KR20170008140A - System and method for predicting states of polymer considering temperature based on degradation model - Google Patents

Abstract

The present invention relates to a system for predicting states of a polymer material state by various environment conditions and, more specifically, to a system and a method for predicting states of a polymer material state considering temperature, for predicting states of the polymer material state by the state change of the polymer material in accordance with temperature and time based on a degradation model.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a polymer material,

The present invention relates to a material state prediction system for predicting a state change of a material by various environmental conditions, and more particularly, to a material state prediction system for predicting a state change of a material by various environmental conditions, And a system and method for predicting material states.

Recently, polymer materials have been used in various products in various fields due to various advantages such as light weight, and the application fields are being expanded more and more. In this regard, it is important to understand the performance and life span of the product in which the polymer material is used, but the development of systems and methods for understanding the properties of such polymer materials is not active.

Particularly, polymer materials are used in various fields such as gas pipes, storage tanks, automobiles, etc., and their use is increasing. These polymers are influenced by a variety of environmental factors, especially heat, that is, temperature.

For example, polymers are widely used in automotive interior materials. When the vehicle is parked outside the summer, the temperature of the interior is increased due to the direct sunlight transmitted through the windshield, and the internal temperature is increased to three times higher than the external temperature due to the greenhouse effect of the closed room. (Deterioration behavior) due to temperature may occur.

There is a demand for a method of predicting the deterioration behavior for such a polymer material in response to various environmental conditions, i.e., temperature conditions, and applying a polymer having an optimum deterioration property value in accordance with the predicted deterioration behavior.

Registration No. 10-1489709 (Feb. 29, 2015)

SUMMARY OF THE INVENTION It is therefore an object of the present invention to provide a system and method for predicting polymeric material states in consideration of temperature that more accurately predicts the state of a polymer material with respect to temperature and time based on a degradation model.

According to another aspect of the present invention, there is provided a system for predicting the state of a polymer material in consideration of temperature, comprising: a means for applying a complex deterioration model in which a Leathon deterioration model and an Abrahamic degradation model are applied in combination, Detecting a proportional constant and parameters of the composite deterioration model and applying the detected deterioration constant to the composite deterioration model to predict a characteristic of a physical property of the polymer to output a point; And storing the proportional constant, parameter and experimental sample data output by the incrementing unit, resampling the experimental sample data using the proportional constant, parameter and standard deviation, and applying the same again to the incremental unit, And a section estimator for estimating a parameter set included in a certain confidence interval after cumulatively storing a plurality of proportional coefficient sets and parameter sets by repeatedly detecting constants and resampling parameters and outputting result information corresponding thereto, .

The point estimator may include a proportional constant detector for detecting a proportional constant of the complex deterioration model for each isothermal temperature using the maximum likelihood method for the isothermal temperature to be measured; A parameter detector for detecting a parameter of a proportional constant using the Arrhenius relation by applying the proportional constant to each of the isothermal temperatures; And a physical property detector for detecting and outputting physical properties of the polymer by applying the composite deterioration model to which the parameter is applied.

The point estimator may include: a data storage unit for storing experimental data; And a secant detector for extracting experimental sample data, which is experimental data corresponding to 1% secant modulus of the experimental data, and storing the extracted sample data in the data storage unit.

The physical property detecting unit is characterized by detecting a material property of the polymer by a deterioration model of the following formula.

[Mathematical Expression]

Where A, B, C and D are parameters, T is isothermal temperature and T _ref is reference temperature. At this time, the temperature uses absolute temperature.

Wherein the interval estimator includes a standard deviation calculator for receiving the proportional constant and parameters and calculating and outputting a standard deviation; The proportional constant and the standard deviation are input. The proportional constant is resampled by the standard deviation. The sampling data is resampled by using the resampled proportional constant and the standard deviation. A point estimate data accumulating unit for accumulating and storing a plurality of proportional coefficient sets and parameter sets repeatedly a plurality of times; And a confidence interval determining unit for determining a parameter set included in the 95% confidence interval of the accumulated parameter sets, estimating a confidence interval, and outputting result information according to the reliability interval.

