Glass transition temperature test method, device, equipment, medium and program product

Abstract

The embodiment of the present application discloses a glass transition temperature test method, device, equipment, medium and program product. The method includes: performing a near infrared spectrum test on an object to be tested in a target environment to obtain a near infrared spectrum signal of the object to be tested; inputting the near infrared spectrum signal of the object to be tested into a pre-trained glass transition temperature determination model; and determining the measured glass transition temperature corresponding to the near infrared spectrum signal of the object to be tested according to the correspondence between the near infrared spectrum signal and the glass transition temperature through the trained glass transition temperature model. The embodiment of the present application can realize the rapid and accurate determination of the glass transition temperature at the industrial production site.

Description

Technical Field

[0001] The present application belongs to the technical field of image glass transition temperature testing, and in particular, relates to a glass transition temperature testing method, device, equipment, computer storage medium and computer program product. Background Art

[0002] Glass transition temperature (Tg) is a key parameter that characterizes the unique physical behavior of polymer materials. It marks the critical point at which the polymer chain segments obtain enough energy from the frozen glass state to begin to rotate randomly. At this specific temperature Tg, the material undergoes a fundamental transformation from a hard and brittle glass state to a soft and flowing rubber state (or viscoelastic state), and its mechanical properties such as elastic modulus and toughness will change significantly. Therefore, the glass transition temperature is not only an important basis for determining the temperature range of polymer materials, but also a core indicator for evaluating the processing performance of materials and the quality of finished products. It is of great significance in the field of materials science and engineering, and directly affects the design, material selection, and optimization and control of the manufacturing process of products.

[0003] At present, the commonly used methods for measuring glass transition temperature in industrial production are differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). Although DSC and DMA can provide relatively accurate results, they need to be performed in a laboratory environment, the operation is time-consuming and requires professional technicians to perform data analysis. In modern factories that pursue efficient production, rapid quality inspection and cost control, traditional detection methods are difficult to meet production needs. Therefore, it is urgent to provide an accurate glass transition temperature test method that can be used in industrial production sites. Summary of the invention

[0004] The embodiments of the present application provide a glass transition temperature testing method, device, equipment, computer storage medium and computer program product, which can realize rapid and accurate determination of the glass transition temperature.

[0005] In a first aspect, the present application provides a method for testing glass transition temperature, comprising:

[0006] Performing a near infrared spectroscopy test on an object to be tested in a target environment to obtain a near infrared spectroscopy signal of the object to be tested, wherein the object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer;

[0007] Inputting the near-infrared spectral signal of the object to be measured into a pre-trained glass transition temperature determination model, wherein the glass transition temperature determination model is obtained by training a training sample set; the training sample set includes multiple training samples; the multiple training samples include multiple near-infrared spectral signals obtained by performing near-infrared spectral tests on multiple standard samples in a target environment under multiple preset conditions, and multiple glass transition temperature labels corresponding to the multiple near-infrared spectral signals; the multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters; the multiple standard samples include a target polymer, and the multiple standard samples include multiple standard samples with different uncontrollable product parameters, and the uncontrollable product parameters include color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structure parameters. At least one of the parameters; the glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near-infrared spectral signal;

[0008] Through the trained glass transition temperature model, according to the corresponding relationship between the near-infrared spectrum signal and the glass transition temperature, the measured glass transition temperature corresponding to the near-infrared spectrum signal of the object to be measured is determined.

[0009] In an optional embodiment, before inputting the near infrared spectrum signal of the object to be measured into a pre-trained glass transition temperature measurement model, the method further includes:

[0010] Performing near infrared spectroscopy tests on a plurality of standard samples respectively under a plurality of preset conditions and in a target environment to obtain a near infrared spectroscopy signal of each standard sample in the plurality of standard samples under each preset condition;

[0011] Obtaining the calibrated glass transition temperature of the target polymer in each standard sample;

[0012] The near-infrared spectral signal corresponding to each standard sample under each preset condition and the calibrated glass transition temperature corresponding to the standard sample are respectively used to create training samples to obtain a training sample set;

[0013] Inputting the training sample set into the glass transition temperature determination model;

[0014] Through the glass transition temperature determination model, according to the preset correspondence between the near-infrared spectrum signal and the glass transition temperature, the measured glass transition temperature corresponding to each training sample is obtained;

[0015] According to the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample, the glass transition temperature measurement model is iteratively trained to adjust the corresponding relationship between the near-infrared spectral signal and the glass transition temperature, so as to obtain a trained glass transition temperature measurement model.

[0016] In an optional embodiment, before performing near infrared spectroscopy tests on multiple standard samples in a target environment under multiple preset conditions, the method further includes:

[0017] Obtain the boundary values ​​of the uncontrollable parameters corresponding to the target environment;

[0018] Determine the value range of the uncontrollable parameter according to the boundary value of the uncontrollable parameter;

[0019] According to the value range of the uncontrollable parameter, a plurality of preset conditions are designed, wherein the plurality of preset conditions include test conditions corresponding to boundary values ​​of the uncontrollable parameter and test conditions corresponding to an intermediate value of at least one uncontrollable parameter.

[0020] In an optional embodiment, before performing near infrared spectroscopy tests on multiple standard samples in a target environment under multiple preset conditions, the method further includes:

[0021] Obtain the boundary values ​​of the uncontrollable product parameters corresponding to the standard samples in the target production line;

[0022] Determine the value range of the uncontrollable product parameter according to the boundary value of the uncontrollable product parameter;

[0023] According to the value range of the uncontrollable product parameter, a plurality of standard samples are prepared, wherein the plurality of standard samples include standard samples corresponding to the boundary values ​​of the uncontrollable product parameter and standard samples corresponding to the intermediate values ​​of at least one uncontrollable product parameter.

[0024] In an optional embodiment, obtaining the calibrated glass transition temperature of the target polymer in each standard sample includes:

[0025] Each standard sample is tested by a target glass transition temperature determination method to obtain a calibrated glass transition temperature of the target polymer in each standard sample, wherein the target glass transition temperature determination method includes differential scanning calorimetry and / or dynamic mechanical analysis.

[0026] In an optional embodiment, the wavelength of the near infrared spectroscopy test is 780nm to 2526nm.

[0027] In an optional embodiment, the target polymer includes a thermosetting polymer material and / or a thermoplastic polymer material.

[0028] In a second aspect, an embodiment of the present application provides a glass transition temperature testing device, comprising:

[0029] A testing module, used for performing a near infrared spectrum test on an object to be tested in a target environment to obtain a near infrared spectrum signal of the object to be tested, wherein the object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer;

[0030] An input module is used to input the near-infrared spectrum signal of the object to be tested into a pre-trained glass transition temperature determination model, wherein the glass transition temperature determination model is obtained by training a training sample set; the training sample set includes multiple training samples; the multiple training samples include multiple near-infrared spectrum signals obtained by performing near-infrared spectrum tests on multiple standard samples in a target environment under multiple preset conditions, and multiple glass transition temperature labels corresponding to the multiple near-infrared spectrum signals; the multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters; the multiple standard samples include a target polymer, and the multiple standard samples include multiple standard samples with different uncontrollable product parameters, and the uncontrollable product parameters include color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structure parameters. At least one of the parameters; the glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near-infrared spectrum signal;

[0031] The determination module is used to determine the measured glass transition temperature corresponding to the near-infrared spectrum signal of the object to be measured according to the corresponding relationship between the near-infrared spectrum signal and the glass transition temperature through the trained glass transition temperature model.

