A high hardness polycarbonate material performance test system

Abstract

The application discloses a high-hardness polycarbonate material performance test system, and relates to the technical field of material testing.The system comprises an optical frequency comb interference detection module and a digital signal processing unit.The optical frequency comb interference detection module utilizes an optical frequency comb laser to output wideband and multiphase coherent laser pulses.Through optical beam splitting and interference optical modulation devices, the laser signals are divided into reference light paths and detection light paths.The detection light paths pass through polycarbonate material production to generate coherent interference, and form interference patterns that are coherent with the microstructure defects of the local refractive index changes in the polycarbonate material.The application designs the optical frequency comb interference detection module, realizes real-time, non-contact and high-resolution detection of the local refractive index changes, micro stress field and microstructure defects in the polycarbonate material, overcomes the resolution deficiency problem caused by mechanical wear and environmental interference in traditional contact measurement, improves the detection accuracy and real-time performance, effectively protects the material structure, and prolongs the service life of the equipment.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of material testing, and particularly relates to a high-hardness polycarbonate material performance testing system. BACKGROUND

[0002] At present, high-hardness polycarbonate materials are widely used in the fields of aviation, automobiles and protective equipment due to excellent mechanical and thermal properties in industrial applications. Traditional detection methods mainly rely on mechanical contact measurement, which has the disadvantages of low resolution, data island and strong environmental sensitivity. A new generation of non-contact, high-speed and cross-scale testing technology is becoming an urgent demand for improving the accuracy of material testing and real-time monitoring capability.

[0003] The application patent authorization announcement CN114507430B discloses a polycarbonate modified material for 3D printing, a preparation method thereof, and a test method for layer strength of 3D printing wire. The modified PC material obtained by adding aliphatic aromatic copolyester and epoxy type polyester chain extender to modify the PC material has obviously improved the bonding strength between layers.

[0004] The above-mentioned patent solves the problem that the interlayer bonding strength of polycarbonate is too low and the product structure is easily damaged, which is not suitable for 3D printing applications. However, the above-mentioned test method uses mechanical contact measurement, which has the technical problem of low resolution.

[0005] Therefore, the application provides a high-hardness polycarbonate material performance testing system for realizing real-time, non-contact and high-resolution detection of local refractive index changes, micro stress field and micro structure defects in the polycarbonate material. SUMMARY

[0006] The application aims to provide a high-hardness polycarbonate material performance testing system to solve the technical problem of low resolution in mechanical contact measurement in the background art.

[0007] To achieve the above-mentioned purpose, the application provides the following technical solution: a high-hardness polycarbonate material performance testing system, comprising an optical frequency comb interference detection module and a digital signal processing unit, the optical frequency comb interference detection module is connected with the digital signal processing unit through a data signal line, the optical frequency comb interference detection module uses an optical frequency comb laser to output wideband and multi-coherent laser pulses, and through optical beam splitting and interference optical modulation devices, the laser signal is divided into a reference light path and a detection light path, the detection light path passes through polycarbonate material production to form an interference pattern coherent with the micro structure defects of the local refractive index changes in the polycarbonate material.

[0008] The digital signal processing unit adopts multi-channel high-speed data acquisition and FFT algorithm to perform spectral analysis on the interference pattern, and reconstructs the digital micro stress field distribution of the material surface and near surface layer by using a nonlinear data fitting model.

[0009] Based on the obtained digital micro stress field image, the test loading parameters are dynamically adjusted by the preset intelligent control strategy in the closed loop control unit, so as to realize real-time adaptive testing.

[0010] Preferably, the optical frequency comb interference detection module further comprises:

[0011] An optical frequency comb laser with ultra-narrow linewidth and high coherence is used to generate stable broadband laser pulses.

[0012] A set of precise fine-tuning beam splitters are optimally designed in structure and arrangement to equally divide the optical signals from the optical frequency comb laser into reference light paths and detection light paths.

[0013] An interference modulation device uses an adjustable phase modulator and an optical coupler to dynamically modulate the light waves after passing through the material in the detection light path, so that high-contrast interference fringes are generated between the detection light path and the reference light path, thereby realizing high-sensitivity collection of the material internal micro refractive index changes and local structure abnormalities.

[0014] Preferably, the digital signal processing unit comprises:

[0015] A multi-channel high-speed sampling front end can simultaneously collect high-frequency signals from each point of the interference detection module.

[0016] A fast Fourier transform and time-frequency analysis module uses a digital signal processor to convert the time-domain interference pattern to the frequency domain in real time, and analyzes the frequency spectrum characteristics related to local stress and micro stress fluctuations.

[0017] A nonlinear data fitting and inversion algorithm is used to convert the frequency spectrum characteristics into micro stress values and strain distribution maps of each region in the material, and supports dynamic data visualization and quantitative analysis.

[0018] Preferably, the digital signal processing unit further comprises a self-correction module, which combines a pre-established environmental compensation database and real-time detected temperature, humidity and vibration parameters, and uses an adaptive algorithm to automatically correct the signal phase, frequency and amplitude offset caused by environmental changes or instrument drift, so as to ensure that the reconstructed micro stress field image has high precision and low noise.

[0019] Preferably, the closed loop control unit is combined with an intelligent bionic feedback control module in the system, and the intelligent bionic feedback control module comprises:

[0020] An artificial neural network-based self-learning control algorithm that simulates the feedback mechanism of the biological nervous system to weak external stimuli, rapidly judges the local abnormal area based on the preliminary collected micro-stress images;

[0021] A feedback signal generator that, when detecting stress peaks or initial micro-cracks, converts the prediction results into adjustment instructions through real-time calculation, dynamically adjusts the action parameters of the loading instrument, thereby achieving tracking optimization of material response and enhancing the ability to capture transient stress changes.

