A method for automatic optimization of connection system assembly performance based on digital twin

Abstract

The present invention discloses a method for automatically optimizing the assembly performance of a connection system based on digital twins, the method comprising: obtaining data of a connection system bolt group; establishing a digital twin model based on the data; correcting the friction coefficient based on the digital twin model; calculating the theoretical assembly torque of the bolt based on the service load and connection requirements; predicting the preload distribution and connection performance of the connection system bolt group based on the actual monitoring data of local bolts; calculating the correction value of the friction coefficient based on the actual monitored preload; recalculating the theoretical assembly torque based on the correction value of the friction coefficient until the error between the actual preload and the theoretical preload is less than a preset value; establishing a digital twin interface analysis system to display the contact state, stress distribution, bolt contact stiffness, and torsional stiffness of the connection system joint surface in real time. Through the processing scheme disclosed in the present invention, the twin digital model is made more accurate and reliable, and the assembly efficiency and quality are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twins, and in particular to a method for automatically optimizing assembly performance of a connection system based on digital twins. Background Art

[0002] With the improvement of machining accuracy and quality stability of connection structures, assembly accuracy has gradually become a key factor affecting product accuracy. At present, there is no convenient and effective method to accurately characterize the deviation characteristics of the installation surface morphology, which makes it impossible to accurately analyze its impact on assembly. Especially for precision mechanical products such as aircraft engines, small geometric deviations in the morphology of the assembly interface will have a significant impact on product quality and stability under high load conditions. In recent years, although fastener preload monitoring technology has gradually matured, it is costly and can only evaluate the axial force level of a single bolt, and cannot evaluate the state of the connection structure.

[0003] It can be seen that how to create a new method for automatically optimizing the assembly performance of the connection system has become a goal that the industry urgently needs to improve. Summary of the invention

[0004] In view of this, an embodiment of the present disclosure provides a method for automatically optimizing the assembly performance of a connection system based on digital twins, which at least partially solves the problems existing in the prior art.

[0005] In a first aspect, an embodiment of the present disclosure provides a method for automatically optimizing assembly performance of a connection system based on digital twins, the method comprising the following steps:

[0006] Get data of bolt groups of connection systems;

[0007] Establishing a digital twin model based on the data;

[0008] The friction coefficient is corrected based on the digital twin model.

[0009] According to a specific implementation of the embodiment of the present disclosure, the data of the bolt group of the connection system includes:

[0010] The contact state of the connection system joint surface, stress distribution, bolt contact stiffness, bolt preload data, torsional stiffness, tightening torque data, structural dimension data, service load and connection requirements.

[0011] According to a specific implementation of the embodiment of the present disclosure, the correcting the friction coefficient based on the digital twin model includes:

[0012] Calculate the theoretical assembly preload target value and assembly torque of the bolt based on the service load and connection requirements;

[0013] Predict the preload distribution and connection performance of the bolt group in the connection system based on the actual monitoring data of local bolts;

[0014] Calculate the correction value of the friction coefficient based on the actual monitored preload force;

[0015] The theoretical assembly torque is recalculated based on the corrected value of the friction coefficient until the error between the actual preload force and the theoretical preload force is less than a preset value.

[0016] According to a specific implementation of the embodiment of the present disclosure, the calculation of the theoretical assembly preload target value and assembly torque of the bolt based on the service load and the connection requirements includes:

[0017] The theoretical assembly torque of the bolt is calculated based on the following formula:

[0018]

[0019] in, is the theoretical assembly torque; is the theoretical preload of the bolt; is the friction coefficient constant; is the effective diameter of the bolt;

[0020] The theoretical preload force of the bolt is calculated based on the following formula: :

[0021]

[0022] in, is the minimum assembly preload of the bolt; is the maximum assembly preload of the bolt;

[0023] The minimum assembly preload force of the bolt is calculated based on the following formula: :

[0024]

[0025] in, is the axial load on the connection structure; is the anti-slip coefficient; is the lateral shear force of a single bolt; is the bending moment of the connecting structure; is the friction coefficient of the joint surface; n is the number of joint surfaces; R is the distribution radius of the bolt;

[0026] The maximum assembly preload force of the bolt is calculated based on the following formula

[0027]

[0028] in, is the yield strength of the fastener; is the dangerous cross-sectional area of ​​the fastener; is the thread pitch; is the stress utilization factor of the bolt yield point; is the thread friction coefficient; is the bolt mid-diameter; is the bolt diameter.

[0029] According to a specific implementation of the embodiment of the present disclosure, the method of predicting the preload force distribution and connection performance of the bolt group of the connection system based on the actual monitoring data of the local bolts includes:

[0030] The bolt is calculated based on the following formula After tightening, the node Axial deformation at:

[0031]

[0032] in, For Node Axial deformation at For bolts Initial preload force; is the axial stiffness at the bolted node; To tighten the bolts When the node The ability to resist the elastic deformation of the interference fit; the node refers to the contact point between the end face of the bolt or nut and the end face of the connected structure;

[0033] When tightening the When there are bolts, the node The deformation coordination equation at is:

[0034]

[0035] in, For the After the bolts are tightened, the node The amount of change in deformation; For the After tightening the first bolt, The change in the deformation of the bolts;

[0036] When After tightening the first bolt, The residual preload of the bolts is:

[0037]

[0038] in, For the Residual preload of the bolts; is the initial preload of the bolt; For the +1 bolt is tightened, Changes in residual preload of bolts; For the After the first bolt is tightened, Changes in residual preload of bolts; For the After the first bolt is tightened, The variation of residual preload of bolts.

