Injection molding method and injection molding device for vehicle-mounted electronic structural component

Abstract

The invention discloses an injection molding method and an injection molding device for a vehicle-mounted electronic structural member, and the method comprises the following steps: applying an excitation light source to obtain a polarization emission spectrum of engineering plastic, and forming a quantum optical fingerprint spectrum; carrying out polarization analysis on the spectral characteristic peak, and calculating a dichroic ratio to obtain molecular orientation three-dimensional distribution data; applying a laser pulse to acquire a coherent Raman scattering signal and a harmonic generation signal to obtain nonlinear optical response data; analyzing a molecular chain rearrangement characteristic and a stress relaxation characteristic based on the data to obtain kinetic parameters; and analyzing a molecular orientation evolution rule according to the kinetic parameters to obtain structure evolution prediction data. According to the technical scheme, the molecular orientation state of the engineering plastic can be monitored in real time in the injection molding process, and accurate characterization and prediction of the dynamic arrangement behavior of molecular chain segments are achieved through a method combining quantum spectrum analysis and nonlinear optical response characterization. And a scientific basis is provided for optimizing injection molding process parameters and controlling residual stress distribution in a product.

Description

Technical Field

[0001] The present invention relates to the technical field of injection molding process detection, and particularly to an injection molding method and an injection molding device for vehicle-mounted electronic structural parts. Background Art

[0002] Vehicle-mounted electronic structural parts refer to structural components installed on vehicles to provide support, fixation, and protection for various electronic devices, mainly including instrument panel skeletons, center console frames, vehicle-mounted display housings, navigation system brackets, various control module housings, and battery management system housings, etc. These structural parts not only need to meet precise dimensional tolerance requirements, but also must possess excellent mechanical strength, heat resistance, electrical insulation, and electromagnetic shielding performance, and at the same time maintain shape stability and physical property consistency during the long-term use of the vehicle. Therefore, the manufacturing quality of vehicle-mounted electronic structural parts directly affects the reliability and safety of automotive electronic systems.

[0003] In the prior art, vehicle-mounted electronic structural parts are mainly prepared by injection molding of high-performance engineering plastics. During the injection molding process, the high-temperature molten plastic rapidly fills the mold cavity under high pressure and then rapidly cools and solidifies in the mold cavity. Due to the usually complex geometric shapes and uneven wall thickness distributions of vehicle-mounted electronic structural parts, there are significant differences in the melt flow state, shear rate, and cooling rate in different regions during the injection molding process, which results in different degrees of orientation arrangement of polymer chains inside the structural parts. This non-uniformity of molecular orientation generates residual stresses inside the structural parts, causing problems such as warping deformation, cracking, and even functional failure of the products under vehicle-mounted environments such as thermal cycling and mechanical vibration. Especially for vehicle-mounted electronic structural parts with high dimensional accuracy requirements and large wall thickness differences, this problem of non-uniform molecular orientation is more prominent. However, the prior art lacks effective methods for real-time monitoring and prediction of the molecular orientation degree during the injection molding process, and can only indirectly control the product quality through empirical parameter settings and post-event quality inspection, with relatively large technical blind spots. Summary of the Invention

[0004] The main objective of the present invention is to solve the technical problem that the molecular orientation degree during the injection molding process of existing vehicle-mounted electronic structural parts cannot be monitored and predicted in real time.

[0005] The first aspect of the present invention provides an injection molding method for vehicle-mounted electronic structural parts, and the injection molding method for vehicle-mounted electronic structural parts includes: Applying an excitation light source to the engineering plastic during the injection molding process to obtain polarization emission spectra along the injection flow direction and perpendicular to the injection flow direction, and forming a quantum optical fingerprint spectrum; Performing polarization analysis on the characteristic peaks in the quantum optical fingerprint spectrum, calculating the dichroic ratio of the polarization spectra in two directions, analyzing the orientation characteristics of the engineering plastic molecular chains, and obtaining three-dimensional molecular orientation distribution data; Apply a laser pulse to the engineering plastic, collect the coherent Raman scattering signal and harmonic generation signal of the engineering plastic along the injection molding flow direction and perpendicular to the injection molding flow direction, and obtain the non-linear optical response data characterizing the molecular chain segment movement; According to the three-dimensional molecular orientation distribution data and the non-linear optical response data, analyze the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic, and obtain the kinetic parameters characterizing the molecular chain movement ability; According to the kinetic parameters, analyze the molecular orientation evolution law of the engineering plastic during the injection molding process, and obtain the predicted data of the molecular chain structure evolution.

[0006] Preferably, an excitation light source is applied to the engineering plastic during the injection molding process, and the polarization emission spectra along the injection molding flow direction and perpendicular to the injection molding flow direction are obtained to form a quantum optical fingerprint spectrum, including: Apply a third-harmonic pulsed laser to the high stress concentration area, shear stress gradient area and melt pressure jump area in the injection mold respectively to obtain the zonal transient emission spectrum; Perform time-resolved detection according to the zonal transient emission spectrum, distinguish the fluorescence component, delayed fluorescence component and phosphorescence component, and obtain the multi-channel time-resolved spectrum; Perform quantum efficiency correction on the delayed fluorescence component and phosphorescence component in the multi-channel time-resolved spectrum according to the molecular vibration intensity to obtain the corrected emission spectrum; According to the corrected emission spectrum, obtain the polarization spectrum signals along the injection molding flow direction and perpendicular to the injection molding flow direction respectively according to the polarization spectrum intensity ratio, and obtain the zonal polarization emission spectrum; form a quantum optical fingerprint spectrum.

[0007] Preferably, the time-resolved detection according to the zonal transient emission spectrum, distinguishing the fluorescence component, delayed fluorescence component and phosphorescence component, and obtaining the multi-channel time-resolved spectrum includes: Perform time-gated detection on the zonal transient emission spectrum, separate the spectral components according to the excited state lifetime of the molecular chain, and obtain three groups of time-resolved fluorescence spectra; Perform electron-vibrational energy level transition analysis on the three groups of time-resolved fluorescence spectra to determine the excited state lifetime distribution of the molecular chain segments under different stress fields, and obtain the fluorescence quantum yield data; According to the fluorescence quantum yield data, use the time-correlated single photon counting method to analyze the contribution ratio of different lifetime components to obtain the multi-channel time-resolved spectrum.

[0008] Preferably, perform polarization analysis on the characteristic peaks in the quantum optical fingerprint spectrum, calculate the dichroic ratio of the polarization spectra in two directions, analyze the orientation characteristics of the molecular chains of the engineering plastic, and obtain the three-dimensional molecular orientation distribution data, including: Background noise elimination and baseline correction are performed on the spectral peaks in the high stress concentration region of the quantum optical fingerprint spectrum to obtain the chemical bond vibration characteristic peaks of the engineering plastic; For the benzene ring torsional vibration peak and amide stretching vibration peak in the chemical bond vibration characteristic peaks, the polarization spectral intensity ratios along the injection molding flow direction and perpendicular to the injection molding flow direction are calculated respectively to obtain the dichroic ratio data of the molecular chain orientation of the engineering plastic; According to the dichroic ratio data, the included angle relationship between the main axis direction of the molecular chain and the reference coordinate system is calculated to obtain the spatial orientation data of the molecular chain; According to the spatial orientation data, combined with the shear stress field distribution, a rotation matrix transformation is performed on the molecular chain movement trajectory to obtain the three-dimensional distribution data of the molecular orientation.

[0009] Preferably, a laser pulse is applied to the engineering plastic, and the coherent Raman scattering signals and harmonic generation signals of the engineering plastic along the injection molding flow direction and perpendicular to the injection molding flow direction are collected to obtain the non-linear optical response data characterizing the molecular chain segment movement, including: Femtosecond laser pulses are applied to the high stress concentration region and shear stress gradient region of the engineering plastic respectively, and the coherent Raman scattering signals along the injection molding flow direction and perpendicular to the injection molding flow direction are collected to obtain the molecular vibration response data; According to the molecular vibration response data, the second harmonic generation signals of parallel polarization and perpendicular polarization are collected, and partition analysis is performed according to the Raman spectral characteristic peak intensity to obtain the crystallization orientation data of the engineering plastic; According to the crystallization orientation data, through the ratio analysis of the coherent anti-Stokes Raman scattering signal intensity and the harmonic generation signal intensity, the degree of freedom data characterizing the molecular movement restriction is obtained; According to the degree of freedom data and the shear stress field distribution, polarization-dependent analysis of the molecular chain movement is performed to obtain the non-linear optical response data characterizing the molecular chain segment movement.

[0010] Preferably, the degree of freedom data characterizing the molecular movement restriction is obtained by ratio analysis of the coherent anti-Stokes Raman scattering signal intensity and the harmonic generation signal intensity according to the crystallization orientation data, including: Frequency domain deconvolution is performed on the coherent anti-Stokes Raman scattering signal and the harmonic generation signal to separate the local vibration mode and crystal region vibration mode of the molecular chain to obtain a bimodal vibration spectrum; According to the bimodal vibration spectrum, the ratio of the coherent anti-Stokes Raman scattering signal intensity to the harmonic generation signal intensity is calculated, and the vibration restriction degree of the molecular chain segment in the shear flow field is analyzed to obtain the local movement parameters of the molecular chain; The coherent Raman gain spectrum analysis method is used to perform correlation analysis on the vibration mode and orientation state of the molecular chain to obtain the degree of freedom data characterizing the molecular movement restriction.

[0011] Preferably, based on the three-dimensional distribution data of molecular orientation and the non-linear optical response data, analyze the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic to obtain kinetic parameters characterizing the molecular chain movement ability, including: Based on the three-dimensional distribution data of molecular orientation, through cross-correlation analysis of the molecular chain segment movement frequency and the non-linear optical response intensity, calculate the molecular chain relaxation characteristic frequency under different temperature conditions to obtain the multiple relaxation time spectrum of the molecular chain; Perform frequency-domain deconvolution on the local chain segment movement process and the molecular chain cooperative movement process in the multiple relaxation time spectrum respectively, extract the contribution components of the local movement and cooperative movement of the molecular chain segments to obtain the internal friction coefficient of the molecular chain segments; Based on the internal friction coefficient of the molecular chain segments, combined with the spatial distribution gradient of the shear stress field, use the stress-optical coefficient calibration method to calculate the critical stress of molecular chain rearrangement to obtain the activation energy spectrum of stress-induced rearrangement; Perform a bivariate response analysis of the stress field and temperature field on the activation energy spectrum, and use the non-equilibrium state scaling method to calculate the orientation entropy change and conformational entropy change of the molecular chain to obtain the thermodynamic parameters characterizing the non-equilibrium state movement of the molecular chain; Based on the thermodynamic parameters, use the generalized Langevin dynamics equation to calculate the movement characteristic parameters of the molecular chain under the coupled action of the shear flow field and temperature field to obtain the kinetic parameters characterizing the molecular chain movement ability.

[0012] Preferably, perform a bivariate response analysis of the stress field and temperature field on the activation energy spectrum, and use the non-equilibrium state scaling method to calculate the orientation entropy change and conformational entropy change of the molecular chain to obtain the thermodynamic parameters characterizing the non-equilibrium state movement of the molecular chain, including: Perform a coupled response analysis of the stress field and temperature field on the activation energy spectrum simultaneously, use the thermal perturbation response function to analyze the conformational transition process of the molecular chain under the shear field to obtain the non-equilibrium state response function of the molecular chain; Perform stress field scaling analysis based on the non-equilibrium state response function, calculate the orientation entropy change and conformational entropy change of the molecular chain respectively to obtain the entropy change function of the molecular chain; Based on the entropy change function and the stress field distribution, use the non-equilibrium state fluctuation-dissipation theorem to calculate the dynamic structure factor of the molecular chain to obtain the thermodynamic parameters characterizing the non-equilibrium state movement of the molecular chain.

