Multidimensional analysis method for compression resistance of explosion-proof glass
By synchronously collecting and analyzing multidimensional signals of explosion-proof glass during the compression process, the problem of the inability to comprehensively monitor dynamic response and identify damage events in existing technologies has been solved, enabling accurate assessment of the compression resistance performance and failure mechanism analysis of explosion-proof glass.
Patent Information
- Application Number
- CN202610655942.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-13
- Publication Date
- 2026-07-07
AI Technical Summary
Existing methods for evaluating the compressive strength of explosion-proof glass cannot comprehensively monitor dynamic response, identify damage events, and quantify energy distribution, resulting in superficial failure mechanism analysis and insufficient critical point early warning capabilities.
A compression test device with controllable loading rate, a high-speed optical camera, and a piezoelectric sensor array are used to simultaneously acquire three-dimensional force-displacement signals, high-speed full-field strain image sequences, and vibration waveform signals. Time-frequency analysis and event matching are performed to quantify the energy distribution ratio and generate a dynamic evolution spectrum.
It enables simultaneous acquisition of multiple signals, comprehensive monitoring of the dynamic response during compression, accurate identification of material damage events and quantification of energy distribution, thereby improving the accuracy of the assessment of the protective performance of explosion-proof glass.
Smart Images
Figure CN122345528A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of testing the strength characteristics of solid materials by applying pressure, and in particular to a multi-dimensional analysis method for the compressive strength of explosion-proof glass. Background Technology
[0002] Currently, the evaluation of the compressive strength of explosion-proof glass generally adopts a single testing device and isolated signal acquisition mode, which can only obtain the static failure mode after compression and cannot reflect the entire dynamic response process.
[0003] For example, some methods for testing the compressive strength of explosion-proof glass focus on the mechanical response of local areas, lacking the ability to monitor the overall structural behavior. This limitation results in a lack of dynamic correlation information between the compression contact surface and the back of the sample, making it difficult to construct a complete model of the compression energy transfer path. Simultaneously, it cannot effectively separate the low-frequency stress wave propagation component from high-frequency material damage events in the vibration waveform, creating blind spots in the identification of key damage processes such as microcrack propagation and interlaminar delamination. Furthermore, the evaluation results are limited to static indicators such as damage area statistics or peak load records, failing to present the evolutionary trajectory of energy dissipation.
[0004] Therefore, the failure mechanism analysis of explosion-proof glass is superficial, the early warning capability of the critical point is seriously insufficient, and ultimately the accurate determination of protective effectiveness is restricted. Summary of the Invention
[0005] The present invention aims to solve the above-mentioned technical problems by providing a multi-dimensional analysis method for the compressive strength of explosion-proof glass.
[0006] The technical solution of this invention is a multi-dimensional analysis method for the compressive strength of explosion-proof glass, based on a detection system including a compression testing device with a controllable loading rate and a high-speed optical camera. The method includes... Synchronous signal acquisition steps: During the process of the indenter applying compressive load to the explosion-proof glass sample, the three-dimensional force-displacement time history signal of the compression contact surface, the high-speed full-field strain image sequence of the back of the sample, and the vibration waveform signal of the piezoelectric sensor array are acquired in parallel through hardware synchronous triggering. Process analysis and quantification steps: Time-frequency analysis and event localization are performed on the vibration waveform signal to distinguish between low-frequency components characterizing stress wave propagation and high-frequency sudden events characterizing material damage; the spatiotemporal information of the high-frequency sudden events is matched with the strain concentration areas in the full-field strain image sequence at the same time to identify damage events caused by microcrack propagation and / or interlayer delamination; based on the three-dimensional force-displacement time history signal, the full-field strain image sequence, and the identified damage events, the distribution ratio and evolution process of the work done by external forces during compression through overall structural deformation, material damage dissipation, and fragment ejection kinetic energy are quantified respectively. Comprehensive performance evaluation steps: Based on the energy distribution ratio and evolution process obtained from the process analysis and quantification steps, calculate performance indicators in at least two dimensions, including: the effective energy dissipation ratio reflecting the material's ability to convert external mechanical energy into controllable energy dissipation, and the evolution sequence of the dominant failure mode reflecting the stability of the failure process; finally, generate a dynamic evolution spectrum of compressive strength performance that integrates the aforementioned performance indicators over time, which is used to evaluate the protective effectiveness, failure mechanism, and critical point of the explosion-proof glass.
