Performance testing system for high-hardness polycarbonate material

Abstract

The invention discloses a high-hardness polycarbonate material performance test system, and relates to the technical field of material testing, the high-hardness polycarbonate material performance test system comprises an optical frequency comb interference detection module and a digital signal processing unit, the optical frequency comb interference detection module uses an optical frequency comb laser to output broadband and multi-coherent laser pulses, and the digital signal processing unit outputs the broadband and multi-coherent laser pulses through an optical beam splitting and interference optical modulation device; a laser signal is divided into a reference light path and a detection light path, and the detection light path generates coherent interference through the polycarbonate material to form an interference pattern coherent with a microstructure defect with local refractive index change in the polycarbonate material. According to the invention, by designing the optical frequency comb interference detection module, real-time, non-contact and high-resolution detection of local refractive index change, a micro stress field and microstructure defects in the polycarbonate material is realized, the problem of insufficient resolution caused by mechanical wear and environmental interference in traditional contact measurement is overcome, the detection precision and real-time performance are improved, and the detection efficiency is improved. The material structure is effectively protected and the service life of equipment is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of material testing, and particularly to a performance testing system for high-hardness polycarbonate materials. Background Art

[0002] Currently, high-hardness polycarbonate materials are widely used in the fields of aviation, automobiles, and protective equipment in industrial applications due to their excellent mechanical and thermal properties. Traditional detection methods mostly rely on mechanical contact measurement, which has deficiencies such as low resolution, data islands, and strong environmental sensitivity; while the new generation of non-contact, high-speed, cross-scale testing technologies are becoming an urgent need to improve the accuracy of material detection and real-time monitoring capabilities.

[0003] Patent CN114507430B discloses a polycarbonate modified material for 3D printing, its preparation method, a 3D printing filament, and a testing method for interlayer strength. The above patent modifies the PC material by adding an aliphatic-aromatic copolyester and an epoxy-type polyester chain extender, and the adhesive strength between the layers of the obtained PC modified material has been significantly improved.

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

[0005] Therefore, this application proposes a performance testing system for high-hardness polycarbonate materials that can achieve real-time, non-contact, and high-resolution detection of local refractive index changes, micro stress fields, and microstructural defects inside polycarbonate materials. Summary of the Invention

[0006] The purpose of the present invention is to provide a performance testing system for high-hardness polycarbonate materials to solve the technical problem of low resolution in mechanical contact measurement proposed in the above background art.

[0007] To achieve the above purpose, the present invention provides the following technical solution: A performance testing system for high-hardness polycarbonate materials, including an optical frequency comb interference detection module and a digital signal processing unit. The optical frequency comb interference detection module is connected to 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 broadband, multi-coherent laser pulses. Through an optical beam splitting and interference optical modulation device, the laser signal is divided into a reference optical path and a detection optical path. The detection optical path generates coherent interference through the polycarbonate material to form an interference pattern coherent with the microstructural defects of local refractive index changes inside the polycarbonate material;

[0008] The digital signal processing unit uses multi-channel high-speed data acquisition and the FFT algorithm to perform spectral analysis on the interference pattern, and uses a non-linear data fitting model to reconstruct the digital micro-stress field distribution on the surface and near-surface layer of the material;

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

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

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

[0012] A group of precision fine-tuning beam splitters, whose structure and arrangement are optimized, and the optical signal from the optical frequency comb laser is equally divided into a reference optical path and a detection optical path;

[0013] An interference modulation device, which uses an adjustable phase modulator and an optical coupler to dynamically modulate the light wave after passing through the material in the detection optical path, so that high-contrast interference fringes are generated between the detection optical path and the reference optical path, thereby achieving highly sensitive acquisition of small refractive index changes and local structural anomalies inside the material.

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

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

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

[0017] A non-linear data fitting and inversion algorithm, used to convert the spectral characteristics into the micro-stress values and strain distribution maps of each region inside the material, and support dynamic data visualization and quantitative analysis.

[0018] Preferably, the digital signal processing unit further includes a self-calibration module, which combines a pre-established environmental compensation database and the temperature, humidity, and vibration parameters detected in real time, and uses an adaptive algorithm to automatically correct the signal phase, frequency, and amplitude offsets 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 regulation module in the system. The intelligent bionic feedback regulation module includes:

[0020] Self-learning control algorithm based on artificial neural network, which simulates the feedback mechanism of biological nervous system to weak external stimuli and quickly judges local abnormal areas according to the preliminarily collected micro-stress images;

[0021] A feedback signal generator, when detecting the stress peak or the initial appearance of micro-cracks, converts the prediction result into an adjustment instruction through real-time calculation, dynamically adjusts the action parameters of the loading instrument, so as to realize the tracking optimization of the material response and enhance the ability to capture transient stress changes.

