A segmented blade assembly precision analysis method and system based on digital twin simulation

Abstract

The application is suitable for the technical field of wind power generation equipment manufacturing, and provides a segmented blade assembly precision analysis method based on digital twin simulation, which comprises the following steps: calling a digital twin simulation model for target segmented blade assembly, performing a clamping adjustment operation according to an intervention time delay within a reference intervention time period interval to obtain a corresponding simulation final assembly edge deviation; and when the simulation final assembly edge deviation falls within a reference assembly edge deviation range, performing a clamping adjustment operation according to an intervention time delay within a reference intervention time period interval in an actual gluing assembly process. The application combines historical assembly data analysis and digital twin simulation verification to realize prospective control of the clamping adjustment operation timing, thereby avoiding assembly adjustment depending on experience judgment and improving the stability of the final assembly edge deviation and the assembly precision in the segmented blade gluing assembly process.

Description

Technical Field

[0001] This invention belongs to the field of wind power equipment manufacturing technology, and in particular relates to a method and system for analyzing the assembly accuracy of segmented blades based on digital twin simulation. Background Technology

[0002] As the capacity of wind turbine generators continues to increase, the size and weight of wind turbine blades are also growing. Integral blades face significant engineering constraints during manufacturing, transportation, and installation. Therefore, segmented blade structures are increasingly being adopted in practical engineering. Segmented blades typically consist of multiple segments, such as the root segment, middle segment, and tip segment. These segments are manufactured and then assembled on-site to form a complete blade structure. During assembly, adhesive bonding is usually used to connect the segments, and assembly fixtures are used to position and clamp them to ensure the geometric accuracy of the segment mating positions. Because blades have high requirements for aerodynamic shape and structural connection accuracy, controlling the misalignment at the segment mating positions becomes a crucial factor affecting the assembly quality of segmented blades.

[0003] To improve the controllability of the assembly process, existing technologies have begun to incorporate digital assembly and simulation analysis techniques. For example, by establishing assembly simulation models based on three-dimensional structural models or finite element analysis, the segment docking status, structural deformation, and assembly errors during blade assembly can be predicted. Simultaneously, during actual assembly, sensors can monitor changes in the thickness or related displacement of the adhesive bonding area. When abnormal changes are detected, the assembly state can be adjusted by modifying the clamping force of the assembly tooling or the position of the fixtures, thereby maintaining the stability of the assembly process.

[0004] However, in existing assembly control methods, when abnormal changes are detected in the assembly process, the timing of clamping adjustment operations is usually determined by the assembly control system or operators based on real-time monitoring information. This decision-making relies heavily on real-time status assessments or empirical process strategies, lacking a systematic analysis of the relationship between intervention delays and assembly results in historical assembly data. Furthermore, while existing technologies can simulate the assembly process, they typically focus on structural deformation or process simulation, without incorporating historical assembly data to identify the impact of intervention delays on the final assembly misalignment. Therefore, in actual assembly processes, it remains difficult to proactively optimize the timing of clamping adjustment operations, leaving room for further improvement in the stability of segmented blade assembly accuracy. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for analyzing the assembly accuracy of segmented blades based on digital twin simulation, in order to solve the problems mentioned in the background art.

[0006] This invention is implemented as follows: a method for analyzing the assembly accuracy of segmented blades based on digital twin simulation, the method comprising:

[0007] Obtain the historical assembly database and filter out several historical assembly samples that match the target segmented blade assembly conditions. Analyze each historical assembly sample to determine the trigger time when the rate of change of adhesive layer thickness reaches the preset abnormal threshold during the adhesive bonding assembly process, and determine the intervention delay from the trigger time to the execution of the clamping adjustment operation. At the same time, obtain the final assembly misalignment amount of the corresponding historical assembly sample.

[0008] Determine whether there is a trend in which the final assembly misalignment amount first decreases and then increases as the intervention time delay increases in several historical assembly samples. If so, determine the turning point of the trend as the reference intervention time period interval and determine the reference assembly misalignment amount range corresponding to the reference intervention time period interval.

[0009] The digital twin simulation model for the target segmented blade assembly is invoked, and the clamping adjustment operation is performed according to the intervention delay within the reference intervention time interval to obtain the corresponding simulation final assembly misalignment amount;

[0010] When the final assembly misalignment in the simulation falls within the range of the reference assembly misalignment, the clamping adjustment operation is performed during the actual adhesive bonding assembly process according to the intervention delay within the reference intervention time interval.

[0011] As a further limitation of the technical solution of the present invention, the matching with the target segmented blade assembly conditions means that the historical assembly sample and the target segmented blade all meet the corresponding preset similarity threshold in terms of blade structure type, bonding position, assembly environment parameters, assembly tooling configuration parameters and bonding process parameters.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the blade structure type includes blade model, segment structure form and material system, the bonding position includes the connection position of root segment-middle segment or middle segment-tip segment, the assembly environment parameters include temperature and humidity, the assembly tooling configuration parameters include fixture layout, support position and clamping point, and the bonding process parameters include adhesive type, adhesive layer design thickness, adhesive application amount and clamping force parameters.

[0013] As a further limitation of the technical solution of the present invention, the final assembly misalignment amount corresponding to the historical assembly sample is within the preset allowable tolerance range.

[0014] As a further limitation of the technical solution of this invention, the steps of calling the digital twin simulation model for the target segmented blade assembly, performing clamping adjustment operations according to the intervention delay within the reference intervention time interval, and obtaining the corresponding final simulated assembly misalignment amount include:

[0015] The assembly process is simulated by calling a digital twin simulation model for the target segmented blade assembly and inputting the assembly conditions of the target segmented blade into the digital twin simulation model.