[0022] Preferably, the intelligent bionic feedback regulation module further comprises a data fusion interface that can interactively fuse the multi-point micro-stress data parsed by the digital signal processing unit with the real-time prediction data of the feedback control module, form a global stress field monitoring graph, and assist the closed-loop control strategy in accurately adjusting the next test parameters, ensuring continuous, accurate and dynamic response in the test process.

[0023] Preferably, the test system further comprises an adaptive spectral transformation nanoprobing interface, which is arranged after the optical frequency comb interference detection module and comprises:

[0024] A tunable full-spectrum resolution instrument for capturing the full-spectrum response signal emitted by the material during loading;

[0025] A ultrafast optical acquisition unit with sub-nanosecond response capability, capable of capturing transient spectral changes;

[0026] A machine learning-based spectral analysis algorithm module that performs multi-scale noise reduction and feature extraction on the collected broadband spectral signals, determines the material local phase change, molecular rearrangement or thermal response through the coupling relationship between spectral characteristics and material molecular-level structure information.

[0027] Preferably, the adaptive spectral transformation nanoprobing interface and the digital signal processing unit cooperate with each other to form a cross-scale multi-modal data fusion system. This system simultaneously acquires interference patterns and spectral signals, cross-checks macro stress field data and nanoscale dynamic response data using relevant algorithms, thereby providing accurate material state evaluation and qualitative and quantitative description of the formation mechanism of local defects and initial cracks.

[0028] Preferably, the test system further comprises a digital twin real-time monitoring platform that uses a high-speed data transmission interface to access data from the digital signal processing unit, the intelligent feedback module and the adaptive spectral probing interface in real time, and constructs a real-time digital twin model based on the coupling of physical fields, stress fields and thermal fields. This model has:

[0029] Dynamic data synchronization and visualization function, real-time display of the performance state of each level of the material;

[0030] Model prediction function, through deep learning algorithm, comprehensively analyzes the history and current data, and predicts the future micro stress evolution and damage trend of the material;

[0031] Virtual-real contrast interface, realizes real-time mutual correction of digital twin model and actual test feedback, and ensures the foresight and fine adjustment of the test strategy.

[0032] Preferably, the digital twin real-time monitoring platform, the closed-loop control unit and the intelligent bionic feedback regulation module constitute a complete closed-loop feedback control system;

[0033] The digital twin model is updated according to real-time data, and provides prediction information and error feedback to the closed-loop control unit;

[0034] The closed-loop control unit adjusts the dynamic parameters of the loading device according to the feedback information, and realizes accurate control of the test process;

[0035] The intelligent feedback regulation module optimizes its neural network algorithm by combining digital twin prediction and actual micro stress field data, further improves the response sensitivity of the test system to sudden micro strain changes and local material defects, and ensures that the entire test system can realize continuous, stable and high-resolution performance testing under multiple working conditions.

[0036] Compared with the prior art, the beneficial effects of the present application are:

[0037] 1. The present application realizes real-time, non-contact and high-resolution detection of the internal local refractive index change, micro stress field and micro structure defect of the polycarbonate material by designing the optical frequency comb interference detection module, overcomes the resolution problem caused by mechanical wear and environmental interference in traditional contact measurement, improves the detection accuracy and real-time performance, effectively protects the material structure, and prolongs the service life of the equipment;

[0038] 2. The present application realizes comprehensive inversion of the macroscopic micro stress distribution and nanoscale dynamic response of the material, global state evaluation, elimination of single data source noise and incomplete local defect information, data complementation and cross verification, improvement of the accuracy and reliability of material state evaluation, and effective realization of early defect warning by designing the digital signal processing unit;

[0039] 3. The present application realizes real-time display of the performance state of each level of the material, predicts the future micro stress evolution and damage trend, solves the problems of poor real-time performance, lagging post-processing and insufficient prediction ability in traditional testing, provides dynamic and forward-looking test data support, guides the optimization of loading parameters, and helps material life evaluation and reliability design;

[0040] 4. The present application realizes automatic optimization of the loading process, captures sudden micro-strain changes and initial defects, overcomes the defects of fixed loading parameters, insufficient dynamic response and data feedback lag in traditional testing, improves test continuity and system stability, and thus ensures that high-resolution performance testing can be successfully implemented under multiple working conditions according to the pre-judgment information and actual test feedback of the digital twin platform. BRIEF DESCRIPTION OF DRAWINGS

[0041] Figure 1 It is a schematic diagram of the test system structure framework of the present application.

[0042] Figure 2 It is a schematic diagram of the test process of the present application.

[0043] Figure 3 It is an interference fringe diagram of the optical frequency comb interference signal generated by the present application.

[0044] Figure 4 It is a schematic diagram of the small change of the refractive index of the present application.