[0039] According to a specific implementation method of the embodiment of the present disclosure, After tightening the bolts, The preload force of the bolts changes as follows:

[0040]

[0041]

[0042] in, is the inverse of the compression stiffness of node 1; for The reciprocal of is the reciprocal of the stiffness of bolt 1; for The reciprocal of is the ability of node 1 to resist the elastic deformation caused by the preload of bolt n-1; yes The reciprocal of is the ability of node n-1 to resist the elastic deformation caused by the preload of bolt n-1; yes The reciprocal of It indicates the ability of node n-1 to resist the elastic deformation of the interference fit when tightening bolt n-1; yes The reciprocal of represents the stiffness of bolt n-1; is the change in the preload force of bolt 1 after tightening bolt n; is the change in the preload force of bolt n-1 after tightening bolt n; for The reciprocal of It represents the ability of node 1 to resist the elastic deformation caused by the preload of bolt n; for The reciprocal of It represents the ability of node n-1 to resist the elastic deformation caused by the preload of bolt n; is the initial preload force of bolt n.

[0043] According to a specific implementation of the embodiment of the present disclosure, the correction value of the friction coefficient is calculated based on the actually monitored preload force, including:

[0044] The preload error is calculated based on the following formula:

[0045]

[0046] in, For the The preload error of the bolts; The actual measured The preload of the bolts; is the theoretical preload; is the theoretical assembly torque; For the The actual friction coefficient of the bolts; is the theoretical friction coefficient; is the effective diameter of the bolt;

[0047] The average corrected friction coefficient is calculated based on the following formula :

[0048]

[0049] in, is the number of bolts tested; For the The actual friction coefficient of the bolts;

[0050] The relationship between bolt preload and assembly torque is calculated based on the following formula:

[0051]

[0052] in, is the corrected assembly torque; is the average corrected friction coefficient; is the theoretical preload of the bolt; is the effective diameter of the bolt.

[0053] According to a specific implementation of the embodiment of the present disclosure, recalculating the theoretical assembly torque based on the correction value of the friction coefficient until the error between the actual preload force and the theoretical preload force is less than a preset value includes:

[0054] Correction is based on the following formula:

[0055]

[0056] in, is the preload error updated during the iteration process; is the actual measured value of the nth preload force; is the theoretical preload of the bolt;

[0057] when When , the iteration stops; is the set error accuracy threshold.

[0058] According to a specific implementation of the embodiment of the present disclosure, establishing a digital twin model based on the data includes:

[0059] Establish the correlation between the axial stiffness and torsional stiffness of the local position according to the bolt preload data, tightening torque data and structural dimension data during the assembly process;

[0060] Based on the deep learning algorithm, the tightening friction coefficient is corrected according to the local data to obtain the corrected axial stiffness variation range;

[0061] The overall stiffness matrix of the connection structure is corrected according to the smart bolt data at different positions to obtain the digital twin model of assembly performance. The installation sequence and order of the bolts are automatically selected based on the measured preload force data of local bolts.

[0062] According to a specific implementation of the embodiment of the present disclosure, the automatic selection of the installation sequence and installation order of the bolts based on the measured data of the preload force of the local bolts includes:

[0063] Calculate the preload error of all bolts, thus reflecting the friction coefficient fluctuation value of all bolts;

[0064] Arrange the bolt installation sequence from high to low according to the stability of the friction coefficient; the smaller the fluctuation of the friction coefficient, the higher its stability.

[0065] The method for automatic optimization of connection system assembly performance based on digital twin in the disclosed embodiment makes the digital model more accurate and reliable through real-time monitoring and feedback of the connection system; and greatly improves the assembly efficiency and quality through automatic optimization of the assembly preload and tightening torque of the connection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The above is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0067] Figure 1 A schematic flow chart of a method for automatically optimizing assembly performance of a connection system based on digital twins provided in an embodiment of the present disclosure;

[0068] Figure 2A technical roadmap for a method for automatically optimizing assembly performance of a connection system based on digital twins provided in an embodiment of the present disclosure;

[0069] Figure 3 A schematic diagram of bolt numbering in a connection structure provided in an embodiment of the present disclosure;

[0070] Figure 4 A schematic diagram of a connection system state prediction process based on local bolt sensor information provided by an embodiment of the present disclosure;

[0071] Figure 5 A schematic diagram of an intelligent decision-making process for an assembly process of a fastening connection system provided by an embodiment of the present disclosure;

[0072] Figure 6 A schematic diagram of a digital twin system interface provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0073] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0074] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0075] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement a device and / or practice a method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0076] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0077] An embodiment of the present invention provides a method for automatically optimizing the assembly performance of a connection system based on digital twins. By constructing a high-precision digital twin prediction model of a typical fastening connection system based on real assembly and application scenarios, and combining test data to correct and improve the accuracy of the prediction model, the assembly preload and tightening torque of a typical fastening connection system can be designed and automatically corrected, the quality control of bolt group assembly can be achieved, and key performance parameters such as the contact state of the connection surface during the assembly service stage, contact stiffness, and assembly uniformity of the bolt group can be displayed in real time. This provides a solution for the maintenance and inspection of connecting bolts and the overall structural stress analysis, avoids frequent periodic maintenance, and ensures the safety of the connection system.