[0013] Preferably, based on the kinetic parameters, analyze the molecular orientation evolution law of the engineering plastic during the injection molding process to obtain the predicted data of the molecular chain structure evolution, including: According to the kinetic parameters, through the molecular chain segment cooperative motion equation and the orientation entropy change calculation method, analyze the molecular chain motion trajectory and the evolution process of the orientation angle of the engineering plastic in the shear flow field to obtain the molecular chain conformation transition data; Perform stress tensor decomposition on the molecular chain conformation transition data under non-isothermal conditions, and adopt the correlation analysis method of stress optical coefficient and nonlinear optical susceptibility to calculate the stress relaxation function of the molecular chain during the melt flow process to obtain the molecular chain segment motion response curve; According to the molecular chain segment motion response curve, adopt the generalized molecular chain stress relaxation model and the molecular chain cooperative motion theory to conduct a coupled analysis of the segment motion and the change of entanglement density of the engineering plastic under shear stress to obtain the molecular chain network structure evolution data; Through the molecular chain network structure evolution data, combined with the temperature field gradient and the shear rate field distribution, use the non-equilibrium state kinetic equation to calculate the evolution laws of the molecular chain orientation degree and crystallinity in the high stress area and the low stress area to obtain the predicted data of the molecular chain structure evolution.

[0014] The second aspect of the present invention provides an injection molding device for vehicle-mounted electronic structural parts, and the injection molding device for vehicle-mounted electronic structural parts includes: An excitation spectrum detection module for applying an excitation light source to the engineering plastic during the injection molding process to obtain the polarized emission spectra along the injection molding flow direction and perpendicular to the injection molding flow direction, and form a quantum optical fingerprint spectrum; A polarization analysis module for performing polarization analysis on the characteristic peaks in the quantum optical fingerprint spectrum, calculating the dichroic ratio of the polarized spectra in two directions, analyzing the orientation characteristics of the molecular chains of the engineering plastic, and obtaining the three-dimensional distribution data of molecular orientation; A nonlinear optical response module for applying laser pulses to the engineering plastic and collecting the coherent Raman scattering signals and harmonic generation signals of the engineering plastic along the injection molding flow direction and perpendicular to the injection molding flow direction to obtain the nonlinear optical response data characterizing the molecular chain segment motion; A kinetic characteristic analysis module for analyzing the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic according to the three-dimensional distribution data of molecular orientation and the nonlinear optical response data to obtain the kinetic parameters characterizing the molecular chain motion ability; A structure prediction module for analyzing the molecular orientation evolution law of the engineering plastic during the injection molding process according to the kinetic parameters to obtain the predicted data of the molecular chain structure evolution.

[0015] The third aspect of the present invention provides an injection molding device for vehicle-mounted electronic structural parts, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected by a circuit; the at least one processor calls the instructions in the memory to enable the injection molding device for vehicle-mounted electronic structural parts to execute the steps of the above-mentioned injection molding method for vehicle-mounted electronic structural parts.

[0016] The fourth aspect of the present invention provides a computer-readable storage medium, in which instructions are stored, and when it runs on a computer, it enables the computer to execute the steps of the above-mentioned injection molding method for vehicle-mounted electronic structural parts.

[0017] The injection molding method for vehicle-mounted electronic structural parts provided by the present invention obtains polarized emission spectra along the injection molding flow direction and perpendicular to the injection molding flow direction during the injection molding process, and forms a quantum optical fingerprint spectrum, which directly reflects the orientation state of the molecular chains of the engineering plastic. This detection method based on polarized spectra can capture the molecular chain arrangement characteristics in different regions in real time, laying a foundation for subsequent orientation analysis. By performing polarization analysis on the characteristic peaks in the quantum optical fingerprint spectrum and calculating the dichroic ratio of the polarized spectra in two directions, the orientation characteristics of the molecular chains of the engineering plastic can be quantitatively characterized, and then three-dimensional distribution data of molecular orientation can be obtained. At the same time, by applying laser pulses to the engineering plastic and collecting coherent Raman scattering signals and harmonic generation signals, the motion state of the molecular segments can be deeply understood, and non-linear optical response data can be obtained, which provides important information for analyzing the dynamic behavior of the molecular chains during the injection molding process.

[0018] Based on the obtained three-dimensional distribution data of molecular orientation and non-linear optical response data, this method can analyze the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic, and obtain kinetic parameters characterizing the molecular chain motion ability. These kinetic parameters reflect the motion law of the molecular chains during the injection molding process. By deeply analyzing these parameters, the evolution law of molecular orientation can be accurately predicted, and prediction data of the molecular chain structure evolution can be obtained. This detection and prediction method starting from the molecular scale breaks through the limitation that traditional detection means cannot monitor molecular orientation in real time, enabling the accurate grasp of the molecular orientation state inside the engineering plastic during the injection molding process, and providing scientific guidance for optimizing injection molding process parameters and reducing the accumulation of residual stress inside the product. Description of the Drawings

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on the structures shown in these drawings without creative efforts.

[0020] Figure 1 This is a schematic diagram of an embodiment of the injection molding method for in-vehicle electronic structural parts in an embodiment of the present invention; Figure 2 This is a schematic diagram of an embodiment of the injection molding device for in-vehicle electronic structural parts in an embodiment of the present invention; Figure 3 This is a schematic diagram of an embodiment of the injection molding equipment for in-vehicle electronic structural parts in an embodiment of the present invention.

[0021] The realization of the purpose, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to 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 of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0023] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present invention, the directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0024] In addition, the descriptions involving "first", "second", etc. in the present invention are only for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In addition, "and / or" throughout the text includes three solutions. Taking A and / or B as an example, it includes the technical solution of A, the technical solution of B, and the technical solution that both A and B are satisfied; in addition, the technical solutions between various embodiments can be combined with each other, which must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0025] An embodiment of the present application provides an injection molding method for in-vehicle electronic structural parts. Figure 1 This is a flowchart of the injection molding method for in-vehicle electronic structural parts provided by an embodiment of the present application. In this embodiment, the method includes: Please refer to Figure 1, an excitation light source is applied to the engineering plastic during the injection molding process to obtain polarization emission spectra along the injection molding flow direction and perpendicular to the injection molding flow direction, and a quantum optical fingerprint spectrum is formed. In one embodiment of the present invention, the step of applying an excitation light source to the engineering plastic during the injection molding process to obtain polarization emission spectra along the injection molding flow direction and perpendicular to the injection molding flow direction, and forming a quantum optical fingerprint spectrum includes: Applying a third-harmonic pulsed laser to the high stress concentration region, shear stress gradient region, and melt pressure jump region in the injection mold respectively to obtain zonal transient emission spectra; Performing time-resolved detection based on the zonal transient emission spectra to distinguish fluorescence components, delayed fluorescence components, and phosphorescence components, and obtaining multi-channel time-resolved spectra; Performing quantum efficiency correction on the delayed fluorescence components and phosphorescence components in the multi-channel time-resolved spectra according to the molecular vibration intensity to obtain corrected emission spectra; According to the corrected emission spectra, polarization spectral signals along the injection molding flow direction and perpendicular to the injection molding flow direction are obtained respectively according to the polarization spectral intensity ratio, and a zonal polarization emission spectrum is obtained; a quantum optical fingerprint spectrum is formed.

[0026] The following specifically describes the steps involved in the above embodiments: Applying a third-harmonic pulsed laser to the high stress concentration region (such as the corners and wall thickness mutation points of structural parts), shear stress gradient region (such as the flow path turning points and wall thickness change regions), and melt pressure jump region (such as near the gate and flow front) in the injection mold respectively is achieved by installing sapphire optical windows at key positions of the injection mold. The specific operation is as follows: The 1064nm fundamental frequency laser generated by a Nd:YAG laser is converted into a 355nm ultraviolet laser pulse through a frequency tripling crystal (such as an LBO or BBO crystal). The pulse width is controlled within 5ns - 10ns, and the repetition frequency is 10Hz - 20Hz. This pulsed laser is introduced into the optical window pre-installed in the mold through an optical fiber transmission system. The window is usually made of sapphire with a diameter of 3mm - 5mm, which can withstand the high pressure and high temperature environment during the injection molding process. After passing through the collimation and focusing systems, the laser forms an excitation region of about 0.5mm - 1mm in the engineering plastic, triggering the electronic state transition of the material and generating transient emission spectra. These spectra are collected through the same optical window, transmitted through the optical fiber to the spectrometer for real-time recording. The third-harmonic 355nm ultraviolet laser is selected because this wavelength can effectively excite aromatic groups in most engineering plastics, and at the same time has sufficient energy to trigger electronic transitions without causing thermal damage to the material.

[0027] Time-resolved detection based on the collected transient emission spectra of different regions is achieved through a time-correlated single-photon counting (TCSPC) system. This system uses a fast photomultiplier tube and a time-correlated counting circuit to classify and count photons with different lifetimes, and the time resolution can reach 100 ps. The specific implementation process is as follows: First, three time windows are set, corresponding to the fluorescence component (lifetime < 10 ns), the delayed fluorescence component (lifetime 10 ns - 100 ns), and the phosphorescence component (lifetime > 100 ns). The fluorescence component mainly comes from the fast radiative transition of the singlet excited state of molecules, the delayed fluorescence component comes from the molecules that return to the singlet state after intersystem crossing, and the phosphorescence component comes from the slow radiative transition of the triplet state. By integrating the spectral signals in each time window respectively, the spectral distributions of the three components are obtained. This time-resolved detection method is particularly suitable for detecting the spectral changes of polymer materials under different stress states because different degrees of molecular orientation will significantly affect the intersystem crossing efficiency and the triplet state formation rate, thus changing the relative intensities of delayed fluorescence and phosphorescence.

[0028] Quantum efficiency correction of the delayed fluorescence component and the phosphorescence component in the multi-channel time-resolved spectrum according to the molecular vibration intensity is achieved through a reference standard sample. First, standard emission spectra are obtained using standard fluorescent substances with known quantum yields (such as anthracene, naphthalene, etc.) under the same conditions. Then, according to the known quantum efficiency of the standard sample, the actual quantum efficiency of different vibration modes of the engineering plastics to be measured is calculated. The specific correction process is as follows: For the main vibration peaks in the delayed fluorescence and phosphorescence spectra (such as C=C stretching vibration, benzene ring breathing vibration, etc.), the integral intensity ratios of these peaks to the corresponding peaks of the standard sample are calculated respectively. Combining with the absolute quantum efficiency of the standard sample, the correction coefficients of each vibration mode of the engineering plastics are obtained. Multiply the original spectrum by the corresponding correction coefficient to obtain the corrected emission spectrum. This correction process ensures the comparability of the spectral intensities of different vibration modes and different lifetime components, making the subsequent orientation analysis more accurate.

[0029] Obtaining the polarized spectral signal based on the corrected emission spectrum is achieved by adding a polarization beam splitter prism in the detection optical path. The beam splitter prism is adjusted to two polarization states parallel and perpendicular to the injection molding flow direction respectively, and the spectral signals I‖ and I⊥ in the two directions are recorded respectively. By calculating the dichroic ratio R = I‖ / I⊥, a quantitative characterization of the molecular chain orientation degree in different regions is obtained. Combine the polarized spectral data of each key region into a complete regionalized polarized emission spectrum, and finally form the quantum optical fingerprint spectrum of the engineering plastics. This fingerprint spectrum contains the orientation information of the material at the molecular level and can accurately reflect the arrangement state of molecular chains during the injection molding process. The reason why the quantum optical fingerprint spectrum has the "fingerprint" characteristic is that it not only reflects the chemical composition of the material, but more importantly, records the unique characteristics of the molecular arrangement in space and dynamic behavior, just like a person's fingerprint, which has a high degree of specificity and recognition value.

[0030] In one embodiment of the present invention, the time-resolved detection is performed according to the zonal transient emission spectrum to distinguish fluorescence components, delayed fluorescence components, and phosphorescence components, and a multi-channel time-resolved spectrum is obtained, including: Performing time-gated detection on the zonal transient emission spectrum, separating spectral components according to the excited state lifetimes of molecular chains, and obtaining three groups of time-resolved fluorescence spectra; Performing electron-vibrational energy level transition analysis on the three groups of time-resolved fluorescence spectra, determining the excited state lifetime distribution of molecular chain segments under different stress fields, and obtaining fluorescence quantum yield data; According to the fluorescence quantum yield data, using the time-correlated single photon counting method to analyze the contribution ratios of components with different lifetimes, and obtaining a multi-channel time-resolved spectrum.

