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

Abstract

The present invention discloses an injection molding method and device for automotive electronic structural parts. The method includes: obtaining the polarized emission spectrum of engineering plastics by applying an excitation light source to form a quantum optical fingerprint spectrum; performing polarization analysis on the characteristic peaks of the spectrum and calculating the dichroic ratio to obtain three-dimensional distribution data of molecular orientation; applying laser pulses to collect coherent Raman scattering signals and harmonic generation signals to obtain nonlinear optical response data; analyzing the molecular chain rearrangement characteristics and stress relaxation characteristics based on the above data to obtain kinetic parameters; and analyzing the molecular orientation evolution law based on the kinetic parameters to obtain structural evolution prediction data. The technical solution of the present invention can monitor the molecular orientation state of engineering plastics in real time during the injection molding process. By combining quantum spectral analysis with nonlinear optical response characterization, it can achieve accurate characterization and prediction of the dynamic arrangement behavior of molecular chain segments, providing a scientific basis for optimizing injection molding process parameters and controlling the residual stress distribution within the product.

Description

Technical Field

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

[0002] Automotive electronic components are structural parts installed in vehicles to support, secure, and protect various electronic devices. These primarily include instrument panel frames, center console frames, display screen housings, navigation system brackets, various control module housings, and battery management system housings. These components must not only meet precise dimensional tolerances but also possess excellent mechanical strength, heat resistance, electrical insulation, and electromagnetic shielding properties, while maintaining dimensional stability and consistent physical properties over the long-term operation of the vehicle. Therefore, the manufacturing quality of automotive electronic components directly impacts the reliability and safety of automotive electronic systems.

[0003] In the prior art, automotive electronic components are primarily manufactured through injection molding of high-performance engineering plastics. During the injection molding process, high-temperature molten plastic rapidly fills the mold cavity under high pressure and then rapidly cools and solidifies within the mold cavity. Because automotive electronic components typically have complex geometries and uneven wall thickness distributions, there are significant differences in the melt flow state, shear rate, and cooling rate in different areas during the injection molding process, which results in varying degrees of orientation of the polymer chains within the component. This molecular orientation heterogeneity can generate residual stress within the component, causing the product to warp, crack, or even fail in vehicle-mounted environments such as thermal cycling and mechanical vibration. This molecular orientation heterogeneity problem is particularly prominent for automotive electronic components with high dimensional accuracy requirements and large variations in wall thickness. However, the prior art lacks an effective method for real-time monitoring and prediction of molecular orientation during the injection molding process. Product quality can only be indirectly controlled through empirical parameter settings and subsequent quality inspections, resulting in significant technical blind spots. Summary of the Invention

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

[0005] A first aspect of the present invention provides an injection molding method for an on-vehicle electronic structural component, the injection molding method comprising:

[0006] During the injection molding process, an excitation light source is applied to the engineering plastic to obtain the polarized emission spectra along and perpendicular to the injection molding flow direction to form a quantum optical fingerprint spectrum;

[0007] 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 engineering plastic molecular chains, and obtaining three-dimensional distribution data of molecular orientation;

[0008] Apply laser pulses to engineering plastics, collect coherent Raman scattering signals and harmonic generation signals of engineering plastics along and perpendicular to the injection flow direction, and obtain nonlinear optical response data characterizing the motion of molecular chain segments;

[0009] Analyzing the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic based on the three-dimensional molecular orientation distribution data and the nonlinear optical response data to obtain kinetic parameters characterizing the molecular chain motion ability;

[0010] 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.

[0011] Preferably, the excitation light source is applied 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, including:

[0012] 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;

[0013] 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;

[0014] Performing quantum efficiency correction on the delayed fluorescence component and the phosphorescence component in the multi-channel time-resolved spectrum according to the molecular vibration intensity to obtain a corrected emission spectrum;

[0015] 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 regional polarization emission spectrum; and a quantum optical fingerprint spectrum is formed.

[0016] Preferably, performing time-resolved detection based on the partitioned transient emission spectrum to distinguish fluorescent components, delayed fluorescent components, and phosphorescent components to obtain a multi-channel time-resolved spectrum includes:

[0017] 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 sets of time-resolved fluorescence spectra;

[0018] 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;

[0019] According to the fluorescence quantum yield data, the contribution ratios of different lifetime components are analyzed using a time-correlated single photon counting method to obtain a multi-channel time-resolved spectrum.

[0020] Preferably, the polarization analysis of the characteristic peaks in the quantum optical fingerprint spectrum is performed, 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 three-dimensional distribution data of the molecular orientation is obtained, including:

[0021] performing background noise elimination and baseline correction on the spectral peaks in the high stress concentration region of the quantum optical fingerprint spectrum to obtain characteristic peaks of chemical bond vibration of the engineering plastic;

[0022] Calculating the polarization spectrum intensity ratios of the benzene ring torsional vibration peak and the amide stretching vibration peak in the chemical bond vibration characteristic peak along the injection molding flow direction and perpendicular to the injection molding flow direction, respectively, to obtain dichroic ratio data of the engineering plastic molecular chain orientation;

[0023] Calculating the angle between the main axis of the molecular chain and the reference coordinate system based on the dichroic ratio data to obtain the spatial orientation data of the molecular chain;

[0024] 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.

[0025] Preferably, applying laser pulses to the engineering plastic, collecting coherent Raman scattering signals and harmonic generation signals of the engineering plastic along and perpendicular to the injection molding flow direction, and obtaining nonlinear optical response data characterizing the motion of molecular segments include:

[0026] Femtosecond laser pulses are applied to high stress concentration areas and shear stress gradient areas of engineering plastics, respectively, and coherent Raman scattering signals along and perpendicular to the injection flow direction are collected to obtain molecular vibration response data;

[0027] According to the molecular vibration response data, the parallel polarization and perpendicular polarization second harmonic generation signals are collected, and the zoning analysis is performed according to the intensity of the characteristic peaks of the Raman spectrum to obtain the crystal orientation data of the engineering plastic;

[0028] 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;

[0029] Polarization dependence analysis of the molecular chain motion is performed based on 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.

[0030] Preferably, 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 based on the crystal orientation data, including:

[0031] The coherent anti-Stokes Raman scattering signal and the harmonic generation signal are deconvolved in the frequency domain to separate the local vibration mode of the molecular chain and the crystal region vibration mode to obtain a dual-mode vibration spectrum.

[0032] Based on the dual-mode vibration spectrum, the ratio of the coherent anti-Stokes Raman scattering signal intensity to the harmonic generation signal intensity is calculated to analyze the degree of vibration restriction of the molecular chain segments under the shear flow field and obtain the local motion parameters of the molecular chain;

[0033] The coherent Raman gain spectrum analysis method is used to correlate the vibration modes and orientation states of the molecular chain, and the degree of freedom data that characterizes the degree of restriction of molecular motion is obtained.

[0034] Preferably, the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic are analyzed based on the molecular orientation three-dimensional distribution data and the nonlinear optical response data to obtain kinetic parameters characterizing the molecular chain motion ability, including:

[0035] Based on the three-dimensional molecular orientation distribution data, 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;

[0036] Performing frequency domain deconvolution on the local segment motion process and the molecular chain cooperative motion process in the multiple relaxation time spectrum, extracting the contribution components of the molecular segment local motion and cooperative motion, and obtaining the molecular segment internal friction coefficient;

[0037] 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 the molecular chain rearrangement is calculated using the stress-optical coefficient calibration method to obtain the activation energy spectrum of the stress-induced rearrangement;

[0038] 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 conformational 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;

[0039] 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.

[0040] Preferably, the activation energy spectrum is subjected to a bivariate response analysis of stress field and temperature field, and the orientation entropy change and conformational 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:

[0041] Simultaneously performing coupled response analysis of stress field and temperature field on the activation energy spectrum, using a thermal perturbation 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;

[0042] Performing stress field scaling analysis based on the non-equilibrium response function, respectively calculating the orientation entropy change and conformation entropy change of the molecular chain to obtain an entropy change function of the molecular chain;

[0043] 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.

[0044] Preferably, the analyzing the molecular orientation evolution law of the engineering plastic during the injection molding process based on the kinetic parameters to obtain the molecular chain structure evolution prediction data includes:

[0045] According to the kinetic parameters, the molecular chain motion trajectory and orientation angle evolution process of the engineering plastic 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;

[0046] Decomposing the molecular chain conformational transition data into a stress tensor under non-isothermal conditions, calculating the stress relaxation function of the molecular chain during melt flow using a correlation analysis method of stress optical coefficient and nonlinear optical polarizability, and obtaining a molecular chain segment motion response curve;

[0047] Based on the molecular chain segment motion response curve, the generalized molecular chain stress relaxation model and molecular chain cooperative motion theory are used to couple the segment motion and entanglement density changes of engineering plastics under shear stress to obtain molecular chain network structure evolution data;

[0048] By using 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 low stress area, and the molecular chain structure evolution prediction data is obtained.

[0049] A second aspect of the present invention provides an injection molding device for an on-vehicle electronic structural component, the injection molding device for the on-vehicle electronic structural component comprising:

[0050] 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 spectra along and perpendicular to the injection molding flow direction, and form a quantum optical fingerprint spectrum;

[0051] A polarization analysis module is used 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 engineering plastic molecular chains, and obtain three-dimensional distribution data of molecular orientation;

[0052] The nonlinear optical response module is used to apply laser pulses to engineering plastics, collect coherent Raman scattering signals and harmonic generation signals of the engineering plastics along and perpendicular to the injection flow direction, and obtain nonlinear optical response data that characterizes the motion of molecular chain segments;

[0053] A dynamic characteristic analysis module is used to analyze the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic based on the three-dimensional molecular orientation distribution data and the nonlinear optical response data, and obtain dynamic parameters that characterize the molecular chain movement ability;

[0054] The structure prediction module is used to analyze the molecular orientation evolution law of engineering plastics during the injection molding process based on the kinetic parameters and obtain molecular chain structure evolution prediction data.