[0078] First, for typical bolt connection structures, based on known service loads and connection requirements, the target values ​​of bolt assembly preload and tightening torque are automatically designed. Secondly, combined with the actual assembly preload data of local intelligent bolts, the friction coefficient fluctuation range of the bolt group of the connection system is analyzed, the preload-installation torque conversion relationship is corrected, and a prediction model is established to intelligently feedback the actual tightening torque. Then, a mathematical model that can reflect the assembly quality of the connection system and feedback the assembly process when manufacturing errors and assembly deviations exist is established. Finally, a digital twin interface analysis system is established to achieve real-time display capabilities of parameters such as the contact state of the connection system joint surface, stress distribution, bolt contact stiffness, and torsional stiffness based on sensor information such as preload data.

[0079] Figure 1 A schematic diagram of the process of the method for automatically optimizing the assembly performance of a connection system based on digital twins provided in an embodiment of the present disclosure.

[0080] Figure 2 For Figure 1 Corresponding technical roadmap for automatic optimization method of connection system assembly performance based on digital twin.

[0081] like Figure 1 As shown, at step S110, data of the bolt group of the connection system is obtained.

[0082] In an embodiment of the present invention, the data of the bolt group of the connection system include: contact state of the connection system joint surface, stress distribution, bolt contact stiffness, bolt preload data, torsional stiffness, tightening torque data, structural dimension data, service load and connection requirements.

[0083] More specifically, the process proceeds to step S120.

[0084] In step S120, a digital twin model is established based on the data.

[0085] In an embodiment of the present invention, the digital twin model is established based on the data, including: establishing a correlation relationship between the axial stiffness and torsional stiffness of a local position according to the preload data, tightening torque data, and structural dimension data of the bolts during the assembly process; based on a deep learning algorithm, correcting the tightening friction coefficient according to the local data to obtain a corrected axial stiffness variation range; correcting the overall stiffness matrix of the connection structure according to the smart bolt data at different positions to obtain an assembly performance digital twin model, and automatically selecting the installation sequence and installation order of the bolts based on the actual measured preload data of the local bolts.

[0086] More specifically, the comprehensive monitoring of the assembly status of the fastening connection structure includes the following steps:

[0087] Based on the digital twin model for predicting and optimizing the assembly performance of the connection system, an intelligent decision-making system for installation strategy based on digital twin is formed, which mainly includes:

[0088] (1) Working condition display module: By installing multiple types of sensors on the bolt connection structure, the collected multi-source sensor data is calculated and processed and displayed on the working condition display interface. The specific display content includes: connection structure ambient temperature, connection structure load change, etc.

[0089] (2) Status sensing module: By installing multiple types of sensors on the bolted connection structure, the collected multi-source sensor data is calculated and processed to display the relevant status of each component of the testing machine, including: bolt tightening torque, bolt service preload, and changes in bolt preload when the connection structure load changes.

[0090] (3) Preload calculation and stress analysis module

[0091] Taking the working condition information as input, the bolt deformation coordination theory, bolt creep model and bolt high-temperature preload change model are used to analyze and calculate the bolt service status through the digital twin model. Under the corresponding bolt connection structure working conditions, the bolt preload and the contact stress of the mating surface of the connected structure are displayed, realizing the visualization of the deformation cloud map and stress cloud map of the key positions of the bolt connection structure.

[0092] (4) Intelligent decision-making module

[0093] Through the preload calculation and stress analysis module, the use conditions of the clamped parts and bolts can be intelligently judged according to the contact stress requirements of the joint surfaces of the clamped parts, the strength and life standards of the bolts, and intelligent decisions can be made on the assembly process of the bolted connection structure.

[0094] More specifically, the process proceeds to step S130.

[0095] In step S130 , the friction coefficient is corrected based on the digital twin model.

[0096] In an embodiment of the present invention, the correction of the friction coefficient based on the digital twin model includes: calculating the theoretical assembly preload target value and assembly torque of the bolt based on the service load and connection requirements; predicting the preload distribution and connection performance of the bolt group of the connection system based on the actual monitoring data of local bolts; calculating the correction value of the friction coefficient based on the actual monitored preload; and recalculating the theoretical assembly torque based on the correction value of the friction coefficient until the error between the actual preload and the theoretical preload is less than a preset value.

[0097] In an embodiment of the present invention, the automatic selection of the installation sequence and installation order of the bolts based on the measured preload force data of local bolts includes: calculating the preload force error of all bolts, thereby reflecting the friction coefficient fluctuation value of all bolts; arranging the installation order of the bolts from high to low according to the stability of the friction coefficient; wherein, the smaller the fluctuation of the friction coefficient, the higher its stability.

[0098] More specifically, the basic principles are:

[0099] 1. For multi-bolt connection systems, evenly distribute some of the monitored bolts to the connection structure.

[0100] 2. By monitoring the actual preload of some bolts, the preload of all bolts can be mapped out from the local preload;

[0101] 3. Then, the preload error of all bolts is calculated, which reflects the fluctuation value of the friction coefficient of all bolts;

[0102] 4. Based on the principle of friction coefficient stability, arrange the bolt installation order from high to low. The smaller the fluctuation of the friction coefficient, the higher its stability;

[0103] 5. Finally, combined with the finite element simulation data of the connection structure, the bolt installation sequence (rounds) of the connection system is selected.