[0022] Preferably, the intelligent bionic feedback regulation module further includes a data fusion interface, which can interactively fuse the multi-point micro-stress data analyzed by the digital signal processing unit with the real-time prediction data of the feedback control module to form a global stress field monitoring map, and assist the closed-loop control strategy to accurately adjust the next test parameters to ensure the continuity, accuracy and dynamic response of the test process.

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

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

[0025] An ultrafast optical acquisition unit with sub-nanosecond response ability, which can capture transient spectral changes;

[0026] A spectral analysis algorithm module based on machine learning, which performs multi-scale noise reduction and feature extraction on the collected broadband spectral 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.

[0027] Preferably, the data of the adaptive spectral transformation nano-detection interface and the digital signal processing unit cooperate with each other to form a cross-scale multi-modal data fusion system, which simultaneously obtains the interference pattern and the spectral signal, and uses relevant algorithms to cross-check the macroscopic stress field data and the nano-scale dynamic response data, so as to provide accurate material state evaluation and qualitatively and quantitatively describe the formation mechanism of local defects and initial cracks.

[0028] Preferably, the test system further includes a digital twin real-time monitoring platform, which uses a high-speed data transmission interface to access the data from the digital signal processing source, 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, and this model has:

[0029] Dynamic data synchronization and visualization function, which can display the performance status of each level of the material in real time;

[0030] The model prediction function comprehensively analyzes historical and current data through deep learning algorithms to predict the future micro-stress evolution and damage trend of materials;

[0031] The virtual-real comparison interface realizes real-time mutual calibration between the digital twin model and the actual test feedback, ensuring the forward-looking 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 form a complete closed-loop feedback control system;

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

[0034] The closed-loop control unit dynamically adjusts the parameters of the loading device based on the feedback information to achieve precise control of the test process;

[0035] The intelligent feedback regulation module combines digital twin prediction and actual micro-stress field data to optimize its own neural network algorithm, further improving the response sensitivity of the test system to sudden micro-strain force changes and local defects of materials, and ensuring that the entire test system can achieve continuous, stable, and high-resolution performance tests under multiple working conditions.

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

[0037] 1. By designing an optical frequency comb interference detection module, the present invention realizes real-time, non-contact, and high-resolution detection of local refractive index changes, micro-stress fields, and micro-structural defects inside polycarbonate materials, overcomes the problem of insufficient resolution caused by mechanical wear and environmental interference in traditional contact measurements, improves detection accuracy and real-time performance, effectively protects the material structure, and extends the service life of the equipment;

[0038] 2. By designing a digital signal processing unit, the present invention realizes the comprehensive inversion of the macroscopic micro-stress distribution and nano-scale dynamic response of materials, conducts global state evaluation, eliminates the problems of noise from a single data source and incomplete local defect information, realizes data complementarity and cross-verification, improves the accuracy and reliability of material state evaluation, and effectively realizes early defect warning;

[0039] 3. By designing a digital twin model, the present invention can display the performance states of materials at all levels in real time and predict 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 tests, provides dynamic and forward-looking test data support, guides the optimization of loading parameters, and helps in material life evaluation and reliability design;

[0040] 4. The present invention realizes the 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 lagging data feedback in traditional tests, improves the test continuity and system stability, and thus ensures the smooth realization of high-resolution performance tests under multiple working conditions by designing a closed-loop control unit and an intelligent bionic feedback regulation module according to the prediction information of the digital twin platform and the actual test feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic structural framework diagram of the test system of the present invention;

[0042] Figure 2 It is a schematic diagram of the test process of the present invention;

[0043] Figure 3 It is an interference fringe pattern generated by an optical frequency comb interference signal of the present invention;

[0044] Figure 4 It is a schematic diagram of a slight change in refractive index of the present invention;

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

[0046] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "inner", "outer", "front end", "rear end", "both ends", "one end", "the other end", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0048] In the description of the present invention, it should be noted that, unless otherwise clearly specified and defined, terms such as "installation", "equipped with", "connection", etc. should be understood in a broad sense. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0049] Please refer to Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5 As shown in

[0050] 、

[0051] and

[0052] , an embodiment provided by the present invention: a performance testing system for a high-hardness polycarbonate material, including an optical frequency comb interference detection module and a digital signal processing unit. The optical frequency comb interference detection module is connected to 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 broadband and multi-coherent laser pulses. Through an optical beam splitting and interference optical modulation device, the laser signal is divided into a reference optical path and a detection optical path. The detection optical path produces coherent interference through the polycarbonate material, forming an interference pattern coherent with the microstructural defects of the local refractive index change inside the polycarbonate material;

