A kind of low temperature based on optical fiber coating detection equipment and method under extremely cold conditions

Abstract

The application discloses a kind of low temperature extremely cold environment under optical fiber coating detection equipment and method, belong to coating detection technical field, comprising: obtaining temperature cycle times, coating thickness, heating gradient and other detection condition data, and coating adhesion, shrinkage, cracking length and other performance data;Construct detection condition evaluation model output detection condition coefficient;Based on detection condition, coating performance analysis model is established to output performance evaluation coefficient;Combined with the content of silicon dioxide nanoparticles and polyether polyurethane data, material analysis model is output material analysis coefficient;If the coefficient exceeds the preset threshold, then determine that the coating is unqualified and generate target formulation parameters.The application solves the technical problems of coating performance quantification evaluation and formulation precise optimization under extreme environment, significantly improves detection efficiency and reliability.

Description

Technical Field

[0001] This invention belongs to the field of coating testing technology, and in particular relates to a fiber optic coating testing device and method under extremely cold conditions. Background Technology

[0002] In the current field of coating testing technology, there are significant technical challenges in testing fiber optic coatings under extremely low-temperature environments. Traditional testing methods often struggle to comprehensively and accurately assess the overall performance of coatings under extreme low-temperature conditions, especially key coating indicators such as adhesion, shrinkage rate, and crack length, as well as the specific impact of key components in the coating, such as silica nanoparticles and polyether polyurethane, on the overall performance of the coating. This technological gap leads to uncertainty in the performance of fiber optic coatings under extreme environments, which may in turn affect the stability and reliability of fiber optic communication. Therefore, developing a testing device and method specifically for fiber optic coatings under extremely low-temperature conditions is of great significance for ensuring the reliability and stability of coatings in extreme environments. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a device and method for testing fiber optic coatings under extremely low temperature conditions, thus solving the aforementioned problems.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting fiber optic coatings under extremely low temperature conditions, comprising the following steps:

[0005] Acquire data on testing conditions, coating performance, silica nanoparticle content in the coating, and polyether polyurethane (polyether polyol-based polyurethane acrylate).

[0006] A detection condition evaluation model is constructed based on the detection condition data, and the detection condition evaluation coefficients are output.

[0007] Based on the coating performance data under the evaluation coefficient of the detection conditions, a coating performance analysis model is constructed to output the coating performance evaluation coefficient;

[0008] Based on the coating performance evaluation coefficient, silica nanoparticle content, and polyether-type polyurethane construct material analysis model, the material analysis coefficient is output.

[0009] The obtained material analysis coefficients are compared with the preset material analysis coefficient thresholds. If the material analysis coefficients are not within the material analysis coefficient thresholds, a judgment information is generated that the current coating performance is unqualified.

[0010] Based on the above technical solutions, the present invention also provides the following optional technical solutions:

[0011] Further technical solutions: Based on the judgment information, according to the material analysis coefficient, silica nanoparticle content, and polyether polyurethane, construct silica nanoparticle content model and polyether polyurethane model respectively, and output the target silica nanoparticle content and target polyether polyurethane content.

[0012] Further technical solution: The detection condition data includes the number of temperature cycles, coating thickness, and temperature gradient.

[0013] Further technical solution: The coating performance data includes coating adhesion, shrinkage rate, and crack length.

[0014] Further technical solution: The steps for constructing a detection condition evaluation model based on detection condition data and outputting detection condition evaluation coefficients are as follows:

[0015] The heating gradient exponent is obtained by processing the ratio of the heating gradient to the maximum allowable heating gradient.

[0016] The thickness deviation index is obtained by comparing the coating thickness with the reference coating thickness.

[0017] The thickness index is obtained by subtracting the reference coating thickness from the coating thickness and then comparing it with the thickness tolerance bandwidth.

[0018] The temperature cycle number index is obtained by processing the ratio of the number of temperature cycles to the maximum allowable number of temperature cycles;

[0019] A detection condition evaluation model is constructed based on the heating gradient index, thickness deviation index, thickness index, and temperature cycle number index. The detection condition evaluation model is expressed as follows:

[0020]

[0021] in, This represents the evaluation coefficient for testing conditions. Indicates the cyclic decay factor. Indicates the number of temperature cycles, This indicates a thickness deviation index. Indicates the thickness index. Indicates the temperature gradient exponent. Indicates the power exponent of the temperature gradient;

[0022] Input the current heating gradient index, current thickness deviation index, current thickness index, and current temperature cycle number index into the detection condition evaluation model to output the current detection condition evaluation coefficient.

[0023] Further technical solution: The steps for constructing a coating performance analysis model based on coating performance data under testing condition evaluation coefficients and outputting coating performance evaluation coefficients are as follows:

[0024] The shrinkage rate, crack length, and adhesion of the coating were subjected to maximum-minimum normalization to obtain the shrinkage rate index, crack length index, and adhesion index.

[0025] A coating performance analysis model is constructed based on the shrinkage index, coating crack length index, and adhesion index under the evaluation coefficient of the detection conditions. The coating performance analysis model is expressed as follows:

[0026]

[0027] in, Indicates the performance evaluation coefficient of the coating. Indicates the adhesion index. Indicates the adhesion weight index. Indicates the shrinkage rate index. Indicates the coating crack length index. Indicates the crack correction amount (to prevent division by zero). Represents the conditional coupling coefficient. Indicates the evaluation coefficient of the testing conditions;

[0028] Import the current test condition evaluation coefficient, current shrinkage index, current coating crack length index, and current adhesion index into the coating performance analysis model to output the current coating performance evaluation coefficient.

[0029] Further technical solution: The steps for outputting material analysis coefficients based on the coating performance evaluation coefficient, silica nanoparticle content, and polyether-type polyurethane construct material analysis model are as follows:

[0030] The polyether polyurethane content index is obtained by comparing the polyether polyurethane content with the optimal polyether polyurethane content.