[0045] Figure 5 It is a schematic diagram of the micro-stress field distribution of the present application. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0047] In the description of the present application, it should be noted that the terms "upper", "lower", "inner", "outer", "front end", "rear end", "two ends", "one end", "the other end" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application. In addition, the terms "first" and "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0048] In the description of the present application, it should be noted that unless otherwise expressly specified and limited, the terms "mounting", "provided with", "connected" and the like should be understood in a broad sense, for example, "connected" can be fixed connection, can also be detachable connection, or integral connection; can be mechanical connection, can also be electrical connection; can be directly connected, can also be indirectly connected through an intermediate medium, can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0049] Please refer to Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 And Figure 5 , the present application provides an embodiment: a high hardness polycarbonate material performance test system, including optical frequency comb interference detection module and digital signal processing unit, the optical frequency comb interference detection module and digital signal processing unit are connected through data signal line, the optical frequency comb interference detection module utilizes the output broadband, multiple coherent laser pulse of optical frequency comb laser, through optical beam splitter and interference optical modulation device, the laser signal is divided into reference light path and detection light path, the detection light path is coherent interfered by polycarbonate material production, forming the interference pattern coherent with the microstructure defect of the local refractive index change in the polycarbonate material internal;

[0050] The digital signal processing unit adopts multi-channel high-speed data acquisition and FFT algorithm, carries out spectrum analysis on the interference pattern, and reconstructs the digital microstress field distribution of the material surface and near surface layer by using nonlinear data fitting model;

[0051] Based on the obtained digital microstress field image, the test loading parameters are dynamically adjusted through the intelligent control strategy preset in the closed loop control unit, and real-time adaptive test is realized;

[0052] The optical frequency comb interference detection module further comprises:

[0053] An optical frequency comb laser with ultra-narrow linewidth and high coherence is used to generate stable broadband laser pulses;

[0054] A set of precision fine-tuning beam splitters, the structure and arrangement are optimized and designed, the optical signals from the optical frequency comb laser are equally divided into reference light path and detection light path;

[0055] An interference modulation device, using adjustable phase modulator and optical coupler to dynamically modulate the light wave after the detection light path passes through the material, so that the detection light path and the reference light path produce high-contrast interference fringes, thereby realizing high-sensitivity collection of the material internal small refractive index change and local structure anomaly;

[0056] Further, after starting the optical frequency comb laser, the broadband, multi-coherent laser pulses output by the optical frequency comb laser are evenly divided into two paths by a beam splitter. The reference light path directly enters a digital signal processing unit or is saved as a control signal. The detection light path passes through the polycarbonate sample to be tested. When the internal microstructure of the sample changes due to microstructure defects or local refractive index, the light wave passing through the detection light path will produce a corresponding phase shift. After the detection light path passes through an interference modulation device, it is superimposed with the reference light path at the coupler, and finally an interference fringe with high sensitivity is formed. The interference fringe directly reflects the distribution of the internal microstress state and structural defects of the sample.

[0057] The high-speed multi-channel data acquisition module acquires the interference pattern signal in real time, converts the time domain data to the frequency domain through the FFT module, decomposes the signal characteristics in different frequency bands caused by local stress changes, maps the signal amplitude and phase change in the frequency spectrum to the microstress distribution image of the polycarbonate material surface and near surface layer using a nonlinear data fitting model, and realizes digital representation. In order to offset environmental noise and equipment drift, the built-in self-correcting module automatically compares the collected data with the preset standard, compensates for the small errors of phase, frequency and amplitude, and thus ensures the high precision and reliability of the reconstructed data.

[0058] The real-time microstress field image generated by the digital signal processing unit is transmitted to the closed-loop control unit. The preset intelligent control strategy analyzes the real-time image. When the signal of significant stress concentration or initial microcrack formation in the material is analyzed, the system automatically adjusts the loading parameters, such as changing the loading rate, energy density or waveform of the applied load, so that the test process is always in the optimal acquisition state in the dynamic response. The data signal line closely connects the optical frequency comb interference detection module, the digital signal processing unit and the closed-loop control unit, ensures that the data transmission delay between optical detection, digital processing and feedback control is very low, and thus realizes real-time adaptive testing.

[0059] Optical frequency comb interference signal generation diagram: interference fringe diagram obtained under three working conditions of loading stresses σ1=10 MPa, σ2=30 MPa and σ3=50 MPa. The fringe spacing is inversely proportional to the local refractive index change.

[0060] Refractive index change diagram: refractive index distribution Δn(x, y) obtained after FFT phase shift calculation in the optical frequency comb interference signal generation diagram, which is inverted according to the photoelastic effect Δn=C·Δσ, C is the material photoelastic constant -4.22×10 -6 MPa -1 ;

[0061] Refractive index change formula Δn(x, y)=C·Δσ(x, y),

[0062] Wherein, Δn(x, y) is a small change of refractive index at the sample surface plane coordinate (x, y), reflecting the change of optical path difference caused by external load at the point, directly corresponding to the spatial mapping of the stress distribution inside the material; Δσ(x, y) is the micro stress increment at (x, y), that is, the difference between the stress σ under the loading state and the stress σ0 under the reference state, unit MPa, describing the load intensity change actually borne by the point, which is the driving factor of the refractive index change;

[0063] When the high-hardness polycarbonate sample is subjected to 10 MPa, 30 MPa and 50 MPa loads respectively, the interference fringe is collected by the optical frequency comb interference detection module, and after FFT processing, the phase shift is converted into a small change of refractive index according to the refractive index change formula, finally, the nonlinear inversion algorithm calibrated in advance is used to invert Δn into the micro stress field distribution, and the digital micro stress field distribution map is obtained.

[0064] The digital micro stress field distribution map: the nonlinear data fitting and inversion algorithm is used to map Δn in the small change of refractive index into the local stress value, and the color spectrum (0-50 MPa) is used to represent the micro stress field distribution of the material surface and the near surface layer.