[0104] In an embodiment of the present invention, the calculation of the theoretical assembly preload target value and assembly torque of the bolt based on the service load and the connection requirements includes:

[0105] The theoretical assembly torque of the bolt is calculated based on the following formula:

[0106]

[0107] in, is the theoretical assembly torque; is the theoretical preload of the bolt; is the friction coefficient constant; is the effective diameter of the bolt;

[0108] The theoretical preload force of the bolt is calculated based on the following formula: :

[0109]

[0110] in, is the minimum assembly preload of the bolt; is the maximum assembly preload of the bolt;

[0111] The minimum assembly preload force of the bolt is calculated based on the following formula: :

[0112]

[0113] in, is the axial load on the connection structure; is the anti-slip coefficient; is the lateral shear force of a single bolt; is the bending moment of the connecting structure; is the friction coefficient of the joint surface; n is the number of joint surfaces; R is the distribution radius of the bolt;

[0114] The maximum assembly preload force of the bolt is calculated based on the following formula

[0115]

[0116] in, is the yield strength of the fastener; is the dangerous cross-sectional area of ​​the fastener; P is the thread pitch; ν is the stress utilization factor of the bolt yield point; is the thread friction coefficient; is the bolt mid-diameter; is the bolt diameter.

[0117] More specifically, the forward design of fastener assembly preload based on service conditions includes the following steps:

[0118] Firstly, for typical bolt connection structures, the minimum assembly preload of the bolts is designed through engineering algorithms and simulation based on known service loads and connection requirements, such as axial force, lateral shear force, bending moment and torque of the connection structure.

[0119]

[0120] in: For the axial load on the connecting structure, is the anti-slip coefficient, is the transverse shear force of a single bolt, is the bending moment of the connecting structure, is the friction coefficient of the joint surface, and n is the number of joint surfaces.

[0121] According to the actual bolt information, by defining the bolt usage coefficient, the maximum assembly preload of the bolt is determined within the elastic range of the material:

[0122]

[0123] Consider using 70% of the yield strength (refer to the Mechanical Design Manual: Generally, the pre-tightening stress of the threaded connection after tightening shall not be greater than the yield point of its material. For general connection steel bolts, the recommended preload is Includes: Carbon steel bolts ; Alloy steel bolts , is the nominal stress cross-sectional area of ​​the bolt)

[0124] in, is the yield strength of the fastener; is the dangerous cross-sectional area of ​​the fastener; P is the thread pitch; ν is the stress utilization factor of the bolt yield point; is the thread friction coefficient; is the bolt mid-diameter; is the bolt diameter.

[0125] Then, determine the design preload target value:

[0126]

[0127] Finally, the designed tightening torque value is calculated according to the preload target value. During the assembly process, the torque is applied to make the bolt reach the predetermined preload. The ideal assembly torque should be calculated based on the friction coefficient and the geometric characteristics of the bolt in order to effectively achieve the required preload. The required assembly torque for each bolt can be derived from the calculated theoretical preload, combined with the friction coefficient and the geometric parameters of the bolt.

[0128]

[0129] in, is the theoretical assembly torque; is the theoretical preload of the bolt; is the friction coefficient constant; is the effective diameter of the bolt;

[0130] In an embodiment of the present invention, the method of predicting the preload force distribution and connection performance of the bolt group of the connection system based on the actual monitoring data of the local bolts includes:

[0131] The bolt is calculated based on the following formula After tightening, the node Axial deformation at:

[0132]

[0133] in, For Node Axial deformation at For bolts Initial preload of is the axial stiffness at the bolted node; To tighten the bolts When the node The ability to resist the elastic deformation of the interference fit; the node refers to the contact point between the end face of the bolt or nut and the end face of the connected structure;

[0134] When tightening When there are bolts, the node The deformation coordination equation at is:

[0135]

[0136] in, For the After the bolts are tightened, the node The amount of change in deformation; For the After tightening the first bolt, The change in the deformation of the bolts;

[0137] When After tightening the first bolt, The residual preload of the bolts is:

[0138]

[0139] in, For the Residual preload of the bolts; is the initial preload of the bolt; For the +1 bolt is tightened, Changes in residual preload of bolts; For the After the first bolt is tightened, Changes in residual preload of bolts; For the After the first bolt is tightened, The variation of residual preload of bolts.

[0140] In the embodiment of the present invention, After tightening the bolts, The preload force of the bolts changes as follows:

[0141]

[0142]

[0143] Among them, among them, is the inverse of the compression stiffness of node 1; for The reciprocal of is the reciprocal of the stiffness of bolt 1; for The reciprocal of is the ability of node 1 to resist the elastic deformation caused by the preload of bolt n-1; yes The reciprocal of is the ability of node n-1 to resist the elastic deformation caused by the preload of bolt n-1; yes The reciprocal of It indicates the ability of node n-1 to resist the elastic deformation of the interference fit when tightening bolt n-1; yes The reciprocal of represents the stiffness of bolt n-1; is the change in the preload force of bolt 1 after tightening bolt n; is the change in the preload force of bolt n-1 after tightening bolt n; for The reciprocal of It represents the ability of node 1 to resist the elastic deformation caused by the preload of bolt n; for The reciprocal of It represents the ability of node n-1 to resist the elastic deformation caused by the preload of bolt n; is the initial preload force of bolt n.

