beam additive manufacturing equipment; determining the predictive maintenance strategy according to the equipment state parameters of the multi-laser beam additive manufacturing equipment and the product quality parameters of different working blocks during the processing; according to the predictive maintenance strategy, controlling the multi-laser beam additive manufacturing equipment to stop processing and feedback abnormal information; or, stopping the processing of the abnormal laser head and re-matching the laser head for the corresponding working block of the abnormal laser head for processing; or, feedback the abnormal information and keep the original processing strategy unchanged; or, keep the original processing strategy unchanged.

[0109] In addition, the logic instructions in the above-mentioned memory 630 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0110] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the predictive maintenance method for multi-laser beam additive manufacturing equipment provided by the above methods, the method including: allocating working blocks to multiple laser heads based on the initial state score, standby state score and equipment-block matching degree of the multi-laser beam additive manufacturing equipment; determining a predictive maintenance strategy based on the equipment state parameters of the multi-laser beam additive manufacturing equipment and the product quality parameters of different working blocks during the processing; according to the predictive maintenance strategy, controlling the multi-laser beam additive manufacturing equipment to stop processing and feedback abnormal information; or, stopping the processing of the abnormal laser head and re-matching the laser head for processing for the corresponding working block of the abnormal laser head; or, feedback abnormal information and keep the original processing strategy unchanged; or, keep the original processing strategy unchanged.

[0111] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the predictive maintenance method for a multi-laser beam additive manufacturing device provided by the above-mentioned methods, the method comprising: allocating working blocks to multiple laser heads based on the initial state score, standby state score and device-block matching degree of the multi-laser beam additive manufacturing device; determining a predictive maintenance strategy based on the device state parameters of the multi-laser beam additive manufacturing device and the product quality parameters of different working blocks during the processing; according to the predictive maintenance strategy, controlling the multi-laser beam additive manufacturing device to stop processing and feedback abnormal information; alternatively, stopping the processing of the abnormal laser head and re-matching the laser head for processing for the corresponding working block of the abnormal laser head; alternatively, feedback the abnormal information and keep the original processing strategy unchanged; alternatively, keep the original processing strategy unchanged.

[0112] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0113] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A predictive maintenance method for a multi-laser beam additive manufacturing device, characterized in that: The multi-laser beam additive manufacturing device includes a plurality of laser heads, and the method includes: Allocate working blocks to multiple laser heads based on the initial state score, standby state score, and device-block matching of the multi-laser beam additive manufacturing equipment; Determine the predictive maintenance strategy based on the equipment status parameters of the multi-laser beam additive manufacturing equipment and the product quality parameters of different working blocks during the processing; According to the predictive maintenance strategy, the multi-laser beam additive manufacturing equipment is controlled to stop processing and feedback abnormal information; or, the processing of the abnormal laser head is stopped and the laser head corresponding to the abnormal laser head is re-matched for processing; or, the abnormal information is fed back and the original processing strategy is kept unchanged; or, the original processing strategy is kept unchanged.

2. The predictive maintenance method of a multi-laser beam additive manufacturing device according to claim 1, characterized in that: The method of allocating working blocks to multiple laser heads based on the initial state score, the standby state score and the device-block matching degree of the multi-laser beam additive manufacturing equipment previously includes: Obtaining initial equipment state parameters of a multi-laser beam additive manufacturing equipment; Comparing the initial equipment state parameters with theoretical parameters of equipment startup in a pre-constructed process case data set to obtain a comparison result; Performing a score evaluation on the multi-laser beam additive manufacturing device according to the comparison result to obtain an initial state score; When the initial status score is lower than a warning threshold, providing error feedback and maintenance to the multi-laser beam additive manufacturing device; When the initial status score is higher than the warning threshold, the device enters the standby stage.