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

[0054] A set of precision fine-tuning beam splitters, whose structure and arrangement are optimized, equally divide the optical signal from the optical frequency comb laser into a reference optical path and a detection optical path;

[0055] An interference modulation device dynamically modulates the light wave passing through the material in the detection optical path by using a tunable phase modulator and an optical coupler, so that high-contrast interference fringes are generated between the detection optical path and the reference optical path, thereby achieving highly sensitive acquisition of the tiny refractive index change and local structural abnormality inside the material;

[0056] Furthermore, after the optical frequency comb laser is started, the broadband and multi-coherent laser pulses output by it are evenly divided into two paths by a beam splitter. The reference optical path directly enters the digital signal processing unit or is saved as a reference signal; the detection optical path passes through the polycarbonate sample to be measured. When there are microstructural defects or slight changes in the local refractive index inside the sample, it will cause a corresponding phase shift of the light wave passing through the detection optical path. After the detection optical path is processed by the interference modulation device, it is superimposed with the reference optical path at the coupler output, and finally an interference fringe with high sensitivity is formed. This interference fringe directly reflects the distribution of the internal micro-stress state and structural defects of the sample;

[0057] The high-speed multi-channel data acquisition module obtains the interference pattern signal in real time, converts the time-domain data to the frequency domain through the FFT module, decomposes the signal characteristics caused by local stress changes in different frequency bands, and uses a non-linear data fitting model to map the signal amplitude and phase change amounts in the frequency spectrum to the micro-stress distribution image on the surface and near-surface layer of the polycarbonate material to achieve digital representation; to cancel the environmental noise and the drift of the device itself, the built-in self-calibration module automatically compares the collected data with the preset standard, compensates for the small errors in phase, frequency and amplitude, so as to ensure the high-precision and reliability of the reconstructed data;

[0058] The real-time micro-stress field image generated by the digital signal processing unit is transmitted to the closed-loop control unit. The preset intelligent control strategy will analyze based on the real-time image. When the signal of significant stress concentration or initial micro-crack formation appears locally in the material is parsed, the system will automatically adjust the loading parameters, such as changing the loading rate, energy density or the waveform of the applied load, so that the test process is always in the optimal acquisition state during the dynamic response; the data signal line tightly connects the optical frequency comb interference detection module with the digital signal processing unit and the closed-loop control unit, ensuring that the data transmission delay from optical detection to digital processing and feedback control is extremely low, so as to achieve real-time adaptive testing;

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

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

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

[0062] Among them, Δn(x,y) is the small change in refractive index at the sample surface plane coordinates (x,y), which reflects the change in optical path difference caused by the external load at this point and directly corresponds to the spatial mapping of the internal stress distribution of 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, with the unit of MPa, which describes the change in the load intensity actually borne by this point and is the driving factor for the change in refractive index.

[0063] When applying loads of 10 MPa, 30 MPa, and 50 MPa to the high-hardness polycarbonate samples respectively, the optical frequency comb interference detection module collects interference fringes. After FFT processing, the phase offset is converted into a small change map of refractive index according to the refractive index change formula. Finally, using the pre-calibrated non-linear inversion model, Δn is inverted into the micro-stress field distribution to obtain a digital micro-stress field distribution map.

[0064] Digital micro-stress field distribution map: Using non-linear data fitting and inversion algorithms, Δn in the small change map of refractive index is mapped to local stress values and represented by a chromatogram (0 - 50 MPa), presenting the micro-stress field distribution on the material surface and near the surface layer.

[0065] Please refer to Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5 As an embodiment provided by the present invention: A performance testing system for high-hardness polycarbonate materials, the digital signal processing unit includes:

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

[0067] A fast Fourier transform and time-frequency analysis module, which uses a digital signal processor to convert the time-domain interference pattern to the frequency domain in real time and analyze the spectral characteristics related to local stress and micro-stress fluctuations;

[0068] Non-linear data fitting and inversion algorithms, which are used to convert the spectral characteristics into micro-stress values and strain distribution maps of each region inside the material, supporting dynamic data visualization and quantitative analysis;

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

[0070] Furthermore, during the testing process, the high-frequency signals output by the interference detection module are transmitted along the data signal lines to the sampling front end. Each channel independently collects a continuous time-domain signal data, ensuring that the signals at each sampling point within the entire detection area are captured in real time. This not only ensures the spatial resolution but also captures the instantaneous signal changes caused by the variation of the micro-stress field inside the material;

