Polymer stress detection method and detection system

Abstract

The invention provides a polymer stress detection method and system. The detection method comprises the following steps: obtaining a standard sample which is a polymer material containing a carbon material; determining a detection model between the standard sample and the stress according to the first Raman signals corresponding to the standard sample under different stress conditions; and collecting a second Raman signal corresponding to a to-be-detected polymer material, and determining a stress value corresponding to the to-be-detected polymer material based on the detection model and the second Raman signal. The polymer stress detection system comprises a stress regulation and control device, a Raman spectrum detector and a polymerization stress detection device. According to the invention, a carbon material is used as an indicator, and an in-situ nondestructive non-contact method for directly detecting the stress in the polymer by using the Raman spectrum is provided. The detection method can be used for analyzing different stress states of the polymer, and provides theoretical guidance and failure analysis basis for stress characterization application of the polymer.

Description

Technical Field

[0001] The invention belongs to the technical field of polymer materials, and in particular relates to a polymer stress detection method and a detection system. Background Art

[0002] Polymers are a class of macromolecular compounds composed of many repeated monomer molecules linked by chemical bonds. They are widely used in daily life and industrial production, covering various fields from packaging materials, textiles to medical devices and building materials. Stress detection of polymers is extremely important in the fields of engineering and materials science, which is directly related to the reliability and service life of materials in practical applications. Through stress detection, structural deformation, crack propagation or stress concentration problems that may occur in the process of processing or use of materials can be discovered in time. At the same time, accurate stress detection helps to optimize material formulation and processing technology, improve the performance and quality of polymer components, and reduce production costs.

[0003] Mechanical testing methods, dynamic mechanical analysis, thermomechanical analysis, thermogravimetric analysis and photoelastic analysis are commonly used to detect and analyze the stress state of polymers. These methods not only require complex equipment, but most of them also require the destruction or change of samples, and have requirements on the shape, size and test conditions of the samples, making it difficult to obtain characterization information on the stress state of polymers.

[0004] At present, stress detection for polymers is usually offline and destructive, which can only provide limited information and cannot achieve non-destructive detection of polymer stress state. Raman spectroscopy, as a powerful spectral detection method, has many advantages, such as high spatial resolution, non-contact and non-destructive detection, high sensitivity, etc. The prior art also discloses technical solutions for stress detection using Raman spectroscopy, such as using a polarization conversion system as a core device and using multi-polarization state incident micro-Raman spectroscopy to detect micro-scale three-axis principal stress of silicon material and gallium arsenide material semiconductor devices; in addition, a fiber / polymer interface shear crystallization online detector based on stress monitoring can be used, which uses a force sensor and a polarizing microscope as core components, and is used to detect the shear stress and interface crystallization morphology introduced by pulling fibers in semi-crystalline polymer melts.

[0005] However, Raman spectroscopy cannot directly characterize stress in polymers. Therefore, how to provide an in-situ, non-destructive, non-contact method for directly detecting stress in polymers using Raman spectroscopy has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention aims to provide a method and system for detecting polymer stress. The present invention uses carbon materials as indicators to provide an in-situ, non-destructive, non-contact method for directly detecting stress in polymers using Raman spectroscopy. The detection method can be used to analyze different stress states of polymers, providing theoretical guidance for the application of polymer stress characterization and a basis for failure analysis.

[0007] To achieve this object, the present invention adopts the following technical solutions:

[0008] In a first aspect, the present invention provides a method for detecting polymer stress, the detection method comprising the following steps:

[0009] Obtaining a standard sample, wherein the standard sample is a polymer material containing a carbon material;

[0010] Determining a detection model between the standard sample and the stress according to a first Raman signal corresponding to the standard sample under different stress conditions;

[0011] A second Raman signal corresponding to the polymer material to be tested is collected, and a stress value corresponding to the polymer material to be tested is determined based on the detection model and the second Raman signal.

[0012] The present invention uses carbon material as an indicator and provides an in-situ, non-destructive, non-contact method for directly detecting stress in polymers using Raman spectroscopy; the detection method can be used to analyze different stress states of polymers, providing theoretical guidance for the stress characterization application of polymers and a basis for failure analysis.

