In-situ monitoring method and system for structural pre-tightening assembly state

Abstract

This invention provides an in-situ monitoring method and system for structural pre-tightening assembly state, relating to the field of structural pre-tightening force assembly state monitoring technology. Specifically, it includes the following steps: acquiring information about the target structure and detection conditions, constructing a unified structural state description vector, and establishing a three-dimensional digital model of the target; based on the structural state description vector, conducting preliminary adaptive design of the detection system and outputting the system design parameters; this method utilizes laser transmission in space to achieve in-situ detection. After acquiring the dynamic interferogram sequence of the pre-tightening process and extracting the surface structural feature vector, it directly uses an evaluation model to judge the pre-tightening quality online; this solves the problem of detection lag and inability to promptly feedback and adjust assembly process parameters in the current production practice of monitoring pre-tightening assembly quality, achieving efficient monitoring of the structural pre-tightening assembly state.

Description

Technical Field

[0001] This invention relates to the field of structural preload assembly state monitoring technology, specifically to an in-situ monitoring method and system for structural preload assembly state. Background Technology

[0002] Precision assembly is a core process in modern high-end manufacturing, widely used in strategic industries such as aerospace, precision optical instruments, microelectronic packaging, and new energy vehicles. Assembly quality directly determines the final performance, service life, and reliability of a product. For precision structural components using pre-tightening methods such as threaded connections, interference fits, and adhesive bonding, the distribution and contact state of the pre-tightening force formed during assembly are key factors affecting their mechanical behavior. Excessive pre-tightening force may lead to structural damage or localized yielding, while insufficient pre-tightening force may cause loose connections, fatigue failure, or even catastrophic accidents. Therefore, achieving online quality detection and precise control of the pre-tightening assembly process is of paramount engineering significance for ensuring the manufacturing precision and long-term stable operation of high-end equipment.

[0003] Currently, monitoring the quality of pre-tightened assembly in production practice mainly relies on two methods: indirect parameter control and offline sampling inspection. On the one hand, widely used process control methods such as torque method and torque-angle method indirectly infer the connection status by monitoring the output parameters of assembly tools. They cannot directly perceive the stress and deformation state of the assembly, and it is difficult to detect abnormal pre-tightening force or off-center loading caused by factors such as fluctuations in friction coefficient, differences in the micro-morphology of mating surfaces, and the introduction of foreign objects. On the other hand, offline sampling inspection methods after assembly, such as X-ray inspection and ultrasonic scanning, are not only inefficient and unable to achieve full inspection, but also have detection lag, failing to provide timely feedback and adjustment of assembly process parameters, which can easily lead to batch quality risks. In addition, traditional inspection methods usually require transferring the workpiece to a dedicated inspection station. If the inspection fails, it needs to be returned to the assembly station for reassembly. This non-in-situ quality control method disrupts the continuity of the production process, and most of them use contact measurement, which is difficult to meet the urgent needs of modern production lines for online in-situ inspection.

[0004] Therefore, how to stably monitor the actual pre-tightening state of the assembly structure in a complex production environment is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an in-situ monitoring method and system for pre-tightened assembly, which solves the problems in current production practice regarding the monitoring of pre-tightened assembly quality, such as detection lag, inability to provide timely feedback and adjust assembly process parameters, and difficulty in detecting abnormal pre-tightening force or off-center loading caused by factors such as fluctuations in friction coefficient, differences in the microstructure of mating surfaces, and the introduction of foreign objects.

[0006] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: In the first aspect of this invention, an in-situ monitoring method for pre-tightened assembly of a structure specifically includes the following steps: Acquire the structural information and detection condition information of the target under test, construct a unified structural state description vector, and establish a three-dimensional digital model of the target under test; Based on the structural state description vector, a preliminary adaptive design of the detection system is carried out, and the detection system design parameters are output; based on the detection system design parameters, a detection system adaptability evaluation function is constructed. The three-dimensional digital model of the target to be tested is imported into the finite element analysis software to construct the finite element analysis model of the target to be tested; Based on the results of finite element analysis, an evaluation model for pre-tightened state is established; Acquire phase images of the structure under test during the pre-tightening process; According to the input parameter type of the pre-tightening state evaluation model, the micro-deformation RMS value of the structure under test is extracted from the phase image to obtain the pre-tightening state of the target structure.

[0007] Secondly, an in-situ monitoring system for structural pre-tightened assembly states includes: The modeling and parameter characterization module is used to acquire structural information and detection condition information of the target under test, construct a unified structural state description vector, and establish a three-dimensional digital model of the target under test. The detection system adaptive design module is connected to the modeling and parameter characterization module, and is used to perform preliminary adaptive design of the detection system based on the structural state description vector, and output the detection system design parameters. The adaptability evaluation module, connected to the adaptive design module of the detection system, is used to construct a detection system adaptability evaluation function based on the design parameters of the detection system. The finite element simulation analysis module is connected to the modeling and parameter characterization module. It is used to import the three-dimensional digital model of the target under test, establish the finite element analysis model of the target under test, carry out finite element analysis, and output the finite element analysis results. The pre-tightening state modeling module is connected to the finite element simulation analysis module and is used to construct a pre-tightening state evaluation model based on the finite element analysis results. The image acquisition module is used to acquire structural phase images of the target under test during the pre-tightening process; The state solution module is connected to the pre-tightening state modeling module and the image acquisition module, respectively. It is used to extract the micro-deformation RMS value of the structure under test from the acquired phase image according to the input parameter type of the pre-tightening state evaluation model, and solve for the pre-tightening state of the target structure.