[0071] The time-domain data collected by each channel undergoes preprocessing including DC removal, filtering, etc., and is then sent to the FFT module for frequency-domain conversion. Through time-domain analysis, the system analyzes the spectral features related to the local stress variation of the material, such as the abnormal increase in amplitude within a specific frequency band, phase mutation, etc. These spectral feature data provide a quantitative basis for subsequent non-linear data fitting, that is, each peak or frequency band in the spectrum corresponds to the micro-stress response of a certain area of the material;

[0072] The preset non-linear data fitting model is obtained through experimental data during the system calibration stage to form a standard curve or mapping function. During the testing process, the system performs data fitting on the spectral data of each sampling point to obtain a numerical micro-stress value. After integrating the data of all sampling points, a micro-stress field distribution map of the material surface and near-surface layer is constructed, and at the same time, it supports quantitative analysis and visual display of the strain distribution in the entire area. In addition, by continuously updating the fitting results in real time, the system realizes the continuous output of dynamic data, which is convenient for subsequent feedback control;

[0073] When the system is designed, a reference database under standard environment is established to record the signal offset data corresponding to various environmental parameters. The real-time detection module collects the current temperature, humidity, and vibration parameters, compares them with the data in the reference database, and the adaptive algorithm calculates the corresponding compensation amount according to the comparison results, including phase, frequency, and amplitude correction, and adjusts the FFT output results in real time. The corrected data undergoes the non-linear data fitting process to ensure that the finally reconstructed micro-stress field image has high precision and low noise, ensuring the stability and reliability of the data;

[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 nano-scale micro-stress and strain information can be retrieved. The self-calibration module combines the environmental compensation algorithm, effectively reducing the interference of the external environment on the signal and the error caused by instrument drift, ensuring data stability. The non-linear data fitting and inversion algorithm can quickly convert the spectral data into a specific stress distribution image, support real-time dynamic visualization, and facilitate the tester to immediately evaluate and judge the material state;

[0075] The reference database measures the influence of environmental changes on the interference phase drift through a calibration experiment under standard environmental conditions (T0 = 20 °C, P0 = 1013.25 hPa, H0 = 50% RH), and uses the least squares method to fit and obtain a linear compensation model:

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

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

[0078] ΔΨ comp is the phase compensation amount. Due to the phase drift of the interference signal caused by environmental changes, the phase or equivalent frequency offset amount to be compensated is subtracted from the original interference phase before FFT analysis to eliminate environmental interference; α T is the phase drift amount caused by a 1 °C change 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, T0 is the reference standard environmental temperature, usually taken as 20 °C; is the phase drift amount caused by a 1 hPa change in the environmental air pressure; p is the current atmospheric pressure, P0 is the reference air pressure; α H is the phase drift amount caused by a 1%RH change in the relative humidity; H is the current relative humidity, H0 is the reference humidity;

[0079] The environmental compensation calculation is as follows:

[0080] Calibration data collection:

[0081] Near the standard environmental point (T0, P0, H0), N groups of different environmental conditions (T i , P i , H i ) are selected, and the corresponding uncompensated phase drift ΔΨ i is measured;

[0082] Construct the design matrix and the observation vector:

[0083] Let , ;

[0084] Among them, ΔT i = T i - T0, ΔP i = P i - P0, ΔH i = H i - H0;

[0085] Solve the coefficient vector:

[0086] Use the least squares method to solve the linear model y = Xα, where , the standard solution is: α’ = (X T X) -1 X T y;

[0087] Steps of the algorithm calculation:

[0088] 1. Calculate X T X:

[0089] ;

[0090] 2. Calculate X T y:

[0091] ;

[0092] 3. Invert and multiply:

[0093] α’ = M -1 b, that is, α T , α P , α H .

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

[0095] A self-learning control algorithm based on an artificial neural network, which simulates the feedback mechanism of the biological nervous system to weak external stimuli and quickly judges local abnormal areas based on the preliminarily collected micro-stress images;

[0096] A feedback signal generator, when a stress peak or the initial appearance of a micro-crack is detected, converts the prediction result into an adjustment instruction through real-time calculation, dynamically adjusts the action parameters of the loading instrument, so as to realize the tracking optimization of the material response and enhance the ability to capture transient stress changes;

[0097] The intelligent bionic feedback control module further includes a data fusion interface, which can interact and 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 to form a global stress field monitoring map, and assist the closed-loop control strategy to accurately adjust the next test parameters to ensure the continuity, accuracy and dynamic response of the test process;