[0144] More specifically, the connection system state prediction based on local bolt sensing information includes the following steps:

[0145] This step aims to predict the assembly performance of the entire connection system by monitoring the connection status of some bolts (m bolts) in real time and calculating the connection status of all bolts (n bolts). The prediction accuracy can be further improved by combining the elastic deformation coordination equation with simulation data. This method can achieve real-time control of the health status of the connection system and provide data support for subsequent assembly optimization. The specific method is:

[0146] Using the elastic deformation coordination equation, the connection status of all bolts in the entire connection system is predicted through the actual monitoring data of some bolts, combined with the material properties, dimensions and loading conditions of the bolts. This method is based on the principle of elastic mechanics and assumes that the bolts will deform slightly under the preload, and there is a linear relationship between the deformation and the applied force. The specific method is as follows:

[0147] For a connection system with n bolts, assume that the initial preload of each bolt is The axial stiffness at each bolted node is Then when the bolt After tightening, the corresponding node The axial deformation at is:

[0148]

[0149] in, For Node Axial deformation at For bolts Initial preload force; is the axial stiffness at the bolted node; To tighten the bolts When the node The ability to resist the elastic deformation of the interference fit; the node refers to the contact point between the end face of the bolt or nut and the end face of the connected structure;

[0150] After that, when tightening When there are bolts, the node The deformation coordination equation at :

[0151]

[0152] In the formula, For the After the bolts are tightened, the node The change in deformation, For the After tightening the first bolt, The change in the deformation of the bolts.

[0153] Accordingly, After tightening the bolts, The equations for the change of the preload force of the bolts are:

[0154]

[0155]

[0156] in, is the inverse of the compression stiffness of node 1; for The reciprocal of is the reciprocal of the stiffness of bolt 1; for The reciprocal of is the elastic interaction stiffness, which represents the ability of node 1 to resist the elastic deformation caused by the preload of bolt n-1; yes The reciprocal of is the elastic interaction stiffness, which represents the ability of node n-1 to resist the elastic deformation caused by the preload of bolt n-1; yes The reciprocal of It indicates the effect of the stop on the compression stiffness of node n-1 when tightening bolt n-1, and the ability of node n-1 to resist the elastic deformation of the interference stop; yes The reciprocal of represents the stiffness of bolt n-1; is the change in the preload force of bolt 1 after tightening bolt n; is the change in the preload force of bolt n-1 after tightening bolt n; for The reciprocal of is the elastic interaction stiffness, which represents the ability of node 1 to resist the elastic deformation caused by the preload of bolt n; for The reciprocal of is the elastic interaction stiffness, which represents the ability of node n-1 to resist the elastic deformation caused by the preload of bolt n; is the initial preload force of bolt n.

[0157] Furthermore, when After tightening the first bolt, The residual preload of the bolts is:

[0158]

[0159] in, For the Residual preload of the bolts; is the initial preload of the bolt; For the After the first bolt is tightened, Changes in residual preload of bolts; For the After the first bolt is tightened, Changes in residual preload of bolts; For the After the first bolt is tightened, The variation of residual preload of bolts.

[0160] By measuring the preload fluctuation state of some bolts, combined with the material properties, geometric dimensions and external loads of the bolts, the connection state of the remaining bolts can be calculated. Using the known information of some bolts, the preload distribution and connection performance of the entire connection system can be inferred.

[0161] In an embodiment of the present invention, the calculation of the correction value of the friction coefficient based on the actually monitored preload force includes: calculating the preload force error based on the following formula:

[0162]

[0163] in, For the The preload error of the bolts; The actual measured The preload of the bolts; is the theoretical preload; is the theoretical assembly torque; For the The actual friction coefficient of the bolts; is the theoretical friction coefficient; is the effective diameter of the bolt;

[0164] The average corrected friction coefficient is calculated based on the following formula :

[0165]

[0166] in, is the number of bolts tested; For the The actual friction coefficient of the bolts;

[0167] The relationship between bolt preload and assembly torque is calculated based on the following formula:

[0168]

[0169] in, is the corrected assembly torque; is the average corrected friction coefficient; is the theoretical preload of the bolt; is the effective diameter of the bolt.

[0170] More specifically, constructing an intelligent decision-making model for the assembly process of a fastening connection system includes the following steps:

[0171] The core goal of this step is to optimize the assembly accuracy by monitoring the preload force after assembly in real time, analyzing the preload force error caused by the fluctuation of the friction coefficient, and correcting the friction coefficient accordingly. The assembly process is repeatedly iterated using a closed-loop control mechanism until the error between the actual preload force and the theoretical preload force meets the accuracy requirements. This method can greatly improve the assembly accuracy of the connection system, especially when the friction coefficient fluctuates greatly, it can effectively reduce the assembly error and ensure the accuracy of the preload force. The specific method is:

[0172] Intelligent decision making of assembly torque:

[0173] Calculate the preload error.

[0174] After the bolts with locally measurable preload are assembled with the assembly torque designed in step 1, real-time monitoring is performed. The actual preload force of the bolts. The preload force error is calculated based on the difference between the known theoretical preload force value and the actual preload force value. The error formula is:

[0175]

[0176] in, For the The preload error of the bolts; The actual measured The preload of the bolts; is the theoretical preload;

[0177] Correct the friction coefficient and recalculate the assembly torque.

[0178] Establish the relationship between error analysis and friction coefficient: By analyzing the relationship between the preload error and the friction coefficient, a mathematical model that reflects the influence of the friction coefficient can be established. Assume that in the theoretical preload calculation, the friction coefficient is , and the actual friction coefficient is , the error of preload force can be described by the following formula:

[0179]

[0180] in, For the The actual friction coefficient of the bolt. Through this formula, the measured The actual value of the friction coefficient of the bolt.