[0032] In a third aspect, an embodiment of the present application provides a glass transition temperature testing device, the device comprising:

[0033] A processor and a memory storing computer program instructions; when the processor executes the computer program instructions, any one of the above glass transition temperature test methods is implemented.

[0034] In a fourth aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, any one of the above-mentioned glass transition temperature test methods is implemented.

[0035] In a fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is enabled to perform any of the above-mentioned glass transition temperature test methods.

[0036] The glass transition temperature test method, device, equipment, computer storage medium and computer program product of the embodiment of the present application can perform near-infrared spectroscopy test on the object to be tested in the target environment to obtain the near-infrared spectral signal of the object to be tested, the object to be tested includes the material and / or product to be tested, and the object to be tested includes the target polymer. Then, the near-infrared spectral signal of the object to be tested is input into the pre-trained glass transition temperature determination model. Among them, the glass transition temperature determination model is obtained by training the training sample set. The training sample set includes multiple training samples. The above-mentioned multiple training samples include multiple near-infrared spectral signals obtained by performing near-infrared spectral tests on multiple standard samples in the target environment under multiple preset conditions, and multiple glass transition temperature labels corresponding to the multiple near-infrared spectral signals. The near-infrared spectral signals of the above-mentioned training sample set are all measured in the target environment. In this way, for a relatively fixed and controllable production site environment, a corresponding target environment can be designed so that the glass transition temperature determination model can be applied to a specific production site environment. The above-mentioned multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters. A plurality of standard samples include a target polymer, and the plurality of standard samples include a plurality of standard samples with different uncontrollable product parameters, and the uncontrollable product parameters include at least one of color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structure parameters. The glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near-infrared spectral signal. In this way, the training samples contained in the training sample set are more comprehensive, and can cover the results that may appear in the glass transition temperature test site under industrial environment, facing a variety of uncontrollable test environment factors and uncontrollable product factors as much as possible. The calibrated glass transition temperature of the target polymer in the standard sample is used as the glass transition temperature label of the training sample. In this way, the glass transition temperature determination model obtained by training can accurately determine the glass transition temperature of the target polymer in the object to be tested according to the near-infrared spectral signal collected at the production site. In this way, it is allowed to use a portable near-infrared spectrometer as a measuring tool for non-destructive testing at the industrial production site, so that the glass transition temperature test has both high convenience and accuracy. The embodiment of the present application determines the measured glass transition temperature corresponding to the near-infrared spectrum signal of the object to be measured according to the corresponding relationship between the near-infrared spectrum signal and the glass transition temperature through the trained glass transition temperature model. In this way, the glass transition temperature can be measured quickly and accurately in a variety of industrial application scenarios, which is conducive to improving production efficiency and product quality management capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0038] Figure 1 It is a schematic diagram of a process for testing a glass transition temperature provided by an embodiment of the present application;

[0039] Figure 2 is a flow chart of a glass transition temperature testing method provided by another embodiment of the present application;

[0040] Figure 3 is a structural schematic diagram of a glass transition temperature testing device provided in yet another embodiment of the present application;

[0041] Figure 4 It is a structural schematic diagram of a glass transition temperature testing device provided in yet another embodiment of the present application. DETAILED DESCRIPTION

[0042] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0043] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0044] At present, the commonly used methods for measuring glass transition temperature in industrial production are differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). Although DSC and DMA can provide relatively accurate results, they need to be performed in a laboratory environment, the operation is time-consuming and requires professional technicians to perform data analysis. In modern factories that pursue efficient production, rapid quality inspection and cost control, traditional detection methods are difficult to meet production needs.

[0045] Near infrared spectroscopy (NIR) technology is currently widely used as an efficient and non-destructive quality inspection tool in the food, chemical and pharmaceutical industries. NIR is a region between visible light and mid-infrared light, with a wavelength range of 780nm to 2526nm and a wavenumber range of 12500cm -1 ~4000cm -1 . Near-infrared spectroscopy belongs to molecular vibration spectroscopy, which is generated by the anharmonic vibration of covalent chemical bonds and is the frequency doubling and combination of molecular vibration. The near-infrared spectral signal of polymers covers the vibration frequency doubling and combination frequency bands of various molecular groups such as CH, OH and NH. The subtle changes in these frequency bands directly reflect the microscopic changes in the internal structure and physical state of the polymer. When the component content of the sample changes, its near-infrared spectral signal will also change accordingly.

[0046] The related technology involves exploring the feasibility of measuring the glass transition temperature of epoxy resin using NIR. Specifically, it involves using near-infrared light with a wavelength range of 830nm to 2630nm to measure the glass transition temperature of epoxy resin with a glass transition temperature range of 45°C to 98°C. However, the accuracy of this solution is low and is not enough to meet industrial-level requirements.

[0047] Related technologies also involve near-infrared spectroscopy analysis using near-infrared light in the wavelength range of 2000nm to 2450nm to measure the glass transition temperature of epoxy prepregs with a glass transition temperature between 25°C and 42°C. However, this solution has a limited scope of application and is only applicable to epoxy resin prepregs with a narrow temperature range.

[0048] In addition, the above two solutions have common limitations: on the one hand, both of them only consider laboratory conditions. In the complex application scenarios of factories, it is difficult for both to meet the accuracy requirements of industrial applications. On the other hand, the above two solutions use near-infrared spectrometers for laboratory environments. The whole set of equipment is heavy and not conducive to use in industrial production sites.

[0049] In summary, although the relevant technology has been initially explored and studied in the laboratory in terms of using NIR technology to measure the glass transition temperature, it is not applicable to industrial application needs. The accuracy, scope of application and convenience of NIR technology in measuring the glass transition temperature in industrial scenarios need to be improved.

[0050] In order to solve the above problems, the inventor, after in-depth thinking, cleverly proposed a glass transition temperature testing method, device, equipment, computer storage medium and computer program product.

[0051] In conjunction with the accompanying drawings, the glass transition temperature test method provided in the embodiment of the present application is introduced through specific embodiments and their application scenarios. The glass transition temperature test method provided in the embodiment of the present application, the device for executing the method can be a glass transition temperature test device, or a partial module in the glass transition temperature test device for executing the glass transition temperature test method. In the embodiment of the present application, the glass transition temperature test method provided in the embodiment of the present application is described in detail by taking the glass transition temperature test device executing the glass transition temperature test method as an example.

[0052] In addition, it should be noted that the glass transition temperature test method provided in the embodiment of the present application, after performing a near-infrared spectrum test on the object to be tested in the target environment and obtaining the near-infrared spectrum signal of the object to be tested, needs to determine the measured glass transition temperature corresponding to the near-infrared spectrum signal through the glass transition temperature measurement model. Therefore, before determining the measured glass transition temperature corresponding to the near-infrared spectrum signal through the glass transition temperature measurement model, it is necessary to first train the glass transition temperature measurement model. The specific implementation method of the training method of the glass transition temperature measurement model used in the glass transition temperature test method provided in the embodiment of the present application is described below.