[0016] In the assembly process simulation, the rate of change of adhesive layer thickness is detected. When the rate of change of adhesive layer thickness reaches a preset abnormal threshold, at least one intervention delay is selected from the reference intervention time interval, and a clamping adjustment operation is performed according to the selected intervention delay after the abnormal threshold is triggered.

[0017] After completing the simulation assembly process, obtain the final assembly misalignment amount under the corresponding intervention delay.

[0018] As a further limitation of the technical solution of this invention, when the simulated final assembly misalignment falls within the reference assembly misalignment range, the steps of performing clamping adjustment operation according to the intervention delay within the reference intervention time interval during the actual adhesive bonding assembly process include:

[0019] Determine whether the final assembly misalignment amount in the simulation falls within the range of the reference assembly misalignment amount;

[0020] When the final assembly misalignment amount in the simulation falls within the range of the reference assembly misalignment amount, the corresponding intervention delay is determined as the reference intervention delay for the actual assembly execution.

[0021] During the actual bonding and assembly of the target segmented blade, the rate of change of adhesive layer thickness is monitored in real time. When the rate of change of adhesive layer thickness reaches a preset abnormal threshold, a clamping adjustment operation is performed according to the reference intervention delay.

[0022] A segmented blade assembly accuracy analysis system based on digital twin simulation, the system comprising:

[0023] The historical sample acquisition and analysis module is used to acquire the historical assembly database, filter out several historical assembly samples that match the target segmented blade assembly conditions, analyze each historical assembly sample, determine the trigger time when the rate of change of adhesive layer thickness reaches the preset abnormal threshold during the adhesive bonding assembly process, determine the intervention delay from the trigger time to the execution of the clamping adjustment operation, and at the same time acquire the final assembly misalignment amount of the corresponding historical assembly sample.

[0024] The intervention time period analysis module is used to determine whether there is a trend of first decreasing and then increasing in the final assembly misalignment amount in several historical assembly samples as the intervention time delay increases. If so, the turning point of the trend is determined as the reference intervention time period interval, and the reference assembly misalignment amount range corresponding to the reference intervention time period interval is determined.

[0025] The digital twin simulation module is used to call the digital twin simulation model for the target segmented blade assembly, perform clamping adjustment operations according to the intervention delay within the reference intervention time interval, and obtain the corresponding simulation final assembly misalignment amount.

[0026] The assembly control execution module is used to perform clamping adjustment operations during the actual adhesive bonding assembly process when the simulated final assembly misalignment falls within the reference assembly misalignment range, according to the intervention delay within the reference intervention time interval.

[0027] As a further limitation of the technical solution of the present invention, the matching with the target segmented blade assembly conditions means that the historical assembly sample and the target segmented blade all meet the corresponding preset similarity threshold in terms of blade structure type, bonding position, assembly environment parameters, assembly tooling configuration parameters and bonding process parameters.

[0028] As a further limitation of the technical solution of the embodiment of the present invention, the blade structure type includes blade model, segment structure form and material system, the bonding position includes the connection position of root segment-middle segment or middle segment-tip segment, the assembly environment parameters include temperature and humidity, the assembly tooling configuration parameters include fixture layout, support position and clamping point, and the bonding process parameters include adhesive type, adhesive layer design thickness, adhesive application amount and clamping force parameters.

[0029] As a further limitation of the technical solution of the present invention, the final assembly misalignment amount corresponding to the historical assembly sample is within the preset allowable tolerance range.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] This invention analyzes historical assembly samples from a historical assembly database to identify the variation pattern between intervention delay and final assembly misalignment. Based on this, it determines the reference intervention time interval and the corresponding reference assembly misalignment range, thus providing data basis for the timing of clamping adjustment operations. Furthermore, by calling a digital twin simulation model for the target segmented blade assembly, the intervention delay within the reference intervention time interval is simulated and verified to obtain the corresponding simulated final assembly misalignment, thereby determining the reference intervention delay used in the actual assembly process. Through this method, combining historical assembly data analysis with digital twin simulation verification, proactive control of the timing of clamping adjustment operations is achieved, avoiding reliance solely on experience-based judgments for assembly adjustments and improving the stability and accuracy of the final assembly misalignment during segmented blade bonding assembly. This method optimizes assembly control strategies without increasing assembly complexity, which is of great significance for improving the quality of segmented blade assembly. Attached Figure Description

[0032] Figure 1 A flowchart of the method provided in the embodiments of the present invention;

[0033] Figure 2 This is a flowchart illustrating the method for obtaining the final assembly misalignment amount in the simulation provided by the embodiments of the present invention;

[0034] Figure 3 This is a flowchart illustrating the method provided in this embodiment of the invention for determining the reference intervention delay based on simulation results and performing a clamping adjustment operation;

[0035] Figure 4 The application architecture diagram of the system provided in the embodiments of the present invention. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0037] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0038] Specifically, a method for analyzing the assembly accuracy of segmented blades based on digital twin simulation includes the following steps:

[0039] Step S100: Obtain the historical assembly database and filter out several historical assembly samples that match the target segmented blade assembly conditions. Analyze each historical assembly sample to determine the trigger moment when the rate of change of adhesive layer thickness during the bonding assembly process reaches a preset abnormal threshold, and determine the intervention delay from the trigger moment to the execution of the clamping adjustment operation. Simultaneously, obtain the final assembly misalignment amount of the corresponding historical assembly sample. The final assembly misalignment amounts corresponding to the historical assembly samples are all within the preset allowable tolerance range.

