Deterioration prediction device, deterioration prediction method, and program

Abstract

To provide a deterioration prediction device, a deterioration prediction method, and a program that can shorten the test time for accelerated deterioration tests for a resin material.SOLUTION: The deterioration prediction device 100 according to an embodiment of the present disclosure comprises a fraction regression calculation part 101 that calculates a regression of a fraction of a degradation promotion factor and a fraction with an explanatory physical property values as explanatory variables and a fraction as an objective variable, based on the ratio in each resin material between a degradation rate of the physical property value that is an evaluation object of the degradation in the plurality of resin materials with the predetermined degradation promotion factor as the explanatory variable when the degradation promotion factor is the predetermined value and the target degradation rate when the degradation promotion factor is the predetermined prediction object value, and the explanatory physical property values in each resin material other than the physical property value to be evaluated for degradation.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a deterioration prediction device, a deterioration prediction method, and a program. [Background technology]

[0002] Resin materials are used in a wide range of fields, including as materials for various products and as paints. Particularly outdoors, resin materials are exposed to ultraviolet rays, rain, and other factors, which can cause deterioration over time, such as a loss in strength and discoloration. To predict the degree of weathering, accelerated degradation tests are sometimes conducted, in which outdoor environments are replicated using laboratory light sources and degradation-accelerating factors, such as temperature, are adjusted to forcibly accelerate the degradation of resin materials.

[0003] For example, Patent Document 1 describes a method for predicting weathering deterioration of an organic material, focusing on the ratio of a parameter relating to the amount of NH groups and / or OH groups to a parameter relating to the amount of CH groups in the organic material. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2004-286707 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the above-mentioned accelerated aging tests have the problem of requiring long test times for the following reasons: The deterioration behavior of each type of resin material differs. Therefore, in order to determine the acceleration rate of deterioration when comparing the behavior in outdoor exposure tests with that in accelerated aging tests (hereinafter also defined as the accelerated deterioration factor), it is necessary to conduct accelerated aging tests under two or more levels of conditions for each resin material in order to determine the correlation between deterioration-accelerating factors such as temperature and accelerated aging behavior. Accelerated aging tests under two or more levels of conditions are expected to take a long time.

[0006] An object of the present disclosure is to provide a deterioration prediction device, a deterioration prediction method, and a program that can shorten the testing time for resin materials. [Means for solving the problem]

[0007] A degradation prediction device according to one aspect of the present disclosure includes a ratio regression equation calculation unit that calculates a regression equation of a ratio in which a predetermined degradation-accelerating factor and an explanatory physical property value are explanatory variables and a ratio is a target variable, based on the ratio in each resin material between the degradation rate when the degradation-accelerating factor is a predetermined value and the target degradation rate, which is the degradation rate when the degradation-accelerating factor is a predetermined predicted target value, for a physical property value that is the target of degradation evaluation in a plurality of types of resin materials, with the predetermined degradation-accelerating factor as an explanatory variable, and on explanatory physical property values ​​in each resin material other than the physical property value that is the target of degradation evaluation.

[0008] A degradation prediction method performed by a degradation prediction device according to one aspect of the present disclosure calculates a regression equation of a ratio in which a predetermined degradation-accelerating factor and an explanatory physical property value are explanatory variables and the ratio is a target variable, based on the ratio in each resin material between the degradation rate when the degradation-accelerating factor is a predetermined value and the target degradation rate, which is the degradation rate when the degradation-accelerating factor is a predetermined predicted target value, and the explanatory physical property values ​​in each resin material other than the physical property value of the degradation evaluation target, with the degradation-accelerating factor and the explanatory physical property value as explanatory variables and the ratio as a target variable.