[0055] A 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 through lines; 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.

[0056] A fourth aspect of the present invention provides a computer-readable storage medium having instructions stored therein, which, when executed on a computer, causes the computer to execute the steps of the above-mentioned method for injection molding of vehicle-mounted electronic structural parts.

[0057] The method for injection molding of automotive electronic structural parts provided by the present invention applies an excitation light source during the injection molding process to obtain polarized emission spectra along the injection molding flow direction and perpendicular to the injection molding flow direction, forming a quantum optical fingerprint spectrum, which directly reflects the orientation state of the engineering plastic molecular chain. This detection method based on polarization spectroscopy can capture the molecular chain arrangement characteristics of different regions in real time, laying the 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 polarization spectra in two directions, the orientation characteristics of the engineering plastic molecular chain can be quantitatively characterized, and then the three-dimensional distribution data of the 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 chain segments can be deeply understood and nonlinear optical response data can be obtained, which provides important information for analyzing the dynamic behavior of the molecular chain during the injection molding process.

[0058] Based on the acquired three-dimensional molecular orientation distribution data and nonlinear optical response data, this method can analyze the molecular chain rearrangement and stress relaxation characteristics of engineering plastics, and obtain kinetic parameters that characterize the molecular chain's mobility. These kinetic parameters reflect the motion patterns of the molecular chains during the injection molding process. Through in-depth analysis of these parameters, the evolution of molecular orientation can be accurately predicted, and predicted data for the molecular chain structure evolution can be obtained. This molecular-scale detection and prediction method overcomes the limitations of traditional detection methods that cannot monitor molecular orientation in real time. It enables precise understanding of the molecular orientation state within engineering plastics during the injection molding process, providing scientific guidance for optimizing injection molding process parameters and reducing residual stress accumulation within the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0060] Figure 1 Schematic diagram of an embodiment of an injection molding method for an on-vehicle electronic structural component according to an embodiment of the present invention;

[0061] Figure 2 Schematic diagram of an embodiment of an injection molding device for an on-vehicle electronic structural component according to an embodiment of the present invention;

[0062] Figure 3 Schematic diagram of an embodiment of an injection molding device for an on-board electronic structural component in an embodiment of the present invention.

[0063] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0065] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture. If the specific posture changes, the directional indications will also change accordingly.

[0066] In addition, the descriptions of "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 number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, and must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0067] An embodiment of the present application provides an injection molding method for an in-vehicle electronic structural component. Figure 1 A flow chart of a method for injection molding a vehicle-mounted electronic structural component provided in one embodiment of the present application. In this embodiment, the method includes:

[0068] See also Figure 1 , during the injection molding process, an excitation light source is applied to the engineering plastic to obtain the polarized emission spectra along the injection molding flow direction and perpendicular to the injection molding flow direction to form a quantum optical fingerprint spectrum;

[0069] In one embodiment of the present invention, the excitation light source is applied 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, including:

[0070] 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;

[0071] 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;

[0072] Performing quantum efficiency correction on the delayed fluorescence component and the phosphorescence component in the multi-channel time-resolved spectrum according to the molecular vibration intensity to obtain a corrected emission spectrum;

[0073] 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 regional polarization emission spectrum; and a quantum optical fingerprint spectrum is formed.

[0074] The following is a detailed description of the steps involved in the above embodiment:

[0075] Sapphire optical windows installed at key locations within the injection mold are used to apply tripled-frequency pulsed laser light to areas of high stress concentration (such as corners of structural components and areas of sudden wall thickness changes), areas of shear stress gradient (such as runner bends and areas of wall thickness variation), and areas of melt pressure jump (such as near the gate and at the flow front). Specifically, a 1064nm fundamental frequency Nd:YAG laser is converted into 355nm UV laser pulses via a frequency-tripled crystal (such as LBO or BBO crystal). The pulse width is controlled between 5ns and 10ns, and the repetition rate is 10Hz to 20Hz. This pulsed laser is introduced via an optical fiber transmission system to a pre-installed optical window in the mold. The window is typically made of sapphire with a diameter of 3mm to 5mm and is designed to withstand the high pressure and high temperature environment of the injection molding process. After passing through a collimation and focusing system, the laser light creates an excitation region approximately 0.5mm to 1mm in the engineering plastic, inducing electronic state transitions in the material and generating transient emission spectra. These spectra are collected through the same optical window and transmitted via optical fiber to a spectrometer for real-time recording. The tripled frequency 355nm ultraviolet laser was chosen because this wavelength can effectively excite aromatic groups in most engineering plastics, while having enough energy to trigger electronic transitions without causing thermal damage to the material.

[0076] Time-resolved detection of the acquired transient emission spectra is achieved using a time-correlated single-photon counting (TCSPC) system. This system utilizes fast photomultiplier tubes and time-correlated counting circuits to perform temporal classification and statistics of photons with varying lifetimes, achieving a temporal resolution of 100 ps. The specific implementation process involves first setting three time windows, corresponding to the fluorescence component (lifetime <10 ns), the delayed fluorescence component (lifetime 10-100 ns), and the phosphorescence component (lifetime >100 ns). The fluorescence component primarily originates from fast radiative transitions from the singlet excited state of the molecule, the delayed fluorescence component originates from molecules returning to the singlet state after intersystem crossing, and the phosphorescence component originates from slow radiative transitions from the triplet state. By integrating the spectral signal within each time window, the spectral distributions of the three components are obtained. This time-resolved detection method is particularly suitable for examining spectral changes in polymer materials under different stress states, as varying molecular orientation significantly affects the intersystem crossing efficiency and triplet formation rate, thereby altering the relative intensities of delayed fluorescence and phosphorescence.

[0077] The quantum efficiency correction of the delayed fluorescence and phosphorescence components in the multi-channel time-resolved spectrum according to the molecular vibration intensity is achieved by referring to standard samples. First, a standard fluorescent substance with a known quantum yield (such as anthracene, naphthalene, etc.) is used to obtain a standard emission spectrum under the same conditions. Then, based on the known quantum efficiency of the standard sample, the actual quantum efficiency of the different vibration modes of the engineering plastic to be tested is calculated. The specific correction process is: for the main vibration peaks in the delayed fluorescence and phosphorescence spectra (such as C=C stretching vibration, benzene ring breathing vibration, etc.), the integrated intensity ratio of the corresponding peak of the standard sample is calculated respectively, and the correction coefficient of each vibration mode of the engineering plastic is obtained by combining the absolute quantum efficiency of the standard sample. The original spectrum is multiplied by the corresponding correction coefficient to obtain the corrected emission spectrum. This correction process ensures that the spectral intensities of different vibration modes and components with different lifetimes are comparable, making the subsequent orientation analysis more accurate.

[0078] Obtaining the polarization spectrum signal based on the corrected emission spectrum is achieved by adding a polarization beam splitter to the detection light path. The beam splitter is adjusted to two polarization states, parallel to the injection flow direction and perpendicular to the flow direction, 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 orientation degree of the molecular chains in different regions is obtained. The polarization spectrum data of each key area is combined into a complete regionalized polarization emission spectrum, and finally a quantum optical fingerprint spectrum of the engineering plastic is formed. This fingerprint spectrum contains the orientation information of the material at the molecular level and can accurately reflect the arrangement state of the 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 molecules in terms of spatial arrangement and dynamic behavior. Just like a human fingerprint, it has a high degree of specificity and identification value.

[0079] In one embodiment of the present invention, performing time-resolved detection based on the partitioned transient emission spectrum to distinguish fluorescent components, delayed fluorescent components, and phosphorescent components to obtain a multi-channel time-resolved spectrum includes:

[0080] 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 sets of time-resolved fluorescence spectra;

[0081] 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;

[0082] According to the fluorescence quantum yield data, the contribution ratios of different lifetime components are analyzed using a time-correlated single photon counting method to obtain a multi-channel time-resolved spectrum.

[0083] The following is a detailed description of the steps involved in the above embodiment:

[0084] Time-gated detection of the segmented transient emission spectrum is achieved using a high-speed photoelectric gating system. This system uses an ultrafast photodetector (such as a streak camera or a fast photomultiplier tube) in conjunction with a precise time delay control circuit to acquire the spectral signal in time segments. The specific implementation process is as follows: First, the segmented transient emission spectrum signal is input into the photoelectric detection system, and three time gates are set: 0-10 nanoseconds (for the fluorescent component), 10-100 nanoseconds (for the delayed fluorescence component), and 100-1000 nanoseconds (for the phosphorescent component). These time gates are based on the characteristic lifetimes of the molecular chains of high-performance engineering plastics commonly used in automotive electronic components (such as polycarbonate and polyphenylene sulfide) at different excited states. For example, when the carbonate group in polycarbonate is excited, its singlet fluorescence lifetime is approximately 2-5 nanoseconds, while the delayed fluorescence of aromatic ring structures can reach 30-80 nanoseconds. Spectral signals are collected separately in these three time windows, generating three sets of time-resolved fluorescence spectra corresponding to different molecular excited states. This time-gating technique can distinguish the contributions of different functional groups within a molecular structure, particularly those spectral components significantly affected by molecular orientation, providing an accurate signal source for subsequent analysis. This time-resolving capability is particularly important in areas with varying wall thicknesses or weld lines in automotive electronic components, where molecular orientation varies significantly. It can capture subtle changes that are difficult to distinguish using conventional methods.

