A real-time detection system for tube forming

Abstract

The present application relates to a kind of real-time detection system for pipe forming, belong to pipe forming technical field, solve the problem that existing pipe cannot be detected in pipe forming process in real time pipe category and lead to pipe rejection rate high.The present application includes: pipe forming equipment, twin space and monitoring terminal;Pipe forming equipment is used to send the process data collected by the data acquisition component installed to twin space, process pipe according to the rebound amount compensation pipe parameters shown by monitoring terminal, adjust process data according to the reference data shown by monitoring terminal;Twin space constructs multiple pattern recognition models, predicts rebound amount and pipe category according to received process data, predicts reference data according to pipe category and decision rule;Monitoring terminal is used to show the data of twin space, including: received process data, predicted rebound amount, pipe category and reference data.Real-time detection of pipe forming is realized, and pipe accuracy and quality are improved.

Description

Technical Field

[0001] This invention relates to the field of pipe forming technology, and in particular to a real-time detection system for pipe forming. Background Technology

[0002] Pipe bending technology is widely used in the production of pipes in various industries such as central air conditioning, automobile industry, aerospace industry, and shipbuilding. The quality of pipe bending directly affects the structural rationality, safety, and reliability of products in these industries.

[0003] Current pipeline forming processing equipment mainly revolves around pipeline routing measurement. In the process of pipeline model input-processing-measurement-comparison measurement-compensation-processing-measurement-output, theoretical and actual measurement data comparison and feedback are used to focus on the entire bend direction target. However, during the processing, the pipeline is prone to wrinkles, cracks and elliptical distortion.

[0004] In existing automated, digitalized production and digital twin-based pipe bending systems, the pressure of the front and rear clamping blocks, rotation, clamping die position, conduit diameter, conduit thickness, and bending radius are often used as fixed quantities or experimental conditions, while the bending angle and mandrel position are used as variables, springback as a dependent variable, and wrinkling and cracking are used as quality rejection items. After pipe forming, inspectors visually check for fractures, wrinkles, excessive thinning, etc., and then use gauges to further inspect the bending angle of products that have passed this step, thereby preventing defective products from being shipped out as much as possible. This inspection is a process of distinguishing between qualified and defective pipes, but it cannot detect and quickly identify wrinkles and cracks in real time during pipe processing, increasing the pipe scrap rate, reducing pipe accuracy and quality, and is also not conducive to fully automated production. Summary of the Invention

[0005] Based on the above analysis, the present invention aims to provide a real-time inspection system for pipe forming, in order to solve the problem of high pipe scrap rate caused by the inability to detect wrinkles and cracks in real time during the pipe forming process.

[0006] This invention provides a real-time detection system for pipe forming, comprising: a pipe forming device, a twin space, and a monitoring terminal; the pipe forming device is used to send process data collected by an installed data acquisition component to the twin space, process the pipe after compensating for pipe parameters based on the springback amount displayed by the monitoring terminal, and adjust the process data based on reference data displayed by the monitoring terminal; the twin space constructs multiple pattern recognition models, predicts the springback amount and pipe type based on the received process data, and predicts reference data based on the pipe type and decision rules; the monitoring terminal is used to display the data from the twin space, including: the received process data, the predicted springback amount, the pipe type, and the reference data.

[0007] Based on further improvements to the above system, the data acquisition components installed in the pipe forming equipment include: an in-pipe laser head, a chuck drive cylinder, a pressure block drive cylinder, and an auxiliary push drive cylinder; the in-pipe laser head is connected to the outermost ball head front end on the mandrel via a ball joint, and is used to acquire the inner diameter and inner surface dimensions of the pipe, as well as the position of the anti-wrinkle block; the chuck drive cylinder is mounted on the chuck frame, the pressure block drive cylinder is mounted on the Y-axis direction of the pressure block frame, and the auxiliary push drive cylinder is mounted on the X-axis direction of the pressure block frame and / or on the jaws of the bundle clamp; the drive cylinder is equipped with a pressure sensor, and the chuck pressure, pressure block pressure, and auxiliary push force are acquired based on the pressure sensor when the drive cylinder moves.

[0008] Based on further improvements to the above system, the twin space also includes a sample database, which is acquired through the following steps:

[0009] Defects such as wrinkling, cracking, and elliptical distortion are simulated on the designed pipeline model. The process parameters are then adjusted until the defects are resolved, and the adjusted process parameters are used as preset parameters. Based on the preset parameters and pipeline parameters, the pipeline forming equipment is tested to obtain test result data. The preset parameters are then updated according to the test result data, and the test result data and the updated preset parameters are combined to obtain a qualified data sample.

[0010] Based on qualified data samples, defect data samples of different degrees are added through simulation; based on qualified data samples and defect data samples, stress-strain samples, dynamic parameter samples, geometric curvature samples and anti-wrinkle block position samples are obtained through motion simulation and pipe bending test.

[0011] Based on further improvements to the above system, multiple pattern recognition models employ one or more of Bayesian decision models, clustering models, and deep neural network models, including: predicting pipeline type based on stress-strain samples, dynamic parameter samples, geometric curvature samples, and anti-wrinkle block location samples in the sample database; and predicting springback based on dynamic parameter samples.