[0098] Furthermore, the multi-point micro-stress data obtained by parsing the digital signal processing unit, such as the stress values, phase information and spectral characteristics of each region, are first normalized and noise-filtered and then input into the neural network. The neural network uses structures such as convolutional layers to extract local features in the micro-stress image, quickly judge the location and change trend of the abnormal region. During the continuous test process, the network continuously learns the actual test feedback, compares it with the preset mode, and automatically updates the weight parameters to ensure a higher capture ability for subtle stress and transient phenomena;

[0099] When the neural network quickly judges that the local stress exceeds the set threshold or has a non-linear mutation, the feedback signal generator is activated at this time. Based on the current test data and historical model prediction, the feedback signal generator determines the optimal load parameter modification value through real-time calculation, such as using an adaptive PID or incremental update algorithm, such as changing the loading rate, energy density or loading waveform. The generated control instruction is fed back to the loading instrument through the closed-loop control unit to immediately adjust the applied load. The adjusted loading instrument parameters can enable the system to obtain more ideal stress distribution data in the next test cycle and further capture transient stress change information;

[0100] The digital signal processing unit provides the stress values and corresponding spectral characteristics of each sampling point in the test area. At the same time, the prediction results of the feedback control module, such as the trend that the stress in a certain area is expected to further rise or there is a risk of crack appearance, are interactively fused with these collected data in real time through the data fusion interface. The system uses a weighted average fusion algorithm to reconstruct a continuous and global stress field monitoring map, which not only contains the quantitative stress distribution, but also marks the predicted evolution trend of the abnormal area. This global monitoring map 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, so as to ensure the continuity, accuracy and dynamic response of the test process;

[0101] Through the self-learning control algorithm based on the artificial neural network, the system can identify and respond to local stress anomalies in materials within microseconds, realizing real-time online regulation. The data fusion interface effectively integrates multi-point data and prediction results, provides a global stress field view for the closed-loop control unit, and supports refined loading parameter adjustment. When a stress peak or the initial appearance of a micro-crack is detected, the feedback signal generator can immediately generate a control instruction, thereby enhancing the capture and tracking ability of transient stress changes and reducing the test risk.

[0102] Please refer toFigure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 , an embodiment provided by the present invention: a performance test system for a high-hardness polycarbonate material, the test system further includes an adaptive spectral transformation nano-detection interface, the adaptive spectral transformation nano-detection interface is arranged behind the optical frequency comb interference detection module, and includes:

[0103] A tunable full-spectrum resolver for capturing the full-spectrum response signal emitted by the material during the loading process;

[0104] An ultrafast optical acquisition unit with sub-nanosecond response ability, capable of capturing transient spectral changes;

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

[0106] The data of the adaptive spectral transformation nano-detection 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 the interference pattern and the spectral signal, and uses relevant algorithms to cross-check the macroscopic stress field data and the nano-scale dynamic response data, so as to provide accurate material state evaluation, and qualitatively and quantitatively describe the formation mechanism of local defects and initial cracks;

[0107] Furthermore, the tunable full-spectrum resolver captures the overall spectral characteristic information of the material by collecting the broadband spectral response emitted by the material during the loading process. By adjusting the working band and resolution of the resolver, different bands in the range from visible light to near-infrared can be covered to adapt to the characteristics of the polycarbonate material. The wavelength range of the resolver is set to 400 - 1100 nm, and the resolution can be adjusted to the 0.1 nm level, enabling the device to capture subtle spectral changes and meet the detection requirements for the micro-environment state of the material such as phase change and micro-crack omen;

[0108] The ultrafast optical acquisition unit has sub-nanosecond response ability and can capture transient spectral changes during the loading process in real time. This is crucial for capturing short-lived and rapidly occurring spectral responses, such as local temperature mutations or transient strain effects. By using high-speed photodetectors and high-speed data collectors, the response time can be as low as 500 ps, ensuring that transient changes under loading will not be lost due to acquisition delay, thus providing a complete signal 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. This module is based on a pre-trained machine learning model and uses algorithms such as convolutional neural network (CNN) or support vector machine (SVM) to extract characteristic parameters of local phase change, molecular rearrangement, or thermal response from spectral data, and couples and determines them with the molecular-level structure information of the material. Before data input, wavelet transform or principal component analysis (PCA) is used for preliminary noise reduction, and then the spectral signals in different bands are classified and feature-recognized through a neural network. For example, when a shift in the absorption peak position or a change in intensity is detected within a specific band, it can be determined that the molecular structure of the material in this area has undergone subtle changes, which may indicate the occurrence of local phase change or the appearance of initial cracks;

