A method and digital twin system for real-time monitoring and prediction of pipe bending process status

Abstract

The present invention discloses a method and digital twin system for real-time monitoring and prediction of the state of a pipe bending process. By installing gyroscopes, force sensors, etc. on the bending dies, pressing dies, and other molds of the pipe bending equipment, the position, speed, acceleration of each mold, and the pressure, friction, and other data between each mold and the pipe fitting can be collected in real time. A displacement sensor is installed on the anti-wrinkle mold to realize wrinkle monitoring, a camera is installed above the pipe fitting to realize pipe wall state collection, and a gyroscope is installed at the end of the core rod to realize pipe fitting unloading rebound monitoring. The collected state information of the pipe bending equipment mold and pipe fitting is processed by the data processing system and then subjected to spatiotemporal fusion by the spatiotemporal fusion conversion module based on multi-task learning to realize the construction of a digital twin model, and finally realize real-time monitoring and prediction of the state of the pipe bending process. The present invention can realize the collection of the bending state of the pipe bending equipment mold and pipe fitting, and can realize real-time monitoring and prediction of the state of the pipe bending process, so as to improve the accuracy of the pipe bending equipment and the quality of the pipe bending.

Description

Technical Field

[0001] The present invention relates to the field of digital twins, specifically a method and a digital twin system for real-time monitoring and prediction of pipe bending process status. Background Art

[0002] Bent pipe components are formed by bending straight hollow pipes through bending equipment coupled with various dies. They are widely used in high-tech fields such as aviation and aerospace. The most precise and widely used bending equipment is CNC bending equipment, which uses a bending die, clamping die, pressing die, anti-wrinkle die, and mandrel. However, traditional bending methods use open-loop control and lack intelligence. This makes it impossible to obtain data on the actual operating status of the bending equipment dies and the deformation process during the bending process. To achieve higher-quality pipes, manual bending and offline measurement are required, relying on experience, resulting in low efficiency and difficulty in ensuring accuracy.

[0003] Digital twin systems use physical geometry models, real-time state data, and historical operational data to create a twin model in virtual space. This allows for the simulation and evolution of multiple timescales and multiple physical fields within the twin system, and provides online guidance and control of the actual scenario through a feedback system. With the application of digital twin systems in process manufacturing, mapping physical models to virtual models through data acquisition and predicting future states through digital twin modeling have become crucial foundational tasks.

[0004] To map the actual physical process of the full tube bending process to a virtual twin model and predict its future state, a reasonable data acquisition system must be constructed to enable online, real-time status monitoring and future state prediction of both the tube bending equipment mold state and the tube bending process. Current digital twin models for machining processes only model the equipment or workpiece. However, the workpiece's state monitoring and future state are affected by the equipment's state. Considering only a single dimension of information weakens the generalization capability of the digital twin model. Therefore, we propose a data acquisition method and digital twin system for real-time monitoring and prediction of the tube bending process state. Summary of the Invention

[0005] In order to solve the problems in the background technology, the present invention discloses a real-time monitoring and prediction method and digital twin system for the status of the pipe bending process, which effectively realizes multi-directional online real-time monitoring and prediction of the mold status of the pipe bending equipment and the pipe bending process, and can improve the intelligence level of the pipe bending process and improve the quality of pipe bending forming.

[0006] The technical solution adopted in the present invention is as follows:

[0007] 1. A digital twin system for real-time monitoring and prediction of pipe bending process status

[0008] It includes pipe bending equipment, pipe fittings, data acquisition system, data processing system and twin model system. The data acquisition system is used to collect the status data of each mold of the pipe bending equipment and the deformation data of the pipe fittings during the bending process. The data processing system is used to preprocess the data collected by the data acquisition system. The twin model system predicts the mold status and pipe fitting bending status of the pipe bending equipment at the current time and in the future based on the data preprocessed by the data processing system.

[0009] The pipe bending equipment includes a bending die, an insert, a clamping die, a pressing die, an anti-wrinkle die, a booster trolley, a core shaft, and a core ball; the tail of the pipe fitting is clamped by the chuck of the booster trolley, the insert and the clamping die clamp the other end of the pipe fitting, and the middle is clamped by the pressing die and the anti-wrinkle die; a core ball and a core shaft for supporting the pipe wall are arranged in the pipe fitting, and multiple core balls are hinged on the core shaft after being connected in series, and the bending die and the insert are fixedly connected; the bending die, the insert, and the clamping die rotate synchronously, and apply torque to the pipe fitting through the synergistic action of pressure and friction. As the rotation angle increases, the pipe fitting undergoes plastic deformation; during the bending process, the anti-wrinkle die and the core shaft remain stationary, and the multiple core balls swing at a certain angle as the axial shape of the pipe fitting changes; during the bending process, the pressing die and the booster trolley move forward at a certain speed, and provide forward power to the unbent part of the pipe fitting through pressure and friction, so as to avoid defects such as fracture and cross-section collapse that cause failure of the pipe fitting;

[0010] The data acquisition system includes a pipe bending equipment mold state monitoring module and a pipe bending process monitoring module; the pipe bending equipment mold state monitoring module collects the state data of each pipe bending equipment mold, and the pipe bending process monitoring module collects the deformation data of the pipe during the pipe bending process;

[0011] The data processing system includes a data filtering and denoising module, a time series preprocessing module, a pipe bending process comprehensive information model, and a historical information storage module;

[0012] The twin model system includes a spatiotemporal fusion conversion module based on multi-task learning and a twin model visualization presentation module.