[0013] The present invention uses carbon materials as indicators to indirectly and accurately obtain information about the stress state changes of polymer materials, which is helpful for the evaluation of stress characterization and the judgment of failure analysis; and can cooperate with microscope observation to detect stress changes in real time and obtain stress information of samples non-destructively.

[0014] The following are preferred technical solutions of the present invention, but are not intended to limit the technical solutions provided by the present invention. Through the following preferred technical solutions, the objectives and beneficial effects of the present invention can be better achieved and realized.

[0015] As a preferred technical solution of the present invention, determining the detection model between the standard sample and the stress according to the first Raman signal corresponding to the standard sample under different stress conditions includes:

[0016] Under different stress conditions, sending laser to the standard sample and collecting a first Raman signal corresponding to the standard sample;

[0017] determining, according to the first Raman signal, corresponding Raman spectra under different stress conditions;

[0018] For each of the Raman spectra, extracting corresponding Raman features;

[0019] A detection model between the standard sample and the polymer stress is determined according to the Raman characteristics and the polymerization stress corresponding to the stress condition.

[0020] As a preferred technical solution of the present invention, the Raman characteristics include the intensity value of the characteristic peak, the displacement of the characteristic peak, the intensity ratio of the characteristic peak and / or the morphology of the characteristic peak.

[0021] Preferably, the Raman characteristic comprises an intensity ratio of a D peak to a G peak.

[0022] As a preferred technical solution of the present invention, the detection model includes a linear regression model, a machine learning model, a variable statistics model and / or a feature comparison model.

[0023] Preferably, the stress condition includes at least one of a temperature condition, a humidity condition, a mechanical stress condition or a pressure condition.

[0024] As a preferred technical solution of the present invention, the detection model type is a linear regression model, and the detection model is:

[0025] σ=k·A–b;

[0026] Wherein, σ is the polymer stress, A is the intensity ratio of the D peak to the G peak, k is the slope, and b is a constant.

[0027] As a preferred technical solution of the present invention, before obtaining the standard sample, the method further comprises:

[0028] Obtain pre-processed materials;

[0029] When the pre-treated material is a polymer material containing a carbon material, the pre-treated material is used as the standard sample;

[0030] When the pre-treated material is a polymer material that does not contain a carbon material, the pre-treated material is modified with a carbon material to obtain the standard sample.

[0031] Preferably, the carbon material modification method comprises any one of doping, spraying, in-situ polymerization or electrochemical deposition.

[0032] It should be noted that when a polymer material that does not contain carbon material is modified with a carbon material, the modification needs to be as uniform as possible.

[0033] As a preferred technical solution of the present invention, the polymer material containing carbon material includes any one of underfill (UF), epoxy molding compound (EMC), and thermal interface material (TIM).

[0034] Preferably, the polymer material containing no carbon material comprises at least one of epoxy resin, silicone material, polyurethane or polyimide;

[0035] Preferably, the carbon material includes at least one of carbon black, carbon nanotubes, graphite or diamond.

[0036] As a preferred technical solution of the present invention, taking the mass percentage of the standard sample as 100%, the mass percentage of the carbon material in the standard sample is 0.1% to 45%, for example, it can be 0.1%, 0.5%, 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40% or 45%, etc.

[0037] As a preferred technical solution of the present invention, the laser wavelength includes at least one of 266nm, 325nm, 514nm, 532nm, 633nm, 785nm, 830m, and 1064nm.

[0038] Generally speaking, the shorter the laser wavelength, the higher the energy and the stronger the Raman signal, but it is easy to damage the sample. It is necessary to select the appropriate wavelength according to different types of polymer materials.

[0039] Preferably, the power of the laser is 1 mW to 100 mW, for example, it can be 1 mW, 2 mW, 5 mW, 10 mW, 20 mW, 30 mW, 40 mW, 50 mW, 60 mW, 70 mW, 80 mW, 90 mW or 100 mW.

[0040] Preferably, the integration time corresponding to the laser is 1s to 1000s, for example, it can be 1s, 5s, 20s, 50s, 100s, 200s, 300s, 400s, 500s, 600s, 700s, 800s, 900s or 1000s.

[0041] The present invention controls the integration time within a specific range, thereby obtaining a significant signal without damaging the sample. If the integration time is too low, it is difficult to obtain a significant signal; if the integration time is too high, the signal is likely to overflow.