[0012] The twin space obtains dynamic parameters, geometric curvature, and anti-wrinkle block position parameters based on the received process data, which are used as inputs to the pattern recognition model.

[0013] Based on further improvements to the above system, stress-strain samples are obtained through the following steps:

[0014] A circular grid with a diameter of 0.2–1.5 mm is printed on the surface of the bent portion of the pipe in the pipe forming equipment using laser marking, chemical etching, or electro-etching. Defects are generated through pipe bending tests, and image processing and analysis are performed using an automatic grid strain measurement system to obtain the FLC curve in the FLD diagram. Based on the FLC curve, the principal strain, secondary strain, and principal stress data and their corresponding pipe categories are obtained to construct a stress-strain sample.

[0015] Based on further improvements to the above system, the decision rules include: when the pipe type is front wrinkle, increase the clamp pressure and auxiliary thrust, and decrease the pressure block pressure; when the pipe type is back wrinkle and internal crack, increase the clamp pressure and pressure block pressure, and decrease the auxiliary thrust; when the pipe type is external crack, increase the auxiliary thrust, and decrease the clamp pressure and pressure block pressure; when the pipe type is elliptical distortion, increase the auxiliary thrust and decrease the pressure block pressure.

[0016] Based on further improvements to the above system, the sample database is continuously updated by acquiring data from each stage of the perception test; the perception test involves inputting the pipeline parameters of the pipeline model into the pipeline forming equipment, and performing pipe bending and adjustment tests using a pattern recognition model in twin space based on automatically collected process data.

[0017] Based on further improvements to the above system, in the perception test, before each bend at a corner, the collected process data is transmitted to the twin space to obtain the predicted springback amount, and the pipeline parameters are compensated according to the predicted springback amount. When bending to the pre-designed angle, the clamp of the pipeline forming equipment is opened, and the actual springback amount is measured. If the difference between the actual springback amount and the predicted springback amount exceeds the difference threshold, the actual springback amount and process data are added to the sample database; otherwise, the bending process continues. During the bending process, the twin space predicts the pipeline type based on the real-time collected process data. If the pipeline type is not qualified, the process data of the pipeline forming equipment is adjusted until the pipeline type is qualified, and the bending process continues until each corner is bent to the design angle. Decision rules are extracted based on the adjustment method of the process data.

[0018] Based on further improvements to the above system, reference data is predicted according to pipeline type and decision rules, including:

[0019] When the pipeline category is not qualified, the received process data is adjusted according to the decision rule corresponding to the pipeline category and the preset difference to obtain the test data. The test data is input into the pattern recognition model to predict the new pipeline category. If the new pipeline category is qualified, the test data is used as reference data. Otherwise, the power parameters are adjusted according to the corresponding decision rule until the predicted new pipeline category is qualified.

[0020] Based on further improvements to the above system, the pipe forming equipment is also equipped with an indentation monitoring component. The indentation monitoring component includes an indentation laser head bracket and an indentation laser head. The indentation laser head bracket is fixed on the clamp, and the indentation laser head is installed and fixed through the hole of the indentation laser head bracket. The pipe position at a distance of 0.1mm from the front end of the clamp is measured. When the position deviation is greater than the pipe threshold, it is determined that an indentation has occurred, and the monitoring terminal issues an alarm.

[0021] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0022] 1. By constructing a sample database and multiple pattern recognition models through twin space, and in conjunction with improved pipe forming equipment, defects such as wrinkles, cracks and elliptical distortions in multiple stages are automatically identified. Based on decision rules, reference data is predicted, which facilitates rapid parameter compensation during processing, outputs qualified pipes, reduces pipe scrap rate, and improves pipe accuracy and quality.

[0023] 2. This study explores and researches multidimensional intelligent analysis by using front and rear clamping block pressure, rotation, clamping die position, conduit diameter, conduit thickness, and bending radius as variables, supplemented by bending angle and mandrel position. It also considers pipe wrinkling, cracking, and springback as dependent variables. Through intelligent analysis methods, it accumulates parameters, predicts quality problems, and provides parameter optimization methods. This improves the experience window period from zero to the first processing of the pipe, reducing the impact on efficiency and effectiveness caused by repeated problems, incomplete consideration, and chaotic quality adjustment directions resulting from the intellectual labor involved in controlling wrinkling, cracking, and springback during production.

[0024] 3. It provides a high-speed and accurate pipeline processing equipment and real-time detection system that incorporates the basic principles of intelligent manufacturing, pattern recognition, statistical analysis, and quality management, thereby greatly improving the automation and intelligence of pipeline forming, helping to ensure the progress of scientific research and production, and reducing production costs.

[0025] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0026] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0027] Figure 1 This is a schematic diagram of a real-time detection system for pipeline forming according to an embodiment of the present invention;

[0028] Figure 2 This is a schematic diagram of the existing pipe forming equipment.