[0008] Thirdly, a computer-readable storage medium stores a computer program for an in-situ monitoring system for a pre-tightened assembly state of a structure, wherein the computer program causes a computer to execute an in-situ monitoring method for a pre-tightened assembly state of a structure.

[0009] Fourthly, an electronic device comprising: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing in-situ monitoring of pre-tightened assembly states of structures.

[0010] (III) Beneficial Effects This invention provides an in-situ monitoring method and system for structural pre-tightened assembly. Compared with existing technologies, it has the following advantages: This method utilizes laser transmission in space to achieve in-situ detection. After acquiring the dynamic interferogram sequence of the pre-tightening process and extracting the surface structural feature vector, the pre-tightening quality is directly judged online using the evaluation model. This solves the problems of detection lag, inability to timely feedback and adjust assembly process parameters, and difficulty in detecting abnormal pre-tightening force or off-center loading caused by factors such as friction coefficient fluctuations, micro-morphological differences of mating surfaces, and foreign object intervention in the current production practice of monitoring pre-tightening assembly quality. This method achieves efficient monitoring of the pre-tightening assembly status of the structure. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0012] Figure 1 This is a block diagram of the in-situ monitoring method for pre-tightened assembly state of the present invention; Figure 2 This is a schematic flowchart of an in-situ monitoring method for pre-tightened assembly state according to an embodiment of the present invention; Figure 3 This is a diagram of the target model to be tested in an embodiment of the present invention; Figure 4 This is a schematic diagram of a single-channel shear speckle interference optical path; Figure 5 This is a schematic diagram of a dual-channel shear speckle interference optical path; Figure 6 The results of finite element analysis of the target under test in an embodiment of the present invention; Figure 7 Error analysis of the finite element model of the target under test in an embodiment of the present invention; Figure 8 This is the strain analysis result of the sensitive region of the target under test when the measured value is 0.4 N·m, according to an embodiment of the present invention. Figure 9 The above is the intensity map result of the target under test in an embodiment of the present invention; Figure 10 The phase extraction result of the target under test in an embodiment of the present invention; Figure 11 The strain distribution results of the target under test in an embodiment of the present invention are shown. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] This application provides an in-situ monitoring method and system for pre-tightened assembly, which solves the problems of detection lag, inability to timely feedback and adjustment of assembly process parameters, and difficulty in detecting abnormal pre-tightening force or off-center loading caused by factors such as friction coefficient fluctuations, differences in the micro-morphology of mating surfaces, and foreign object intervention in the current production practice of monitoring the quality of pre-tightened assembly. It achieves efficient monitoring of the pre-tightened assembly state of the structure.

[0015] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0016] like Figure 1 As shown, an in-situ monitoring method for structural pre-tightened assembly states specifically includes the following steps: S1. Obtain the structure and detection conditions of the target under test, construct a unified structural state description vector, and establish a three-dimensional digital model of the target under test. The unified structural state description vector is constructed as follows: In step S1, the constructed structural state description vector is as follows: This includes the geometric parameters of the object under test; in, L : Feature length; W :width; H Thickness or height; κ : Structural curvature; ξ : Pre-tightening assembly interface morphology parameters; This includes information about the material of the object being tested; in, : Elastic modulus; υ Poisson's ratio; σ y Yield strength; This includes the optical parameters of the surface of the object under test; in, ρ Surface reflectivity; R a Surface roughness; This includes assembly working condition and environmental information; in, T Ambient temperature; h Ambient humidity; α v Vibration frequency; This includes assembly process parameters; in: ω : Tightening speed; Π: Pre-tightening sequence; M t Preload torque.

[0017] In summary, it can be seen that obtaining the target structure and testing condition information in step S1 is to obtain the basic information and environmental information of the structure to be pre-tightened, specifically including geometric parameters: dimensions, pre-tightening assembly interface morphology parameters; material parameters: elastic modulus, Poisson's ratio, yield strength; surface optical parameters: reflectivity, roughness; environmental parameters: temperature, humidity, vibration; and initial values ​​of process parameters: tightening speed, pre-tightening sequence, and pre-tightening torque.

[0018] S2. Based on the structural state description vector obtained in step S1 X The detection system is initially designed adaptively, and the design parameters of the detection system are output. Based on the design parameters of the detection system, a detection system adaptability evaluation function is constructed; The design parameters for the output detection system are as follows: in, l Laser speckle detection scheme; λ: laser wavelength;P Laser power; d Working distance; θ Imaging field of view; ψ Laser beam expansion angle; δ Spatial resolution; f : Focal length of the imaging lens; s: Shearing amount; The mapping relationship between the design scheme of the detection system and the state of the structure under test is established as follows: It should be noted that the design parameters of the output detection system satisfy the following basic physical and geometric constraints: 1. Laser wavelength λ Based on the surface reflectivity of the object to be measured ρ Considering the ambient noise level, the optimal laser wavelength λ is selected to maximize the system's signal-to-noise ratio. ; in, The laser wavelength at which the system signal-to-noise ratio is maximized; For the optimization function; For CCD quantum efficiency; Optical transmittance; This represents the system's background noise. This is environmental noise.