[0007] In one embodiment, the piezoelectric sensor array is arranged in a grid pattern of a set density on the back of the explosion-proof glass sample, and the sampling frequency of each sensor is not less than 1MHz.
[0008] In one implementation, the hardware synchronization trigger is triggered by a pulse signal generated when the three-dimensional force signal of the compression contact surface first exceeds a preset threshold.
[0009] As one implementation method, the energy dissipation of the overall structural deformation in the energy distribution is obtained by reconstructing the full-field strain-stress field and integrating the full-field strain image sequence.
[0010] In one implementation, the energy dissipation due to material damage in the energy distribution is obtained by summing the energy of the vibration waveform signals corresponding to all identified damage events.
[0011] In one implementation, the kinetic energy of the debris splash is obtained by analyzing the motion vector of the debris at the moment of separation in the full-field strain image sequence and calculating it based on a preset debris mass model.
[0012] As one implementation method, the evolution sequence of the dominant failure mode is determined by the following method: taking a preset time window as a unit, the ratio of the material damage dissipation energy to the fragment splash kinetic energy within each time window is counted. When the ratio is continuously lower than a preset threshold, it is determined that the dominant failure mode is changing to brittle fracture.
[0013] As one implementation, the method further includes Data archiving and database construction steps: All raw data obtained in the synchronous signal acquisition step, all feature parameters extracted in the process analysis and quantification step, and all indicators and graphs generated in the comprehensive performance evaluation step are stored in the associated database to construct a compressive strength performance sample library.
[0014] The advantages of this invention compared to existing technologies are that the multi-dimensional analysis method for the compressive strength of explosion-proof glass provided by this invention, through hardware synchronous triggering and parallel acquisition of three-dimensional force-displacement signals, high-speed full-field strain image sequences and vibration waveform signals, and performing time-frequency analysis, event matching and energy quantification, calculates multi-dimensional performance indicators and generates dynamic evolution spectra. This solves the limitations of existing technologies in that they cannot comprehensively monitor dynamic response, identify damage events and quantify energy distribution. It has the ability to realize multi-signal synchronous acquisition, comprehensive monitoring of dynamic response during compression, accurate identification of material damage events, quantification of energy distribution ratio and generation of dynamic performance evolution spectra, thereby improving the accuracy of explosion-proof glass protective performance evaluation. Attached Figure Description
[0015] Figure 1 A flowchart of a multi-dimensional analysis method for the compressive strength of explosion-proof glass provided in one embodiment of the present invention; Figure 2 A flowchart of a multi-dimensional analysis method for the compressive strength of explosion-proof glass provided in another embodiment of the present invention. Detailed Implementation
[0016] The above and other embodiments and advantages of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.
[0017] In one implementation, such as Figure 1 As shown.