The system may further include: a thermal cycle analyzing unit for applying the physical property estimation information at the isothermal temperature to a specific thermal cycle (a cycle in which the temperature is changed) to predict the deterioration property of the polymer and output the predicted result information do.

According to another aspect of the present invention, there is provided a method for predicting a state of a polymer material in consideration of temperature, comprising: applying a composite deterioration model in which a Leathon deterioration model and an Abrahamic degradation model are applied in combination, A point estimation process for detecting a proportional constant and parameters of the complex deterioration model and applying the same to the complex deterioration model to predict a characteristic of a physical property of the polymer and output the characteristic; And storing the proportional constant, parameter, and experimental sample data output by the point estimation, calculating a standard deviation by resampling the proportional constant, resampling the experimental sample data using the standard deviation, and applying the same to the decompression unit Estimating a parameter set included in a confidence interval after cumulatively storing a plurality of proportional coefficient sets and parameter sets by repeatedly detecting the proportional constants and parameters, and outputting result information according to the parameter sets; .

The point estimating step may include: a proportional constant detecting step of detecting a proportional constant using a temperature and a maximum likelihood method for the isothermal temperature to be measured; A parameter detection step of detecting a parameter by using the temperature and the linear regression for the isothermal temperature to be measured and applying the proportional constant; And a physical property detecting step of detecting and outputting physical properties of the polymer by applying the composite deterioration model to which the parameter is applied.

The point estimating step may further include a secant detecting step of extracting experimental sample data that is experimental data corresponding to 1% secant modulus of the experimental data and storing the extracted sample data in a data storage unit, wherein the test sample data obtained through the secant detecting step To perform the parameter detection step.

And the material characteristic of the polymer in the physical property detecting step is generated by a deterioration model of the following equation.

[Mathematical Expression]

Where A, B, C and D are parameters, T is isothermal temperature and T _ref is reference temperature. At this time, the temperature uses absolute temperature.

Calculating a standard deviation by calculating the standard deviation by receiving the proportional constant and the parameter, and outputting the standard deviation; The proportional constant and the standard deviation are input. The proportional constant is resampled by the standard deviation. The sampling data is resampled by using the resampled proportional constant and the standard deviation. A point estimation data accumulating step of accumulating and storing a plurality of proportional coefficient sets and parameter sets repeatedly a plurality of times; And a confidence interval determining step of determining a parameter set included in the 95% confidence interval of the accumulated parameter sets, estimating a confidence interval, and outputting result information according to the confidence interval.

The present invention has an effect of predicting a more accurate deterioration behavior by applying a new deterioration model generated by reflecting the Rayton model and the Avrami model.

The present invention is a function of the deterioration time and the deterioration temperature. The deterioration property in the isothermal state can be predicted in the confidence interval of 95%, so that it is possible to perform more accurate prediction.

Further, the present invention relates to a change in physical properties with respect to a temperature change by incorporating a physical property prediction for a temperature cycle into an isothermal deterioration model to take into account that the environment where the actual polymer is constituted, for example, the temperature of the internal environment of the automobile, And has a predictable effect.

In addition, the present invention has an effect that when the user inputs the deterioration temperature and the deterioration time with the isothermal deterioration model, the value when the 1% secant modulus of the polymer is deteriorated can be known, and a specific heat cycle The 1% Secant Modulus value of the polymer undergoing thermal cycling also has a predictable effect.