[0040] The matching with the target segmented blade assembly conditions means that the historical assembly samples and the target segmented blade all meet the corresponding preset similarity thresholds in terms of blade structure type, bonding location, assembly environment parameters, assembly tooling configuration parameters, and bonding process parameters.

[0041] The blade structure type includes blade model, segment structure form and material system; the bonding position includes the connection position of root segment-middle segment or middle segment-tip segment; the assembly environment parameters include temperature and humidity; the assembly tooling configuration parameters include fixture layout, support position and clamping point; and the bonding process parameters include adhesive type, adhesive layer design thickness, adhesive application amount and clamping force parameters.

[0042] In this embodiment of the invention, the segmented blades involved are typically large wind turbine blades. As the capacity of individual wind turbine units continues to increase, blade lengths are also increasing, with blades exceeding 80 meters or even 100 meters gradually becoming mainstream. Due to significant size and weight limitations in manufacturing, transportation, and installation of integral blades, the industry has increasingly adopted segmented blade structures. This involves dividing the blade into multiple segments, such as the root, middle, and tip sections, which are then assembled into a complete blade structure after manufacturing. These segmented blades require high precision in structural alignment, aerodynamic shape, and uniform stress distribution during assembly; therefore, assembly accuracy becomes one of the key factors affecting blade performance and reliability.

[0043] In practical engineering, the connection between segmented blade components is usually achieved through adhesive bonding. Adhesive bonding is a common technique in composite material structure connections. By applying structural adhesive to the connection interface to form a stable adhesive layer, a reliable structural connection is achieved between the two segments. Compared to mechanical connections, adhesive bonding effectively avoids fiber damage caused by drilling in composite structures and allows for more uniform load distribution within the connection area. Therefore, adhesive bonding has become a mature and widely used connection technology in segmented blade assembly. During adhesive bonding assembly, assembly fixtures are typically used to position and clamp the blade segments to ensure that the relative positional relationship between the segments meets design requirements and a stable structural connection is formed after the adhesive layer cures.

[0044] On the other hand, with the development of digital manufacturing technology, assembly simulation models built based on digital twin technology can now accurately simulate the assembly process of segmented blades. By modeling the blade structure, assembly tooling, environmental conditions, and assembly process parameters, the entire blade assembly process can be simulated in a virtual environment, including segment docking, hoisting, clamping, and bonding processes. Common simulation models in existing technologies include structural deformation simulation models based on finite element analysis, assembly process simulation models based on assembly constraints, and digital twin assembly models incorporating sensor data. These simulation models can predict the relative pose changes, structural deformation, and assembly errors between segments during assembly, and can achieve dynamic simulation of blade assembly and hoisting processes. Their simulation results typically show high consistency with actual assembly results, with minimal error. Therefore, in the current segmented blade assembly process, by inputting relevant assembly condition parameters, such as environmental conditions, tooling configuration, and bonding process parameters, a relatively accurate simulation analysis of the target blade assembly process can be performed.

[0045] During the bonding assembly process, the thickness of the adhesive layer will change with factors such as the clamping force, the docking state of the components, and the flow of the adhesive. When the adhesive layer shows obvious flow or compression under the stress state, the change rate of the adhesive layer thickness may increase abnormally. When the change rate of the adhesive layer thickness reaches the preset abnormal threshold, it usually means that the state of the adhesive layer has changed significantly, such as local flow of the adhesive, change in the contact state of the components, or fluctuation in the clamping force distribution. In the existing assembly control technology, the thickness of the adhesive layer or the change in the relevant displacement is usually monitored in real time by sensors, and the change rate of the adhesive layer thickness is obtained through calculation. When this change rate exceeds the preset abnormal threshold, the system will consider that there is an abnormal trend in the current assembly state and needs to be intervened by adjusting the clamping state and other means. The preset abnormal threshold can usually be obtained through empirical data, statistical analysis of historical assembly data, or experimental calibration, so it belongs to the common technical means in this field.

[0046] After detecting that the change rate of the adhesive layer thickness reaches the preset abnormal threshold, it is usually necessary to perform a clamping adjustment operation. The clamping adjustment operation is generally achieved by adjusting the clamping mechanism in the assembly tooling, such as changing the clamping force magnitude, adjusting the fixture position, or reallocating the clamping points, so as to change the stress state between the components and make the adhesive layer thickness gradually stable. Since in the actual assembly process, after detecting that the change rate of the adhesive layer thickness reaches the preset abnormal threshold, it is usually necessary to first analyze the current assembly state, generate the corresponding clamping adjustment operation strategy, and then confirm and execute the clamping adjustment operation by the assembly control system or the operator. Therefore, there is usually a certain time interval between the abnormal trigger moment and the actual execution of the clamping adjustment operation, and this time interval is the intervention delay. The intervention delay is usually related to the response process of the assembly control process, such as the time consumed in the processes of abnormal recognition, adjustment strategy generation, operation confirmation, and clamping mechanism execution. Therefore, there may be certain differences in the intervention delay in different historical assembly samples.

[0047] After the blade assembly is completed, the final assembly misalignment amount can be obtained by measuring the geometric relationship at the docking position of the components. The final assembly misalignment amount is usually used to characterize the relative misalignment degree of the two blade components at the docking position, such as the height difference between the upper and lower edges or the misalignment amount of the docking surface. In most normal assembly cases, this final assembly misalignment amount will be within the preset allowable tolerance range because the blade assembly process itself has been strictly controlled and will only be recognized as a qualified assembly when the tolerance requirements are met. Therefore, in the actual assembly data, the final assembly misalignment amounts of most historical assembly samples are within the allowable tolerance range.