[0013] The pipe bending equipment mold status monitoring module includes:

[0014] Bend die gyroscope, embedded in the bend die surface, used to measure the bend die rotation angle, bending velocity and angular acceleration;

[0015] Several bend die temperature sensors are evenly distributed on the bend die and pass through the bend die from top to bottom, used to measure the temperature of the bend die;

[0016] The clamping die force sensor has an arc-shaped sensing surface and is embedded in the inner surface of the clamping die, that is, the contact surface with the pipe fitting, and the sensor sensing surface is completely in contact with the pipe fitting. The clamping die force sensor is used to measure the force between the clamping die and the pipe fitting, including the pressure between the clamping die and the pipe fitting and the friction between the clamping die and the pipe fitting.

[0017] The die gyroscope is embedded in the die surface and is used to measure the feed displacement, velocity and acceleration of the die;

[0018] The die force sensor has an arc-shaped sensing surface and is embedded in the inner surface of the die, that is, the contact surface with the pipe fitting, and the sensor sensing surface is completely in contact with the pipe fitting. The die force sensor is used to measure the force between the die and the pipe fitting, including the pressure between the die and the pipe fitting and the friction between the die and the pipe fitting.

[0019] Several die temperature sensors are evenly distributed on the die and pass through the die from top to bottom, for measuring the temperature of the die;

[0020] The booster trolley gyroscope is embedded in the outer surface of the booster trolley and is used to measure the feed displacement, speed and acceleration of the booster trolley;

[0021] The booster trolley force sensor has an arc-shaped sensing surface and is embedded in the inner surface of the chuck of the booster trolley, that is, the contact surface with the pipe fitting, and the sensor sensing surface is completely in contact with the pipe fitting; the booster trolley force sensor is used to measure the force between the booster trolley and the pipe fitting, including the pressure between the booster trolley and the pipe fitting, and the friction between the booster trolley and the pipe fitting.

[0022] The pipe bending process monitoring module includes:

[0023] The anti-wrinkle mold displacement sensor is embedded in the anti-wrinkle mold's curved surface and located at the very bottom of the curved surface, where the anti-wrinkle mold contacts the innermost concave side of the straight pipe section of the pipe. The anti-wrinkle mold displacement sensor probe contacts the pipe, with the probe axis perpendicular to the axis of the straight pipe section. It is used to monitor wrinkling during pipe bending, that is, to measure the displacement of wrinkle corrugations when the pipe is bent and wrinkled.

[0024] The core ball end gyroscope is installed at the tail of the core ball link to monitor the core ball status during the pipe bending process and the rebound angle of the pipe when the pipe is unloaded after bending.

[0025] A camera, mounted above the pipe using a camera bracket, monitors the pipe's bending state, including cross-sectional distortion, in real time. This camera, a depth camera, is mounted horizontally to the pipe's bending plane and can measure the deformation of the exposed portion of the pipe that is not in contact with the mold during bending.

[0026] All gyroscopes are high-precision six-axis gyroscopes that can measure displacement, angle, speed, angular velocity, acceleration, and angular acceleration; all force sensors are multi-dimensional force sensors that can measure pressure and friction; all temperature sensors use thermocouple temperature acquisition probes. The temperature sensor is only used during the heating bending process and can be selected not to be used when bending at room temperature.

[0027] 2. A method for real-time monitoring and prediction of pipe bending process status

[0028] Step 1) Data acquisition: The multi-sensor data of the data acquisition system is used to collect the state data of the pipe bending equipment and the deformation data of the pipe in real time during the pipe bending process;

[0029] Step 2) Data processing: The data collected by the data acquisition system is preprocessed by the data processing system, specifically:

[0030] The data collected by the data acquisition system is filtered and denoised by the data filtering and denoising module, and then the time stamps are unified by the time series preprocessing module to convert the data into data with the same time stamp and the same time interval. The data processed by the time series preprocessing module is integrated to obtain the comprehensive information model IM of the pipe bending process. The historical information storage module performs structured storage on the data processed by the comprehensive information model of the pipe bending process.

[0031] The present invention adopts Kalman filtering algorithm to filter the data and perform denoising to avoid adverse effects on the data caused by inherent vibration of the pipe bending equipment;

[0032] Step 3) Prediction and visualization of bending equipment and fittings status:

[0033] The spatiotemporal fusion conversion module based on multi-task learning predicts the status information of the pipe bending equipment and the bending status of the pipe fittings at the current and future moments according to the data preprocessed by the data processing system;

[0034] The twin model visualization module uses Unity to visualize the current and future status of the bending equipment mold and the pipe bending process, and then conducts feedback control on the bending equipment to improve the forming quality.