[0085] The electron-vibration energy level transition analysis of the three sets of time-resolved fluorescence spectra was achieved by spectral deconvolution and peak fitting techniques. Each set of time-resolved fluorescence spectra was processed using spectral analysis software. First, baseline correction and noise filtering were performed, and then the Gaussian-Lorentzian mixing function was used to fit and separate the peaks in the spectrum. Each peak corresponds to the characteristic vibration of a specific chemical bond. For example, for polycarbonate, 1775 cm -1 The peak at 1230 cm corresponds to the C=O stretching vibration of the carbonate group, while the peak at 1230 cm -1 The peak at corresponds to the COC stretching vibration. By analyzing the peak position shift, intensity change and half-peak width change of these peaks under different stress fields, the response characteristics of the molecular chain segments under different stress states can be determined. Specifically, the spectra of the high stress area (such as the wall thickness change) and the low stress area (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, the relationship between these changes and the molecular energy level structure is analyzed through the electron-vibration transition theory (such as the Franck-Condon principle), and the excited state lifetime distribution of the molecular chain under different stress fields is calculated, and the fluorescence quantum yield data is obtained. This step reveals the subtle changes in the electronic energy level and vibration energy level of the molecular chain after being subjected to stress. These changes directly reflect the degree of molecular orientation and the internal stress state, which has important guiding significance for predicting the performance stability of automotive electronic structural parts under thermal cycling and vibration environments.

[0086] Fluorescence quantum yield data is analyzed using time-correlated single photon counting (TCSPC) using a high-precision TCSPC system. This system comprises a pulsed laser source, a high-sensitivity photomultiplier tube, and a multi-channel time analyzer. The specific operation involves first importing fluorescence quantum yield data into the TCSPC system as a reference threshold. Then, the engineering plastic sample is repeatedly excited, and the precise time of arrival of each photon at the detector (relative to the excitation pulse) is recorded. This time information is then divided into thousands of time channels with a temporal resolution of 0.1 nanosecond, generating a statistical histogram of photon arrival times. Photon arrival time distributions are then obtained for different functional regions of an automotive electronic component (such as high-strength support areas and precision mating areas). These distribution curves are then fitted to a multi-exponential decay model to isolate the contribution of different lifetime components. For example, in regions with high orientation, the proportion of the short-lived component (corresponding to singlet fluorescence) is generally higher, while in regions with low orientation, the proportion of the long-lived component (corresponding to triplet phosphorescence) is relatively higher. This difference stems from the influence of molecular orientation on intersystem crossing efficiency. Finally, the spectral signals of the different lifetime components are reconstructed 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 automotive electronic components under extreme temperature fluctuations and long-term vibration environments.

[0087] Please continue reading Figure 1 , 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 distribution data of the molecular orientation;

[0088] In one embodiment of the present invention, the polarization analysis of the characteristic peaks in the quantum optical fingerprint spectrum is performed, 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 three-dimensional distribution data of the molecular orientation is obtained, including:

[0089] performing background noise elimination and baseline correction on the spectral peaks in the high stress concentration region of the quantum optical fingerprint spectrum to obtain characteristic peaks of chemical bond vibration of the engineering plastic;

[0090] Calculating the polarization spectrum intensity ratios of the benzene ring torsional vibration peak and the amide stretching vibration peak in the chemical bond vibration characteristic peak along the injection molding flow direction and perpendicular to the injection molding flow direction, respectively, to obtain dichroic ratio data of the engineering plastic molecular chain orientation;

[0091] Calculating the angle between the main axis of the molecular chain and the reference coordinate system based on the dichroic ratio data to obtain the spatial orientation data of the molecular chain;

[0092] 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.

[0093] The following is a detailed description of the steps involved in the above embodiment:

[0094] The background noise elimination and baseline correction of the spectral peaks in the high stress concentration area in the quantum optical fingerprint spectrum are achieved through spectrum preprocessing technology. The specific operation process is: first, the original quantum optical fingerprint spectrum is imported into the spectrum analysis software, and the wavelet transform noise reduction algorithm (usually Daubechies wavelet is selected) is used to perform multi-scale decomposition of the spectral data, retaining the large-scale coefficients representing the real signal, while suppressing the small-scale details representing the noise. For the spectra of high stress concentration areas of automotive electronic structural parts (such as bracket connection points and wall thickness mutations), the noise threshold is set to 1 / 20 of the root mean square of the signal to achieve effective removal of high-frequency random noise. The improved polynomial fitting method is then used to correct the spectral baseline, and a 5-7th order polynomial function is selected to fit the background curve to ensure that it adapts to the baseline change characteristics of different bands. During the correction process, the 1500cm -1 -1800cm -1 region (corresponding to C=O stretching vibration) and 1100cm -1 -1300cm -1 Iterative adaptive weighted fitting is used for characteristic peak regions, such as the spectral region (corresponding to COC stretching vibration), to avoid interference from these peaks on the baseline fit. After noise removal and baseline correction, the characteristic peaks of chemical bond vibrations in the spectrum are clearly presented, and the peak signal-to-noise ratio is improved by 5-10 times. This preprocessing technique is crucial for capturing subtle molecular orientation changes during the injection molding process of automotive electronic structural parts, especially in high-stress areas. Spectral changes caused by molecular orientation are often subtle and easily masked by noise. Extracting this critical information requires precise preprocessing.

[0095] The calculation of polarization spectrum intensity ratio of chemical bond vibration characteristic peak is achieved through polarization dependence analysis. First, the peaks of the pre-processed spectrum are identified and separated. For the characteristic peaks of commonly used materials for automotive electronic components, such as the torsional vibration peak of the benzene ring of polycarbonate (about 640cm -1 -660cm -1 ) and the amide stretching vibration peak of modified polyamide (about 1630 cm -1 -1650cm -1), and a mixed Gaussian-Lorentzian function is used for precise fitting. The integrated intensity of these characteristic peaks in the two polarization directions parallel to the injection flow direction (I‖) and perpendicular to the injection flow direction (I⊥) is then 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 instrument panel bracket of an automotive 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 area have a high degree of orientation. The dichroic ratio data directly reflects the degree of orientation of the molecular chains in space. An R value close to 1 indicates that the molecular chains are randomly arranged, and an R value significantly greater than 1 indicates that the molecular chains are preferentially arranged 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 automotive electronic structural components, because different functional regions have different requirements for molecular orientation. For example, the load-bearing connection area requires a higher degree of orientation to provide sufficient strength, while the precision matching area requires a lower degree of orientation to ensure dimensional stability.

[0096] The calculation of molecular chain spatial orientation data based on dichroic ratio data is based on the orientation theory of molecular spectroscopy. First, the dichroic ratio R is converted into an orientation function value according to the classic Hermans orientation function f=(R-1) / (R+2). The range of the orientation function f is -0.5 to 1, where f=0 indicates completely random orientation, f=1 indicates orientation completely along the flow direction, and f=-0.5 indicates orientation completely perpendicular to the flow direction. Then, based on the ellipsoid 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 automotive electronic structure to form a spatial distribution map of the main axis orientation of the molecular chain. For example, for the corner area of ​​the instrument panel frame, if the measured orientation function f=0.6, then the calculated cos 2 θ = 0.87, corresponding to θ of approximately 22°, indicates that the molecular chains in this region are primarily aligned close to the flow direction, with a deviation angle of approximately 22°. This angle analysis can reveal the alignment of molecular chains in three-dimensional space. This is particularly true for complex automotive electronic structures, where different regions have varying flow directions. It is difficult to fully characterize their three-dimensional orientation using only the dichroic ratio. However, by calculating the angle relationship with the reference coordinate system, a unified spatial orientation representation can be obtained.

[0097] The rotation matrix transformation based on spatial orientation data is achieved through tensor calculation method. First, a three-dimensional shear stress field distribution model of the injection molding process of automotive electronic structural parts is established, and the main shear direction and shear strength of each area are calculated using flow analysis software. Then, the spatial orientation data of the molecular chain is expressed as a second-order orientation tensor S, whose diagonal elements S xx 、S yy 、S zz Indicates the degree of orientation of the molecular chain in the three coordinate axes, and the non-diagonal elements represent the cross-orientation components. For each measurement point, according to the principal axis direction of the local shear stress field, a rotation matrix R is constructed, and the tensor transformation is performed. The molecular orientation tensor is converted from the reference coordinate system to a coordinate system consistent with the local shear field. For example, in the curved area of ​​the center console of an on-board electronic structural component, the local shear field may deviate from the global flow direction by 30°-45°. 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 motion trajectory of the molecular chain, making the molecular orientation analysis more consistent with physical reality. The final 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 on-board electronic structural components under complex stress environments. This molecular orientation analysis method that takes into account the influence of the shear field breaks through the limitations of traditional simplified models and can accurately describe the molecular arrangement state in complex geometric structural components. It has direct guiding significance for optimizing injection molding process parameters and improving product performance.