[0031] The following specifically describes the steps involved in the above embodiments: The time-gated detection of the zonal transient emission spectrum is realized by a high-speed photoelectric gating system. This system uses an ultrafast photodetector (such as a streak camera or a fast photomultiplier tube) in cooperation with a precision time delay control circuit to collect the spectral signal in time segments. The specific implementation process is as follows: First, input the zonal transient emission spectrum signal into the photoelectric detection system, and set three time gates: 0 nanoseconds - 10 nanoseconds (for fluorescence components), 10 nanoseconds - 100 nanoseconds (for delayed fluorescence components), and 100 nanoseconds - 1000 nanoseconds (for phosphorescence components). The settings of these time gates are based on the characteristic lifetimes of the molecular chains of commonly used high-performance engineering plastics (such as polycarbonate and polyphenylene sulfide) in vehicle-mounted electronic components. For example, when the carbonate group in polycarbonate is excited, its singlet fluorescence lifetime is about 2 nanoseconds - 5 nanoseconds, while the delayed fluorescence containing an aromatic ring structure can reach 30 nanoseconds - 80 nanoseconds. The spectral signals are collected through these three time windows respectively to form three groups of time-resolved fluorescence spectra corresponding to different molecular excited states. This time-gating technology can distinguish the contributions of different functional groups in the molecular structure, especially can separate those spectral components that are significantly affected by molecular orientation, providing an accurate signal source for subsequent analysis. In the wall thickness mutation area or weld line area of vehicle-mounted electronic components, due to the large difference in molecular orientation, this time-resolving ability is particularly important and can capture subtle changes that cannot be distinguished by conventional methods.

[0032] The analysis of the electronic-vibrational energy level transitions for three groups of time-resolved fluorescence spectra is achieved through spectral deconvolution and peak fitting techniques. Each group of time-resolved fluorescence spectra is processed using spectral analysis software. First, baseline correction and noise filtering are performed, and then a Gaussian-Lorentzian mixed function is used to fit and separate each peak in the spectrum. Each peak corresponds to the characteristic vibration of a specific chemical bond. For example, for polycarbonate, the peak at 1775 cm -1 corresponds to the C=O stretching vibration of the carbonate group, while the peak at 1230 cm -1 corresponds to the C-O-C stretching vibration. By analyzing the peak position shift, intensity change, and full width at half maximum change of these peaks under different stress fields, the response characteristics of molecular chain segments under different stress states can be determined. Specifically, the spectra of the high-stress regions (such as the wall thickness change) and low-stress regions (such as the uniform wall thickness) in the injection mold are compared, the relative intensity change and peak position shift of each characteristic peak are calculated, and a stress field-spectral parameter relationship diagram is established. Then, through the electronic-vibrational transition theory (such as the Franck-Condon principle), the relationship between these changes and the molecular energy level structure is analyzed, the excited state lifetime distribution of the molecular chain under different stress fields is calculated, and the fluorescence quantum yield data are obtained. This step reveals the subtle changes in the electronic and vibrational energy levels of the molecular chain under stress, which directly reflect the degree of molecular orientation and the internal stress state, and has important guiding significance for predicting the performance stability of in-vehicle electronic structural components under thermal cycling and vibration environments.

[0033] The analysis by the time-correlated single photon counting method based on fluorescence quantum yield data is realized through a high-precision TCSPC (Time-Correlated Single Photon Counting) system. This system includes a pulsed laser light source, a high-sensitivity photomultiplier tube, and a multi-channel time analyzer. The specific operation process is as follows: First, import the fluorescence quantum yield data into the TCSPC system as a reference threshold, and then repeatedly excite the engineering plastic sample, recording the exact time (relative to the excitation pulse) when each photon reaches the detector. The system divides these time information into thousands of time channels with a time resolution of 0.1 nanoseconds to form a statistical histogram of the photon arrival time. Samples are taken separately for different functional regions (such as high-strength support regions and precision mating regions) in the in-vehicle electronic structural components to obtain the distribution of photon arrival times. Then, fit these distribution curves with a multi-exponential decay model to separate the contribution ratios of different lifetime components. For example, in the highly oriented region, the proportion of short-lived components (corresponding to singlet fluorescence) is usually high, while in the low-oriented region, the proportion of long-lived components (corresponding to triplet phosphorescence) relatively increases. This difference stems from the influence of molecular orientation on the intersystem crossing efficiency. Finally, reconstruct the spectral signals of different lifetime components according to their contribution ratios to form a complete multi-channel time-resolved spectrum. This high-precision time-resolved analysis method can capture the structural changes of engineering plastics at the molecular scale, providing a microscopic basis for accurately predicting the performance degradation of in-vehicle electronic structural components under extreme temperature changes and long-term vibration environments.

[0034] Please continue to refer to Figure 1 , perform polarization analysis on the characteristic peaks in the quantum optical fingerprint spectrum, calculate the dichroic ratio of the polarization spectra in two directions, analyze the orientation characteristics of the molecular chains of the engineering plastic, and obtain three-dimensional distribution data of molecular orientation; In an embodiment of the present invention, the performing polarization analysis on the characteristic peaks in the quantum optical fingerprint spectrum, calculating the dichroic ratio of the polarization spectra in two directions, analyzing the orientation characteristics of the molecular chains of the engineering plastic, and obtaining three-dimensional distribution data of molecular orientation includes: Perform background noise elimination and baseline correction on the spectral peaks in the high-stress concentration region of the quantum optical fingerprint spectrum to obtain the chemical bond vibration characteristic peaks of the engineering plastic; Calculate the intensity ratio of the polarization spectra along the injection molding flow direction and perpendicular to the injection molding flow direction for the benzene ring torsional vibration peak and the amide stretching vibration peak in the chemical bond vibration characteristic peaks respectively to obtain the dichroic ratio data of the molecular chain orientation of the engineering plastic; Calculate the angle relationship between the main axis direction of the molecular chain and the reference coordinate system according to the dichroic ratio data to obtain the spatial orientation data of the molecular chain; According to the spatial orientation data, a rotation matrix transformation is performed on the molecular chain movement trajectory in combination with the shear stress field distribution to obtain three-dimensional molecular orientation distribution data.

[0035] The following specifically describes the steps involved in the above embodiments: The elimination of background noise and baseline correction of the spectral peaks in the high stress concentration region of the quantum optical fingerprint spectrum are achieved through spectral preprocessing techniques. The specific operation process is as follows: First, the original quantum optical fingerprint spectrum is imported into the spectral analysis software, and the wavelet transform denoising algorithm (usually choosing Daubechies wavelet) is applied to perform multi-scale decomposition on the spectral data, retaining the large-scale coefficients representing the true signal while suppressing the small-scale details representing noise. For the spectra in the high stress concentration regions of vehicle-mounted electronic structural components (such as bracket connection points, wall thickness mutation points), the noise threshold is set to 1 / 20 of the root mean square of the signal to effectively remove high-frequency random noise. Then, an improved polynomial fitting method is used to correct the spectral baseline. A 5-7th order polynomial function is selected to fit the background curve to ensure adaptation to the baseline change characteristics of different bands. During the correction process, iterative adaptive weighted fitting is used for the characteristic peak regions such as the 1500 cm -1 -1800 cm -1 region (corresponding to C=O stretching vibration) and the 1100 cm -1 -1300 cm -1 region (corresponding to C-O-C stretching vibration) of polycarbonate materials to avoid interference from the characteristic peak regions on the baseline fitting. After noise elimination and baseline correction, the chemical bond vibration characteristic peaks in the spectrum are clearly presented, and the peak signal-to-noise ratio is increased by 5-10 times. This preprocessing technique is crucial for capturing weak molecular orientation changes during the injection molding process of vehicle-mounted electronic structural components. Especially in high stress regions, the spectral changes caused by molecular orientation are often weak and easily masked by noise, and these key information can only be extracted through precise preprocessing.

[0036] The calculation of the polarization spectral intensity ratio for the chemical bond vibration characteristic peaks is achieved through polarization-dependent analysis. First, peak identification and separation are performed on the preprocessed spectrum. For the characteristic peaks of the commonly used materials of vehicle-mounted electronic structural components, such as the benzene ring torsional vibration peak (about 640 cm -1 -660 cm -1 ) of polycarbonate and the amide stretching vibration peak (about 1630 cm -1 -1650 cm -1),Precise fitting is carried out using a mixed Gaussian-Lorentz function. Then, the integrated intensities of these characteristic peaks in two polarization directions, parallel to the injection molding flow direction (I‖) and perpendicular to the injection molding flow direction (I⊥), are extracted. For each characteristic peak, the dichroic ratio R = I‖ / I⊥ is calculated to form a dichroic ratio data set. For example, at the connection of the dashboard bracket of the in-vehicle electronic structural component, the intensity of the benzene ring torsional vibration peak of polycarbonate in the flow direction is usually 1.5 - 3 times higher than that in the perpendicular direction, indicating that the molecular chains in this region are arranged with a relatively high degree of orientation. The dichroic ratio data directly reflects the degree of molecular chain orientation in space. An R value close to 1 indicates random arrangement of molecular chains, while a significantly larger R value indicates preferential arrangement of molecular chains along the flow direction. This dichroic ratio analysis method can quantitatively characterize the molecular orientation state of different regions, which is of great significance for the quality control of in-vehicle electronic structural components. Since different functional regions have different requirements for molecular orientation. For example, load-bearing connection regions require a higher degree of orientation to provide sufficient strength, while precision fitting regions require a lower degree of orientation to ensure dimensional stability.

[0037] Calculating the molecular chain spatial orientation data based on the dichroic ratio data is achieved based on the orientation theory of molecular spectroscopy. First, the dichroic ratio R is converted into an orientation function value according to the classical Hermans orientation function f = (R - 1) / (R + 2). The range of the orientation function f is from -0.5 to 1, where f = 0 represents completely random orientation, f = 1 represents completely oriented along the flow direction, and f = -0.5 represents completely perpendicular to the flow direction. Then, based on the ellipsoidal statistical distribution model, the angular relationship between the main axis direction of the molecular chain and the reference coordinate system established by the injection molding flow is established. The specific calculation formula is: cos 2 θ = (3f + 1) / 3, where θ is the angle between the main axis of the molecular chain and the Z-axis of the reference coordinate system (usually defined as the flow direction). The angle θ is calculated for each measurement point of the in-vehicle electronic structural component to form a spatial distribution map of the main axis orientation of the molecular chain. For example, for the corner region of the instrument panel frame, if the measured orientation function f = 0.6, then cos 2 θ = 0.87 is calculated, corresponding to θ of approximately 22°, indicating that the molecular chains in this region are mainly arranged along a direction close to the flow direction, with a deviation angle of approximately 22°. This angular analysis can reveal the arrangement state of molecular chains in three-dimensional space. Especially for in-vehicle electronic structural components with complex shapes, the flow directions in different regions are different, and it is difficult to comprehensively characterize their three-dimensional orientation characteristics only through the dichroic ratio. By calculating the angular relationship with the reference coordinate system, a unified spatial orientation description can be obtained.