[0019] 2. Laser power P To ensure uniformity and brightness of the speckle field illumination, considering speckle contrast, irradiance, and CCD saturation limitations, the laser power... P Should meet: ; in, D The diameter of the laser speckle field; Minimum average irradiance of the target surface to meet the signal-to-noise ratio requirements of speckle imaging.

[0020] 3. Imaging field of view θ To ensure that the feature region of the target object is completely within the detection field of view of the speckle interferometry system, the imaging field of view must cover the maximum feature size of the object. ;in, d This refers to the working distance.

[0021] 4. Laser beam expansion angle ψ Considering that laser light sources typically exhibit a Gaussian beam distribution with weaker edge intensity, to ensure the contrast and signal-to-noise ratio of the interference fringes across the entire field, the expanded laser beam spot must completely cover and be larger than the imaging field of view. ; in, k 1 is the margin factor, used to compensate for energy attenuation at the edge of the Gaussian beam.

[0022] 5. Spatial resolution δ To cover the region of the feature structure to be measured and ensure measurement accuracy, the spatial resolution must meet the following requirements: ; in Pixel size; This refers to the imaging magnification. To meet the spatial resolution requirements of the measurement task, a selection is typically made. , It is the smallest feature size that needs to be detected in the target object, used to characterize the scale of the smallest structural feature or the smallest effective deformation region in the object being detected.

[0023] 6. Shear quantity s: In a shear interferometry system, to ensure that the detection sensitivity matches the degree of micro-deformation of the structure under test, the shear quantity s must theoretically meet the basic control range. ; in, This represents the minimum detectable strain by the system. In actual operation, the specific value of the shearing amount is dynamically adjusted within the above range, with the optimal setting being the ability to observe clear and identifiable phase-wrapped interference fringes.

[0024] In practical applications, the selection of the laser speckle detection scheme in step S2 is as follows: Laser speckle interferometry uses coherent laser light to illuminate a rough surface, generating a random speckle field. By shearing an interference optical path, spatially displaced images of the same speckle field are subjected to self-interference, thus enabling the measurement of displacement gradients and exhibiting strong resistance to environmental interference. Let the object's optical recovery amplitude be: ;in For the optical recovery amplitude; The amplitude of the object light; i The imaginary unit; The phase of the object light.

[0025] The reference optical recovery amplitude is: ;in, For reference optical recovery amplitude; The reference optical amplitude; i The imaginary unit; The phase difference resulting from the introduction of shearing in an optical system.

[0026] The interference light intensity at the CCD target surface before deformation is: ;in As background light, For modulation terms; The interference light intensity at the CCD target surface after deformation is: ;in, This represents the phase increment of the interference light at the CCD target surface caused by deformation; This represents the displacement gradient along the shear direction; By utilizing the equal-step spatial phase shift technique, an unknown equal-step phase increment is actively introduced into the interferometric optical path. α Four equal-step phase-shift interferograms were acquired, and the phase increments before and after deformation were calculated based on the equal-step phase-shift algorithm. : This method has the ability to resist environmental interference. If environmental vibration interference exists, the introduced phase disturbance is denoted as... ,but: Physical light: Reference light: Phase difference: in, , To add perturbation to the phase distribution of the object beam and reference beam; , To add perturbation to the phase distribution of the object beam and reference beam As a result, the phase error caused by environmental vibration is eliminated.

[0027] It should be noted that this solution determines the detection system hardware based on the optical system parameters, including illumination components, detection components, image acquisition components, electrical control and image processing components, etc. The parameters of each hardware are matched with the designed optical system parameters, and each hardware can be selected from existing hardware of the optical detection system, all of which are existing technologies, and will not be elaborated here.

[0028] Specifically, based on the initial detection system parameters, a detection system fit evaluation function is constructed as follows: A comprehensive evaluation of the detection system's structural coverage, measurement sensitivity, image quality, and data processing capabilities was conducted. ; is a weighting constant, with a value range of [0,1); To improve the overall compatibility of the detection system; For spatial resolution adaptation; Field of view coverage adaptation; For lighting quality adaptation; To detect sensitivity fit; To adapt to data acquisition and processing capabilities; And the optimization goal is to maximize the fit: The detection system parameters are iteratively optimized to output design parameters that meet the adaptability requirements.

[0029] S3. Import the three-dimensional digital model of the target to be tested into the finite element analysis software to construct the finite element analysis model of the target to be tested; Based on the results of finite element analysis, an evaluation model for pre-tightened state is established; The finite element analysis model is as follows: The finite element analysis results under different process parameters and environmental conditions are output, forming the surface region structure feature vector F, which can be expressed as: ; in, For the strain field on the structural surface, For displacement field, This represents the stress field.

