Ultralow-temperature mechanical property in-situ testing method based on CT (Computed Tomography) technology

Abstract

The invention discloses an ultralow-temperature mechanical property in-situ testing method based on a CT technology, and relates to the technical field of material science testing, and the method comprises the steps: according to a steady state confirmation record, collecting no-load three-dimensional body data, extracting a no-load reference feature set, and forming a no-load reference binding table through binding baseline signals; executing in-situ excitation on the no-load reference binding table, collecting a data stream of a mechanical sensor, and obtaining loading state three-dimensional body data through CT to form a loading state binding table; performing volume data registration and digital volume correlation inversion on the loading state binding table to obtain DVC displacement field data, and obtaining a material state judgment parameter set in combination with multi-parameter mapping constraint; and based on the material state judgment parameter set, solidifying the grouping judgment rule and endowing a state label to form an ultralow-temperature in-situ test report. According to the invention, accurate quantification of internal full-field mechanical parameters is realized, and the effect of enhancing test depth and reliability is achieved.

Description

Technical Field

[0001] This invention relates to the field of materials science testing technology, and in particular to an in-situ method for testing the mechanical properties of ultra-low temperature mechanical properties based on CT technology. Background Technology

[0002] In the fields of aerospace, defense industry, and cryogenic engineering, accurate testing of the mechanical properties of materials under ultra-low temperature environments is of great significance. For example, in high-end equipment such as deep space exploration and superconducting maglev trains, resin-based composite materials need to operate at extremely low temperatures (such as -196°C), and their mechanical behavior directly affects the reliability of the equipment. Conventional in-situ testing methods for ultra-low temperature mechanical properties based on CT technology integrate mechanical sensors for load control and data acquisition, combine a cryogenic environment chamber to simulate steady-state ultra-low temperature conditions, and use X-ray computed tomography to obtain three-dimensional information of the internal structure of the material, thereby characterizing the macroscopic properties of the material under tensile, compressive, and other loads. This method relies on existing CT platforms and mechanical sensing technologies to provide fundamental support for the performance evaluation of materials in extreme environments and promotes the development of cryogenic materials research.

[0003] However, conventional methods have certain limitations in practical applications. On the one hand, CT scanning is time-consuming, with high-resolution scans taking several hours or more, which can easily introduce time-related errors such as stress relaxation or creep, affecting the accuracy of the mechanical response signal. On the other hand, the characterization of the internal deformation field depends on complete three-dimensional reconstruction, and the computational load of digital volume correlation methods is huge, making it difficult to efficiently invert the internal displacement field, thus limiting its ability to monitor the real-time evolution of material damage. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides an in-situ testing method for ultra-low temperature mechanical properties based on CT technology to solve the problems of low scanning efficiency and incomplete characterization of internal deformation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides an in-situ method for testing the mechanical properties of cryogenic surfaces based on CT technology, comprising:

[0008] Pre-treat and group the ultra-low temperature resistant resin-based composite material plates to generate a composite material grouping index table;

[0009] Clamping is performed based on the composite material grouping index table, and liquid nitrogen steady state is configured to generate a steady state confirmation record;

[0010] Based on the steady-state confirmation record, unloaded 3D volume data is collected and unloaded reference feature set is extracted. Unloaded reference binding table is formed by binding baseline signal.

[0011] In-situ excitation was performed on the unloaded reference binding table, and mechanical sensor data streams were collected. Loaded state three-dimensional volume data were obtained through CT to form a loaded state binding table.

[0012] The DVC displacement field data is obtained by performing volume data registration and digital volume correlation inversion on the loaded state binding table, and the material state determination parameter set is obtained by combining multi-parameter mapping constraints;

[0013] Based on the material state determination parameter set, solidify the grouping determination rules and assign state labels to generate an ultra-low temperature in-situ test report.

[0014] As a preferred embodiment of the in-situ testing method for ultra-low temperature mechanical properties based on CT technology described in this invention, the steps of pretreating and grouping the ultra-low temperature resistant resin-based composite material plates to generate a composite material grouping index table are as follows.

[0015] The ultra-low temperature resistant resin-based composite material plates were machined and deburred, and a set of ultra-low temperature resistant composite material samples was generated by dimensional verification.

[0016] Collect the property fields of the ultra-low temperature resistant composite material and write them into the ultra-low temperature resistant composite material sample set in a structured manner to generate a sample material state identification table;

[0017] The sample material state identification table is encoded using combination keys and deduplicated by grouping to obtain the composite material grouping index.

[0018] As a preferred embodiment of the in-situ testing method for ultra-low temperature mechanical properties based on CT technology described in this invention, the steps of performing clamping based on the composite material grouping index table, configuring liquid nitrogen steady state, and generating a steady state confirmation record are as follows.

[0019] Based on the composite material grouping index table, the clamping and loading boundary conditions of the cured specimen are defined, and a clamping definition table is generated.

[0020] According to the clamping definition table, clamping operations are performed on the set of ultra-low temperature resistant composite material samples, and consistency verification and registration are carried out to form a clamping execution record.