[0009] A program according to one aspect of the present disclosure causes a computer to calculate a regression equation for a ratio in which a predetermined deterioration-accelerating factor and an explanatory physical property value are explanatory variables and the ratio is a target variable, based on the ratio in each resin material between the deterioration rate when the deterioration-accelerating factor is a predetermined value and the target deterioration rate, which is the deterioration rate when the deterioration-accelerating factor is a predetermined predicted target value, and the explanatory physical property values ​​in each resin material other than the physical property value being the target of deterioration evaluation, with the deterioration-accelerating factor and the explanatory physical property value as explanatory variables and the ratio as a target variable. [Effects of the Invention]

[0010] According to the present disclosure, it is possible to provide a deterioration prediction device, a deterioration prediction method, and a program that can shorten the testing time for resin materials. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a block diagram showing an example of a deterioration prediction device according to a first embodiment; [Figure 2] 4 is a flowchart illustrating an example of a process executed by the deterioration prediction device according to the first embodiment. [Figure 3] FIG. 10 is a block diagram showing an example of a deterioration prediction device according to a second embodiment. [Figure 4A] FIG. 10 is a conceptual diagram showing an outline of the entire degradation prediction method according to the second embodiment. [Figure 4B] FIG. 10 is a conceptual diagram showing an outline of the entire degradation prediction method according to the second embodiment. [Figure 5] FIG. 2 is a block diagram illustrating an example of a hardware configuration of an apparatus according to each embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Embodiment 1 Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0013] 1 is a block diagram showing an example of a deterioration prediction device. The deterioration prediction device 100 is a device capable of predicting deterioration of any resin material, and includes a ratio regression equation calculation unit 101.

[0014] The ratio regression equation calculation unit 101 calculates a regression equation of the ratio, in which a predetermined deterioration accelerating factor is used as an explanatory variable, for two types of deterioration rates of physical property values ​​that are the subject of deterioration evaluation in multiple types of sample resin materials, and in which the deterioration accelerating factor and the explanatory physical property values ​​are used as explanatory variables and the ratio is used as a response variable, based on the explanatory physical property values ​​of each sample resin material other than the physical property value that is the subject of deterioration evaluation. The ratio of the two types of deterioration rates refers to the ratio, in each sample resin material, between the deterioration rate when the deterioration accelerating factor is a predetermined value (e.g., a set value used in an accelerated deterioration test) and the target deterioration rate, which is the deterioration rate when the deterioration accelerating factor is a predetermined predicted target value (e.g., a value in the environment in which the resin material is used).

[0015] The predetermined degradation acceleration factor can be any parameter of an environmental condition in an accelerated degradation test, such as temperature, water vapor pressure, or ultraviolet irradiance. The physical property value of the degradation evaluation target can be any evaluation target in an accelerated degradation test, such as Charpy impact strength, gloss, or tensile strength. Furthermore, the explanatory physical property value in the regression equation for the ratio can be any one or more physical property values ​​of the resin material, such as at least one of density, water absorption, mold shrinkage, deflection temperature under load, linear expansion coefficient, dielectric constant, and melt flow rate.

[0016] FIG. 2 is a flowchart showing an example of a typical process of the deterioration prediction device 100, and the process of the deterioration prediction device 100 will be explained using this flowchart.

[0017] The ratio regression equation calculation unit 101 acquires ratios of two types of deterioration rates of physical property values ​​that are the subject of deterioration evaluation for multiple types of sample resin materials, with a predetermined deterioration acceleration factor as an explanatory variable, and explanatory physical property values ​​for each sample resin material other than the physical property value that is the subject of deterioration evaluation. The ratios may be calculated by other components within the deterioration prediction device 100, or the deterioration prediction device 100 may acquire them from another device. Furthermore, data on the explanatory physical property values ​​for each sample resin material may be pre-stored within the deterioration prediction device 100, or the deterioration prediction device 100 may acquire them from another device. Based on the ratios and the explanatory physical property values, the ratio regression equation calculation unit 101 calculates a regression equation of the ratios, with the deterioration acceleration factor and the explanatory physical property values ​​as explanatory variables and the ratio as a response variable (step S11).

[0018] In this way, the deterioration prediction device 100 calculates a regression equation of the ratio for each sample resin material. Therefore, by applying the regression equation to data on a resin material other than the sample resin material, it is possible to calculate the physical property values ​​of the deterioration evaluation target after a predetermined time has elapsed for that other resin material while reducing the number of steps required for the accelerated deterioration test. This makes it possible to shorten the total test time required to calculate the physical property values ​​of the deterioration evaluation target.