[0181] Corrected assembly torque: According to The actual friction coefficient of the tested bolts , calculate the average corrected friction coefficient , and recalculate the assembly torque in combination with the expected preload. According to the relationship between bolt preload and assembly torque:

[0182]

[0183] By accurately inferring the correction value of the friction coefficient through error analysis, the preload error caused by friction fluctuation can be eliminated, thereby improving the accuracy of the entire assembly process. In addition, the fluctuation of the friction coefficient is not only affected by the material and surface state, but also by external conditions such as ambient temperature and humidity. Therefore, automatically adjusting the friction coefficient through data feedback can improve the adaptability and stability of the assembly process.

[0184] In an embodiment of the present invention, recalculating the theoretical assembly torque based on the correction value of the friction coefficient until the error between the actual preload force and the theoretical preload force is less than a preset value includes:

[0185] Correction is based on the following formula:

[0186]

[0187] in, is the preload error updated during the iteration process; is the actual measured value of the nth preload force; is the theoretical preload of the bolt;

[0188] when When , the iteration stops; is the set error accuracy threshold.

[0189] Repeat until the accuracy reaches the required level

[0190] Once the friction coefficient is corrected and the assembly torque is recalculated, the assembly torque of each bolt can be adjusted repeatedly through the closed-loop control mechanism until the error between the actual preload and the theoretical preload is within an acceptable range. The control steps are:

[0191] Based on monitoring the preload of some bolts, new preload and torque values ​​are obtained after each iteration. When the difference between the actual preload and the theoretical preload is less than the predetermined accuracy requirement, the control system considers that the target accuracy has been achieved and stops the iteration.

[0192] The iteration formula is as follows:

[0193]

[0194] when When , the iteration stops, is the error accuracy threshold set, Represents the actual measured value of the nth preload force.

[0195] The present invention is described in detail below by way of examples.

[0196] Taking a flange connection structure as the application object, the butt joint is fixed by 24 MJ8 titanium alloy bolts. The specific structure is as follows: Figure 3 shown.

[0197] Through the study of digital twin technology in the assembly process of circular connection systems, it is possible to predict the preload size and distribution changes of the overall bolt group of the connection structure during the assembly and service process, as well as the overall stress distribution and performance changes of the connection system by relying on the input of local external loads, and to visualize and display them in real time through simulation prototypes. The specific steps are as follows:

[0198] Step 1: Forward design of fastener assembly preload based on service conditions.

[0199] First, according to the service conditions of the target connection structure, the flange is mainly subjected to tensile and bending loads, with an axial tensile force of 24kN and a bending moment of 200N.m. The assembly preload is calculated in combination with the preload design requirements:

[0200]

[0201] The assembly preload of the flange connection structure under load service requirements is obtained is 1.5 kN.

[0202] Then, using the yield strength, dangerous cross-sectional area, pitch and other parameters, considering the yield strength of the bolt as a factor of 0.8, the maximum assembly preload of the bolt is calculated:

[0203] =20.57 kN

[0204] Ensure safe operation within the elastic range of the material. Finally, based on the friction coefficient, bolt geometry and yield point stress, combined with the above calculation results, the design preload and theoretical torque required for bolt assembly are derived.

[0205] =20.57kN. Considering the error in the installation process, the target value of bolt preload is 20 kN.

[0206] Depend on . If the bolts are anodized, the design installation torque is =20 =24N•m.

[0207] Step 2: Connection system status prediction based on local bolt sensing information.

[0208] like Figure 4As shown in the figure, sensors are installed at local bolts to monitor the changes in the preload force of the bolts in real time. Through the elastic deformation coordination equation, it is confirmed that two bolts are arranged in each quadrant of the connection flange, and a total of 8 bolts are arranged as local measurement points (bolt numbers are 3, 5, 9, 11, 15, 17, 21, and 23). The monitoring data of some bolts are combined with their material properties, dimensions, and loading conditions to predict the connection status of all bolts in the entire connection system. Subsequently, the axial deformation of the bolts is derived based on the initial preload force and stiffness of the bolts using the principle of elastic mechanics. The tightening sequence and preload force changes of different bolts are calculated through this equation, and the connection status and preload force distribution of the entire connection system are inferred. Combined with the status information of local bolts, the preload force distribution and its changes of the entire connection system are calculated, which can evaluate the health status of the connection system in real time and provide data support for subsequent assembly optimization.

[0209] Step 3: Construct an intelligent decision-making model for the assembly process of the fastening connection system.

[0210] like Figure 5 As shown in the figure, during the assembly process, the 8 monitoring point bolts are tested with the designed tightening torque. First, the actual preload force distribution value of the 8 bolts is monitored in real time, and the known theoretical preload force value is used to calculate the preload force distribution value. The difference between 20kN and the actual preload value is used to calculate the average preload error. =4kN.

[0211] Then calculate the average corrected friction coefficient based on the preload error Recalculate the actual assembly torque based on the expected preload. Based on the relationship between bolt preload and assembly torque: , correct the actual installation torque to = =30N•m.

[0212] Finally, the preload error of all bolts is calculated through the mapping relationship between local bolts and the overall bolt group, which reflects the fluctuation value of the friction coefficient of all bolts. The installation order of the bolts is arranged from low to high according to the stability of the friction coefficient. The bolt number sequence is 1→13→7→19→4→16→10→22→2→14→8→20→5→17→11→23→3→15→9→21→6→18→12→24. The assembly sequence of the flange is a cross-tightening method. The installation sequence is confirmed to be a two-tightening method with the help of data comparison of the flange assembly process simulation model. The first tightening is to 40% of the target value, and the second tightening is to 100% of the target value.