[0021] Based on the clamping execution record, liquid nitrogen cooling conditions and circulating nitrogen insulation conditions are configured, temperature time series are collected, steady-state criteria are verified, and a steady-state confirmation record is formed.

[0022] As a preferred embodiment of the in-situ testing method for ultra-low temperature mechanical properties based on CT technology described in this invention, the steps of acquiring unloaded three-dimensional volume data and extracting the unloaded reference feature set based on steady-state confirmation records are as follows:

[0023] Based on the steady-state confirmation record, solidify the acquisition nodes before loading, collect unloaded 3D volume data and archive them according to the group index number to form an unloaded CT reference volume dataset.

[0024] By extracting benchmark features, a set of unloaded benchmark features is obtained from the unloaded CT benchmark dataset.

[0025] As a preferred embodiment of the in-situ testing method for ultra-low temperature mechanical properties based on CT technology described in this invention, the step of forming a no-load reference binding table by binding baseline signals comprises the following steps:

[0026] Based on steady-state confirmation records, baseline signals of mechanical sensors are acquired and registered under zero-load boundary conditions to form unloaded mechanical sensor baseline records.

[0027] The unloaded reference feature set is aligned with the unloaded mechanical sensor baseline records by grouping index numbers and timestamps to form an unloaded reference binding table.

[0028] As a preferred embodiment of the in-situ testing method for ultra-low temperature mechanical properties based on CT technology described in this invention, the steps of performing in-situ excitation on the unloaded reference binding table and acquiring the mechanical sensor data stream are as follows:

[0029] Extract the excitation parameters from the unloaded baseline binding table, solidify them, and generate a set of loaded excitation parameters;

[0030] In-situ excitation is performed on the set of loading excitation parameters, and mechanical response signals are collected by mechanical sensors to generate mechanical response time series and mechanical sensor data stream.

[0031] As a preferred embodiment of the in-situ testing method for ultra-low temperature mechanical properties based on CT technology described in this invention, the steps of acquiring three-dimensional volume data of the loaded state through CT and forming a loaded state binding table are as follows:

[0032] Verify the mechanical sensor data stream that meets the set value holding condition and record the trigger time to form a set value holding trigger mark;

[0033] By setting a value to keep the trigger marker, CT is triggered to acquire loaded 3D volume data and archived according to the load point number to generate a loaded projection dataset.

[0034] Align and bind the loaded state projection dataset with the mechanical sensor data stream according to the load point number, and generate a loaded state binding table.

[0035] As a preferred embodiment of the in-situ testing method for cryogenic mechanical properties based on CT technology described in this invention, the steps for obtaining DVC displacement field data by performing volume data registration and digital volume correlation inversion on the loaded state binding table are as follows:

[0036] Based on the unloaded CT reference volume dataset and the loaded projection dataset, volume data registration is performed and spatial alignment parameters are registered to form a volume data registration set;

[0037] Perform digital volume correlation inversion based on few-angle projection data on the volume data registration set, and invert the internal displacement field to generate DVC displacement field data.

[0038] As a preferred embodiment of the in-situ testing method for ultra-low temperature mechanical properties based on CT technology described in this invention, the steps for obtaining the material state determination parameter set by combining multi-parameter mapping constraints are as follows:

[0039] Perform material characterization calculations on DVC displacement field data to obtain a set of material characterization indices;

[0040] Establish a coupling mapping relationship between the material characterization index set and the loading state binding table to form a material coupling mapping table;

[0041] Based on the material coupling mapping table, the set of material state determination parameters is extracted through multi-parameter mapping constraints.

[0042] As a preferred embodiment of the in-situ ultra-low temperature mechanical property testing method based on CT technology described in this invention, the steps for forming an ultra-low temperature in-situ test report based on a material state determination parameter set, solidifying grouping determination rules, assigning state labels, and establishing the report are as follows.

[0043] The material condition determination parameter set is solidified into group determination rules according to the grouping index table, and a group determination rule table is generated.

[0044] Based on the grouping judgment rule table, the sample material state identification table is judged and the state label is output to generate a set of state labels for cryogenic materials.

[0045] Based on the cryogenic material status label set, archive the group index number and status label and generate a cryogenic in-situ test report.

[0046] The beneficial effects of this invention are as follows: by acquiring data streams through mechanical sensors and combining them with CT scans to obtain three-dimensional volume data under loading conditions, real-time synchronous monitoring of the internal structure of materials under ultra-low temperature environments is realized, revealing the damage evolution behavior of materials under extreme conditions, thereby improving the real-time performance and accuracy of testing; through volume data registration and digital volume correlation inversion, the precise quantification of internal full-field mechanical parameters is achieved, thereby enhancing the depth and reliability of testing. Attached Figure Description

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0048] Figure 1 This is a flowchart of an in-situ testing method for cryogenic mechanical properties based on CT technology.

[0049] Figure 2 This is a flowchart for hierarchical decision-making.

[0050] Figure 3 This is a flowchart illustrating the interaction between in-situ excitation and CT acquisition.