[0053] Figure 1 A flow chart of a glass transition temperature testing method provided in one embodiment of the present application is shown, and specifically, it may be a flow chart of a training method for a glass transition temperature determination model used in the glass transition temperature testing method provided in an embodiment of the present application.

[0054] like Figure 1 As shown, the training method of the glass transition temperature determination module used in the glass transition temperature testing method provided in the embodiment of the present application may include steps S110 to S160.

[0055] S110, performing near-infrared spectroscopy tests on multiple standard samples under multiple preset conditions and in a target environment, respectively, to obtain near-infrared spectral signals of each standard sample in the multiple standard samples under each preset condition; wherein the multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters; the multiple standard samples include a target polymer, and the multiple standard samples include multiple standard samples with different uncontrollable product parameters, and the uncontrollable product parameters include color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and at least one of composite material structure parameters.

[0056] Step S110 can distinguish between controllable parameters and uncontrollable parameters for the parameters that affect the accuracy of glass transition temperature measurement in the actual factory environment, fix the controllable parameters, and design comprehensive and representative measurement samples and test environments for the uncontrollable parameters. Specifically, in step S110, the target environment can be a fixed test environment designed according to the controllable parameters of the industrial production site, for example, the target environment can be a factory environment with a fixed background for the measurement. The multiple preset conditions can be multiple measurement environments designed according to the uncontrollable environmental parameters of the industrial production site. Exemplarily, the uncontrollable parameters corresponding to the target environment can include uncontrollable environmental parameters in the factory environment, for example, including but not limited to one or more of the environmental pollutants that may exist in the factory environment, such as ambient temperature, relative humidity, and environmental contaminants that may exist in the factory environment. The multiple standard samples can be measurement samples with fixed product structures designed according to the controllable structural parameters and uncontrollable product parameters of the material and / or product, but with different uncontrollable product parameters, for example, they can be measurement samples specially made based on the surface structure of the product. Among the uncontrollable product parameters, the composite material parameters of the target polymer may include, for example, the types of other materials compounded with the target polymer in the composite material, including but not limited to the types of fabrics and core materials; the composite material structural parameters may include but not limited to the relative orientation between fabrics and the surface morphology structure of the core material. It is understandable that the above controllable parameters and uncontrollable parameters are not fixed, and the controllable parameters and uncontrollable parameters can be adjusted according to actual needs. For example, some industrial production environments have strict requirements on ambient temperature and relative humidity, so ambient temperature and relative humidity can also be used as controllable parameters.

[0057] It is understandable that the target environment may include a fixed test environment designed for the environment of the same industrial production site, and the multiple standard samples may include measurement samples with different uncontrollable product parameters designed for the same material or product. When it is necessary to train a glass transition temperature determination model suitable for a variety of industrial production sites and / or for a variety of materials and / or products, controllable parameters and uncontrollable parameters may be distinguished for each material and / or product at each industrial production site, and the controllable parameters may be fixed. For the uncontrollable parameters, a comprehensive and representative measurement sample and test environment may be designed, and the corresponding near-infrared spectral signal may be collected.

[0058] S120, obtaining the calibrated glass transition temperature of the target polymer in each standard sample.

[0059] In step S120, the calibrated glass transition temperature may include a glass transition temperature measured by a glass transition temperature testing method with high accuracy. The calibrated glass transition temperature has high accuracy and can be considered as the actual glass transition temperature of the target polymer in the standard sample.

[0060] S130, creating training samples by respectively using the near-infrared spectral signal corresponding to each standard sample under each preset condition and the calibrated glass transition temperature corresponding to the standard sample to obtain a training sample set.

[0061] In step S130, the calibrated glass transition temperature corresponding to the standard sample can be used as a glass transition temperature label to measure the effect of model training.

[0062] S140, inputting the training sample set into the glass transition temperature determination model.

[0063] In step S140, the glass transition temperature determination model may include a correction prediction model constructed by a mathematical algorithm, for example, a correction prediction model constructed based on partial least squares regression, principal component regression, multivariate linear regression, or a neural network algorithm, etc. The present application embodiment does not specifically limit this.

[0064] S150, obtaining the measured glass transition temperature corresponding to each training sample through the glass transition temperature measurement model according to the preset correspondence relationship between the near-infrared spectrum signal and the glass transition temperature.

[0065] S160, iteratively training the glass transition temperature measurement model according to the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample, so as to adjust the corresponding relationship between the near-infrared spectral signal and the glass transition temperature, and obtain a trained glass transition temperature measurement model.

[0066] In step S160, the glass transition temperature measurement model can be iteratively trained according to the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample until a preset training stop condition is met to obtain a trained glass transition temperature measurement model. The training stop condition can be, for example, reaching a preset number of iterations, or the performance of the glass transition temperature measurement model meets preset requirements, which is not limited here. Exemplarily, the measured glass transition temperature and the calibrated glass transition temperature of each training sample can be calculated to determine the deviation function value of the glass transition temperature measurement model. When the deviation function value is less than a preset threshold, a trained glass transition temperature measurement model is obtained.

[0067] According to the above embodiment, multiple standard samples can be tested for near infrared spectroscopy under multiple preset conditions and in the target environment, and the near infrared spectral signal of each standard sample in the multiple standard samples under each preset condition can be obtained. Among them, the multiple preset conditions include test conditions designed according to the uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters. Multiple standard samples include target polymers, and multiple standard samples include multiple standard samples with different uncontrollable product parameters, and the uncontrollable product parameters include color, raw material ratio of target polymers, moisture content, composite material parameters of target polymers, and at least one of composite material structure parameters. In this way, the corresponding target environment can be designed for a relatively fixed and controllable production site environment; multiple standard samples with different uncontrollable product parameters can be designed for uncontrollable uncontrollable product parameters; and multiple preset conditions can be designed for uncontrollable test environment factors. In this way, the near infrared spectral signal of each standard sample collected under each preset condition is relatively comprehensive, and can cover the results that may appear in the glass transition temperature test site under an industrial environment as much as possible.

[0068] Subsequently, the calibrated glass transition temperature of the target polymer in each standard sample can be obtained, and the calibrated glass transition temperature has a high accuracy and can be considered as the actual glass transition temperature of the target polymer in the standard sample. Then, the near-infrared spectral signal corresponding to each standard sample under each preset condition and the calibrated glass transition temperature corresponding to the standard sample are used to create training samples to obtain a training sample set. The training sample set created in this way includes rich training samples and has a high representativeness. Then the training sample set is input into the glass transition temperature determination model. Through the glass transition temperature determination model, according to the preset correspondence between the near-infrared spectral signal and the glass transition temperature, the measured glass transition temperature corresponding to each training sample is obtained. According to the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample, the glass transition temperature determination model is iteratively trained to adjust the correspondence between the near-infrared spectral signal and the glass transition temperature, and a trained glass transition temperature determination model is obtained, which is used to determine the glass transition temperature of the target polymer in the object to be measured by the trained glass transition temperature determination model. In this way, the trained glass transition temperature determination model can accurately determine the glass transition temperature of the corresponding target polymer based on the near-infrared spectral signal collected at the production site.