[0030] Specifically, key points on the measurement surface are selected, and physical sensors such as force sensors, displacement sensors, and strain gauges are used to measure the force, displacement, and strain data at these key points under the simulated load conditions in finite element simulation. ;in, This represents the sensor measurement results at different locations; Simultaneously extract simulation analysis results for corresponding points in the finite element analysis model: ;in, Represents the finite element simulation results at different points; The simulation error is obtained by comparing the finite element simulation results with the actual sensor measurement results. ; If the error e < the standard value e0, the confidence level of the finite element simulation model meets the standard; if the error e ≥ the standard value e0, the finite element analysis model is corrected to achieve the optimization of the finite element analysis model. Based on the finite element analysis results, prior knowledge for online monitoring of structural preload is established, and information on surface micro-deformation during the structural preload process is obtained, thus establishing a prior knowledge base based on finite element analysis. ; in This is the optimal detection area; The results show the domain structure characteristics under standard process parameters; This is a reference evaluation value for the pre-tightened state; These correspond to the standard process parameters.

[0031] The multi-factor coupled pre-tightening state evaluation model is constructed as follows: By coupling simulation analysis of factors such as errors, assembly environment conditions, assembly process parameters, and optical system parameters, and combining the surface region structural feature vectors in the prior knowledge base of finite element analysis, a pre-tightening state evaluation model is constructed: Where Y represents the various factors affecting the pre-tightening state of the structure; F' represents the structural surface region feature extraction result during the pre-tightening process; and Q is a weight matrix constructed considering the interaction of various factors. The weights of different factors can be adaptively adjusted for different pre-tightening scenarios to improve the applicability of the evaluation model.

[0032] Based on the structural assembly accuracy and by coupling multiple factors, the pre-tightening state reference range JS is determined as the basis for in-situ monitoring of the structural pre-process.

[0033] S4. Acquire phase images of the structure under test during the pre-tightening process; According to the input parameter type of the pre-tightening state evaluation model, the micro-deformation RMS value of the structure under test is extracted from the phase image to obtain the pre-tightening state of the target structure. The specific acquisition and processing of speckle sequence images during the pre-tightening process in the target monitoring process is as follows: 1. Acquiring Intensity Images: During the pre-tightening process of the structure under test, laser speckle interferometry is performed, and an optical detection system is used to acquire a sequence of interferometric images in real time. ,in, t For the pre-tightening process; I CCD interferogram intensity distribution; obtaining dynamically changing image sequences: ; 2. Obtaining phase images: Using the equal-step phase shift phase extraction algorithm, four consecutive intensity images are grouped together to extract a phase wrapping image. The wrapping image is then unwrapped based on the least squares method to obtain a continuous phase image. 3. Key Feature Extraction: Based on the phase values ​​in the phase diagram, the micro-deformation state of the pre-tightened structure is obtained, and the strain, displacement field, and stress field of the surface to be measured are obtained. , ; Extracting the feature vector of the surface region structure: ; Finally, the pre-tightening quality is judged according to the pre-tightening state evaluation model. If JS1≤J´≤JS2, the assembly is qualified, where J´ is the model evaluation result after pre-tightening, and JS1 and JS2 are the pre-tightening state reference ranges, respectively. Otherwise, the pre-tightening state is compensated, the environmental conditions and optical system parameters are optimized, and the assembly process parameters are updated and iterated until the pre-tightening state is qualified.

[0034] Example 1, the process is as follows Figure 2 As shown, the details are as follows: S1: Acquisition of information on the target structure and testing conditions; In this embodiment, the target to be tested is a circular flange connection structure, such as... Figure 3 As shown, the outer diameter is 125mm, the material is 6061 aluminum alloy, the structure is fixed and constrained around the perimeter, and the central area of ​​the back is pre-tightened by bolts. The designed pre-tightening torque is 0.4N·m. A digital three-dimensional model of the structure under test is established using three-dimensional modeling software as the subsequent structural mechanics analysis model. Based on the structural design drawings and material parameters, the geometric dimensions, material mechanical properties, and assembly condition information of the structure under test are obtained, including: Geometric parameters are expressed as ,in, R =125mm, which is the radius of the circular part to be measured; H =2mm, which is the structural thickness; W =6mm, which is the diameter of the effective contact area; k =8, which represents the number of effective contact areas; Material parameters are expressed as follows In this embodiment =68.9 GPa, which is the material's elastic modulus; ν =0.33, which is the Poisson's ratio of the material; The optical parameters of the surface under test are obtained using a surface roughness meter and reflectivity testing equipment and are expressed as follows: ,in, ρ =88%, which is the surface reflectance; R a =1.7μm, which is the surface roughness; Environmental information is collected and monitored using environmental monitoring devices, and is represented as follows: ,in, T =24±2℃, which is the ambient temperature; h =45±3%RH, which is the ambient humidity; α v =10Hz~50Hz, which is the ambient vibration frequency; Set the initial process parameters according to the assembly process requirements: Where ω = 1 rad / s, is the preload speed; M t =0.4 N•m, which is the preload torque; In summary, a unified structural state description vector is constructed: It serves as a unified input for subsequent adaptive design of the detection system, establishment of the finite element analysis model, and evaluation of the pre-tightening state.