[0051] Figure 4 A flowchart for data processing and report generation. Detailed Implementation

[0052] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0054] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0055] Reference Figures 1-4 This is one embodiment of the present invention, which provides an in-situ testing method for cryogenic mechanical properties based on CT technology, comprising the following steps:

[0056] S1. Pre-treat and group the ultra-low temperature resistant resin-based composite material sheets to generate a composite material grouping index table.

[0057] The ultra-low temperature resistant resin-based composite material plates were machined and deburred, and a set of ultra-low temperature resistant composite material samples was generated by dimensional verification.

[0058] Furthermore, the ultra-low temperature resistant resin-based composite material sheet is fixed on a CNC machining table and subjected to contour machining (e.g., feed rate of 0.02–0.05 mm / tooth), followed by deburring to remove microcrack initiation sources and suppress stress concentration under low temperature load, thereby generating an ultra-low temperature resistant resin-based composite material sample. Under constant temperature conditions, the ultra-low temperature resistant resin-based composite material sample is subjected to three-dimensional verification (e.g., length and width deviation ≤ ±0.05 mm, thickness deviation ≤ ±0.02 mm). The ultra-low temperature resistant resin-based composite material samples that pass the dimensional verification are written into the sample registration record, forming an ultra-low temperature resistant composite material sample set.

[0059] Collect the property fields of the ultra-low temperature resistant composite material and write them into the ultra-low temperature resistant composite material sample set in a structured manner to generate a sample material state identification table.

[0060] Furthermore, based on the set of ultra-low temperature resistant composite material samples, the following properties of the ultra-low temperature resistant composite materials were collected (e.g., source batch field, layup structure field, fiber volume fraction field, resin system field, and nominal service temperature range field, where the source batch field and layup structure field are from the manufacturing process record, the fiber volume fraction field is preferentially based on the value given in the manufacturing record, and is obtained by density method or ignition method when the manufacturing record is missing, the resin system field is from the material specification document, and the nominal service temperature range field is from the material design parameter table). The calibration baseline response field of the mechanical sensor was collected under room temperature and zero load conditions, and the structured splicing with the properties of the ultra-low temperature resistant composite materials was used to generate the identification code of the ultra-low temperature resistant resin-based composite material sample. The field consistency and value legality of the identification code of the ultra-low temperature resistant resin-based composite material sample were verified, and the code was written into the set of ultra-low temperature resistant resin-based composite material samples to form a sample material state identification table.

[0061] It should be noted that the calibration baseline of the mechanical sensor collected under room temperature and zero-load conditions is only used for the initial state calibration and consistency verification of the sensor, and is not used as the baseline data for the subsequent no-load reference binding table. Subsequent material state analysis and binding shall be based on the no-load mechanical sensor baseline record collected under ultra-low temperature steady-state conditions.

[0062] The sample material state identification table is encoded using combination keys and deduplicated by grouping to obtain the composite material grouping index.

[0063] Furthermore, batch information, layup structure information, and fiber volume fraction information of the low-temperature resin-based composite material are obtained from the sample material state identification table to generate ultra-low temperature resistant composite material attribute fields, and these fields are spliced ​​together in a fixed order to generate combination key codes. A line-by-line comparison is performed using the combination key codes as the sole criterion, and records with the same combination key codes are merged into the same group and the corresponding sample identification code set is registered. A group index number is assigned to the corresponding sample identification code set and written back to the sample material state identification table to form a composite material group index.

[0064] S2. Perform clamping based on the composite material grouping index table, configure liquid nitrogen steady state, and generate steady state confirmation record.

[0065] Based on the composite material grouping index table, the clamping and loading boundary conditions of the cured specimens are defined, and a clamping definition table is generated.

[0066] Furthermore, using the consistency between the principal axis of the ultra-low temperature resistant resin-based composite material specimen and the loading direction of the mechanical sensor as a constraint, a unified end clamping method and symmetrical loading boundary are determined for each group; the clamping contact area parameters (including clamping contact area position, contact length, and effective loading gauge length) are solidified for each group, and the loading boundary parameters (including mechanical sensor loading direction, loading degree of freedom constraint, and zero-load reference boundary) are solidified simultaneously; the group index number, specimen identification code set, clamping contact area parameters, and loading boundary parameters are written and locked, so that all ultra-low temperature resistant resin-based composite material specimens in the same group share a completely consistent clamping and loading definition, generating a clamping definition table.

[0067] According to the clamping definition table, clamping operations are performed on the set of ultra-low temperature resistant composite material samples, and consistency verification and registration are carried out to form a clamping execution record.

[0068] Furthermore, after adjusting the main axis of the length of each ultra-low temperature resistant composite material sample in the sample set to coincide with the loading direction of the mechanical sensor, end clamping operation is performed according to the clamping contact area position and contact length parameters, and the effective loading gauge length is simultaneously controlled to meet the preset fixed value in the clamping definition table; consistency verification is performed on the main axis direction, clamping position deviation and clamping symmetry of the ultra-low temperature resistant composite material sample, and the corresponding group index number, sample identification code, clamping contact area parameters, loading boundary parameters and verification result identifier are integrated to form a data-driven clamping execution record.