[0019] Embodiment 2 A second embodiment of the present invention will be described below with reference to the drawings. In the second embodiment, a specific example of the deterioration prediction device 100 described in the first embodiment will be disclosed.

[0020] 3 is a block diagram showing an example of a deterioration prediction device according to embodiment 2. The deterioration prediction device 200 is a device capable of predicting the degree of deterioration of a certain resin material, and includes a first test environment deterioration rate calculation unit 201, a deterioration rate regression equation calculation unit 202, an actual environment deterioration rate calculation unit 203, a first accelerated deterioration magnification calculation unit 204, an accelerated deterioration magnification regression equation calculation unit 205, a second accelerated deterioration magnification calculation unit 206, a second test environment deterioration rate calculation unit 207, and a physical property value prediction unit 208.

[0021] 4A and 4B are conceptual diagrams for explaining the overall flow of the degradation prediction method and the processes executed by each unit of degradation prediction device 200 within the method. Fig. 4A shows tests on a sample resin material and calculation processes using the data, while Fig. 4B shows tests on a resin material to be evaluated and processes for predicting weather resistance degradation using the data. The following explanation will be made with reference to Figs. 4A and 4B.

[0022] First, the user (tester) performs an accelerated degradation test T on weather resistance under JIS K 7350-2 conditions under multiple temperature conditions 1, 2, etc. (multiple temperature levels) on multiple types of resin materials to be samples. This allows the Charpy impact strength Et under JIS K 7111-1 conditions to be measured for multiple resin materials at each sampling period. The user inputs data on the measured Charpy impact strength Et, the test time, and the test temperature conditions into the degradation prediction device 200.

[0023] In this example, Charpy impact strength was selected as the physical property to evaluate weathering degradation. Compared to other mechanical properties such as tensile strength and bending strength, Charpy impact strength exhibits a significant change due to weathering degradation, making it a particularly suitable physical property to measure.

[0024] In particular, the temperature conditions for the accelerated aging test are preferably set to a temperature equal to or lower than the glass transition temperature of the resin material being tested. This is because, when the resin exceeds its glass transition temperature, unique physical properties may be exhibited, potentially affecting the measured values. Furthermore, it is preferable that the temperature levels under the multiple temperature conditions differ by 5°C or more.

[0025] Furthermore, considering the variation in degradation behavior between the early and later stages of an accelerated degradation test, it is preferable to set the test time to 500 hours or more and to sample measurements every 100 hours. Furthermore, to prevent bias in measurement results due to the influence of physical properties of the type of resin, it is particularly preferable to conduct an accelerated degradation test on eight or more types of resin.

[0026] However, the details described above are merely examples, and other variations are also possible. For example, instead of Charpy impact strength, other types of physical properties can also be measured and calculated in the same manner as in the present disclosure. In addition, any parameters can be used for the temperature conditions, test time, and sampling period in the accelerated aging test. Furthermore, any standard can be used for the measurement.

[0027] The first test environment degradation rate calculation unit 201 acquires data on the measured value of the Charpy impact strength E, which is a variable of time t, and the test time obtained by this accelerated degradation test T. Then, for each resin, a degradation rate v1 in the test environment (test temperature) for the Charpy impact strength E is calculated (step S21). The degradation rate v1 is defined by the following mathematical formula (1). (Number 1) E = v1* log(t) + E0 (1) Note that E0 is a constant that can be calculated.

[0028] The degradation rate regression equation calculation unit 202 acquires data on the test temperature conditions in the accelerated degradation test T and the degradation rate v1 for each resin calculated by the first test environment degradation rate calculation unit 201. Then, using these data, a regression equation f1 of the degradation rate under the temperature condition D0 in the test environment is calculated for each resin (step S22). The regression equation f1 is defined by the following equation (2). (Number 2) v1= f1(D0) (2) The degradation rate regression equation calculation unit 202 can calculate the regression equation shown in (2) using a known method such as the least squares method. In other words, the degradation rate regression equation calculation unit 202 calculates the correlation between temperature, which is a degradation acceleration factor, and the degradation rate.