[0042] In the present invention, the integration time needs to be set according to the power. Generally, the greater the power, the shorter the integration time.

[0043] In a second aspect, the present invention provides a polymer stress detection system, which includes a stress regulation device, a Raman spectrometer and a polymerization stress detection device. The polymerization stress detection device is electrically connected to the stress regulation device and the Raman spectrometer, respectively, and performs the polymer stress detection method described in the first aspect.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention uses carbon materials as indicators to provide an in-situ, non-destructive, non-contact method for directly detecting stress in polymers using Raman spectroscopy. The detection method can be used to analyze different stress states of polymers, providing theoretical guidance for the application of stress characterization of polymers and a basis for failure analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a schematic flow chart of a method for detecting polymer stress provided in Example 1 of the present invention;

[0047] Figure 2 Schematic diagram of the structure of a device for detecting polymer stress provided in Example 1 of the present invention;

[0048] Figure 3 is the change in the Raman spectrum of the polymer during the heating process provided by Example 1 of the present invention;

[0049] Figure 4 The change in the characteristic peak intensity ratio of the Raman spectrum of the polymer during the heating process provided in Example 1 of the present application;

[0050] Figure 5 is the change in the Raman spectrum of the polymer during the heating process provided by Example 2 of the present invention;

[0051] Figure 6 The change in the characteristic peak intensity ratio of the Raman spectrum of the polymer during the heating process provided in Example 2 of the present application;

[0052] Figure 7 is the change in the Raman spectrum of the polymer during the heating process provided by Example 3 of the present invention;

[0053] Figure 8 The change in the ratio of characteristic peak intensities of the Raman spectrum of the polymer during the heating process provided in Example 3 of the present application. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is further described below in conjunction with the accompanying drawings and through specific implementation methods. It should be understood by those skilled in the art that the embodiments are only to help understand the present invention and should not be regarded as specific limitations of the present invention.

[0055] This embodiment provides a method for detecting polymer stress, the detection method comprising:

[0056] S10. Obtain a standard sample, wherein the standard sample is a polymer material containing carbon.

[0057] Specifically, a standard sample is prepared in advance, and the standard sample should be a polymer material containing a carbon material. The carbon material in this embodiment may include at least one of carbon black, carbon nanotubes, graphite or diamond. The polymer material may contain carbon material itself or may not contain carbon material. For example, materials such as epoxy resin, silicone material, polyurethane, polyimide, etc. do not contain carbon material, while materials such as bottom filling glue, epoxy resin molding compound, thermal interface material, etc. themselves contain carbon material.

[0058] A polymer material is used as a pretreatment material. When the pretreatment material is a polymer material containing carbon material, the pretreatment material is used as the standard sample; when the pretreatment material is a polymer material not containing carbon material, the pretreatment material is modified with carbon material to obtain the standard sample.

[0059] Further, the carbon material modification method includes any one or more combinations of doping, spraying, in-situ polymerization or electrochemical deposition, so that the standard sample contains carbon material. Further, based on the mass percentage of the standard sample as 100%, the mass percentage of the carbon material is 0.1% to 45%, so as to obtain a more accurate test result.

[0060] S20, determining a detection model between the standard sample and the stress according to the first Raman signal corresponding to the standard sample under different stress conditions.

[0061] Specifically, the stress condition of the standard sample is changed. Based on the detection system of this embodiment, the standard sample can be placed in a stress control device for stress adjustment, and the stress control device includes a temperature and humidity control box to change the temperature condition and / or humidity condition, an external force applying device to change the mechanical stress, a pressure control device to change the pressure condition, etc.

[0062] The Raman spectrometer is controlled to emit laser to the standard sample, and the stress conditions are changed synchronously to collect the first Raman signal corresponding to the standard sample under different stress conditions, and the detection model between the standard sample and the stress is determined based on the first Raman signal. In the first implementation, the Raman spectrum composed of the first Raman signals under different stress conditions can be used for subsequent comparison, but the detection accuracy and efficiency of this method are low. In the second implementation, the Raman features in the Raman spectrum can be extracted as subsequent detection objects. Specifically including:

[0063] S21. Under different stress conditions, send laser to the standard sample, and collect a first Raman signal corresponding to the standard sample.

[0064] S22. Determine Raman spectra corresponding to different stress conditions according to the first Raman signal.