[0069] It should be noted that the preset fixed values ​​in the clamping definition table are set based on the mechanical sensor's travel capability and the CT scan's field of view coverage. For example, the clamping contact length is set to 3–5 mm, and the effective loading gauge length is set to 15–30 mm. The effective loading gauge length refers to the effective load-bearing length that actually bears the axial load during loading and serves as the mechanical response and CT analysis, located between the edges of the clamping contact areas at both ends after the end clamping is completed. For example, it is set to 15–30 mm.

[0070] Based on the clamping execution record, liquid nitrogen cooling conditions and circulating nitrogen insulation conditions are configured, temperature time series are collected, steady-state criteria are verified, and a steady-state confirmation record is formed.

[0071] Furthermore, while keeping the clamping contact area parameters and loading boundary parameters unchanged, liquid nitrogen cooling conditions and circulating nitrogen insulation conditions to form a gas insulation layer around the sample are configured, and liquid nitrogen supply is started, so that the overall temperature of the ultra-low temperature resistant composite material sample monotonically decreases towards the target ultra-low temperature range (e.g., −196 ℃ to −150 ℃); during the liquid nitrogen cooling process, a circulating nitrogen gas flow is simultaneously formed around the ultra-low temperature resistant composite material sample to form an insulation layer, weakening external heat exchange and suppressing temperature fluctuations; with a fixed sampling period (e.g., sampling period of 1–5...). s) Continuously collect temperature data at key locations of the ultra-low temperature resistant composite material sample (including the middle gauge length area and the adjacent areas of the clamping contact areas at both ends of the sample) and form a temperature time series. Simultaneously, perform time difference calculation on the temperature values ​​of adjacent sampling times in the temperature time series and obtain the temperature change rate by combining the sampling time interval. Determine the temperature time series within the time window whose temperature change rate is less than the steady-state criterion threshold and assign a steady-state label. Register the group index number, sample identification code, liquid nitrogen cooling conditions, circulating nitrogen insulation conditions and steady-state determination time interval corresponding to the assigned steady-state label to form a traceable steady-state confirmation record.

[0072] It should be noted that the steady-state criterion threshold is set based on the upper limit of allowable temperature drift during no-load CT acquisition and the stability requirements of the zero-load baseline of the mechanical sensor. It is achieved by limiting the temperature change amplitude and temperature change rate within a continuous time window to not exceed the preset upper limit. For example, the temperature change amplitude range is ±0.2 ℃ to ±1.0 ℃ and the temperature change rate range is 0.01 ℃ / s–0.05 ℃ / s.

[0073] S3. Based on the steady-state confirmation record, collect unloaded three-dimensional volume data and extract the unloaded reference feature set, and form an unloaded reference binding table by binding the baseline signal.

[0074] Based on the steady-state confirmation record, solidify the acquisition nodes before loading, collect unloaded 3D volume data and archive them according to the group index number to form an unloaded CT reference volume dataset.

[0075] Furthermore, the start time of the steady-state determination time interval in the steady-state confirmation record is fixed as the pre-loading acquisition node; under the condition that the clamping contact area parameters and loading boundary parameters remain unchanged and the mechanical sensor is in a zero-load state, a CT scan is started on the pre-loading acquisition node (e.g., the scanning angle covers 360°), a full-angle no-load scan is performed on the ultra-low temperature resistant composite material sample to obtain no-load three-dimensional body data, and it is bound and registered with the corresponding group index number, sample identification code and acquisition timestamp, and cataloged and archived according to the group index number to generate a no-load CT reference body dataset.

[0076] By extracting benchmark features, a set of unloaded benchmark features is obtained from the unloaded CT benchmark dataset.

[0077] Furthermore, while maintaining the spatial resolution of the volume data, the voxel gray values ​​of the unloaded 3D volume data are linearly mapped according to a fixed gray range (e.g., 0.1–99.9 percentile range), and local neighborhood smoothing is simultaneously used to suppress random noise, so as to unify the gray scale of different scanning batches and reduce high-frequency interference. Gauge-length voxel sub-regions are loaded into the unloaded 3D volume data, and reference features reflecting the internal material state (including voxel gray statistical features, internal pore distribution features, and structural interface continuity features) are extracted. The reference features corresponding to each ultra-low temperature resistant composite material sample are bound and registered with the group index number and sample identification code to form an unloaded reference feature set.

[0078] It should be noted that the loaded gauge volume sub-region refers to the continuous volume sub-region located between the two clamping contact regions and corresponding to the loaded region in the unloaded three-dimensional volume data based on the clamping contact region parameters fixed in the clamping definition table, used to characterize the reference space range of subsequent loaded evolution.

[0079] Based on steady-state confirmation records, baseline signals of mechanical sensors are acquired and registered under zero-load boundary conditions to form baseline records of unloaded mechanical sensors.