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

[0111] Data cross-checking and fusion algorithm: Interference data analysis: The digital signal processing unit uses FFT and non-linear fitting algorithms to process the interference pattern data and reconstructs the micro-stress field distribution map on the material surface; Spectral data analysis: Based on machine learning algorithms, the spectral analysis module extracts features from the full-spectrum data and identifies characteristic parameters related to local phase change, molecular rearrangement, and thermal response; Data cross-checking: A fusion algorithm such as Kalman filtering or weighted fusion method is used to perform spatio-temporal correlation analysis on the macroscopic stress data and spectral feature data. Utilizing the complementary characteristics of the two, the consistency detection of micro-stress and molecular-level changes in the same local area is achieved; Cross-scale data fusion not only corrects the errors caused by noise and environmental impacts in a single data source, but also can quantitatively describe the mechanism of the formation of initial cracks or local defects. For example, in a certain area, the interference pattern shows local stress concentration, and at the same time, the spectral feature shows a shift in the absorption peak position. The combination of the two indicates that this area may be in the initial stage of micro-crack initiation;

[0112] Global Material State Assessment and Qualitative and Quantitative Analysis: The fused data is presented as a global stress field monitoring map and related spectral response maps through the digital twin real-time monitoring platform. Combining historical data and model predictions, the system can accurately evaluate the overall state of the material and qualitatively and quantitatively describe the formation mechanisms of local defects and initial cracks. Using the data fusion results, the system can generate reports including multi-dimensional information such as local stress intensity, strain distribution, and molecular-level response parameters, providing a scientific basis for material reliability assessment and subsequent improvement.

[0113] Please refer to Figure 1 、 Figure 2 、 Figure 3 、 Figure 4 and Figure 5 For an embodiment provided by the present invention: A performance testing system for a high-hardness polycarbonate material, the testing system further includes a digital twin real-time monitoring platform, which uses a high-speed data transmission interface to access data from a digital signal processing source, 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 states of each layer of the material in real time;

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

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

[0117] Furthermore, the data signal processing unit, intelligent feedback module, and adaptive spectral detection interface in the testing system all send the real-time data collected and analyzed by each of them to the digital twin platform through the high-speed data transmission interface. The data transmission uses fiber optic or gigabit Ethernet connections to ensure that the delay is controlled within milliseconds, ensuring strict temporal alignment of the data of each module. The transmitted data includes multi-modal data such as digitalized 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 is built with a data synchronization module to accurately align the multi-modal data from each data source in terms of time stamps, construct the state data of each layer of the material. The synchronization mechanism is based on time tags and buffer queue technology to update the global data state in real time. Through the graphical user interface, the platform converts this data into intuitive three-dimensional stress field models, thermal field maps, and physical field distribution maps, highlighting the details of areas with abnormal stress concentration or spectral anomalies, and showing the changing trends of micro-stress, temperature, and spectral characteristics over time in the form of charts.

[0119] Integrate a prediction module based on a deep neural network within the digital twin platform. This module utilizes historical test data and current real-time data, extracts key features after data preprocessing, constructs a prediction model, processes the input data using normalization, filtering, and noise reduction techniques to ensure data quality, applies structures such as convolutional neural networks, recurrent neural networks, or long short-term memory networks, establishes prediction sub-models for different data features respectively, and then conducts comprehensive prediction through a fusion layer. The module outputs the micro-stress evolution trend, local stress distribution changes, and possible damage development trends of the material over a period of time in the future, forming a prediction map and a prediction curve.

[0120] The virtual-real comparison 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. It calculates the difference between the model prediction value and the actual test value using methods such as Kalman filtering and least squares fitting. If the system detects a large deviation, the interface will automatically send a correction instruction to the closed-loop control unit, and at the same time feedback the deviation information to the deep learning prediction module to trigger model update; on the visualization interface, the prediction map generated by the virtual digital twin model and the actually detected image are simultaneously displayed, and the matching degree between the virtual and real is visually presented through methods such as superposition, difference map, or dynamic chart.

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

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

[0123] The closed-loop control unit dynamically adjusts the parameters of the loading device based on the feedback information to achieve precise control of the test process;

[0124] The intelligent feedback regulation module combines digital twin prediction and actual micro-stress field data, optimizes its own neural network algorithm, further improves the response sensitivity of the test system to sudden micro-strain force changes and local defects of the material, and ensures that the entire test system can achieve continuous, stable, and high-resolution performance tests under multiple working conditions;