[0031] The silica nanoparticle content index is obtained by comparing the difference between the silica nanoparticle content and the optimal silica nanoparticle content with the silica nanoparticle content tolerance bandwidth.

[0032] A material analysis model is constructed based on the coating performance evaluation coefficient, the polyether polyurethane content index, and the silica nanoparticle content index. This material analysis model is expressed as follows:

[0033]

[0034] in, Indicates the material analysis coefficients. Indicates the performance evaluation coefficient of the coating. Indicates the lowest coating performance evaluation coefficient. This represents the gain coefficient, which is used to evaluate the performance of coatings. Indicates the polyether polyurethane content index. Indicates the response index of polyether polyurethane. Indicates the silica nanoparticle content index. Represents the weight coefficient and ;

[0035] Import the current coating performance evaluation coefficient, the current polyether polyurethane content index, and the current silica nanoparticle content index into the material analysis model to output the current material analysis coefficient.

[0036] Further technical solution: The polyether-type polyurethane model is represented as follows:

[0037]

[0038] in, Indicates the target polyether polyurethane content. This indicates the optimal content of polyether polyurethane. Indicates the range of content adjustment. This represents the current material analysis coefficient. This represents the equilibrium value of the material analysis coefficients. This indicates the degree of sensitivity to adjustment.

[0039] A further technical solution: The silica nanoparticle content model is expressed as follows:

[0040]

[0041] in, This indicates the content of the target silica nanoparticles. This indicates the optimal content of silica nanoparticles. This indicates the tolerance bandwidth for silica content. Represents the sensitivity coefficient. This represents the current material analysis coefficient. This represents the equilibrium value of the material analysis coefficients. This indicates the lower limit of the material analysis coefficient.

[0042] Further technical solution: A fiber optic coating testing device based on low temperature and extreme cold conditions, employing the above-mentioned fiber optic coating testing method based on low temperature and extreme cold conditions.

[0043] This invention provides a device and method for testing fiber optic coatings under extremely low temperature conditions, which has the following advantages compared with the prior art:

[0044] 1. By constructing a testing condition evaluation model, a coating performance analysis model, and a material analysis model, this invention can comprehensively and deeply evaluate the overall performance of coatings under low temperature and extreme cold conditions, ensuring the accuracy and reliability of the evaluation results;

[0045] 2. When the coating performance is unqualified, the present invention can further construct a model to output the target silica nanoparticle content and the target polyether polyurethane content, providing precise guidance for coating improvement and improving the reliability and stability of the coating.

[0046] 3. This invention is specifically designed for testing optical fiber coatings under extremely low temperature and cold conditions. It can ensure that the performance of the coating meets the requirements under extreme environments, improve the reliability of optical fiber coatings under extremely cold conditions, reduce the risk of coating failure, and broaden the application range of the coating. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0049] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0050] In existing technologies, performance testing of optical fiber coatings in extremely low-temperature environments faces significant challenges. Traditional testing methods typically employ single environmental parameter tests or isolated performance index assessments, which fail to reflect the synergistic effects between coating materials and components under extreme conditions. For example, conventional methods judge coating performance solely through adhesion tests at fixed temperatures or static component analysis, neglecting the dynamic correlation between temperature cycling, coating thickness changes, and material composition, leading to discrepancies between evaluation results and actual application environments. In the deployment of optical fiber cables in the Arctic region, coatings experience crack propagation due to repeated freeze-thaw cycles, and existing testing methods cannot predict the stress-matching relationship between silica nanoparticle dispersion and the polyurethane matrix under such conditions, resulting in the risk of early failure.

[0051] Please see Figure 1 The present invention provides a method for detecting optical fiber coatings under extremely low temperature conditions, comprising the following steps:

[0052] Acquire data on testing conditions, coating performance, silica nanoparticle content in the coating, and polyether polyurethane (polyether polyol-based polyurethane acrylate).

[0053] A detection condition evaluation model is constructed based on the detection condition data, and the detection condition evaluation coefficients are output.

[0054] Based on the coating performance data under the evaluation coefficient of the detection conditions, a coating performance analysis model is constructed to output the coating performance evaluation coefficient;

[0055] Based on the coating performance evaluation coefficient, silica nanoparticle content, and polyether-type polyurethane construct material analysis model, the material analysis coefficient is output.

[0056] The obtained material analysis coefficients are compared with the preset material analysis coefficient thresholds. If the material analysis coefficients are not within the material analysis coefficient thresholds, a judgment information is generated that the current coating performance is unqualified.

[0057] Specifically, this method achieves multi-dimensional data fusion through phased modeling. In the detection condition evaluation stage, the ratio of the number of temperature cycles to the maximum allowable value is used to quantify the cumulative damage effect caused by cyclic freeze-thaw cycles. Coating thickness data is calculated using both the baseline thickness and the tolerance bandwidth to distinguish the impact of differences between absolute deviations and relative tolerances. In the performance analysis stage, shrinkage rate and crack length are normalized and combined with the adhesion index to construct a product relationship model, revealing the interaction mechanism between deformation and failure. In the material analysis stage, the deviation between component content and optimal values ​​is calculated, and combined with the logarithmic transformation of performance evaluation coefficients, to establish a quantitative correlation between component distribution ratio and overall performance. Finally, by comparing preset thresholds, a pass / fail determination based on multi-factor coupled analysis is achieved.

[0058] Compared to existing technologies, traditional methods rely on component testing and manual judgment, resulting in a disconnect between environmental conditions and material performance evaluation. For example, conventional component analysis focuses only on whether the silica content meets standards, neglecting the impact of its dispersion stability under temperature gradients on coating cracking. This solution establishes a dynamic coupling model between testing condition evaluation coefficients and performance data, quantifying environmental stress as a calculable correction factor, thus ensuring that performance evaluation results accurately reflect material behavior under extreme conditions. Furthermore, the component deviation tolerance bandwidth calculation in the material analysis model addresses the limitation of traditional binary pass / fail judgments in adapting to different low-temperature scenarios.