[0069] The glass transition temperature determination model obtained by training according to the above-mentioned embodiment can have a higher accuracy at the industrial production site. In this way, it is possible to allow the use of a portable near-infrared spectrometer (such as a handheld near-infrared spectrometer) as a measuring tool to non-destructively, quickly and accurately measure the glass transition temperature of raw materials and products in industrial applications. Thus, the glass transition temperature test method combined with the glass transition temperature determination model has a higher applicability and can be applied to the production and maintenance of parts and products in the fields of aerospace, wind power, energy storage, ocean and automobile. Its applicable raw materials may include thermosetting and thermoplastic polymer materials, as well as composite materials based on these materials. Its applicable products may include, but are not limited to, composite material parts, wind turbine blades, energy storage tanks, automotive parts, coatings, adhesives, and structural adhesives used on aircraft.

[0070] In one embodiment, before performing near infrared spectroscopy tests on a plurality of standard samples in a target environment under a plurality of preset conditions, the method may further include:

[0071] Get the boundary values ​​of the uncontrollable parameters corresponding to the target environment.

[0072] According to the boundary value of the uncontrollable parameter, the value range of the uncontrollable parameter is determined.

[0073] According to the value range of the uncontrollable parameter, a plurality of preset conditions are designed, wherein the plurality of preset conditions include test conditions corresponding to boundary values ​​of the uncontrollable parameter and test conditions corresponding to an intermediate value of at least one uncontrollable parameter.

[0074] In the above-mentioned implementation, the boundary value may include the extreme value that the uncontrollable parameter can reach in the target environment, or the extreme value that may be reached. The value range of the uncontrollable parameter may represent the optional range of the uncontrollable parameter. According to the value range of the uncontrollable parameter, multiple preset conditions are designed, which may include designing test conditions corresponding to the boundary value of the uncontrollable parameter and test conditions corresponding to the intermediate value of at least one uncontrollable parameter. In some examples, the uncontrollable parameter may include multiple environmental parameters, and the multiple environmental parameters may be combined based on the value range of each environmental parameter so that the multiple preset conditions designed can cover various changes in the actual production environment as much as possible.

[0075] According to the above implementation, by obtaining the boundary value of the uncontrollable parameter corresponding to the target environment, the value range of the uncontrollable parameter is determined, and then multiple preset conditions are designed according to the value range of the uncontrollable parameter. In this way, the multiple preset conditions can cover various changes in the actual production environment as much as possible, thereby improving the comprehensiveness and representativeness of the training samples. In this way, the glass transition temperature measurement model can still maintain a high degree of accuracy when facing the variable uncontrollable environmental parameters in the target environment, which is conducive to improving the accuracy and flexibility of the glass transition temperature test.

[0076] In one embodiment, before performing near infrared spectroscopy tests on a plurality of standard samples in a target environment under a plurality of preset conditions, the method may further include:

[0077] Obtain the boundary values ​​of the uncontrollable product parameters corresponding to the standard samples in the target production line.

[0078] According to the boundary value of the uncontrollable product parameter, the value range of the uncontrollable product parameter is determined.

[0079] According to the value range of the uncontrollable product parameter, a plurality of standard samples are prepared, wherein the plurality of standard samples include standard samples corresponding to the boundary values ​​of the uncontrollable product parameter and standard samples corresponding to the intermediate values ​​of at least one uncontrollable product parameter.

[0080] In the above embodiment, the boundary value may include the extreme value that the uncontrollable product parameter corresponding to the standard sample in the target production line can reach, or the extreme value that may be reached. The value range of the uncontrollable product parameter can represent the optional range of the uncontrollable parameter. According to the value range of the uncontrollable product parameter, multiple standard samples are prepared, which may include preparing standard samples corresponding to the boundary values ​​of the uncontrollable product parameters and standard samples corresponding to the intermediate values ​​of at least one uncontrollable product parameter. For example, taking the target polymer color as an example, for the natural color difference between different batches, it is necessary to design a sample set containing a color gradient, from the lightest to the darkest, and its span needs to exceed the color range used in actual production. In this way, the influence of these uncontrollable variables can be effectively corrected for the slight differences between raw materials or product batches by means of chemical quantitative analysis. In some examples, the number of uncontrollable product parameters can be multiple, and multiple uncontrollable product parameters can be combined based on the value range of each uncontrollable product parameter so that the prepared multiple standard samples can cover various changes in the actual production environment as much as possible.

[0081] According to the above embodiment, by obtaining the boundary value of the uncontrollable product parameter corresponding to the target environment, the value range of the uncontrollable product parameter is determined, and then multiple standard samples are prepared according to the value range of the uncontrollable product parameter. In this way, multiple standard samples can cover various changes in the actual production environment as much as possible, thereby improving the comprehensiveness and representativeness of the training samples. In this way, the glass transition temperature determination model can still maintain a high degree of accuracy when facing the variable uncontrollable product parameters in industrial production, which is conducive to improving the accuracy and flexibility of the glass transition temperature test.

[0082] In one embodiment, obtaining the calibrated glass transition temperature of the target polymer in each standard sample may specifically include:

[0083] Each standard sample is tested by a target glass transition temperature determination method to obtain a calibrated glass transition temperature of the target polymer in each standard sample, wherein the target glass transition temperature determination method includes differential scanning calorimetry and / or dynamic mechanical analysis.

[0084] According to the above embodiment, the calibrated glass transition temperature of the target polymer in each standard sample is determined by differential scanning calorimetry and / or dynamic mechanical analysis. Both differential scanning calorimetry and dynamic mechanical analysis have high accuracy, and the calibrated glass transition temperature determined by these two methods is close to the actual glass transition temperature of the target polymer in the standard sample. Therefore, using the calibrated glass transition temperature as a label for the training sample can improve the accuracy of the glass transition temperature determination model.

[0085] In one embodiment, performing near infrared spectroscopy tests on multiple standard samples in a target environment under multiple preset conditions to obtain near infrared spectroscopy signals of each standard sample in the multiple standard samples under each preset condition may specifically include:

[0086] Performing near infrared spectroscopy tests on a plurality of standard samples respectively under a plurality of preset conditions and in a target environment, and obtaining an original near infrared spectroscopy signal of each standard sample in the plurality of standard samples under each preset condition;

[0087] The original near-infrared spectral signal of each standard sample under each preset condition is preprocessed to obtain the near-infrared spectral signal of each standard sample in the plurality of standard samples under each preset condition.

[0088] The above-mentioned preprocessing can be achieved by preprocessing methods known in the art, for example, it may include but is not limited to applying smoothing techniques to remove random noise, performing normalization operations to standardize data distribution, and revealing at least one of the potential spectral features through first-order derivative and second-order derivative transformations.