[0098] Please continue reading Figure 1 , applying laser pulses to engineering plastics, collecting coherent Raman scattering signals and harmonic generation signals of engineering plastics along and perpendicular to the injection flow direction, and obtaining nonlinear optical response data characterizing the motion of molecular chain segments;

[0099] In one embodiment of the present invention, applying laser pulses to the engineering plastic, collecting coherent Raman scattering signals and harmonic generation signals of the engineering plastic along and perpendicular to the injection molding flow direction, and obtaining nonlinear optical response data characterizing the motion of molecular segments include:

[0100] Femtosecond laser pulses are applied to high stress concentration areas and shear stress gradient areas of engineering plastics, respectively, and coherent Raman scattering signals along and perpendicular to the injection flow direction are collected to obtain molecular vibration response data;

[0101] According to the molecular vibration response data, the parallel polarization and perpendicular polarization second harmonic generation signals are collected, and the zoning analysis is performed according to the intensity of the characteristic peaks of the Raman spectrum to obtain the crystal orientation data of the engineering plastic;

[0102] 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;

[0103] Polarization dependence analysis of the molecular chain motion is performed based on 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.

[0104] The following is a detailed description of the steps involved in the above embodiment:

[0105] Applying femtosecond laser pulses to high stress concentration areas and shear stress gradient areas of engineering plastics is achieved through an ultrafast laser system. The specific operation is: using a titanium sapphire femtosecond laser (central wavelength 800nm, pulse width 100 femtoseconds-200 femtoseconds, repetition frequency 80MHz) as the light source, and focusing the laser to the target area of ​​the vehicle electronic structural parts through an optical window. High stress concentration areas are usually located at the corners of structural parts, bracket connection points and other locations, while shear stress gradient areas are mainly distributed at flow channel bends and wall thickness change areas. The laser power density is controlled at 0.5GW / cm 2 -2GW / cm 2 The laser focus area is approximately 2 to 5 microns in diameter, and precise positioning of the target area is achieved through a high-precision 3D 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 a spectral range of 200 cm -1 -3200cm -1 , resolution better than 1cm -1 Coherent Raman scattering is a nonlinear optical effect affected by molecular vibration modes, and its signal intensity is highly correlated with the molecular orientation. For example, in the high stress area of ​​polycarbonate material, 1775cm -1 The coherent Raman signal intensity of the C=O stretching vibration peak at the flow direction is 2-4 times higher than that in the perpendicular direction, which directly reflects the high degree of orientation of the molecular chains. The use of femtosecond lasers is crucial for generating strong nonlinear effects, as their ultrashort pulses can provide extremely high instantaneous light intensity without causing thermal effects on the material, effectively stimulating the transient response of molecules and obtaining molecular structural information that is difficult to capture with conventional methods.

[0106] The acquisition of second harmonic generation signals based on molecular vibration response data is achieved by changing the polarizer and filter in the optical path. Under the same femtosecond laser irradiation, a bandpass filter (central wavelength 400nm, corresponding to the second harmonic of the fundamental frequency 800nm) is installed to collect second harmonic generation (SHG) signals parallel to and perpendicular to the injection flow direction, respectively. Second harmonic generation is a nonlinear optical process that is extremely sensitive to the breaking of molecular symmetry. It only produces significant signals in non-centrosymmetric structures. Therefore, it is particularly suitable for detecting the crystalline region orientation of semi-crystalline engineering plastics. The collected SHG signals are divided into regions and intensity normalized according to the characteristic peaks in the coherent Raman spectrum. For example, for polyphenylene sulfide materials, according to 630cm -1 -650cm -1 (CS stretching vibration), 1070cm -1 -1090cm -1 (benzene ring breathing vibration) and 1570cm -1 -1590cm -1 (C=C stretching vibration) The anisotropy ratios of the SHG signals (parallel polarization signal / vertical polarization signal) are calculated for the three characteristic regions respectively. 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, direction of the main crystallization axis, and crystallization uniformity. Taking the center console bracket of an automotive electronic structural part as an example, the crystallinity of the high shear area is usually 30%-40%, and the main crystallization axis deviates from the flow direction within the range of ±15°. This crystallization orientation state directly affects the mechanical strength and dimensional stability of the product. The advantage of second harmonic generation technology lies in its high selectivity for crystallization areas and high sensitivity to orientation, which can provide microstructural information that is difficult to obtain by conventional testing methods.

[0107] The analysis of the ratio of the coherent anti-Stokes Raman scattering signal to the harmonic generation signal is achieved using a dual-beam coherent modulation technique. Specifically, the two aforementioned signals (CARS and SHG) are collected and stored separately, and after pixel-level registration, the CARS / SHG signal intensity ratio is calculated for each measurement point. This ratio is extremely sensitive to the molecular degrees of freedom of motion, because the CARS signal primarily reflects the molecular vibrational activity, while the SHG signal primarily reflects the order of molecular arrangement. In highly crystalline and uniformly oriented regions, the SHG signal is strong and the CARS signal is relatively weak, resulting in a small ratio. In contrast, in regions where the molecular segments move more freely, the CARS signal is relatively enhanced, resulting in a larger ratio. This ratio data is converted into a degree of freedom parameter that represents the degree of restriction of molecular motion, ranging from 0 (completely restricted) to 1 (completely free). For example, in the precision mating surface area of ​​automotive electronic structural parts, the degree of freedom parameter is usually in the range of 0.3-0.5, indicating moderate molecular chain motion restrictions, which is conducive to maintaining dimensional stability; while in the flexible connection area of ​​the structural parts, the degree of freedom parameter reaches 0.6-0.8, indicating a large molecular chain motion ability, which is conducive to buffering mechanical shock. The spatial distribution diagram of this degree of freedom parameter directly reveals the inhomogeneity of the internal microstructure of the material. Especially for components such as automotive electronic structural parts that are subjected to complex stresses and environmental conditions, the distribution of molecular chain motion degrees of freedom is crucial for predicting long-term performance. The unique advantage of CARS / SHG ratio analysis technology is that it simultaneously obtains information on both molecular vibration and structural order, achieving a comprehensive characterization of the material's microscopic dynamic behavior.

[0108] Polarization-dependent analysis based on degree-of-freedom data and shear stress field distribution is achieved using a rotating polarizer system. First, a shear stress field distribution model for the injection molding process of automotive electronic components is established to determine the principal shear direction and shear intensity at each measurement point. Then, at each measurement point, the polarization direction of the incident laser is rotated (0°-180°, in 15° steps), and the nonlinear optical response signals (including CARS and SHG) are recorded as a function of polarization angle. Fourier transform analysis is performed on these curves to extract parameters characterizing polarization dependence, including anisotropy (the ratio of the maximum to minimum response values) and principal axis orientation (the polarization angle corresponding to the maximum response value). These parameters are then correlated with the aforementioned degree-of-freedom data and shear stress field data to establish a quantitative relationship between the three. For example, for polycarbonate materials, in the high shear stress region (>50 MPa), the anisotropy of the nonlinear optical response is generally positively correlated with the shear stress, with a slope of approximately 0.05 / MPa-0.1 / MPa. However, this correlation is significantly weakened in the low shear region (<20 MPa). This correlation directly reflects the mechanism by which the shear flow field affects the motion and arrangement of molecular chains. Through this full-angle polarization scanning analysis, a complete nonlinear optical response data set characterizing the motion of molecular segments is obtained, including multidimensional information such as response intensity, directional dependence, and spatial distribution. These data show significant differences in different functional areas of automotive electronic structural components. For example, the load-bearing connection area shows strong polarization dependence and low degrees of freedom, while the elastic buffer area shows weaker polarization dependence and high degrees of freedom. This multidimensional 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 automotive electronic structural components.

[0109] In one embodiment of the present invention, 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 based on the crystal orientation data, including:

[0110] The coherent anti-Stokes Raman scattering signal and the harmonic generation signal are deconvolved in the frequency domain to separate the local vibration mode of the molecular chain and the crystal region vibration mode to obtain a dual-mode vibration spectrum.

[0111] Based on the dual-mode vibration spectrum, the ratio of the coherent anti-Stokes Raman scattering signal intensity to the harmonic generation signal intensity is calculated to analyze the degree of vibration restriction of the molecular chain segments under the shear flow field and obtain the local motion parameters of the molecular chain;

[0112] The coherent Raman gain spectrum analysis method is used to correlate the vibration modes and orientation states of the molecular chain, and the degree of freedom data that characterizes the degree of restriction of molecular motion is obtained.

[0113] The following is a detailed description of the steps involved in the above embodiment:

[0114] Frequency domain deconvolution of coherent anti-Stokes Raman scattering (CARS) signals and harmonic generation (SHG) signals is achieved through a multi-component spectral decomposition algorithm. First, the raw CARS and SHG spectral data are imported into a dedicated spectral analysis software, and the data are preprocessed, including denoising, baseline correction, and intensity normalization. Then, blind signal separation technology (such as independent component analysis or non-negative matrix decomposition algorithm) is used to perform frequency domain deconvolution of the spectrum. During the deconvolution process, two main vibration modes are set as targets: local vibration modes (mainly corresponding to local motion of molecular chain segments, such as methylene torsion, benzene ring swing, etc.) and crystalline vibration modes (mainly corresponding to cooperative vibrations of ordered arrangement areas, such as lattice vibrations). For polycarbonate materials, the local vibration modes are mainly concentrated at 700 cm -1 -900cm -1 and 1150cm -1 -1250cm -1 region, and the crystal region vibration mode is mainly manifested at 1430cm -1 -1500cm -1 and 1770cm -1 -1800cm -1 region. The optimal decomposition parameters are determined through an iterative optimization algorithm 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 the analysis of the molecular chain motion state in different areas of vehicle-mounted electronic structural parts (such as the center console bracket, instrument panel frame, etc.). The key advantage of this frequency domain deconvolution method is that it can separate overlapping vibration modes that are difficult to distinguish using traditional spectral methods, especially for semi-crystalline engineering plastics, where the vibration responses of the amorphous region and the crystalline region are often overlapped in conventional spectra. This method can achieve effective separation, thereby enabling targeted analysis of the molecular orientation and motion characteristics of different phase regions.