[0065] Please refer to Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5 , the present application provides an embodiment: a high-hardness polycarbonate material performance test system, the digital signal processing unit comprises:

[0066] The multi-channel high-speed sampling front end can simultaneously collect high-frequency signals from each point of the interference detection module;

[0067] The fast Fourier transform and time-frequency analysis module converts the time-domain interference pattern into the frequency domain in real time by using the digital signal processor, and analyzes the frequency spectrum characteristics related to the local stress and micro stress fluctuation;

[0068] The nonlinear data fitting and inversion algorithm is used to convert the frequency spectrum characteristics into the micro stress value and strain distribution map of each region inside the material, and supports dynamic data visualization and quantitative analysis;

[0069] The digital signal processing unit further comprises a self-correction module, which automatically corrects the signal phase, frequency and amplitude offset caused by environmental changes or instrument drift by combining the pre-established environmental compensation database and the real-time detected temperature, humidity and vibration parameters, so as to ensure that the reconstructed micro stress field image has high precision and low noise;

[0070] Further, during the test process, the high-frequency signal output by the interference detection module is transmitted along the data signal line to the sampling front end, and a continuous time domain signal data is independently collected by each channel, ensuring that the signals of all sampling points in the entire detection area are captured in real time, which not only ensures the spatial resolution, but also captures the instantaneous signal changes caused by the changes of the internal micro stress field of the material;

[0071] The time domain data collected by each channel is preprocessed, including removing direct current and filtering, and is sent to the FFT module for frequency domain conversion. Through time domain analysis, the system analyzes the frequency spectrum characteristics related to the local stress changes of the material, such as abnormal increase of amplitude in a specific frequency band and phase mutation, etc. These frequency spectrum characteristic data provide quantitative basis for subsequent nonlinear data fitting, that is, each peak or frequency band in the frequency spectrum corresponds to the micro stress response of a certain area of the material;

[0072] The preset nonlinear data fitting model is obtained through experimental data in the system calibration stage, forming a standard brick curve or mapping function. In the test process, the system performs data fitting on the frequency spectrum data of each sampling point to obtain the numerical micro stress value. After integrating the data of all sampling points, the micro stress field distribution map of the surface and near-surface is constructed, and the strain distribution of the entire area is quantitatively analyzed and visually displayed. In addition, by updating the fitting results in real time, the system realizes the continuous output of dynamic data, which is convenient for subsequent feedback control;

[0073] The system establishes a reference database under standard environment during design, records the signal offset data corresponding to each environmental parameter, and compares the current temperature, humidity and vibration parameters collected by the real-time detection module with the data in the reference database. The self-adaptive algorithm calculates the corresponding compensation amount, including phase, frequency and amplitude correction, adjusts the FFT output result in real time, and performs nonlinear data fitting process on the corrected data, so as to ensure that the finally reconstructed micro stress field image has high precision and low noise, and the stability and reliability of the data are ensured;

[0074] Multi-channel high-speed sampling and FFT processing ensure that all subtle signal changes in the interference pattern are accurately captured and analyzed, so that the nanoscale micro stress and strain information can be inverted. The self-correction module combined with the environmental compensation algorithm effectively reduces the errors caused by external environment interference and instrument drift, ensuring the stability of the data. The nonlinear data fitting and inversion algorithm can quickly convert the frequency spectrum data into specific stress distribution image, support real-time dynamic visualization, and facilitate the test personnel to evaluate and judge the material state in real time;

[0075] The reference database determines the influence of environmental changes on the interference phase shift through calibration experiments under standard environmental conditions (T0=20℃, P0=1013.25 hPa, H0=50%RH), and a linear compensation model is obtained by least squares fitting:

[0076] ΔΨ comp =α T (T-T0)+α P (P-P0)+α H (H-H0)

[0077] Wherein, α T ≈0.96ppm / ℃, α P ≈-0.27ppm / hPa, α H ≈0.0084ppm / %RH, the real-time self-correcting module automatically calculates and corrects the FFT phase according to the model, and the signal-to-noise ratio is improved by about 20dB after correction. The non-linear fitting process is carried out on the compensated data to ensure that the reconstructed micro stress field image has high precision and low noise. In addition, the improved Edlen formula combined with particle swarm optimization algorithm can be selected to further reduce the compensation error to <10ppm;

[0078] ΔΨ comp is the phase compensation amount, the interference signal phase shift caused by environmental changes, the phase or equivalent frequency offset amount that needs to be compensated, which is deducted from the original interference phase before FFT analysis to eliminate environmental interference; α T The phase shift amount caused by a change of 1℃ in the environmental temperature, which needs to be calibrated according to the specific optical path length and material parameters; T is the current test environmental temperature, and T0 is the reference standard environmental temperature, usually 20℃; The phase shift amount caused by a change of 1hPa in the environmental air pressure; p is the current atmospheric pressure, and P0 is the reference air pressure; α H The phase shift amount caused by a change of 1%RH in the relative humidity; H is the current relative humidity, and H0 is the reference humidity;

[0079] The environmental compensation algorithm is as follows:

[0080] Calibration data collection:

[0081] Near the standard environmental point (T0, P0, H0), select N groups of different environmental conditions (T i , P i , H i ), and measure the corresponding uncompensated phase shift ΔΨ i ;

[0082] Construct the design matrix and observation vector:

[0083] Let X= , y= ;

[0084] where, Delta T i = T i -T0, Delta P i = P i -P0, Delta H i = H i -H0;

[0085] Solving the coefficient vector:

[0086] Using the least square method to solve the linear model y=Xa, where, a=[ ] T , the standard solution is: alpha'=(X T X) -1 X T y;

[0087] Algorithm operation steps:

[0088] 1, calculate X T X:

[0089] M= ;

[0090] 2, calculate X T y:

[0091] b= ;

[0092] 3, inverse and multiply:

[0093] alpha'=M -1 b, that is, alpha T , alpha P , alpha H .

