Flange pipeline multi-source coupling error prediction, regulation and control method and device

Abstract

The invention provides a flange pipeline multi-source coupling error prediction, regulation and control method and device. The flange pipeline multi-source coupling error prediction, regulation and control method comprises the steps that a multi-source error coupling model is constructed based on structure parameters and precision parameters of a flange pipeline; constructing an assembly error migration accumulation model based on the Jacobian matrix and the multi-source error coupling model; a Halton low-difference sequence is adopted to generate random assembly errors meeting spinor constraints, iterative simulation is conducted on the assembly error migration accumulation model, and a prediction accumulation assembly error simulation result is obtained; when the result meets the expected function requirement, updating and reconstructing the assembly error migration accumulation model based on the measured data, and predicting the assembly error of the flange pipeline system based on the reconstructed assembly error migration accumulation model; and when the result does not conform to the expectation, an optimal tolerance allocation scheme is obtained. According to the method, the error sources of flange pipeline assembly are classified and represented, the tolerance optimization model is provided to realize reasonable distribution of tolerance parameters, and the assembly prediction precision and the assembly success rate are improved.

Description

Technical Field

[0001] This invention relates to the field of pipeline assembly technology, specifically to a method and device for predicting and controlling multi-source coupling errors in flange pipelines. Background Technology

[0002] Pipelines play a crucial role in delivering fluids such as oil and gas to various equipment, and are widely used in complex equipment such as vehicles, ships, and aerospace systems, highlighting their importance. However, due to factors such as large spans, complex connection structures, and harsh operating conditions, controlling the assembly precision of pipeline systems is quite challenging. Traditional pipeline assembly precision control methods suffer from difficulties in pipe closure, low assembly efficiency, and high production costs because they cannot accurately characterize pipeline assembly errors and lack a clear understanding of the transmission mechanism. Statistics show that pipeline costs account for more than 20% of the total cost; therefore, pipeline assembly deviations are a key factor affecting production costs and operational performance.

[0003] Currently, the domestic industry lacks a systematic process for precision calibration of pipeline assembly. Most manufacturers still use "blind assembly and adjustment" and "blind selection and matching" methods, which are difficult to guarantee assembly accuracy and easily lead to assembly defects. Furthermore, because the error propagation patterns during actual product assembly cannot be predicted, deviations in the assembly process may result in significant differences between the final assembly accuracy and the theoretical design value, requiring subsequent rework and adjustments to meet functional requirements, thus increasing production costs and affecting assembly efficiency and quality. With the development of the industry, the introduction of modern manufacturing technologies has made it possible to improve the precision and advancement of design and manufacturing. Against this backdrop, how to effectively shorten the pipeline assembly cycle and improve assembly quality has become one of the core issues that the industry urgently needs to address. To overcome the technical problems of "blind assembly and adjustment" and "blind selection and matching" methods, a technical solution based on the Monte Carlo method for predicting assembly accuracy was proposed. However, due to insufficient consideration of the random number generation mechanism in the Monte Carlo method, unstable results and slow convergence speed occur when performing repeated calculations on large samples. At the same time, the types of error sources in the pipeline assembly process have not been thoroughly studied, resulting in low assembly efficiency and low assembly accuracy.

[0004] Therefore, there is an urgent need to provide a method and device for predicting and controlling multi-source coupling errors in flange pipelines, so as to improve the assembly efficiency and assembly accuracy of flange pipelines. Summary of the Invention

[0005] In view of this, it is necessary to provide a method and device for multi-source coupling error prediction and control of flange pipelines, so as to solve the technical problems of low assembly efficiency and assembly accuracy of flange pipelines due to insufficient consideration of the random number generation mechanism in the Monte Carlo method and unclear error sources in the existing technology.

[0006] Firstly, in order to solve the above-mentioned technical problems, the present invention provides a method for predicting and controlling multi-source coupling errors in flange pipelines, comprising: Obtain the structural and precision parameters of the flange pipeline, and construct a multi-source error coupling model of six-degree-of-freedom screws based on the structural and precision parameters of the flange pipeline. An assembly error migration and accumulation model characterizing the overall assembly accuracy of the flange piping system is constructed based on the Jacobian matrix and the multi-source error coupling model. Halton low-discrepancy sequences are used to generate random assembly errors that satisfy screw constraints. Based on the random assembly errors, a quasi-Monte Carlo simulation is performed on the assembly error migration accumulation model to obtain the simulation results of the predicted cumulative assembly error. When the simulation results of the predicted cumulative assembly error meet the expected functional requirements, the measured data of the assembly site are collected, the assembly error migration accumulation model is updated and reconstructed based on the measured data, and the assembly error of the flange pipeline system is predicted again based on the reconstructed assembly error migration accumulation model. When the simulation results of the predicted cumulative assembly error do not meet the expected functional requirements, a tolerance optimization model is established with the goal of minimizing processing costs and achieving the assembly accuracy pass rate. The tolerance optimization model is then solved to obtain the optimal tolerance allocation scheme, and the accuracy parameters are updated based on the optimal tolerance allocation scheme.

[0007] In one possible implementation, the flanged pipeline includes multiple pipe segments and flanges connecting adjacent pipe segments, wherein the pipe segments include straight pipes and bends, and the multi-source error coupling model includes a straight pipe manufacturing error model, a bend manufacturing error model, and a flange connection error model.