[0089] According to the above implementation, by preprocessing the original near-infrared spectrum signal, it is helpful to extract the spectral characteristics of the target polymer system as the glass transition temperature changes under the production environment. In this way, it is helpful for the glass transition temperature determination model to accurately identify the potential relationship between the glass transition temperature and the near-infrared spectrum signal, thereby helping to improve the accuracy of the glass transition temperature test.

[0090] The embodiment of the present application does not limit the wavelength of the near infrared spectrum test. In the embodiment of the present application, the glass transition temperature determination model used by the glass transition temperature test method has high accuracy and applicability and has a wide range of application. Thus, according to the different chemical structures of the target polymer, the wavelength of the near infrared spectrum test can be any part of the wavelength between 780nm and 2526nm. For example, it can be 780nm to 2526nm, 780nm to 2400nm, 780nm to 2200nm, 780nm to 2000nm, 780nm to 1800nm, 780nm to 1600nm, 780nm to 1200nm, 780nm to 1000nm, 800nm ​​to 2526nm, 800nm ​​to 2300nm, 800nm ​​to 2100nm, 800nm ​​to 1900nm, 800nm ​​to 1500nm, and so on.

[0091] In this way, different test wavelengths can be selected for different target polymers, so that the measurement results of the glass transition temperatures of different target polymers have higher accuracy.

[0092] In one embodiment, after iteratively training the glass transition temperature determination model according to the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample, the method may further include:

[0093] The performance of the glass transition temperature determination model is verified by using actual industrial materials and / or actual products corresponding to the standard samples, so as to adjust the model parameters of the glass transition temperature determination model.

[0094] In this way, the glass transition temperature determination model can be optimized and calibrated using actual industrial materials and / or actual products, thereby further improving the accuracy of the model.

[0095] In one embodiment, the wavelength of the near infrared spectrum test may be 950 nm to 1650 nm, which is beneficial for achieving higher accuracy, wider applicability and lower instrument production cost.

[0096] In one embodiment, the target polymer may include a thermosetting polymer material and / or a thermoplastic polymer material.

[0097] Exemplarily, the thermosetting polymer material may include epoxy resin, unsaturated polyester, polyurethane, and their modified, block, hybrid or blended materials and systems. Exemplarily, the thermoplastic polymer material may include free radical polymerized polyolefin, and their modified, block, hybrid or blended polyolefin materials and systems.

[0098] The following is combined with Figure 2 The glass transition temperature testing method provided in the examples of the present application is described in detail.

[0099] Figure 2 FIG. 1 is a flow chart of a method for testing glass transition temperature provided by an embodiment of the present application. Figure 2 As shown, the glass transition temperature testing method may specifically include the following steps S210 to S230.

[0100] S210, performing a near infrared spectrum test on an object to be tested in a target environment to obtain a near infrared spectrum signal of the object to be tested, wherein the object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer.

[0101] In step S210, the object to be tested may include the surface structure or raw materials of the product.

[0102] S220, input the near infrared spectrum signal of the object to be measured into a pre-trained glass transition temperature determination model, wherein the glass transition temperature determination model is obtained by training a training sample set. The training sample set includes multiple training samples. The multiple training samples include multiple near infrared spectrum signals obtained by performing near infrared spectrum tests on multiple standard samples in a target environment under multiple preset conditions, and multiple glass transition temperature labels corresponding to the multiple near infrared spectrum signals. The multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters. The multiple standard samples include a target polymer, and the multiple standard samples include multiple standard samples with different uncontrollable product parameters, and the uncontrollable product parameters include color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structure parameters. At least one of the parameters. The glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near infrared spectrum signal.

[0103] S230, determining the measured glass transition temperature corresponding to the near infrared spectrum signal of the object to be measured according to the corresponding relationship between the near infrared spectrum signal and the glass transition temperature through the trained glass transition temperature model.

[0104] The glass transition temperature test method of the embodiment of the present application can perform near-infrared spectroscopy test on the object to be tested in the target environment to obtain the near-infrared spectral signal of the object to be tested, the object to be tested includes the material and / or product to be tested, and the object to be tested includes the target polymer. Then, the near-infrared spectral signal of the object to be tested is input into the pre-trained glass transition temperature determination model. Among them, the glass transition temperature determination model is obtained by training a training sample set. The training sample set includes a plurality of training samples. The above-mentioned multiple training samples include a plurality of near-infrared spectral signals obtained by performing near-infrared spectroscopy test on a plurality of standard samples in the target environment under a plurality of preset conditions, and a plurality of glass transition temperature labels corresponding to the plurality of near-infrared spectral signals. The near-infrared spectral signals of the above-mentioned training sample set are all measured in the target environment. In this way, for a relatively fixed and controllable production site environment, a corresponding target environment can be designed so that the glass transition temperature determination model can be applied to a specific production site environment. The above-mentioned multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters. A plurality of standard samples include a target polymer, and the plurality of standard samples include a plurality of standard samples with different uncontrollable product parameters, and the uncontrollable product parameters include at least one of color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structure parameters. The glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near-infrared spectral signal. In this way, the training samples contained in the training sample set are more comprehensive, and can cover the results that may appear in the glass transition temperature test site under industrial environment, facing a variety of uncontrollable test environment factors and uncontrollable product factors as much as possible. The calibrated glass transition temperature of the target polymer in the standard sample is used as the glass transition temperature label of the training sample. In this way, the glass transition temperature determination model obtained by training can accurately determine the glass transition temperature of the target polymer in the object to be tested according to the near-infrared spectral signal collected at the production site. In this way, it is allowed to use a portable near-infrared spectrometer as a measuring tool for non-destructive testing at the industrial production site, so that the glass transition temperature test has both high convenience and accuracy. The embodiment of the present application determines the measured glass transition temperature corresponding to the near-infrared spectrum signal of the object to be measured according to the corresponding relationship between the near-infrared spectrum signal and the glass transition temperature through the trained glass transition temperature model. In this way, the glass transition temperature can be measured quickly and accurately in a variety of industrial application scenarios, which is conducive to improving production efficiency and product quality management capabilities.

[0105] In order to better describe the entire scheme, based on the above embodiments, a specific example is given to illustrate the glass transition temperature test method of the embodiment of the present application. This is explained in detail below. It should be noted that the following examples are only for explaining the embodiment of the present application, and do not constitute a limitation on the embodiment of the present application.

[0106] As an example, the glass transition temperature testing method may include the following steps S310 to S360 .

[0107] S310, determining parameters that affect the accuracy of glass transition temperature testing in a factory environment.