[0094] Please refer to Figure 1 , Figure 2 , Figure 3 , Figure 4 And Figure 5 , an embodiment provided by the application: a high-hardness polycarbonate material performance test system, the closed-loop control unit is combined with the intelligent bionic feedback control module in the system, and the intelligent bionic feedback control module comprises:

[0095] The self-learning control algorithm based on artificial neural network simulates the feedback mechanism of the biological nervous system to weak external stimulation, rapidly judges the local abnormal area according to the preliminary collected micro stress image;

[0096] A feedback signal generator, when detecting stress peak or micro-crack initial appearance, converts the prediction results into adjustment instructions through real-time calculation, dynamically adjusts the action parameters of the loading instrument, thereby realizing the tracking optimization of material response and enhancing the capture ability of transient stress changes;

[0097] The intelligent bionic feedback regulation module further comprises a data fusion interface, which can interactively fuse the multi-point micro stress data parsed by the digital signal processing unit and the real-time prediction data of the feedback control module, form a global stress field monitoring graph, and assist the closed-loop control strategy in accurately adjusting the next test parameters, ensuring continuous, accurate and dynamic response of the test process;

[0098] Further, the multi-point micro stress data such as regional stress values, phase information and frequency spectrum characteristics parsed by the digital signal processing unit are first input into the neural network after normalization and noise filtering, and the neural network uses convolutional layers and other structures to extract local features in the micro stress image, quickly determine the position and change trend of the abnormal area, and in the continuous test process, the network continuously learns the actual test feedback and compares with the preset mode, automatically updates the weight parameters, and ensures higher capture ability for subtle stress and transient phenomena;

[0099] When the neural network quickly determines that the local stress exceeds the set threshold or has a nonlinear mutation, the feedback signal generator is aerodynamic, and the feedback signal generator determines the optimal loading parameter modification value, such as changing the loading rate, energy density or loading waveform, based on the current test data and historical model prediction through real-time calculation, for example, using adaptive PID or incremental update algorithm, generates control instructions, which are fed back to the loading instrument through the closed-loop control unit, immediately adjust the applied load, and the adjusted loading instrument parameters can make the system obtain more ideal stress distribution data in the next test period, further capturing transient stress change information;

[0100] The digital signal processing unit provides stress values and corresponding frequency spectrum characteristics from each sampling point in the test area, while the prediction results of the feedback control module, such as the estimated trend of further rising of stress in a certain area or the risk of crack, are interactively fused with these collected data in real time through the data fusion interface, and the system reconstructs a continuous and global stress field monitoring graph using a weighted average fusion algorithm, which not only contains quantitative stress distribution but also marks the predicted evolution trend of the abnormal area. The global monitoring graph is fed back to the closed-loop control unit to provide data support for the system to automatically adjust the loading parameters in the next step, thereby ensuring continuous, accurate and dynamic response of the test process;

[0101] Through the self-learning control algorithm based on artificial neural network, the system can identify and respond to local stress abnormalities of the material within microseconds, realize real-time online regulation and control, and effectively integrate multi-point data and prediction results through the data fusion interface to provide a global stress field view for the closed-loop control unit, support fine loading parameter adjustment, and when detecting stress peaks or initial micro-cracks, the feedback signal generator can generate control instructions in real time, thereby enhancing the ability to capture and track transient stress changes and reducing test risks.

[0102] Please refer to Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5 , the present application provides an embodiment: a high-hardness polycarbonate material performance test system, the test system further comprises an adaptive spectral transformation nanodetection interface, the adaptive spectral transformation nanodetection interface is arranged after the optical frequency comb interference detection module, and comprises:

[0103] a tunable full-spectrum spectrometer for capturing full-spectrum response signals emitted by the material during loading;

[0104] a superfast optical acquisition unit with sub-nanosecond response capability, capable of capturing transient spectral changes;

[0105] a machine learning-based spectral analysis algorithm module, which performs multi-scale noise reduction and feature extraction on the collected broadband spectrum signals, and determines the local phase change, molecular rearrangement or thermal response of the material through the coupling relationship between spectral characteristics and material molecular structure information;

[0106] The adaptive spectral transformation nanodetection interface and the digital signal processing unit cooperate with each other to form a cross-scale multi-modal data fusion system, which simultaneously acquires interference patterns and spectral signals, cross-checks macro stress field data and nanoscale dynamic response data using relevant algorithms, thereby providing accurate material state evaluation and qualitative and quantitative description of the formation mechanism of local defects and initial cracks;

[0107] Further, the tunable full-spectrum spectrometer collects broadband spectrum responses emitted by the material during loading to capture overall spectral characteristic information of the material. By adjusting the working wavelength range and resolution of the spectrometer, different wavelength ranges from visible light to near-infrared can be covered to adapt to the characteristics of polycarbonate materials. The wavelength range of the spectrometer is set to 400-1100 nm, and the resolution can be adjusted to 0.1 nm level, so that the device can capture subtle spectral changes and meet the detection requirements for material microenvironment states such as phase change and micro-crack precursors;

[0108] The ultrafast optical acquisition unit has sub-nanosecond response capability, which can capture the transient spectral changes in the loading process in real time. This is crucial for capturing short and rapid spectral responses such as local temperature mutations or transient strain effects. The use of high-speed photodetectors and high-speed data acquisition devices can reduce the response time to 500 ps, ensuring that transient changes under loading will not be lost due to acquisition delay, thus providing complete signals for subsequent data analysis;

[0109] The spectral analysis algorithm module based on machine learning performs multi-scale noise reduction and feature extraction on the collected broadband spectral signals. Based on the pre-trained machine learning model, the module uses convolutional neural network (CNN) or support vector machine (SVM) algorithms to extract feature parameters of material local phase change, molecular rearrangement, or thermal response from spectral data, and to couple with material molecular structure information for judgment. Wavelet transform or principal component analysis (PCA) is used for preliminary noise reduction before data input, and then neural network is used for classification and feature recognition of spectral signals in different wavebands. For example, when a specific waveband absorption peak shift or intensity change is detected, it indicates that the material molecular structure in that region has changed slightly, possibly indicating the emergence of local phase change or initial crack;