[0008] In one possible implementation, the construction of a six-degree-of-freedom spinor multi-source error coupling model based on the structural and accuracy parameters of the flange pipeline includes: Based on the structural parameters of the flange pipeline, various error sources of different types are determined during the assembly process. These error sources include straight pipe manufacturing errors, bend pipe manufacturing errors, and flange connection errors. Based on the variation of the assembly features within the tolerance domain under coupling error, the straight pipe manufacturing error and the flange connection error are respectively represented by a six-degree-of-freedom spinor in the form of Lie algebra se(3), and the straight pipe manufacturing error model and the flange connection error model are obtained accordingly. Based on the spinor exponential mapping and homogeneous coordinate pose transformation, the manufacturing error of the bent pipe is transformed from the rigid body transformation pose of SE(3) to its Lie algebra se(3) form of a six-degree-of-freedom spinor representation, thus obtaining the manufacturing error model of the bent pipe.

[0009] In one possible implementation, the straight pipe manufacturing error and the flange connection error include dimensional error, perpendicularity error and coaxiality error, and the bent pipe manufacturing error includes angular error and length offset error.

[0010] In one possible implementation, the assembly error transfer accumulation model is as follows:

[0011]

[0012] In the formula, These represent the translational amounts of the flange piping system end along the x, y, and z directions in the global coordinate system. These represent the rotation of the flange piping system end around the x, y, and z directions in the global coordinate system, respectively. For the first i Jacobian matrix for error propagation in a local coordinate system; For the first i Characteristic spinor error in a local coordinate system; This is an antisymmetric matrix, representing the spatial differences between various local coordinate systems; For the tolerance domain analysis coordinate system and local coordinate system i When poses do not coincide, the tolerance domain coordinate system and the local coordinate system i The pose transformation matrix between them; O 3×3 It is a zero matrix.

[0013] In one possible implementation, the iterative simulation of the assembly error migration accumulation model based on quasi-Monte Carlo simulation to obtain the simulation result of the predicted cumulative assembly error includes: Based on the random assembly error, a quasi-Monte Carlo simulation is performed on the assembly error migration accumulation model to obtain multiple predicted assembly error results; the number of quasi-Monte Carlo simulations is equal to the set number of simulations. Statistical analysis is performed on the multiple predicted assembly error results to obtain the simulation results of the predicted cumulative assembly error. The simulation results of the predicted cumulative assembly error include the distribution range of the cumulative assembly error of the flange pipeline, the assembly qualification rate, and the three-dimensional spatial pose variation.

[0014] In one possible implementation, determining whether the simulation results of the predicted cumulative assembly error meet the expected assembly functional requirements includes: Obtain the characteristic parameters of the simulation results of the predicted cumulative assembly error, including the mean, standard deviation, error range, maximum deviation value, minimum deviation value, and probability distribution value; Based on whether the characteristic parameters are within the theoretical parameter range, if yes, the simulation result of the predicted cumulative assembly error meets expectations; otherwise, the simulation result of the predicted cumulative assembly error does not meet the expected functional requirements, the assembly qualification rate cannot be guaranteed, and subsequent accuracy optimization and adjustment are required.

[0015] In one possible implementation, when the simulation results of the predicted cumulative assembly error do not meet the expected functional requirements, the method further includes: Based on the simulation results of the predicted cumulative assembly error, the contribution of each assembly feature to the error source is determined, and based on the contribution of the error source, key features and non-key features are determined. Control the precision of key component features, and relax the precision requirements for non-key components while meeting the precision requirements.

[0016] In one possible implementation, the tolerance optimization model is:

[0017] The constraints for solving the tolerance optimization model are:

[0018] In the formula, For the assembly cumulative error rate function; For processing cost function; , , These represent the cumulative offset errors in the three directions under the global coordinate system; t i For the first i Tolerances for each assembly feature; For the first i Manufacturing cost function for assembly features; FR x , FR y , FR z These are the assembly function requirements for flange pipelines in three different directions under the global coordinate system; Minimum tolerance requirement; This is the maximum tolerance requirement.

[0019] Secondly, the present invention also provides a flange pipeline multi-source coupling error prediction and control device, comprising: A multi-source error coupling model construction unit is used to obtain the structural parameters and accuracy parameters of the flange pipeline, and to construct a six-degree-of-freedom screw multi-source error coupling model based on the structural parameters and accuracy parameters of the flange pipeline. The assembly error migration accumulation model construction unit is used to construct an assembly error migration accumulation model characterizing the overall assembly accuracy of the flange pipeline system based on the Jacobian matrix and the multi-source error coupling model. The pre-assembly accuracy simulation prediction unit is used to generate random assembly errors that satisfy screw constraints using Halton low-difference sequences. Based on the random assembly errors, the assembly error migration accumulation model is simulated using quasi-Monte Carlo simulation to obtain the simulation results of the predicted cumulative assembly errors. The actual assembly error simulation prediction unit is used to collect measured data from the assembly site when the predicted cumulative assembly error simulation result meets the expected functional requirements, update and reconstruct the assembly error migration accumulation model based on the measured data, and perform assembly error simulation prediction on the flange pipeline again based on the reconstructed assembly error migration accumulation model. The tolerance optimization allocation unit is used to establish a tolerance optimization model with the goal of minimizing processing costs and achieving the assembly accuracy pass rate when the simulation results of the predicted cumulative assembly error do not meet the expected functional requirements. The unit then solves the tolerance optimization model to obtain the optimal tolerance allocation scheme and updates the accuracy parameters based on the optimal tolerance allocation scheme.