[0018] The multi-dimensional analysis method for the compressive strength of explosion-proof glass provided in this embodiment is based on a compression testing device with a controllable loading rate and a detection system with a high-speed optical camera. The method includes a synchronous signal acquisition step, a process analysis and quantification step, and a comprehensive performance evaluation step. In the synchronous signal acquisition step, during the process of the indenter applying a compressive load to the explosion-proof glass sample, the three-dimensional force-displacement time history signal of the compression contact surface, the high-speed full-field strain image sequence of the back of the sample, and the vibration waveform signal of the piezoelectric sensor array are acquired in parallel through hardware synchronous triggering. In the process analysis and quantification step: the vibration waveform signal is subjected to time-frequency analysis and event localization to distinguish the low-frequency component characterizing stress wave propagation and the high-frequency sudden event characterizing material damage; the spatiotemporal information of the high-frequency sudden event is matched with the strain concentration area in the full-field strain image sequence at the same time to identify damage events caused by microcrack propagation and / or interlaminar delamination; based on the three-dimensional force-displacement time history signal, the full-field strain image sequence, and the identified damage events, the work done by the external force during the compression process is quantified through the distribution ratio and evolution process of overall structural deformation, material damage dissipation, and fragment splash kinetic energy. In the comprehensive performance evaluation step: based on the energy distribution ratio and evolution process obtained from the process analysis and quantification steps, at least two dimensions of performance indicators are calculated, including: the effective energy consumption ratio reflecting the material's ability to convert external mechanical energy into controllable energy consumption, and the dominant failure mode evolution sequence reflecting the stability of the failure process; finally, a dynamic evolution spectrum of compressive strength performance over time is generated, which integrates the aforementioned performance indicators, and is used to evaluate the protective effectiveness, failure mechanism, and critical point of the explosion-proof glass.
[0019] In this embodiment, the multi-dimensional analysis method for the compressive strength of explosion-proof glass aims to comprehensively and deeply analyze the dynamic response, energy dissipation mechanism, and failure process of explosion-proof glass under compressive load, thereby providing richer performance evaluation results than traditional single-index methods. The controllable loading rate compression test device is used to apply quasi-static or dynamic compressive loads, and a high-speed optical camera is used to capture image sequences of the explosion-proof glass sample surface at an extremely high frame rate during compression, thereby acquiring dynamic visual information such as macroscopic deformation, crack initiation and propagation, and fragmentation under compression. A piezoelectric sensor array is used to convert stress waves or vibration signals within the material into electrical signals to monitor stress wave propagation, damage events within the material, and acoustic emission activity.
[0020] In the synchronous signal acquisition step, the three-dimensional force-displacement time history signal of the compression contact surface can be acquired using force and displacement sensors mounted on the indenter or the explosion-proof glass sample. This reflects the changes in compressive force in three orthogonal directions and the displacement changes of the indenter. A high-speed full-field strain image sequence can be obtained by continuously capturing images of the back of the sample using a high-speed optical camera. This reflects the full-field strain distribution of the explosion-proof glass sample surface under compression over time. The vibration waveform signal of the piezoelectric sensor array can be acquired by attaching multiple piezoelectric sensors to the back of the explosion-proof glass sample and connecting them to the data acquisition system. This reflects the elastic and inelastic waves generated within the material during compression. Analyzing this signal can reveal the propagation characteristics of stress waves and acoustic emission events caused by microscopic damage. The acquisition of these signals can be initiated separately and then time-aligned through post-processing. To ensure accurate temporal correspondence of all signals, parallel acquisition is preferably achieved through a hardware synchronous triggering mechanism. For example, an independent trigger can be set up to generate an electrical pulse signal when the indenter comes into contact with the explosion-proof glass sample. This pulse signal is simultaneously sent to the data acquisition system of the force-displacement sensor, the high-speed optical camera, and the piezoelectric sensor array, so that all devices start recording data at the same time.