[0029] Figure 3 This is a schematic diagram of the pipe forming equipment in an embodiment of the present invention;

[0030] Figure 4 This is a magnified schematic diagram of the internal laser head structure in an embodiment of the present invention;

[0031] Figure 5 This is a schematic diagram of a circular grid on the surface of the curved portion of a pipe in the pipe forming equipment of this embodiment of the invention;

[0032] Figure 6 This is a schematic diagram of the FLC curve of the pipe forming equipment in an embodiment of the present invention;

[0033] Figure 7 This is a schematic diagram of data category division based on FLC curves in an embodiment of the present invention;

[0034] Figure 8 This is a flowchart illustrating the construction and application of the pipeline forming equipment and twin space in an embodiment of the present invention;

[0035] Figure label:

[0036] 1-Mandrel; 2-Bundle clamp; 3-Anti-wrinkle block; 4-Anti-wrinkle block bracket; 5-Bending wheel; 6-Equipment motor; 7-Chuck; 8-Chuck frame; 9-Pressure block frame; 10-Pressure block; 11-In-tube laser head; 12-Auxiliary push drive cylinder; 13-X-direction anti-wrinkle drive cylinder; 14-Y-direction anti-wrinkle drive cylinder; 15-Pipeline; 16-Indentation laser head; 17-Indentation laser head bracket; 18-Chuck drive cylinder; 19-First pressure block drive cylinder; 20-Second pressure block drive cylinder; 101-First ball head; 102-Second ball head. Detailed Implementation

[0037] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0038] One specific embodiment of the present invention discloses a real-time detection system for pipeline forming, such as... Figure 1As shown, it includes: a pipe forming device, a twin space, and a monitoring terminal; the pipe forming device is used to send process data collected by the installed data acquisition components to the twin space, process the pipe after compensating the pipe parameters according to the springback amount displayed by the monitoring terminal, and adjust the process data according to the reference data displayed by the monitoring terminal; the twin space constructs multiple pattern recognition models, predicts the springback amount and pipe type according to the received process data, and predicts the reference data according to the pipe type and decision rules; the monitoring terminal is used to display the data of the twin space, including: the received process data, the predicted springback amount, the pipe type, and the reference data.

[0039] It should be noted that, Figure 2 It is an existing pipe forming equipment, including: mandrel 1, bundle clamp 2, anti-wrinkle block 3, 4 is an anti-wrinkle block support, bending wheel 5, equipment motor 6, chuck 7, chuck frame 8, pressing block frame 9, pressing block 10, first ball head 101, second ball head 102 and pipe 15.

[0040] In this embodiment, in order to acquire and monitor process data in real time, Figure 2 The pipeline forming equipment was equipped with data acquisition components, displacement control components, and indentation monitoring components, such as... Figure 3 As shown.

[0041] Specifically, the data acquisition components include: an in-tube laser head 11, a clamp drive cylinder 18, a first pressure block drive cylinder 19, a second pressure block drive cylinder 20, and an auxiliary push drive cylinder 12.

[0042] The laser head 11 inside the tube is connected to the front end of the outermost ball head (second ball head 102) on the mandrel via a ball hinge. In conjunction with the core feeding and pulling action, it detects the surrounding working surface, obtains the inner diameter and inner surface dimensions of the tube, and determines the position of the anti-wrinkle block by measuring its front end. For example... Figure 4 The enlarged view of the laser head 11 inside the tube shows that it is formed by embedding 8 optical fibers into the mechanical base in eight directions to form an 8-directional four-dimensional probe, which can measure the surrounding area. At each point, it can measure the position of a point on a curved surface with a diameter of 0.2 to 1 mm.

[0043] It should be noted that data measurement includes, but is not limited to, laser measurement, and can employ mechanical or brush-type sensors for measuring electrical signals.

[0044] The chuck drive cylinder 18 is mounted on the chuck frame 8, the first pressure block drive cylinder 19 and the second pressure block drive cylinder 20 are mounted on the Y-axis of the pressure block frame 9, and the auxiliary push drive cylinder 12 is mounted on the X-axis of the pressure block frame 9, or it can be mounted on the jaws of the tube clamp 2. The drive cylinders are equipped with pressure sensors, which may be located inside, at the front and rear ends of the drive cylinders, or inside, outside, and at contact points of the chuck 7, chuck frame 8, and tubing 15. During the movement of the drive cylinders, the pressure sensors acquire the chuck pressure F1, pressure block pressure F2, and auxiliary push force F3.

[0045] It should be noted that in commonly used equipment, the chuck pressure and drive stroke are fixed; only the chuck position can be manually fine-tuned. Therefore, fluctuations in pressure often lead to relative slippage between the tubing and the chuck. Often, even after manual parameter adjustments, wrinkles and cracks appear in the tubing during automatic bending, resulting in wasted time, effort, and materials, and hindering the adjustment of forming parameters. Therefore, this invention adds a pressure sensor to the chuck frame 8. A pressure sensor is installed at the force transmission end of the hydraulic or motor system to collect the actual pressure value during bending, transmitting the measurement information to the twin space for sample construction, pattern recognition, and decision-making.

[0046] Specifically, the displacement control component includes an X-axis anti-wrinkle drive cylinder 13 and a Y-axis anti-wrinkle drive cylinder 14 mounted on the anti-wrinkle block bracket 4. During pipeline processing, the X-axis anti-wrinkle drive cylinder 13 and the Y-axis anti-wrinkle drive cylinder 14 are moved to compensate for displacement based on the deviation between the anti-wrinkle block position measured by the laser head 11 inside the pipe and the reference data provided by the twin space.