[0115] Calculating the ratio of CARS and SHG signal intensities based on the bimodal vibration spectrum is achieved through regional integration and ratio mapping techniques. First, the separated local vibration modes and crystal region vibration modes are integrated separately to obtain their spatial intensity maps. The CARS signal mainly corresponds to the intensity of the local vibration mode, reflecting the vibration activity of the molecular chain; while the SHG signal mainly corresponds to the intensity of the crystal region vibration mode, reflecting the orderliness of the molecular arrangement. The CARS / SHG signal intensity ratio is calculated for each measurement point to generate a ratio distribution map. This ratio is directly related to the degree of vibration restriction of the molecular chain under the shear flow field: a larger ratio indicates that local vibration dominates and the molecular chain movement is relatively free; a smaller ratio indicates that crystal region vibration dominates and the molecular chain movement is significantly restricted. This ratio shows significant differences for different functional areas of automotive electronic structural components. For example, in areas subjected to high shear stress (>60MPa), such as the corners of the instrument panel bracket, the CARS / SHG ratio is usually in the range of 0.5-1.0, indicating strong molecular orientation and restricted motion; while in low shear regions (<20MPa), such as flat plate regions, the ratio reaches 2.0-3.0, indicating a relatively free molecular motion state. By comparing the correspondence between the contrast value distribution and the shear stress field distribution, a quantitative mapping of shear stress-molecular motion restriction is established, forming a set of local motion parameters of the molecular chain, including motion amplitude, motion frequency, and directional correlation. This analysis method based on dual-mode vibration spectrum breaks through the limitations of traditional single spectrum and can simultaneously obtain information on the degree of freedom and orientation of molecular motion, providing a microscopic mechanism basis for the performance prediction of automotive electronic structural components under dynamic environments such as vibration and impact.

[0116] The correlation analysis using the coherent Raman gain spectrum analysis method is achieved through the dual-beam pump-probe technology. The specific operation is: using a tunable femtosecond laser as the pump light source (wavelength 760nm-840nm, tunable step size 2nm), and a fixed wavelength femtosecond laser as the probe light source. By changing the wavelength difference between the pump light and the probe light, the various vibration resonance frequencies of the molecules are scanned to obtain the coherent Raman gain spectrum (CRG). For engineering plastics of automotive electronic structural parts, the focus is on 600cm -1 -1800cm -1 The peak position and line width of the CRG spectrum directly reflect the resonance frequency and relaxation time of the molecular vibration mode. These parameters are closely related to the molecular orientation and freedom of movement. For example, for a highly oriented molecular chain, the characteristic peak in its CRG spectrum (such as 1775 cm -1 The line width of the C=O stretching vibration peak is narrowed by 25%-40%, and the resonance frequency is blue-shifted by 3cm. -1 -8cm -1 For the molecular chain with restricted motion, its low-frequency vibration mode (such as 600cm-1 -800cm -1 The CRG signal intensity of the skeleton torsional vibration in the region is reduced by 40%-60%. By analyzing the correlation between the CRG spectral parameters and the aforementioned local motion parameters of the molecular chain, a three-dimensional correlation diagram of vibration mode-orientation state-motion freedom is established, and then a degree of freedom database that characterizes the degree of restriction of molecular motion is constructed. For different parts of automotive electronic structural parts, such as electronic component mounting surfaces, mechanical connection areas, and stress buffer zones, this degree of freedom database provides a panoramic view of motion characteristics at the molecular scale, which is directly related to the performance of the product under different environmental conditions (such as -40°C to 125°C temperature cycles, 5Hz-2000Hz vibrations). The unique advantage of the CRG analysis method lies in its high sensitivity to the dynamic characteristics of molecular vibrations, and its ability to capture subtle changes that are difficult to distinguish with conventional Raman spectroscopy, especially for the subtle structural evolution of polymer materials during flow-induced orientation. This method provides unprecedented detection accuracy and information richness.

[0117] Please continue reading Figure 1 , analyzing the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastics based on the molecular orientation three-dimensional distribution data and the nonlinear optical response data, and obtaining kinetic parameters characterizing the molecular chain movement ability;

[0118] In one embodiment of the present invention, the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic are analyzed based on the molecular orientation three-dimensional distribution data and the nonlinear optical response data to obtain kinetic parameters characterizing the molecular chain motion ability, including:

[0119] Based on the three-dimensional molecular orientation distribution data, 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;

[0120] Performing frequency domain deconvolution on the local segment motion process and the molecular chain cooperative motion process in the multiple relaxation time spectrum, extracting the contribution components of the molecular segment local motion and cooperative motion, and obtaining the molecular segment internal friction coefficient;

[0121] 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 the molecular chain rearrangement is calculated using the stress-optical coefficient calibration method to obtain the activation energy spectrum of the stress-induced rearrangement;

[0122] 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 conformational 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;

[0123] 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.

[0124] The following is a detailed description of the steps involved in the above embodiment:

[0125] The cross-correlation analysis based on the three-dimensional distribution data of molecular orientation is achieved through temperature modulation spectroscopy. The specific operation is: install a micro temperature control device in multiple characteristic areas of the vehicle-mounted electronic structural parts (such as the center console frame, instrument panel bracket, etc.), set the temperature range to -40°C to 150°C, and the step size is 10°C. At each temperature point, a femtosecond laser is used to excite the engineering plastic, and the decay curve of the nonlinear optical response signal (such as CARS signal) over time is recorded at the same time. The sampling frequency is 10GHz and the measurement time window is 0 nanoseconds-100 nanoseconds. The collected time domain data is Fourier transformed 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 vibration frequency and the nonlinear optical response intensity, the characteristic frequency of the main relaxation process is identified. For example, in polycarbonate materials, three main relaxation processes are observed: β relaxation (10 8 Hz-10 9 Hz, corresponding to side chain motion), α relaxation (10 6 Hz-10 7 Hz, corresponding to the main chain local motion) and α ' Relaxation (10 5 Hz-10 6 Hz, corresponding to the coordinated motion of multiple chain segments). Arrhenius analysis was performed on the characteristic frequencies at each temperature point to obtain the activation energy and pre-exponential factor, which was then used to construct a complete multiple relaxation time spectrum. Different regions of automotive electronic structural components exhibit significant differences. For example, the activation energy of the α-relaxation process in highly oriented regions (such as areas with sudden changes in wall thickness) is 20%-30% higher than that in low-oriented regions, indicating that a higher energy threshold is required for molecular chain motion. This multiple relaxation time spectrum directly reveals the time scale and energy characteristics of the microscopic motion within automotive electronic structural components, providing a theoretical basis for predicting the mechanical response of materials under different temperature environments.

[0126] Frequency domain deconvolution of multiple relaxation time spectra is achieved by fitting the Havriliak-Negami function. The multiple relaxation time spectra are imported into dedicated data analysis software, and two main motion modes are set as targets: local 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 skeleton and the cooperative rearrangement of multiple segments). A modified Havriliak-Negami function is used to fit each relaxation process, and the function form is:

[0127] ;

[0128] in is the relaxation strength, Indicates an 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 so that the root mean square deviation between the fitting curve and the experimental data is less than 3%. After deconvolution, the contribution ratio and characteristic parameters of each motion mode are obtained. According to the viscoelasticity theory, the internal friction coefficient of the molecular chain segment ζ=kT / D 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 high stress areas of automotive electronic structural parts, such as bracket connection points, the internal friction coefficient of local segment motion is usually 10 -12 Ns / m-10 -11 Ns / m, and 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 of the internal friction coefficient directly reflects the heterogeneity of the material's internal structure and the spatial limitations of molecular chain motion, which is of great significance for understanding the energy dissipation mechanism of automotive electronic structural components in vibration environments.

[0129] The calculation of the critical stress based on the internal friction coefficient of the molecular chain segment is achieved through the stress-optical coefficient calibration method. First, micro stress sensors are installed in multiple areas of the on-board electronic structural parts, and a gradient stress field (range 5MPa-100MPa, step size 5MPa) is applied, while recording the changes in the nonlinear optical response signal. By calculating the response function of stress and optical signal, the stress-optical coefficient C=Δn / σ is determined, where Δn is optical birefringence and σ is stress. For different regions and different directions, there are significant differences in the stress-optical coefficient. For example, the stress-optical coefficient along the flow direction is usually 30%-50% higher than that in the vertical direction. Combined with the aforementioned internal friction coefficient ζ, the critical stress σ of the molecular chain rearrangement is calculated by molecular dynamics theory. c=Gηeff / ζ, where G is the shear modulus and ηeff is the effective viscosity. For each functional area of ​​the automotive electronic structure, the relationship between critical stress and temperature is plotted, and then the activation energy of stress-induced rearrangement ΔE=R·d(lnσ c ) / d(1 / T a ), where ΔE represents the activation energy of stress-induced molecular chain rearrangement, expressed in joules per mole (J / mol), reflecting the energy barrier that the molecular chain needs to overcome to transition from one conformational state to another, R is the gas constant (8.314 J / (mol·K)), and σ c Indicates the critical stress for molecular chain rearrangement, in Pascals (Pa), T a Represents absolute temperature in Kelvin (K). A complete activation energy spectrum is constructed, including the activation energy distribution in different regions, different orientations, and different temperature conditions. For example, for polycarbonate materials, the typical activation energy value for high-orientation regions is 80kJ / mol-120kJ / mol, while that for low-orientation regions is 50kJ / mol-70kJ / mol. This critical stress analysis method based on microscopic mechanisms breaks through the limitations of traditional macroscopic mechanical testing and can accurately predict the molecular rearrangement behavior and potential stress relaxation risks of automotive electronic structural components under complex stress environments.