[0020] The beneficial effects of this invention are as follows: The multi-source coupling error prediction and control method for flange pipelines provided by this invention constructs a six-degree-of-freedom screw-based multi-source error coupling model based on the structural and precision parameters of the flange pipeline. This clarifies the error sources and achieves the goal of uniformly representing different error sources with six-degree-of-freedom screws, making the multi-source error coupling model compatible with different types of pipeline errors and improving the construction efficiency of the multi-source error coupling model. Secondly, based on the Jacobian matrix and the multi-source error coupling model, an assembly error migration and accumulation model is constructed. The Jacobian matrix explains the error propagation mechanism, realizing a quantitative mapping from the local characteristic error of a single part to the overall assembly precision of the flange pipeline system. This improves the adaptability of the constructed assembly error migration and accumulation model to the errors in actual flange pipelines, thereby improving the error prediction accuracy. Furthermore, by using Halton low-discrepancy sequences to generate random assembly errors that satisfy screw constraints, compared to the traditional Monte Carlo method, the quasi-Monte Carlo simulation based on Halton low-discrepancy sequences, while ensuring the super-uniformity of the deviation sequence, incorporates a certain degree of randomization, thus avoiding the problem of large differences in simulation results leading to instability. Simultaneously, using low-discrepancy sequences as sampling points reduces the error order, thereby improving the error convergence speed in the Monte Carlo simulation process and thus increasing error prediction efficiency. Even further, when the simulation results predicting cumulative assembly errors meet the expected functional requirements, during the actual assembly stage, the assembly error migration accumulation model is reconstructed based on measured data. This compensates for errors caused by pipeline errors, personnel calculations, and positioning installation issues during pipeline assembly, thus avoiding the problem of the predicted cumulative assembly error continuously increasing as the assembly process progresses. This mitigates the impact of excessive assembly errors on pipeline closure and assembly quality, improving the pipeline assembly success rate and service life. Meanwhile, when the simulation results of the predicted cumulative assembly error do not meet the expected functional requirements, a tolerance optimization model is established with the goal of minimizing processing costs and achieving the assembly accuracy pass rate. Based on this, the optimal tolerance allocation scheme is determined, which can provide guidance for the precision design of assembly parts.

[0021] In summary, this invention achieves accurate characterization of error sources and error propagation by constructing a multi-source error coupling model and an assembly error migration accumulation model, thereby improving the efficiency of assembly error and propagation modeling. Furthermore, by using Halton low-difference sequences to generate screw-constrained stochastic assembly errors for quasi-Monte Carlo simulation, the simulation accuracy and convergence speed are improved, further enhancing the accuracy and computational efficiency of assembly error prediction. Moreover, by setting different processing methods at different stages of determining whether the simulation results of predicted cumulative assembly errors meet the expected assembly functional requirements, the actual assembly of flange pipelines is ensured to be accurate and effective, further guaranteeing assembly quality and success rate. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A schematic diagram of an embodiment of the flange pipeline provided by the present invention; Figure 2 A schematic flowchart of an embodiment of the multi-source coupling error prediction and control method for flange pipelines provided by the present invention; Figure 3 This is a schematic diagram of an embodiment of the present invention for constructing a multi-source error coupling model; Figure 4 A schematic diagram of an embodiment of the straight pipe error characterization provided by the present invention; Figure 5 A schematic diagram of an embodiment of the pipe bending error characterization provided by the present invention; Figure 6 A schematic diagram of an embodiment of the connection error characterization provided by the present invention; Figure 7 This is a schematic diagram of the simulation results of the pipeline end face error provided by the present invention; Figure 8 This is a schematic flowchart of an embodiment of the strategy for adjusting pipeline assembly features based on error contribution provided by the present invention. Figure 9 A schematic diagram illustrating the calculation results of the contribution of assembly feature error sources provided by this invention; Figure 10 This is a schematic diagram of an embodiment of the flange pipeline multi-source coupling error prediction and control device provided by the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0025] It should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this invention illustrate operations implemented according to some embodiments of the invention. It should be understood that the operations in the flowcharts may be implemented out of order, and steps without logical contextual relationships may be reversed or performed simultaneously. Furthermore, those skilled in the art, guided by the content of this invention, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] This invention provides a method and apparatus for predicting and controlling multi-source coupling errors in flange pipelines, which will be described below.

[0028] Before demonstrating specific embodiments, it is necessary to first explain that: Figure 1 As shown, the flanged pipeline in this embodiment of the invention includes multiple pipe sections and flanges connecting adjacent pipe sections. The pipe sections include straight pipes and bends. Specifically, Figure 1 P1-P2, P4-P5, and P7-P8 are straight pipes, while P2-P4 and P5-P7 are bent pipes. The connection between the straight pipes and the bent pipes is a flange connection.

[0029] Figure 2 This is a schematic flowchart of an embodiment of the flange pipeline multi-source coupling error prediction and control method provided by the present invention, as shown below. Figure 2 As shown, the method for predicting and controlling multi-source coupling errors in flange pipelines includes: S201. Obtain the structural and accuracy parameters of the flange pipeline, and construct a multi-source error coupling model of six-degree-of-freedom spinors based on the structural and accuracy parameters of the flange pipeline.

[0030] The structural parameters include, but are not limited to, flange dimensions, straight pipe characteristic dimensions, and bend characteristic dimensions. Precision parameters include, but are not limited to, dimensional tolerances, bend angle deviations, sealing surface perpendicularity, coaxiality, and bolt hole position accuracy.

[0031] Since the cumulative error of flanged pipelines is jointly affected by several components, including straight pipes, connecting flanges, and bends with directional transitions, the multi-source error coupling model in this invention includes a straight pipe manufacturing error model, a bend manufacturing error model, and a flange connection error model. This solves the current problem of unclear characterization of error sources in flanged pipeline assembly. By comprehensively considering the influence of each error source and accurately characterizing the errors through a unified mathematical model, the accuracy and efficiency of subsequent pipeline assembly can be improved.

[0032] S202. Based on the Jacobian matrix and the multi-source error coupling model, an assembly error migration and accumulation model is constructed to characterize the overall assembly accuracy of the flange pipeline system.