[0080] Furthermore, under the condition of keeping the clamping contact area parameters and loading boundary parameters unchanged and without applying external load, the mechanical sensor is placed in a zero-load boundary state; the starting time within the steady-state determination time interval is used as the baseline acquisition starting point, and the output signal of the mechanical sensor (e.g., acquisition time of 10–60 s) is continuously acquired to form a mechanical sensor signal sequence; the temporal stability of the mechanical sensor signal sequence is checked to determine that the absolute difference between the force signal output by the mechanical sensor at adjacent sampling times does not exceed the preset fluctuation limit of the full scale of the force signal of the mechanical sensor, thereby confirming that there are no sudden changes or drifts in the mechanical sensor signal during the acquisition time period. The corresponding group index number, zero-load state identifier, acquisition time interval and mechanical sensor signal are bound and registered to form an unloaded mechanical sensor baseline record.

[0081] It should be noted that the preset fluctuation upper limit is based on the zero-load repeatability error setting of the mechanical sensor. The natural noise amplitude of the zero-load signal within the recording time interval is confirmed by statistical steady-state analysis and its upper limit is set. For example, the amplitude variation of the output signal does not exceed 0.05%–0.2% of the full scale. The full scale refers to the upper limit of the maximum load range that the mechanical sensor can measure and output a valid signal under calibrated conditions.

[0082] The unloaded reference feature set is aligned with the unloaded mechanical sensor baseline records by grouping index numbers and timestamps to form an unloaded reference binding table.

[0083] Furthermore, based on the unloaded reference feature set and the unloaded mechanical sensor baseline record, the corresponding ultra-low temperature resistant composite material sample record is matched one by one according to the group index number, and the alignment is completed by using the group index number as the association key; the acquisition node solidified in the steady-state confirmation record is used as the time reference to perform timestamp correction, so that the unloaded reference feature set and the unloaded mechanical sensor baseline record correspond to the same steady-state time interval; the unloaded reference feature field and the mechanical sensor baseline signal field are bound and registered in a fixed field order to form an unloaded reference binding table with consistent structure and time sequence.

[0084] S4. Perform in-situ excitation on the unloaded reference binding table and collect the mechanical sensor data stream. Obtain the loaded three-dimensional body data through CT to form the loaded state binding table.

[0085] Extract the excitation parameters from the unloaded baseline binding table, solidify them, and generate a set of loaded excitation parameters.

[0086] Furthermore, the baseline signal of the unloaded mechanical sensor is extracted from the unloaded reference binding table. The loading start reference point is fixed within the stable section of the unloaded mechanical sensor baseline signal. Combined with the fixed mechanical sensor loading direction and zero-load reference boundary in the clamping definition table, a unified loading control method is determined (displacement control method for tensile loading and force control method for compressive loading). The initial loading amplitude, loading rate (e.g., 0.5%–2% of full scale per second) and loading holding time are set with the unloaded mechanical sensor baseline record as a reference. The loading start reference point, loading amplitude, loading rate and loading holding time are bound and registered according to the group index number to form a loading excitation parameter set.

[0087] In-situ excitation is performed on the set of loading excitation parameters, and mechanical response signals are collected by mechanical sensors to generate mechanical response time series and mechanical sensor data stream.

[0088] Furthermore, under the premise that the clamping contact area parameters, loading boundary parameters, and steady-state cryogenic conditions (obtained by liquid nitrogen cooling and circulating nitrogen insulation) remain unchanged, in-situ loading is initiated from the loading start reference point, causing the mechanical sensor to apply load along the predetermined loading direction at a curing rate (e.g., 0.5%–2% of full scale per second) and enter the holding phase. Throughout the loading process, the mechanical response signal of the mechanical sensor is continuously acquired at a fixed sampling period (e.g., sampling period of 1–10 ms) and timestamped to form a mechanical response time series covering the loading process. The mechanical response time series is bound and registered with the group index number, sample identification code, and loading excitation parameter set to form a mechanical sensor data stream.

[0089] Verify the mechanical sensor data stream that meets the set value holding condition and record the trigger time to form a set value holding trigger mark.

[0090] Furthermore, based on the load amplitude centrally fixed by the loading excitation parameters, the load used as the judgment benchmark is fixed as a set value at a fixed percentage (e.g., 10%) (e.g., the set value is 1%–5% of the full scale as the initial level and gradually increased to 10%–80%). The actual load value is compared with the set value point by point in the mechanical response time series. When the actual load enters the allowable deviation range of the set value and remains unchanged in a continuous time period, it is determined that the set value holding condition is met. For example, the actual load deviation does not exceed ±1% of the set value and the duration is not less than the time required for a single CT scan with a time margin. At the time point when the set value holding condition is met, the corresponding timestamp is fixed as the trigger time, and the group index number, sample identification code, set value number and trigger time are registered to form a set value holding trigger mark.

[0091] By setting a value to keep the trigger marker, CT is triggered to acquire loaded 3D volume data and archived according to the load point number to generate a loaded projection dataset.

[0092] Furthermore, the trigger time is extracted from the set value holding trigger mark and a CT scan is started. The ultra-low temperature resistant composite material sample in the set value holding state is scanned at all angles (e.g., the scanning angle covers 360°) to obtain the three-dimensional volume data of the loaded state. The three-dimensional volume data of the loaded state is bound and registered with the corresponding group index number, sample identification code, set value number and trigger time. The set value number is used as the load point number for cataloging and archiving to form a loaded state projection dataset.