[0130] The bivariate response analysis of the activation energy spectrum is achieved by constructing a thermodynamic phase diagram. In the temperature-stress two-dimensional parameter space (temperature: -40℃ to 150℃, stress: 0MPa-100MPa), the contour map of the molecular chain rearrangement activation energy is drawn. For each parameter combination point (T i , σ j ), measure the rate of change of the nonlinear optical response signal, and convert it into the rate of change of the molecular chain orientation degree dP / dt, where P is the orientation parameter. According to the nonequilibrium thermodynamics theory, the orientation entropy change ΔSor=∫(dP / dt)·(dσ / dT)dt, where P represents the orientation parameter of the molecular chain, is dimensionless, and usually ranges from 0-1, where 0 represents completely random orientation and 1 represents completely directional arrangement. dP / dt represents the rate of change of the orientation parameter with time, dσ / dT represents the partial derivative of stress with respect to temperature, and describes the sensitivity of stress to temperature change. 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 rate of change of the conformational parameter with time, and dσ / dT also represents the partial derivative of stress with respect to temperature. Using the nonequilibrium scaling method, the scaling relationship between temperature and stress is established: 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 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 α value is usually in the range of 1.2-1.5, indicating that the effect of temperature on molecular motion is stronger than 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 generation rate, non-equilibrium degree and relaxation time. These parameters show significant differences in different functional areas of automotive electronic structural parts. For example, on the surface where electronic components are mounted, the entropy generation rate remains low (0.1J / mol·K·s-0.5J / mol·K·s), indicating high structural stability. In the stress buffer zone, however, the entropy generation rate reaches as high as 1J / mol·K·s-3J / mol·K·s, indicating that the material's internal structure is undergoing continuous rearrangement. This non-equilibrium thermodynamic analysis method provides a theoretical tool for predicting the performance evolution of automotive electronic components under extreme environmental conditions.

[0131] Calculating kinetic parameters from thermodynamic parameters is achieved by solving the generalized Langevin equation. A mathematical model for 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 rate of change of the molecular chain position (in nanometers per second), v is the deterministic drift term (indicating deterministic motion under the action of an external force field, in nanometers per second), σ is the stress tensor (describing the mechanical stress on 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 time (in seconds).

[0132] The drift term v is directly related to the aforementioned thermodynamic parameters: ,in is the diffusion coefficient matrix (describing the diffusion ability of the molecular chain in all directions, the unit is square meters per second), is the Gibbs free energy (characterizing the thermodynamic state of the system, in joules), represents the gradient operator (indicates the rate of change of the function in space), is the Boltzmann constant (1.38×10 -23 Joules / Kelvin), is the absolute temperature (in Kelvin).

[0133] Random force term Satisfies the fluctuation-dissipation relation: , Represents random force in time and The correlation function (i.e., statistical mean) of is the Dirac delta function (representing the uncorrelatedness of random forces at different time points), and are two different time points (in seconds).

[0134] The Langevin equation is solved using numerical integration methods (such as the modified Euler-Malua algorithm) to simulate the motion of molecular chains under the coupled shear flow and temperature fields. Key kinetic parameters are calculated, 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 Newtons per meter), k is the Boltzmann constant, T is the absolute temperature, exp represents the exponential function, ΔG is the activation free energy (in joules per mole), and R is the gas constant (8.314 joules per (mole per Kelvin)); and the dynamic friction coefficient μd = FD / FN, where μd is the dimensionless dynamic friction coefficient, FD is the dissipative force (in Newtons), and FN is the normal force (in Newtons).

[0135] and the viscoelastic response function ,in represents the response function (the unit depends on the specific physical quantity), is the angular frequency (in radians per second), is the imaginary unit, To store the response (real part), is the loss response (imaginary part).

[0136] These parameters comprehensively characterize the dynamic characteristics of the molecular chain's mobility. The dynamic parameters show significant differences in different parts of automotive electronic structural components. For example, in the high-stress area of ​​the center console frame, the rate at which the relaxation time decreases with increasing temperature is 40%-60% slower than that in the low-stress area, indicating that the high-orientation molecular structure is less sensitive to temperature changes. The dynamic friction coefficient is 30%-50% higher in the high-orientation area than in the low-orientation area, indicating enhanced intermolecular interactions. This systematic dynamic analysis method breaks through the limitations of traditional static analysis and can accurately predict the dynamic response characteristics of automotive electronic structural components in actual use environments (temperature fluctuations, vibration shock, etc.), providing microscopic mechanism guidance for product design and material optimization.

[0137] In one embodiment of the present invention, the activation energy spectrum is subjected to a bivariate response analysis of stress field and temperature field, and the orientation entropy change and conformational 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:

[0138] Simultaneously performing coupled response analysis of stress field and temperature field on the activation energy spectrum, using a thermal perturbation 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;

[0139] Performing stress field scaling analysis based on the non-equilibrium response function, respectively calculating the orientation entropy change and conformation entropy change of the molecular chain to obtain an entropy change function of the molecular chain;

[0140] 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.

[0141] The following is a detailed description of the steps involved in the above embodiment:

[0142] The coupled response analysis of stress field and temperature field of activation energy spectrum is realized by combining temperature jump technology with in-situ nonlinear spectrum measurement. The specific operation is: install micro temperature control devices in multiple characteristic areas of vehicle-mounted electronic structural parts (such as the connection of center console bracket and the corner of instrument panel frame), which can realize temperature jump within 1 millisecond to 5 milliseconds (temperature change ΔT = ±20℃). At the same time, a gradient stress field with a control accuracy of ±2MPa is applied. At each temperature-stress combination point (T i is a specific temperature point, σ j For a specific stress point), record the evolution curve of the nonlinear optical response signal (such as the CARS signal) over time. The sampling time window is 0 seconds to 1000 seconds, and the sampling interval is 0.1 seconds. By analyzing the signal intensity, frequency shift and phase change, a thermal perturbation response function is constructed:

[0143] ;

[0144] in is the thermal disturbance response function (indicating the rate of change of molecular orientation 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 molecular orientation parameter versus temperature (in Kelvin).

[0145] For different regions of the vehicle electronic structure, this function exhibits a double exponential decay form:

[0146]

[0147] in and is the amplitude coefficient, is the fast relaxation time (about 1 second to 10 seconds), is the slow relaxation time (about 50 seconds to 500 seconds), Represents the exponential function.

[0148] Convert the time domain response function into frequency domain form through Fourier transform:

[0149] ;

[0150] in is the frequency domain response function in complex form, is the angular frequency (in radians per second), is the imaginary unit, is the real part (stored response), is the imaginary part (loss response).

[0151] The stress field scaling analysis based on the non-equilibrium response function is realized by constructing a generalized stress-response phase diagram. The modulus value With stress The change curve of is plotted on a double logarithmic coordinate and it is observed that at the critical stress (unit: Pascal) where the slope of the curve changes significantly. Applying power law scaling theory ,in It is proportional to, is the scaling exponent (dimensionless). Based on the scaling behavior, the orientation entropy change of the molecular chain is calculated. and conformational entropy change The formula for calculating orientation entropy change is:

[0152] ;

[0153] where ΔSor is the orientation entropy change (in joules / (mole·Kelvin)), is the partial derivative of the molecular orientation parameter with respect to temperature, is the partial derivative of stress with respect to the orientation parameter, is a small change in the orientation parameter.

[0154] The formula for calculating conformational entropy change is:

[0155] ;

[0156] in is the conformational entropy change (in joules / (mole·Kelvin)), is the molecular radius of gyration (in nanometers), is the partial derivative of the radius of gyration with respect to temperature, is the partial derivative of stress with respect to the radius of gyration, is a small change in the radius of gyration.

[0157] The total entropy change function is:

[0158] ;in is the total entropy change (in joules / (mole·Kelvin)).

[0159] The calculation of dynamic structure factor based on entropy change function and stress field distribution is realized through non-equilibrium fluctuation dissipation theory. First, the mapping relationship between spatial position r (unit is meter) and local stress σ(r) is constructed. The local non-equilibrium degree is calculated. ,in is the degree of imbalance (dimensionless), is the temperature, is entropy change, is the Boltzmann constant (1.38×10 -23 joules / kelvin).

[0160] Apply the non-equilibrium fluctuation dissipation theorem to 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 (the unit is meter -1 ), ω is the angular frequency. The dynamic structure factor satisfies the relationship with the dissipation function and the response function:

[0161] S(q,ω)=(2k B T / ω)·R(q,ω)·Im[χ(q,ω)];

[0162] where R(q,ω) is the dissipation function and Im[χ(q,ω)] represents the imaginary part of the response function χ(q,ω).

[0163] Extract key thermodynamic parameters, including effective temperature Teff=S(q,0)·q 2 / (2kB 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 ,in D * is the non-equilibrium diffusion coefficient (in square meters per second);

[0164] Non-equilibrium free energy dissipation rate ,in is the dissipation rate (in watts per cubic meter), are the stress tensor components, This analysis method provides a new perspective for understanding the microscopic dynamic behavior of automotive electronic components in actual use environments.