[0035] S2. Based on the structural state description vector obtained in step S1 X The detection system is initially designed adaptively, and the design parameters of the detection system are output. Based on the design parameters of the detection system, a detection system adaptability evaluation function is constructed; The specific design of the detection system is as follows: Including the selection of testing solutions: In this embodiment, the target under test is an axisymmetric circular flange structure with a large overall size. The pre-tightening area is located at the center of the structure, and the pre-tightening process mainly produces small, axisymmetrically distributed deformations and changes in surface strain gradient. Since laser shear speckle interferometry can directly measure the surface displacement gradient and strain distribution of an object, and has the characteristics of strong resistance to environmental vibration and full-field measurement, this embodiment uses a simplified single-channel shear speckle interferometry optical path, given that the target is a circular symmetrical structure. Figure 4 As shown; The initial parameters of the detection system are designed as follows: Based on the obtained structural state description vector, a preliminary adaptive design of the detection system is performed, and the design parameters of the detection system are output. In this embodiment, the specific details are as follows: (1) Working distance: The working distance is determined based on the target size and installation space. d =600mm; (2) Laser wavelength: Based on the surface reflectivity ρ of the object to be measured and the ambient noise level, the laser wavelength λ is selected to maximize the system signal-to-noise ratio: In this embodiment, a laser with a wavelength of 532nm is selected based on the principle of maximizing the signal-to-noise ratio.

[0036] (3) Laser power: To ensure the uniformity and brightness of the speckle field illumination, considering speckle contrast, irradiance, and CCD saturation limitations, the laser power is... P Should meet: Based on the target size and surface reflectivity in this embodiment, a laser power of 200mW is selected.

[0037] (4) Imaging field of view: To ensure that the target can completely cover the camera's field of view, it should meet the following requirements. In this embodiment, the calculation is obtained θ For angles ≥23.5°, an imaging system with a field of view of approximately 24° should be selected.

[0038] (5) Laser beam expansion angle: To ensure the contrast and signal-to-noise ratio of the interference fringes across the entire field, the laser beam expansion spot must completely cover and be larger than the imaging field of view. Selecting the margin factor k 1 is 1.2, and the laser beam expansion angle ψ is selected to be approximately 29°.

[0039] (6) Spatial resolution: The spatial resolution must meet the following requirements: ,in Pixel size; This refers to the imaging magnification. To meet spatial resolution requirements, typically select , The smallest feature size to be detected in the target is used to characterize the scale of the smallest structural feature or the smallest effective deformation area in the object being detected. In this embodiment, the camera pixel size is 2.74 μm.

[0040] (7) Shear quantity: The shear quantity s should theoretically meet the basic control range. ,in To ensure the system can detect the minimum strain, the shear amount of the optical system in this embodiment is selected as 5mm.

[0041] Hardware design of the detection system: (1) Lighting component 1: The illumination assembly includes a laser, a fiber collimator, a beam expander assembly, and a beam expansion adjustment mechanism. The laser outputs a continuous laser beam with a wavelength of 532nm and a power of 200mW, which is collimated by the fiber collimator before entering the beam expander assembly. The beam expander assembly employs a Galilean beam expander structure, consisting of a 3mm focal length positive lens and a focal length... Composed of 19mm negative lenses, it achieves beam expansion illumination of >29°. The expanded laser beam is then directed onto the area to be measured via a universal ball joint adjustment mechanism, forming a uniform speckle field on the structural surface.

[0042] (2) Detection component 2: like Figure 4 As shown, the single-channel shear speckle interferometric optical route consists of an illumination component, a detection component, an image acquisition component, and an electrical control component. After the laser speckle on the surface of the target under test enters the detection component, it is split into an object beam (reflected) and a reference beam (transmitted) after passing through the first beam splitter. The object beam forms a first reflected beam after passing through the object beam mirror, and the reference beam forms a second reflected beam after passing through the reference beam mirror. The first and second reflected beams re-enter the first beam splitter and are combined, and then received by the imaging system.

[0043] A dual-channel shear speckle interference optical path can be used depending on the shape, size, detection accuracy, and strain direction of the object being detected, such as... Figure 5 As shown, the detection assembly includes an object light reflector I, an object light reflector II, a reference light reflector I, a piezoelectric ceramic component, a first beam splitter, and a second beam splitter. Object light reflectors I and II are coated with dichroic films. Object light reflector I reflects laser light of wavelength I and transmits laser light of wavelength II; object light reflector II reflects laser light of wavelength II and transmits laser light of wavelength I. The coating parameters on the reflector surfaces are matched to the wavelengths of the dual-band laser generated by the laser. The object light, after being reflected by the object light reflector, re-enters the first beam splitter and then transmits to the second beam splitter. The reference light, after being reflected by the reference light reflector, re-enters the first beam splitter and then reflects to the second beam splitter, interfering with the object light to form a dual-channel sheared speckle interference optical path.

[0044] The reference mirror is mounted on a piezoelectric ceramic actuator, which performs a four-step phase shift according to the Carre phase shift method, with each step displacement of 78nm, to introduce a constant-step phase change for the reference light.

[0045] (3) Image acquisition component 3: The image acquisition component includes a 100mm focal length, F3.5 imaging lens, a 2800×28008 pixel CMOS camera, and an image acquisition card. The camera has a pixel size of 2.74μm, a maximum acquisition frame rate of 100 fps, and uses a global shutter and an RJ45 gigabit network interface to achieve high-speed speckle image acquisition.