[0065] S23. For each of the Raman spectra, extract corresponding Raman features.

[0066] Specifically, for each Raman spectrum, the intensity value of its characteristic peak, the displacement of the characteristic peak, the intensity ratio of the characteristic peak, the morphology of the characteristic peak, etc. can be extracted as Raman features. In this embodiment, the intensity ratio of the D peak to the G peak is preferably used as the Raman feature.

[0067] S24. Determine a detection model between the standard sample and the polymer stress according to the Raman characteristics and the polymerization stress corresponding to the stress condition.

[0068] Specifically, the Raman feature can be used as the input value, and the polymerization stress corresponding to different stress conditions (obtained by testing the standard sample) can be used as the output value to determine the detection model between the standard sample and the polymer stress. For example, a linear regression model can be constructed through a large amount of data statistics, an algorithm can be constructed and a machine learning model can be obtained through machine learning training, a variable statistical model can be constructed based on probability theory, or a feature comparison model can be determined based on the similarity between Raman features.

[0069] S30, collecting a second Raman signal corresponding to the polymer material to be tested, and determining a stress value corresponding to the polymer material to be tested based on the detection model and the second Raman signal.

[0070] Specifically, a second Raman signal corresponding to the polymer material to be tested is collected, the second Raman signal is input as an input value into the detection model, and based on the detection model, a stress value corresponding to the polymer material to be tested is calculated. The entire process of this embodiment does not require cutting or other destruction of the polymer material to be tested, and only requires comparison of the Raman signals between the standard sample and the polymer material to be tested to achieve in-situ detection of the stress value corresponding to the polymer material to be tested.

[0071] The sources of some components in the specific embodiments and comparative examples are described as follows:

[0072] Bottom filling glue 1: standard sample, the bottom filling glue uses bisphenol A epoxy resin (viscosity value is 8000-10000mPa·s) as the base material (mass percentage is about 7.5%), and is filled with about 75% by mass of silicon dioxide particles (average particle size is 1 micron) and no more than 1% of carbon black, wherein the carbon black can be used as an indicator for stress detection.

[0073] Bottom filling glue 2: the sample to be tested, the bottom filling glue uses bisphenol A epoxy resin (viscosity value is 8000-10000mPa·s) as the base material (mass percentage is about 7.5%), and is filled with 75% by mass of silicon dioxide particles (average particle size is 1 micron) and no more than 1% of carbon black, wherein the carbon black can be used as an indicator for stress detection.

[0074] Thermal interface material 1: standard sample, the thermal interface material uses thermal conductive silicone grease (viscosity value is 180Pa·s) as the base material (mass percentage is about 7.5%), and is filled with about 75% by mass of aluminum oxide (particle size is 3 microns) and aluminum nitride (particle size is 5 microns), and the mass ratio of aluminum oxide to aluminum nitride is 2:1, and no more than 1% of carbon black, wherein the carbon black can be used as an indicator for stress detection.

[0075] Thermal interface material 2: The sample to be tested, the thermal interface material uses thermal conductive silicone grease (viscosity value is 180Pa·s) as the base material (mass percentage is about 7.5%), and is filled with 75% by mass of aluminum oxide (particle size is 3 microns) and aluminum nitride (particle size is 5 microns), and the mass ratio of aluminum oxide to aluminum nitride is 2:1, and no more than 1% of carbon black, wherein the carbon black can be used as an indicator for stress detection.

[0076] Epoxy resin 1: standard sample, bisphenol A epoxy resin (viscosity value is 8000-10000 mPa·s).

[0077] Epoxy resin 2: sample to be tested, bisphenol A epoxy resin (viscosity value is 8000-10000 mPa·s).

[0078] The carbon material used in the following examples is a general-grade pigment carbon black sold on the market, with a particle size of 13-30 nm.