[0108] In step S310, the parameters affecting the accuracy of the glass transition temperature test may include parameters affecting the acquisition of near-infrared spectrum signals, especially factors that may affect the absorption characteristics of the near-infrared spectrum and the signal intensity feedback. For example, it may include environmental parameters, polymer-related parameters, polymer composite material parameters, composite material structure parameters, etc. The composite material parameters of the polymer may include the types of materials within the depth range that the near-infrared light can penetrate during measurement, and the composite material structure parameters may include the ply structure sequence and interface structure of the composite material. Exemplarily, environmental factors may include ambient temperature, relative humidity, environmental pollutants, etc. The polymer-related parameters may include color, polymer raw material ratio (such as the mixing ratio of resin and curing agent), moisture content, etc. The composite material parameters of the polymer and the composite material structure parameters may be determined by the actual situation of the product. Exemplarily, the composite material parameters of the polymer may include, in addition to the polymer matrix material, the types of composite materials within the depth range that the near-infrared light can penetrate, for example, may include but are not limited to reinforcing materials composited with the polymer matrix material, such as glass fiber, carbon fiber, aramid fiber, boron fiber, silicon carbide fiber, natural fiber, etc. For the same type of fiber, it can also be divided into different types of fabrics, such as normal modulus uniaxial fabric, normal modulus biaxial fabric, normal modulus triaxial fabric, high modulus uniaxial fabric, high modulus biaxial fabric, high modulus triaxial fabric, ultra-high modulus uniaxial fabric, ultra-high modulus biaxial fabric, ultra-high modulus triaxial fabric, etc. Exemplarily, the types of materials within the depth range that near-infrared light can penetrate can also include foams for weight reduction and stiffness increase, such as polyethylene terephthalate (PET), polyvinyl chloride (PVC), polyurethane (PU), polystyrene (PS), polymethacrylimide (PMI), acrylonitrile-styrene foam, polyethylene foam, balsa wood, engineering foam, etc. Composite material structural parameters can include the ply structure of various composite materials in different combinations, the number of layers of fabrics used, and the angles between fabrics. Composite material structural parameters can also include the surface results of composite materials, such as foam and balsa wood surface structures (including but not limited to the structure of grooves, the structure and distribution of engineering foam reinforcement materials, etc.).

[0109] S320, fixing the controllable parameters and designing a training sample set for the uncontrollable parameters.

[0110] In step S320, the fixed controllable parameters may include a fixed test environment and a fixed test object. For example, the fixed test environment may include a fixed test environment as a certain factory environment, and the background of the control measurement is a fixed background. The fixed test object may include a fixed test object as a certain type of product or raw material, and the specifications of the test object are fixed. Fixed controllable parameters may specifically refer to simplifying the influencing factor system when the production process allows. For example, in the case of reinforced composite materials, the outer surface material contacted by the near-infrared spectrometer is used as a design prototype to create a sample that can be peeled off from the product or component without causing damage. When the outer surface of the product is composed of a reinforced composite material, the sample thickness can be adjusted according to the number of layers of the fabric, and can be single-layer, double-layer or multi-layer, depending on the influence of the fabric thickness on the accuracy of the glass transition temperature measurement, and the balance between the accuracy of the glass transition temperature measurement and the convenience of operation. When the sample is a multi-layer fabric structure, the fabric fibers can be designed to have the same direction or maintain a certain angle. Under the premise of not affecting the accuracy of the glass transition temperature measurement, it is not necessary to strictly control the relative orientation between the fabric fibers of each layer. After the production process of a product or component is completed, these samples can be safely removed from the production line and their glass transition temperature can be measured using near-infrared spectroscopy without affecting the spectral quality.

[0111] Designing a training sample set for uncontrollable parameters can mean designing and constructing a comprehensive training sample set for uncontrollable parameters that are difficult to predict or adjust, so as to ensure that the training sample set can fully represent various scenarios in the actual production environment. When designing a training sample set, in addition to covering all changes under conventional production conditions, it is also necessary to expand the scope to ensure that the training samples can maintain their representativeness and effectiveness even under extreme or boundary conditions. Taking polymer color as an example, for the natural color difference between different batches, it is necessary to design a sample set containing a color gradient, from the lightest to the darkest, and its span needs to exceed the color range used in actual production. In this way, the slight differences between batches of raw materials can be effectively corrected by chemical quantitative analysis methods. Exemplarily, designing a training sample set for uncontrollable parameters can include designing test conditions corresponding to multiple different environmental parameters for uncontrollable environmental parameters, and designing multiple standard samples with different uncontrollable product parameters for uncontrollable product parameters. The above has described in detail the implementation method of constructing a training sample set, which will not be repeated here.

[0112] S330, collecting near infrared spectral signals and calibrated glass transition temperatures of training samples in the training sample set.

[0113] In step S330, collecting near-infrared spectral signals of training samples in the training sample set may include collecting the corresponding original near-infrared spectral signals under the designed test conditions for each training sample in the training sample set, and preprocessing the original spectral signals, including but not limited to applying smoothing technology to remove random noise, performing normalization operations to standardize data distribution, and revealing potential spectral features through first-order derivative and second-order derivative transformations. Collecting and calibrating glass transition temperature may include using differential scanning calorimetry or dynamic mechanical analysis to determine the glass transition temperature of each sample as a glass transition temperature label for each training sample.

[0114] S340, training a glass transition temperature determination model using the training sample set.

[0115] S350, validation using actual industrial raw materials and / or actual products from a factory to optimize the glass transition temperature determination model.

[0116] S360, collecting near infrared spectrum signals of the object to be measured, inputting them into a glass transition temperature measurement model, and obtaining the glass transition temperature of the target polymer in the object to be measured.

[0117] The above example identifies and controls the parameters that affect the accuracy of measuring the glass transition temperature through a systematic method, distinguishes between controllable parameters and uncontrollable parameters, designs and constructs a training sample set, improves the accuracy of measuring the glass transition temperature with near-infrared spectroscopy in industrial applications, solves the problem of large measurement deviations caused by differences in environment, materials and product structure in actual production applications of near-infrared spectroscopy technology, and ensures the reliability of measurement data. The glass transition temperature test method is not only applicable to a variety of thermosetting and thermoplastic polymer materials and their composite materials, but can also be widely used in quality inspection of products in multiple industrial fields including aerospace, wind power, energy storage, ocean, automobiles and sports equipment, with high flexibility and universal applicability. The glass transition temperature test method of the embodiment of the present application effectively solves the limitations of low measurement accuracy, narrow scope of application and inconvenient on-site operation of instruments in laboratory exploration and research of near-infrared spectroscopy technology for measuring glass transition temperature in industrial applications, and provides a fast and accurate non-destructive measurement technology for industrial-related industry materials and products.

[0118] For the measurement application of the glass transition temperature of the shell and web of the wind turbine blade, a near-infrared spectroscopy measurement technology is developed for the infusion epoxy resin system A based on the glass transition temperature test method of the embodiment of the present application, which is used to non-destructively measure the glass transition temperature of the glass fiber reinforced composite material during the curing process. A glass transition temperature test model was established using the method of the embodiment of the present application, and the glass transition temperature of the factory shell and web was tested using the model. The deviations of more than two thousand glass transition temperature data output by the model and the glass transition temperature data measured by differential scanning calorimetry were statistically analyzed, and the standard deviation of the glass transition temperature measured by near-infrared spectroscopy and the glass transition temperature measured by differential scanning calorimetry was 3.0517°C. A measurement system analysis (MSA) was performed on the method of measuring the glass transition temperature of epoxy resin A using near-infrared spectroscopy technology, and the analysis results are shown in Table 1. As can be seen from Table 1, the coefficient of variation (SV) of this method is 1.67%, which meets the requirements of the quality monitoring system.