[0038] The rotation matrix transformation based on spatial orientation data is achieved through tensor calculation methods. First, a three-dimensional shear stress field distribution model of the in-vehicle electronic structural part injection molding process is established, and the principal shear direction and shear strength of each region are calculated through flow analysis software. Then, the spatial orientation data of the molecular chains is represented as a second-order orientation tensor S, and its diagonal elements S xx 、S yy 、S zz represent the orientation degrees of the molecular chains in the three coordinate axis directions, and the off-diagonal elements represent the cross-orientation components. For each measurement point, a rotation matrix R is constructed according to the principal axis direction of the local shear stress field, and through tensor transformation the molecular orientation tensor is transformed from the reference coordinate system to the coordinate system consistent with the local shear field. For example, in the curved surface area of the center console of the in-vehicle electronic structural part, the local shear field may have a deviation of 30° - 45° from the global flow direction. Through the rotation matrix transformation, a molecular orientation distribution consistent with the actual shear direction can be obtained. This transformation takes into account the influence of the shear flow field on the movement trajectory of the molecular chains, making the molecular orientation analysis more in line with physical reality. The finally generated three-dimensional molecular orientation distribution data contains complete information such as orientation degree, orientation direction, and orientation uniformity, providing a microscopic basis for predicting the performance of in-vehicle electronic structural parts under complex stress environments. This molecular orientation analysis method considering the influence of the shear field breaks through the limitations of traditional simplified models, can accurately describe the molecular arrangement state in structural parts with complex geometries, and has direct guiding significance for optimizing injection molding process parameters and improving product performance.

[0039] Please continue to refer to Figure 1 and apply laser pulses to the engineering plastic, and collect the coherent Raman scattering signals and harmonic generation signals of the engineering plastic along the injection molding flow direction and perpendicular to the injection molding flow direction to obtain non-linear optical response data characterizing the molecular chain segment motion; In an embodiment of the present invention, the applying laser pulses to the engineering plastic, collecting the coherent Raman scattering signals and harmonic generation signals of the engineering plastic along the injection molding flow direction and perpendicular to the injection molding flow direction, and obtaining the non-linear optical response data characterizing the molecular chain segment motion includes: Applying femtosecond laser pulses to the high stress concentration region and shear stress gradient region of the engineering plastic respectively, and collecting the coherent Raman scattering signals along the injection molding flow direction and perpendicular to the injection molding flow direction to obtain molecular vibration response data; According to the molecular vibration response data, collecting the second harmonic generation signals of parallel polarization and perpendicular polarization, and performing zonal analysis according to the Raman spectral characteristic peak intensity to obtain the crystallization orientation data of the engineering plastic; According to the crystallization orientation data, through the ratio analysis of the coherent anti-Stokes Raman scattering signal intensity and the harmonic generation signal intensity, the degree of freedom data characterizing the degree of molecular motion restriction is obtained; Perform polarization-dependent analysis on the molecular chain motion based on the degree-of-freedom data and the shear stress field distribution to obtain non-linear optical response data characterizing the motion of molecular chain segments.

[0040] The following specifically describes the steps involved in the above embodiments: Applying femtosecond laser pulses to the high stress concentration regions and shear stress gradient regions of engineering plastics is achieved through an ultrafast laser system. The specific operation is as follows: Use a titanium sapphire femtosecond laser (center wavelength 800 nm, pulse width 100 femtoseconds - 200 femtoseconds, repetition frequency 80 MHz) as the light source, and focus the laser through an optical window onto the target region of the in-vehicle electronic structure. High stress concentration regions are usually located at positions such as the corners and bracket connection points of the structure, while shear stress gradient regions are mainly distributed at the turns of the flow channels and the wall thickness change areas. The laser power density is controlled within the range of 0.5 GW / cm 2 -2 GW / cm 2 This parameter range is sufficient to excite non-linear optical effects without causing thermal damage to the material. The diameter of the laser focus region is approximately 2 micrometers - 5 micrometers, and precise positioning of the target region is achieved through a high-precision three-dimensional scanning platform. After excitation, a confocal Raman spectrometer is used to simultaneously collect coherent Raman scattering signals along the injection molding flow direction and perpendicular to the flow direction, with the spectral range covering 200 cm -1 -3200 cm -1 and the resolution being better than 1 cm -1 . Coherent Raman scattering is a non-linear optical effect affected by molecular vibration modes, and its signal intensity is highly correlated with the molecular orientation. For example, in the high stress region of polycarbonate material, the coherent Raman signal intensity of the C=O stretching vibration peak at 1775 cm -1 in the flow direction is 2 to 4 times higher than that in the perpendicular direction, which directly reflects the highly oriented arrangement of molecular chains. The use of femtosecond lasers is crucial for generating strong non-linear effects because their ultrashort pulses can provide extremely high instantaneous light intensities without causing thermal effects in the material, thereby effectively exciting the transient response of molecules and obtaining molecular structure information that is difficult to capture by conventional methods.

[0041] Generating the second harmonic generation (SHG) signal based on the molecular vibration response data is achieved by changing the polarizer and filter in the optical path. Under the same femtosecond laser irradiation, a band-pass filter (center wavelength 400 nm, corresponding to the second harmonic of the fundamental frequency 800 nm) is installed, and the SHG signals parallel and perpendicular to the injection molding flow direction are collected respectively. Second harmonic generation is a non-linear optical process that is extremely sensitive to the breaking of molecular symmetry, and significant signals are only generated in non-centrosymmetric structures. Therefore, it is particularly suitable for detecting the orientation of crystalline regions in semi-crystalline engineering plastics. The collected SHG signals are divided into regions and intensity-normalized according to the characteristic peaks in coherent Raman spectroscopy. For example, for polyphenylene sulfide materials, the anisotropy ratios (parallel polarization signal / perpendicular polarization signal) of the SHG signals are calculated for three characteristic regions of 630 cm -1 -650 cm -1 (C-S stretching vibration), 1070 cm -1 -1090 cm -1 (benzene ring breathing vibration) and 1570 cm -1 -1590 cm -1 (C=C stretching vibration). These ratios are directly related to the spatial orientation states of different chemical bonds. The SHG anisotropy data of each region are converted into crystallization orientation parameters, including crystallinity, the direction of the crystallization major axis, and crystallization uniformity. Taking the center console bracket of a vehicle electronic structural component as an example, the crystallinity in the high-shear region is usually 30% - 40%, and the crystallization major axis deviates within ±15° from the flow direction. This crystallization orientation state directly affects the mechanical strength and dimensional stability of the product. The advantage of the second harmonic generation technique lies in its high selectivity for crystalline regions and high sensitivity to orientation, capable of providing microstructural information that is difficult to obtain by conventional testing methods.

[0042] The ratio analysis of coherent anti-Stokes Raman scattering signals and harmonic generation signals is achieved by using a two-beam coherent modulation technique. The specific operation is as follows: The aforementioned two signals (CARS and SHG) are collected and stored separately. After pixel-level registration, the CARS / SHG signal intensity ratio at each measurement point is calculated. This ratio is extremely sensitive to the degrees of freedom of molecular motion because the CARS signal mainly reflects the vibrational activity of molecules, while the SHG signal mainly reflects the orderliness of molecular arrangement. For highly crystalline and uniformly oriented regions, the SHG signal is strong and the CARS signal is relatively weak, resulting in a small ratio; while for regions where molecular chain segments are more mobile, the CARS signal is relatively enhanced and the ratio is larger. This ratio data is converted into a degree-of-freedom parameter characterizing the degree of molecular motion restriction, ranging from 0 (completely restricted) to 1 (completely free). For example, in the precision mating surface region of vehicle-mounted electronic structural components, the degree-of-freedom parameter is usually in the range of 0.3 - 0.5, indicating a moderate restriction of molecular chain motion, which is beneficial for maintaining dimensional stability; while in the flexible connection region of the structural components, the degree-of-freedom parameter reaches 0.6 - 0.8, indicating a large molecular chain motion ability, which is beneficial for buffering mechanical shocks. The spatial distribution map of this degree-of-freedom parameter directly reveals the inhomogeneity of the internal microstructure of the material. Especially for components such as vehicle-mounted electronic structural components that are subjected to complex stresses and environmental conditions, the distribution of the degrees of freedom of molecular chain motion is crucial for predicting long-term performance. The unique advantage of the CARS / SHG ratio analysis technique lies in simultaneously obtaining information on both molecular vibration and structural orderliness, achieving a comprehensive characterization of the microscopic dynamic behavior of the material.

[0043] The polarization-dependent analysis based on the degree-of-freedom data and the shear stress field distribution is achieved by rotating the polarizer system. First, a shear stress field distribution model of the in-vehicle electronic structural part injection molding process is established to obtain the principal shear direction and shear strength at each measurement point. Then, at each measurement point, the polarization direction of the incident laser is rotated (0° - 180°, step size 15°), and the variation curves of the nonlinear optical response signals (including CARS and SHG) with the polarization angle are recorded. Fourier analysis is performed on these curves to extract the parameters characterizing polarization dependence, including the degree of anisotropy (the ratio of the maximum to the minimum response) and the principal axis orientation angle (the polarization angle corresponding to the maximum response). These parameters are correlated with the aforementioned degree-of-freedom data and shear stress field data to establish a quantitative relationship among the three. For example, for polycarbonate materials, in the high shear stress region (>50 MPa), the degree of anisotropy of the nonlinear optical response is usually positively correlated with the shear stress, with a slope of approximately 0.05 / MPa - 0.1 / MPa; while in the low shear region (<20 MPa), this correlation is significantly weakened. This correlation directly reflects the influence mechanism of the shear flow field on the motion and arrangement of molecular chains. Through this full-angle polarization scanning analysis, a complete set of nonlinear optical response data characterizing the motion of molecular segments is obtained, including multi-dimensional information such as response intensity, direction dependence, and spatial distribution. In different functional regions of the in-vehicle electronic structural parts, these data show significant differences. For example, the load-bearing connection region shows strong polarization dependence and low degrees of freedom, while the elastic buffer region shows weak polarization dependence and high degrees of freedom. This multi-dimensional nonlinear optical characterization method breaks through the limitations of traditional linear spectroscopy and can reveal the motion characteristics and structural evolution laws of materials at the molecular scale, providing microscopic guidance for the optimized design of in-vehicle electronic structural parts.

[0044] In one embodiment of the present invention, based on the crystallization orientation data, the degree-of-freedom data characterizing the degree of molecular motion restriction is obtained through the ratio analysis of the coherent anti-Stokes Raman scattering signal intensity and the harmonic generation signal intensity, including: Perform frequency-domain deconvolution on the coherent anti-Stokes Raman scattering signal and the harmonic generation signal to separate the local vibration mode and the crystalline region vibration mode of the molecular chain, and obtain a bimodal vibration spectrum; According to the bimodal vibration spectrum, calculate the ratio of the coherent anti-Stokes Raman scattering signal intensity to the harmonic generation signal intensity, analyze the degree of vibration restriction of the molecular segments under the shear flow field, and obtain the local motion parameters of the molecular chain; Adopt the coherent Raman gain spectrum analysis method to perform a correlation analysis on the vibration mode and orientation state of the molecular chain, and obtain the degree-of-freedom data characterizing the degree of molecular motion restriction.

[0045] The following specifically describes the steps involved in the above embodiments: Frequency-domain deconvolution of coherent anti-Stokes Raman scattering (CARS) signals and second harmonic generation (SHG) signals is achieved through a multi-component spectral decomposition algorithm. First, the original CARS and SHG spectral data are imported into specialized spectral analysis software, and the data are preprocessed, including denoising, baseline correction, and intensity normalization. Then, blind signal separation techniques (such as independent component analysis or non-negative matrix factorization algorithms) are used to perform frequency-domain deconvolution on the spectra. During the deconvolution process, two main types of vibration modes are set as targets: local vibration modes (mainly corresponding to the local motion of molecular segments, such as methylene torsion, benzene ring wagging, etc.) and crystalline region vibration modes (mainly corresponding to the cooperative vibration of ordered regions, such as lattice vibration). For polycarbonate materials, the local vibration modes are mainly concentrated in the 700 cm -1 -900 cm -1 and 1150 cm -1 -1250 cm -1 regions, and the crystalline region vibration modes are mainly manifested in the 1430 cm -1 -1500 cm -1 and 1770 cm -1 -1800 cm -1 regions. Through an iterative optimization algorithm, the optimal decomposition parameters are determined to make the residual error less than 5%. The bimodal vibration spectrum obtained after deconvolution clearly distinguishes the two vibration contributions, providing an accurate basis for analyzing the molecular chain motion states in different regions of vehicle-mounted electronic structural parts (such as the center console bracket, instrument panel skeleton, etc.). The key advantage of this frequency-domain deconvolution method is its ability to separate overlapping vibration modes that are difficult to distinguish by traditional spectral methods. Especially for semi-crystalline engineering plastics, the vibration responses of their amorphous and crystalline regions often overlap in conventional spectra, while this method can effectively separate them, thus enabling targeted analysis of the molecular orientation and motion characteristics of different phase regions.