1 is a graph showing a 1% Secant Modulus calculation graph applied to the present invention.
FIG. 2 is a graph showing the results of a deterioration static property test for confirming an estimated result according to an embodiment of the present invention.
3 is a graph showing a parameter estimation graph of k using the Arrhenius relation according to an embodiment of the present invention.
FIG. 4 is a graph showing a degradation model graph for each type of polymer estimated according to an embodiment of the present invention.
5 is a diagram showing the configuration of a polymer material state prediction system considering the temperature according to the present invention.
6 is a view showing a detailed configuration of a point estimating unit of a polymer material state predicting system according to the present invention.
7 is a flowchart illustrating a method of predicting a polymer material state in consideration of a temperature according to the present invention.
8 is a diagram showing the configuration of a section estimating unit of the polymer material state predicting system according to the present invention.
9 is a diagram illustrating a concept of bootstrap sampling and confidence interval estimation according to the present invention.
FIG. 10 is a graph illustrating a 95% confidence interval estimation result using a bootstrap according to an embodiment of the present invention.
11 is a diagram illustrating a thermal cycle condition applied according to an embodiment of the present invention.
FIG. 12 is a graph illustrating a concept of cumulative degradation according to an embodiment of the present invention.
13 is a graph showing cumulative deterioration result values according to an embodiment of the present invention.

The concept of predicting the state of the polymer material in consideration of the temperature according to the present invention will be described first with reference to the accompanying drawings and then the construction and operation of the polymer material state prediction system considering the temperature will be described, The state prediction method will be described.

FIG. 1 is a graph showing a 1% Secant Modulus calculation graph applied to the present invention. FIG. 2 is a graph showing the results of a deterioration static property test for confirming an estimated result according to an embodiment of the present invention. 3 is a graph showing a parameter estimation curve of k using the Arrhenius relation according to an embodiment of the present invention, and FIG. 4 is a graph illustrating a degradation model graph for each polymer type estimated according to an embodiment of the present invention. Will be described with reference to Figs. 1 to 4. Fig.

The present invention is to develop a deterioration model of physical properties of a polymer material, which is widely used as an automobile component material, and to predict a deterioration property value. Considering the deterioration of the most important temperature among various environmental conditions that cause deterioration. As a result, the following tests were conducted on three polymeric materials PPF-A (MT62CP), PPF-D (MT42HS) and Polypropylene with Long Fiber Thermoplastics (LFT [HGL72B]), Isothermal deterioration was induced and Elastic Modulus (1% Secant Modulus), which is static property, was measured by tensile test (ASTM D638). First, the test standard for isothermal conditions is the same as that of Test Standard of Environmental Condition (Isothermal).

[Table 1]

Table 1 shows the condition settings for the actual environmental test. According to Table 1, the temperature condition of automotive instrument panel is affected in the range of -40 ~ 113 ℃. However, since the actual vehicle is not applied at extreme temperature conditions of -40 ° C and 113 ° C for a long time, the deterioration characteristics for three temperatures are measured as shown in Table 2, which shows the following recommended environmental conditions and Table 3, which shows the test condition.

[Table 2]

[Table 3]

Temperature was deteriorated by isothermal deterioration at 40 ℃, 65 ℃ and 85 ℃. After 15, 30, 50 and 70 days of deterioration, material specimens were taken out and tested at room temperature. Among the static properties of the polymer, the modulus of elasticity was calculated as 1% Secant Modulus as shown in the graph of FIG.

 1% Secant Modulus is obtained by using the strain value and the stress value at 1% point from the strain-stress curve as shown in FIG.

There are the following representative models of deterioration models that represent existing deterioration.

There are Layton model and Avrami model with Arrhenius equation which shows the relationship between temperature and reaction rate constant.

First, the Layton model is as shown in the following Equation 1, and is used for predicting mechanical properties and deterioration behavior of a polymer material.

[Equation 1]

는 열화시점 t와

에서의 물성값이며,

는 초기의 열화시작 시점이다. k는 비례상수(Rate Constant)이며 수학식 2와 같이 쓸 수 있다. Here, P and

Lt; RTI ID = 0.0 > t &

Lt; RTI ID = 0.0 >

Is the initial deterioration start point. k is a rate constant and can be written as in Equation (2).

&Quot; (2) "

Equation (2) represents the k value according to the temperature change and is called the Arrhenius equation. Here, A and B in the equation (2) represent the parameters of the Arrhenius equation and T represents the absolute temperature. If the Layton model of Equation (1) is a physical property change in log form according to the deterioration time, the physical property change of the exponential form can be considered, and it can be expressed as Equation (3).