[0033] It should be noted that the multi-source error coupling model refers to the error model of each pipeline component, namely, a single bend, straight pipe, and connecting flange, while the assembly error migration accumulation model based on the Jacobian matrix represents the overall assembly accumulation error of the flange pipeline system, realizing a quantitative mapping from local error to global error.

[0034] S203. Use Halton low-difference sequences to generate random assembly errors that satisfy screw constraints. Based on the random assembly errors, perform quasi-Monte Carlo simulation on the assembly error migration accumulation model to obtain the simulation results of the predicted cumulative assembly error.

[0035] The specific generation process of Halton low-difference sequences and the quasi-Monte Carlo simulation are existing technologies and will not be elaborated here.

[0036] S204. When the simulation results of the predicted cumulative assembly error meet the expected functional requirements, collect the measured data from the assembly site, update and reconstruct the assembly error migration cumulative model based on the measured data, and predict the assembly error of the flange pipeline system again based on the reconstructed assembly error migration cumulative model.

[0037] Among them, the measured data at the assembly site includes, but is not limited to: measuring the actual six-degree-of-freedom pose of the assembly features through methods such as three-dimensional laser scanning and coordinate measuring machine, and measuring the spatial position of the local coordinate system of the assembly features in the global coordinate system after actual assembly.

[0038] S205. When the simulation results of the predicted cumulative assembly error do not meet the expected functional requirements, establish a tolerance optimization model with the goal of minimizing processing costs and achieving the assembly accuracy pass rate, solve the tolerance optimization model to obtain the optimal tolerance allocation scheme, and update the accuracy parameters based on the optimal tolerance allocation scheme.

[0039] It should be understood that after executing S205, the process should return to step S201 and execute steps S201 to S204 to ensure that the optimal tolerance allocation scheme meets the requirements.

[0040] It should also be understood that the flange pipeline multi-source coupling error prediction and control method in the embodiments of the present invention can be implemented in any device based on the flange pipeline multi-source coupling error prediction and control method, such as electronic devices for assembly purposes. Specifically, the flange pipeline multi-source coupling error prediction and control method is stored in the aforementioned device as a pre-programmed program. When the device is started, the program is called, and the flange pipeline multi-source coupling error prediction and control method is implemented.

[0041] Compared with existing technologies, the flange pipeline multi-source coupling error prediction and control method provided in this invention constructs a six-degree-of-freedom screw-based multi-source error coupling model based on the structural and precision parameters of the flange pipeline. This clarifies the error sources and achieves the goal of uniformly representing different error sources with six-degree-of-freedom screws, making the multi-source error coupling model compatible with different types of errors and improving the construction efficiency of the multi-source error coupling model. Secondly, based on the Jacobian matrix and the multi-source error coupling model, an assembly error migration and accumulation model is constructed. The Jacobian matrix explains the error propagation mechanism, realizing a quantitative mapping from the local characteristic error of a single part to the overall assembly precision of the flange pipeline system. This improves the adaptability of the constructed assembly error migration and accumulation model to the errors in actual flange pipelines, thereby improving the error prediction accuracy. Furthermore, by using Halton low-discrepancy sequences to generate random assembly errors that satisfy screw geometry constraints, compared to the traditional Monte Carlo method, the quasi-Monte Carlo simulation based on Halton low-discrepancy sequences, while ensuring the hyperuniformity of the deviation sequence, incorporates a certain degree of randomization. This avoids the problem of excessively large differences in simulation results, leading to instability. Simultaneously, using low-discrepancy sequences as sampling points reduces the error order, thus improving the error convergence speed in the Monte Carlo simulation process and consequently increasing error prediction efficiency. Even further, when the simulation results predicting cumulative assembly errors meet expectations, the assembly error migration accumulation model is reconstructed based on measured data during the actual assembly stage. This compensates for errors caused by pipeline errors, personnel calculations, and positioning during pipeline assembly, thus mitigating the problem of cumulative assembly errors continuously increasing as the assembly process progresses. This reduces the impact of excessive assembly errors on pipeline closure and assembly quality, improving the pipeline assembly success rate and service life. Meanwhile, when the simulation results of the predicted cumulative assembly error do not meet expectations, a tolerance optimization model is established with the goal of minimizing processing costs and achieving the assembly accuracy pass rate. Based on this, the optimal tolerance allocation scheme is determined, which can provide guidance for the precision design of assembly parts.

[0042] In summary, this invention achieves accurate characterization of error sources and error propagation by constructing a multi-source error coupling model and an assembly error migration accumulation model, thereby improving the efficiency of assembly error and propagation modeling. Furthermore, by using Halton low-discrepancy sequences to generate random assembly errors that satisfy screw constraints for simulation, the simulation accuracy and convergence speed are improved, further enhancing the accuracy and computational efficiency of assembly error prediction. Moreover, by setting different processing methods at different stages—whether the simulation results of predicted cumulative assembly errors meet or do not meet the expected assembly functional requirements—the actual assembly of flange pipelines is ensured to be accurate and effective, further guaranteeing assembly quality and success rate.

[0043] In some embodiments of the present invention, such as Figure 3 As shown, step S201, which involves constructing a six-degree-of-freedom spinor multi-source error coupling model based on the structural and accuracy parameters of the flange pipeline, includes: S301. Based on the structural parameters of the flange pipeline, determine various error sources of different types, including straight pipe manufacturing error, bend pipe manufacturing error and flange connection error.

[0044] Since the error analysis methods for straight pipe manufacturing errors and flange connection errors are the same, the following steps will characterize straight pipe manufacturing errors and flange connection errors using the same method.

[0045] S302. Based on the variation of assembly features within the tolerance domain under coupling error, the straight pipe error and flange connection error are respectively expressed as six-degree-of-freedom spinors in the form of Lie algebra se(3), and the straight pipe error model and flange connection error model are obtained accordingly. S303. Based on the spinor exponential mapping and homogeneous coordinate pose transformation, the bending pipe error is converted from the rigid body transformation pose of SE(3) to its Lie algebra se(3) form of a six-degree-of-freedom spinor representation, and the bending pipe error model is obtained.