[0035] In the step 2):

[0036] The pipe bending process comprehensive information model IM includes the total data of the pipe bending equipment mold status monitoring part Data Mach And the total data of pipe bending process monitoring part Tube , where each data is a time series data with equal time intervals. The pipe bending process comprehensive information model IM includes the total data of the pipe bending equipment mold status monitoring part Data MachAnd the total data of pipe bending process monitoring part Tube ,Right now

[0037] IM={Data Mach ,Data Tube}

[0038] Among them, the total data of the pipe bending equipment mold status monitoring part Mach The bending die rotates by an angle θ 弯 , bending die bending speed ω 弯 , bending die angular acceleration α 弯 , bending die temperature T 弯 , the pressure P between the clamping die and the tube 夹紧-管 , the friction force F between the clamping die and the tube 夹紧-管 , die displacement d 压 , die speed v 压 , die acceleration α 压 , pressure P between die and tube 压-管 , the friction force F between the die and the tube 压-管 , die temperature T 压 , booster car displacement d 助推 , booster car speed v 助推 , booster car acceleration α 助推 , the pressure P between the booster trolley and the pipe 助推-管 , the friction force F between the booster trolley and the tube 助推-管 Composition, that is

[0039] Data Mach ={θ 弯 ,ω 弯 , α 弯 , T 弯 , P 夹紧-管 , F 夹紧-管 , d 压 , v 压 , α 压 , P 压-管 , F 压-管 , T 压 ,

[0040] d 助推 , v 助推 , α 助推 , P 助推-管 , F 助推-管}

[0041] Total data of pipe bending process monitoring part Tube The displacement of the wrinkle ripple d 皱 、Bending angle of pipe θ 管 、Pipe section deformation data ε 管 Composition, that is

[0042] Data Tube ={d 皱 ,θ 管 , ε 管}

[0043] All data in the integrated information model IM of the pipe bending process are in the form of time series.

[0044] In step 3), the spatiotemporal fusion conversion module based on multi-task learning includes three parts: an input layer, a private-shared layer, and a task output layer;

[0045] The input layer receives the data of the integrated information model IM of the pipe bending process, including the total data Data of the pipe bending equipment mold status monitoring part Mach And the total data of pipe bending process monitoring part Tube ;

[0046] The private-shared layer includes a shared LSTM module and two private LSTM modules, the two private LSTM modules are the auxiliary task private LSTM module and the main task private LSTM module; the shared LSTM module receives the total data Data of the mold status monitoring part of the bending equipment Mach And the total data of pipe bending process monitoring part Tube Two parts of data are used to share common feature extraction and explore the interaction between bending equipment and bending pipes; the auxiliary task private LSTM module and the main task private LSTM module receive the total data of the bending equipment mold status monitoring part respectively Mach And the total data of pipe bending process monitoring part Tube , used to extract the temporal evolution law of the tube bending equipment die state and the temporal evolution law of the tube bending process;

[0047] The task output layer includes an auxiliary task Dense module, a feature fusion Concatenate module and a main task Dense module; the output results of the auxiliary task private LSTM module and the shared LSTM module are added element by element and then input into the auxiliary task Dense module; the output results of the main task private LSTM module and the shared LSTM module are added element by element, and then input into the feature fusion Concatenate module together with the output results of the auxiliary task Dense module for series splicing to achieve feature fusion, and the fused data is input into the main task Dense module; the auxiliary task Dense module and the main task Dense module respectively output the prediction results of the bending equipment status and the prediction results of the pipe fitting bending forming as the final output results of the module.

[0048] The spatiotemporal fusion conversion module based on multi-task learning is trained through a joint loss function and adapted to the multi-task learning scenario. The joint loss function is defined as the auxiliary task loss L of the bending equipment mold state. 辅助 and the main task loss L of the pipe bending process state 主 The sum of the principal weights:

[0049] L=L 主 +αL 辅助

[0050] Among them, L is the total loss of the model, and the weight α is determined according to the empirical method.

[0051] In the step 3):

[0052] Based on the bending equipment information and the bending state of the pipe fittings before the current moment, the state of the bending equipment mold and the deformation state of the pipe fittings at the current moment and in the future are predicted;

[0053] The state of the pipe bending equipment mold includes an abnormal state of the pipe bending equipment, such as vibration of the pipe bending equipment;

[0054] The predicted deformation state of the pipe fitting includes predicting the distortion defect of the cross section of the pipe fitting.

[0055] The pipe bending equipment can compensate online based on the wrinkle ripples directly measured by the sensor and the cross-sectional distortion defects obtained through prediction. The compensation can be achieved by speeding up or slowing down the speed of the bending die, pressing die, and booster trolley, as well as increasing or decreasing the pressure of the pressing die and booster trolley on the pipe.

[0056] The pipe bending equipment can bend and compensate again online based on the springback angle of the pipe measured after bending unloading.