[0048] Through long-term assembly practice and analysis of a large amount of historical assembly data, those skilled in the art have found that the length of the intervention delay has a certain impact on the final assembly misalignment. Although this impact is usually small, and the final assembly misalignment is still within the preset allowable tolerance, different intervention delays can still cause the final assembly misalignment to change within the tolerance range. Further research has found that a shorter or longer intervention delay is not necessarily better.

[0049] Specifically, when the rate of change in adhesive layer thickness reaches a preset abnormal threshold, the assembly system typically needs to go through control processes such as abnormal identification, clamping adjustment operation strategy generation, and execution. Therefore, there is a certain intervention delay between the triggering moment and the actual execution of the clamping adjustment operation. During this time period, the adhesive layer material may still experience slight flow and stress redistribution under stress. If the clamping adjustment operation is executed too early, the adhesive layer state has not yet stabilized sufficiently, and subsequent slight displacement may still occur, affecting the segment docking position; while if the clamping adjustment operation is executed too late, the opportunity to effectively adjust the relative position of the segments may be missed. Therefore, there is usually a specific time period after the abnormality is triggered, and performing the clamping adjustment operation within this time period can achieve a better final assembly misalignment.

[0050] Based on the above understanding, those skilled in the art realize that the selection of intervention delay can actually be actively controlled. If the variation law between intervention delay and final assembly misalignment can be identified under specific assembly conditions, and the assembly results corresponding to different intervention delays can be predicted and verified using a digital twin simulation model, then a better intervention delay can be actively selected by proactively setting or controlling the intervention delay during the actual adhesive bonding assembly process, thereby further optimizing the final assembly misalignment and improving the assembly accuracy of segmented blades.

[0051] In step S100, it is necessary to acquire a historical assembly database. This database typically originates from assembly records during actual production, such as assembly sensor data, assembly process monitoring data, and assembly quality inspection data. This data can be accumulated and stored over a long period through production line data acquisition systems, manufacturing execution systems, or quality management systems; therefore, its acquisition and collection are existing technological means. Since wind turbine blades typically employ standardized designs, the structure and process conditions of blades of the same model are often quite similar across different batches. Therefore, historical assembly data has significant reference value for subsequent assembly processes.

[0052] After acquiring the historical assembly database, the historical assembly samples need to be screened. The purpose of screening samples is to ensure that the selected data can accurately reflect the assembly behavior of the target segmented blade under similar assembly conditions, thereby improving the reliability of subsequent analysis results. Therefore, this invention employs relatively strict screening conditions during the sample screening process. For example, it requires that historical assembly samples and the target segmented blade meet corresponding preset similarity thresholds in terms of blade structure type, bonding location, assembly environment parameters, assembly tooling configuration parameters, and bonding process parameters. This approach ensures a high degree of similarity between historical samples and the target assembly scenario, thereby avoiding data deviations caused by structural or process differences.

[0053] In addition to the screening criteria mentioned above, other screening criteria can be added as needed in practical applications, such as component size parameters, adhesive bonding length parameters, assembly sequence parameters, or clamping strategy parameters, to further improve the comparability of sample data. The preset similarity threshold is usually determined based on historical data statistics or engineering experience; it does not require complete consistency but allows for differences within a certain range. This is because in actual production environments, there are often slight differences between different assembly batches, and requiring complete consistency would make it difficult to obtain a sufficient number of valid samples. Therefore, by setting a reasonable similarity threshold, sufficient data volume can be retained while ensuring sample similarity, thereby improving the stability and reliability of data analysis.

[0054] Furthermore, the method for analyzing the assembly accuracy of segmented blades based on digital twin simulation also includes the following steps:

[0055] Step S200: Determine whether there is a trend in which the final assembly misalignment amount first decreases and then increases as the intervention delay increases in several historical assembly samples. If so, determine the turning point of the trend as the reference intervention time period interval, and determine the reference assembly misalignment amount range corresponding to the reference intervention time period interval.

[0056] In this embodiment of the invention, the purpose of step S200 is to further analyze the historical assembly samples obtained in step S100 to identify the variation pattern between the intervention delay and the final assembly misalignment, and thereby determine the intervention time period with better assembly effect under the current assembly conditions. In step S100, historical assembly samples matching the target segmented blade assembly conditions have been obtained by screening the historical assembly database, and key data such as trigger time, intervention delay, and final assembly misalignment have been extracted from each historical assembly sample. Therefore, step S200 is actually performing pattern mining and relationship analysis based on the above data to identify the changes in assembly results corresponding to different intervention delays, thereby providing a basis for subsequently determining the reference intervention time period interval.

[0057] Specifically, after obtaining several historical assembly samples, a dataset can be established using the intervention delay corresponding to each historical assembly sample as the x-axis and the corresponding final assembly misalignment amount as the y-axis. This dataset can then be analyzed for trend changes. To identify the trend between the intervention delay and the final assembly misalignment amount, various data analysis techniques can be employed, such as statistical regression analysis, curve fitting analysis, sliding window analysis, or data clustering analysis. Through these analytical methods, the historical sample data can be smoothed, and a curve showing the relationship between the intervention delay and the final assembly misalignment amount can be constructed. This allows for the determination of whether a trend exists in the historical assembly samples where the final assembly misalignment amount first decreases and then increases with increasing intervention delay. When this trend is detected, the minimum value region or trend inflection region within the curve can be further identified.