[0047] Specifically, the indentation monitoring component includes an indentation laser head bracket 17 and an indentation laser head 16. The indentation laser head bracket 17 is fixed to the clamp 7, and the indentation laser head 16 is installed and fixed through the holes in the indentation laser head bracket. It measures the position of the pipeline at a distance of 0.1 mm from the front end of the clamp 7. The structure is the same as that of the laser head 11 inside the pipeline, with eight optical fibers embedded in the mechanical base in eight directions to form an 8-directional four-dimensional probe. When the position deviation is greater than the pipeline threshold, it is determined that an indentation has occurred, and the monitoring terminal issues an alarm. For example, the pipeline threshold is 0.1 times the pipeline wall thickness, and the multiple is set based on the results of multiple tests.

[0048] It should be noted that the diameters of the first ball head 101 and the second ball head 102 are less than or equal to the inner diameter of the pipe minus 0.3 mm and greater than or equal to the inner diameter of the pipe minus 0.7 mm. In existing equipment, when multiple ball heads are used, the diameters of the ball heads are the same. In this embodiment, the ball heads are designed with progressively smaller diameters, and the diameter of the second ball head 102 is smaller than that of the first ball head 101. For example, the diameter of the first ball head 101 is designed to be 30.7 mm and the diameter of the second ball head 102 is designed to be 30.5 mm. Furthermore, if more than two ball heads are used, a stepped design is selected. For example, three ball heads are respectively one 30.7 mm ball head, one 30.5 mm ball head, and one 30.3 mm ball head, and six ball heads are respectively two 30.7 mm ball heads, two 30.5 mm ball heads, and two 30.3 mm ball heads.

[0049] It should be noted that the process data collected by the in-tube laser head 11, the indentation laser head 16, and various pressure sensors are transmitted to the memory via cable connection or other wired or wireless transmission methods, and then aggregated into the twin space, or directly transmitted to the monitoring database of the twin space.

[0050] In this embodiment, the twin space is used to store and analyze data, and to train pattern recognition and decision-making rules to obtain various pattern recognition models for prediction. Before, during, and after pipe processing, based on the twin space and in conjunction with improved pipe forming equipment, the system automatically identifies the presence and specific types of defects based on collected process data. It also provides reference compensation data for decision-making methods adopted for various defects, thereby replacing existing manual experience and continuous trial and error. The twin space can be deployed on a monitoring terminal or a cloud server.

[0051] Specifically, the sample database stores simulation, experimental, and historical actual bending data, and is collected through the following steps:

[0052] 1) By simulating the defects of wrinkling, cracking and elliptical distortion in the designed pipeline model, the process parameters are adjusted until the defects are resolved. The adjusted process parameters are used as preset parameters.

[0053] The basic information of the designed 3D pipeline model includes: bending radius r, pipeline diameter D, thickness t, surface curvature K, straight segment length Y, bending angle C, spatial rotation angle B, and material (titanium alloy, aluminum alloy, stainless steel), which serve as inputs for simulation analysis of the simplified pipeline forming equipment model.

[0054] First, initialize the process parameters, including: chuck pressure F1, pressure block pressure F2, auxiliary thrust F3; core ball diameter D1, core ball thickness H1, core ball distance H2; number of core balls N; tube chuck friction coefficient f1, tube pressure block friction coefficient f2, tube anti-wrinkle friction coefficient f3, and tube core ball friction coefficient f4.

[0055] For example, f1 = 0.5, f2 = 0.2, f3 = 0.2, f4 = 0.1; for pipelines made of aluminum alloy and stainless steel, H1 = 20, H2 = 2; for pipelines made of titanium alloy, H1 = 10, H2 = 1.

[0056] The initial values ​​of the clamp pressure F1, the block pressure F2, and the auxiliary thrust F3 are set using the following formula:

[0057]

[0058] Among them, Y min This represents the minimum straight segment length in the pipeline; for example, it is set to 65. s This indicates the yield strength of the material.

[0059] The mandrel diameter is initially set using the following formula:

[0060] D1=D-2t-0.2n Formula (2)

[0061] Where n represents the number of simulations, initially n=1.

[0062] The mandrel structure is initialized based on the pipe diameter and bending angle using the following formula:

[0063]

[0064] Where N=0 indicates no mandrel is used; N=0.5 indicates a spherical mandrel is used without movable balls; N=1,2,3 indicates a chain ball mandrel is used, and N is the number of movable balls.

[0065] Based on the initial process parameters, when defects such as wrinkling, cracking, or elliptical distortion are detected in the simulation, the process parameters are adjusted until the defects are resolved. After multiple simulations, the process parameters for pipe forming are obtained and used as preset parameters.

[0066] Preferably, the difference is used to increase or decrease the value after rounding down to 0.1 times the preset value. When wrinkles appear, the chuck pressure F1, the block pressure F2, the auxiliary thrust F3, the core ball thickness H1, the core ball distance H2, and the core rod diameter D1 are increased. When cracks appear, the adjustment is performed in the opposite way to the wrinkles.

[0067] 2) Based on the preset parameters and pipeline parameters, the pipeline forming equipment is tested to obtain test result data. The preset parameters are then updated according to the test result data. The test result data and the updated preset parameters are combined to obtain a qualified data sample.