[0046] It should be noted that: in the process of representing the bending error with six degrees of freedom using screw index mapping and homogeneous coordinate pose transformation, there is no need to establish a local coordinate system. The bending error is only coupled by the global coordinate system fixed at the starting end face of the bending pipe and the terminal coordinate system at the end face. The obtained SE(3) type end space pose is combined with homogeneous transformation theory and small displacement screw assumption to convert the pose error into a six-degree-of-freedom screw in the form of se(3) Lie algebra. This solves the problem that the traditional small displacement screw theory can only characterize the error variation of conventional cylindrical and planar features, and realizes the screw characterization of special structural features of the bending pipe.

[0047] This invention unifies the various error sources of a pipeline system with a six-degree-of-freedom spinor, overcoming the limitations of the traditional exponential product method, which is only applicable to welded pipeline connections and cannot characterize pipeline flange connection errors and error coupling. It provides a new solution for the accuracy prediction of flange pipeline assembly.

[0048] In a specific embodiment of the present invention, the straight pipe error and the connection error include dimensional error, perpendicularity error, and coaxiality error, and the bending pipe error includes angular error and length offset error. In a specific embodiment of the present invention, the manufacturing error of the straight pipe is characterized as follows: Figure 4 As shown, the limiting deflection angles of the variable planar feature around the Y and Z axes within the tolerance domain relative to the theoretical position S The value of 0 must not exceed S 1. The axial limit offset of the dimensional tolerance relative to the theoretical position. S 0 must not exceed S 2.

[0049] The six-degree-of-freedom spinor model for straight pipe manufacturing error is as follows:

[0050] In the formula, For straight pipe error rotation; This represents the minimum axial offset of the theoretical position under dimensional tolerance constraints. This represents the maximum axial offset of the theoretical position under dimensional tolerance constraints. D The diameter of the sealing surface of the straight pipe flange; t 1 represents the perpendicularity of the sealing surface of the straight pipe flange; t 2 refers to the coaxiality of the sealing surface of the straight pipe flange.

[0051] In a specific embodiment of the present invention, the manufacturing error of the bent pipe is characterized as follows: Figure 5 As shown, since the installation direction of the bend is divided into the following six cases in the global coordinate system, XY, XZ, YX, YZ, ZX, ZY, the bend error screw model is constructed based on the following formula for the above six cases.

[0052]

[0053] Substituting the bending error into the above formula for calculation, we obtain a six-degree-of-freedom spinor model of the bending manufacturing error.

[0054] In the formula, This represents the actual pose of the pipeline end feature in the global coordinate system. This represents the theoretical pose of the pipeline end features in the global coordinate system. G The transformation matrix characterizing the influence of pipe bending manufacturing errors on the characteristic pose of the pipe end; To account for the influence of angular deviation at the control point in the pipeline bend; The influence of length deviation at the pipeline control point.

[0055] Specifically, when the installation direction is XY, the bending error rotation is... for:

[0056] When the installation direction is XZ, the bending error rotation amount for:

[0057] When the installation direction is YX, the bending error rotation amount for:

[0058] When the installation direction is YZ, the bending error rotation amount for:

[0059] When the installation direction is ZX, the bending error rotation amount for:

[0060] When the installation direction is ZY, the bending error rotation amount for:

[0061] In the formula, x , y , z These are the characteristic lengths of the bend in the assembly direction; This is the length offset of P1P2; This is the length offset of P2P3; This represents the angle error at the bend.

[0062] In a specific embodiment of the present invention, the connection error of the MFM flange sealing surface is characterized as follows: Figure 6 As shown in (a), based on the variation analysis of the connection features, it can be seen that for the shaft-hole mating assembly feature, the amount of clearance between the sealing surface and the connection is affected. and the coaxiality of the sealing surface t 2. Constraint effects, i.e., the axis of the connection feature to be assembled is in The size varies within the tolerance range. Because the sealing surface connection, in addition to the shaft-hole fit, also has a planar fit of the sealing surface, it belongs to a locally parallel structure. Analysis shows that for the winding... y , z Shaft rotation is affected by perpendicularity t1 constraint, Figure 6 (b) Connection error in the winding x Shaft rotational deviation The screw model for flange connection error is then:

[0063] In the formula, For connection error spinor; This refers to the connection gap between the flange sealing surfaces. t 1 represents the perpendicularity of the flange sealing surface; t 2 refers to the coaxiality of the flange sealing surface; t 3 is the position of the bolt holes on the flange end face; For bolt hole size tolerances; R The base circle radius of the bolt hole. D This refers to the diameter of the flange sealing surface.

[0064] It should be noted that flange sealing face types include five types: raised face (RF), tongue / groove face (TG), concave face / convex face (MFM), flat face (FF), and O-ring face (OSG). The above flange connection error model applies to MFM, OSG, and TG sealing face types. When the sealing face type is RF or FF, the flange connection error model is as follows:

[0065] In the formula, t 3 is the position of the bolt holes on the flange end face; t 4 represents the surface profile tolerance of the sealing surface; D The diameter of the sealing surface; R The radius of the base circle of the bolt hole.

[0066] In a specific embodiment of the present invention, the assembly error migration accumulation model is as follows:

[0067]

[0068] In the formula, These represent the translational amounts of the flange pipe end along the x, y, and z directions in the global coordinate system; These represent the rotation amounts of the flange pipe end feature around the x, y, and z directions in the global coordinate system; For the first i Jacobian matrix for error propagation in a local coordinate system; For the first i Characteristic spinor error in a local coordinate system; This is an antisymmetric matrix, representing the spatial differences between various local coordinate systems; For the coordinate system and local coordinate system in the tolerance domain analysisi When directions do not coincide, the tolerance domain coordinate system and the local coordinate system i The pose transformation matrix between them; O 3×3 It is a zero matrix.