[0021] In the process analysis and quantification steps, time-frequency analysis and event localization are performed on the acquired vibration waveform signals. For example, short-time Fourier transform or wavelet transform methods can be used to decompose the vibration signal into different frequency components that change over time. By analyzing these time-frequency diagrams, the occurrence time and duration of different frequency components in the signal can be identified. In one feasible approach, low-frequency and high-frequency components can be roughly distinguished by observing the energy distribution of the signal. This distinguishes the low-frequency components characterizing stress wave propagation and the high-frequency burst events characterizing material damage. For example, low-frequency components usually correspond to the macroscopic propagation of stress waves in the material, with lower frequencies and longer durations; while high-frequency burst events usually manifest as short-duration, high-energy pulse signals, corresponding to local damage events such as the initiation, propagation, or delamination of microcracks within the material. Then, the spatiotemporal information of the high-frequency burst events is matched with strain concentration areas in a simultaneous high-speed full-field strain image sequence to identify damage events caused by microcrack propagation and / or delamination. For example, regions with large strain gradients or abnormally high strain values can be identified first from high-speed full-field strain image sequences. These regions may indicate local deformation or damage to the material. Then, the timing and location of these strain concentration areas are compared with the timing of high-frequency burst events and the location information of the piezoelectric sensor array to determine the specific type and location of the damage event. Based on the three-dimensional force-displacement time history signal, the high-speed full-field strain image sequence, and the identified damage events, the distribution ratio and evolution process of the work done by external forces during compression through overall structural deformation, material damage dissipation, and fragment ejection kinetic energy can be quantified. For example, the energy dissipation due to overall structural deformation can be estimated by measuring the macroscopic deformation of the explosion-proof glass sample during compression and combining it with the material's mechanical property parameters. The energy dissipation due to material damage can be roughly estimated by statistically analyzing the number and intensity of damage events. The fragment ejection kinetic energy can be calculated by collecting fragments after compression, measuring their total mass, and then estimating their average velocity using empirical formulas.
[0022] In the comprehensive performance evaluation step, based on the energy distribution ratio and evolution process obtained from the process analysis and quantification steps, performance indicators in at least two dimensions are calculated. For example, the effective energy dissipation ratio, reflecting the material's ability to convert external mechanical energy into controllable energy dissipation, can be calculated. This ratio can be obtained by comparing the energy dissipated by structural deformation with the total input mechanical energy. This ratio quantifies the ability of the explosion-proof glass to convert input mechanical energy into controllable energy dissipation during compression; a higher effective energy dissipation ratio generally indicates better protective performance. Simultaneously, the evolution sequence of the dominant failure mode, reflecting the stability of the failure process, can be calculated, for example, qualitatively described by observing the type and sequence of damage events. This sequence describes the trend of the failure mechanism of the explosion-proof glass under compression over time. Finally, a dynamic evolution spectrum of the compressive strength performance over time is generated, used to evaluate the protective effectiveness, failure mechanism, and critical point of the explosion-proof glass. This spectrum is a comprehensive graph that visualizes the changing trends of multiple performance indicators, such as the effective energy dissipation ratio and the evolution sequence of the dominant failure mode, over time. For example, the spectrum can be a two-dimensional chart, with the horizontal axis representing time and the vertical axis representing different performance indicators, such as effective energy dissipation ratio and frequency of damage events. By analyzing this spectrum, one can intuitively understand the performance change trend of explosion-proof glass during compression, thereby judging its protective capability, identifying key factors leading to failure, and predicting the critical conditions for it to reach a failure state.
[0023] In this embodiment, the multi-dimensional analysis method for the compressive strength of explosion-proof glass synchronously triggers and acquires three-dimensional force-displacement signals, high-speed full-field strain image sequences, and vibration waveform signals in parallel. It then performs time-frequency analysis, event matching, and energy quantification to calculate multi-dimensional performance indicators and generate a dynamic evolution spectrum. This method overcomes the limitations of existing technologies in comprehensively monitoring dynamic response, identifying damage events, and quantifying energy distribution. It enables simultaneous acquisition of multiple signals, comprehensive monitoring of dynamic response during compression, accurate identification of material damage events, quantification of energy distribution ratios, and generation of dynamic performance evolution spectra, thereby improving the accuracy of explosion-proof glass protection performance evaluation.
[0024] In one embodiment, the multi-dimensional analysis method for the compressive strength of explosion-proof glass uses a piezoelectric sensor array arranged in a grid pattern with a set density on the back of the explosion-proof glass sample, and the sampling frequency of each sensor is not less than 1MHz.