&Quot; (3) "

와 t,

는 수학식 1 에서의 의미와 같으며 k는 수학식 2 이다. 이러한 Avrami Model 은 폴리머 소재의 인장 강도(Tensile Strength)와 크립 강도(Creep Strength)는 물론 전체적인 기계적 물성을 예측하는 데 많이 활용된다. 각각의 폴리머 재료(PPF-A,PPF-D,LFT)에 대한 열화 정적물성(1% Secant Modulus) 실험 결과는 도 2와 같이 나타난다.Equation (3) is called Avrami Model, and P ,

And t,

Is the same as that in Equation (1), and k is Equation (2). These Avrami models are widely used to predict the tensile strength and creep strength of polymeric materials as well as the overall mechanical properties. The results of the deterioration static properties (1% Secant Modulus) for each polymer material (PPF-A, PPF-D, LFT) are shown in FIG.

As shown in FIG. 2, the deterioration static properties of the three materials showed that the behavior of the 1% Secant Modulus physical properties was decreased at the beginning of deterioration as compared with the initial physical properties. In order to express such a behavior, the present invention modifies and combines the existing model Abraham model and the Layton model. The completion model (hereinafter referred to as "complex deterioration model") according to the present invention is represented by the following equation (4).

&Quot; (4) "

는 기준온도이다. 이때 온도는 절대온도를 사용한다.Here, A, B, C and D are parameters, T is isothermal temperature,

Is the reference temperature. At this time, the temperature uses absolute temperature.

The parameter estimation process of the complex degradation model is as follows. To the Arrhenius equation model of a complex degradation place to k ₁ and k ₂ as in equation (5) estimates the k _1, k ₂ at each temperature. At this time, the parameter is estimated using Maximum Likelihood Estimation.

&Quot; (5) "

이고, 추정될 파라미터가

라 하면 우도함수는 하기 수학식 6과 같이 정의된다.The maximum-likelihood method is a method of maximizing the likelihood function to obtain the parameter most likely to generate the given data. The experimental sample data

And the parameter to be estimated is

The likelihood function is defined by Equation (6).

&Quot; (6) "

The likelihood function when the probability density function is assumed to be a normal distribution is expressed by Equation (7).

&Quot; (7) "

는 실제 실험 샘플데이터가 되며, μ는 열화 예측 모델식, t는 열화시간이 된다. 우도함수의 최적화를 통해

,

,

,

값을 추정할 수 있다.

Is actual experiment sample data, μ is the deterioration prediction model equation, and t is the deterioration time. Through the optimization of the likelihood function

,

,

,

Value can be estimated.

,

는 아레니우스 관계(Arrhenius Relation)를 이용하여

의 파라미터 A,B,

의 파라미터 C,D를 추정하여 전체 모델식을 완성한다. 아레니우스 관계를 이용한 파라미터 추정 방법은 다음과 같다. 아레니우스 방정식 양변에 각각 log를 취하면 하기 수학식 8과 같이 나타낼 수 있다.Estimated at each temperature

,

Using the Arrhenius Relation

The parameters A, B,

The parameters C and D are estimated to complete the entire model equation. The parameter estimation method using the Arrhenius relation is as follows. The logarithm of each of the Arrhenius equations can be expressed by the following equation (8).

&Quot; (8) "

라 하면 하기 수학식 9와 같은 선형식이 된다.And

Is expressed by the following equation (9).

&Quot; (9) "

&Quot; (10) "

의 파라미터 A,B,

의 파라미터 C,D를 구할 수 있다. 이 때 상기 수학식 10을 적용하여 표준편차

,

를 구한다.Using the respective isothermal temperatures (40 ° C, 65 ° C and 85 ° C) and proportional constants, the Arrhenius relationship (linear regression)

The parameters A, B,

The parameters C, At this time, the standard deviation

,

.