[0068] It should be noted that, based on the simulation results, a preliminary single-pipe bending test was conducted. YBC pipeline parameters were preset, and wrinkling and cracking were predicted according to the FLC curves (Forming Limit Curves) in the FLD diagram (Forming Limit Diagrams). Unreasonable preset parameters were removed based on the actual situation, and new process parameters were set according to the actual situation or based on reasonable preset parameters to obtain pipeline test results data that were smooth and did not produce cracks.

[0069] In addition to the process parameters in step 1), the test results data also include the springback amount ΔC obtained from each angle and the corresponding measurement during the test.

[0070] 3) Based on qualified data samples, defect data samples of different degrees are added through simulation; based on qualified data samples and defect data samples, stress-strain samples, dynamic parameter samples, geometric curvature samples and anti-wrinkle block position samples are obtained through motion simulation and pipe bending test.

[0071] It should be noted that, based on qualified data samples, wrinkles are formed by simulating the positional movement of each finite element node. This is done according to the pipeline stress state, and bending tests are conducted using curves and principal stress values ​​in the FLD diagram. The distance from any point to the boundary curve can be used as the criterion for determining the occurrence of cracks, thus obtaining defect data samples.

[0072] In simulations and experiments, various types of samples are collected and calculated for qualified and defective pipelines, corresponding to different pipeline categories. Among them, stress-strain samples include, but are not limited to: principal strain, secondary strain, and principal stress; dynamic parameter samples include, but are not limited to: clamp pressure, clamping block pressure, and auxiliary thrust; geometric curvature samples include, but are not limited to: bending direction, cross section, and Gaussian curvature; anti-wrinkle block position samples include, but are not limited to: anti-wrinkle block X-direction position, anti-wrinkle block Y-direction position, and clamping block pressure; pipeline categories include: front wrinkle, back wrinkle, external crack, internal crack, elliptical distortion, and qualified.

[0073] As shown in Tables 1-4, each type of sample contains various information, and different numbers are used to represent different pipeline categories.

[0074] Table 1 Sample of Stress-Strain Parameters

[0075]

[0076]

[0077] Table 2 Sample of Power Parameters

[0078] 1 F11 F21 F31 1 2 F12 F22 F32 2 3 F13 F23 F33 3 4 F14 F24 F34 4 …… …… …… …… …… j F1j F2j F3j 6

[0079] Table 3 Geometric Curvature Samples

[0080] 1 K11 K21 K31 1 2 K12 K22 K32 2 3 K13 K23 K33 3 4 K14 K24 K34 4 …… …… …… …… …… p K1p K2p K3p 6

[0081] Table 4 Sample of anti-wrinkle block locations

[0082] 1 X11 Y21 F21 1 2 X12 Y22 F22 2 3 X13 Y23 F23 3 4 X14 Y24 F24 4 …… …… …… …… …… q X1q Y2q F2q 6

[0083] Specifically, stress-strain samples are obtained through the following steps:

[0084] In the pipe forming equipment, a circular grid with a diameter d0 of 0.2–1.5 mm is printed on the surface of the curved portion of the pipe using laser marking, chemical etching, or electro-etching. Figure 5 As shown; defects are generated through pipe bending tests, and image processing and analysis are performed using a grid strain automatic measurement system to obtain the FLC curve in the FLD diagram; based on the FLC curve, the principal strain, secondary strain, and principal stress data and their corresponding categories are obtained, and a stress-strain sample is constructed. For example, as... Figure 6 and Figure 7 As shown.

[0085] In existing technologies, the forming limit curve FLC in the FLD diagram is based on cupping, hole enlargement, and other tests performed on sheet metal. It forms a strip-shaped region or curve based on the local instability limit strain and engineering strain or actual strain under different strain paths, reflecting the local forming limit of the sheet metal under unidirectional and bidirectional tensile stress. In contrast, this embodiment obtains the limit strain force when different problems occur in the pipeline through pipe bending tests, facilitating the differentiation of pipeline types.

[0086] The dynamic parameter samples and anti-wrinkle block position samples are obtained from the data acquisition components on the pipe forming equipment, and the geometric curvature samples are calculated using the curvature formula based on the surface dimensions of the bent parts of the pipe forming equipment.

[0087] Based on a sample database, multiple pattern recognition models are trained. These models employ one or more of Bayesian decision models, clustering models, and deep neural network models to predict pipeline type and rebound amount.

[0088] A pattern recognition model is trained for each type of parameter sample. At the same time, features of different dimensions from different types of parameter samples are aggregated through a fully connected linear layer neural network and then fused to train a pattern recognition model, which is suitable for scenarios that comprehensively consider various types of data.

[0089] Preferably, a corresponding pattern recognition model is trained based on stress-strain samples, dynamic parameter samples, geometric curvature samples, and anti-wrinkle block position samples in the sample database to predict the pipeline category; a pattern recognition model is trained based on the dynamic parameter samples to predict the springback level, with each springback level corresponding to a springback amount, thereby obtaining the springback amount; a fusion model recognition model is trained based on the dynamic parameter samples, geometric curvature samples, and anti-wrinkle block position samples to predict the pipeline category.

[0090] It should be noted that after the sample database is constructed, a further perception test is conducted on the pipe forming equipment. The perception test involves inputting the pipe parameters of the pipe model into the pipe forming equipment, and using the pattern recognition model of twin space to perform pipe bending processing and adjustment based on the automatically collected process data.