[0058] In actual data, due to inherent measurement errors and production fluctuations in the assembly process, the intervention delay and final assembly misalignment between different historical assembly samples often do not present a single precise point, but rather form a distribution within a certain range. Therefore, when identifying trends, we usually do not only obtain a single time point, but rather identify an intervention delay interval within which the overall final assembly misalignment is at a relatively optimal level. In other words, when clamping adjustment operations are performed within this interval, the final assembly misalignment can usually be kept at a low level; therefore, this time interval is determined as the reference intervention time interval.

[0059] After determining the reference intervention time period, the final assembly misalignment amount corresponding to the historical assembly samples within that period can be further statistically analyzed, and the reference assembly misalignment amount range can be determined based on this data. For example, a reasonable reference assembly misalignment amount range can be determined by statistically analyzing the maximum, minimum, or average value of the final assembly misalignment amount within that period, combined with production quality control standards. Since step S100 has already specified that the final assembly misalignment amount of the historical assembly samples is within the preset allowable tolerance range, the reference assembly misalignment amount range obtained in the above manner will usually also be within this allowable tolerance range and can represent a relatively optimal assembly accuracy level that can be obtained within the reference intervention time period.

[0060] Therefore, step S200 is essentially a further pattern extraction and data analysis of the historical assembly sample data obtained in step S100. Its purpose is to identify the influence of intervention delay on the final assembly misalignment and to determine a reference intervention time interval that yields optimal assembly accuracy. The reference intervention time interval and reference assembly misalignment range obtained in this step provide a basis for subsequent verification analysis using a digital twin simulation model, enabling the subsequent assembly process to make more reasonable intervention delay selections based on historical data patterns and simulation analysis results.

[0061] Furthermore, the method for analyzing the assembly accuracy of segmented blades based on digital twin simulation also includes the following steps:

[0062] Step S300: Call the digital twin simulation model for the target segmented blade assembly, perform clamping adjustment operation according to the intervention delay within the reference intervention time interval, and obtain the corresponding simulation final assembly misalignment amount.

[0063] Specifically, Figure 2 A flowchart for obtaining the final assembly misalignment amount in the simulation is shown.

[0064] The process of calling the digital twin simulation model for the target segmented blade assembly, performing clamping adjustment operations according to the intervention delay within the reference intervention time interval, and obtaining the corresponding final simulated assembly misalignment includes the following steps:

[0065] Step S301: Call the digital twin simulation model for the assembly of the target segmented blade, and input the assembly conditions of the target segmented blade into the digital twin simulation model to simulate the assembly process.

[0066] Step S302: In the assembly process simulation, the rate of change of adhesive layer thickness is detected. When the rate of change of adhesive layer thickness reaches a preset abnormal threshold, at least one intervention delay is selected from the reference intervention time interval, and clamping adjustment operation is performed according to the selected intervention delay after the abnormal threshold is triggered.

[0067] Step S303: After completing the simulation assembly process, obtain the final assembly misalignment amount under the corresponding intervention delay.

[0068] In this embodiment of the invention, the purpose of step S300 is to further verify the intervention delay within the reference intervention time interval using a digital twin simulation model based on the analysis of historical assembly data. This allows for the determination of the assembly effect corresponding to different intervention delays under the current target segmented blade assembly conditions, and the acquisition of the corresponding simulated final assembly misalignment amount. Although step S200 has identified the variation pattern between intervention delay and final assembly misalignment amount based on historical assembly samples and determined the reference intervention time interval, the historical assembly samples are essentially derived from existing production data, and their assembly conditions may still have certain differences. Therefore, before actual assembly, simulating the assembly process of the target segmented blade under the current assembly conditions using a digital twin simulation model allows for forward verification of the assembly effect of different intervention delays within the reference intervention time interval, thereby further improving the reliability of the intervention delay selection.

[0069] In step S301, a digital twin simulation model for the target segmented blade assembly is first invoked, and the assembly conditions of the target segmented blade are input into the digital twin simulation model to simulate the assembly process. The digital twin simulation model can be built based on existing assembly simulation technologies, such as assembly simulation models based on three-dimensional structural models, structural deformation simulation models based on finite element analysis, and digital twin models incorporating production data. This model typically includes a three-dimensional geometric model of the blade segment, an assembly tooling model, a bonding interface model, and an assembly constraint relationship model. By inputting the assembly conditions of the target segmented blade into the digital twin simulation model, the blade segment docking, clamping, and bonding assembly process can be reproduced in a virtual environment. The assembly conditions may include blade structure type, bonding location, assembly environment parameters, assembly tooling configuration parameters, and bonding process parameters. These parameters are consistent with the parameter types used in step S100 for screening historical assembly samples, thereby ensuring a high degree of consistency between the assembly scenario reflected by the simulation model and the actual assembly scenario. In the existing technology, similar digital twin assembly simulation can usually be achieved through industrial simulation software or assembly simulation platforms, such as software systems based on finite element analysis or industrial assembly simulation systems. The assembly process simulation can be completed by inputting structural models and assembly process parameters.