[0059] Through the above technical solutions, this application achieves a systematic evaluation of the performance of optical fiber coatings under low-temperature environments. By using a dynamic correlation model between environmental parameters and performance indicators, the crack propagation trend of the coating under temperature cycling is accurately predicted. Composite analysis of component content index and performance evaluation coefficient effectively identifies the synergistic failure problem between the polyurethane matrix and nanoparticles. The threshold comparison mechanism allows for adjustment of judgment criteria according to different application scenarios, improving the engineering applicability of the test results. This method overcomes the technical shortcomings of traditional testing methods that lead to misjudgments due to isolated analysis of environmental factors and material properties, providing a reliable means of coating quality evaluation for special scenarios such as polar communication optical cables.

[0060] Based on the judgment information, the material analysis coefficient, the content of silica nanoparticles, and the polyether polyurethane, respectively construct silica nanoparticle content models and polyether polyurethane models, and output the target silica nanoparticle content and the target polyether polyurethane content.

[0061] Among them, the material analysis coefficient refers to a quantitative index generated by comprehensively analyzing coating performance data and testing conditions. Specifically, it can be calculated using a multi-parameter coupled exponential function model to characterize the degree of deviation in the overall performance of the coating. The silica nanoparticle content model is a dynamic adjustment model established based on the nonlinear relationship between the material analysis coefficient and the optimal content. Specifically, it can be implemented using a hyperbolic tangent function combined with a tolerance bandwidth parameter, used to adaptively adjust the amount of nanoparticles added according to the performance deviation. The polyether-type polyurethane model is a content optimization model established through the correlation between the material analysis coefficient and the equilibrium state. Specifically, it can be implemented using an exponential adjustment function combined with a sensitivity coefficient, used to maintain the flexibility of the matrix material at low temperatures.

[0062] Specifically, when the material analysis coefficient is detected to exceed a preset threshold, the target content is calculated using a silica nanoparticle content model. This model inputs the deviation between the material analysis coefficient and the equilibrium value into a hyperbolic tangent function, and combines this with a tolerance bandwidth parameter to generate an adjustment range relative to the optimal content, making the amount of nanoparticles added change non-linearly with performance deviation. Simultaneously, a polyether-type polyurethane model correlates the material analysis coefficient with the equilibrium state through an exponential function, dynamically adjusting the polyurethane content based on the degree of coefficient deviation to ensure the structural stability of the matrix material at low temperatures. The two models work synergistically through a weighting mechanism, enhancing the mechanical strength of the coating while maintaining the flexibility of the matrix, thus systematically solving the problem of component ratio imbalance.

[0063] Compared to existing technologies, traditional methods typically employ fixed formulation ratios or linear adjustment strategies for single parameters, making them ill-suited for complex operating conditions involving multiple coupled factors at low temperatures. Existing solutions, when detecting performance anomalies, often only provide qualitative adjustment suggestions and lack quantitative optimization models. This solution, by establishing a dynamic adjustment mechanism based on nonlinear functions, achieves precise bidirectional control of the content of key components, overcoming the shortcomings of traditional methods such as adjustment lag and insufficient precision.

[0064] Through the above technical solution, this application effectively solves the problem of dynamic optimization of coating composition under extreme environments, achieving a precise balance between the reinforcing effect of silica nanoparticles and the properties of the polyether-type polyurethane matrix. This solution can automatically generate the optimal component ratio based on real-time monitoring data, significantly improving the coating's crack resistance and dimensional stability under low-temperature cycling conditions, ensuring the long-term reliability of the optical fiber coating in extremely cold environments.

[0065] Preferably, the detection condition data includes the number of temperature cycles, coating thickness, and temperature gradient.

[0066] Among them, the number of temperature cycles refers to the number of complete temperature change cycles that the optical fiber coating undergoes in a low-temperature and extremely cold environment. Specifically, it can be achieved by recording the number of cycles using temperature cycling testing equipment, which is used to quantify the cumulative damage effect of extreme temperature changes on the coating.

[0067] The coating thickness refers to the dimension of the coating layer on the surface of the optical fiber in the vertical direction. It can be measured non-contactly using an optical thickness gauge or an ultrasonic thickness gauge to evaluate the structural stability and deformation resistance of the coating at low temperatures.

[0068] The temperature gradient refers to the rate at which the temperature recovers from a low-temperature, extremely cold state to normal temperature per unit time, and is used to characterize the thermal stress intensity that the coating withstands when the temperature changes abruptly.

[0069] Specifically, the number of temperature cycles, coating thickness, and temperature gradient are used as input parameters for the evaluation model of testing conditions, representing three dimensions: dynamic cyclic load, static structural characteristics, and thermodynamic response. The number of temperature cycles reflects the fatigue accumulation effect of the coating during repeated freeze-thaw cycles; the coating thickness determines the internal stress distribution of the material during low-temperature shrinkage; and the temperature gradient affects the thermal expansion matching between the coating and the optical fiber substrate. When these three parameters work synergistically, they can comprehensively cover the combined action mechanism of mechanical stress, thermal stress, and cyclic load borne by the coating under low-temperature conditions, thus providing multi-dimensional quantitative input for the evaluation model of testing conditions and avoiding model bias caused by a single parameter.

[0070] Compared with existing technologies, traditional testing methods usually only focus on coating performance at static temperature or single cycle count. However, this application solves the problem of model prediction inaccuracies caused by ignoring the temperature change rate and coating structural characteristics in existing technologies by introducing a dynamic coupling parameter of temperature gradient and coating thickness. This enables the testing condition evaluation model to more accurately reflect the quantitative impact of extreme temperature change rate on coating cracking risk.