[0046] Calculating the ratio of CARS to SHG signal intensities based on the bimodal vibration spectrum is achieved through region integration and ratio mapping techniques. First, the isolated local vibration modes and crystalline region vibration modes are integrated separately to obtain their intensity maps in the spatial distribution. The CARS signal mainly corresponds to the intensity of the local vibration modes, reflecting the vibrational activity of the molecular chains; while the SHG signal mainly corresponds to the intensity of the crystalline region vibration modes, reflecting the orderliness of the molecular arrangement. Calculate the CARS / SHG signal intensity ratio at each measurement point to generate a ratio distribution map. This ratio is directly related to the degree of vibration restriction of the molecular chains under the shear flow field: a larger ratio indicates that local vibrations dominate and the molecular chains move relatively freely; a smaller ratio indicates that crystalline region vibrations dominate and the molecular chain movement is significantly restricted. For different functional regions of vehicle-mounted electronic structural components, this ratio shows obvious differences. For example, in regions subjected to high shear stress (>60 MPa), such as the corners of the dashboard bracket, the CARS / SHG ratio is usually in the range of 0.5 - 1.0, indicating strong molecular orientation and restricted movement; while in low shear regions (<20 MPa), such as flat regions, this ratio reaches 2.0 - 3.0, indicating a relatively free molecular movement state. By comparing the correspondence between the ratio distribution and the shear stress field distribution, a quantitative mapping of shear stress - molecular movement limitation is established, forming a set of local movement parameters of the molecular chains, including movement amplitude, movement frequency, and direction correlation, etc. This analysis method based on the bimodal vibration spectrum breaks through the limitations of traditional single spectra and can simultaneously obtain information on the degree of freedom and orientation of molecular movement, providing a microscopic mechanism basis for predicting the performance of vehicle-mounted electronic structural components in dynamic environments such as vibration and impact.

[0047] Performing correlation analysis using the coherent Raman gain spectrum analysis method is achieved through the two-beam pump-probe technique. The specific operation is as follows: Use a tunable femtosecond laser as the pump light source (wavelength 760 nm - 840 nm, tunable step 2 nm), and a femtosecond laser with a fixed wavelength as the probe light source. By changing the wavelength difference between the pump light and the probe light, scan the various vibrational resonance frequencies of the molecules to obtain the coherent Raman gain spectrum (CRG). For the engineering plastics of vehicle-mounted electronic structural components, focus on the characteristic vibration frequencies in the range of 600 cm -1 -1800 cm -1 . The peak position and linewidth of the CRG spectrum directly reflect the resonance frequency and relaxation time of the molecular vibration modes, and these parameters are closely related to the molecular orientation and movement degree of freedom. For example, for highly oriented molecular chains, the linewidth of the characteristic peak (such as the C=O stretching vibration peak at 1775 cm -1 in polycarbonate) in their CRG spectrum narrows by 25% - 40%, and the resonance frequency blueshifts by 3 cm -1 -8 cm -1 ; while for molecular chains with restricted movement, their low-frequency vibration modes (such as 600 cm-1 -800 cm -1 The CRG signal intensity of the skeletal torsional vibration in the region decreases by 40% - 60%. By analyzing the correlation between CRG spectral parameters and the aforementioned molecular chain local motion parameters, a three-dimensional correlation diagram of vibration mode - orientation state - degree of freedom of motion is established, and then a degree-of-freedom database characterizing the degree of molecular motion restriction is constructed. For different parts of vehicle-mounted electronic structural components, such as the electronic component mounting surface, mechanical connection area, and stress buffer area, etc., this degree-of-freedom database provides a panoramic view of the motion characteristics at the molecular scale, which is directly related to the performance of the product under different environmental conditions (such as temperature cycling from -40°C to 125°C, vibration from 5 Hz to 2000 Hz). The unique advantage of the CRG analysis method lies in its high sensitivity to the dynamic characteristics of molecular vibration, which can capture subtle changes that are difficult to distinguish by conventional Raman spectroscopy. Especially for the subtle structural evolution of polymer materials during the flow-induced orientation process, this method provides unprecedented detection accuracy and richness of information.

[0048] Please continue to refer to Figure 1 According to the three-dimensional molecular orientation distribution data and the nonlinear optical response data, analyze the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic to obtain kinetic parameters characterizing the molecular chain motion ability; In an embodiment of the present invention, the analyzing the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic according to the three-dimensional molecular orientation distribution data and the nonlinear optical response data to obtain kinetic parameters characterizing the molecular chain motion ability includes: According to the three-dimensional molecular orientation distribution data, through the cross-correlation analysis of the molecular chain segment motion frequency and the nonlinear optical response intensity, calculate the molecular chain relaxation characteristic frequency under different temperature conditions to obtain the multi-relaxation time spectrum of the molecular chain; Perform frequency-domain deconvolution on the local chain segment motion process and the molecular chain cooperative motion process in the multi-relaxation time spectrum respectively, extract the contribution components of the local motion and cooperative motion of the molecular chain segments to obtain the internal friction coefficient of the molecular chain segments; According to the internal friction coefficient of the molecular chain segments, combined with the spatial distribution gradient of the shear stress field, use the stress - optical coefficient calibration method to calculate the critical stress for molecular chain rearrangement to obtain the activation energy spectrum of stress-induced rearrangement; Perform a bivariate response analysis of the stress field and the temperature field on the activation energy spectrum, and use the non-equilibrium state scaling method to calculate the orientation entropy change and conformational entropy change of the molecular chain to obtain thermodynamic parameters characterizing the non-equilibrium state motion of the molecular chain; According to the thermodynamic parameters, use the generalized Langevin dynamics equation to calculate the motion characteristic parameters of the molecular chain under the coupled action of the shear flow field and the temperature field to obtain kinetic parameters characterizing the molecular chain motion ability.

[0049] The following specifically describes the steps involved in the above embodiments: The cross-correlation analysis based on the three-dimensional distribution data of molecular orientation is achieved by temperature-modulated spectroscopy. The specific operation is as follows: Install micro temperature control devices in multiple characteristic regions of the in-vehicle electronic structural components (such as the center console frame, dashboard bracket, etc.), set the temperature range from -40°C to 150°C, and the step size is 10°C. At each temperature point, use a femtosecond laser to excite the engineering plastic, and simultaneously record the decay curve of the non-linear optical response signal (such as the CARS signal) over time. The sampling frequency is 10 GHz, and the measurement time window is 0 nanoseconds - 100 nanoseconds. Perform Fourier transform on the collected time-domain data to obtain the frequency-domain spectrum, and the frequency range covers 10 6 Hz - 10 12 Hz. By calculating the cross-correlation function between the molecular chain segment vibration frequency and the non-linear optical response intensity, the characteristic frequencies of the main relaxation processes are identified. For example, in polycarbonate materials, three main relaxation processes are observed: β relaxation (10 8 Hz - 10 9 Hz, corresponding to the side chain movement), α relaxation (10 6 Hz - 10 7 Hz, corresponding to the local movement of the main chain), and α ' relaxation (10 5 Hz - 10 6 Hz, corresponding to the cooperative movement of multiple chain segments). Perform Arrhenius analysis on the characteristic frequencies at each temperature point to obtain the activation energy and the pre-exponential factor, and then construct a complete multiple relaxation time spectrum. Different regions of the in-vehicle electronic structural components show significant differences. For example, the activation energy of the α relaxation process in the highly oriented region (such as the wall thickness mutation) is 20% - 30% higher than that in the low-oriented region, indicating that the molecular chain movement requires a higher energy threshold. This multiple relaxation time spectrum directly reveals the time scale and energy characteristics of the internal microscopic movement of the in-vehicle electronic structural components, providing a theoretical basis for predicting the mechanical response of the material in different temperature environments.

[0050] The frequency-domain deconvolution of the multiple relaxation time spectrum is achieved by fitting with the Havriliak-Negami function. Import the multiple relaxation time spectrum into a dedicated data analysis software, and set two main motion modes as the targets: local chain segment motion (mainly including the rotation and swing of local structures such as side groups and bridge bonds) and molecular chain cooperative motion (mainly referring to the large-scale motion of the main chain backbone and the cooperative rearrangement of multiple chain segments). Use the modified Havriliak-Negami function to fit each relaxation process, and the function form is: ; where is the relaxation intensity, represents the ordinal unit, represents the angular frequency, is the characteristic relaxation time, and is the shape parameter. The fitting parameters are optimized by the least squares method to make the root mean square deviation between the fitting curve and the experimental data less than 3%. After deconvolution, the contribution ratio and characteristic parameters of each motion mode are obtained. According to the viscoelastic theory, the internal friction coefficient ζ = kT / D of the molecular chain segments is calculated, where k is the Boltzmann constant, T is the temperature, and D is the diffusion coefficient of each motion mode obtained by deconvolution. For the high-stress regions of vehicle-mounted electronic structural components, such as the bracket connection points, the internal friction coefficient of local chain segment motion is usually 10 -12 Ns / m - 10 -11 Ns / m, while the internal friction coefficient of cooperative motion is as high as 10 -10 Ns / m - 10 -9 Ns / m, indicating that large-scale motion is significantly hindered. The spatial distribution map of the internal friction coefficient directly reflects the inhomogeneity of the material internal structure and the spatial restriction of molecular chain motion, which is of great significance for understanding the energy dissipation mechanism of vehicle-mounted electronic structural components in the vibration environment.

[0051] Calculating the critical stress based on the internal friction coefficient of the molecular chain segments is achieved through the stress-optical coefficient calibration method. First, miniature stress sensors are installed in multiple regions of the vehicle-mounted electronic structural components, and a gradient stress field (range 5 MPa - 100 MPa, step size 5 MPa) is applied, while recording the changes in the nonlinear optical response signals. By calculating the response function of stress and optical signals, the stress-optical coefficient C = Δn / σ is determined, where Δn is the optical birefringence and σ is the stress. For different regions and different directions, the stress-optical coefficient shows significant differences. For example, the stress-optical coefficient along the flow direction is usually 30% - 50% higher than that in the perpendicular direction. Combining the aforementioned internal friction coefficient ζ, the critical stress σ c = Gηeff / ζ of molecular chain rearrangement is calculated through the molecular dynamics theory, where G is the shear modulus and ηeff is the effective viscosity. For each functional region of the vehicle-mounted electronic structural components, the relationship curve between the critical stress and temperature is plotted, and then the activation energy ΔE = R·d(lnσ c ) / d(1 / T a ) of stress-induced rearrangement is calculated, where ΔE represents the activation energy of stress-induced molecular chain rearrangement, with the unit of joule per mole (J / mol), reflecting the energy barrier that the molecular chain needs to overcome to change from one conformational state to another, R is the gas constant (with a value of 8.314 J / (mol·K)), σ c represents the critical stress of molecular chain rearrangement, with the unit of pascal (Pa), and T aDenotes the absolute temperature, with the unit of Kelvin (K). Construct a complete activation energy spectrum, including the activation energy distributions under different regions, different orientations, and different temperature conditions. For example, for polycarbonate materials, the typical activation energy values in the highly oriented region are 80 kJ / mol - 120 kJ / mol, while those in the lowly oriented region are 50 kJ / mol - 70 kJ / mol. This critical stress analysis method based on the microscopic mechanism breaks through the limitations of traditional macroscopic mechanical tests and can accurately predict the molecular rearrangement behavior and potential stress relaxation risks of in-vehicle electronic structural components in complex stress environments.