[0070] In step S302, the rate of change of adhesive layer thickness is detected during the assembly process simulation. When the rate of change of adhesive layer thickness reaches a preset abnormal threshold during the simulation, at least one intervention delay is selected from the reference intervention time interval, and a clamping adjustment operation is performed according to the selected intervention delay after the abnormal threshold is triggered. In specific implementation, virtual monitoring nodes or monitoring points can be set in the digital twin simulation model to acquire thickness change data at the adhesive interface in real time and calculate the rate of change of adhesive layer thickness based on the thickness data. When the rate of change reaches the preset abnormal threshold, the system records the corresponding trigger time and uses the trigger time as the start time of the intervention delay. Subsequently, one or more intervention delays are selected from the reference intervention time interval as candidate intervention delays, such as the start delay, middle delay, and end delay of the interval, which are respectively used as the intervention delays for simulation testing. Then, a clamping adjustment operation is performed in the simulation model according to the selected intervention delay, such as by changing the clamping force parameters in the simulation tooling, adjusting the clamping point constraints, or changing the contact state between the segments, thereby simulating the assembly state change after the clamping adjustment operation is performed in actual assembly.

[0071] In step S303, after completing the simulation assembly process, the final simulated assembly misalignment amount under the corresponding intervention delay is obtained. Specifically, after the simulation assembly is completed, the geometric relationship of the segment component docking position can be measured to calculate the corresponding final simulated assembly misalignment amount. For example, the relative displacement of the docking edges of two segments can be obtained through measurement points set in the simulation model, and this relative displacement can be used as the final simulated assembly misalignment amount. For different intervention delays, the simulation model will output the corresponding final simulated assembly misalignment amount, thus forming a correspondence between the intervention delay and the final simulated assembly misalignment amount. By comparing these simulation results, it can be determined which intervention delays within the reference intervention time interval can achieve better assembly results.

[0072] Through steps S301 to S303, the intervention delay within the reference intervention time interval can be verified and analyzed using a digital twin simulation model before actual assembly, thereby obtaining the simulated final assembly misalignment amount under the current target segmented blade assembly conditions. This process not only reduces the trial-and-error costs in the actual assembly process but also further improves the accuracy of the intervention delay selection, providing a reliable basis for subsequent clamping and adjustment operations during the actual adhesive bonding assembly process.

[0073] Furthermore, the method for analyzing the assembly accuracy of segmented blades based on digital twin simulation also includes the following steps:

[0074] Step S400: When the final assembly misalignment falls within the range of the reference assembly misalignment, clamping adjustment is performed during the actual adhesive bonding process according to the intervention delay within the reference intervention time interval.

[0075] Specifically, Figure 3 A flowchart is shown to determine the reference intervention delay based on simulation results and to perform clamping adjustment operations.

[0076] Specifically, when the simulated final assembly misalignment falls within the reference assembly misalignment range, the clamping adjustment operation performed during the actual adhesive bonding assembly process, according to the intervention delay within the reference intervention time interval, includes the following steps:

[0077] Step S401: Determine whether the final assembly misalignment amount in the simulation falls within the range of the reference assembly misalignment amount.

[0078] Step S402: When the simulated final assembly misalignment falls within the range of the reference assembly misalignment, the corresponding intervention delay is determined as the reference intervention delay for actual assembly execution.

[0079] Step S403: During the actual adhesive bonding and assembly process of the target segmented blade, the rate of change of adhesive layer thickness is monitored in real time. When the rate of change of adhesive layer thickness reaches the preset abnormal threshold, the clamping adjustment operation is performed according to the reference intervention delay.

[0080] In this embodiment of the invention, step S400 is used to apply the simulation analysis results obtained in step S300 to the actual adhesive bonding assembly process, thereby achieving optimized control of the timing of the clamping adjustment operation. Through this step, the clamping adjustment operation can be performed according to the verified reference intervention delay during the actual assembly process, so that the assembly control process is consistent with the simulation analysis results, thereby improving the stability and assembly accuracy of the final assembly misalignment.

[0081] In step S401, it is necessary to determine whether the simulated final assembly misalignment falls within the reference assembly misalignment range. This determination is to verify the validity of the simulation results. Although the reference intervention time period and reference assembly misalignment range were obtained in step S200 through historical assembly sample analysis, the patterns derived from historical data analysis are still empirical, and the specific assembly behavior of the target segmented blade under the current assembly conditions may still differ. Therefore, the simulation analysis in step S300 can obtain the simulated final assembly misalignment corresponding to different intervention delays under the current assembly conditions, and step S401 determines whether the simulation result falls within the reference assembly misalignment range.

[0082] If the final simulated assembly misalignment falls within the range of the reference assembly misalignment, it indicates that under the current assembly conditions, the intervention delay can still achieve an assembly effect similar to the historical assembly samples, thus demonstrating good feasibility. If the final simulated assembly misalignment does not fall within the range of the reference assembly misalignment, it indicates that there may be differences between the current assembly conditions and historical samples. In this case, other intervention delays can be selected for simulation verification, or historical assembly samples can be re-analyzed, thereby avoiding the direct application of unsuitable intervention delay strategies in the actual assembly process.

[0083] In step S402, when the final assembly misalignment amount in the simulation falls within the reference assembly misalignment amount range, the corresponding intervention delay is determined as the reference intervention delay for actual assembly execution. Through this step, the simulation-verified intervention delay can be directly used in the actual assembly control process, thereby mapping the assembly control strategy from the virtual simulation environment to the actual assembly process. In other words, the simulation-verified intervention delay becomes the time parameter used to perform clamping and adjustment operations in the actual assembly process, thus ensuring that the assembly control strategy has reliable data support.