[0071] Through the above technical solution, this application achieves comprehensive parameter coverage of optical fiber coating testing conditions in low-temperature and extremely cold environments, ensuring that the testing condition evaluation model can accurately quantify the superimposed effects of temperature cycle number, coating thickness deviation, and heating rate on coating performance, thereby providing a reliable data foundation for subsequent coating performance analysis and material optimization.

[0072] Preferably, the coating performance data includes the coating's adhesion, shrinkage rate, and crack length.

[0073] Adhesion refers to the bonding strength between the coating and the optical fiber substrate. It can be tested using a tensile testing machine to quantify the coating's resistance to peeling under drastic temperature changes.

[0074] Shrinkage rate refers to the rate of volume change of the coating during the low-temperature curing process. Specifically, it can be achieved by measuring the percentage change in coating thickness before and after curing relative to the original thickness, and is used to reflect the level of internal stress caused by the temperature gradient.

[0075] Crack length refers to the linear dimension of crack propagation on the coating surface. Specifically, it can be achieved by observing the maximum crack length after low-temperature cycling with a microscope, and is used to characterize the brittle deterioration trend of the material at extreme temperatures.

[0076] Specifically, in extremely cold environments, coating adhesion is tested using a tensile testing machine, for example, by placing the sample in a -60°C environment for a peel test; shrinkage is measured by the difference in thickness before and after curing, for example, by using an optical thickness gauge or an ultrasonic thickness gauge to record the thickness change when the temperature drops sharply; crack length is analyzed using a microscopic image analysis system, for example, by statistically analyzing the crack length of samples that have undergone 50 cycles from -40°C to 25°C. These three data points correspond to the bonding strength between the coating and the substrate, the distribution of thermal deformation stress, and the degree of structural integrity degradation, respectively. Through combined analysis, the mechanical stability of the coating under extreme conditions can be comprehensively evaluated, providing multi-dimensional performance inputs for subsequent material analysis models.

[0077] Compared to existing technologies, traditional testing methods typically focus on only a single performance indicator or use indirect estimation methods, such as testing only hardness or elastic modulus to infer coating performance. This solution, however, directly obtains measured data in three dimensions: adhesion, shrinkage, and crack length, forming a complete evaluation system for interfacial bonding, thermal stress response, and mechanical durability, effectively solving the problem of missing dimensions in coating performance evaluation under low-temperature conditions.

[0078] Through the above technical solution, this application can accurately obtain the key performance parameters of the coating under extreme temperature conditions, providing a direct basis for determining whether the coating meets the requirements for low-temperature use. This multi-dimensional data acquisition method significantly improves the comprehensiveness of performance evaluation, helps to accurately identify the failure modes of the coating in low-temperature environments, and guides the optimization of the ratio of silica nanoparticles to polyether polyurethane.

[0079] Preferably, the step of constructing a detection condition evaluation model based on detection condition data and outputting detection condition evaluation coefficients is as follows:

[0080] The heating gradient exponent is obtained by processing the ratio of the heating gradient to the maximum allowable heating gradient.

[0081] The thickness deviation index is obtained by comparing the coating thickness with the reference coating thickness.

[0082] The thickness index is obtained by subtracting the reference coating thickness from the coating thickness and then comparing it with the thickness tolerance bandwidth.

[0083] The temperature cycle number index is obtained by processing the ratio of the number of temperature cycles to the maximum allowable number of temperature cycles;

[0084] A detection condition evaluation model is constructed based on the heating gradient index, thickness deviation index, thickness index, and temperature cycle number index. The detection condition evaluation model is expressed as follows:

[0085]

[0086] in, This represents the evaluation coefficient for testing conditions. Indicates the cyclic decay factor. Indicates the number of temperature cycles, This indicates a thickness deviation index. Indicates the thickness index. Indicates the temperature gradient exponent. Indicates the power exponent of the temperature gradient;

[0087] Input the current heating gradient index, current thickness deviation index, current thickness index, and current temperature cycle number index into the detection condition evaluation model to output the current detection condition evaluation coefficient.

[0088] The heating gradient index is the ratio of the actual heating gradient to the preset maximum allowable value. This can be achieved by calculating the gradient ratio after real-time temperature data acquisition using a temperature sensor, quantifying the thermal stress impact on the coating during the heating process. The thickness deviation index is the ratio of the measured coating thickness to the reference thickness. This can be achieved by dividing the measured thickness by the preset reference value using a laser thickness gauge, characterizing the absolute degree of coating thickness deviation from the ideal value. The thickness index is the ratio of the coating thickness deviation to the thickness tolerance bandwidth. This can be achieved by calculating the difference between the measured thickness and the reference thickness and dividing by the allowable thickness fluctuation range, reflecting the relative deviation of the coating thickness within the tolerance range. The temperature cycle count index is the ratio of the actual number of temperature cycles to the preset maximum allowable number of cycles. This can be achieved by recording the number of cycles with a counter and dividing by a threshold, characterizing the cumulative damage effect of temperature cycling on the coating. The cycle decay factor is the weighting coefficient of the temperature cycle count index in the exponential function, used to adjust the decay effect of temperature cycling on the evaluation coefficient. The power exponent of the heating gradient refers to the power parameter of the heating gradient in the model, which is used to control the nonlinear effect of the heating gradient on the evaluation results.

[0089] Specifically, this technical solution transforms testing conditions such as temperature cycle count, coating thickness, and temperature gradient into standardized indices, constructing a mathematical model that incorporates a coupling relationship between exponential and power functions. For example, the temperature cycle count index, combined with a cycle decay factor, reflects the nonlinear cumulative effect of multiple temperature changes on the coating; the thickness deviation index, correlated with the thickness index through an exponential function, simultaneously characterizes the dual impact of absolute deviation and relative tolerance range; the temperature gradient index, participating in the calculation through a power function, highlights its sensitivity to differences in testing conditions. During model computation, the weights of each index are dynamically adjusted through a combination of product and exponential decay, ultimately outputting a comprehensive testing condition evaluation coefficient. This coefficient quantifies the overall severity of testing conditions under extreme environments, providing standardized input parameters for subsequent coating performance analysis.