[0029] The actual environment deterioration rate calculation unit 203 acquires the regression equation f1 for each resin calculated by the deterioration rate regression equation calculation unit 202 and the actual environment temperature D1 set by the user and input to the deterioration prediction device 200. The actual environment temperature D1 is the temperature of the environment in which the resin material to be tested is used, and for example, if the resin material is used at room temperature, room temperature is set. The actual environment deterioration rate calculation unit 203 calculates the deterioration rate v2 (target deterioration rate) at the actual environment temperature D1 by substituting the actual environment temperature D1 into the regression equation f1 as the temperature (step S23). The deterioration rate v2 is defined by the following mathematical formula (3). (Number 3) v2= f1(D1) (3)

[0030] The first accelerated deterioration magnification calculation unit 204 acquires, for each resin, the regression equation f1 of the deterioration rate calculated by the deterioration rate regression equation calculation unit 202 and the deterioration rate v2 at the actual environmental temperature D1 calculated by the actual environmental deterioration rate calculation unit 203. Then, using these data, it calculates an accelerated deterioration magnification C for each resin (step S24). This accelerated deterioration magnification C means the ratio between the deterioration rate v1 under temperature condition D0 and the deterioration rate v2 at the actual environmental temperature D1, and is defined by the following mathematical formula (4). (Number 4) C = f1(D0) / v2(= f1(D0) / f1(D1)) ···(4)

[0031] The accelerated degradation magnification factor regression equation calculation unit 205 corresponds to the ratio regression equation calculation unit 101 in the first embodiment, and acquires, for each resin, the accelerated degradation magnification factor C calculated by the first accelerated degradation magnification factor calculation unit 204 and the physical property value data (basic physical property data 1) of the sample resin material. This physical property value data is, for example, data of basic physical property values ​​obtained from a material physical property table published by the material manufacturer or a physical property evaluation test conducted by the user for the resin material that is the subject of the accelerated degradation test T.

[0032] The basic physical property values ​​refer to, for example, the physical property values ​​listed in a list of physical property values ​​of materials publicly released by material manufacturers, and examples include at least one of density (ISO 1183), water absorption (ISO 62), mold shrinkage, deflection temperature under load (1.8 MPa) (ISO 178), coefficient of linear expansion (ISO 11359-2), relative dielectric constant (IEC 60250), melt flow rate (ISO 1133-1), etc. Furthermore, if there are other physical properties that are easily measurable and increase the coefficient of determination of the regression equation for the accelerated degradation factor calculated by the accelerated degradation factor regression equation calculation unit 205, it is desirable to acquire these as basic physical property values ​​as well. Examples of such physical property values ​​include water absorption at high temperatures, and the ratio between the strength at room temperature (F0) and the strength at high temperatures (F h ) change rate (F h / F0) are possible. However, the parameters shown above are merely examples, and other parameters may be used as basic physical property values. Furthermore, the international standards applied to the test are not limited to the above examples.

[0033] The accelerated deterioration magnification regression equation calculation unit 205 calculates a regression equation f2 of the accelerated deterioration magnification C, which can be commonly applied to resin materials, using the acquired accelerated deterioration magnification C and basic physical property value a for each resin (step S25). As described above, the accelerated deterioration magnification defines the acceleration rate of deterioration when comparing the behavior in the outdoor exposure test with that in the accelerated deterioration test. In this example, a1 to a are used as the physical property value a. n Here, the regression equation f2 is defined by the following equation (5), which uses the temperature condition D0 (set value) and the physical property value a as explanatory variables. (Number 5) C = f2(D0, a1, a2,..., a n ) ···(5) The accelerated deterioration magnification factor regression equation calculation unit 205 can calculate the regression equation shown in (5) using a known method such as the least squares method. In other words, the accelerated deterioration magnification factor regression equation calculation unit 205 calculates the correlation between the basic physical property value and the accelerated deterioration magnification factor C.

[0034] It is preferable that the coefficient of determination of regression equation f2 is 0.8 or more so that it has a predetermined accuracy. When the accelerated deterioration magnification regression equation calculation unit 205 calculates regression equation f2, it determines whether the coefficient of determination is equal to or greater than a predetermined threshold, and if it is equal to or greater than the predetermined threshold, the deterioration prediction device 200 executes the process shown in Fig. 4B. On the other hand, if it is less than the predetermined threshold, the accelerated deterioration magnification regression equation calculation unit 205 operates to acquire new data of basic physical property values ​​and recalculate regression equation f2 so that regression equation f2 has a predetermined accuracy.