[0057] Beneficial effects of the present invention:

[0058] The present invention sets corresponding sensors at appropriate positions of the pipe bending equipment and the pipe bending site to collect the time series of the operation status of the pipe bending equipment and the deformation status of the pipe fittings, and processes the data through the data processing system to obtain a digital twin data model of the pipe bending process, thereby realizing the integration of the state data of the pipe bending equipment and the pipe fittings in the pipe bending process, and modeling the integrated time series information through the spatiotemporal fusion conversion module based on multi-task learning. The multi-task learning comprehensively considers the mutual influence between the state of the pipe bending equipment and the pipe fittings in the pipe bending process, and can realize more accurate pipe forming by considering the influence of the future state of the pipe bending equipment information on the future state of the pipe fittings. The forming state prediction of the pipe bending equipment and pipe fittings at the current moment is predicted by using the data before the current moment, which compensates for the time lag problem caused by the data processing process, improves the real-time performance of the digital twin model, and can predict the future state at the same time, providing a basis for the digital twin system to optimize the pipe bending process in real time through decision-making. The real-time monitoring state and future prediction results are finally visualized through the twin model, effectively realizing the multi-dimensional online real-time monitoring and future prediction of the pipe bending equipment mold state and the pipe bending process, which can improve the intelligence level of the pipe bending process and improve the quality of pipe bending. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 Schematic diagram of the pipe bending process of the present invention;

[0060] Figure 2 It is a simplified schematic diagram of the pipe bending equipment of the present invention;

[0061] Figure 3 It is a simplified schematic diagram of the overall structure of the system of the present invention;

[0062] Figure 4 A cross-sectional view of the overall structure of the system of the present invention;

[0063] Figure 5 This is a schematic diagram of the installation of the anti-wrinkle mold displacement sensor of the present invention;

[0064] Figure 6 This is a structural diagram of the anti-wrinkle mold of the present invention;

[0065] Figure 7 It is the system principle diagram of the present invention;

[0066] Figure 8 Schematic diagram of the spatiotemporal fusion conversion module based on multi-task learning.

[0067] In the figure: 1. Pipe bending equipment, 2. Pipe fittings, 3. Data acquisition system, 4. Data processing system, 5. Twin model system, 6. Bending die, 7. Insert, 8. Clamping die, 9. Pressing die, 10. Anti-wrinkle die, 11. Booster trolley, 12. Mandrel, 13. Core ball, 14. Bending die gyroscope, 15. Bending die temperature sensor, 16. Clamping die force sensor, 17. Clamping die temperature sensor, 18. Pressing die gyroscope, 19. Pressing die force sensor, 20. Die temperature sensor, 21. Booster trolley gyroscope, 22. Booster trolley force sensor, 23. Anti-wrinkle mold displacement sensor, 24. Core ball end gyroscope, 25. Camera bracket, 26. Camera, 41. Data filtering and denoising module, 42. Time series preprocessing module, 43. Integrated information model IM of bending process, 44. Historical information storage module, 51. Spatiotemporal fusion conversion module based on multi-task learning, 52. Twin model visualization presentation module. DETAILED DESCRIPTION

[0068] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0069] like Figures 1 to 7 As shown, the present invention includes pipe bending equipment 1, pipe fittings 2, a data acquisition system 3, a data processing system 4, and a twin model system 5.

[0070] like Figure 2 As shown, the pipe bending equipment 1 includes a bending die 6, an insert 7, a clamping die 8, a pressing die 9, an anti-wrinkle die 10, a booster trolley 11, a core shaft 12, and a core ball 13.

[0071] like Figures 3 and 4 As shown, the data acquisition system 3 includes a tube bending equipment mold state monitoring part and a tube bending process monitoring part, wherein the tube bending equipment mold state monitoring part includes a bending die gyroscope 14, a bending die temperature sensor 15, a clamping die force sensor 16, a clamping die temperature sensor 17, a pressing die gyroscope 18, a pressing die force sensor 19, a pressing die temperature sensor 20, a booster trolley gyroscope 21, and a booster trolley force sensor 22; the tube bending process monitoring part includes an anti-wrinkle die displacement sensor 23, a core ball end gyroscope 24, a camera bracket 25 and a camera 26, wherein all gyroscopes are high-precision six-axis gyroscopes, which can measure displacement, angle, speed, angular velocity, acceleration, and angular acceleration; all force sensors are multi-dimensional force sensors; and the temperature sensor uses a thermocouple temperature acquisition probe. The temperature sensor is only used in the heating bending process and can be selected not to be used when bending at room temperature.