[0119] Table 1

[0120]

[0121] Based on the same inventive concept, an embodiment of the present application also provides a glass transition temperature testing device.

[0122] like Figure 3 As shown, the glass transition temperature testing device 300 may include a first testing module 301 , a first input module 302 , and a first determining module 303 .

[0123] The first testing module 301 is used to perform a near infrared spectrum test on an object to be tested in a target environment to obtain a near infrared spectrum signal of the object to be tested. The object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer.

[0124] The first input module 302 is used to input the near-infrared spectrum signal of the object to be tested into a pre-trained glass transition temperature determination model, wherein the glass transition temperature determination model is obtained by training a training sample set; the training sample set includes multiple training samples; the multiple training samples include multiple near-infrared spectrum signals obtained by performing near-infrared spectrum tests on multiple standard samples in a target environment under multiple preset conditions, and multiple glass transition temperature labels corresponding to the multiple near-infrared spectrum signals; the multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters; the multiple standard samples include a target polymer, and the multiple standard samples include multiple standard samples with different uncontrollable product parameters, and the uncontrollable product parameters include color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structure parameters. At least one of the parameters; the glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near-infrared spectrum signal.

[0125] The first determination module 303 is used to determine the measured glass transition temperature corresponding to the near infrared spectrum signal of the object to be measured according to the corresponding relationship between the near infrared spectrum signal and the glass transition temperature through the trained glass transition temperature model.

[0126] The glass transition temperature testing device of the embodiment of the present application can perform near infrared spectroscopy test on the object to be tested in the target environment to obtain the near infrared spectroscopy signal of the object to be tested, the object to be tested includes the material and / or product to be tested, and the object to be tested includes the target polymer. Then, the near infrared spectroscopy signal of the object to be tested is input into the pre-trained glass transition temperature determination model. Among them, the glass transition temperature determination model is obtained by training the training sample set. The training sample set includes a plurality of training samples. The above-mentioned multiple training samples include a plurality of near infrared spectroscopy signals obtained by performing near infrared spectroscopy test on a plurality of standard samples in the target environment under a plurality of preset conditions, and a plurality of glass transition temperature labels corresponding to the plurality of near infrared spectroscopy signals. The near infrared spectroscopy signals of the above-mentioned training sample set are all measured in the target environment. In this way, for a relatively fixed and controllable production site environment, a corresponding target environment can be designed so that the glass transition temperature determination model can be applied to a specific production site environment. The above-mentioned multiple preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, and the uncontrollable parameters include environmental parameters. A plurality of standard samples include a target polymer, and the plurality of standard samples include a plurality of standard samples with different uncontrollable product parameters, and the uncontrollable product parameters include at least one of color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structure parameters. The glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near-infrared spectral signal. In this way, the training samples contained in the training sample set are more comprehensive, and can cover the results that may appear in the glass transition temperature test site under industrial environment, facing a variety of uncontrollable test environment factors and uncontrollable product factors as much as possible. The calibrated glass transition temperature of the target polymer in the standard sample is used as the glass transition temperature label of the training sample. In this way, the glass transition temperature determination model obtained by training can accurately determine the glass transition temperature of the target polymer in the object to be tested according to the near-infrared spectral signal collected at the production site. In this way, it is allowed to use a portable near-infrared spectrometer as a measuring tool for non-destructive testing at the industrial production site, so that the glass transition temperature test has both high convenience and accuracy. The embodiment of the present application determines the measured glass transition temperature corresponding to the near-infrared spectrum signal of the object to be measured according to the corresponding relationship between the near-infrared spectrum signal and the glass transition temperature through the trained glass transition temperature model. In this way, the glass transition temperature can be measured quickly and accurately in a variety of industrial application scenarios, which is conducive to improving production efficiency and product quality management capabilities.

[0127] In one embodiment, the apparatus may further include:

[0128] The second test module is used to perform near-infrared spectral tests on multiple standard samples under multiple preset conditions and in a target environment before inputting the near-infrared spectral signal of the object to be tested into a pre-trained glass transition temperature measurement model, so as to obtain the near-infrared spectral signal of each standard sample in the multiple standard samples under each preset condition.

[0129] The first acquisition module is used to acquire the calibrated glass transition temperature of the target polymer in each standard sample.

[0130] The creation module is used to create training samples by respectively using the near-infrared spectral signal corresponding to each standard sample under each preset condition and the calibrated glass transition temperature corresponding to the standard sample to obtain a training sample set.

[0131] The second input module is used to input the training sample set into the glass transition temperature determination model.

[0132] The processing module is used to obtain the measured glass transition temperature corresponding to each training sample according to the preset correspondence between the near-infrared spectrum signal and the glass transition temperature through the glass transition temperature measurement model.

[0133] The training module is used to iteratively train the glass transition temperature measurement model according to the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample, so as to adjust the corresponding relationship between the near-infrared spectral signal and the glass transition temperature, and obtain a trained glass transition temperature measurement model.

[0134] In one embodiment, the device may further include:

[0135] The second acquisition module is used to obtain the boundary value of the uncontrollable parameter corresponding to the target environment before performing near-infrared spectrum tests on multiple standard samples in the target environment under multiple preset conditions respectively.

[0136] The second determination module is used to determine the value range of the uncontrollable parameter according to the boundary value of the uncontrollable parameter.

[0137] The design module is used to design multiple preset conditions according to the value range of the uncontrollable parameter, and the multiple preset conditions include test conditions corresponding to the boundary values ​​of the uncontrollable parameter and test conditions corresponding to the intermediate value of at least one uncontrollable parameter.

[0138] In one embodiment, the apparatus may further include:

[0139] The third acquisition module is used to obtain the boundary values ​​of the uncontrollable product parameters corresponding to the standard samples in the target production line before performing near-infrared spectroscopy tests on multiple standard samples in the target environment under multiple preset conditions.

[0140] The third determination module is used to determine the value range of the uncontrollable product parameter according to the boundary value of the uncontrollable product parameter.

[0141] The preparation module is used to prepare multiple standard samples according to the value range of the uncontrollable product parameter, and the multiple standard samples include standard samples corresponding to the boundary values ​​of the uncontrollable product parameter and standard samples corresponding to the intermediate value of at least one uncontrollable product parameter.

[0142] In one embodiment, the first acquisition module is used to acquire the calibrated glass transition temperature of the target polymer in each standard sample, which may specifically include:

[0143] The testing submodule is used to test each standard sample by a target glass transition temperature determination method to obtain a calibrated glass transition temperature of the target polymer in each standard sample, wherein the target glass transition temperature determination method includes differential scanning calorimetry and / or dynamic mechanical analysis.

[0144] In one embodiment, the wavelength of the near infrared spectrum test may be 780 nm to 2526 nm.

[0145] In one embodiment, the target polymer may include a thermosetting polymer material and / or a thermoplastic polymer material.

[0146] The glass transition temperature testing device provided in the embodiment of the present application can achieve Figure 2 To avoid repetition, the various processes implemented by the method embodiment are not described here.

[0147] Figure 4 A schematic diagram of the hardware structure of a glass transition temperature testing device provided in an embodiment of the present application is shown.