[0079] Example 1

[0080] This embodiment provides a method for detecting polymer stress, and its flow chart is as follows: Figure 1 The structural diagram of the detection system is shown in Figure 2 As shown, the detection method is as follows:

[0081] (1) Take bottom filling glue 1 as the standard sample, place it in a temperature control table, and place the whole on the sample table of the Raman detection platform;

[0082] (2) Set the Raman excitation wavelength to 532 nm, the laser power to 50 mW, the integration time to 5 s, the temperature control stage heating rate to 10 °C / min, the holding time to 15 min, and perform Raman detection on the surface of the standard sample every 20 °C from room temperature. Set the highest temperature to 220 °C, and collect the Raman spectrum information changes of the standard sample during the heating process (such as Figure 3 As shown), the change in the intensity ratio of the D peak to the G peak is taken as the observed value (as shown Figure 4 As shown), a detection model between the intensity ratio (A) of carbon black D peak to G peak and the bottom filling glue stress (σ) was established through statistical analysis: σ = 124Α-3.2;

[0083] (3) taking the bottom filling glue 2 as the sample to be tested, collecting the Raman spectrum of the carbon material in the bottom filling glue 2, and calculating the stress value of the bottom filling glue 2 to be tested according to the relationship between the Raman spectrum of the carbon material in the standard sample obtained in step (2) and the polymer stress;

[0084] The parameter conditions for collecting the Raman spectrum of the carbon material in the bottom filling glue 2 are the same as the parameter conditions for the Raman spectrum test of the standard bottom filling glue 1 in step (2);

[0085] After calculation, it is found that the stress value of the bottom filling glue 2 at 50° C. is 106 MPa.

[0086] Example 2

[0087] This embodiment provides a method for detecting polymer stress, and the detection method is as follows:

[0088] (1) Taking thermal interface material 1 as a standard sample, placing it in a temperature control table and placing the whole on a sample table of a Raman detection platform;

[0089] (2) Set the Raman excitation wavelength to 532 nm, the laser power to 50 mW, the integration time to 5 s, the temperature control stage heating rate to 10 °C / min, the holding time to 15 min, and perform Raman detection on the surface of the standard sample every 20 °C from room temperature. Set the highest temperature to 220 °C, and collect the Raman spectrum information changes of the standard sample during the heating process (such as Figure 5 The change in the intensity ratio of the D peak to the G peak is the Raman characteristic (as shown in Figure 6 As shown), a detection model between the intensity ratio (A) of carbon black D peak to G peak and the bottom filling glue stress (σ) was established through statistical analysis: σ = 118Α-4.1;

[0090] (3) taking the thermal interface material 2 as the sample to be tested, collecting the Raman spectrum of the carbon material in the thermal interface material 2, and calculating the stress value of the thermal interface material 2 to be tested according to the relationship between the Raman spectrum of the carbon material in the standard sample obtained in step (2) and the polymer stress;

[0091] The parameter conditions for collecting the Raman spectrum of the carbon material in the thermal interface material 2 are the same as the parameter conditions for the Raman spectrum test of the standard thermal interface material 1 in step (2);

[0092] After calculation, it is found that the stress value of thermal interface material 2 at 50° C. is 84.4 MPa.

[0093] Example 3

[0094] This embodiment provides a method for detecting polymer stress, and the detection method is as follows:

[0095] (1) Epoxy resin 1 and carbon black are doped at a mass ratio of 10%, mixed evenly, and used as a standard sample, which is placed in a temperature control table and placed as a whole on a sample table of a Raman detection platform;

[0096] (2) Set the Raman excitation wavelength to 532 nm, the laser power to 50 mW, the integration time to 5 s, the temperature control stage heating rate to 10 °C / min, the holding time to 15 min, and perform Raman detection on the surface of the standard sample every 20 °C from room temperature. Set the highest temperature to 220 °C, and collect the Raman spectrum information changes of the standard sample during the heating process (such as Figure 7 The change in the intensity ratio of the D peak to the G peak is the Raman characteristic (as shown in Figure 8 As shown), a detection model between the intensity ratio of carbon black D peak to G peak and the bottom filling glue stress is established through statistical analysis: σ = 121Α-2.9;

[0097] (3) doping epoxy resin 2 and carbon black at a mass ratio of 10%, mixing evenly, taking this as the sample to be tested, collecting the Raman spectrum of the carbon material in the sample to be tested, and calculating the stress value of the sample to be tested based on the relationship between the Raman spectrum of the carbon material in the standard sample obtained in step (2) and the polymer stress;

[0098] The parameter conditions for collecting the Raman spectrum of the carbon material in the sample to be tested are the same as the parameter conditions for the Raman spectrum test of the standard sample in step (2);

[0099] After calculation, the stress value of the sample to be tested at 50°C is 93.9MPa.