[0072] like Figures 3 and 4As shown, in the mold status monitoring section of the pipe bending equipment, a bend die gyroscope 14 is embedded in the upper surface of the bend die 6 and can accurately measure the rotation angle, bending speed, and angular acceleration of the bend die 6. Several bend die temperature sensors 15 are embedded in the bend die 6, penetrating the upper and lower surfaces of the bend die 6 to ensure that the temperature of the contact portion between the bend die 6 and the pipe fitting 2 is measured. A clamping die force sensor 16 is embedded in the contact surface with the pipe fitting 2. The contact portion of the clamping die force sensor 16 with the pipe fitting 2 forms an arc surface, which is co-circular with the contact surface of the clamping die 8 and the pipe fitting 2. The clamping die force sensor 16 is at least a two-dimensional force sensor, one dimension of which measures the pressure between the clamping die 8 and the pipe fitting 2, and the other dimension measures the friction between the clamping die 8 and the pipe fitting 2. Since the clamping die 8 rotates synchronously with the bend die 6, no gyroscope is provided in the clamping die 8. The die gyroscope 18 is embedded in the outer surface of the die 9 and can accurately measure the feed displacement, velocity, and acceleration of the die 9. The die force sensor 19 is embedded in the surface in contact with the pipe 2. The contact portion of the die force sensor 19 with the pipe 2 is an arc surface, co-circular with the contact surface between the die 9 and the pipe 2. The die force sensor 19 is at least a two-dimensional force sensor, one dimension of which measures the pressure between the die 9 and the pipe 2, and the other dimension measures the friction between the die 9 and the pipe 2. Several die temperature sensors 20 are evenly embedded in the die 9, extending across the upper and lower surfaces of the die 9 to ensure that the temperature of the contact portion between the die and the pipe 2 is measured. The booster trolley gyroscope 21 is embedded in the outer surface of the booster trolley 11 and can accurately measure the feed displacement, speed and acceleration of the booster trolley 11. The booster trolley force sensor 22 is embedded in the contact surface between the chuck of the booster trolley 11 and the pipe fitting 2. The contact part between the booster trolley force sensor 22 and the pipe fitting 2 is an arc surface, and is a cocircular arc surface with the contact surface between the chuck of the booster trolley 11 and the pipe fitting 2. The booster trolley force sensor 22 is at least a two-dimensional force sensor, one dimension of which measures the pressure between the booster trolley 11 and the pipe fitting 2, and the other dimension measures the friction between the booster trolley 11 and the pipe fitting 2.

[0073] like Figures 3 to 6As shown, in the pipe bending process monitoring part, a hole is opened at the bottom of the arc surface of the anti-wrinkle mold 10 (that is, the contact position with the innermost concave side of the straight pipe section of the pipe) to embed an anti-wrinkle mold displacement sensor 23. The probe of the anti-wrinkle mold displacement sensor 23 extends out and contacts the pipe 2. When the pipe 2 is bent and wrinkled, the wrinkle corrugation displacement can be measured, which is used to monitor the wrinkling condition during the pipe bending process. The core ball end gyroscope 24 is installed at the end of the core ball 13 link, which can monitor the state of the core ball 13 during the pipe bending process, and when the pipe 2 is bent and unloaded, the rebound angle of the pipe 2 can be measured. The pipe bending equipment 1 can bend and compensate again online according to the measured rebound angle. The camera 26 is installed on the camera bracket 25. The camera 26 is a depth camera and is installed parallel to the bending plane of the pipe 2. It can measure the deformation state of the part of the pipe 2 that is not in contact with the mold and exposed to the outside when the pipe 2 is bent, and is used to monitor the cross-sectional deformation data of the pipe 2 during the bending deformation process in real time to observe the distortion state.

[0074] like Figure 7 As shown, the digital twin model system of the pipe bending equipment includes a pipe bending equipment 1, a pipe fitting 2, a data acquisition system 3, a data processing system 4, and a twin model system 5. The data processing system 4 includes a data filtering and denoising module 41, a time series preprocessing module 42, a pipe bending process comprehensive information model IM 43, and a historical information storage module 44. The data filtering and denoising module 41 filters and denoises the data collected by multiple sensors in the data acquisition system 3 respectively. The present invention adopts a Kalman filter algorithm to filter the data and perform denoising processing to avoid the adverse effects of the inherent vibration of the pipe bending equipment 1 on the data. Then, the time stamp is unified through the time series preprocessing module 42 and converted into data with the same time stamp and the same time interval. The pipe bending process comprehensive information model IM 43 includes the total data Data of the pipe bending equipment mold status monitoring part Mach And the total data of pipe bending process monitoring part Tube , where each data is a time series data with equal time intervals. The pipe bending process comprehensive information model IM 43 includes the total data of the pipe bending equipment mold status monitoring part Data Mach And the total data of pipe bending process monitoring part Tube ,Right now

[0075] IM={Data Mach ,Data Tube}

[0076] Among them, the total data of the pipe bending equipment mold status monitoring part Mach The bending die rotates by an angle θ 弯 , bending die bending speed ω 弯 , bending die angular acceleration α 弯 , bending die temperature T 弯 , the pressure P between the clamping die and the tube夹紧-管 , the friction force F between the clamping die and the tube 夹紧-管 , die displacement d 压 , die speed v 压 , die acceleration α 压 , pressure P between die and tube 压-管 , the friction force F between the die and the tube 压-管 , die temperature T 压 , booster car displacement d 助推 , booster car speed v 助推 , booster car acceleration α 助推 , the pressure P between the booster trolley and the pipe 助推-管 , the friction force F between the booster trolley and the tube 助推-管 Composition, that is

[0077] Data Mach ={θ 弯 ,ω 弯 , α 弯 , T 弯 , P 夹紧-管 , F 夹紧-管 , d 压 , v 压 , α 压 , P 压-管 , F 压-管 , T 压 ,

[0078] d 助推 , v 助推 , α 助推 , P 助推-管 , F 助推-管}

[0079] Total data of pipe bending process monitoring part Tube The displacement of the wrinkle ripple d 皱 , pipe bending angle θ 管 , pipe section deformation data ε 管 Composition, that is

[0080] Data Tube ={d 皱 ,θ 管 , ε 管}

[0081] All data in the pipe bending process integrated information model IM 43 is stored in a time series format. Finally, the historical information storage module 44 stores the data processed by the pipe bending process integrated information model IM 43 in a structured manner. The twin model system 5 includes a multi-task learning-based spatiotemporal fusion conversion module 51 and a twin model visualization presentation module 52. The multi-task learning-based spatiotemporal fusion conversion module 51, based on a private-shared multi-task learning framework, receives the pipe bending process integrated information model IM 43 and performs time series predictions on the state of the pipe bending equipment mold and the pipe bending process, thereby enabling prediction of the current and future states of the pipe bending equipment mold and the pipe bending process (including defect monitoring and prediction of cross-sectional distortion). Finally, the twin model visualization presentation module 52 uses Unity to visualize the state of the pipe bending equipment mold and the pipe bending process, thereby providing feedback control for the pipe bending equipment 1 to improve forming quality.