[0093] Align and bind the loaded state projection dataset with the mechanical sensor data stream according to the load point number, and generate a loaded state binding table.

[0094] Furthermore, the corresponding group index number, sample identification code, and trigger time in the loaded projection dataset are read, and the mechanical sensor records covering the trigger time under the same load point number are retrieved in the mechanical sensor data stream; the trigger time registered in the set value holding trigger mark is used as the time alignment reference, and the mechanical sensor record is truncated to make the mechanical sensor record correspond to the same loading holding stage as the loaded projection dataset; the loaded CT body data identifier, mechanical sensor record, group index number, and sample identification code are bound and registered to generate a loaded binding table.

[0095] S5. Perform volume data registration and digital volume correlation inversion on the loaded state binding table to obtain DVC displacement field data, and combine multi-parameter mapping constraints to obtain the material state determination parameter set.

[0096] Based on the unloaded CT reference volume dataset and the loaded projection dataset, volume data registration is performed and spatial alignment parameters are registered to form a volume data registration set.

[0097] Furthermore, while maintaining consistent spatial resolution of the volumetric data, the unloaded 3D volumetric data is used as a reference volume, and rigid spatial alignment is performed on the loaded 3D volumetric data to minimize voxel overlap error. The 3D translation and rotation are then solved to eliminate differences in clamping and scanning postures. The spatially aligned 3D translation and rotation are then bound to the corresponding group index number, sample identification code, and load point number to generate a volumetric data registration set.

[0098] Perform digital volume correlation inversion based on few-angle projection data on the volume data registration set, and invert the internal displacement field to generate DVC displacement field data.

[0099] Furthermore, within the loaded gauge length voxel sub-region, the unloaded 3D volume data is represented as a reference volume function; the loaded 3D volume data is used as the deformation prediction object after being mapped by the displacement field, and for each projection angle, the corresponding loaded projection dataset is read; a 3D correlation calculation window is constructed within the loaded gauge length voxel sub-region, and an internal displacement field function is introduced to map the reference volume function to the deformation position; under the condition of few-angle projection, for each projection angle and the corresponding detector position, the orthographic projection calculation is performed on the displacement-mapped reference volume function through the projection operator to predict the projection, and the residual between the predicted projection and the actual loaded projection data in the loaded projection dataset is solved by the minimum value solver to realize the inversion of the displacement vector of each voxel sub-block; after completing the inversion calculation of all voxel sub-blocks, the obtained 3D displacement vectors are reorganized according to spatial coordinates to form a continuous internal displacement field covering the loaded gauge length voxel sub-region, and the internal displacement field is bound and registered with the group index number, sample identification code, load point number and spatial alignment parameters to generate DVC displacement field data.

[0100] It should be noted that the loaded gauge length voxel sub-region refers to the continuous three-dimensional voxel space range located between the clamping contact areas at both ends and corresponding to the actual loaded interval in the unloaded three-dimensional volume data; the internal displacement field function is a continuous vector function that takes the spatial coordinates of the voxels within the loaded gauge length voxel sub-region as input and the three-dimensional displacement vector of the voxels in the loaded state relative to the unloaded reference state as output, and is used to describe the displacement distribution of each spatial position inside the material.

[0101] The objective function expression for displacement field inversion is:

[0102] ;

[0103] in, To represent the field function of all allowed displacements Within the value space of , find the displacement field that minimizes the overall residual of the objective function, which corresponds to the mathematical description of the "optimal inversion result"; For projection angle; To be at the projection angle of And the detector's position is Under these conditions, the loaded projection dataset obtained from actual CT scans; The detector position represents the coordinates of the ray position on the detector plane; For projection operators, it means that at the projection angle The CT orthographic projection operation performed on the three-dimensional volume data maps the three-dimensional volume function to two-dimensional projection data; The reference volume function is the result of displacement mapping, indicating that the reference volume data is mapped according to the displacement field. The predicted loaded state volume data obtained after mapping is used to compare with the actual loaded state projection data; Let be the internal displacement field function, representing the spatial position of the material under load. The three-dimensional displacement vector of the internal material point relative to the unloaded reference state; For detector spatial integration operator, it represents the continuous or discrete integration accumulation of the projection residuals corresponding to all ray positions along the detector spatial coordinate direction.

[0104] It should be noted that before substituting the loaded projection dataset and the two-dimensional projection data into the displacement field inversion objective function expression, both were normalized to unify the dimensions.

[0105] Material characterization calculations are performed on the DVC displacement field data to obtain a set of material characterization indices.

[0106] Furthermore, under the condition of consistent spatial coordinates, numerical differentiation operations are performed on the three-dimensional displacement vectors of each voxel position in the DVC displacement field data (used to determine the spatial coordinate position of a single voxel in the three-dimensional CT volume data) in the spatial coordinate direction to obtain the partial derivatives of the displacement components with respect to the spatial coordinates and combine them according to the strain definition relationship to obtain the corresponding strain tensor field; the strain tensor field is statistically summarized in the loaded gauge length voxel sub-region to obtain material characterization quantities (such as maximum principal strain and equivalent strain mean) that characterize the degree and distribution characteristics of internal deformation; the material characterization quantities are bound and registered with the group index number, sample identification code and load point number to generate a material characterization index set.