[0100] In summary, the present invention uses carbon materials as indicators to provide an in-situ, non-destructive, non-contact method for directly detecting stress in polymers using Raman spectroscopy. This detection method can be used to analyze different stress states of polymers, providing theoretical guidance for the application of stress characterization of polymers and a basis for failure analysis.

[0101] The applicant declares that the present invention illustrates the detailed process flow of the present invention through the above-mentioned embodiments, but the present invention is not limited to the above-mentioned detailed process flow, that is, it does not mean that the present invention must rely on the above-mentioned detailed process flow to be implemented. Those skilled in the art should understand that any improvement of the present invention, equivalent replacement of various raw materials of the product of the present invention, addition of auxiliary components, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A method for detecting polymer stress, characterized in that: The detection method comprises the following steps: Obtaining a standard sample, wherein the standard sample is a polymer material containing a carbon material; Determining a detection model between the standard sample and the stress according to a first Raman signal corresponding to the standard sample under different stress conditions; A second Raman signal corresponding to the polymer material to be tested is collected, and a stress value corresponding to the polymer material to be tested is determined based on the detection model and the second Raman signal.

2. The method for detecting polymer stress according to claim 1, characterized in that: Determining the detection model between the standard sample and the stress according to the first Raman signal corresponding to the standard sample under different stress conditions includes: Under different stress conditions, sending laser to the standard sample and collecting a first Raman signal corresponding to the standard sample; determining, according to the first Raman signal, corresponding Raman spectra under different stress conditions; For each of the Raman spectra, extracting corresponding Raman features; A detection model between the standard sample and the polymer stress is determined according to the Raman characteristics and the polymerization stress corresponding to the stress condition.

3. The method for detecting polymer stress according to claim 2, characterized in that: The Raman characteristics include the intensity value of the characteristic peak, the displacement of the characteristic peak, the intensity ratio of the characteristic peak and / or the morphology of the characteristic peak; Preferably, the Raman characteristic comprises an intensity ratio of a D peak to a G peak.

4. The method for detecting polymer stress according to any one of claims 1 to 3, characterized in that: The detection model includes a linear regression model, a machine learning model, a variable statistical model or / and a feature comparison model; Preferably, the stress condition includes at least one of a temperature condition, a humidity condition, a mechanical stress condition or a pressure condition.

5. The method for detecting polymer stress according to claim 4, characterized in that: The detection model type is a linear regression model, and the detection model is: σ=k·A–b; Wherein, σ is the polymer stress, A is the intensity ratio of the D peak to the G peak, k is the slope, and b is a constant.

6. The method for detecting polymer stress according to claim 1, characterized in that: Before obtaining the standard sample, the method further includes: Obtain pre-processed materials; When the pre-treated material is a polymer material containing a carbon material, the pre-treated material is used as the standard sample; When the pre-treated material is a polymer material that does not contain a carbon material, the pre-treated material is modified with a carbon material to obtain the standard sample; Preferably, the carbon material modification method comprises any one of doping, spraying, in-situ polymerization or electrochemical deposition.

7. The method for detecting polymer stress according to claim 6, characterized in that: The polymer material containing carbon material includes any one of bottom filling glue, epoxy resin molding compound and thermal interface material; Preferably, the polymer material containing no carbon material comprises at least one of epoxy resin, silicone material, polyurethane or polyimide; Preferably, the carbon material includes at least one of carbon black, carbon nanotubes, graphite or diamond.

8. The method for detecting polymer stress according to claim 1, characterized in that: Taking the mass percentage of the standard sample as 100%, the mass percentage of the carbon material in the standard sample is 0.1% to 45%.

9. The method for detecting polymer stress according to claim 2, characterized in that: The laser wavelength includes at least one of 266nm, 325nm, 514nm, 532nm, 633nm, 785nm, 830nm, and 1064nm; Preferably, the power of the laser is 1mW to 100mW; Preferably, the integration time corresponding to the laser is 1s to 1000s.

10. A polymer stress detection system, characterized in that: The polymer stress detection system includes a stress regulation device, a Raman spectrometer and a polymerization stress detection device. The polymerization stress detection device is electrically connected to the stress regulation device and the Raman spectrometer, respectively, and performs the polymer stress detection method as described in any one of claims 1 to 9.

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