[0110] When working synchronously with the optical frequency comb interference detection module, the adaptive spectral transformation nanoprobing interface is responsible for collecting material full-spectrum response data. The two parts of data, interference pattern data and spectral signal data, are transmitted to the digital signal processing unit through high-speed data signal lines, ensuring that the collected time-domain data and spectral data can be aligned in real time. This synchronous acquisition ensures that macroscopic stress field information and nanoscale dynamic response information can be obtained simultaneously under the same loading state, laying the foundation for subsequent data fusion;

[0111] Data cross-checking and fusion algorithm: interference data analysis: the digital signal processing unit uses FFT and nonlinear fitting algorithms to process the interference pattern data and reconstruct the micro-stress field distribution map of the material surface; spectral data analysis: based on machine learning algorithms, the spectral analysis module extracts features from full-spectrum data and identifies feature parameters related to local phase change, molecular rearrangement, and thermal response; data cross-checking: using fusion algorithms such as Kalman filtering or weighted fusion methods, macroscopic stress data and spectral feature data are analyzed in time and space. By utilizing the complementary characteristics of both, the consistency of micro-stress and molecular-level changes in the same local region is achieved; cross-scale data fusion not only corrects errors caused by noise and environmental influences in a single data source, but also quantitatively describes the mechanism of initial crack or local defect formation. For example, if the interference pattern in a certain region shows local stress concentration, and the spectral features also show absorption peak shift, the combination of the two indicates that the region may be in the initial stage of micro-crack initiation;

[0112] Global material state evaluation and qualitative and quantitative analysis: the fused data is presented as a global stress field monitoring chart and a related spectral response chart through the digital twin real-time monitoring platform. Combined with historical data and model prediction, the system can accurately evaluate the overall state of the material and qualitatively and quantitatively describe the formation mechanism of local defects and initial cracks; using the data fusion results, the system can generate a report including multi-dimensional information such as local stress intensity, strain distribution, and molecular-level response parameters, providing a scientific basis for material reliability evaluation and subsequent improvement.

[0113] Please refer to Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5 , an embodiment provided by the application: a high-hardness polycarbonate material performance testing system, the testing system further comprises a digital twin real-time monitoring platform, which uses a high-speed data transmission interface to access data from a digital signal processing module, an intelligent feedback module, and an adaptive spectral detection interface in real time, and constructs a real-time digital twin model based on the coupling of physical fields, stress fields, and thermal fields. This model has:

[0114] Dynamic data synchronization and visualization function, which can display the performance state of the material at all levels in real time;

[0115] Model prediction function, which uses deep learning algorithms to analyze historical and current data comprehensively to predict the future micro-stress evolution and damage trend of the material;

[0116] Virtual-real comparison interface, which realizes real-time mutual correction between the digital twin model and the actual test feedback, ensuring the forward-looking and fine adjustment of the test strategy;

[0117] Furthermore, the data signal processing unit, the intelligent feedback module, and the adaptive spectral detection interface in the testing system all send the real-time data obtained through their respective collection and analysis to the digital twin platform through a high-speed data transmission interface. The data transmission uses optical fiber or gigabit Ethernet connection to ensure that the delay control is within milliseconds, ensuring the strict alignment of data from each module in time. The transmitted data includes multi-modal data such as digitized micro-stress field images, local stress values at each sampling point, time-frequency characteristics, spectral characteristics, and feedback control parameters;

[0118] The digital twin platform has a built-in data synchronization module that accurately aligns multi-modal data from various data sources in time stamps to construct the state data of the material at all levels. The synchronization mechanism is based on time tags and buffer queue technology, and updates the global data state in real time. Through the graphical user interface, the platform converts these data into intuitive three-dimensional stress field models, thermal field graphs, and physical field distribution graphs, focusing on displaying the details of areas with abnormal stress concentration or spectral anomalies, and using charts to show the trends of micro-stress, temperature, and spectral characteristics over time.

[0119] The prediction module based on deep neural network is integrated in the digital twin platform, which utilizes historical test data and current real-time data, extracts key features after data preprocessing, constructs a prediction model, processes input data using normalization, filtering and noise reduction techniques to ensure data quality, applies convolutional neural network, recurrent neural network or long short-term memory network structure, establishes a prediction sub-model for different data features, and then integrates through a fusion layer for comprehensive prediction. The module outputs the micro stress evolution trend, local stress distribution change and possible damage development trend of the material in the future period of time, forming a prediction atlas and a prediction curve;

[0120] The virtual-real contrast interface compares the prediction data in the digital twin model with the actual test feedback data in real time, forms an error index, and automatically adjusts the prediction model and test parameters. Methods such as Kalman filtering and least squares fitting are used to calculate the difference between the model prediction value and the actual test value. If the system detects a large deviation, the interface will automatically send a correction instruction to the closed-loop control unit and feedback the deviation information to the deep learning prediction module to trigger model updating. On the visualization interface, the prediction graph generated by the virtual digital twin model and the actually detected image are displayed simultaneously, and the matching degree between virtual and real is intuitively displayed through superposition, difference graph or dynamic chart.

[0121] Please refer to Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 , an embodiment provided by the present application: a high-hardness polycarbonate material performance test system, the digital twin real-time monitoring platform, the closed-loop control unit and the intelligent bionic feedback regulation module constitute a complete closed-loop feedback control system.