[0165] Please continue reading Figure 1 According to the kinetic parameters, the molecular orientation evolution law of engineering plastics during the injection molding process is analyzed to obtain the molecular chain structure evolution prediction data.

[0166] In one embodiment of the present invention, analyzing the molecular orientation evolution of the engineering plastic during the injection molding process based on the kinetic parameters to obtain molecular chain structure evolution prediction data includes:

[0167] According to the kinetic parameters, the molecular chain motion trajectory and orientation angle evolution process of the engineering plastic 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;

[0168] Decomposing the molecular chain conformational transition data into a stress tensor under non-isothermal conditions, calculating the stress relaxation function of the molecular chain during melt flow using a correlation analysis method of stress optical coefficient and nonlinear optical polarizability, and obtaining a molecular chain segment motion response curve;

[0169] Based on the molecular chain segment motion response curve, the generalized molecular chain stress relaxation model and molecular chain cooperative motion theory are used to couple the segment motion and entanglement density changes of engineering plastics under shear stress to obtain molecular chain network structure evolution data;

[0170] By using 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 low stress area, and the molecular chain structure evolution prediction data is obtained.

[0171] The following is a detailed description of the steps involved in the above embodiment:

[0172] The analysis of molecular chain motion trajectory based on kinetic parameters is achieved through the modified Doi-Edwards equation. This equation describes the creep and relaxation process of constrained chain segments in the tubular region in the form of:

[0173] ;

[0174] in is the probability distribution function (describing the molecular chain segments at positions ,direction ,time probability density), represents the rate of change of the probability distribution over 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 velocity field (in meters per second), represents the probability distribution convection caused by the flow, is the unit direction vector of the molecular chain segment (dimensionless), is the rotational diffusion coefficient (in radians 2 / Second), represents the orientation diffusion term.

[0175] During the calculation process, the on-board electronic structural parts are divided into 500-1000 discrete units, and the aforementioned dynamic parameters (such as relaxation time spectrum, internal friction coefficient, etc.) are input. The equations are solved by the finite difference method to obtain the motion trajectory of the molecular chains in each unit and the evolution curve of the orientation angle over time. For polycarbonate materials, in high shear areas (such as the corners of injection molded parts), the orientation angle quickly changes from the initial random distribution (mean 45°) to the flow direction orientation (mean 10°-15°), and the time scale is about 0.1 seconds to 0.5 seconds; in the low shear area, 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:

[0176] ;

[0177] in is the orientation entropy change (in joules per kelvin), is the Boltzmann constant (1.38×10 -23 Joules / Kelvin), is the orientation angle In time The probability distribution of is a uniform distribution (reference state), ln represents the natural logarithm, is the infinitesimal element of the orientation angle.

[0178] Ultimately, a complete dataset of molecular chain conformational transitions is generated, including conformational transition rates, orientation angle distribution, and the spatial distribution of orientation entropy changes. This molecular dynamics-based analysis method can reveal the microscopic behavior of molecular chains in automotive electronic structural components during the injection molding process, particularly capturing transient dynamic processes that are difficult to capture with conventional analysis.

[0179] The stress tensor decomposition of molecular chain conformational transition data is achieved through polarization modulation photoelastic analysis. First, the stress tensor σ under non-isothermal conditions is decomposed into ij Decomposed into orientation stress σ o r ij and fluid stress σflow ijTwo parts, σ ij =σ o r ij +σflow ij . Where σ ij is the total stress tensor (in Pascals), i and j represent the row and column indices of the tensor (the values ​​are 1, 2, and 3, corresponding to the three spatial directions of x, y, and z, respectively), σ o r ij is the orientation stress tensor (stress component caused by molecular chain orientation), σflow ij is the fluid stress tensor (the stress component caused by fluid flow). Stress decomposition is based on the law of stress optics, that is, measuring the photoelastic coefficient C of engineering plastics under shear flow field. ij =Δn ij / σ ij , where C ij is the photoelastic coefficient (unit: square meters / Newton), Δn ij is the birefringence tensor (dimensionless, describing the difference in the refractive index of a material in different directions). The key areas of the on-board electronic structural components were scanned by rotating the polarization photoelasticity instrument to measure the birefringence distribution at different temperatures (range 80℃-280℃, interval 10℃). At the same time, the electro-optical modulation technology was used to measure the relationship between the nonlinear optical polarization rate χ(2) and stress, and the correlation function χ(2)=f(σ) was constructed, where χ(2) is the second-order nonlinear optical polarization rate (unit is m / V), 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 flow state is approximately χ(2)∝σ 1.5-1.8 , where ∝ represents a proportional relationship, and 1.5-1.8 is a power exponent (dimensionless). The stress relaxation function G(t)=σ(t) / γ0 is calculated based on the measured data, where G(t) is the stress relaxation function (in Pascals), σ(t) is the stress that changes with time t, and γ0 is the initial strain (dimensionless). In the center console frame area of ​​​​the vehicle-mounted electronic structure, G(t) is usually expressed as a polynomial form G(t)=G0+G1t -α1 +G2t -α2 , where G0, G1, and G2 are relaxation modulus coefficients (in Pascals), α1 and α2 are dimensionless relaxation exponents, and t is time (in seconds). The molecular segment motion response curves obtained using this method directly reveal the rheological behavior and internal structural evolution of the material in the molten state, providing a scientific basis for optimizing injection molding process parameters.

[0180] The coupling analysis based on the molecular chain segment motion response curve is achieved by establishing a generalized Maxwell model. The stress relaxation function G(t) is expressed as a superposition of a series of exponential relaxation terms G(t) = ∑G i exp(-t / τi ), where G(t) is the stress relaxation function (in Pascals), ∑ represents the summation, G i is the intensity of the ith relaxation mode (in Pascals), exp represents the exponential function, t is the time (in seconds), τ i is the characteristic relaxation time of the i-th relaxation mode (in seconds). Based on the Doi-Edwards tube model theory, the chain segment motion is restricted by the "tube" structure formed by the surrounding molecular chains. The relaxation process includes three stages: Rouse relaxation, tube reorientation, and chain crawling. The chain segment entanglement density v=1 / (Me·NA / ρ) is calculated, where v is the entanglement density (in units of per cubic meter), Me is the entanglement molecular weight (in units of grams per mole), and NA is the Avogadro constant (6.022×10 23 / mol), ρ is the density (in grams per cubic meter). For high-performance engineering plastics for automotive electronic components, the entanglement density is usually (0.2-1.0) × 10 26 / m 3 Under the action of shear stress, the entanglement density changes dynamically, and the decoupling equations dv / dt=k f -k d v 2 -k s γ1v, where dv / dt is the rate of change of tangle density with time (in cubic meters per second), k f is the tangle formation rate (in cubic meters per second), k d is the entanglement dissociation coefficient (in cubic meters per second), v 2 represents the square of the tangle density, k s is the shear disentanglement coefficient (dimensionless), and γ1 is the shear rate (in seconds). In the high-shear region of the center console bracket, the disentanglement rate is typically 3-5 times higher than the formation rate, resulting in a significant decrease in entanglement density (40%-60%) and the formation of a distinct molecular chain slip zone. The molecular chain network structure evolution data obtained through this analysis comprehensively describes the microstructural changes of engineering plastics during the injection molding process, providing a solid foundation for understanding and predicting final product performance.

[0181] The calculation of structural evolution prediction data from molecular chain network structure evolution data is achieved through the non-equilibrium Fokker-Planck equation. The kinetic equation describing the evolution of the molecular chain orientation distribution function Ψ(θ,φ,t) is constructed (describing the probability density of a molecular chain having a polar angle θ and an azimuthal angle φ at time t):

[0182] ;

[0183] in, represents the rate of change of the distribution function over time, is the divergence operator, is the convective flux (unit is 1 / (radian 2 ·Second)), is the diffusion coefficient tensor (in radians 2 / Second), represents the gradient of the distribution function.

[0184] Input the aforementioned molecular chain network structure data, temperature field gradient ( ) and shear rate field distribution γ1, the equation is solved by multi-scale calculation method. is the temperature gradient (in Kelvin / meter), and γ1 is the shear rate (in seconds). For regions such as the dashboard bracket of an automotive electronic structural component, simulations predicted the molecular chain orientation S = <3cos²θ-1> / 2 and crystallinity Xc under different cooling rates (1°C / s-50°C / s) and shear rates (10 / s-1000 / s). Here, S is the orientation parameter (dimensionless, ranging from -0.5 to 1), <3cos²θ-1> represents the statistical average of the expression in parentheses, 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 (γ1 > 500 / s) generally form regions with high orientation (S > 0.6) and high crystallinity (Xc increases by 30%-50%), while rapid cooling regions (cooling rates > 30°C / s) tend to form structures with high orientation but low crystallinity. This analysis can predict microstructural heterogeneity within automotive electronic components, specifically identifying potential areas of high residual stress and structural weaknesses, providing scientific guidance for optimizing product design and injection molding process parameters. This molecular dynamics-based prediction method can significantly improve the dimensional stability and service life of automotive electronic components, addressing product deformation and failure issues caused by uneven molecular orientation.