[0069] It should be noted that the local coordinate system refers to the coordinate system established with the geometric center of each feature of the assembly path as the origin, and the global coordinate system is constructed with the center of the starting pipeline feature as the origin. When the local coordinate system and the global coordinate system are in the same orientation, that is, when the feature is not tilted, the pipeline assembly error transfer accumulation model is constructed.

[0070] In a specific embodiment of the present invention, step S203, which iteratively simulates the assembly error migration accumulation model based on the random assembly error generated by quasi-Monte Carlo simulation to obtain the simulation result of the predicted cumulative assembly error, includes: Quasi-Monte Carlo simulations were performed on the assembly error migration accumulation model based on random assembly errors to obtain multiple predicted assembly error results; the number of quasi-Monte Carlo simulations was equal to the set number of simulations. Statistical analysis was performed on the results of multiple predicted assembly errors to obtain the simulation results of the predicted cumulative assembly error.

[0071] Specifically, after each simulation, it is determined whether the number of simulations equals the set number of simulations. If not, the simulation is performed again; if so, the simulation is stopped.

[0072] Among them, the simulation results of predicted cumulative assembly error refer to the simulation results of the pose variation error of the end features of the flange pipeline system in the six degrees of freedom in the global coordinate system, including the simulation results of the cumulative assembly error distribution range of the flange pipeline, the assembly qualification rate, and the pose variation in three-dimensional space.

[0073] It should be noted that, in order to provide intuitive guidance to designers and assemblers, in some embodiments of the present invention, key indicators such as pipeline assembly qualification rate, error distribution and assembly spatial pose can be determined based on the simulation results of predicted cumulative assembly errors, and these indicators can be displayed in the form of two-dimensional and three-dimensional visualization graphics.

[0074] In a specific embodiment of the present invention, determining whether the simulation result of the predicted cumulative assembly error meets expectations includes: Obtain the characteristic parameters of the simulation results for predicting cumulative assembly errors. The characteristic parameters include mean, standard deviation, error range, maximum deviation, minimum deviation, and probability distribution. Based on whether the characteristic parameters are within the range of theoretical parameters, if so, the simulation result of the predicted cumulative assembly error meets the expected functional requirements; otherwise, the simulation result of the predicted cumulative assembly error does not meet the expected functional requirements, the assembly qualification rate cannot be guaranteed, and subsequent accuracy optimization and adjustment are required.

[0075] In a specific embodiment of the present invention, the theoretical value of the probability distribution is selected with a confidence level of ±3. For the interval.

[0076] In one specific embodiment of the present invention, such as Figure 7 As shown, the actual center point positions are all within the limit offset requirements of the end flange face, indicating that the predicted assembly accuracy meets expectations.

[0077] In practical applications, since multiple assembly features affecting assembly errors include critical features with significant impact and non-critical features with less significant impact, when the simulation results of predicted cumulative assembly errors do not meet expectations, precision design can be achieved by focusing on adjusting the precision parameters of critical features. For non-critical features, the precision parameters can be appropriately relaxed to ensure processing economy and efficiency. Therefore, to further improve assembly efficiency, in some embodiments of the present invention, such as... Figure 8 As shown, when the simulation results of the predicted cumulative assembly error do not meet the expected functional requirements, the prediction and control of multi-source coupling errors in flange piping also includes: S801. Based on the simulation results of the predicted cumulative assembly error, determine the error source contribution of each assembly feature, and determine the key features and non-key features based on the error source contribution.

[0078] The contribution of the error source can be calculated based on the following formula:

[0079] In the formula, For a certain assembly feature in the first i Standard deviation of vectors in each functional direction; Let be the vector standard deviation of a certain assembly feature.

[0080] Specifically, due to the normal distribution assumption, for straight pipe errors and flange connection errors, a 6-fold distribution is followed at a confidence level of 99.73%. In principle, its statistical parameters follow the following formula:

[0081]

[0082] In the formula, μ The mean of the random variable distribution; σ Let the standard deviation be the distribution of the random variable. V LSL The upper limit of assembly requirements specifications; V LUL This represents the lower limit of the assembly requirements specifications.

[0083] For bends, the vector expressions of bends in different assembly directions exhibit nonlinear functions. The mean and standard deviation of their random variable distributions are calculated using the following example.

[0084] l and θ All are normally distributed independent random variables, among which Based on the properties of the normal distribution and its linear combination, the tolerance function of the spinor of bending manufacturing error in a certain vector direction can be obtained. Z Expectation and variance.

[0085]

[0086]

[0087] The expression can be obtained from Taylor's expansion. Z The statistical mean and standard deviation.

[0088]

[0089]

[0090] Based on the aforementioned nonlinear transformation, when the bending rotation error is relatively small, the tolerance function Z can be approximated as a normal distribution through Taylor expansion. The contribution analysis of pipeline system assembly error is achieved by substituting the above statistical parameter expressions into the contribution calculation formula.

[0091] S802. Control the precision of key component features, and relax the precision requirements of non-key components while meeting the precision requirements.

[0092] The specific adjustments to the tolerance values ​​of specific parts can be set or adjusted based on processing capabilities, economic requirements, or empirical values ​​to guide the precision design and assembly process; however, they will not be quantified here.