[0090] Compared to existing technologies, traditional methods typically use a single threshold judgment or linear weighting to process test condition data. For example, they may only compare whether the number of temperature cycles exceeds an upper limit, or simply perform a weighted summation of coating thickness deviations. These methods fail to reflect the cumulative decay effect of temperature cycling, the dynamic relationship between thickness deviation and tolerance range, and the nonlinear interactions between different test conditions. This proposed solution, by constructing a composite model incorporating exponential and power functions, can more accurately characterize the coupled impact of test conditions on coating performance, thus solving the problems of single evaluation dimensions and lack of data correlation in traditional methods.

[0091] Through the above technical solution, this application achieves dynamic quantitative evaluation of testing conditions under low-temperature and extremely cold environments, effectively avoiding evaluation errors caused by factors such as the failure to convert the number of temperature cycles into attenuation effects, the failure to distinguish between absolute and relative indicators of coating thickness deviation, and the failure to nonlinearly weight the influence of temperature gradient. This method can provide accurate quantitative parameters of testing conditions for the performance analysis of optical fiber coatings under extreme environments, thereby improving the reliability of subsequent coating composition adjustments and performance optimization.

[0092] Preferably, the step of constructing a coating performance analysis model based on coating performance data under detection condition evaluation coefficients and outputting coating performance evaluation coefficients is as follows:

[0093] The shrinkage rate, crack length, and adhesion of the coating were subjected to maximum-minimum normalization to obtain the shrinkage rate index, crack length index, and adhesion index.

[0094] A coating performance analysis model is constructed based on the shrinkage index, coating crack length index, and adhesion index under the evaluation coefficient of the detection conditions. The coating performance analysis model is expressed as follows:

[0095]

[0096] in, Indicates the performance evaluation coefficient of the coating. Indicates the adhesion index. Indicates the adhesion weight index. Indicates the shrinkage rate index. Indicates the coating crack length index. Indicates the crack correction amount (to prevent division by zero). Represents the conditional coupling coefficient. Indicates the evaluation coefficient of the testing conditions;

[0097] Import the current test condition evaluation coefficient, current shrinkage index, current coating crack length index, and current adhesion index into the coating performance analysis model to output the current coating performance evaluation coefficient.

[0098] Among them, max-min normalization refers to linearly transforming the original data to the range of 0 to 1, used to eliminate numerical differences in different dimensions of indicators such as coating shrinkage, crack length, and adhesion. (Effective weighting index) This refers to adjusting the contribution of adhesion as an index in the model. It can be set through experimental calibration or empirical values, for example, a value between 1.2 and 1.8, to strengthen the dominant role of adhesion in coating performance. Cracking correction amount. This refers to a very small constant introduced to prevent the denominator from failing due to a zero exponent in the coating crack length, for example, a value between 0.001 and 0.01, used to maintain the stability of the model calculation. Conditional coupling coefficient. It refers to the dynamic influence factor of the test condition evaluation coefficient E on performance evaluation. Specifically, it can be determined by fitting the correlation between test conditions and performance indicators under low temperature conditions. For example, the value can be taken as 0.05 to 0.15 to reflect the accelerating effect of extreme temperature cycling on coating cracking.

[0099] Specifically, coating shrinkage, crack length, and adhesion are normalized into standardized indices, making performance indicators of different dimensions comparable. In the coating performance analysis model, the adhesion index serves as the numerator, and its positive contribution is amplified through index weighting, while the shrinkage and crack length indices serve as the denominator, reflecting their inhibitory effects on performance. Evaluation coefficients for testing conditions. Through exponential function Dynamically adjust performance evaluation results, for example, when the number of temperature cycles increases... When decreasing, The amount is increased to reflect the negative impact of extreme conditions on performance. Crack correction amount. The introduction of this avoids the problem of model calculation failure when the coating is not cracked, for example when... When the denominator term remains valid, the calculation remains valid.

[0100] Compared with existing technologies, traditional methods typically use linear weighting to evaluate coating performance, failing to consider the dynamic coupling relationship between testing conditions and performance indicators, and neglecting the normalization problem for indicators with different dimensions. This scheme quantifies the interaction between testing conditions and performance indicators by constructing a nonlinear model. For example, it dynamically correlates the number of temperature cycles with the degree of coating cracking through an exponential function, and eliminates the interference of dimensional differences on the evaluation results through normalization.

[0101] Through the above technical solutions, this application solves the problem of the difficulty in uniformly quantifying and evaluating indicators such as coating shrinkage, crack length, and adhesion under low-temperature environments. For example, at -50℃, the performance degradation caused by the increase in coating crack length can be accurately captured by the model. By dynamically coupling the influence of detection conditions, the accelerated decay effect of temperature gradient changes on coating adhesion can be reflected. For example, for every 10 increases in the number of temperature cycles, the coating performance evaluation coefficient output by the model decreases by approximately 8%-12%. The introduction of normalization processing and correction parameters improves the model's adaptability to data under different operating conditions. For example, when the coating thickness deviates from the baseline value, the change in the thickness index can be used to adjust the sensitivity of the performance evaluation coefficient.

[0102] Preferably, the step of outputting material analysis coefficients based on the coating performance evaluation coefficient, silica nanoparticle content, and polyether-type polyurethane construct material analysis model is as follows:

[0103] The polyether polyurethane content index is obtained by comparing the polyether polyurethane content with the optimal polyether polyurethane content.

[0104] The silica nanoparticle content index is obtained by comparing the difference between the silica nanoparticle content and the optimal silica nanoparticle content with the silica nanoparticle content tolerance bandwidth.