By applying equations (8) and (9), a parameter estimation graph using the Arrhenius relation as shown in FIG. 3 can be obtained.

The parameter estimation results and the model fit (Coefficient of Determination) for each material will be shown in FIG. 4 and Table 4.

[Table 4]

Here, R ² means Coefficient of Determination, which indicates how much the model of the present invention follows the data, and has a value between 0 and 1. The closer to 1, the better the model follows the trend of the data.

5 is a diagram showing the configuration of a polymer material state prediction system considering the temperature according to the present invention. The configuration and operation of the polymer material state prediction system according to the present invention will be described below with reference to FIG.

The polymer material state prediction system considering the temperature according to the present invention includes the thermal expansion unit 100 and the interval estimating unit 200 and may further include the thermal cycle analyzing unit 300 according to the embodiment.

The decompression unit 100 collects experimental data at isothermal temperature, takes experimental sample data (x _i ) from the collected experimental data, and applies it to the complex deterioration model of Equation (4) according to the present invention to determine the properties of the polymer And generates and outputs information according to the prediction. The proportional constants k ₁ and k ₂ for obtaining the model parameters A, B, C and D and the model parameters A, B, C and D and the proportional constants k ₁ and k ₂ should be obtained . The detailed configuration and operation of the increment unit 100 for performing the point estimation by applying the parameters will be described in detail with reference to FIG. 2 to be described later.

The interval estimator 200 resamples the test sample data using the standard deviation S ₁ and S ₂ to account for the uncertainty with respect to the prediction model and outputs the resampled number to the incrementing unit 100 parameters included in N) N of k _1, accumulating k ₂ data set and parameter set to store, by applying the method of the accumulated samples per set of parameters to estimate the confidence interval of the bootstrap 95% confidence interval in accordance with the Estimates the sets, and outputs the result information.

In addition, the temperature of the actual surrounding environment does not remain constant but changes. In order to accurately predict the physical properties of the polymer according to the temperature, a thermal cycle analyzer 300 for predicting the physical properties according to the temperature change is required.

The thermal cycle analyzer 300 estimates the deterioration properties of the polymer by applying the physical property estimation information at the isothermal temperature to a specific thermal cycle (temperature change), and outputs the predicted result information. The operation of the thermal cycle analyzer 300 will be described in detail with reference to FIGS. 11 to 13, which will be described later.

6 is a view showing a detailed configuration of a point estimating unit of a polymer material state predicting system according to the present invention. The detailed configuration and operation of the point estimating unit will be described with reference to FIG.

The incrementing unit 100 includes a data input unit 10, a data storage unit 20, a sequent detecting unit 30, a proportional constant detecting unit 40, a parameter detecting unit 50, and a property detecting unit 60.

The data input unit 10 receives physical property test data for at least one kind of polymer and stores the data in the data storage unit 20.

The data storage unit 20 stores experimental data, experimental data (X: experiment sample data) calculated with 1% secant modulus among experimental data, proportional to the experiment sample data (X) by a constant (k _1, k _2), the parameter (a, B, C, D ), the standard deviation (S _1, S _2), wherein the parameter (a, B, C, D ) and standard deviation (S ₂₎ The resampling test sample data X ^* resampling the test sample data by the resampling proportion constant k ^* resampling the proportional constant, the resampling proportion constant and the standard deviation S ₁ , the resampling proportion constant and the resampling experiment The resampling parameters (A _n , B _n , C _n , and D _n : n = 1, 2, 3, ...) for the sample data are stored. The polymer physical property information may be stored as general data that may be stored in the form of a graph of a deterioration static physical property experiment result as shown in FIG. 2, a k parameter estimation graph as shown in FIG. 3, a degradation model graph as shown in FIG.

The secant detecting unit 30 calculates experimental sample data X corresponding to 1% secant modulus from the experimental data stored in the data storing unit 20 and stores the calculated experimental sample data X in the data storing unit 20, (X). ≪ / RTI >

The proportional constant detector 40 calculates proportional constants k ₁ and k ₂ using Equation (7) and outputs the proportional constants k ₁ and k ₂ to the parameter detector 50 and the interval estimator 200 when the experiment sample data X is detected.