[0046] (4) Electrical control and image processing: The electrical control unit uses an industrial computer as its control core, equipped with a 20-core processor, 128GB of memory, and a high-performance GPU to achieve real-time processing of speckle images. Image processing employs the Carre four-step phase-shifting algorithm for phase extraction, utilizes a 7×7 sine and cosine mean filter to suppress speckle noise, and employs a least-squares phase unwrapping algorithm to restore the continuous phase distribution, thereby obtaining micro-deformation feature information of the structural surface domain.

[0047] The system adaptation evaluation function is as follows: Based on the initial detection system parameters, a detection system fit evaluation function is constructed to comprehensively evaluate the detection system's structural coverage capability, measurement sensitivity, image quality, and data processing capability. ; in, is a weighting constant, with a value range of [0,1); To improve the overall compatibility of the detection system; For spatial resolution adaptation; Field of view coverage adaptation; For lighting quality adaptation; To detect sensitivity fit; To optimize for the data acquisition and processing capabilities, and with maximizing the optimization rate as the target: The parameters of the detection system are iteratively optimized.

[0048] In this embodiment, the evaluation functions are as follows: ; ; ; ; ; in, To detect the spatial resolution of the system, R represents the target spatial resolution required by the mission; R represents the actual field of view coverage of the detection system. The required equivalent field of view; Average image gray level / average light intensity of the detection area The desired average gray level or target light intensity. The maximum gray level / light intensity allowed for the image. The minimum gray level / light intensity allowed for the image; The minimum strain that the system can detect. The minimum strain required by the task; This represents the actual data processing rate. This refers to the actual data acquisition rate of the detection system.

[0049] In this embodiment, based on the requirement for full-field micro-deformation detection of the pre-tightened structure, the weights of spatial resolution and measurement sensitivity indicators are increased: After evaluation and optimization of the detection system's compatibility, the following parameters were determined: Laser wavelength: λ =532nm; Laser power: P =200mW; Working distance: d =600mm; Imaging focal length: f =100mm; Imaging field of view: θ =24°; Laser beam expander angle: ψ =29°; Shear rate: s =5mm; The above parameters meet the requirements of the structure to be tested size, micro-strain detection accuracy and real-time data processing.

[0050] S3. Import the three-dimensional digital model of the target to be tested into the finite element analysis software to construct the finite element analysis model of the target to be tested; Based on the results of finite element analysis, an evaluation model for pre-tightened state is established; The finite element model established in this embodiment is as follows: Based on the three-dimensional digital model of the target object established in S1, the model is imported into the finite element analysis software to establish a finite element model of the structure under test. According to the material properties, geometric features, and assembly boundary conditions of the structure under test, material values, mesh generation, and boundary constraint settings are performed on the model.

[0051] In this embodiment, the object under test is a circular flange connection structure. Fixed constraints are set around the structure, and a bolt preload is applied to the central region of the back. The bolt preload torque is set to 0.4 N·m. The stress, strain, and displacement changes of the structure during the preload process are simulated using a contact nonlinear analysis method to obtain the full-field mechanical response of the structure surface during the preload process.

[0052] Establish a finite element model and obtain its mapping relationship with the structural state parameters: Finite element output results as follows Figure 6 As shown, it can be represented as: ,in, For the strain field on the structural surface, For displacement field, This represents the stress field.

[0053] The finite element model optimization is as follows: A finite-point experiment was conducted using displacement and force sensors. Based on the finite element analysis results, typical measuring points were selected in areas with significant structural deformation to measure displacement and preload, and the results were compared with the finite element calculation results. In this embodiment, the finite element analysis error needs to be controlled within 10%. Error analysis results are as follows Figure 7 As shown, the finite element analysis model error in this embodiment meets the requirements, and the finite element model can accurately reflect the actual stress state of the structure, which can serve as the theoretical basis for subsequent structural surface region analysis and online monitoring. The establishment of the finite element prior knowledge base is as follows: After verifying the finite element model, the validated finite element model was used to obtain the full-field mechanical response information of the structure, and the surface structural features related to the pre-tightening state were extracted to provide prior knowledge for subsequent online monitoring. Pre-tightening assembly prior knowledge base: ; in This is the optimal detection area; The results show the domain structure characteristics under standard process parameters; This is a reference evaluation value for the pre-tightened state; For corresponding standard process parameters; The construction of the multi-factor coupled pre-tightening state evaluation model is as follows: In this embodiment, based on the finite element analysis results, the region with a diameter of approximately 200 mm at the center of the structure is the sensitive area for preload, where strain changes are most pronounced. Therefore, this region is designated as the target detection area for the laser shear speckle interferometry system. During the preload state evaluation process, considering the combined influence of multiple factors on the structural response, a multi-factor coupled evaluation model is established: A subjective weighting model was established based on the analytic hierarchy process, and the influencing factors of the pre-tightening state were divided into the target layer, the criterion layer and the indicator layer. The target layer represents the pre-tightening state of the structure; the criterion layer includes assembly process factors, environmental impact factors, detection system factors, and model prediction factors; the indicator layer corresponds to specific parameters, establishes a pairwise comparison judgment matrix of factors through expert experience, calculates the maximum eigenvalue, and obtains the weight of each influencing factor. Based on the weight calculation results, the assembly process parameters were determined to have the highest influence on the pre-tightening state of the structure and were therefore the main control factors. At the same time, combined with the surface structural features obtained by S4, the out-of-surface strain RMS value within the target detection area was determined to be the most sensitive state evaluation index.