[0052] The bivariate response analysis of the activation energy spectrum is achieved by constructing a thermodynamic phase diagram. In the two-dimensional parameter space of temperature - stress (temperature: -40 °C to 150 °C, stress: 0 MPa - 100 MPa), draw the contour map of the activation energy of molecular chain rearrangement. For each parameter combination point (T i , σ j ), measure the change rate of the nonlinear optical response signal and convert it into the change rate of the molecular chain orientation degree dP / dt, where P is the orientation parameter. According to the non-equilibrium thermodynamics theory, the orientation entropy change ΔSor = ∫(dP / dt)·(dσ / dT)dt, where P represents the orientation parameter of the molecular chain, dimensionless, usually with a value range of 0 - 1, where 0 represents completely random orientation and 1 represents completely oriented arrangement, dP / dt represents the change rate of the orientation parameter with time, dσ / dT represents the partial derivative of stress with respect to temperature, describing the sensitivity of stress change with temperature, and dt represents the time element. The conformational entropy change ΔScon = ∫(dΦ / dt)·(dσ / dT)dt, where Φ is the conformational parameter, dΦ / dt represents the change rate of the conformational parameter with time, and dσ / dT also represents the partial derivative of stress with respect to temperature. Using the non-equilibrium scaling method, establish the scaling relationship between temperature and stress σ(T) / σ(T0) = (T / T0) α, where σ(T) represents the stress value at temperature T (in Pascals), σ(T0) represents the stress value at the reference temperature T0 (in Pascals), T represents the current temperature (in Kelvin), T0 represents the reference temperature (in Kelvin), and α represents the temperature-stress scaling exponent (dimensionless), which describes the proportional relationship between stress and temperature. For polycarbonate materials, the value of α is typically in the range of 1.2 - 1.5, indicating that the influence of temperature on molecular motion is stronger than that of stress. By calculating the entropy change distribution in different regions, a set of thermodynamic parameters characterizing the non-equilibrium motion of molecular chains is obtained, including entropy production rate, degree of non-equilibrium, and relaxation time, etc. In different functional regions of vehicle-mounted electronic structural components, these parameters show significant differences. For example, on the mounting surface of electronic components, the entropy production rate remains at a relatively low level (0.1 J / mol·K·s - 0.5 J / mol·K·s), indicating high structural stability; while in the stress buffer zone, the entropy production rate is as high as 1 J / mol·K·s - 3 J / mol·K·s, indicating that the internal structure of the material is in a state of continuous rearrangement. This non-equilibrium thermodynamics analysis method provides a theoretical tool for predicting the performance evolution of vehicle-mounted electronic structural components under extreme environmental conditions.

[0053] Calculating the kinetic parameters based on the thermodynamic parameters is achieved by solving the generalized Langevin equation. A mathematical model of the molecular chain motion is established: dx / dt = v(x, σ, T) + η(t), where x is the molecular chain position vector (describing the position and conformation of the molecular chain in space, in nanometers), dx / dt represents the time change rate of the molecular chain position (in nanometers per second), v is the deterministic drift term (representing the deterministic motion under the action of an external force field, in nanometers per second), σ is the stress tensor (describing the mechanical stress borne by the material, in Pascals), T is the absolute temperature (in Kelvin), η(t) is the random force term (describing the random perturbation caused by thermal motion, in nanometers per second), and t is the time (in seconds).

[0054] The drift term v is directly related to the aforementioned thermodynamic parameters: , where is the diffusion coefficient matrix (describing the diffusion ability of the molecular chain in each direction, in square meters per second), is the Gibbs free energy (characterizing the thermodynamic state of the system, in joules), represents the gradient operator (representing the rate of change of a function in space), is the Boltzmann constant (1.38×10 -23 joules per Kelvin), is the absolute temperature (in Kelvin).

[0055] The random force term satisfies the fluctuation-dissipation relation: , represents the correlation function of the random force at time and , which is the statistical average, is the Dirac delta function (representing the uncorrelation of the random force at different time points), and are two different time points (in seconds).

[0056] Solve the Langevin equation by numerical integration methods (such as the improved Euler-Maruyama algorithm) to simulate the motion trajectory of the molecular chain under the coupled action of the shear flow field and the temperature field. Calculate the key kinetic parameters, including the relaxation time spectrum τ(σ, T)=ζ / kT·exp(ΔG / RT), where τ is the relaxation time (in seconds), ζ is the friction coefficient (in N·s / m), k is the Boltzmann constant, T is the absolute temperature, exp represents the exponential function, ΔG is the activation free energy (in J / mol), R is the gas constant (8.314 J / (mol·K)); the dynamic friction coefficient μd=FD / FN, where μd is the dynamic friction coefficient (dimensionless), FD is the dissipative force (in N), and FN is the normal force (in N).

[0057] and the viscoelastic response function , where represents the response function (the unit depends on the specific physical quantity), is the angular frequency (in rad / s), is the imaginary unit, is the storage response (real part), is the loss response (imaginary part).

[0058] These parameters comprehensively characterize the dynamic characteristics of the molecular chain's motion ability. For different parts of the vehicle-mounted electronic structural components, the kinetic parameters show obvious differences. For example, in the high-stress area of the center console frame, the rate of decrease of the relaxation time with increasing temperature is 40%-60% slower than that in the low-stress area, indicating that the sensitivity of the highly oriented molecular structure to temperature changes is reduced. The dynamic friction coefficient is 30%-50% higher in the highly oriented area than in the low-oriented area, indicating that the intermolecular interaction is enhanced. This systematic kinetic analysis method breaks through the limitations of traditional static analysis and can accurately predict the dynamic response characteristics of vehicle-mounted electronic structural components under actual use environments (such as temperature fluctuations, vibration shocks, etc.), providing micro-mechanism guidance for product design and material optimization.

[0059] In an embodiment of the present invention, for the bivariate response analysis of the activation energy spectrum under the stress field and the temperature field, the non-equilibrium scaling method is used to calculate the orientation entropy change and the conformational entropy change of the molecular chain, and the thermodynamic parameters characterizing the non-equilibrium motion of the molecular chain are obtained, including: Perform a coupled response analysis of the stress field and temperature field on the activation energy spectrum, use the thermal perturbation response function to analyze the conformational transition process of molecular chains under the shear field, and obtain the non-equilibrium response function of molecular chains; Perform stress field scaling analysis according to the non-equilibrium response function, calculate the orientation entropy change and conformational entropy change of molecular chains respectively, and obtain the entropy change function of molecular chains; According to the entropy change function and the stress field distribution, use the non-equilibrium fluctuation-dissipation theorem to calculate the dynamic structure factor of molecular chains, and obtain the thermodynamic parameters characterizing the non-equilibrium motion of molecular chains.

[0060] The following specifically describes the steps involved in the above embodiments: The coupled response analysis of the stress field and temperature field on the activation energy spectrum is realized by combining the temperature jump technique with in-situ nonlinear spectroscopy measurement. The specific operation is as follows: Install micro temperature control devices in multiple characteristic regions of the in-vehicle electronic structural parts (such as the connection of the center console bracket and the corner of the dashboard skeleton), which can achieve temperature jump (temperature change amount ΔT = ±20 °C) within 1 millisecond - 5 milliseconds. At the same time, apply a gradient stress field with a control accuracy of ±2 MPa. At each temperature-stress combination point (T i is a specific temperature point, σ j is a specific stress point), record the evolution curve of the nonlinear optical response signal (such as CARS signal) over time, with a sampling time window of 0 seconds - 1000 seconds and a sampling interval of 0.1 second. By analyzing the signal intensity, frequency shift, and phase change, construct the thermal perturbation response function: ; where is the thermal perturbation response function (representing the molecular orientation change rate caused by temperature change), is the molecular orientation parameter (dimensionless, ranging from 0 to 1) as a function of time t (in seconds), represents the partial derivative of the molecular orientation parameter with respect to temperature (in Kelvin).

[0061] For different regions in the in-vehicle electronic structural parts, this function shows a double-exponential decay form: where and are amplitude coefficients, is the fast relaxation time (about 1 second - 10 seconds), is the slow relaxation time (about 50 seconds - 500 seconds), represents the exponential function.

[0062] Convert the time-domain response function to the frequency-domain form through Fourier transform: ; where is the frequency-domain response function in the complex form, is the angular frequency (unit: radian / second), is the imaginary unit, is the real part (storage response), is the imaginary part (loss response).

[0063] The stress field scaling analysis based on the non-equilibrium response function is realized by constructing a generalized stress-response phase diagram. Plot the curve of the modulus value of the complex response function versus the stress in double-logarithmic coordinates. It is observed that the slope of the curve changes significantly at the critical stress (unit: Pascal). Apply the power-law scaling theory , where means proportional to, is the scaling exponent (dimensionless). Based on the scaling behavior, calculate the orientation entropy change and the conformational entropy change of the molecular chain. The formula for calculating the orientation entropy change is: ; where ΔSor is the orientation entropy change (unit: Joule / (mole·Kelvin)), is the partial derivative of the molecular orientation parameter with respect to temperature, is the partial derivative of the stress with respect to the orientation parameter, is the small change in the orientation parameter.

[0064] The formula for calculating the conformational entropy change is: ; where is the conformational entropy change (unit: Joule / (mole·Kelvin)), is the radius of gyration of the molecule (unit: nanometer), is the partial derivative of the radius of gyration with respect to temperature, is the partial derivative of the stress with respect to the radius of gyration, is the small change in the radius of gyration.

[0065] The total entropy change function is: ; where is the total entropy change (unit: Joule / (mole·Kelvin)).

[0066] Calculating the dynamic structure factor based on the entropy change function and the stress field distribution is achieved through the non-equilibrium fluctuation-dissipation theory. First, a mapping relationship between the spatial position r (in meters) and the local stress σ(r) is constructed. Calculate the local non-equilibrium degree , where is the non-equilibrium degree (dimensionless), is the temperature, is the entropy change, is the Boltzmann constant (1.38×10 -23 Joules / Kelvin).

[0067] Applying the non-equilibrium fluctuation-dissipation theorem, calculate the dynamic structure factor S(q,ω) of the molecular chain, where S is the dynamic structure factor (the unit depends on the specific physical quantity), q is the scattering vector (in meters -1 ), and ω is the angular frequency. The dynamic structure factor satisfies the relationship with the dissipation function and the response function: S(q,ω) = (2k B T / ω)·R(q,ω)·Im[χ(q,ω)]; where R(q,ω) is the dissipation function, and Im[χ(q,ω)] represents the imaginary part of the response function χ(q,ω).

[0068] Extract the key thermodynamic parameters, including the effective temperature Teff = S(q,0)·q 2 / (2kBχ T ), where Teff is the effective temperature (in Kelvin), χ T is the isothermal response coefficient; the non-equilibrium diffusion coefficient D * = ∫S(q,ω)·ω 2 dω / q 4 , where D * is the non-equilibrium diffusion coefficient (in square meters per second); The non-equilibrium free energy dissipation rate , where is the dissipation rate (in Watts per cubic meter), is the stress tensor component, is the strain rate tensor component. This analysis method provides a new perspective for understanding the microscopic dynamic behavior of in-vehicle electronic structural components in the actual use environment.

[0069] Please continue to refer to Figure 1 , and according to the kinetic parameters, analyze the molecular orientation evolution law of engineering plastics during the injection molding process to obtain the predicted data of the molecular chain structure evolution.

[0070] In one embodiment of the present invention, based on the kinetic parameters, analyzing the molecular orientation evolution law of engineering plastics during the injection molding process to obtain predicted data on the evolution of the molecular chain structure, including: Based on the kinetic parameters, through the molecular chain segment cooperative motion equation and the orientation entropy change calculation method, analyzing the molecular chain motion trajectory and the evolution process of the orientation angle of engineering plastics under a shear flow field to obtain molecular chain conformation transition data; Performing stress tensor decomposition on the molecular chain conformation transition data under non-isothermal conditions, and using the correlation analysis method of stress optical coefficient and nonlinear optical susceptibility to calculate the stress relaxation function of the molecular chain during the melt flow process to obtain the molecular chain segment motion response curve; Based on the molecular chain segment motion response curve, using the generalized molecular chain stress relaxation model and the molecular chain cooperative motion theory, coupling and analyzing the segment motion and the change of entanglement density of engineering plastics under shear stress to obtain the molecular chain network structure evolution data; Through the molecular chain network structure evolution data, combining the temperature field gradient and the shear rate field distribution, using the non-equilibrium state kinetic equation to calculate the evolution laws of the molecular chain orientation degree and crystallinity in the high-stress region and the low-stress region to obtain the predicted data on the evolution of the molecular chain structure.