The parameter detector 50 receives the proportional constant from the proportional constant detector 40 and detects and outputs the parameters A, B, C, and D by applying Equations (8) and (9). The detected parameters will be input to the property detector 60 and the thermal cycle analyzer 300.

The physical property detector 60 receives the proportional constant from the proportional constant detector 40, receives the parameter from the parameter detector 50, applies the parameter to the equation (4), performs point estimation, and generates and outputs physical property estimation information .

The physical property estimation information may be input to the data storage unit 20 and stored or may be input to the interval estimation unit 200 and the thermal cycle analysis unit 300.

7 is a flowchart illustrating a method of predicting a polymer material state in consideration of a temperature according to the present invention. This will be described below with reference to Figs. 6 and 7. Fig.

First, the secant detector 30 calculates experimental sample data from the experiment data stored in the data storage 20 by 1% Secant Modulus (S111).

When the experimental sample data is extracted, the proportional constant detector 40 loads the experimental sample data output from the sequencer 30 or the experimental sample data stored in the data storage 20, and calculates the proportional constant ( k ₁ , k ₂ ) is calculated (S113).

When the proportional constants are calculated, the parameter detector 50 receives proportional constants from the proportional constant 40, loads the experiment sample data from the data storage unit 20 or receives the proportional constants from the proportional constant detector 40, And the parameters A, B, C, and D are detected by applying Equation (9) (S115).

The physical property detecting unit 60 receives a parameter from the parameter detecting unit 50 and outputs a proportional constant from the proportional constant detecting unit 40 to the data storing unit 20, (Step S119). In step S119, the point estimation is performed on the physical property of the polymer by applying equation (4).

When the point estimation information is generated, an output unit (not shown) may be configured to output a deterioration static physical property test result graph, a deterioration model graph, and the like for each polymer type as shown in FIGS. 2 and 4 based on the point estimation information, (A, B, C, D) estimation graph using the Arrhenius relation as shown in FIG.

In the present invention, to consider the uncertainty of the degradation model, the confidence interval is estimated using the bootstrap method using the interval estimator 200 of FIG.

FIG. 8 is a block diagram illustrating a configuration of a section estimating unit of a polymer material state predicting system according to the present invention. FIG. 9 is a diagram illustrating a concept of bootstrap sampling and confidence interval estimation according to the present invention, And 95% confidence interval estimation results using the strap.

8 to 10, the interval estimating unit 200 includes a standard deviation calculating unit 110, a point estimation data accumulating unit 120, and a confidence interval determining unit 130.

The standard deviation calculation unit 110 calculates and outputs the standard deviation S1 by applying Equation 7 when k ₁ and k ₂ are inputted from the proportional constant detection unit 40 and outputs the parameters A, B, C, and D ) Is inputted, the standard deviation S2 is calculated by applying Equation (10), and the standard deviation S2 is calculated and outputted.

The point estimation data accumulation unit 120 receives the proportional constants k ₁ and k ₂ and the standard deviation S and the parameters A, B, C, and D output from the increment unit 100, (S ₁ , S ₂ ) are input from the standard deviation calculation unit 110, and performs resampling to obtain N new parameter sets and experimental samples Data sets are generated and accumulated.

,

에 대한 불확실성을 고려하기 위해

를 이용하여 N개의 새로운 k ₁ , k ₂ 데이터 세트를 생성. 물성에 대한 불확실성을 고려하기 위해

과 새롭게 생성한 N개의 k ₁ , k ₂ 데이터 세트를 이용하여 새로운 물성 데이터 세트인 리생플링 실험 샘플데이터(X_n ^*) N개를 생성한다.More specifically, referring to FIG. 9,

,

To consider the uncertainty of

To generate N new k ₁ , k ₂ data sets. To account for uncertainties in physical properties

And the N sets of k ₁ and k ₂ data sets are used to generate N pieces of new sample data X _n ^* of the new physical property data sets.