[0093] In specific embodiments of the present invention, such as Figure 9 As shown, a total of 9 features were analyzed. Figure 9 The dashed line represents the contribution of a single feature in a certain functional requirement direction, while the solid line represents the cumulative contribution of the pipeline system in that direction. Figure 9 It can be seen that features 5, 7, and 9 are key features, corresponding to Figure 1 The manufacturing error sources for straight pipes P4P5, P7P8, and bends P5P7 in the flanged piping system are not the other key features that affect the accuracy of pipe assembly.

[0094] This invention improves the manufacturing and installation accuracy of critical pipeline assembly features while appropriately relaxing the accuracy requirements of some non-critical features, thereby reducing the impact of critical features on cumulative errors, improving assembly quality while reducing production costs.

[0095] In a specific embodiment of the present invention, the tolerance optimization model is as follows:

[0096] The constraints for solving the tolerance optimization model are:

[0097] In the formula, For the assembly cumulative error rate function; For processing cost function; , , These represent the cumulative offset errors in the three directions under the global coordinate system; t i For the first i Tolerances for each assembly feature; For the first i Manufacturing cost function for assembly features; FR x , FR y , FR z These are the assembly function requirements for flange pipelines in three different directions under the global coordinate system; Minimum tolerance requirement; This is the maximum tolerance requirement.

[0098] In summary, the multi-source coupling error prediction and control method for flange pipelines proposed in this invention takes into account the characteristics of flange pipeline assembly, such as large span, complex connection structure, and irregular error propagation path. It uses screw theory to perform unified modeling of pipeline assembly errors, simulates the errors of the actual assembly process through quasi-Monte Carlo simulation based on Halton low-difference sequences, analyzes key pipeline links, and combines an optimization model oriented towards pipeline system assembly quality and manufacturing cost to achieve pipeline system tolerance allocation to control the cumulative error of the pipeline. This can improve assembly quality and efficiency and reduce production costs.

[0099] To better implement the flange pipeline multi-source coupling error prediction and control method in the embodiments of the present invention, based on the flange pipeline multi-source coupling error prediction and control method, the embodiments of the present invention also provide a flange pipeline multi-source coupling error prediction and control device, such as... Figure 10 As shown, the flange pipeline multi-source coupling error prediction and control device 1000 includes: The multi-source error coupling model construction unit 1001 is used to obtain the structural and accuracy parameters of the flange pipeline, and to construct a six-degree-of-freedom screw multi-source error coupling model based on the structural and accuracy parameters of the flange pipeline. Assembly error migration accumulation model construction unit 1002 is used to construct an assembly error migration accumulation model characterizing the overall assembly accuracy of the flange pipeline system based on the Jacobian matrix and the multi-source error coupling model. The pre-assembly accuracy simulation prediction unit 1003 is used to generate random assembly errors that satisfy screw constraints using Halton low-difference sequences, and to perform quasi-Monte Carlo simulation on the assembly error migration accumulation model based on the random assembly errors to obtain the simulation results of the predicted cumulative assembly errors. The actual assembly error simulation prediction unit 1004 is used to collect measured data from the assembly site when the simulation result of the predicted cumulative assembly error meets the expected functional requirements, update and reconstruct the assembly error migration accumulation model based on the measured data, and predict the error of the flange pipeline based on the reconstructed assembly error migration accumulation model. The tolerance optimization allocation unit 1005 is used to establish a tolerance optimization model with the goal of minimizing processing costs and achieving the assembly accuracy pass rate when the simulation results of the predicted cumulative assembly error do not meet the expected functional requirements. The unit then solves the tolerance optimization model to obtain the optimal tolerance allocation scheme and updates the accuracy parameters based on the optimal tolerance allocation scheme.

[0100] The flange pipeline multi-source coupling error prediction and control device 1000 provided in the above embodiments can realize the technical solutions described in the above flange pipeline multi-source coupling error prediction and control method embodiments. The specific implementation principles of each module or unit can be found in the corresponding content of the above flange pipeline multi-source coupling error prediction and control method embodiments, which will not be repeated here.

[0101] The foregoing has provided a detailed description of a method and apparatus for multi-source coupling error prediction and control of flange pipelines provided by the present invention. Specific examples have been used to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for predicting and controlling multi-source coupling errors in flange pipelines, characterized in that, include: Obtain the structural and precision parameters of the flange pipeline, and construct a multi-source error coupling model of six-degree-of-freedom screws based on the structural and precision parameters of the flange pipeline. An assembly error migration and accumulation model characterizing the overall assembly accuracy of the flange piping system is constructed based on the Jacobian matrix and the multi-source error coupling model. Halton low-discrepancy sequences are used to generate random assembly errors that satisfy screw constraints. Based on the random assembly errors, a quasi-Monte Carlo simulation is performed on the assembly error migration accumulation model to obtain the simulation results of the predicted cumulative assembly error. When the simulation results of the predicted cumulative assembly error meet the expected functional requirements, the measured data of the assembly site are collected, the assembly error migration accumulation model is updated and reconstructed based on the measured data, and the assembly error of the flange pipeline system is predicted again based on the reconstructed assembly error migration accumulation model. When the simulation results of the predicted cumulative assembly error do not meet the expected functional requirements, a tolerance optimization model is established with the goal of minimizing processing costs and achieving the assembly accuracy pass rate. The tolerance optimization model is then solved to obtain the optimal tolerance allocation scheme, and the accuracy parameters are updated based on the optimal tolerance allocation scheme.

2. The method for multi-source coupling error prediction and control of flange pipelines according to claim 1, characterized in that, The flanged pipeline includes multiple pipe segments and flanges connecting adjacent pipe segments. The pipe segments include straight pipes and bends. The multi-source error coupling model includes a straight pipe manufacturing error model, a bend manufacturing error model, and a flange connection error model.