[0107] Establish a coupling mapping relationship between the material characterization index set and the loading state binding table to form a material coupling mapping table.

[0108] Furthermore, using the group index number and load point number as the association key, a one-to-one matching is performed between the material characterization index set and the loading state binding table, so that a definite association is established between the material characterization index set and the mechanical sensor loading information in the loading state binding table for the same ultra-low temperature resistant composite material sample under the same load point; during the matching process, the consistency between the sample identification code and the load point number is verified to ensure that each material characterization index in the material characterization index set corresponds to only one loading state record; the material characterization index field and the load amplitude field and trigger time field in the loading state binding table are combined and registered to generate a material coupling mapping table.

[0109] Based on the material coupling mapping table, the set of material state determination parameters is extracted through multi-parameter mapping constraints.

[0110] Furthermore, the material characterization quantities in the material characterization index set and the load amplitudes in the loading state binding table are jointly sorted by load point number. Under the increasing load order, a consistency comparison is performed on multiple indices (including load amplitude and load point number) of adjacent load points to identify adjacent load points that meet the consistency requirements and whose change amplitude does not show abrupt changes (e.g., equivalent strain changes monotonically with load). The judgment parameters characterizing the evolution of material state (e.g., the slope of strain change with load) are extracted to generate a set of material state judgment parameters.

[0111] S6. Based on the material state determination parameter set, solidify the grouping determination rules and assign state labels to generate an ultra-low temperature in-situ test report.

[0112] The material condition determination parameter set is solidified into group determination rules according to the group index table, and a group determination rule table is generated.

[0113] Furthermore, within the grouping dimension, statistical merging is performed on the material state determination parameters in the set of material state determination parameters. Determination features used to distinguish the stages of material state evolution are selected, and the determination features are bound to fixed determination conditions (for example, using the slope of strain change with load reaching a set limit as the basis for state demarcation) to form a generated grouping determination rule table.

[0114] It should be noted that the fixed judgment condition is based on the statistical distribution characteristics of the material state judgment parameters within the same group during the load increase process. It is obtained by determining and solidifying the parameters that can distinguish the inflection points of the deformation evolution stage. For example, the condition judgment is based on the slope of strain changing with load exceeding the statistical limit within the group.

[0115] Based on the grouping judgment rule table, the state of the sample material state identification table is judged and the state label is output to generate a set of state labels for cryogenic materials.

[0116] Furthermore, the material state determination parameters are compared with the fixed determination conditions in the group determination rule table (e.g., the slope of strain change with load in the material state determination parameters remains monotonically increasing between adjacent load points and does not exceed the statistical upper limit within the group). Based on the state boundary conditions pre-locked in the group determination rule table (e.g., the load point where the trend characteristics first change is used as the boundary between different deformation stages and the corresponding material state category label is assigned accordingly), deterministic discrimination is performed on the material state determination parameters. Records that meet the fixed determination conditions in the group determination rule table are mapped to the corresponding material state category label (e.g., when the slope of strain change with load exceeds the group limit, it is marked as the next deformation stage), forming a traceable set of cryogenic material state labels.

[0117] Based on the cryogenic material status label set, archive the group index number and status label and generate a cryogenic in-situ test report.

[0118] Furthermore, the status labels of the corresponding ultra-low temperature resistant composite material samples are collected according to the group index number, and the status labels are arranged according to the load point number order. The group index number, sample identification code, load point number and status label are archived in a unified manner, and associated with the steady state confirmation record, the data number of the unloaded and loaded CT body and the load amplitude of the mechanical sensor. Finally, the ultra-low temperature in-situ test report is output according to the fixed field order.

[0119] In summary, this invention achieves real-time synchronous monitoring of the internal structure of materials under ultra-low temperature conditions by using mechanical sensors to collect data streams and combining them with CT scans to obtain three-dimensional volume data under loading conditions. This reveals the damage evolution behavior of materials under extreme conditions and improves the real-time performance and accuracy of testing. Furthermore, through volume data registration and digital volume correlation inversion, it achieves precise quantification of internal full-field mechanical parameters, thereby enhancing the depth and reliability of testing.

[0120] It should be noted that 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for in-situ testing of ultra-low temperature mechanical properties based on CT technology, characterized in that: include, Pre-treat and group the ultra-low temperature resistant resin-based composite material plates to generate a composite material grouping index table; Clamping is performed based on the composite material grouping index table, and liquid nitrogen steady state is configured to generate a steady state confirmation record; Based on the steady-state confirmation record, unloaded 3D volume data is collected and unloaded reference feature set is extracted. Unloaded reference binding table is formed by binding baseline signal. In-situ excitation was performed on the unloaded reference binding table, and mechanical sensor data streams were collected. Loaded state three-dimensional volume data were obtained through CT to form a loaded state binding table. The DVC displacement field data is obtained by performing volume data registration and digital volume correlation inversion on the loaded state binding table, and the material state determination parameter set is obtained by combining multi-parameter mapping constraints; Based on the material state determination parameter set, solidify the grouping determination rules and assign state labels to generate an ultra-low temperature in-situ test report.