[0054] Therefore, in engineering applications, the evaluation model is dimensionality-reduced to establish a pre-tightening state evaluation model that takes assembly process parameters as input and the RMS value of strain in the monitoring area as output. This model establishes a mapping relationship between assembly process parameters and the actual pre-tightening state of the structure, and provides a basis for evaluating online detection results and optimizing process parameters in subsequent steps.

[0055] In this embodiment, when the bolt preload torque is 0.4 N·m, the finite element analysis yields a strain RMS of 39.5 με within the target detection area. Figure 8 As shown. Based on the finite element calculation error and model verification results, the RMS reference range of strain in the target region under normal assembly conditions is determined to be 35.5με~43.5με through uncertainty analysis.

[0056] S4. Acquire phase images of the structure under test during the pre-tightening process; According to the input parameter type of the pre-tightening state evaluation model, the micro-deformation RMS value of the structure under test is extracted from the phase image to obtain the pre-tightening state of the target structure. The specific details of speckle sequence image acquisition and processing during the pre-tightening process are as follows: Pre-tightening process speckle sequence image acquisition includes intensity image acquisition: The detection system acquires speckle intensity images of the target detection area to form an intensity image sequence. According to the phase extraction algorithm, every four adjacent intensity images are grouped together for phase extraction.

[0057] In this embodiment, a set of intensity image acquisition results are as follows: Figure 9 As shown.

[0058] Phase image acquisition: The Carre phase shift algorithm is used to calculate and store the surface distribution of the test object. The phase distribution in the initial preload state is used as a reference, and a differential calculation is performed with the subsequent phase map to obtain the phase difference caused by the micro-deformation of the structure after the preload is applied. Figure 10 As shown, a continuous phase diagram is obtained after unwrapping. The continuous phase has the following relationship with the out-of-plane strain on the surface of the structure under test: Feature extraction results: Based on the input parameter type of the pre-tightening state evaluation model, the RMS value of the micro-deformation of the structure under test is extracted based on the phase diagram for pre-tightening state evaluation, thereby obtaining the pre-tightening state of the structure.

[0059] More specifically, the pre-tensioning state evaluation and closed-loop compensation are as follows: After the structure under test enters the assembly station and completes its initial positioning, the control system sends a detection trigger signal to the laser shear speckle interferometry detection system. The system executes step S6 to acquire the initial speckle phase image as a reference. Then, the actuator moves, and the system repeats step S6 to continuously acquire and output the pre-tightening state of the structure during the application of pre-tightening force. There are three states: 1) The preset process parameters have not been reached, and the pre-tightening process continues; 2) The preset process parameters have been reached, the pre-tightening state is qualified, and the assembly ends; 3) The preset process parameters have been reached, the pre-tightening state is unqualified, and the closed-loop control process is executed to control the actuator to load or unload, and adjust the pre-tightening speed, holding time, and other process parameters until the assembly is qualified.

[0060] In this embodiment, when the preset process parameters are reached, the strain distribution on the surface to be measured is as follows: Figure 11 As shown, the effective area strain RMS value is 38.6με at this time, the pre-tightening state evaluation is qualified, and the pre-tightening assembly is completed.

[0061] In summary, compared with existing technologies, it has the following beneficial effects: 1. This invention overcomes the limitations of fixed parameters in traditional testing equipment. By constructing a unified structural state description vector encompassing geometric parameters, material information, surface optical parameters, and environmental conditions, it adaptively derives the optimal design parameters for the optical testing system. A testing system adaptability evaluation function is constructed to verify the adaptability of all hardware and software parameters, including spatial dimensions, accuracy, data transmission speed, and image acquisition frame rate, ensuring that the testing system design parameters match the hardware parameters. Using the best testing adaptability as the optimization objective, the optimal hardware and software parameters are obtained through a multi-objective optimization method; thus achieving adaptive optimization and highly robust online in-situ monitoring of the optical testing system.

[0062] 2. This method establishes a multi-physics field co-simulation finite element model, and introduces force, displacement, and strain data measured by physical sensors to compare errors and verify the confidence level of the finite element simulation model. Furthermore, based on the high-confidence finite element analysis results, theoretical surface domain reference information can be obtained in advance, thereby locking in the optimal area and shear direction for online in-situ monitoring; this avoids the blindness of optical feature extraction and improves the accuracy of strain and displacement field analysis in complex structures.

[0063] 3. This invention constructs a multi-factor coupled pre-tightening state evaluation model, which unifies the mathematical expression of various influencing factors such as simulation analysis error, assembly environment conditions, process parameters, optical system parameters, and feature extraction results. The model introduces a weight matrix that considers the interaction of various factors, which can adaptively adjust the weight distribution for pre-tightening scenarios of different structures. It breaks the limitations of single threshold evaluation and broadens the applicability of the evaluation model in different precision equipment manufacturing fields.