3. The method for multi-source coupling error prediction and control of flange pipelines according to claim 2, characterized in that, The multi-source error coupling model for constructing a six-degree-of-freedom spinor based on the structural and accuracy parameters of the flange pipeline includes: Based on the structural parameters of the flange pipeline, various error sources of different types are determined during the assembly process. These error sources include straight pipe manufacturing errors, bend pipe manufacturing errors, and flange connection errors. Based on the variation of the assembly features within the tolerance domain under coupling error, the straight pipe manufacturing error and the flange connection error are respectively represented by a six-degree-of-freedom spinor in the form of Lie algebra se(3), and the straight pipe manufacturing error model and the flange connection error model are obtained accordingly. Based on the spinor exponential mapping and homogeneous coordinate pose transformation, the manufacturing error of the bent pipe is transformed from the rigid body transformation pose of SE(3) to its Lie algebra se(3) form of a six-degree-of-freedom spinor representation, thus obtaining the manufacturing error model of the bent pipe.

4. The method for multi-source coupling error prediction and control of flange pipelines according to claim 3, characterized in that, The manufacturing errors of the straight pipe and the flange connection errors include dimensional errors, perpendicularity errors and coaxiality errors, and the manufacturing errors of the bent pipe include angular errors and length offset errors.

5. The method for multi-source coupling error prediction and control of flange pipelines according to claim 1, characterized in that, The assembly error migration accumulation model is as follows: In the formula, These represent the translational amounts of the flange piping system end along the x, y, and z directions in the global coordinate system. These represent the rotation of the flange piping system end around the x, y, and z directions in the global coordinate system, respectively. For the first i Jacobian matrix for error propagation in a local coordinate system; For the first i Characteristic spinor error in a local coordinate system; This is an antisymmetric matrix, representing the spatial differences between various local coordinate systems; For the tolerance domain analysis coordinate system and local coordinate system i When poses do not coincide, the tolerance domain coordinate system and the local coordinate system i The pose transformation matrix between them; O 3×3 It is a zero matrix.

6. The method for multi-source coupling error prediction and control of flange pipelines according to claim 1, characterized in that, The iterative simulation of the assembly error migration accumulation model based on quasi-Monte Carlo simulation to obtain the simulation results of predicted cumulative assembly errors includes: Based on the random assembly error, a quasi-Monte Carlo simulation is performed on the assembly error migration accumulation model to obtain multiple predicted assembly error results; the number of quasi-Monte Carlo simulations is equal to the set number of simulations. Statistical analysis is performed on the multiple predicted assembly error results to obtain the simulation results of the predicted cumulative assembly error. The simulation results of the predicted cumulative assembly error include the distribution range of the cumulative assembly error of the flange pipeline, the assembly qualification rate, and the three-dimensional spatial pose variation.

7. The method for multi-source coupling error prediction and control of flange pipelines according to claim 1, characterized in that, Determine whether the simulation results of the predicted cumulative assembly error meet the expected assembly functional requirements, including: Obtain the characteristic parameters of the simulation results of the predicted cumulative assembly error, including the mean, standard deviation, error range, maximum deviation value, minimum deviation value, and probability distribution value; Based on whether the characteristic parameters are within the theoretical parameter range, if yes, the simulation result of the predicted cumulative assembly error meets the expected functional requirements; otherwise, the simulation result of the predicted cumulative assembly error does not meet the expected functional requirements.

8. The method for multi-source coupling error prediction and control of flange pipelines according to claim 1, characterized in that, When the simulation results of the predicted cumulative assembly error do not meet the expected functional requirements, the method further includes: Based on the simulation results of the predicted cumulative assembly error, the contribution of each assembly feature to the error source is determined, and based on the contribution of the error source, key features and non-key features are determined. Control the precision of key component features, and relax the precision requirements for non-key components while meeting the precision requirements.

9. The method for multi-source coupling error prediction and control of flange pipelines according to claim 1, characterized in that, The tolerance optimization model is as follows: The constraints for solving the tolerance optimization model are: In the formula, For the assembly cumulative error rate function; For processing cost function; , , These represent the cumulative offset errors in the three directions under the global coordinate system; t i For the first i Tolerances for each assembly feature; For the first i Manufacturing cost function for assembly features; FR x , FR y , FR z These are the assembly function requirements for flange pipelines in three different directions under the global coordinate system; Minimum tolerance requirement; This is the maximum tolerance requirement.

10. A multi-source coupling error prediction and control device for flange pipelines, characterized in that, include: A multi-source error coupling model construction unit is used to obtain the structural parameters and accuracy parameters of the flange pipeline, and to construct a six-degree-of-freedom screw multi-source error coupling model based on the structural parameters and accuracy parameters of the flange pipeline. The assembly error migration accumulation model construction unit is used to construct an assembly error migration accumulation model characterizing the overall assembly accuracy of the flange pipeline system based on the Jacobian matrix and the multi-source error coupling model. The pre-assembly accuracy simulation prediction unit is used to generate random assembly errors that satisfy screw constraints using Halton low-difference sequences. Based on the random assembly errors, the assembly error migration accumulation model is simulated using quasi-Monte Carlo simulation to obtain the simulation results of the predicted cumulative assembly errors. The actual assembly error simulation prediction unit is used to collect measured data from the assembly site when the predicted cumulative assembly error simulation result meets the expected functional requirements, update and reconstruct the assembly error migration accumulation model based on the measured data, and perform assembly error simulation prediction on the flange pipeline again based on the reconstructed assembly error migration accumulation model. The tolerance optimization allocation unit is used to establish a tolerance optimization model with the goal of minimizing processing costs and achieving the assembly accuracy pass rate when the simulation results of the predicted cumulative assembly error do not meet the expected functional requirements. The unit then solves the tolerance optimization model to obtain the optimal tolerance allocation scheme and updates the accuracy parameters based on the optimal tolerance allocation scheme.

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