[0082] like Figure 7 As shown, the spatiotemporal fusion conversion module 51 based on multi-task learning is based on a private-shared multi-task learning framework. The framework is divided into two sub-tasks: the auxiliary task of the pipe bending equipment mold state and the main task of the pipe bending process state. The main task of the pipe bending process state is used to predict the pipe forming quality, and the auxiliary task of the pipe bending equipment mold state is used to predict the pipe bending equipment state (including predicting whether the pipe bending machine is abnormal: such as vibration, temperature abnormality). Since the pipe bending equipment mold state will affect the pipe forming quality, the output results of the auxiliary tasks of the task output layer are integrated into the main task prediction to improve the robustness and accuracy of the main task of the pipe bending process state.

[0083] The private-shared multi-task learning framework consists of three parts: input layer, private-shared layer, and task output layer. The input layer receives the comprehensive information model IM 43 of the bending process, which is divided into the total data Data of the bending equipment mold status monitoring part Mach And the total data of pipe bending process monitoring part Tube The private-shared layer consists of a shared LSTM module and two private LSTM modules. The two private LSTM modules are the auxiliary task private LSTM module and the main task private LSTM module. The shared LSTM module receives the total data of the mold status monitoring part of the bending equipment. Mach And the total data of pipe bending process monitoring part Tube Two parts of data are used to share common feature extraction and explore the interaction between bending equipment and bending pipes; two private LSTM modules receive the total data of the bending equipment mold status monitoring part respectively Mach And the total data of pipe bending process monitoring part Tube, used to extract the temporal evolution law of the state of the pipe bending equipment mold and the temporal evolution law of the pipe bending process. The output results of the shared LSTM are added element by element to the private LSTM output results of the main task and the auxiliary task respectively; the output layer includes the auxiliary task Dense module, the feature fusion Concatenate module and the main task Dense module. The auxiliary task Dense module of the pipe bending equipment mold state only receives the auxiliary task information of the pipe bending equipment mold state after processing by the private-shared layer. The auxiliary task Dense module processes the input data to obtain the final auxiliary task prediction result. The feature fusion Concatenate module receives the main task information of the pipe bending process state and the auxiliary task output result of the pipe bending equipment mold state after processing by the private-shared layer and performs a series splicing operation to realize feature fusion. The fused data is finally generated by the main task Dense module to generate the future prediction result of the main task. Finally, a joint loss function is used to train the model and adapt it to the multi-task learning scenario. The joint loss function is defined as the auxiliary task loss L of the pipe bending equipment mold state. 辅助 and the main task loss L of the pipe bending process state 主 The sum of the principal weights:

[0084] L=L 主 +αL 辅助

[0085] Among them, L is the total loss of the model, and the weight α can be determined according to the empirical method.

[0086] Through online training, the bending equipment information and pipe forming status before the current moment can be used to predict the bending equipment information (including abnormal operating conditions such as equipment vibration) and pipe bending status (including defects such as predicted cross-sectional distortion) at the current moment t and future moments. In addition, the influence of the future state of the bending equipment information on the future state of the pipe forming can be comprehensively considered to realize the prediction of the pipe forming status, making the prediction more accurate.

Claims

1. A digital twin system for real-time monitoring and prediction of pipe bending process status, characterized in that: The invention comprises a pipe bending device (1), a pipe fitting (2), a data acquisition system (3), a data processing system (4) and a twin model system (5), wherein the data acquisition system (3) is used to acquire status data of each mold of the pipe bending device (1) and deformation data of the pipe fitting (2) during the bending process, the data processing system (4) is used to pre-process the data acquired by the data acquisition system (3), and the twin model system (5) predicts the mold status of the pipe bending device and the bending status of the pipe fitting at the current moment and in the future according to the data pre-processed by the data processing system (4); The pipe bending equipment (1) comprises a bending die (6), an insert (7), a clamping die (8), a pressing die (9), an anti-wrinkle die (10), a booster trolley (11), a core shaft (12), and a core ball (13); the tail end of the pipe fitting (2) is clamped by the chuck of the booster trolley (11), the insert (7) and the clamping die (8) clamp the other end of the pipe fitting (2), and the middle is clamped by the pressing die (9) and the anti-wrinkle die (10); a core ball (13) and a core shaft (12) for supporting the pipe wall are arranged in the pipe fitting, and a plurality of core balls (13) are connected in series and hinged on the core shaft (12). The bending die (6) and the insert (7) are fixedly connected; the bending die (6), the insert (7), and the clamping die (8) rotate synchronously, and apply torque to the pipe (2) through the synergistic effect of pressure and friction, and the pipe produces plastic deformation as the rotation angle increases; during the bending process, the anti-wrinkle die (10) and the core shaft (12) remain stationary, and the multiple core balls (13) swing as the shape of the axis of the pipe (2) changes; during the bending process, the pressing die (9) and the booster trolley (11) move forward, providing forward power to the unbent part of the pipe (2) through pressure and friction; The data acquisition system (3) includes a pipe bending equipment mold state monitoring module and a pipe bending process monitoring module; the pipe bending equipment mold state monitoring module collects state data of each mold of the pipe bending equipment (1), and the pipe bending process monitoring module collects deformation data of the pipe (2) during the bending process; The data processing system (4) includes a data filtering and denoising module (41), a time series preprocessing module (42), a pipe bending process integrated information model IM (43), and a historical information storage module (44); The twin model system (5) includes a spatiotemporal fusion conversion module (51) based on multi-task learning and a twin model visualization presentation module (52).