[0213] Step 4: Achieve comprehensive monitoring of the assembly status of the fastening connection structure.

[0214] A variety of sensors are installed on the bolted connection structure to collect multi-source data such as ambient temperature and load changes in real time, and transmit them to the working condition display module. Through data processing and analysis, real-time monitoring of various working conditions of the connection system is achieved. The digital twin model calculates and displays the preload changes, contact stress and contact status of the mating surfaces of the bolts, and generates visual deformation cloud maps and stress cloud maps. Through stress analysis and preload calculation of the connection structure, the assembly accuracy of the connection system is judged, and assembly process decisions are automatically generated. This module provides real-time feedback on the assembly status based on the bolt strength and life standards to ensure the reliability and safety of the connection system. The visual interface of the assembly process of the fastening connection system is as follows: Figure 6 shown.

[0215] Based on the modified prediction model, a simulation prototype for visual evaluation and prediction of the assembly quality of the fastening connection system is designed and developed, and a real-time data association with the preload force measuring equipment is established to achieve real-time feedback of data information between the measuring equipment and the simulation prototype. This enables the prediction of the assembly quality and accuracy of the fastening connection system under different assembly processes, as well as feedback evaluation of the stress distribution of the fastening connection structure and the reliability level of the connection system.

[0216] The present invention provides an automatic optimization method for the assembly performance of a connection system based on digital twins. Based on the known service load and connection requirements, the optimal assembly preload and tightening torque of the bolts are automatically designed. The actual assembly preload data of the local intelligent bolts are combined to analyze the fluctuation range of the friction coefficient of the bolt group of the connection system, correct the preload-installation torque conversion relationship and prediction model, intelligently feedback the actual tightening torque and tightening process, and can realize the real-time display capability of parameters such as the contact state of the connection system joint surface, stress distribution, bolt contact stiffness, torsional stiffness, etc. according to sensor information such as preload data.

[0217] In summary, the present invention provides a method for automatically optimizing the assembly performance of a connection system based on digital twins. Therefore, it can be widely used in application fields such as the fields of digital twin technology, connection system assembly technology, and performance optimization technology. First, in the field of digital twin technology, the method provided by the present invention can realize real-time monitoring and feedback of the connection system, making the digital model more accurate and reliable. This will greatly promote the development of digital twin technology and make it more widely used in aerospace, automobile manufacturing, wind power and other fields. Secondly, in the field of connection system assembly technology, the method provided by the present invention can realize automatic optimization of the assembly preload and tightening torque of the connection system, greatly improving the assembly efficiency and quality. This will greatly promote the development of connection system assembly technology and make it more widely used in mechanical manufacturing, construction engineering, electronic equipment and other fields. Finally, in the field of performance optimization technology, the method provided by the present invention can realize automatic optimization of the assembly performance of the connection system and improve product performance and reliability. This will greatly promote the development of performance optimization technology and make it more widely used in manufacturing, automobile industry, aerospace and other fields. In general, the technical application prospects of the present invention are broad, the market demand is large, and it is expected to have a significant impact in many fields.

[0218] The above is only a specific implementation of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present disclosure should be included in the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be based on the protection scope of the claims.

Claims

1. A method for automatic optimization of assembly performance of a connection system based on digital twins, characterized in that: The method comprises the following steps: Obtain data on the bolt group of the connection system; set the target values ​​of the bolt assembly preload and tightening torque based on known service loads and connection requirements; A digital twin model is established based on the data, and according to the sensor information, the contact state of the connection system joint surface, stress distribution, bolt contact stiffness, and torsional stiffness are displayed in real time; Correcting a friction coefficient based on the digital twin model; The correction of the friction coefficient based on the digital twin model includes: calculating the theoretical assembly preload target value and assembly torque of the bolt based on the service load and connection requirements; predicting the preload distribution and connection performance of the bolt group of the connection system based on the actual monitoring data of the local bolts; calculating the correction value of the friction coefficient based on the actual monitored preload; recalculating the theoretical assembly torque based on the correction value of the friction coefficient until the error between the actual preload and the theoretical preload is less than a preset value; The method of predicting the preload force distribution and connection performance of the bolt group of the connection system based on the actual monitoring data of the local bolts includes: The axial deformation at node i1 after bolt i is tightened is calculated based on the following formula: in, is the axial deformation at node i1; F i is the initial preload of bolt i; is the axial stiffness at the bolted node; K iα It is the ability of node i1 to resist the elastic deformation of the interference fit when tightening bolt i. The node refers to the contact point between the end face of the bolt and nut and the end face of the connected structure. When the jth (j>i) bolt is tightened, the deformation coordination equation at node i1 is: in, is the change in deformation of node i1 after the jth bolt is tightened; Δδ bi,j is the change in deformation of the i-th bolt after the j-th (j>i)-th bolt is tightened; When the nth bolt is tightened, the residual preload of the i-th bolt is: F in =F i +ΔF i(i+1) +ΔF i(i+2) +…+ΔF in Among them, F in is the residual preload of the i-th bolt; F i is the initial preload of the bolt; ΔF i(i+1) is the change in residual preload of the i-th bolt after the i+1-th bolt is tightened; ΔF i(i+2) is the change in residual preload of the i-th bolt after the i+2-th bolt is tightened; ΔF in is the change of residual preload of the i-th bolt after the n-th bolt is tightened.