[0025] In this embodiment, the set density can be determined based on the size of the explosion-proof glass sample, the size of the expected damage event, and the required spatial resolution. The grid can be a square grid, a triangular grid, or a hexagonal grid. Simultaneously, the sampling frequency of each sensor is no less than 1MHz to ensure accurate capture of high-frequency vibration signals and avoid signal distortion. Through the above technical solution, local blind spots can be effectively avoided, improving the spatial localization capability of damage events.
[0026] In one embodiment, the multi-dimensional analysis method for the pressure resistance of explosion-proof glass is triggered by a pulse signal generated when the three-dimensional force signal of the compression contact surface first exceeds a preset threshold.
[0027] In this embodiment, hardware synchronization triggering refers to a mechanism that ensures multiple data acquisition channels can start recording data simultaneously or with precisely controlled time offsets. Its function is to maintain the temporal correlation between different types of acquired data, thereby ensuring that the dynamic response of the entire compressed event process can be accurately captured.
[0028] In one embodiment, the multi-dimensional analysis method for the compressive strength of the explosion-proof glass obtains the energy dissipation of the overall structural deformation in the energy distribution by reconstructing the full-field strain-stress field from the full-field strain image sequence and performing integral calculation.
[0029] In this embodiment, the energy dissipation due to overall structural deformation refers to the energy absorbed and dissipated by the elastic or plastic deformation of the macroscopic structure of the explosion-proof glass sample when subjected to compressive load. This energy is one of the important mechanisms by which explosion-proof glass resists external compression and maintains structural integrity, and quantifying its value is crucial for understanding the compressive strength of the material. Full-field strain-stress field reconstruction refers to converting the strain distribution information on the surface of the explosion-proof glass sample into corresponding stress distribution information based on the acquired full-field strain image sequence and the mechanical parameters of the explosion-proof glass material, using numerical calculation methods or analytical models. Specifically, after extracting the full-field strain data using digital image correlation technology, the finite element inverse analysis method can be used, with the measured strain data as input, to inversely calculate the stress field inside the sample. Integral calculation refers to performing mathematical integration on the reconstructed full-field stress field and full-field strain field over the entire volume or specific key areas of the explosion-proof glass sample to accurately quantify the total energy dissipated by the overall structural deformation. The above technical solution utilizes synchronously acquired high-speed full-field strain image sequences. Through full-field strain-stress field reconstruction, intuitive deformation information is transformed into an intrinsic mechanical response. Furthermore, through integral calculation, the proportion of work done by external forces during compression in the overall structural deformation is precisely quantified. This directly reflects the dynamic characteristics of energy dissipation during compression evolution, thus providing a data foundation and calculation basis for a comprehensive and accurate assessment of the compression resistance performance of explosion-proof glass.
[0030] In one embodiment, the multi-dimensional analysis method for the compressive strength of the explosion-proof glass obtains the energy dissipation of material damage in the energy distribution by summing the energy of the vibration waveform signals corresponding to all identified damage events.
[0031] In this embodiment, all identified damage events refer to internal material failure events caused by microcrack propagation and / or interlaminar delamination, determined through time-frequency analysis and event localization of vibration waveform signals in a multi-dimensional analysis method for the compressive strength of explosion-proof glass, combined with matching of strain concentration areas in a high-speed full-field strain image sequence. These damage events are key links in the energy dissipation of materials under compressive loads. The corresponding vibration waveform signal energy refers to the energy contained in the high-frequency components characterizing material damage in the vibration signals collected by a piezoelectric sensor array that are highly correlated with the identified damage events in time and space. This energy is a direct manifestation of the elastic wave energy released or dissipated when the internal structure of the material is damaged. Cumulative summation refers to adding up the vibration waveform signal energies corresponding to all identified damage events to obtain the total energy dissipated by material damage during the entire compression process. Through the above technical solution, the energy dissipation due to material damage can be quantified directly and efficiently.
[0032] In one embodiment, the multi-dimensional analysis method for the compressive strength of the explosion-proof glass obtains the fragment splash kinetic energy by analyzing the motion vector of the fragments at the moment of separation in the full-field strain image sequence and calculating it based on a preset fragment mass model.