[0064] 4. This method utilizes the transmission of laser light in space to achieve in-situ detection. After acquiring the dynamic interferogram sequence of the pre-tightening process and extracting the surface region structural feature vector, the pre-tightening quality is directly judged online using the evaluation model. This eliminates the lag of offline sampling inspection and truly realizes real-time online correction and adaptive optimization of process parameters in the assembly process.

[0065] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0066] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An in-situ monitoring method for structural pre-tightened assembly state, characterized in that, Specifically, the following steps are included: Acquire the structural information and detection condition information of the target under test, construct a unified structural state description vector, and establish a three-dimensional digital model of the target under test; Based on the structural state description vector, a preliminary adaptive design of the detection system is carried out, and the design parameters of the detection system are output. Based on the design parameters of the detection system, a detection system adaptability evaluation function is constructed; The three-dimensional digital model of the target to be tested is imported into the finite element analysis software to construct the finite element analysis model of the target to be tested. Based on the results of finite element analysis, an evaluation model for pre-tightened state is established; Acquire phase images of the structure under test during the pre-tightening process; According to the input parameter type of the pre-tightening state evaluation model, the micro-deformation RMS value of the structure under test is extracted from the phase image to obtain the pre-tightening state of the target structure.

2. The in-situ monitoring method for structural pre-tightened assembly state as described in claim 1, characterized in that, The unified structural state description vector is constructed as follows: In the formula, This includes the geometric parameters of the object under test; in, L : Feature length; W :width; H Thickness or height; κ : Structural curvature; ξ : Pre-tightening assembly interface morphology parameters; This includes information about the material of the object being tested; in, : Elastic modulus; υ Poisson's ratio; σ y Yield strength; This includes the optical parameters of the surface of the object under test; in, ρ Surface reflectivity; R a Surface roughness; This includes assembly working condition and environmental information; in, T Ambient temperature; h Ambient humidity; α v Vibration frequency; This includes assembly process parameters; in: ω : Tightening speed; Π: Pre-tightening sequence; M t Preload torque.

3. The in-situ monitoring method for pre-tightened assembly state of a structure as described in claim 1, characterized in that, Based on the design parameters of the detection system, a detection system adaptability evaluation function is constructed; The design parameters for the output detection system are as follows: in, l Laser speckle detection solution; λ Laser wavelength; P Laser power; d Working distance; θ Imaging field of view; ψ Laser beam expansion angle; δ Spatial resolution; f : Focal length of the imaging lens; s : Shear rate.

4. The in-situ monitoring method for structural pre-tightened assembly state as described in claim 1, characterized in that, Based on the initial detection system parameters, a detection system fit evaluation function is constructed as follows: A comprehensive evaluation of the detection system's structural coverage, measurement sensitivity, image quality, and data processing capabilities was conducted. ; in, is a weighting constant, with a value range of [0,1); To improve the overall compatibility of the detection system; For spatial resolution adaptation; Field of view coverage adaptation; For lighting quality adaptation; To detect sensitivity fit; To adapt to data acquisition and processing capabilities; And the optimization goal is to maximize the fit: The detection system parameters are iteratively optimized to output design parameters that meet the adaptability requirements.

5. The in-situ monitoring method for structural pre-tightened assembly state as described in claim 1, characterized in that, The finite element analysis model is as follows: The finite element analysis results under different process parameters and environmental conditions are output to form the feature vector of the surface region structure. F , can be represented as: ; in, For the strain field on the structural surface, For displacement field, This represents the stress field.

6. An in-situ monitoring system for structural pre-tightened assembly states, characterized in that, include: The modeling and parameter characterization module is used to acquire structural information and detection condition information of the target under test, construct a unified structural state description vector, and establish a three-dimensional digital model of the target under test. The detection system adaptive design module is connected to the modeling and parameter characterization module, and is used to perform preliminary adaptive design of the detection system based on the structural state description vector, and output the detection system design parameters. The adaptability evaluation module, connected to the adaptive design module of the detection system, is used to construct a detection system adaptability evaluation function based on the design parameters of the detection system. The finite element simulation analysis module is connected to the modeling and parameter characterization module. It is used to import the three-dimensional digital model of the target under test, establish the finite element analysis model of the target under test, carry out finite element analysis, and output the finite element analysis results. The pre-tightening state modeling module is connected to the finite element simulation analysis module and is used to construct a pre-tightening state evaluation model based on the finite element analysis results. The image acquisition module is used to acquire structural phase images of the target under test during the pre-tightening process; The state solution module is connected to the pre-tightening state modeling module and the image acquisition module, respectively. It is used to extract the micro-deformation RMS value of the structure under test from the acquired phase image according to the input parameter type of the pre-tightening state evaluation model, and solve for the pre-tightening state of the target structure.

7. A computer-readable storage medium, characterized in that, It stores a computer program for an in-situ monitoring system for pre-tightened assembly of a structure, wherein the computer program causes a computer to execute the in-situ monitoring method for pre-tightened assembly of a structure as described in any one of claims 1 to 5.

8. An electronic device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including methods for performing in-situ monitoring of pre-tightened assembly states as described in any one of claims 1 to 5.

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