[0035] For example, assume that data on basic physical properties of the sample resin material stored in the memory unit of the degradation prediction device 200 is stored, and that only a portion of the data on basic physical properties of the sample resin material stored in the memory unit was used in the first calculation of regression equation f2. Furthermore, assume that the coefficient of determination of regression equation f2 is less than a predetermined threshold value of 0.8. In this case, in the second calculation of regression equation f2, the accelerated degradation magnification regression equation calculation unit 205 can calculate regression equation f2 using not only the accelerated degradation magnification C for each resin but also the basic physical property data stored in the memory unit that was not used in the first calculation, in addition to the basic physical property data used in the first calculation. If the degradation prediction device 200 is connected to a network, the accelerated degradation magnification regression equation calculation unit 205 can obtain the basic physical property data that was not used in the first calculation via the network and use that data in the second calculation of regression equation f2.

[0036] As yet another example, if the coefficient of determination is less than 0.8, the accelerated degradation magnification regression equation calculation unit 205 may control a notification unit (e.g., a display unit or an audio output unit) of the degradation prediction device 200 to request the user to input further data on the basic physical property values ​​of the sample resin material because the accuracy of regression equation f2 is insufficient. When the user inputs new data on the basic physical property values ​​into the degradation prediction device 200, the accelerated degradation magnification regression equation calculation unit 205 performs a second calculation of regression equation f2 using not only the data used in the first calculation but also the newly input data on the basic physical property values. The above-described process can be repeated until the coefficient of determination of regression equation f2 calculated by the accelerated degradation magnification regression equation calculation unit 205 becomes 0.8 or greater.

[0037] The deterioration prediction device 200 calculates a regression equation for the accelerated deterioration magnification factor based on the basic physical property values ​​of the resin material using the method shown in Fig. 4A. Then, using the following method shown in Fig. 4B, it calculates the deterioration progression at the actual ambient temperature of the resin material to be evaluated. Here, after the accelerated deterioration magnification regression equation calculation unit 205 has calculated the regression equation f2 for the accelerated deterioration magnification factor once, if an accelerated deterioration test is to be performed with another resin material, it is possible to calculate the deterioration progression at the actual ambient temperature simply by performing a simplified accelerated deterioration test as described below.

[0038] The second accelerated deterioration magnification calculation unit 206 acquires the regression equation f2 of the accelerated deterioration magnification calculated by the accelerated deterioration magnification regression equation calculation unit 205 and the physical property value data (basic physical property data 2) of the resin material to be evaluated. This physical property value data is data of the basic physical property values ​​of the resin material to be evaluated. The basic physical property values ​​are as described above. In this example, the physical property value a * As A1 * ~a n * There are n types of

[0039] Furthermore, the second accelerated deterioration magnification calculation unit 206 calculates the temperature D * This test temperature D * The test temperature D is preferably about 5°C lower than the glass transition temperature of the resin material to be evaluated. * The higher the test temperature D, the shorter the test time. * If the test temperature D is set to a temperature higher than the glass transition temperature, there is a high possibility that the deterioration behavior of the resin material in the accelerated deterioration test will differ from the deterioration behavior in the outdoor exposure test. *is determined to be lower than the glass transition temperature of the resin material to be tested, taking into consideration the trade-off between the accelerated degradation rate calculated by the second accelerated degradation rate calculation unit 206 and the reproducibility of degradation behavior. The user also determines the test time for the accelerated degradation test of the resin material to be evaluated.