[0105] A material analysis model is constructed based on the coating performance evaluation coefficient, the polyether polyurethane content index, and the silica nanoparticle content index. This material analysis model is expressed as follows:

[0106]

[0107] in, Indicates the material analysis coefficients. Indicates the performance evaluation coefficient of the coating. Indicates the lowest coating performance evaluation coefficient. This represents the gain coefficient, which is used to evaluate the performance of coatings. Indicates the polyether polyurethane content index. Indicates the response index of polyether polyurethane. Indicates the silica nanoparticle content index. Represents the weight coefficient and ;

[0108] Import the current coating performance evaluation coefficient, the current polyether polyurethane content index, and the current silica nanoparticle content index into the material analysis model to output the current material analysis coefficient.

[0109] The polyether polyurethane content index is a parameter obtained by comparing the actual content with the optimal content. Specifically, it can be achieved by dividing the actual measured content by the preset optimal content, reflecting the degree to which the polyether polyurethane content deviates from the ideal value. The silica nanoparticle content index is a parameter obtained by combining the difference between the actual content and the optimal content with the tolerance bandwidth. Specifically, it can be achieved by dividing (actual content - optimal content) by the preset tolerance bandwidth, quantifying the deviation of the silica nanoparticle content from the optimal value. (Logarithmic term in the material analysis model) This is used to normalize the performance evaluation coefficients of coatings. Specifically, it can be achieved by combining the natural logarithm function with a preset minimum performance threshold, avoiding model distortion caused by excessive differences in the absolute values ​​of performance coefficients. Polyether-type polyurethane response index To control the nonlinear response characteristics of this component to the material analysis coefficients, a power function approach combined with experimentally calibrated parameters can be used to adapt to the influence of polyurethane on the coating flexibility under different low-temperature conditions. The exponential decay term of the silica nanoparticle term... To characterize the performance degradation when the nanoparticle content deviates from the optimal value, a Gaussian function combined with square operations can be used, which aligns with the physical characteristic that excessive nanoparticles lead to increased coating brittleness. Weighting coefficients To balance the contribution of the two components to the material analysis coefficients, a preset proportional coefficient can be used while satisfying normalization conditions to adapt to the different requirements of material performance under different environmental conditions.

[0110] Specifically, the polyether-type polyurethane content index amplifies the impact of small deviations through ratio processing. For example, when the actual content is 95% of the optimal content, the index is 0.95, directly reflecting the potential impact of insufficient content on the coating's flexibility. The silica nanoparticle content index uses a combination of difference and tolerance bandwidth processing. For example, with a tolerance bandwidth of ±2%, a 1% deviation between the actual content and the optimal content will produce an index value of 0.5, quantifying the degree of deviation in the nanoparticle dispersion state. In the material analysis model, logarithmic terms map the coating performance evaluation coefficients to a relative proportion space. For example, when... for When the value is twice that of the coefficient, the logarithmic term becomes ln2, eliminating the influence of dimensional differences in the performance coefficients on the model. The power function form of the polyether polyurethane term allows for adjustment... The value changes the steepness of the response curve, for example... This can enhance sensitivity to changes in content. The exponential decay term of the silica term in... When the value increases, the contribution of that component decreases rapidly, for example when... At that time, the exponential term decays to 36.8% of its initial value, accurately reflecting the brittleness risk caused by excessive nanoparticles. The weighting coefficients are dynamically allocated according to environmental conditions; for example, at temperatures below -50°C... It can be set to 0.6 to enhance the emphasis on the flexibility of polyurethane.

[0111] Compared to existing technologies, traditional methods typically use fixed thresholds to determine whether component content is acceptable, failing to quantify the dynamic relationship between component deviation and performance degradation. Existing technologies often assess silica and polyurethane content independently, lacking synergistic modeling and failing to consider the guiding role of coating performance evaluation coefficients in component optimization. This solution establishes a multivariate coupled mathematical model, embedding component content deviation exponentially into the performance evaluation system, and combines this with a weighting mechanism to achieve adaptive evaluation under different environmental conditions.

[0112] Through the above technical solution, this application solves the problem of difficulty in quantifying the dynamic correlation between component content and coating performance under low-temperature and extremely cold environments, and achieves accurate evaluation of the comprehensive performance of materials. By differentiating the polyether polyurethane content index and the silica nanoparticle content index, the influence characteristics of component deviations on flexibility and brittleness are captured respectively. The function combination form in the material analysis model effectively characterizes the nonlinear relationship between performance coefficients and component content, providing a quantitative basis for optimizing and adjusting component ratios. The introduction of weighting coefficients enables the model to adapt to the differentiated requirements of material performance under different low-temperature scenarios, improving the applicability and accuracy of the evaluation results.

[0113] Preferably, the polyether-type polyurethane model is represented as follows:

[0114]

[0115] in, Indicates the target polyether polyurethane content. This indicates the optimal content of polyether polyurethane. Indicates the range of content adjustment. This represents the current material analysis coefficient. This represents the equilibrium value of the material analysis coefficients. This indicates the degree of sensitivity to adjustment.

[0116] Among them, the optimal content of polyether polyurethane refers to the content of polyether polyurethane at which the coating performance is optimal under specific low temperature conditions, and is used as a benchmark value for dynamic adjustment.

[0117] The content adjustment range refers to the maximum range within which the polyether polyurethane content can be adjusted relative to the optimal content. It is used to control the upper limit of the adjustment range to avoid excessive deviation in performance.

[0118] Among them, the material analysis coefficient balance value refers to the material analysis coefficient threshold corresponding to the stable state of the coating performance. It can be set through long-term monitoring data or empirical formulas to determine whether the current material state needs to be adjusted.