[0091] Specifically, the system automatically senses parameters and outputs during bending through data acquisition, and continuously updates the sample database with sensing test data. This includes: analyzing sample data in the sample database based on the sensing test data, updating sample data with the same parameters but different pipeline types, and removing sample data corresponding to parameters that cannot be achieved by the sensing test; and supplementing the sample database with sensing test data from each processing stage.

[0092] Furthermore, based on the unqualified pipeline categories predicted by the pattern recognition model, decision rules are formulated and the process data is adjusted through multiple experiments to make the pipeline categories qualified in the next prediction. This updates the decision rules and ensures that accurate reference data is provided in the actual pipe bending process, thereby improving the pipe bending accuracy.

[0093] Specifically, the decision-making rules include: when the pipe type is front wrinkle, increase the clamp pressure, auxiliary thrust, and pipe clamp friction coefficient, and decrease the pressure block pressure; when the pipe type is back wrinkle and internal crack, increase the clamp pressure and pressure block pressure, and decrease the auxiliary thrust; when the pipe type is external crack, increase the auxiliary thrust and decrease the clamp pressure and pressure block pressure; when the pipe type is elliptical distortion, increase the auxiliary thrust and decrease the pressure block pressure.

[0094] Next, combined Figure 8 This document describes the construction and application process of the twin space based on the pipe forming equipment in this embodiment:

[0095] S1. Obtain the three-dimensional pipeline model of the design.

[0096] S2, ① Extract basic information such as thickness t, surface curvature K, straight segment length Y, bending angle C, spatial rotation angle B, and material of the pipeline model, and input them into the simulation analysis as analysis input to obtain the pipeline preset parameters; ② Based on the pipeline preset parameters, initially preset the YBC pipeline parameters to the pipeline forming equipment, further analyze the wrinkle and crack rules and limits, and input them into the twin space.

[0097] S3. ① Design multiple pattern recognition models in the twin space to perform overall analysis and simulation of the pipe bending process, including parameters of the pipe bending forming equipment, experimental and sensing information, and pattern decision samples. Establish multi-dimensional and multi-level collaborative analysis to compare each stage, and store experience data, samples, decisions, and results for each stage. ② Based on the three-dimensional pipeline model, transmit pipeline parameters to the pipe forming equipment as experimental input. Based on the sample database and pattern recognition model in the twin space, conduct sensing experiments on the pipeline to verify and concretize the twin space model, update the sample database, optimize process parameters, and finally obtain reasonable samples that closely resemble actual working conditions.

[0098] S4. Based on the updated sample database, the pattern recognition model is retrained, the decision rules are optimized, and decisions are made on the parameters of the pipeline forming equipment.

[0099] S5. Based on the parameters after the decision, the pipeline to be formed is processed and monitored in real time. When defects occur in the pipeline, the process parameters of the pipeline forming equipment are adjusted according to the reference data predicted by the twin space.

[0100] S6, Delivery Pipeline.

[0101] Preferably, in step S3②, after receiving the pipeline model provided by the designer, it is necessary to pre-check whether the pipeline model collides with the pipeline forming equipment. In the prior art, the operator first judges based on experience which pipeline models to perform software interference checks, and then uses the software built into the pipeline forming equipment or 3D motion simulation software to perform interference checks, feeding back the pipeline models that have interference to the designer. When there are many pipelines and the operator lacks experience, it is necessary to simulate all pipeline models, which is time-consuming, and the existing software interference detection does not consider the springback detection accuracy is not high.

[0102] This embodiment pre-specifies the springback amount (i.e., the springback amount used to check for interference) for each type of pipeline based on the pipeline material. Based on the Cartesian coordinate XYZ data of the pipeline model, the coordinates of the pipeline's axis intersection and end center point are compensated for the springback amount. A two-dimensional interference analysis method for pipeline bending, disclosed in patent CN202211714754.4, is used. This includes: based on the pipeline XYZ data compensated for the springback amount, simplifying straight segments and arc segments into two-dimensional spatial distance curves and two-dimensional spatial angular distance curves; simplifying the bending wheel of the pipeline forming equipment and the pipeline into two-dimensional obstacle curves; determining whether the two-dimensional spatial distance curves and two-dimensional spatial angular distance curves interfere with the two-dimensional obstacle curves; if interference exists, it is considered a pipeline model to be confirmed; the pipeline model to be confirmed is then subjected to three-dimensional interference detection using three-dimensional motion simulation software, and compared with the two-dimensional analysis results to demonstrate the credibility of the interference position, thereby improving detection efficiency and accuracy.

[0103] Specifically, the rebound amount is checked by compensating for the coordinates of the intersection of the pipeline axes and the center point of the pipeline end using the following formula:

[0104]

[0105] Where (X1, Y1, Z1) represents the coordinates of the starting point P1 of the pipeline axis, (X... t ,Y t Z t P represents the coordinates of the intersection of the pipeline axes and the center point of the pipeline's end. t , t≥2; ΔC s This indicates the amount of rebound during the review.

[0106] Pipeline models exhibiting interference are fed back to designers for process optimization, including: optimizing pipe forming equipment parameters, changing the bending start point, and replacing the pipe forming equipment. Once all pipeline models are confirmed to have no interference with the pipe forming equipment, a perception test of pipeline processing is conducted. This perception test involves inputting the pipeline parameters from the pipeline model into the pipe forming equipment and using a twin-space pattern recognition model to perform actual pipe bending and process parameter adjustments.