In other words, the point estimation data accumulation unit 120 provides a new resampling experiment sample data set to the decompression unit 100 to perform multi-point estimation to estimate the parameters for the resampling experiment sample data, After completing the physical property model, it is accumulated and stored.

The confidence interval determining unit 120 determines a 95% confidence interval by applying a bootstrap confidence interval determination method, as shown in FIG. 9, among the completed physical property models accumulated in the point estimation data accumulation unit 120. [ As a method of estimating the confidence interval of the bootstrap, a general method, the percentile method, is applied. The results thus estimated are shown in Fig.

FIG. 11 is a view showing a thermal cycle condition applied to the present invention, FIG. 12 is a diagram showing the concept of cumulative deterioration according to the present invention, FIG. 13 is a graph showing cumulative deterioration result values according to the present invention, Cycle analysis unit 300 according to an embodiment of the present invention.

The temperature of the actual surrounding environment changes not just isothermal but also instantaneously. Therefore, a degradation model according to the temperature change is required. Therefore, it is preferable that the thermal cycle analyzer 300 to which the deterioration model according to the temperature change is applied is further applied.

Since the deterioration model developed for performing the estimated physical property estimation in the burn-in unit 100 and the interval estimating unit 200 is a deterioration model for isothermal temperature, the following method is proposed to predict deterioration properties for a specific thermal cycle .

In other words, the thermal cycle analyzer 300 performs a property estimation for a variable temperature rather than an isothermal temperature by applying a simple cumulative damage model, which is generally used, to the Miner's rule, and outputs result information thereof.

Since the deterioration model applied to the burn-in unit 100 and the interval estimating unit 200 of the present invention expresses an increasing tendency by using the exponential (exp) function and the log (log) function, It is difficult to apply the Miner's rule.

Accordingly, the thermal cycle analyzing unit 300 separately calculates the exp function part and the log function part, separately calculates and finalizes the final value. As shown in Fig. 11, the thermal cycle conditions are three cycles at 100 ° C, three cycles at -40 ° C, and one cycle at 50 ° C for seven cycles.

Referring to FIG. 12, let it be assumed that one cycle is 5h at temperature T1 and 5h at temperature T2. After 5 h at the initial temperature T1, the property value will be y1, which will be tn1 in the T2 temperature behavior model. After 5 h at tn1, it will be y2. Cumulative degradation was calculated using this algorithm. The calculation result is shown in Fig. 13 when the isothermal deterioration model of the actual PPF-A is carried out for 5 cycles with respect to the cycle of Fig. The formula for dividing the log function part and the exp function part used for the calculation and the function for the time t can be expressed by the following equation (11).

 &Quot; (11) "

While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims. It will be easily understood. It is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, it is intended to cover various modifications within the scope of the appended claims.

Claims (11)

A composite deterioration model in which a Leathon deterioration model and an Abrahamic degradation model are combined is used by taking experimental sample data collected at isothermal temperature and the proportional constant and parameters of the composite deterioration model are detected and applied to the composite deterioration model, A pointing unit for performing point estimation for predicting and outputting a characteristic of the pointing device; And
Parameter and experiment sample data output by the incrementor, resampling the experimental sample data using the proportional constant, parameter and standard deviation, and then applying the same again to the incremental portion to update the resampling proportional constant And a section estimator for detecting a resampling parameter and accumulating and storing a plurality of sets of proportional coefficients and parameter sets repeatedly a plurality of times, estimating a parameter set included in a certain confidence interval, and outputting result information corresponding thereto A system for predicting the state of a polymer material considering its temperature.
The method according to claim 1,
The point estimating unit may include:
A proportional constant detector for detecting a proportional constant of the complex deterioration model with respect to each of the isothermal constants using the maximum likelihood method for the isothermal constants to be measured;
A parameter detector for detecting a parameter of a proportional constant using the Arrhenius relation by applying the proportional constant to each of the isothermal temperatures; And
And a physical property detecting unit for detecting and outputting physical properties of the polymer by applying the complex deterioration model to which the parameter is applied.
3. The method of claim 2,
The point estimating unit may include:
A data storage unit for storing experimental data; And
Further comprising a secant detector for extracting experimental sample data, which is experimental data corresponding to 1% secant modulus of the experimental data, and storing the extracted data in the data storage unit.
3. The method of claim 2,
Wherein the physical property detecting unit comprises:
A polymer material state prediction system in consideration of temperature, characterized by detecting a material property of a polymer by a degradation model of the following equation (12).