[0125] Furthermore, the multimodal data of the digital signal processing unit, intelligent feedback module and adaptive spectral detection interface are synchronously transmitted to the digital twin platform through a high-speed interface to build a complete real-time digital twin model. The model is continuously updated and uses deep learning algorithms to generate predictive information on the future material state. At the same time, the virtual-reality comparison interface is used to correct the prediction results and output prediction information and error feedback. The closed-loop control unit dynamically adjusts the parameters (rate, energy, waveform) of the loading equipment in real time based on the prediction information and error information to ensure that the test loading is always in the optimal state. The intelligent bionic feedback control module combines the digital twin prediction and actual microstress field data to generate feedback instructions, and continuously optimizes itself through self-learning algorithms to improve the response sensitivity to sudden changes. The adjusted loading parameters cause the material response to change, and the relevant data is collected and transmitted to the digital twin platform again. The entire closed-loop control system is continuously iterated 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 were tested on the same batch of samples;

[0127] The comparative experimental test results are shown in the following table:

[0128] Index Experimental method Key parameters Traditional mechanical hardness tester The test system of this application Spatial resolution (μm) Scanning method of scribed lines: Perform micro-step scanning on the sample surface Step distance 100 μm; Laser probe diameter 50 μm 100 10 Micro-stress measurement error (%) Stress loading - feedback measurement: Measure the stress value after loading a known stress block Load = 20 MPa; Number of tests = 10 times Average error 8% Average error 2% Minimum defect detection size (μm) Micro-crack precursor test: Create crack precursor defects on the sample surface Crack precursor width = 50–200 μm 100 50 Signal-to-noise ratio (SNR, dB) Continuous laser interference sampling: Measure the 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: Measure the phase drift under the variation of ±5 °C and ±20 %RH Temperature change = ±5 °C; Humidity change = ±20 %RH ≈5 ppm ≈1 ppm Response dynamic range (MPa) Load - response linearity test: Measure the output linearity within the load range of 0–60 MPa Load range = 0–60 MPa; Linear fitting Linear from 0–40 MPa Linear from 0–60 MPa Multi-point synchronous measurement capability Parallel channel test: Simultaneously collect interference signals from 4 spatial points in parallel Number of channels = 4; Synchronization accuracy = 1 ns No parallel Support 4-channel parallel

[0129] Working Principle: The test system uses a high-precision optical frequency comb laser to output broadband, multi-coherent laser pulses. A precisely designed beam splitter divides the laser into a reference optical path and a detection optical path. When the detection optical path passes through the polycarbonate material, slight phase differences caused by local refractive index changes and microstructural defects within the material form a high-contrast interference pattern. This interference signal reflects the microstress field and strain distribution within the material, fundamentally achieving non-contact, high-resolution detection.

[0130] The test system has built-in high-speed multi-channel sampling and FFT modules, which perform video conversion on interference patterns, extract spectral features related to local stress fluctuations, and use nonlinear data fitting and inversion algorithms to map spectral features into micro-specific digital microstress field images. Simultaneously, an adaptive spectral change nano-detection interface is used to collect full-spectral response signals during material loading. Machine learning algorithms are then used to extract features from nanoscale signals, achieving complementary verification and deep fusion of macro and nano data.

[0131] The digital twin real-time monitoring platform synchronously constructs a real-time model from the data of interference detection, spectrum acquisition, and data processing, uses deep learning algorithms to analyze and predict historical and current data, and performs real-time cross-calibration with actual test data. The closed-loop control unit, based on the model prediction information and combined with the self-learning feedback regulation module based on artificial neural networks, dynamically adjusts the loading parameters in real time to achieve precise control and adaptive optimization of the material testing process, ensuring continuous and stable tracking of material property changes during the detection process.

[0132] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A performance testing system for a high-hardness polycarbonate material, comprising an optical frequency comb interference detection module and a digital signal processing unit, characterized in that: The optical frequency comb interference detection module is connected to 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 broadband and multi-coherent laser pulses. Through an optical beam splitting and interference optical modulation device, the laser signal is divided into a reference optical path and a detection optical path. The detection optical path produces coherent interference via a polycarbonate material, forming an interference pattern coherent with the microstructural defects of the local refractive index change inside the polycarbonate material; The digital signal processing unit uses multi-channel high-speed data acquisition and FFT algorithms to perform spectral analysis on the interference pattern, and uses a non-linear data fitting model to reconstruct the digital micro-stress field distribution on the material surface and near-surface layer; Based on the obtained digital micro-stress field image, the test loading parameters are dynamically adjusted through the intelligent regulation strategy preset in the closed-loop control unit to achieve real-time adaptive testing.

2. The performance testing system for a high-hardness polycarbonate material according to claim 1, wherein: The optical frequency comb interference detection module further includes: An optical frequency comb laser with an ultra-narrow linewidth and high coherence, used to generate stable broadband laser pulses; A set of precision fine-tuning beam splitters, whose structure and arrangement are optimized, to equally divide the optical signal from the optical frequency comb laser into a reference optical path and a detection optical path; An interference modulation device, which uses a tunable phase modulator and an optical coupler to dynamically modulate the light wave after the detection optical path passes through the material, so that the detection optical path and the reference optical path generate high-contrast interference fringes, thereby achieving high-sensitivity acquisition of the small refractive index change and local structural abnormality inside the material.