[0107] Specifically, in the perception test, before each bend at an angle, the collected process data is input into the twin space to obtain the predicted springback amount. For example, the clamp pressure, clamp block pressure, and auxiliary thrust are obtained from the process data to construct a dynamic parameter sample and predict the springback amount. The pipeline parameters are compensated based on the predicted springback amount. For example, when the pipeline parameters use CNC machining coordinates YBC, the C-axis is compensated for the predicted springback amount (the value can be positive or negative). When bending to the pre-designed angle, the clamp of the pipeline forming equipment is opened, and the actual springback amount is measured. If the difference between the actual springback amount and the predicted springback amount exceeds the difference threshold, the actual springback amount and process data are added to the sample database; otherwise, the bending process continues. During the bending process, the twin space predicts the pipeline type based on the real-time collected process data. If the pipeline type is not qualified, the process data of the pipeline forming equipment is adjusted until the pipeline type is qualified, and the bending process continues until each angle is bent to the design angle. Based on the adjustment method of the process data, decision rules are extracted. Subsequently, reference data is predicted based on the pipeline type and decision rules to facilitate automatic adjustment of the process data.

[0108] It should be noted that the prefabricated angle is the angle set according to a preset variational ratio before the design angle is reached. For example, if the design angle is 90 degrees and the variational ratio is 1 / 2, then the prefabricated angle is 45 degrees. This embodiment also considers the springback amount and will compensate for the prefabricated angle based on the predicted springback amount. For example, if the springback amount is 1 degree, then the prefabricated angle is 46 degrees.

[0109] When bending to the pre-set angle, the actual springback is large. Therefore, the process parameters are compensated to control bending accuracy by increasing the clamp pressure and pressure block pressure while reducing the auxiliary thrust, based on the principle of increasing the clamp pressure and pressure block pressure and reducing the auxiliary thrust. In existing technologies, bending is performed step-by-step after bending is complete, and the finished pipe is either transferred to a straightening or scrapped state, making further processing on the pipe forming equipment impossible.

[0110] Reference data is predicted based on pipeline type and decision rules, including:

[0111] When the pipeline category is not qualified, the received process data is adjusted according to the decision rule corresponding to the pipeline category and the preset difference to obtain the test data. The test data is input into the pattern recognition model to predict the new pipeline category. If the new pipeline category is qualified, the test data is used as reference data. Otherwise, the power parameters are adjusted according to the corresponding decision rule until the predicted new pipeline category is qualified.

[0112] Preferably, if the new pipeline category is still not qualified after reaching the maximum number of automatic predictions, the system receives manually input process data from the monitoring terminal and performs predictions until the pipeline category is qualified, thus obtaining reference data. In other words, in this embodiment, the monitoring terminal not only displays data but also provides a manual input function, and the manually input process data and actual pipe bending conditions can be added to the sample database.

[0113] As the perception test progresses, the sample database is continuously updated, and the pattern recognition model is continuously trained. The predicted pipeline types become more and more accurate, and the difference between the predicted rebound amount and the actual rebound amount becomes smaller and smaller, thereby improving the actual processing efficiency and pipeline qualification rate.

[0114] Compared with existing technologies, the real-time detection system for pipe forming provided in this embodiment constructs a sample database and multiple pattern recognition models through twin space. Combined with improved pipe forming equipment, it automatically identifies defects such as wrinkles, cracks, and elliptical distortions at multiple stages. Based on decision rules, it predicts reference data, facilitating rapid parameter compensation during processing, outputting qualified pipes, reducing pipe scrap rates, and improving pipe accuracy and quality. The system uses front and rear clamping block pressure, rotation, clamping die position, conduit diameter, conduit thickness, and bending radius as variables, supplemented by bending angle and mandrel position, to explore and study multi-dimensional intelligent analysis of the pipe. Using wrinkling, cracking, and springback as dependent variables, this study employs intelligent analysis methods to accumulate parameters, predict quality problems, and provide parameter optimization methods. This reduces the experience gap between the initial processing and the first processing of pipelines, and helps to minimize the impact of repeated problems, incomplete considerations, and chaotic quality adjustment directions caused by the intellectual labor involved in controlling wrinkling, cracking, and springback in production, thus improving efficiency and effectiveness. Furthermore, it provides a high-speed, accurate pipeline processing equipment and real-time detection method that incorporates the basic principles of intelligent manufacturing, pattern recognition, statistical analysis, and quality management. This significantly improves the automation and intelligence of pipeline forming, helps ensure the progress of scientific research and production, and reduces production costs.