&Quot; (12) "

Where A, B, C and D are parameters, T is isothermal temperature and T _ref is reference temperature. At this time, the temperature uses absolute temperature.
The method according to claim 1,
The section estimator may include:
A standard deviation calculation unit for receiving the proportional constant and parameters and calculating and outputting a standard deviation;
The proportional constant and the standard deviation are input. The proportional constant is resampled by the standard deviation. The sampling data is resampled by using the resampled proportional constant and the standard deviation. A point estimate data accumulating unit for accumulating and storing a plurality of proportional coefficient sets and parameter sets repeatedly a plurality of times; And
And a confidence interval determining unit for determining a parameter set included in the 95% confidence interval among the accumulated parameter sets to estimate a confidence interval and outputting result information according to the confidence interval.
The method according to claim 1,
Further comprising a thermal cycle analyzing unit for applying the physical property estimation information at the isothermal temperature to a specific thermal cycle (cycle in which the temperature is converted) to predict the deterioration properties of the polymer and outputting the predicted result information. Polymer material state prediction system.
A composite deterioration model in which a Leathon deterioration model and an Abrahamic degradation model are combined is used by taking experimental sample data collected at isothermal temperature and the proportional constant and parameters of the composite deterioration model are detected and applied to the composite deterioration model, A point estimating step of estimating and outputting characteristics of a point; And
The method includes: storing a proportional constant, a parameter, and experiment sample data output by the point estimation; calculating a standard deviation by resampling the proportional constant; resampling the experimental sample data using the standard deviation; A step of estimating a parameter set included in the confidence interval after cumulatively storing a plurality of proportional coefficient sets and parameter sets by repeatedly detecting the proportional constants and parameters and outputting result information corresponding thereto Wherein the temperature of the polymer material is in the range of from about 1 to about 10 minutes.
8. The method of claim 7,
The point estimation process includes:
A proportional constant detecting step of detecting a proportional constant using a temperature and a maximum likelihood method for the isothermal temperature to be measured;
A parameter detection step of detecting a parameter by using the temperature and the linear regression for the isothermal temperature to be measured and applying the proportional constant;
And detecting a physical property of the polymer by applying the composite deterioration model to which the parameter is applied, and outputting the detected property.
9. The method of claim 8,
The point estimation process includes:
Further comprising a secant detection step of extracting experimental sample data that is experimental data corresponding to 1% secant modulus of the experimental data and storing the extracted data in a data storage unit, wherein the test sample data obtained through the secant detection step is applied to detect the parameter Wherein the temperature of the polymer material is in the range of about < RTI ID = 0.0 > 100 C < / RTI >
9. The method of claim 8,
In the physical property detecting step,
Is generated by a degradation model of the following equation (13).

&Quot; (13) "

Where A, B, C and D are parameters, T is isothermal temperature and T _ref is reference temperature. At this time, the temperature uses absolute temperature.
8. The method of claim 7,
The section estimating process includes:
A standard deviation calculation step of receiving the proportional constant and parameters and calculating and outputting a standard deviation;
The proportional constant and the standard deviation are input. The proportional constant is resampled by the standard deviation. The sampling data is resampled by using the resampled proportional constant and the standard deviation. A point estimation data accumulating step of accumulating and storing a plurality of proportional coefficient sets and parameter sets repeatedly a plurality of times; And
Determining a parameter set included in the 95% confidence interval of the accumulated sets of parameters to estimate a confidence interval and outputting result information according to the confidence interval; .

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