[0033] In this embodiment, the motion vectors of the fragments at the moment of separation in the full-field strain image sequence are analyzed to accurately capture the instantaneous motion state of the fragments as they detach from the explosion-proof glass sample, including their velocity and direction, which is the basis for calculating kinetic energy. The calculated kinetic energy is based on a preset fragment mass model, aiming to convert the obtained fragment motion vectors into physically meaningful kinetic energy values, ensuring the accuracy and repeatability of energy calculations. Through the above technical solution, analyzing the motion vectors of the fragments at the moment of separation in the full-field strain image sequence enables real-time, high-precision capture of the fragments' trajectory and velocity changes as they detach from the sample, avoiding errors caused by signal acquisition lag or isolation in traditional methods, thereby ensuring the accuracy of the fragment splash kinetic energy data.
[0034] In one embodiment, the multi-dimensional analysis method for the compressive strength of the explosion-proof glass determines the evolution sequence of the dominant failure mode by means of the following: taking a preset time window as a unit, the ratio of material damage dissipation energy to fragment splash kinetic energy within each time window is statistically analyzed. When the ratio is continuously lower than a preset threshold, it is determined that the dominant failure mode is changing to brittle fracture.
[0035] In this embodiment, the method for determining the dominant failure mode evolution sequence aims to provide a quantitative and dynamic tracking method for the transformation of failure modes of explosion-proof glass under compressive loads, with particular focus on the transition from ductile failure to brittle fracture. This determination method can be implemented by defining a series of judgment rules or algorithms, automatically identifying and recording the stage changes of failure modes based on real-time or near-real-time energy distribution data. The setting of a preset time window divides the entire compression process into discrete time periods to allow for the statistical analysis of local energy ratios within these periods. This effectively captures the dynamic and stage-specific characteristics of the failure process, avoiding the obscuring of key details by overall averaging. The preset time window can be set to a fixed duration based on the compression duration, material properties, or experimental experience. Within each preset time window, the ratio of material damage dissipation energy to fragment ejection kinetic energy is a key indicator for quantifying the failure mode. Material damage dissipation energy primarily reflects the microscopic damage within the material. Fragment ejection kinetic energy characterizes the kinetic energy carried by the macroscopic fragmentation and formation of high-speed fragments, and is typically associated with brittle fracture behavior. The magnitude of this ratio directly indicates which failure mechanism is dominant within the current time window. Within each preset time window, the vibration waveform signal energy corresponding to all identified damage events within that time window is accumulated to obtain the energy dissipation due to material damage. Simultaneously, the motion vectors at the moment of fragment separation in the full-field strain image sequence within that time window are analyzed, and the fragment ejection kinetic energy is calculated based on a preset fragment mass model, thus calculating the ratio between the two. When this ratio consistently falls below a preset threshold, it signifies a significant increase in fragment ejection kinetic energy relative to material damage dissipation, indicating a shift in the failure mode from primarily internal damage dissipation to primarily macroscopic fragment ejection, i.e., a shift towards brittle fracture. This preset threshold can be set through statistical analysis and expert experience using a large amount of explosion-proof glass compression test data. Ultimately, the shift to brittle fracture as the dominant failure mode is determined, clearly indicating that the failure mode of the explosion-proof glass has changed from relatively controllable damage dissipation to a more dangerous brittle fracture state, providing a basis for protective effectiveness assessment and critical point early warning.
[0036] In one implementation, such as Figure 2 As shown.
[0037] The multi-dimensional analysis method for the compressive strength of explosion-proof glass provided in this embodiment also includes a data archiving and database construction step. In the data archiving and database construction step, all raw data obtained in the synchronous signal acquisition step, all feature parameters extracted in the process analysis and quantification step, and all indicators and graphs generated in the comprehensive performance evaluation step are stored in an associated database to construct a compressive strength performance sample library.
[0038] In this embodiment, the data archiving and database building steps aim to systematically store and manage all data generated during the analysis of the pressure resistance performance of explosion-proof glass, so as to achieve long-term data accumulation and efficient reuse.