[0084] In step S403, the rate of change of adhesive layer thickness is monitored in real time during the actual bonding assembly of the target segmented blade. When the rate of change of adhesive layer thickness reaches a preset abnormal threshold, a clamping adjustment operation is performed according to the reference intervention delay. In specific applications, displacement sensors, laser rangefinders, or thickness monitoring devices can be arranged in the assembly fixture or blade mating area to collect displacement change data or thickness change data at the bonding interface in real time, and the rate of change of adhesive layer thickness is calculated by the control system. When the rate of change reaches the preset abnormal threshold, the system records the corresponding trigger time and starts the intervention delay timing. After the reference intervention delay is reached, the assembly control system can automatically send control commands to the clamping mechanism of the assembly fixture, such as adjusting the clamping force or adjusting the position of the clamp, thereby performing the clamping adjustment operation. In some embodiments, the assembly control system can also issue prompt signals to the operator, who can then perform the corresponding clamping adjustment operation according to the system prompts, thereby realizing the control execution during the actual assembly process.

[0085] Through the overall implementation of steps S100 to S400, an assembly control method based on a combination of historical assembly data analysis and digital twin simulation verification can be formed. This method first identifies the variation pattern between intervention delay and final assembly misalignment amount using a historical assembly database, and determines a reference intervention time interval. Then, it verifies and analyzes the intervention delay within this time interval using a digital twin simulation model. Finally, during actual assembly, clamping adjustment operations are performed according to the verified reference intervention delay. In this way, an optimal intervention delay can be determined in advance before actual assembly, thus avoiding adjustments based on experience during the actual assembly process.

[0086] Therefore, this invention effectively solves the core research problem raised in step S100, namely, the impact of different intervention delays on the final assembly misalignment. By analyzing historical assembly data and combining it with digital twin simulation verification, the intervention delay range that can achieve better assembly accuracy under specific assembly conditions can be identified. Furthermore, the timing of clamping adjustment operations can be proactively controlled during actual assembly, thereby further improving the assembly accuracy and stability of segmented blades.

[0087] Furthermore, the method of this invention also has promising engineering application prospects. With the continuous increase in wind turbine capacity, segmented blades are being used more and more widely in wind power equipment manufacturing, and blade assembly accuracy has a significant impact on blade aerodynamic performance and structural reliability. This invention, by combining historical assembly data analysis and digital twin simulation technology, optimizes the assembly control strategy, providing a more scientific and reliable control method for the segmented blade assembly process. This method can not only be applied to the adhesive bonding assembly process of wind turbine blades, but can also be extended to other adhesive bonding assembly scenarios for large composite material structures, such as aerospace composite material structure assembly, ship composite material structure assembly, and the connection of large composite material components, thus possessing good application value and promising prospects for promotion.

[0088] Furthermore, Figure 4 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0089] In another preferred embodiment of the present invention, a segmented blade assembly accuracy analysis system based on digital twin simulation includes:

[0090] The historical sample acquisition and analysis module 100 is used to acquire a historical assembly database, filter out several historical assembly samples that match the assembly conditions of the target segmented blade, analyze each historical assembly sample, determine the trigger time when the rate of change of adhesive layer thickness during the adhesive bonding assembly process reaches a preset abnormal threshold, and determine the intervention delay from the trigger time to the execution of the clamping adjustment operation. Simultaneously, it acquires the final assembly misalignment amount of the corresponding historical assembly sample. The final assembly misalignment amounts corresponding to the historical assembly samples are all within a preset allowable tolerance range.

[0091] The matching with the target segmented blade assembly conditions means that the historical assembly samples and the target segmented blade all meet the corresponding preset similarity thresholds in terms of blade structure type, bonding location, assembly environment parameters, assembly tooling configuration parameters, and bonding process parameters.

[0092] The blade structure type includes blade model, segment structure form and material system; the bonding position includes the connection position of root segment-middle segment or middle segment-tip segment; the assembly environment parameters include temperature and humidity; the assembly tooling configuration parameters include fixture layout, support position and clamping point; and the bonding process parameters include adhesive type, adhesive layer design thickness, adhesive application amount and clamping force parameters.

[0093] Furthermore, the segmented blade assembly accuracy analysis system based on digital twin simulation also includes:

[0094] The intervention time period analysis module 200 is used to determine whether, among several historical assembly samples, the final assembly misalignment amount exhibits a trend of first decreasing and then increasing as the intervention time delay increases. If so, the turning point of the trend is determined as the reference intervention time period interval, and the corresponding reference assembly misalignment amount range is determined. If not, the historical assembly samples are re-screened, or digital twin simulation verification is performed based on the original intervention time period range.

[0095] Furthermore, the segmented blade assembly accuracy analysis system based on digital twin simulation also includes:

[0096] The digital twin simulation module 300 is used to call the digital twin simulation model for the target segmented blade assembly, perform clamping adjustment operations according to the intervention delay within the reference intervention time interval, and obtain the corresponding simulation final assembly misalignment amount.

[0097] Furthermore, the segmented blade assembly accuracy analysis system based on digital twin simulation also includes:

[0098] The assembly control execution module 400 is used to perform clamping adjustment operations during the actual adhesive bonding assembly process when the simulated final assembly misalignment falls within the reference assembly misalignment range, according to the intervention delay within the reference intervention time interval.

[0099] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0100] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0101] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0102] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing the assembly accuracy of segmented blades based on digital twin simulation, characterized in that, The method includes: Obtain the historical assembly database and filter out several historical assembly samples that match the target segmented blade assembly conditions. Analyze each historical assembly sample to determine the trigger time when the rate of change of adhesive layer thickness reaches the preset abnormal threshold during the adhesive bonding assembly process, and determine the intervention delay from the trigger time to the execution of the clamping adjustment operation. At the same time, obtain the final assembly misalignment amount of the corresponding historical assembly sample. Determine whether there is a trend in which the final assembly misalignment amount first decreases and then increases as the intervention time delay increases in several historical assembly samples. If so, determine the turning point of the trend as the reference intervention time period interval and determine the reference assembly misalignment amount range corresponding to the reference intervention time period interval. The digital twin simulation model for the target segmented blade assembly is invoked, and the clamping adjustment operation is performed according to the intervention delay within the reference intervention time interval to obtain the corresponding simulation final assembly misalignment amount; When the final assembly misalignment in the simulation falls within the range of the reference assembly misalignment, the clamping adjustment operation is performed during the actual adhesive bonding assembly process according to the intervention delay within the reference intervention time interval.