[0185] The above describes the injection molding method of the vehicle-mounted electronic structural component in the embodiment of the present invention. The following describes the injection molding device of the vehicle-mounted electronic structural component in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, an injection molding device for an on-vehicle electronic structural component includes:

[0186] The excitation spectrum detection module 101 is used to apply an excitation light source to the engineering plastic during the injection molding process, obtain polarized emission spectra along the injection molding flow direction and perpendicular to the injection molding flow direction, and form a quantum optical fingerprint spectrum;

[0187] Polarization analysis module 102, 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 engineering plastic molecular chains, and obtain three-dimensional distribution data of molecular orientation;

[0188] The nonlinear optical response module 103 is used to apply laser pulses to the engineering plastic, collect coherent Raman scattering signals and harmonic generation signals of the engineering plastic along the injection flow direction and perpendicular to the injection flow direction, and obtain nonlinear optical response data characterizing the motion of molecular chain segments;

[0189] A dynamic characteristic analysis module 104 is used to analyze the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic based on the molecular orientation three-dimensional distribution data and the nonlinear optical response data, and obtain dynamic parameters that characterize the molecular chain motion ability;

[0190] The structure prediction module 105 is used to analyze the molecular orientation evolution law of the engineering plastic during the injection molding process based on the kinetic parameters to obtain molecular chain structure evolution prediction data.

[0191] above Figure 2 The injection molding device for the vehicle-mounted electronic structural parts in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The injection molding device for the vehicle-mounted electronic structural parts in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0192] Figure 3 The figure is a schematic diagram of the structure of an injection molding apparatus for an on-vehicle electronic component according to an embodiment of the present invention. The injection molding apparatus 200 for an on-vehicle electronic component may vary significantly depending on its configuration or performance. The apparatus may include one or more processors 210 (e.g., one or more processors), a memory 220, and one or more storage media 230 (e.g., one or more mass storage devices) storing application programs 233 or data 232. The memory 220 and storage media 230 may be either transient or persistent storage. The program stored in the storage medium 230 may include one or more modules (not shown), each of which may include a series of instructions for operating on the injection molding apparatus 200 for an on-vehicle electronic component. Furthermore, the processor 210 may be configured to communicate with the storage medium 230, executing the series of instructions stored in the storage medium 230 on the injection molding apparatus 200 for an on-vehicle electronic component, thereby implementing the steps of the above-described method for injecting an on-vehicle electronic component.

[0193] The injection molding device 200 for automotive 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 and output interfaces 260, and / or one or more operating systems 231, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 3The structure of the injection molding equipment for vehicle-mounted electronic structural parts shown does not constitute a limitation on the injection molding equipment for vehicle-mounted electronic structural parts provided by the present invention, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0194] 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. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the injection molding method for vehicle-mounted electronic structural parts.

[0195] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0196] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0197] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made by using the contents of the present invention description and drawings under the inventive concept of the present invention, or direct / indirect application in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A method for injection molding of a vehicle-mounted electronic structural component, characterized in that: include: During the injection molding process, an excitation light source is applied to the engineering plastic to obtain polarized emission spectra along the injection molding flow direction and perpendicular to the injection molding flow direction, thereby forming a quantum optical fingerprint spectrum. Specifically, the method comprises: applying a triple frequency pulsed laser to the high stress concentration area, the shear stress gradient area, and the melt pressure jump area in the injection mold respectively to obtain a partitioned transient emission spectrum; performing time-resolved detection based on the partitioned transient emission spectrum to distinguish fluorescent components, delayed fluorescent components, and phosphorescent components to obtain a multi-channel time-resolved spectrum; performing quantum efficiency correction on the delayed fluorescent components and phosphorescent components in the multi-channel time-resolved spectrum according to the molecular vibration intensity to obtain a corrected emission spectrum; based on the corrected emission spectrum, obtaining polarized spectrum signals along the injection molding flow direction and perpendicular to the injection molding flow direction according to the polarization spectrum intensity ratio to obtain a regionalized polarized emission spectrum 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 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 flow direction, and obtain nonlinear optical response data characterizing the motion of molecular chain segments; Analyzing the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic based on the three-dimensional molecular orientation distribution data and the nonlinear optical response data to obtain kinetic parameters characterizing the molecular chain motion 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 time-resolved detection is performed 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, including: 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 sets 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 ratios of different lifetime components are analyzed using a time-correlated single photon counting method to obtain a multi-channel time-resolved spectrum.

3. The injection molding method of the vehicle-mounted electronic structural component according to claim 1, characterized in that: The polarization analysis of the characteristic peaks in the quantum optical fingerprint spectrum is performed, 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 three-dimensional distribution data of the molecular orientation is obtained, including: performing background noise elimination and baseline correction on the spectral peaks in the high stress concentration region of the quantum optical fingerprint spectrum to obtain characteristic peaks of chemical bond vibration of the engineering plastic; Calculating the polarization spectrum intensity ratios of the benzene ring torsional vibration peak and the amide stretching vibration peak in the chemical bond vibration characteristic peak along the injection molding flow direction and perpendicular to the injection molding flow direction, respectively, to obtain dichroic ratio data of the engineering plastic molecular chain orientation; Calculating the angle between the main axis of the molecular chain and the reference coordinate system based on 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.

4. The injection molding method of the vehicle-mounted electronic structural component according to claim 1, characterized in that: The laser pulses are applied to the engineering plastics to collect coherent Raman scattering signals and harmonic generation signals of the engineering plastics along and perpendicular to the injection molding flow direction to obtain nonlinear optical response data characterizing the motion of molecular segments, including: Femtosecond laser pulses are applied to high stress concentration areas and shear stress gradient areas of engineering plastics, respectively, and coherent Raman scattering signals along and perpendicular to the injection flow direction are collected to obtain molecular vibration response data; According to the molecular vibration response data, the parallel polarization and perpendicular polarization second harmonic generation signals are collected, and the zoning analysis is performed according to the intensity of the characteristic peaks 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 of the molecular chain motion is performed based on 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.

5. The injection molding method of the vehicle-mounted electronic structural component according to claim 4, characterized in that: 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 based on the crystal orientation data, including: The coherent anti-Stokes Raman scattering signal and the harmonic generation signal are deconvolved in the frequency domain to separate the local vibration mode of the molecular chain and the crystal region vibration mode to obtain a dual-mode vibration spectrum. Based on the dual-mode vibration spectrum, the ratio of the coherent anti-Stokes Raman scattering signal intensity to the harmonic generation signal intensity is calculated to analyze the degree of vibration restriction of the molecular chain segments under the shear flow field and obtain the local motion parameters of the molecular chain; The coherent Raman gain spectrum analysis method is used to correlate the vibration modes and orientation states of the molecular chain, and the degree of freedom data that characterizes the degree of restriction of molecular motion is obtained.

6. The injection molding method of vehicle-mounted electronic structural parts according to claim 1, characterized in that: The molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic are analyzed based on the molecular orientation three-dimensional distribution data and the nonlinear optical response data to obtain kinetic parameters characterizing the molecular chain motion ability, including: Based on the three-dimensional molecular orientation distribution data, 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, extracting the contribution components of the molecular segment local motion and cooperative motion, and obtaining the molecular segment internal friction coefficient; 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 the molecular chain rearrangement is calculated using the stress-optical coefficient calibration method to obtain the activation energy spectrum of the 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 conformational 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.

7. The injection molding method of the vehicle-mounted electronic structural component according to claim 6, 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 conformational 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 perturbation 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 based on the non-equilibrium response function, respectively calculating the orientation entropy change and conformation entropy change of the molecular chain to obtain 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.

8. The injection molding method of vehicle-mounted electronic structural parts according to claim 1, characterized in that: The method of analyzing the molecular orientation evolution of engineering plastics during the injection molding process based on 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 plastic 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 into a stress tensor under non-isothermal conditions, calculating the stress relaxation function of the molecular chain during melt flow using a correlation analysis method of stress optical coefficient and nonlinear optical polarizability, and obtaining a molecular chain segment motion response curve; Based on the molecular chain segment motion response curve, the generalized molecular chain stress relaxation model and molecular chain cooperative motion theory are used to couple the segment motion and entanglement density changes of engineering plastics under shear stress to obtain molecular chain network structure evolution data; By using 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 low stress area, and the molecular chain structure evolution prediction data is obtained.

9. 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 8, 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 to obtain polarized emission spectra along and perpendicular to the injection molding flow direction to form a quantum optical fingerprint spectrum; specifically, it includes: applying a tripled frequency pulse laser to the high stress concentration area, shear stress gradient area and melt pressure jump area in the injection mold to obtain a partitioned transient emission spectrum; performing time-resolved detection based on the partitioned transient emission spectrum to distinguish fluorescent components, delayed fluorescent components and phosphorescent components to obtain a multi-channel time-resolved spectrum; performing quantum efficiency correction on the delayed fluorescent components and phosphorescent components in the multi-channel time-resolved spectrum according to the molecular vibration intensity to obtain a corrected emission spectrum; based on the corrected emission spectrum, obtaining polarized spectrum signals along and perpendicular to the injection molding flow direction according to the polarization spectrum intensity ratio to obtain a regionalized polarized emission spectrum to form a quantum optical fingerprint spectrum; A polarization analysis module is used 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 engineering plastic molecular chains, and obtain three-dimensional distribution data of the molecular orientation; The nonlinear optical response module is used to apply laser pulses to engineering plastics, collect coherent Raman scattering signals and harmonic generation signals of the engineering plastics along and perpendicular to the injection flow direction, and obtain nonlinear optical response data that characterizes the motion of molecular chain segments; A dynamic characteristic analysis module is used to analyze the molecular chain rearrangement characteristics and stress relaxation characteristics of the engineering plastic based on the three-dimensional molecular orientation distribution data and the nonlinear optical response data, and obtain dynamic parameters that characterize the molecular chain movement ability; The structure prediction module is used to analyze the molecular orientation evolution law of engineering plastics during the injection molding process based on the kinetic parameters and obtain molecular chain structure evolution prediction data.

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