2. The method for automatic optimization of connection system assembly performance based on digital twin according to claim 1 is characterized in that: The data of the bolt group of the connection system includes: The contact state of the connection system joint surface, stress distribution, bolt contact stiffness, bolt preload data, torsional stiffness, tightening torque data, structural size data, service load and connection requirements.

3. The method for automatic optimization of connection system assembly performance based on digital twin according to claim 1 is characterized in that: The calculation of the theoretical assembly preload target value and assembly torque of the bolt based on the service load and connection requirements includes: The theoretical assembly torque of the bolt is calculated based on the following formula: M theoretical =F theoretical ·k theoretical ·d Among them, M theoretical is the theoretical assembly torque; F theoretical is the theoretical preload of the bolt; k theoretical ; d is the effective diameter of the bolt; The theoretical preload force F of the bolt is calculated based on the following formula: theoretical : F theoretical =max(F B1 ,F B2 ) Among them, F B1 F is the minimum assembly preload of the bolt; B2 is the maximum assembly preload of the bolt; The minimum bolt assembly preload F is calculated based on the following formula: B1 : Among them, F A K is the axial load on the connection structure; S F is the anti-slip coefficient; ∑Q0 is the transverse shear force of a single bolt; M is the bending moment of the connection structure; μ T is the friction coefficient of the joint surface; n is the number of joint surfaces; R is the distribution radius of the bolt; The maximum assembly preload force F of the bolt is calculated based on the following formula B2 Among them, R p0.2min is the yield strength of the fastener; A0 is the dangerous cross-sectional area of ​​the fastener; P is the pitch; ν is the stress utilization factor of the bolt yield point; μ G is the thread friction coefficient; d2 is the bolt median diameter; d0 is the bolt minor diameter.

4. The method for automatic optimization of connection system assembly performance based on digital twin according to claim 1, characterized in that: After the nth bolt is tightened, the preload force of the first n-1 bolts changes to: in, is the inverse of the compression stiffness of node 1; K iα The reciprocal of is the reciprocal of the stiffness of bolt 1; K 1(n-1) The reciprocal of K 1(n-1) is the ability of node 1 to resist the elastic deformation caused by the preload of bolt n-1; It's K (n-1)(n-1) The reciprocal of K (n-1)(n-1) is the ability of node n-1 to resist the elastic deformation caused by the preload of bolt n-1; It's K (n-1)α The reciprocal of K (n-1)α It indicates the ability of node n-1 to resist the elastic deformation of the interference fit when tightening bolt n-1; It's K b(n-1) The reciprocal of K b(n-1) Indicates the stiffness of bolt n-1; ΔF 1n is the change in the preload force of bolt 1 after tightening bolt n; ΔF (n-1)n is the change in the preload force of bolt n-1 after tightening bolt n; K 1n The reciprocal of K 1n It represents the ability of node 1 to resist the elastic deformation caused by the preload of bolt n; K (n-1)n The reciprocal of K (n-1)n It represents the ability of node n-1 to resist the elastic deformation caused by the preload of bolt n; F n is the initial preload force of bolt n.

5. The method for automatic optimization of connection system assembly performance based on digital twin according to claim 1, characterized in that: The correction value of the friction coefficient is calculated based on the actual monitored preload force, including: The preload error is calculated based on the following formula: Where, ΔF (i) is the preload error of the i-th bolt; is the actual measured preload force of the ith bolt; F theoretical is the theoretical preload; M theoretical is the theoretical assembly torque; is the actual friction coefficient of the i-th bolt; k theoretical is the theoretical friction coefficient; d is the effective diameter of the bolt; The average corrected friction coefficient k is calculated based on the following formula corrected : Where, m is the number of bolts tested; is the actual friction coefficient of the i-th bolt; The relationship between bolt preload and assembly torque is calculated based on the following formula: M corrected =k corrected ·F theoretical ·d Among them, M corrected is the corrected assembly torque; k corrected is the average modified friction coefficient; F theoretical is the theoretical preload of the bolt; d is the effective diameter of the bolt.

6. The method for automatic optimization of connection system assembly performance based on digital twin according to claim 1, characterized in that: Recalculating the theoretical assembly torque based on the corrected value of the friction coefficient until the error between the actual preload force and the theoretical preload force is less than a preset value, including: Correction is based on the following formula: Where, ΔF new is the preload error updated during the iteration process; is the actual measured value of the nth preload force; F theoretical is the theoretical preload of the bolt; When ΔF new When ≤ε, the iteration stops; ε is the set error accuracy threshold.

7. The method for automatic optimization of connection system assembly performance based on digital twin according to claim 1 is characterized in that: The establishing of a digital twin model based on the data comprises: Establish the correlation between the axial stiffness and torsional stiffness of the local position according to the bolt preload data, tightening torque data and structural dimension data during the assembly process; Based on the deep learning algorithm, the tightening friction coefficient is corrected according to the local data to obtain the corrected axial stiffness variation range; The overall stiffness matrix of the connection structure is corrected according to the smart bolt data at different positions to obtain the digital twin model of assembly performance. The installation sequence and order of the bolts are automatically selected based on the measured preload force data of local bolts.

8. The method for automatic optimization of connection system assembly performance based on digital twin according to claim 7, characterized in that: The automatic selection of the bolt installation sequence and installation order based on the measured data of the preload force of the local bolts includes: Calculate the preload error of all bolts, thus reflecting the friction coefficient fluctuation value of all bolts; Arrange the bolt installation sequence from high to low according to the stability of the friction coefficient; the smaller the fluctuation of the friction coefficient, the higher its stability.

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