2. The method for analyzing the assembly accuracy of segmented blades based on digital twin simulation according to claim 1, characterized in that, The matching with the target segmented blade assembly conditions means that the historical assembly samples and the target segmented blade all meet the corresponding preset similarity thresholds in terms of blade structure type, bonding location, assembly environment parameters, assembly tooling configuration parameters, and bonding process parameters.

3. The method for analyzing the assembly accuracy of segmented blades based on digital twin simulation according to claim 2, characterized in that, The blade structure type includes blade model, segment structure form and material system; the bonding position includes the connection position of root segment-middle segment or middle segment-tip segment; the assembly environment parameters include temperature and humidity; the assembly tooling configuration parameters include fixture layout, support position and clamping point; and the bonding process parameters include adhesive type, adhesive layer design thickness, adhesive application amount and clamping force parameters.

4. The method for analyzing the assembly accuracy of segmented blades based on digital twin simulation according to claim 1, characterized in that, The final assembly misalignment amounts corresponding to the historical assembly samples are all within the preset allowable tolerance range.

5. The method for analyzing the assembly accuracy of segmented blades based on digital twin simulation according to claim 1, characterized in that, The steps for calling the digital twin simulation model of the target segmented blade assembly, performing clamping adjustment operations according to the intervention delay within the reference intervention time interval, and obtaining the corresponding final simulated assembly misalignment include: The assembly process is simulated by calling a digital twin simulation model for the target segmented blade assembly and inputting the assembly conditions of the target segmented blade into the digital twin simulation model. In the assembly process simulation, the rate of change of adhesive layer thickness is detected. When the rate of change of adhesive layer thickness reaches a preset abnormal threshold, at least one intervention delay is selected from the reference intervention time interval, and a clamping adjustment operation is performed according to the selected intervention delay after the abnormal threshold is triggered. After completing the simulation assembly process, obtain the final assembly misalignment amount under the corresponding intervention delay.

6. The method for analyzing the assembly accuracy of segmented blades based on digital twin simulation according to claim 5, characterized in that, When the simulated final assembly misalignment falls within the reference assembly misalignment range, the steps for performing clamping adjustment operations according to the intervention delay within the reference intervention time interval during the actual adhesive bonding assembly process include: Determine whether the final assembly misalignment amount in the simulation falls within the range of the reference assembly misalignment amount; When the final assembly misalignment amount in the simulation falls within the range of the reference assembly misalignment amount, the corresponding intervention delay is determined as the reference intervention delay for the actual assembly execution. During the actual bonding and assembly of the target segmented blade, the rate of change of adhesive layer thickness is monitored in real time. When the rate of change of adhesive layer thickness reaches a preset abnormal threshold, a clamping adjustment operation is performed according to the reference intervention delay.

7. A segmented blade assembly accuracy analysis system based on digital twin simulation, characterized in that, The system includes: The historical sample acquisition and analysis module is used to acquire the historical assembly database, filter out several historical assembly samples that match the target segmented blade assembly conditions, analyze each historical assembly sample, determine the trigger time when the rate of change of adhesive layer thickness reaches the preset abnormal threshold during the adhesive bonding assembly process, determine the intervention delay from the trigger time to the execution of the clamping adjustment operation, and at the same time acquire the final assembly misalignment amount of the corresponding historical assembly sample. The intervention time period analysis module is used to determine whether there is a trend of first decreasing and then increasing in the final assembly misalignment amount in several historical assembly samples as the intervention time delay increases. If so, the turning point of the trend is determined as the reference intervention time period interval, and the reference assembly misalignment amount range corresponding to the reference intervention time period interval is determined. The digital twin simulation module is used to call the digital twin simulation model for the target segmented blade assembly, perform clamping adjustment operations according to the intervention delay within the reference intervention time interval, and obtain the corresponding simulation final assembly misalignment amount. The assembly control execution module is used to perform clamping adjustment operations during the actual adhesive bonding assembly process when the simulated final assembly misalignment falls within the reference assembly misalignment range, according to the intervention delay within the reference intervention time interval.

8. The segmented blade assembly accuracy analysis system based on digital twin simulation according to claim 7, characterized in that, The matching with the target segmented blade assembly conditions means that the historical assembly samples and the target segmented blade all meet the corresponding preset similarity thresholds in terms of blade structure type, bonding location, assembly environment parameters, assembly tooling configuration parameters, and bonding process parameters.

9. The segmented blade assembly accuracy analysis system based on digital twin simulation according to claim 8, characterized in that, The blade structure type includes blade model, segment structure form and material system; the bonding position includes the connection position of root segment-middle segment or middle segment-tip segment; the assembly environment parameters include temperature and humidity; the assembly tooling configuration parameters include fixture layout, support position and clamping point; and the bonding process parameters include adhesive type, adhesive layer design thickness, adhesive application amount and clamping force parameters.

10. The segmented blade assembly accuracy analysis system based on digital twin simulation according to claim 7, characterized in that, The final assembly misalignment amounts corresponding to the historical assembly samples are all within the preset allowable tolerance range.

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