2. The digital twin system for real-time monitoring and prediction of pipe bending process status according to claim 1 is characterized in that: The pipe bending equipment mold status monitoring module includes: A bending die gyroscope (14) is embedded in the surface of the bending die (6) and is used to measure the rotation angle, bending speed and angular acceleration of the bending die (6); A plurality of bending die temperature sensors (15) are evenly distributed on the bending die (6) and pass through the bending die (6) from top to bottom, and are used to measure the temperature of the bending die (6); A clamping die force sensor (16) has an arc-shaped sensing surface and is embedded in the inner surface of the clamping die (8), that is, the contact surface with the pipe fitting, and the sensor sensing surface is completely in contact with the pipe fitting; the clamping die force sensor (16) is used to measure the force between the clamping die and the pipe fitting, including the pressure between the clamping die (8) and the pipe fitting (2), and the friction between the clamping die (8) and the pipe fitting (2); A die gyroscope (18) is embedded in the surface of the die (9) and is used to measure the feed displacement, speed and acceleration of the die (9); A die force sensor (19) has an arc-shaped sensing surface and is embedded in the inner surface of the die (9), that is, the contact surface with the pipe fitting, and the sensor sensing surface is completely in contact with the pipe fitting; the die force sensor (19) is used to measure the force between the die (9) and the pipe fitting, including the pressure between the die (9) and the pipe fitting (2) and the friction between the die (9) and the pipe fitting (2); A plurality of die temperature sensors (20) are evenly distributed on the die (9) and pass through the die (9) vertically and are used to measure the temperature of the die (9); A booster trolley gyroscope (21) is embedded in the outer surface of the booster trolley (11) and is used to measure the feed displacement, speed and acceleration of the booster trolley (11); The boosting trolley force sensor (22) has an arc-shaped sensing surface and is embedded in the inner surface of the chuck of the boosting trolley (11), that is, the contact surface with the pipe (2), and the sensor sensing surface is completely in contact with the pipe (2); the boosting trolley force sensor (22) is used to measure the force between the boosting trolley (11) and the pipe (2), including the pressure between the boosting trolley (11) and the pipe (2) and the friction between the boosting trolley (11) and the pipe (2).

3. The digital twin system for real-time monitoring and prediction of pipe bending process status according to claim 1 is characterized in that: The pipe bending process monitoring module includes: The anti-wrinkle mold displacement sensor (23) is embedded and installed on the arc surface of the anti-wrinkle mold (10) and is located at the bottom of the arc surface, that is, the contact position between the anti-wrinkle mold (10) and the innermost concave side of the straight pipe section of the pipe fitting; the anti-wrinkle mold displacement sensor (23) probe is in contact with the pipe fitting (2), and the axis of the probe is perpendicular to the axis of the straight pipe section of the pipe fitting, and is used to measure the displacement of wrinkle corrugations when the pipe fitting (2) is bent and wrinkled; The core ball end gyroscope (24) is installed at the end of the core ball (13) and is used to monitor the state of the core ball (13) during the pipe bending process and the rebound angle of the pipe when the pipe is unloaded after bending. The camera (26) is installed above the pipe (2) via a camera bracket (25) and is used to monitor the bending state of the pipe (2) in a bending deformation process, including the cross-sectional distortion state, in real time.

4. A method for real-time monitoring and prediction using the system according to any one of claims 1 to 3, characterized in that: include: Step 1) Data acquisition: using multiple sensors of a data acquisition system (3) to collect in real time the state data of the pipe bending equipment (1) and the deformation data of the pipe (2) during the pipe bending process; Step 2) Data processing: The data collected by the data acquisition system (3) is pre-processed by the data processing system (4), specifically: The data collected by the data acquisition system (3) is filtered and denoised by a data filtering and denoising module (41), and then the time stamps are unified by a time series preprocessing module (42), and converted into data with the same time stamp and the same time interval; The data processed by the time series preprocessing module (42) is integrated to obtain a pipe bending process integrated information model IM (43), and the historical information storage module (44) performs structured storage on the data processed by the pipe bending process integrated information model IM (43); Step 3) Prediction and visualization of bending equipment and fittings status: The spatiotemporal fusion conversion module (51) based on multi-task learning predicts the status information of the pipe bending equipment and the bending status of the pipe fittings at the current moment and in the future according to the data pre-processed by the data processing system (4); The twin model visualization presentation module (52) visualizes the current and future state of the pipe bending equipment mold and the pipe bending process through Unity, and then performs feedback control on the pipe bending equipment.