[0039] The specific embodiments described above further illustrate the inventive purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, or improvements made by those skilled in the art within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A multi-dimensional analysis method for the compressive strength of explosion-proof glass, characterized in that, The method, based on a compression testing apparatus including a controllable loading rate and a detection system using a high-speed optical camera, includes... Synchronous signal acquisition steps: During the process of the indenter applying compressive load to the explosion-proof glass sample, the three-dimensional force-displacement time history signal of the compression contact surface, the high-speed full-field strain image sequence of the back of the sample, and the vibration waveform signal of the piezoelectric sensor array are acquired in parallel through hardware synchronous triggering. Process analysis and quantification steps: Perform time-frequency analysis and event localization on the vibration waveform signal to distinguish the low-frequency component characterizing stress wave propagation and the high-frequency sudden event characterizing material damage; match the spatiotemporal information of the high-frequency sudden event with the strain concentration area in the full-field strain image sequence at the same time to identify the damage event caused by microcrack propagation and / or interlayer delamination. Based on the three-dimensional force-displacement time history signal, the full-field strain image sequence, and the identified damage events, the work done by external forces during compression is quantified through the distribution ratio and evolution process of overall structural deformation, material damage dissipation, and fragment splash kinetic energy. Comprehensive performance evaluation steps: Based on the energy distribution ratio and evolution process obtained from the process analysis and quantification steps, calculate performance indicators in at least two dimensions, including: the effective energy dissipation ratio, which reflects the material's ability to convert external mechanical energy into controllable energy dissipation, and the evolution sequence of the dominant failure mode, which reflects the stability of the failure process. Finally, a dynamic evolution spectrum of the compressive strength performance over time is generated, which integrates the aforementioned performance indicators, and is used to evaluate the protective effectiveness, failure mechanism, and critical point of the explosion-proof glass.
2. The multi-dimensional analysis method for the compressive strength of explosion-proof glass according to claim 1, characterized in that, The piezoelectric sensor array is arranged in a grid pattern with a set density on the back of the explosion-proof glass sample, and the sampling frequency of each sensor is not less than 1MHz.
3. The multi-dimensional analysis method for the compressive strength of explosion-proof glass according to claim 1, characterized in that, The hardware synchronization trigger is triggered by a pulse signal generated when the three-dimensional force signal of the compression contact surface first exceeds a preset threshold.
4. The multi-dimensional analysis method for the compressive strength of explosion-proof glass according to claim 1, characterized in that, The energy dissipation of the overall structural deformation in the energy distribution is obtained by reconstructing the full-field strain-stress field and integrating the full-field strain image sequence.
5. The multi-dimensional analysis method for the compressive strength of explosion-proof glass according to claim 1, characterized in that, The energy dissipation from material damage in the energy distribution is obtained by summing the energy of the vibration waveform signals corresponding to all identified damage events.
6. The multi-dimensional analysis method for the compressive strength of explosion-proof glass according to claim 1, characterized in that, The kinetic energy of the debris splash is obtained by analyzing the motion vectors of the debris at the moment of separation in the full-field strain image sequence and calculating it based on a preset debris mass model.
7. The multi-dimensional analysis method for the compressive strength of explosion-proof glass according to claim 5, characterized in that, The evolution sequence of the dominant failure mode is determined by the following method: taking a preset time window as the unit, the ratio of the material damage dissipation energy to the fragment splash kinetic energy within each time window is calculated. When the ratio is continuously lower than a preset threshold, it is determined that the dominant failure mode is transforming into brittle fracture.
8. The multi-dimensional analysis method for the compressive strength of explosion-proof glass according to claim 1, characterized in that, The method also includes Data archiving and database construction steps: All raw data obtained in the synchronous signal acquisition step, all feature parameters extracted in the process analysis and quantification step, and all indicators and graphs generated in the comprehensive performance evaluation step are stored in the associated database to construct a compressive strength performance sample library.