[0119] Among them, the adjustment sensitivity refers to the degree of influence of the deviation of the material analysis coefficient on the content adjustment amount. Specifically, it can be determined through empirical calibration, sensitivity analysis or parameter optimization methods, and is used to balance the adjustment response speed and stability.

[0120] Specifically, this model uses the nonlinear characteristics of the hyperbolic tangent function to map the deviation between the material analysis coefficients and the equilibrium values ​​into a smooth adjustment range, allowing the adjustment amount of the polyether polyurethane content to gradually change with the deviation. When the material analysis coefficients approach the equilibrium value, the adjustment amount tends to level off to avoid over-adjustment; when the deviation exceeds the sensitivity threshold, the adjustment amount tends to saturate to limit the maximum adjustment range. By combining the optimal content benchmark and dynamic deviation response, continuous optimization of coating composition can be achieved under low-temperature and extremely cold conditions, while avoiding abrupt changes in composition caused by environmental fluctuations or detection errors.

[0121] Compared to existing technologies, traditional methods typically employ fixed formulations or linear adjustment strategies, which struggle to adapt to the nonlinear changes in material properties at low temperatures. This approach introduces a hyperbolic tangent function to construct a dynamic adjustment model, preserving adjustment flexibility while achieving stability control through sensitivity parameters and adjustment amplitude constraints. This solves the problem of balancing composition adjustment and performance maintenance under extreme conditions.

[0122] Through the above technical solution, this application can dynamically adjust the polyether polyurethane content based on real-time detection data in low-temperature and extremely cold environments, so that the coating composition is always adaptively optimized around the optimal content, thereby effectively suppressing problems such as coating cracking and decreased adhesion caused by sudden temperature changes or stress accumulation, and ensuring the long-term stability of coating performance under extreme conditions.

[0123] Preferably, the silica nanoparticle content model is expressed as follows:

[0124]

[0125] in, This indicates the content of the target silica nanoparticles. This indicates the optimal content of silica nanoparticles. This indicates the tolerance bandwidth for silica content. Represents the sensitivity coefficient. This represents the current material analysis coefficient. This represents the equilibrium value of the material analysis coefficients. This indicates the lower limit of the material analysis coefficient.

[0126] The target silica nanoparticle content refers to the silica content dynamically adjusted according to the material analysis coefficient. Specifically, it can be calculated by inputting the deviation between the current material analysis coefficient and the equilibrium value and the lower limit into the hyperbolic tangent function, which is used to compensate for the insufficient performance of the coating under low temperature conditions.

[0127] The optimal content of silica nanoparticles refers to the baseline value of silica content that performs best under low-temperature conditions, which is determined in advance through experiments. Specifically, it can be determined by the comprehensive evaluation results of coating adhesion and shrinkage rate in multiple sets of low-temperature cycle tests, in order to ensure the basic performance of the coating.

[0128] The tolerance bandwidth of silica content refers to the maximum range in which the silica content is allowed to deviate from the optimal content. Specifically, it can be defined by empirical calibration or by the critical content difference in material endurance testing where the coating does not crack or peel off. It is used to limit the range of content adjustment to avoid performance degradation.

[0129] The sensitivity coefficient refers to the degree of influence of changes in material analysis coefficients on the adjustment amount of silica content, and is used to balance the adjustment speed and stability.

[0130] Among them, the material analysis coefficient balance value refers to the reference value of the material analysis coefficient when the coating performance reaches a stable state. Specifically, it can be calculated from the average material analysis coefficient of qualified coatings in historical test data, and is used to determine whether the current material state needs to be adjusted.

[0131] The lower limit of the material analysis coefficient refers to the minimum allowable threshold of the material analysis coefficient, which can be determined by the statistical results of the material analysis coefficient of the failed coating. It is used to prevent the content from exceeding the safe range during the adjustment process.

[0132] Specifically, the model uses the optimal silica content as a benchmark and converts the deviation between the material analysis coefficient and the equilibrium value into a nonlinear adjustment amount using a hyperbolic tangent function. When the material analysis coefficient deviates from the equilibrium value, the deviation is normalized to a relative value, and the adjustment magnitude is controlled by a sensitivity coefficient. The normalization process eliminates the differences in the magnitude of the deviation under different detection conditions through the denominator term, making the adjustment logic universal. The saturation characteristic of the hyperbolic tangent function limits the silica content to the range of the optimal content plus or minus the tolerance bandwidth, avoiding performance abrupt changes caused by extreme adjustments. The sensitivity coefficient further adjusts the slope of the adjustment curve, triggering a more significant content correction when the material analysis coefficient is close to the lower limit, while maintaining a small adjustment when close to the equilibrium value.

[0133] In some specific implementations, the tolerance bandwidth can be 5%-15% of the optimal silica content, and the sensitivity coefficient can be an empirical value within the range of 0.8-1.2. The equilibrium value of the material analysis coefficient can be obtained by averaging the material analysis coefficients of more than 100 qualified samples, and the lower limit of the material analysis coefficient can be set at 70% of the equilibrium value.

[0134] Compared to existing technologies, traditional methods typically use fixed thresholds to determine whether silica content is up to standard, or directly adjust the content through linear relationships. This proposed solution introduces a hyperbolic tangent function to achieve nonlinear adjustment, effectively suppressing content oscillations caused by minor deviations at low temperatures. Furthermore, normalization processing adapts the adjustment process to different detection conditions. Compared to linear models, this solution exhibits stronger stability when material analysis coefficients approach critical values, and a faster response speed when there are significant deviations.

[0135] Through the above technical solution, this application achieves dynamic optimization of silica nanoparticle content, maintaining a balance between coating adhesion and crack resistance under low-temperature and extremely cold environments. The nonlinear adjustment mechanism avoids the stress concentration problem in the coating caused by abrupt changes in content, as seen in traditional methods, while the tolerance bandwidth constraint ensures that the adjusted content remains within the material's tolerance range. Normalization processing enables the adjustment process to adapt to changes in material state under different testing conditions, improving the robustness of the testing method.