[0040] The second accelerated deterioration magnification calculation unit 206 calculates the basic physical property value a * , and test temperature D * By substituting the above, the accelerated deterioration factor C of the evaluation target at the test temperature can be calculated. * (Step S31) Here, the accelerated deterioration magnification factor C * is calculated as follows: (Number 6) C * = f2(D * , a1 * , a2 * , , a n * ) ···(6) The second accelerated deterioration magnification calculation unit 206 calculates the accelerated deterioration magnification C * Based on the result of the judgment, the test temperature D * For example, the calculated accelerated deterioration factor C * If the test temperature D of the accelerated aging test is less than the predetermined threshold, the time for which the accelerated aging test is shortened will be reduced. * may be reset to a temperature lower than the glass transition temperature of the resin material to be tested, but higher than the previously set test temperature. Then, the second accelerated deterioration factor calculation unit 206 calculates the accelerated deterioration factor C in (6) using the reset test temperature. * Calculate the accelerated deterioration factor C * is set to be equal to or greater than a predetermined threshold value.

[0041] In addition, the user must determine the test temperature D * Based on the test time, accelerated deterioration test T * Accelerated Deterioration Test T* So, test temperature D * A weather resistance test is conducted under JIS K 7350-2 conditions under the above temperature conditions (temperature level), and the Charpy impact strength Et under JIS K 7111-1 conditions is measured for a plurality of resin materials at each sampling cycle.

[0042] At this time, accelerated deterioration test T * Unlike the temperature conditions in the accelerated aging test T, the temperature conditions in the test temperature D do not need to be at multiple temperature levels. * Accelerated deterioration test T * Alternatively, the accelerated aging test T may be performed at a number of temperature levels less than the number of temperature levels of the accelerated aging test T. * In this way, the accelerated deterioration test T * Even if the number of steps is reduced compared to the accelerated aging test T, the Charpy impact strength of the resin material to be evaluated can be calculated as follows. As with the accelerated aging test T, other variations not exemplified here can also be applied.

[0043] Accelerated aging test T other than temperature conditions and test time * The various conditions in the test are set to be the same as those in the accelerated aging test T. Data relating to the measured Charpy impact strength Et, the test time, and the test temperature conditions are input into the degradation prediction device 200 by the user.

[0044] The second test environment deterioration rate calculation unit 207 calculates the deterioration rate of the accelerated deterioration test T * The Charpy impact strength E as a function of time t is given by * The measured values ​​of Charpy impact strength E and test time data are obtained for each resin. * Degradation rate v in the test environment (test temperature) * (Step S32). * is defined by the following equation (7). (Number 7) E * = v ** log(t) + E * 0···(7) In addition, E * 0 is a constant that is calculated.

[0045] The physical property value prediction unit 208 (physical property calculation unit) calculates the accelerated deterioration factor C of the evaluation target at the test temperature calculated by the second accelerated deterioration factor calculation unit 206. * and the deterioration rate v calculated by the second test environment deterioration rate calculation unit 207. * Using the above, the Charpy impact strength E of the evaluation target is calculated at the actual environmental temperature (prediction target value), with the elapsed time t from the initial state as an explanatory variable. * t where Charpy impact strength E * t is calculated (predicted) as shown in the following formula (8). (Number 8) E * t = (v * / C * )* log(t) + E * 0···(8)

[0046] In addition, the deterioration prediction device 200 can also predict the Charpy impact strength E of the sample resin material, where the elapsed time t from the initial state is used as an explanatory variable, by using the regression equation f2(5) of the calculated accelerated deterioration magnification factor C.

[0047] In the degradation prediction device 200 described above, the accelerated degradation magnification regression equation calculation unit 205 can calculate a regression equation f2 of the accelerated degradation magnification that can be commonly applied to resin materials, based on the accelerated degradation magnification C, which is the ratio between the degradation rate v1 under temperature condition D0 and the degradation rate v2 at the actual environmental temperature D1, and the results of the accelerated degradation test performed on the sample resin material (physical property values ​​for explanation). Therefore, for a new resin material to be evaluated, even if the number of steps in the accelerated degradation test is reduced (for example, even if the test is performed only at a single temperature level), the Charpy impact strength E at the actual environmental temperature can be calculated by using the regression equation f2. * t can be derived.

[0048] Charpy impact strength E at actual ambient temperature * t By deriving the above, the user can predict when the resin material to be evaluated will reach a predetermined deterioration level in terms of weather resistance. In addition, the present disclosure has the advantage that it can evaluate the deterioration level of any multiple types of resin materials, not just a specific resin material.