[0071] The following specifically describes the steps involved in the above embodiments: Analyzing the molecular chain motion trajectory based on the kinetic parameters is achieved through the modified Doi-Edwards equation. This equation describes the creep and relaxation processes of the constrained chain segments in the tubular region, and its form is: ; where is the probability distribution function (describing the probability density of the molecular chain segment at position , direction , and time ), represents the rate of change of the probability distribution with time, is the translational diffusion coefficient (in square meters per second), is the Laplace operator (representing the second-order spatial derivative of the function), is the divergence operator, is the flow velocity field (in meters per second), represents the convection of the probability distribution caused by the flow, is the unit direction vector of the molecular chain segment (dimensionless), is the rotational diffusion coefficient (in radians 2 per second), represents the orientation diffusion term.

[0072] During the calculation process, the in-vehicle electronic structural components are divided into 500 to 1000 discrete units, and the aforementioned dynamic parameters (such as relaxation time spectrum, internal friction coefficient, etc.) are input. By solving the equation through the finite difference method, the evolution curves of the molecular chain movement trajectories and orientation angles over time within each unit are obtained. For polycarbonate materials, in the high-shear region (such as the corner of an injection molded part), the orientation angle rapidly changes from an initial random distribution (mean value of 45°) to an orientation in the flow direction (mean value of 10° - 15°), with a time scale of approximately 0.1 second to 0.5 second; while in the low-shear region, this process takes 1 second to 3 seconds. By analyzing the time evolution of the orientation angle distribution function, the orientation entropy change is calculated: ; where is the orientation entropy change (unit: joule per kelvin), is the Boltzmann constant (1.38×10 -23 joule per kelvin), is the orientation angle at time probability distribution, is the uniform distribution (reference state), ln represents the natural logarithm, is the differential element of the orientation angle.

[0073] Finally, a complete data set of the conformational transition of the molecular chain is generated, including the conformational transition rate, the spatial distribution of the orientation angle distribution, and the orientation entropy change. This molecular dynamics-based analysis method can reveal the microscopic behavior of the molecular chain in the in-vehicle electronic structural components during the injection molding process, especially capturing the transient dynamic process that is difficult to obtain by conventional analysis.

[0074] The stress tensor decomposition of the molecular chain conformational transition data is achieved through polarization modulation photoelastic analysis. First, the stress tensor σ ij under non-isothermal conditions is decomposed into the orientation stress σ o r ij and the fluid stress σflow ij two parts, σ ij =σ o r ij +σflow ij . Among them, σ ij is the total stress tensor (unit: pascal), i and j represent the row and column indices of the tensor (taking values of 1, 2, 3, corresponding to the x, y, z three spatial directions respectively), σ o r ij is the orientation stress tensor (the stress component caused by the molecular chain orientation), and σflow ij is the fluid stress tensor (the stress component caused by the fluid flow). The stress decomposition is based on the stress-optical law, that is, measuring the photoelastic coefficient C of the engineering plastic under the shear flow field ij=Δn ij / σ ij , where C ij is the photoelastic coefficient (in square meters per newton), and Δn ij is the birefringence tensor (dimensionless, describing the difference in refractive index of the material in different directions). By rotating the polarized photoelastic instrument to scan the key areas of the in-vehicle electronic structural components, the birefringence distribution at different temperatures (in the range of 80°C - 280°C, with an interval of 10°C) is measured. At the same time, the electro-optic modulation technology is used to measure the relationship between the second-order nonlinear optical susceptibility χ(2) and stress, and a correlation function χ(2)=f(σ) is constructed, where χ(2) is the second-order nonlinear optical susceptibility (in meters per volt), and f(σ) indicates that χ(2) is a function of stress σ. For polycarbonate materials, the relationship between χ(2) and σ in the flowing state is approximately χ(2)∝σ 1.5-1.8 , where ∝ represents a proportional relationship, and 1.5 - 1.8 is the power exponent (dimensionless). According to the measurement data, the stress relaxation function G(t)=σ(t) / γ 0 is calculated, where G(t) is the stress relaxation function (in pascals), σ(t) is the stress varying with time t, and γ 0 is the initial strain (dimensionless). In the center console frame area of the in-vehicle electronic structural components, G(t) usually shows a polynomial form G(t)=G 0 +G 1 t -α1 +G 2 t -α2 , where G 0 , G 1 , G 2 are the relaxation modulus coefficients (in pascals), α1 and α2 are the relaxation exponents (dimensionless), and t is the time (in seconds). The molecular chain segment motion response curve obtained by this method directly reveals the rheological behavior and internal structure evolution of the material in the molten state, providing a scientific basis for optimizing the injection molding process parameters.

[0075] The coupling analysis based on the molecular chain segment motion response curve is realized by establishing a generalized Maxwell model. The aforementioned stress relaxation function G(t) is expressed as the superposition of a series of exponential relaxation terms G(t)=∑G i exp(-t / τ i ), where G(t) is the stress relaxation function (in pascals), ∑ represents summation, G i is the strength of the i-th relaxation mode (in pascals), exp represents the exponential function, t is the time (in seconds), and τ iis the characteristic relaxation time (in seconds) of the i-th relaxation mode. Based on the Doi-Edwards tube model theory, the segmental motion is restricted by the "tube" structure formed by the surrounding molecular chains, and the relaxation process includes three stages: Rouse relaxation, tube reorientation, and segmental reptation. Calculate the segment entanglement density v = 1 / (Me·NA / ρ), where v is the entanglement density (in per cubic meter), Me is the entanglement molecular weight (in grams per mole), NA is Avogadro's constant (6.022×10 23 per mole), and ρ is the density (in grams per cubic meter). For high-performance engineering plastics of vehicle-mounted electronic structural parts, the entanglement density is usually in the range of (0.2 - 1.0)×10 26 / m 3 . Under the action of shear stress, the entanglement density changes dynamically. By solving the coupled equations dv / dt = k f - k d v 2 - k s γ 1 v, where dv / dt is the change rate of the entanglement density with time (in per cubic meter per second), k f is the entanglement formation rate (in per cubic meter per second), k d is the entanglement dissociation coefficient (in cubic meters per second), v 2 represents the square of the entanglement density, k s is the shear disentanglement coefficient (dimensionless), and γ 1 is the shear rate (in per second). For the high-shear region of the center console bracket, the disentanglement rate is usually 3 - 5 times higher than the formation rate, resulting in a significant reduction (40% - 60%) in the entanglement density and forming an obvious molecular chain slip region. The data on the evolution of the molecular chain network structure obtained through this analysis comprehensively describe the microstructural changes of the engineering plastic during the injection molding process, providing a solid foundation for understanding and predicting the performance of the final product.

[0076] Calculating the structure evolution prediction data through the molecular chain network structure evolution data is achieved by the non-equilibrium Fokker-Planck equation. Construct a kinetic equation describing the evolution of the molecular chain orientation distribution function Ψ(θ, φ, t) (describing the probability density that the molecular chain has a polar angle θ and an azimuth angle φ at time t): ; where, represents the change rate of the distribution function with time, is the divergence operator, is the convective flux (in 1 / (radian 2 ·second)), is the diffusion coefficient tensor (in radian 2 / second), Represents the gradient of the distribution function.

[0077] Input the aforementioned molecular chain network structure data, temperature field gradient ( ), and shear rate field distribution γ 1 , and solve the equation through a multi-scale calculation method. Among them is the temperature gradient (in units of Kelvin per meter), and γ 1 is the shear rate (in units of per second). For areas such as the dashboard bracket of vehicle-mounted electronic structural components, simulate and predict the molecular chain orientation degree S = <3cos²θ - 1> / 2 and crystallinity Xc under different cooling rates (1 °C / s - 50 °C / s) and shear rates (10 / s - 1000 / s). Where S is the orientation degree parameter (dimensionless, range -0.5 to 1), <3cos²θ - 1> represents the statistical average of the expression in the brackets, θ is the angle between the molecular chain and the reference direction, and Xc is the crystallinity (dimensionless, representing the percentage of the crystalline region in the total volume). The results show that high shear regions (γ 1 > 500 / s) usually form regions with high orientation degree (S > 0.6) and high crystallinity (Xc increases by 30% - 50%), while rapid cooling regions (cooling rate > 30 °C / s) tend to form structures with high orientation degree but low crystallinity. This analysis can predict the microstructural inhomogeneity inside vehicle-mounted electronic structural components, especially identify potential high residual stress regions and structural weaknesses, providing scientific guidance for optimizing product design and injection molding process parameters. Through this prediction method based on molecular dynamics, the dimensional stability and service life of vehicle-mounted electronic structural components can be significantly improved, solving the problems of product deformation and failure caused by uneven molecular orientation.

[0078] The injection molding method of the vehicle-mounted electronic structural component in the embodiment of the present invention has been described above. Next, the injection molding device of the vehicle-mounted electronic structural component in the embodiment of the present invention will be described. Please refer to Figure 2 , an embodiment of the injection molding device of the vehicle-mounted electronic structural component in the embodiment of the present invention includes: An excitation spectrum detection module 101, configured to apply an excitation light source to the engineering plastic during the injection molding process, obtain the polarization emission spectra along the injection molding flow direction and perpendicular to the injection molding flow direction, and form a quantum optical fingerprint spectrum; A polarization analysis module 102, configured to perform polarization analysis on the characteristic peaks in the quantum optical fingerprint spectrum, calculate the dichroic ratio of the polarization spectra in two directions, analyze the orientation characteristics of the molecular chains of the engineering plastic, and obtain three-dimensional molecular orientation distribution data; A non-linear optical response module 103, configured to apply a laser pulse to the engineering plastic, collect the coherent Raman scattering signals and harmonic generation signals of the engineering plastic along the injection molding flow direction and perpendicular to the injection molding flow direction, and obtain non-linear optical response data characterizing the movement of molecular segments; The kinetic characteristic analysis module 104 is configured to analyze the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic based on the three-dimensional distribution data of molecular orientation and the non-linear optical response data, so as to obtain kinetic parameters characterizing the molecular chain movement ability. The structure prediction module 105 is configured to analyze the molecular orientation evolution law of the engineering plastic during the injection molding process based on the kinetic parameters, so as to obtain the predicted data of the molecular chain structure evolution.

[0079] above Figure 2 The injection molding device for in-vehicle electronic structural parts in the embodiments of the present invention is described in detail from the perspective of modular functional entities. Next, the injection molding equipment for in-vehicle electronic structural parts in the embodiments of the present invention is described in detail from the perspective of hardware processing.

[0080] Figure 3 FIG. is a schematic structural diagram of an injection molding equipment for in-vehicle electronic structural parts provided by an embodiment of the present invention. The injection molding equipment 200 for in-vehicle electronic structural parts may vary greatly due to different configurations or performances, and may include one or more processors 210 (for example, one or more processors) and a memory 220, and one or more storage media 230 (for example, one or more mass storage device terminals) storing application programs 233 or data 232. Among them, the memory 220 and the storage media 230 may be transient storage or persistent storage. The program stored in the storage media 230 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the injection molding equipment 200 for in-vehicle electronic structural parts. Further, the processor 210 may be configured to communicate with the storage media 230 and execute a series of instruction operations in the storage media 230 on the injection molding equipment 200 for in-vehicle electronic structural parts to implement the steps of the above-mentioned injection molding method for in-vehicle electronic structural parts.

[0081] The injection molding equipment 200 for in-vehicle electronic structural parts may further include one or more power supplies 240, one or more wired or wireless network interfaces 250, one or more input / output interfaces 260, and / or one or more operating systems 231, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that Figure 3 The shown structure of the injection molding equipment for in-vehicle electronic structural parts does not limit the injection molding equipment for in-vehicle electronic structural parts provided by the present invention, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0082] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the injection molding method for the vehicle-mounted electronic structural member.