[0115] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0116] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A real-time detection system for pipe forming, characterized in that, include: The system comprises a pipe forming device, a twin space, and a monitoring terminal. The pipe forming device transmits process data collected by the installed data acquisition components to the twin space, processes the pipe after compensating for the pipe parameters based on the springback amount displayed by the monitoring terminal, and adjusts the process data based on the reference data displayed by the monitoring terminal. The twin space constructs multiple pattern recognition models to predict the springback amount and pipe type based on the received process data, and predicts reference data based on the pipe type and decision rules. The monitoring terminal is used to display data from the twin space, including: received process data, predicted rebound amount, pipeline type, and reference data; The twin space also includes a sample database, which is acquired through the following steps: Defects such as wrinkling, cracking, and elliptical distortion are simulated on the designed pipeline model; process parameters are then adjusted until the defects are resolved, and the adjusted process parameters are used as preset parameters; based on the preset parameters and pipeline parameters, the pipeline forming equipment is tested to obtain test result data, and the preset parameters are updated according to the test result data; the test result data and the updated preset parameters are merged to obtain qualified data samples; based on the qualified data samples, defect data samples of different degrees are added through simulation; based on the qualified data samples and defect data samples, stress-strain samples, dynamic parameter samples, geometric curvature samples, and anti-wrinkle block position samples are obtained through motion simulation and pipe bending tests; The multiple pattern recognition models employ one or more of Bayesian decision models, clustering models, and deep neural network models, including: predicting the pipeline type based on stress-strain samples, dynamic parameter samples, geometric curvature samples, and anti-wrinkle block position samples in the sample database; predicting the springback amount based on the dynamic parameter samples; and the twin space obtains the dynamic parameters, geometric curvature, and anti-wrinkle block position parameters based on the received process data, which are used as inputs to the pattern recognition model. The step of predicting reference data based on pipeline category and decision rules includes: when the pipeline category is not qualified, adjusting the received process data according to the decision rules corresponding to the pipeline category and a preset difference to obtain the test data; inputting the test data into the pattern recognition model to predict a new pipeline category; if the new pipeline category is qualified, the test data is used as reference data; otherwise, continuing to adjust the power parameters according to the corresponding decision rules until the predicted new pipeline category is qualified.

2. The real-time detection system for pipeline forming according to claim 1, characterized in that, The data acquisition components installed in the pipe forming equipment include: an in-pipe laser head, a chuck drive cylinder, a pressure block drive cylinder, and an auxiliary push drive cylinder. The in-pipe laser head is connected to the outermost ball head of the mandrel via a ball joint and is used to acquire the inner diameter and inner surface dimensions of the pipe, as well as the position of the anti-wrinkle block. The chuck drive cylinder is mounted on the chuck frame, the pressure block drive cylinder is mounted on the Y-axis of the pressure block frame, and the auxiliary push drive cylinder is mounted on the X-axis of the pressure block frame and / or on the jaws of the bundle clamp. The chuck drive cylinder, the pressure block drive cylinder, and the auxiliary push drive cylinder are all equipped with pressure sensors. When the drive cylinder moves, the pressure sensors acquire the chuck pressure, the pressure block pressure, and the auxiliary push force.

3. The real-time detection system for pipeline forming according to claim 1, characterized in that, The stress-strain samples were obtained through the following steps: A circular grid with a diameter of 0.2~1.5mm is printed on the surface of the bent part of the pipe in the pipe forming equipment using laser marking, chemical etching or electro-etching. Defects are generated through pipe bending tests, and the image is processed and analyzed using an automatic grid strain measurement system to obtain the FLC curve in the FLD diagram. Based on the FLC curve, the principal strain, secondary strain and principal stress data and their corresponding pipe categories are obtained to construct a stress-strain sample.

4. The real-time detection system for pipeline forming according to claim 2, characterized in that, The decision rules include: when the pipe type is front wrinkle, increase the clamp pressure and auxiliary thrust, and decrease the pressure block pressure; when the pipe type is back wrinkle and internal crack, increase the clamp pressure and pressure block pressure, and decrease the auxiliary thrust; when the pipe type is external crack, increase the auxiliary thrust, and decrease the clamp pressure and pressure block pressure; when the pipe type is elliptical distortion, increase the auxiliary thrust and decrease the pressure block pressure.

5. The real-time detection system for pipeline forming according to claim 1, characterized in that, The sample database is continuously updated by acquiring data from each stage of the perception test; the perception test involves inputting the pipeline parameters of the pipeline model into the pipeline forming equipment, and using a twin space pattern recognition model to perform pipe bending processing and adjustment on the automatically collected process data.

6. The real-time detection system for pipeline forming according to claim 5, characterized in that, In the aforementioned perception test, before each bend at a corner, the collected process data is transmitted to the twin space to obtain the predicted springback amount, and the pipeline parameters are compensated based on the predicted springback amount. When bending to the pre-designed angle, the clamp of the pipeline forming equipment is opened, and the actual springback amount is measured. If the difference between the actual springback amount and the predicted springback amount exceeds the difference threshold, the actual springback amount and process data are added to the sample database; otherwise, the bending process continues. During the bending process, the twin space predicts the pipeline type based on the real-time collected process data. If the pipeline type is not qualified, the process data of the pipeline forming equipment is adjusted until the pipeline type is qualified, and the bending process continues until each corner is bent to the design angle. Decision rules are extracted based on the adjustment method of the process data.

7. The real-time detection system for pipeline forming according to claim 2, characterized in that, The pipe forming equipment is also equipped with an indentation monitoring component, which includes an indentation laser head bracket and an indentation laser head. The indentation laser head bracket is fixed on the clamp, and the indentation laser head is installed and fixed through the hole of the indentation laser head bracket. The device measures the position of the pipe at a distance of 0.1 mm from the front end of the clamp. When the position deviation is greater than the pipe threshold, it is determined that an indentation has occurred, and the monitoring terminal issues an alarm.

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