3. A performance testing system for a high-hardness polycarbonate material according to claim 1, characterized in that: The digital signal processing unit includes: 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, which uses a digital signal processor to convert the time-domain interference pattern to the frequency domain in real time, and analyzes the spectral characteristics related to local stress and micro-stress fluctuations; A non-linear data fitting and inversion algorithm, used to convert the spectral characteristics into the micro-stress values and strain distribution maps of each region inside the material, supporting dynamic data visualization and quantitative analysis.

4. The performance testing system for a high-hardness polycarbonate material according to claim 1, characterized in that: The digital signal processing unit also includes a self-calibration module, which combines a pre-established environmental compensation database and the temperature, humidity, and vibration parameters detected in real time, and uses an adaptive algorithm to automatically correct the signal phase, frequency, and amplitude offsets caused by environmental changes or instrument drift, so as to ensure that the reconstructed micro-stress field image has high precision and low noise.

5. A performance testing system for a high-hardness polycarbonate material according to claim 1, characterized in that: The closed-loop control unit is combined with an intelligent bionic feedback regulation module in the system. The intelligent bionic feedback regulation module includes: A self-learning control algorithm based on an artificial neural network, which simulates the feedback mechanism of the biological nervous system to weak external stimuli, and quickly judges local abnormal areas based on the preliminarily collected micro-stress images; A feedback signal generator, when detecting the initial appearance of a stress peak or a micro-crack, converts the prediction result into an adjustment instruction through real-time calculation, and dynamically adjusts the action parameters of the loading instrument, so as to achieve the tracking optimization of the material response and enhance the ability to capture transient stress changes.

6. The performance testing system for a high-hardness polycarbonate material according to claim 5, characterized in that: The intelligent bionic feedback regulation module further includes a data fusion interface, which can interact and 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 to form a global stress field monitoring map, and assist the closed-loop control strategy to accurately adjust the next test parameters to ensure the continuity, accuracy, and dynamic response of the test process.

7. The performance testing system for a high-hardness polycarbonate material according to claim 1, characterized in that: The test system further includes an adaptive spectral transformation nano-detection interface, which is arranged after the optical frequency comb interference detection module and includes: A tunable full-spectrum resolver for capturing the full-spectrum response signal emitted by the material during the loading process; An ultrafast optical acquisition unit with sub-nanosecond response ability to capture transient spectral changes; A spectral analysis algorithm module based on machine learning, which performs multi-scale noise reduction and feature extraction on the collected broadband spectral signals, and determines the local phase change, molecular rearrangement, or thermal response of the material through the coupling relationship between the spectral characteristics and the molecular-level structure information of the material.

8. The high-hardness polycarbonate material performance testing system according to claim 7, characterized in that: The adaptive spectral transformation nano-detection interface cooperates with the data of the digital signal processing unit to form a cross-scale multi-modal data fusion system. This system simultaneously acquires the interference pattern and spectral signal, and uses relevant algorithms to cross-check the macroscopic stress field data and the nano-scale dynamic response data, so as to provide accurate material state evaluation and qualitatively and quantitatively describe the formation mechanism of local defects and initial cracks.

9. The performance testing system for a high-hardness polycarbonate material according to claim 1, wherein: The test system further includes a digital twin real-time monitoring platform, which uses a high-speed data transmission interface to access the data from the digital signal processing source, 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 the physical field, stress field, and thermal field. This model has: Dynamic data synchronization and visualization functions to display the performance status of each layer of the material in real time; Model prediction function, which comprehensively analyzes historical and current data through deep learning algorithms to predict the future micro-stress evolution and damage trend of the material; A virtual-real comparison interface to realize the real-time mutual calibration between the digital twin model and the actual test feedback, and ensure the forward-looking and fine adjustment of the test strategy.

10. The performance testing system for a high-hardness polycarbonate material according to claim 9, wherein: The digital twin real-time monitoring platform and 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 continuously updated according to real-time data and provides pre-judgment information and error feedback to the closed-loop control unit; The closed-loop control unit dynamically adjusts the parameters of the loading device according to the feedback information to achieve precise control of the test process; The intelligent feedback regulation module combines the digital twin prediction and the actual micro-stress field data to optimize its own neural network algorithm, further improving the response sensitivity of the test system to sudden micro-strain force changes and local defects of the material, and ensuring that the entire test system can achieve continuous, stable, and high-resolution performance tests under multiple working conditions.

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