[0122] The digital twin model is continuously updated according to real-time data and provides prediction information and error feedback to the closed-loop control unit.

[0123] The closed-loop control unit adjusts the dynamic parameters of the loading device according to the feedback information to realize precise control of the test process.

[0124] The intelligent feedback regulation module optimizes its neural network algorithm by combining digital twin prediction with actual micro stress field data, further improving the response sensitivity of the test system to sudden micro strain changes and local material defects, ensuring that the entire test system can achieve continuous, stable and high-resolution performance testing under multiple working conditions.

[0125] Further, the multi-modal data of the digital signal processing unit, the intelligent feedback module, and the adaptive optical spectrum detection interface are synchronously transmitted to the digital twin platform through a high-speed interface to construct a complete real-time digital twin model. The model is continuously updated and uses a deep learning algorithm to generate prediction information of the future material state. Meanwhile, the prediction results are corrected using a virtual-real comparison interface, and prediction information and error feedback are output. The closed-loop control unit dynamically adjusts the parameters (rate, energy, and waveform) of the loading device in real time according to the prediction information and error information, ensuring that the test loading is always in an optimal state. The intelligent bionic feedback control module generates feedback instructions by combining digital twin prediction with actual micro-stress field data and continuously optimizes itself through a self-learning algorithm to improve the response sensitivity to sudden changes. The adjusted loading parameters cause the material response to change, and the relevant data are collected again and transmitted to the digital twin platform. The entire closed-loop control system is iterated continuously to ensure that the test process is continuous, stable, and high-resolution.

[0126] The test system of the present application and the existing commercially available mechanical hardness tester are used to test the same batch of samples.

[0127] The comparison test results are as follows:

[0128] Indicator Experimental method Key parameters Conventional mechanical hardness meter Test system of the present application Spatial resolution (um) Scratch line scanning: micro-step scanning on the sample surface Step distance 100 um; laser probe diameter 50 um 100 10 Micro-stress measurement error (%) Stress loading-feedback measurement: measuring stress value after loading a known stress block Load = 20 MPa; number of tests = 10 Average error 8 % Average error 2 % Minimum defect detection size (um) Micro-crack precursor test: making crack precursor defects on the sample surface Crack precursor width = 50 - 200 um 100 50 Signal-to-noise ratio (SNR, dB) Continuous laser interference sampling: measuring signal-to-noise ratio at a sampling rate of 100 kHz Sampling rate = 100 kHz; integration time = 1 ms 30 55 Environmental sensitivity (phase drift ppm) Temperature and humidity cycle test: measuring phase drift under ±5 °C and ±20 %RH changes Temperature change = ±5 °C; humidity change = ±20 %RH ≈5 ppm ≈1 ppm Response dynamic range (MPa) Load-response linearity test: measuring output linearity in the range of 0-60 MPa Load range = 0-60 MPa; linear fitting 0-40 MPa linear 0-60 MPa linear Multi-point synchronous measurement capability Parallel channel test: simultaneously collecting interference signals on 4 spatial points in parallel Number of channels = 4; synchronization accuracy = 1 ns No parallel Supporting 4-channel parallel

[0129] Working principle: The test system uses a high-precision optical frequency comb laser to output broadband and multi-coherent laser pulses. The laser is divided into a reference light path and a detection light path through a precisely designed beam splitter. When the detection light path passes through the polycarbonate material, a small phase difference is caused by the local refractive index change and microstructure defects in the material, forming a high-contrast interference pattern. This interference signal reflects the micro-stress field and strain distribution inside the material, fundamentally realizing non-contact and high-resolution detection.

[0130] The test system has a built-in high-speed multi-channel sampling and FFT module that converts the interference pattern into a video and extracts the frequency spectrum features related to local stress fluctuations. Nonlinear data fitting and inversion algorithms are used to map the frequency spectrum features to specific digital micro-stress field images. At the same time, through an adaptive optical spectrum change nanometer detection interface, the full-spectrum response signal of the material during the loading process is collected. In combination with machine learning algorithms, nanoscale signals are extracted to realize complementary verification and deep fusion of macro and nanoscale data.

[0131] The digital twin real-time monitoring platform synchronously constructs real-time models from data of interference detection, spectrum collection and data processing, analyzes and predicts historical and current data by using deep learning algorithms, and real-time mutual checks with actual test data. The closed-loop control unit adjusts loading parameters in real time and dynamically according to model prediction information and self-learning feedback regulation modules based on artificial neural networks, realizes precise control and self-adaptive optimization of the material testing process, and ensures continuous and stable tracking of material performance changes in the testing process.

[0132] It will be obvious to a person skilled in the art that the application is not limited to the details of the above-described exemplary embodiments, but that the application can be implemented in other concrete forms without departing from the spirit or essential characteristics of the application. The embodiments should therefore be considered in all respects as illustrative and not restrictive, the scope of the application being defined by the appended claims rather than by the above description, and it is therefore intended that all changes and modifications that fall within the meaning and range of equivalency of the elements of the claims are encompassed by the application. No reference signs in the claims should be considered as limiting the scope of the claims to the features to which the reference signs are attached.