2. The in-situ testing method for cryogenic mechanical properties based on CT technology as described in claim 1, characterized in that: The steps for pretreating and grouping the ultra-low temperature resistant resin-based composite material sheets to generate a composite material grouping index table are as follows. The ultra-low temperature resistant resin-based composite material plates were machined and deburred, and a set of ultra-low temperature resistant composite material samples was generated by dimensional verification. Collect the property fields of the ultra-low temperature resistant composite material and write them into the ultra-low temperature resistant composite material sample set in a structured manner to generate a sample material state identification table; The sample material state identification table is encoded using combination keys and deduplicated by grouping to obtain the composite material grouping index.

3. The in-situ testing method for cryogenic mechanical properties based on CT technology as described in claim 2, characterized in that: The steps for performing clamping based on the composite material grouping index table, configuring liquid nitrogen steady state, and generating a steady state confirmation record are as follows. Based on the composite material grouping index table, the clamping and loading boundary conditions of the cured specimen are defined, and a clamping definition table is generated. According to the clamping definition table, clamping operations are performed on the set of ultra-low temperature resistant composite material samples, and consistency verification and registration are carried out to form a clamping execution record. Based on the clamping execution record, liquid nitrogen cooling conditions and circulating nitrogen insulation conditions are configured, temperature time series are collected, steady-state criteria are verified, and a steady-state confirmation record is formed.

4. The in-situ testing method for cryogenic mechanical properties based on CT technology as described in claim 3, characterized in that: The steps for collecting unloaded 3D volume data and extracting the unloaded reference feature set based on steady-state confirmation records are as follows: Based on the steady-state confirmation record, solidify the acquisition nodes before loading, collect unloaded 3D volume data and archive them according to the group index number to form an unloaded CT reference volume dataset. By extracting benchmark features, a set of unloaded benchmark features is obtained from the unloaded CT benchmark dataset.

5. The in-situ testing method for cryogenic mechanical properties based on CT technology as described in claim 4, characterized in that: The steps for forming a no-load reference binding table by binding baseline signals are as follows: Based on steady-state confirmation records, baseline signals of mechanical sensors are acquired and registered under zero-load boundary conditions to form unloaded mechanical sensor baseline records. The unloaded reference feature set is aligned with the unloaded mechanical sensor baseline records by grouping index numbers and timestamps to form an unloaded reference binding table.

6. The in-situ testing method for cryogenic mechanical properties based on CT technology as described in claim 5, characterized in that: The steps for performing in-situ excitation on the unloaded reference binding table and acquiring the mechanical sensor data stream are as follows: Extract the excitation parameters from the unloaded baseline binding table, solidify them, and generate a set of loaded excitation parameters; In-situ excitation is performed on the set of loading excitation parameters, and mechanical response signals are collected by mechanical sensors to generate mechanical response time series and mechanical sensor data stream.

7. The in-situ testing method for cryogenic mechanical properties based on CT technology as described in claim 6, characterized in that: The steps for acquiring loaded 3D volume data via CT and forming a loaded state binding table are as follows. Verify the mechanical sensor data stream that meets the set value holding condition and record the trigger time to form a set value holding trigger mark; By setting a value to keep the trigger marker, CT is triggered to acquire loaded 3D volume data and archived according to the load point number to generate a loaded projection dataset. Align and bind the loaded state projection dataset with the mechanical sensor data stream according to the load point number, and generate a loaded state binding table.

8. The in-situ testing method for cryogenic mechanical properties based on CT technology as described in claim 7, characterized in that: The steps for obtaining DVC displacement field data by performing data registration and digital volume correlation inversion on the loaded state binding table are as follows: Based on the unloaded CT reference volume dataset and the loaded projection dataset, volume data registration is performed and spatial alignment parameters are registered to form a volume data registration set; Perform digital volume correlation inversion based on few-angle projection data on the volume data registration set, and invert the internal displacement field to generate DVC displacement field data.

9. The in-situ testing method for ultra-low temperature mechanical properties based on CT technology as described in claim 8, characterized in that: The steps for obtaining the material state determination parameter set by combining multi-parameter mapping constraints are as follows: Perform material characterization calculations on DVC displacement field data to obtain a set of material characterization indices; Establish a coupling mapping relationship between the material characterization index set and the loading state binding table to form a material coupling mapping table; Based on the material coupling mapping table, the set of material state determination parameters is extracted through multi-parameter mapping constraints.

10. The in-situ testing method for ultra-low temperature mechanical properties based on CT technology as described in claim 9, characterized in that: The process of establishing grouping and assigning status labels based on the material state determination parameter set to generate an ultra-low temperature in-situ test report involves the following steps: The material condition determination parameter set is solidified into group determination rules according to the grouping index table, and a group determination rule table is generated. Based on the grouping judgment rule table, the sample material state identification table is judged and the state label is output to generate a set of state labels for cryogenic materials. Based on the cryogenic material status label set, archive the group index number and status label and generate a cryogenic in-situ test report.

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