[0083] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system, device, or unit can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0084] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that can store program codes.

[0085] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structural transformation made by using the description and drawings of the present invention under the inventive concept of the present invention, or direct / indirect application in other related technical fields, is included in the patent protection scope of the present invention.

Claims

1. A method for injection molding of a vehicle-mounted electronic structural part, characterized in that: include: Apply an excitation light source to the engineering plastic during the injection molding process to obtain the polarized emission spectra along and perpendicular to the injection molding flow direction to form a quantum optical fingerprint spectrum; Performing polarization analysis on characteristic peaks in the quantum optical fingerprint spectrum, calculating the dichroic ratio of polarization spectra in two directions, analyzing the orientation characteristics of the engineering plastic molecular chains, and obtaining three-dimensional distribution data of molecular orientation; Apply laser pulses to engineering plastics, collect coherent Raman scattering signals and harmonic generation signals of engineering plastics along and perpendicular to the injection molding flow direction, and obtain nonlinear optical response data characterizing the motion of molecular chain segments; According to the molecular orientation three-dimensional distribution data and nonlinear optical response data, the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic are analyzed to obtain kinetic parameters characterizing the molecular chain movement ability; According to the kinetic parameters, the molecular orientation evolution law of the engineering plastics during the injection molding process is analyzed to obtain the molecular chain structure evolution prediction data.

2. The injection molding method of the vehicle-mounted electronic structural component according to claim 1, characterized in that: The method of applying an excitation light source to the engineering plastic during the injection molding process to obtain polarized emission spectra along the injection molding flow direction and perpendicular to the injection molding flow direction to form a quantum optical fingerprint spectrum includes: Apply triple frequency pulsed laser to the high stress concentration area, shear stress gradient area and melt pressure jump area in the injection mold to obtain the partitioned transient emission spectrum; Perform time-resolved detection according to the partitioned transient emission spectrum to distinguish the fluorescent component, the delayed fluorescent component and the phosphorescent component to obtain a multi-channel time-resolved spectrum; Performing quantum efficiency correction on delayed fluorescence components and phosphorescence components in the multi-channel time-resolved spectrum according to the molecular vibration intensity to obtain a corrected emission spectrum; According to the corrected emission spectrum, polarization spectrum signals along the injection flow direction and perpendicular to the injection flow direction are respectively obtained according to the polarization spectrum intensity ratio to obtain a regionalized polarization emission spectrum; and a quantum optical fingerprint spectrum is formed.

3. The injection molding method of the vehicle-mounted electronic structural component according to claim 2, characterized in that: The method of performing time-resolved detection according to the partitioned transient emission spectrum to distinguish the fluorescent component, the delayed fluorescent component and the phosphorescent component to obtain a multi-channel time-resolved spectrum includes: Performing time-gated detection on the partitioned transient emission spectrum, separating the spectrum components according to the excited state lifetime of the molecular chain, and obtaining three groups of time-resolved fluorescence spectra; Performing electron-vibration energy level transition analysis on the three sets of time-resolved fluorescence spectra to determine the excited state lifetime distribution of the molecular segments under different stress fields and obtain fluorescence quantum yield data; According to the fluorescence quantum yield data, the contribution ratio of different lifetime components is analyzed by using the time-correlated single photon counting method to obtain a multi-channel time-resolved spectrum.

4. The injection molding method of the vehicle-mounted electronic structural component according to claim 1, characterized in that: The polarization analysis is performed on the characteristic peaks in the quantum optical fingerprint spectrum, the dichroic ratio of the polarization spectra in two directions is calculated, the orientation characteristics of the engineering plastic molecular chains are analyzed, and the molecular orientation three-dimensional distribution data is obtained, including: Performing background noise elimination and baseline correction on the spectral peaks in the high stress concentration region in the quantum optical fingerprint spectrum to obtain the chemical bond vibration characteristic peaks of the engineering plastics; For the benzene ring torsional vibration peak and the amide stretching vibration peak in the chemical bond vibration characteristic peak, respectively calculating the polarization spectrum intensity ratio along the injection molding flow direction and perpendicular to the injection molding flow direction to obtain the dichroic ratio data of the engineering plastic molecular chain orientation; Calculating the angle between the main axis direction of the molecular chain and the reference coordinate system according to the dichroic ratio data to obtain the spatial orientation data of the molecular chain; According to the spatial orientation data, combined with the shear stress field distribution, the molecular chain motion trajectory is subjected to a rotation matrix transformation to obtain the molecular orientation three-dimensional distribution data.

5. The method for injection molding of a vehicle-mounted electronic structural component according to claim 1, characterized in that: The laser pulses are applied to the engineering plastics, and the coherent Raman scattering signals and harmonic wave generation signals of the engineering plastics along the injection molding flow direction and perpendicular to the injection molding flow direction are collected to obtain nonlinear optical response data characterizing the motion of molecular chain segments, including: Femtosecond laser pulses are applied to the high stress concentration area and shear stress gradient area of ​​the engineering plastic, and the coherent Raman scattering signals along the injection molding flow direction and perpendicular to the injection molding flow direction are collected to obtain the molecular vibration response data; According to the molecular vibration response data, the parallel polarization and vertical polarization second harmonic generation signals are collected, and the zoning analysis is performed according to the intensity of the characteristic peak of the Raman spectrum to obtain the crystal orientation data of the engineering plastic; According to the crystal orientation data, the degree of freedom data characterizing the degree of restriction of molecular motion is obtained by analyzing the ratio of the coherent anti-Stokes Raman scattering signal intensity to the harmonic generation signal intensity; Polarization dependence analysis is performed on the molecular chain motion according to the degree of freedom data and the shear stress field distribution to obtain nonlinear optical response data characterizing the motion of the molecular chain segments.

6. The method for injection molding of a vehicle-mounted electronic structural component according to claim 5, characterized in that: According to the crystal orientation data, the degree of freedom data characterizing the degree of restriction of molecular motion is obtained by analyzing the ratio of the coherent anti-Stokes Raman scattering signal intensity to the harmonic generation signal intensity, including: The coherent anti-Stokes Raman scattering signal and the harmonic generation signal are deconvoluted in the frequency domain to separate the local vibration mode of the molecular chain and the vibration mode of the crystal region to obtain a dual-mode vibration spectrum. According to the dual-mode vibration spectrum, the ratio of the coherent anti-Stokes Raman scattering signal intensity to the harmonic generation signal intensity is calculated, the vibration restriction degree of the molecular chain segments under the shear flow field is analyzed, and the local motion parameters of the molecular chain are obtained; The coherent Raman gain spectrum analysis method is used to correlate the vibration mode and orientation state of the molecular chain to obtain the degree of freedom data that characterizes the degree of restriction of molecular motion.

7. The method for injection molding of a vehicle-mounted electronic structural component according to claim 1, characterized in that: The molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastics are analyzed according to the molecular orientation three-dimensional distribution data and the nonlinear optical response data to obtain the kinetic parameters characterizing the molecular chain movement ability, including: According to the three-dimensional distribution data of molecular orientation, the molecular chain relaxation characteristic frequencies under different temperature conditions are calculated by cross-correlation analysis between the molecular chain segment motion frequency and the nonlinear optical response intensity to obtain the multiple relaxation time spectra of the molecular chain; Performing frequency domain deconvolution on the local segment motion process and the molecular chain cooperative motion process in the multiple relaxation time spectrum, respectively, extracting the contribution components of the local motion and cooperative motion of the molecular segment, and obtaining the internal friction coefficient of the molecular segment; According to the internal friction coefficient of the molecular chain segment and the spatial distribution gradient of the shear stress field, the critical stress of molecular chain rearrangement is calculated by using the stress-optical coefficient calibration method to obtain the activation energy spectrum of stress-induced rearrangement; Performing a bivariate response analysis of the activation energy spectrum in terms of stress field and temperature field, and calculating the orientation entropy change and conformation entropy change of the molecular chain using a non-equilibrium scaling method to obtain thermodynamic parameters characterizing the non-equilibrium motion of the molecular chain; According to the thermodynamic parameters, the generalized Langevin kinetic equation is used to calculate the motion characteristic parameters of the molecular chain under the coupling of the shear flow field and the temperature field, and the kinetic parameters characterizing the motion ability of the molecular chain are obtained.

8. The method for injection molding of a vehicle-mounted electronic structural component according to claim 7, characterized in that: The activation energy spectrum is subjected to a bivariate response analysis of stress field and temperature field, and the orientation entropy change and conformation entropy change of the molecular chain are calculated using a non-equilibrium scaling method to obtain thermodynamic parameters characterizing the non-equilibrium motion of the molecular chain, including: Simultaneously performing coupled response analysis of stress field and temperature field on the activation energy spectrum, using a thermal disturbance response function to analyze the conformational transition process of the molecular chain under the shear field, and obtaining a non-equilibrium response function of the molecular chain; Performing stress field scaling analysis according to the non-equilibrium response function, respectively calculating the orientation entropy change and conformation entropy change of the molecular chain, and obtaining an entropy change function of the molecular chain; According to the entropy change function and stress field distribution, the dynamic structure factor of the molecular chain is calculated by adopting the non-equilibrium fluctuation dissipation theorem, and the thermodynamic parameters characterizing the non-equilibrium motion of the molecular chain are obtained.

9. The method for injection molding of a vehicle-mounted electronic structural component according to claim 1, characterized in that: The method of analyzing the molecular orientation evolution law of the engineering plastics during the injection molding process according to the kinetic parameters to obtain molecular chain structure evolution prediction data includes: According to the kinetic parameters, the molecular chain motion trajectory and orientation angle evolution process of the engineering plastics under the shear flow field are analyzed by using the molecular chain segment cooperative motion equation and the orientation entropy change calculation method to obtain the molecular chain conformational transition data; Decomposing the molecular chain conformational transition data by stress tensor under non-isothermal conditions, calculating the stress relaxation function of the molecular chain during melt flow by using a correlation analysis method of stress optical coefficient and nonlinear optical polarizability, and obtaining a molecular chain segment motion response curve; According to the molecular chain segment motion response curve, the generalized molecular chain stress relaxation model and the molecular chain cooperative motion theory are used to couple the segment motion and entanglement density change of the engineering plastic under shear stress to obtain the molecular chain network structure evolution data; Through the molecular chain network structure evolution data, combined with the temperature field gradient and shear rate field distribution, the non-equilibrium kinetic equation is used to calculate the molecular chain orientation degree and crystallinity evolution law in the high stress area and the low stress area, and the molecular chain structure evolution prediction data is obtained.

10. An injection molding device for vehicle-mounted electronic structural parts, characterized in that: The injection molding device for the vehicle-mounted electronic structural component adopts the injection molding method for the vehicle-mounted electronic structural component according to any one of claims 1 to 9, and the injection molding device for the vehicle-mounted electronic structural component comprises: The excitation spectrum detection module is used to apply an excitation light source to the engineering plastic during the injection molding process, obtain the polarized emission spectrum along the injection molding flow direction and perpendicular to the injection molding flow direction, and form a quantum optical fingerprint spectrum; A polarization analysis module is used to perform polarization analysis on characteristic peaks in the quantum optical fingerprint spectrum, calculate the dichroic ratio of polarization spectra in two directions, analyze the orientation characteristics of the engineering plastic molecular chain, and obtain three-dimensional distribution data of molecular orientation; The nonlinear optical response module is used to apply laser pulses to the engineering plastics, collect the coherent Raman scattering signals and harmonic generation signals of the engineering plastics along the injection molding flow direction and perpendicular to the injection molding flow direction, and obtain the nonlinear optical response data characterizing the motion of the molecular chain segments; A dynamic characteristic analysis module is used to analyze the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastics according to the molecular orientation three-dimensional distribution data and the nonlinear optical response data, and obtain dynamic parameters characterizing the molecular chain movement ability; The structure prediction module is used to analyze the molecular orientation evolution law of the engineering plastics during the injection molding process according to the kinetic parameters to obtain molecular chain structure evolution prediction data.

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