Claims

1. A high hardness polycarbonate material performance test system, comprising an optical frequency comb interferometric detection module and a digital signal processing unit, characterized in that: The light frequency comb interference detection module is connected with the digital signal processing unit through a data signal line, the light frequency comb interference detection module utilizes a light frequency comb laser to output wideband and multi-coherent laser pulses, and through optical beam splitting and interference optical modulation devices, the laser signal is divided into a reference light path and a detection light path, the detection light path generates coherent interference through a polycarbonate material, and an interference pattern coherent with a microstructure defect of local refractive index change in the polycarbonate material is formed; The digital signal processing unit adopts a multi-channel high-speed data acquisition and FFT algorithm to perform spectral analysis on the interference pattern, and reconstructs a digitalized micro stress field distribution on the material surface and near the surface layer by using a nonlinear data fitting model; Based on the obtained digitalized micro stress field image, a pre-set intelligent control strategy in the closed loop control unit is used to dynamically adjust the test loading parameters, and real-time adaptive testing is realized; The light frequency comb interference detection module further comprises: A light frequency comb laser with ultra-narrow linewidth and high coherence is used to generate stable wideband laser pulses; A set of precision fine-tuning beam splitters are optimally designed in structure and arrangement to equally divide the optical signal from the light frequency comb laser into the reference light path and the detection light path; An interference modulation device is used to dynamically modulate the light wave after the detection light path passes through the material by using an adjustable phase modulator and an optical coupler, so that the detection light path and the reference light path generate high-contrast interference fringes, thereby realizing high-sensitivity collection of the micro refractive index change and local structure abnormality in the material; The digital signal processing unit comprises: A multi-channel high-speed sampling front end capable of simultaneously collecting high-frequency signals from each point of the interference detection module; A fast Fourier transform and time-frequency analysis module is used to convert the time-domain interference pattern to the frequency domain in real time by using a digital signal processor, and analyze the frequency spectrum characteristics related to local stress and micro stress fluctuation; A nonlinear data fitting and inversion algorithm is used to convert the frequency spectrum characteristics into the micro stress values and strain distribution map of each region in the material, and support dynamic data visualization and quantitative analysis; The digital signal processing unit further comprises a self-correction module, which combines a pre-established environmental compensation database and real-time detected temperature, humidity and vibration parameters, and automatically corrects the signal phase, frequency and amplitude offset caused by environmental changes or instrument drift by using an adaptive algorithm, so as to ensure that the reconstructed micro stress field image has high precision and low noise.

2. The high hardness polycarbonate material performance testing system according to claim 1, wherein: The closed loop control unit is combined with an intelligent bionic feedback control module in the system, and the intelligent bionic feedback control module comprises: An artificial neural network-based self-learning control algorithm simulates the feedback mechanism of the biological nervous system to weak external stimuli, rapidly judges the local abnormal area according to the preliminary collected micro stress image, and generates a feedback signal generator; When the stress peak value or micro crack is detected, the prediction result is converted into an adjustment instruction through real-time calculation, the action parameters of the loading instrument are dynamically adjusted, the tracking optimization of the material response is realized, and the ability to capture transient stress changes is enhanced.

3. The high hardness polycarbonate material performance testing system according to claim 2, wherein: The intelligent bionic feedback regulation module further comprises a data fusion interface capable of interacting and fusing the multi-point micro-stress data resolved by the digital signal processing unit with the real-time prediction data of the feedback control module to form a global stress field monitoring graph and assist the closed-loop control strategy in accurately adjusting the next test parameters, thereby ensuring the continuity, accuracy and dynamic response of the test process.

4. The high hardness polycarbonate material performance testing system according to claim 1, wherein: The test system further comprises an adaptive spectral transformation nanodetection interface arranged after the optical frequency comb interference detection module, which comprises: a tunable full-spectrum spectrometer for capturing the full-spectrum response signal emitted by the material during loading; a sub-nanosecond response capable ultrafast optical acquisition unit capable of capturing transient spectral changes; a machine learning based spectral analysis algorithm module for multi-scale noise reduction and feature extraction of the collected broadband spectral signal, and determining the material local phase change, molecular rearrangement or thermal response condition through the coupling relationship between spectral characteristics and material molecular level structure information.

5. The high hardness polycarbonate material performance testing system according to claim 4, wherein: The adaptive spectral transformation nanodetection interface and the digital signal processing unit cooperate with each other to form a cross-scale multi-modal data fusion system. The multi-modal data fusion system simultaneously acquires interference patterns and spectral signals, cross-checks macro stress field data and nanoscale dynamic response data using relevant algorithms, thereby providing accurate material state evaluation and qualitatively and quantitatively describing the formation mechanism of local defects and initial cracks.

6. The high hardness polycarbonate material performance testing system according to claim 1, wherein: The test system further comprises a digital twin real-time monitoring platform which uses a high-speed data transmission interface to access data from the digital signal processing unit, the intelligent feedback module and the adaptive spectral detection interface in real time, and constructs a real-time digital twin model based on the coupling of physical field, stress field and thermal field. The real-time digital twin model has: dynamic data synchronization and visualization function, which can real-time display the performance state of each level of the material; model prediction function, which can comprehensively analyze the historical and current data through deep learning algorithm to predict the future micro-stress evolution and damage trend of the material; virtual-real comparison interface, which realizes real-time mutual checking between the digital twin model and the actual test feedback, and ensures the forward-looking and fine adjustment of the test strategy.

7. The high-heat polycarbonate material performance testing system of claim 6, wherein: The digital twin real-time monitoring platform, the closed-loop control unit and the intelligent bionic feedback regulation module constitute a complete closed-loop feedback control system; The digital twin model is constantly updated according to real-time data and provides prediction information and error feedback to the closed-loop control unit; The closed-loop control unit adjusts the dynamic parameters of the loading device according to the feedback information to realize accurate control of the test process; The intelligent feedback regulation module optimizes its neural network algorithm by combining digital twin prediction with actual micro-stress field data, further improves the response sensitivity of the test system to sudden micro-strain changes and local material defects, and ensures that the entire test system can realize continuous, stable and high-resolution performance testing under multiple working conditions.

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