[0148] The glass transition temperature testing device may include a processor 401 and a memory 402 storing computer program instructions.

[0149] Specifically, the processor 401 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0150] Memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, memory 402 may include a removable or non-removable (or fixed) medium. In appropriate cases, memory 402 may be inside or outside of an integrated gateway disaster recovery device. In a specific embodiment, memory 402 is a non-volatile solid-state memory.

[0151] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0152] The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any one of the glass transition temperature testing methods in the above embodiments.

[0153] As an example, the glass transition temperature testing device may further include a communication interface 404 and a bus 410. Figure 4 As shown, the processor 401, the memory 402, and the communication interface 404 are connected via a bus 410 and communicate with each other.

[0154] The communication interface 404 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0155] Bus 410 includes hardware, software or both, and the parts of glass transition temperature equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 410 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0156] The glass transition temperature test device can perform the glass transition temperature test method in the embodiment of the present application, thereby realizing the combination Figure 2 and Figure 3 Described is the glass transition temperature test method and apparatus.

[0157] In addition, in combination with the data processing method in the above embodiments, the present application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the data processing methods in the above embodiments is implemented.

[0158] An embodiment of the present application also provides a computer program product, including a computer program, which implements any one of the glass transition temperature testing methods in the above embodiments when the computer program is processed and executed.

[0159] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0160] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0161] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0162] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0163] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A glass transition temperature testing method, characterized in that: include: Performing a near infrared spectroscopy test on an object to be tested in a target environment to obtain a near infrared spectroscopy signal of the object to be tested, wherein the object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer; The near infrared spectrum signal of the object to be measured is input into a pre-trained glass transition temperature determination model, wherein the glass transition temperature determination model is obtained by training a training sample set; the training sample set includes a plurality of training samples; the plurality of training samples include a plurality of near infrared spectrum signals obtained by performing near infrared spectrum tests on a plurality of standard samples in the target environment under a plurality of preset conditions, and a plurality of glass transition temperature labels corresponding to the plurality of near infrared spectrum signals; the plurality of preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, the uncontrollable parameters include environmental parameters; the plurality of standard samples include the target polymer, the plurality of standard samples include a plurality of standard samples with different uncontrollable product parameters, the uncontrollable product parameters include color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structure parameters; the glass transition temperature label is the calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near infrared spectrum signal; The measured glass transition temperature corresponding to the near-infrared spectrum signal of the object to be measured is determined by using the trained glass transition temperature model according to the corresponding relationship between the near-infrared spectrum signal and the glass transition temperature.

2. The method according to claim 1, characterized in that: Before inputting the near infrared spectrum signal of the object to be measured into a pre-trained glass transition temperature measurement model, the method further comprises: Perform near infrared spectroscopy tests on the plurality of standard samples respectively under the plurality of preset conditions and in the target environment to obtain near infrared spectroscopy signals of each standard sample in the plurality of standard samples under each preset condition; Obtaining the calibrated glass transition temperature of the target polymer in each standard sample; Creating training samples using the near-infrared spectral signal corresponding to each standard sample under each preset condition and the calibrated glass transition temperature corresponding to the standard sample to obtain a training sample set; Inputting the training sample set into a glass transition temperature determination model; By using the glass transition temperature determination model, according to the preset correspondence between the near infrared spectrum signal and the glass transition temperature, the measured glass transition temperature corresponding to each training sample is obtained; According to the measured glass transition temperature and the calibrated glass transition temperature corresponding to each training sample, the glass transition temperature measurement model is iteratively trained to adjust the corresponding relationship between the near-infrared spectrum signal and the glass transition temperature, so as to obtain a trained glass transition temperature measurement model.

3. The method according to claim 2, characterized in that Before respectively performing near infrared spectroscopy tests on the plurality of standard samples in the target environment under a plurality of preset conditions, the method further comprises: Obtaining a boundary value of the uncontrollable parameter corresponding to the target environment; Determining a value range of the uncontrollable parameter according to a boundary value of the uncontrollable parameter; According to the value range of the uncontrollable parameter, the multiple preset conditions are designed, and the multiple preset conditions include test conditions corresponding to boundary values ​​of the uncontrollable parameter and test conditions corresponding to at least one intermediate value of the uncontrollable parameter.

4. The method according to claim 2, characterized in that: Before respectively performing near infrared spectroscopy tests on the plurality of standard samples in the target environment under a plurality of preset conditions, the method further comprises: Obtaining the boundary value of the uncontrollable product parameter corresponding to the standard sample in the target production line; Determining a value range of the uncontrollable product parameter according to a boundary value of the uncontrollable product parameter; According to the value range of the uncontrollable product parameter, the multiple standard samples are prepared, and the multiple standard samples include standard samples corresponding to the boundary values ​​of the uncontrollable product parameter and at least one standard sample corresponding to the intermediate value of the uncontrollable product parameter.

5. The method according to claim 2, characterized in that: The step of obtaining the calibrated glass transition temperature of the target polymer in each standard sample comprises: Each standard sample is tested by a target glass transition temperature determination method to obtain a calibrated glass transition temperature of the target polymer in each standard sample, wherein the target glass transition temperature determination method includes differential scanning calorimetry and / or dynamic mechanical analysis.

6. The method according to claim 1, characterized in that The wavelength of the near infrared spectrum test is 780nm to 2526nm.

7. The method according to any one of claims 1 to 6, characterized in that The target polymer includes thermosetting polymer materials and / or thermoplastic polymer materials.

8. A glass transition temperature testing device, characterized in that: include: A testing module, used for performing a near infrared spectrum test on an object to be tested in a target environment to obtain a near infrared spectrum signal of the object to be tested, wherein the object to be tested includes a material and / or product to be tested, and the object to be tested includes a target polymer; An input module, used for inputting the near infrared spectrum signal of the object to be measured into a pre-trained glass transition temperature determination model, wherein the glass transition temperature determination model is obtained by training a training sample set; the training sample set includes a plurality of training samples; the plurality of training samples include a plurality of near infrared spectrum signals obtained by performing near infrared spectrum tests on a plurality of standard samples in the target environment under a plurality of preset conditions, and a plurality of glass transition temperature labels corresponding to the plurality of near infrared spectrum signals; the plurality of preset conditions include test conditions designed according to uncontrollable parameters corresponding to the target environment, the uncontrollable parameters include environmental parameters; the plurality of standard samples include the target polymer, the plurality of standard samples include a plurality of standard samples with different uncontrollable product parameters, the uncontrollable product parameters include color, raw material ratio of the target polymer, moisture content, composite material parameters of the target polymer, and composite material structure parameters; the glass transition temperature label is a calibrated glass transition temperature of the target polymer in the standard sample corresponding to the near infrared spectrum signal; The determination module is used to determine the measured glass transition temperature corresponding to the near-infrared spectrum signal of the object to be measured according to the corresponding relationship between the near-infrared spectrum signal and the glass transition temperature through the trained glass transition temperature model.

9. An electronic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the glass transition temperature testing method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the glass transition temperature testing method according to any one of claims 1 to 7 is implemented.

11. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the glass transition temperature testing method as described in any one of claims 1 to 7.

Images