3. The predictive maintenance method of a multi-laser beam additive manufacturing device according to claim 1, characterized in that: The method of allocating working blocks to multiple laser heads based on the initial state score, the standby state score and the device-block matching degree of the multi-laser beam additive manufacturing equipment also includes: Monitor the standby equipment status parameters of multi-laser beam additive manufacturing equipment within a set period of time; Conduct statistical analysis on the status parameters of standby devices within a set period of time, and predict the status trend of the status parameters of standby devices at the current time and in the future period; According to the state trend, a score evaluation is performed on the multi-laser beam additive manufacturing equipment to obtain a standby state score; When the standby status score does not meet the minimum operating conditions, providing error feedback and maintenance on the multi-laser beam additive manufacturing device; When the standby state score meets the minimum operating condition, the multi-laser beam additive manufacturing device is allowed to process the workpiece.

4. The predictive maintenance method of a multi-laser beam additive manufacturing device according to claim 1, characterized in that: The method of allocating working blocks to multiple laser heads based on the initial state score, the standby state score, and the device-block matching degree of the multi-laser beam additive manufacturing device includes: Predict the theoretical processing time of each laser head based on the laser beam scanning path, laser head operating parameters and workpiece properties; According to the initial state score and the standby state score, the normal working probability of each laser head within the theoretical processing time is evaluated; A laser head with a normal working probability higher than a set probability is determined as a target laser head, and a device-block matching degree between the target laser head and the work block to be assigned is determined according to the processing accuracy; According to the device-block matching degree, a corresponding working block is allocated to the target laser head.

5. The predictive maintenance method of a multi-laser beam additive manufacturing device according to claim 1, characterized in that: The predictive maintenance strategy is determined according to the equipment status parameters of the multi-laser beam additive manufacturing equipment and the product quality parameters of different working blocks in the processing process, including: When the equipment status parameter does not meet the preset safety condition and the overall product quality parameter of the workpiece does not meet the preset processing quality condition, the predictive maintenance strategy is determined as the multi-laser beam additive manufacturing equipment stops processing and feeds back abnormal information; When the equipment status parameters show a performance decline trend or the product quality parameters of the working block show a quality decline trend, and it is predicted that the equipment status parameters will not meet the preset safety conditions within the theoretical processing time, the predictive maintenance strategy is determined to stop the processing of the abnormal laser head and re-match the laser head for the corresponding working block of the abnormal laser head for processing; wherein the abnormal laser head is a laser head that is predicted to fail to meet the preset safety conditions in the future period; When the equipment status parameters show a performance decline trend or the product quality parameters of the work block show a quality decline trend, but it is predicted that the equipment status parameters will always meet the preset safety conditions within the theoretical processing time, the predictive maintenance strategy is determined to feedback abnormal information and keep the original processing strategy unchanged; When the equipment status parameters meet the preset safety conditions and the overall product quality parameters of the workpiece meet the preset processing quality conditions, the original processing strategy remains unchanged.

6. The predictive maintenance method for a multi-laser beam additive manufacturing device according to any one of claims 1 to 5, characterized in that: The device status parameters include one or more combinations of positioning accuracy, temperature drift, device temperature, cooling system efficiency, and motion mechanism zero point deviation; The product quality parameters include one or more combinations of shape deviation, size deviation, surface roughness, molten pool morphology, spatter particle size, spatter particle density and microcrack density.

7. A predictive maintenance device for a multi-laser beam additive manufacturing device, characterized in that: The multi-laser beam additive manufacturing equipment comprises a plurality of laser heads, and the device comprises: A work block allocation module is used to allocate work blocks to multiple laser heads based on the initial state score, standby state score and device-block matching degree of the multi-laser beam additive manufacturing equipment; A predictive maintenance strategy determination module, used to determine a predictive maintenance strategy based on equipment status parameters of the multi-laser beam additive manufacturing equipment and product quality parameters of different working blocks during the processing; A predictive maintenance strategy execution module is used to control the multi-laser beam additive manufacturing equipment to stop processing and feedback abnormal information according to the predictive maintenance strategy; or, stop the processing of the abnormal laser head and re-match the laser head for processing for the corresponding working block of the abnormal laser head; or, feedback the abnormal information and keep the original processing strategy unchanged; or, keep the original processing strategy unchanged.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the predictive maintenance method of the multi-laser beam additive manufacturing equipment according to any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the predictive maintenance method for the multi-laser beam additive manufacturing equipment according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the predictive maintenance method for the multi-laser beam additive manufacturing equipment according to any one of claims 1 to 6 is implemented.

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