[0136] A fiber optic coating testing device based on low-temperature and extremely cold conditions adopts the above-mentioned fiber optic coating testing method based on low-temperature and extremely cold conditions.

[0137] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0138] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for detecting fiber optic coatings under extremely low temperature conditions, characterized in that, Includes the following steps: Acquire data on testing conditions, coating performance, silica nanoparticle content in coatings, and polyether polyurethane; A detection condition evaluation model is constructed based on the detection condition data, and the detection condition evaluation coefficients are output. Based on the coating performance data under the evaluation coefficient of the detection conditions, a coating performance analysis model is constructed to output the coating performance evaluation coefficient; Based on the coating performance evaluation coefficient, silica nanoparticle content, and polyether-type polyurethane construct material analysis model, the material analysis coefficient is output. The obtained material analysis coefficients are compared with the preset material analysis coefficient thresholds. If the material analysis coefficients are not within the material analysis coefficient thresholds, a judgment information is generated that the current coating performance is unqualified. The detection condition data includes the number of temperature cycles, coating thickness, and temperature gradient. The coating performance data includes the coating's adhesion, shrinkage rate, and crack length; The steps for constructing a detection condition evaluation model based on detection condition data and outputting detection condition evaluation coefficients are as follows: The heating gradient exponent is obtained by processing the ratio of the heating gradient to the maximum allowable heating gradient. The thickness deviation index is obtained by comparing the coating thickness with the reference coating thickness. The thickness index is obtained by subtracting the reference coating thickness from the coating thickness and then comparing it with the thickness tolerance bandwidth. The temperature cycle number index is obtained by processing the ratio of the number of temperature cycles to the maximum allowable number of temperature cycles; A detection condition evaluation model is constructed based on the heating gradient index, thickness deviation index, thickness index, and temperature cycle number index. The detection condition evaluation model is expressed as follows: ; in, This represents the evaluation coefficient for testing conditions. Indicates the cyclic decay factor. Indicates the number of temperature cycles, This indicates a thickness deviation index. Indicates the thickness index. Indicates the temperature gradient exponent. Indicates the power exponent of the temperature gradient; Input the current heating gradient index, current thickness deviation index, current thickness index, and current temperature cycle number index into the detection condition evaluation model to output the current detection condition evaluation coefficient; The steps for constructing a coating performance analysis model and outputting coating performance evaluation coefficients based on coating performance data under detection condition evaluation coefficients are as follows: The shrinkage rate, crack length, and adhesion of the coating were subjected to maximum-minimum normalization to obtain the shrinkage rate index, crack length index, and adhesion index. A coating performance analysis model is constructed based on the shrinkage index, coating crack length index, and adhesion index under the evaluation coefficient of the detection conditions. The coating performance analysis model is expressed as follows: ; in, Indicates the performance evaluation coefficient of the coating. Indicates the adhesion index. Indicates the adhesion weight index. Indicates the shrinkage rate index. Indicates the coating crack length index. This indicates the amount of crack correction. Represents the conditional coupling coefficient. Indicates the evaluation coefficient of the testing conditions; Import the current test condition evaluation coefficient, current shrinkage index, current coating crack length index, and current adhesion index into the coating performance analysis model to output the current coating performance evaluation coefficient; The steps for outputting material analysis coefficients based on coating performance evaluation coefficients, silica nanoparticle content, and polyether-type polyurethane construct material analysis model are as follows: The polyether polyurethane content index is obtained by comparing the polyether polyurethane content with the optimal polyether polyurethane content. The silica nanoparticle content index is obtained by comparing the difference between the silica nanoparticle content and the optimal silica nanoparticle content with the silica nanoparticle content tolerance bandwidth. A material analysis model is constructed based on the coating performance evaluation coefficient, the polyether polyurethane content index, and the silica nanoparticle content index. This material analysis model is expressed as follows: ; in, Represents the material analysis coefficients. Indicates the performance evaluation coefficient of the coating. This represents the lowest coating performance evaluation coefficient. This represents the gain coefficient, which is used to evaluate the performance of coatings. Indicates the polyether polyurethane content index. Indicates the response index of polyether polyurethane. Indicates the silica nanoparticle content index. Represents the weight coefficient and ; Import the current coating performance evaluation coefficient, the current polyether polyurethane content index, and the current silica nanoparticle content index into the material analysis model to output the current material analysis coefficient.

2. The method for detecting optical fiber coatings under extremely cold conditions according to claim 1, characterized in that, Based on the judgment information, the material analysis coefficient, the content of silica nanoparticles, and the polyether polyurethane, respectively construct silica nanoparticle content models and polyether polyurethane models, and output the target silica nanoparticle content and the target polyether polyurethane content.

3. The method for detecting optical fiber coatings under extremely low temperature conditions according to claim 2, characterized in that, The polyether-type polyurethane model is represented as follows: ; in, Indicates the target polyether polyurethane content. This indicates the optimal content of polyether polyurethane. Indicates the range of content adjustment. This represents the current material analysis coefficient. This represents the equilibrium value of the material analysis coefficients. This indicates the degree of sensitivity to adjustment.

4. The method for detecting optical fiber coatings under extremely cold conditions according to claim 2, characterized in that, The silica nanoparticle content model is expressed as follows: ; in, This indicates the content of the target silica nanoparticles. This indicates the optimal content of silica nanoparticles. This indicates the tolerance bandwidth for silica content. Represents the sensitivity coefficient. This represents the current material analysis coefficient. This represents the equilibrium value of the material analysis coefficients. This indicates the lower limit of the material analysis coefficient.

5. A fiber optic coating testing device for extremely cold low-temperature conditions, characterized in that, The fiber optic coating detection method based on low temperature and extreme cold conditions as described in any one of claims 1-4 is adopted.

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