5. The real-time monitoring and prediction method according to claim 4, characterized in that: In the step 2): The pipe bending process comprehensive information model IM includes the total data of the pipe bending equipment mold status monitoring part Data Mach And the total data of pipe bending process monitoring part Tube , where each data is a time series data with equal time intervals; the comprehensive information model IM of the pipe bending process includes the total data of the pipe bending equipment mold status monitoring part Data Mach And the total data of pipe bending process monitoring part Tube ,Right now IM={Data Mach ,Data Tube } Among them, the total data of the pipe bending equipment mold status monitoring part Mach The bending die rotates by an angle θ 弯 , bending die bending speed ω 弯 , bending die angular acceleration α 弯 , bending die temperature T 弯 , the pressure P between the clamping die and the tube 夹紧-管 , the friction force F between the clamping die and the tube 夹紧-管 , die displacement d 压 , die speed v 压 , die acceleration α 压 , pressure P between die and tube 压-管 , the friction force F between the die and the tube 压-管 , die temperature T 压 , booster car displacement d 助推 , booster car speed v 助推 , booster car acceleration α 助推 , the pressure P between the booster trolley and the pipe 助推-管 , the friction force F between the booster trolley and the tube 助推-管 Composition, that is Data Mach ={θ 弯 ,ω 弯 ,α 弯 ,T 弯 ,P 夹紧-管 ,F 夹紧-管 ,d 压 ,v 压 ,α 压 ,P 压-管 ,F 压-管 ,T 压 , d 助推 ,v 助推 ,α 助推 ,P 助推-管 ,F 助推-管 } Total data of pipe bending process monitoring part Tube The displacement of the wrinkle ripple d 皱 、Bending angle of pipe θ 管 、Pipe section deformation data ε 管 Composition, that is Data Tube ={d 皱 ,the 管 ,he 管 } All data in the integrated information model IM of the pipe bending process are in the form of time series.

6. The real-time monitoring and prediction method according to claim 4, characterized in that: In the step 3), the spatiotemporal fusion conversion module (51) based on multi-task learning includes three parts: an input layer, a private-shared layer, and a task output layer; The input layer receives data of the integrated information model IM (43) of the pipe bending process, including the total data Data of the pipe bending equipment mold status monitoring part Mach And the total data of pipe bending process monitoring part Tube ; The private-shared layer includes a shared LSTM module and two private LSTM modules, the two private LSTM modules are the auxiliary task private LSTM module and the main task private LSTM module; the shared LSTM module receives the total data Data of the mold status monitoring part of the bending equipment Mach And the total data of pipe bending process monitoring part Tube Two parts of data; the auxiliary task private LSTM module and the main task private LSTM module receive the total data of the bending equipment mold status monitoring part respectively Mach And the total data of pipe bending process monitoring part Tube ; The task output layer includes an auxiliary task Dense module, a feature fusion Concatenate module and a main task Dense module; the output results of the auxiliary task private LSTM module and the shared LSTM module are added element by element and then input into the auxiliary task Dense module; the output results of the main task private LSTM module and the shared LSTM module are added element by element, and then input into the feature fusion Concatenate module together with the output results of the auxiliary task Dense module for series splicing to achieve feature fusion, and the fused data is input into the main task Dense module; the auxiliary task Dense module and the main task Dense module respectively output the prediction results of the bending equipment status and the prediction results of the pipe fitting bending forming as the final output results of the module.

7. The real-time monitoring and prediction method according to claim 6, characterized in that: The spatiotemporal fusion conversion module based on multi-task learning is trained through a joint loss function and adapted to the multi-task learning scenario. The joint loss function is defined as the auxiliary task loss L of the bending equipment mold state. 辅助 and the main task loss L of the pipe bending process state 主 The sum of the principal weights: L=L 主 +αL 辅助 Among them, L is the total loss of the model, and the weight α is determined according to the empirical method.

8. The real-time monitoring and prediction method according to claim 6, characterized in that: In the step 3): Based on the bending equipment information and the bending state of the pipe fittings before the current moment, the state of the bending equipment mold and the deformation state of the pipe fittings at the current moment and in the future are predicted; The state of the pipe bending equipment mold includes an abnormal state of the pipe bending equipment; The predicted deformation state of the pipe fitting includes predicting the distortion defect of the cross section of the pipe fitting.

9. The real-time monitoring and prediction method according to claim 6, characterized in that: The pipe bending equipment (1) can compensate online based on wrinkle ripples directly measured by sensors and cross-sectional distortion defects obtained through prediction. The compensation can be achieved by accelerating or decelerating the speed of the bending die (6), the pressing die (9), and the booster trolley (11), and increasing or decreasing the pressure of the pressing die (9) and the booster trolley (11) on the pipe. The pipe bending equipment (1) can bend and compensate again online according to the springback angle of the pipe measured after bending and unloading.

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