[0049] Resin materials deteriorate due to various factors, including temperature, humidity, chemicals, and ultraviolet light. Therefore, much research has been conducted to rapidly evaluate the deterioration of resin materials. However, the mechanism of weathering deterioration, a phenomenon that occurs under a combination of factors such as temperature, humidity, ultraviolet light, and moisture, remains largely unknown. Furthermore, even if the correlation between the factors and deterioration of a specific type of resin material has been clarified, the correlation between these factors has not been clarified for other types of resin materials. Therefore, it has been difficult to develop a method for predicting deterioration that works for multiple types of resin materials.

[0050] However, as described above, the degradation prediction method according to the present disclosure is applicable to multiple types of resin materials. This makes it possible to reduce the time required for accelerated degradation tests conducted to measure the accelerated degradation ratio. As this test requires approximately 500 hours per level, applying the degradation prediction method according to the present disclosure makes it possible to significantly reduce the amount of work required.

[0051] Furthermore, the physical property value prediction unit 208 uses the basic physical property value a * and test temperature D * Accelerated deterioration factor C * , and test temperature D * The degradation rate of the resin material being evaluated in * Based on this, the Charpy impact strength of the resin material to be evaluated at the actual environmental temperature can be calculated. This allows the degradation prediction device 200 to directly predict the deterioration of the physical property values ​​of a new resin material.

[0052] Furthermore, the deterioration rate regression equation calculation unit 202 calculates a regression equation f1 of the deterioration rate for each resin material based on a plurality of temperature conditions and the deterioration rates v1 for a plurality of types of resin materials under those temperature conditions, and the actual environment deterioration rate calculation unit 203 can calculate the deterioration rate v2 at the actual environment temperature D1 by substituting the actual environment temperature D1 as the temperature into the regression equation f1. This allows the deterioration prediction device 200 to acquire information necessary for calculating the regression equation f2 of the accelerated deterioration magnification within itself.

[0053] The present invention is not limited to the above-described embodiment, and can be modified as appropriate within the scope of the invention.

[0054] For example, in the second embodiment, Charpy impact strength is selected as the physical property to be evaluated for weathering degradation. In this case, it is desirable that the resin material to be evaluated is a resin with a certain level of strength, such as that used for exterior materials. However, as described in the first embodiment, the physical property to be evaluated is not limited to Charpy impact strength, and other physical properties such as glossiness and tensile strength may also be used. In this case, even resin materials with low strength that are difficult to evaluate using Charpy impact strength can be evaluated. This makes it possible to reduce the amount of work required to predict representative physical properties to be evaluated for resin materials.

[0055] In the second embodiment, the explanatory physical property values ​​obtained as a result of the accelerated degradation test may include at least one of density, water absorption, mold shrinkage, deflection temperature under load, linear expansion coefficient, relative dielectric constant, and melt flow rate. Since such physical property values ​​are commonly used in resin materials, the degradation prediction device 200 can predict the physical property values ​​of the resin material without using any special type of data.

[0056] Furthermore, the accelerated deterioration factor is not limited to temperature, but may be other factors related to weather resistance deterioration, such as water vapor pressure or ultraviolet irradiance, or other factors related to deterioration by chemicals, etc. In this way, the prediction of physical property values ​​of resin materials according to the present disclosure can be applied to a wide range of applications.

[0057] In the above-described embodiments, this disclosure has been described as a hardware configuration, but this disclosure is not limited to this. This disclosure can also be realized by having a processor in a computer constituting the deterioration prediction device execute a computer program to perform the processing (steps) of the deterioration prediction device described in each of the above-described embodiments.

[0058] Fig. 5 is a block diagram showing an example of the hardware configuration of an information processing device (computer) on which the processes of the above-described embodiments are executed. Referring to Fig. 5, this information processing device 90 includes a signal processing circuit 91, a processor 92, and a memory 93. The information processing device 90 constitutes the deterioration prediction device 100 or the deterioration prediction device 200.

[0059] The signal processing circuit 91 is a circuit for processing signals in accordance with the control of the processor 92. The signal processing circuit 91 may include a communication circuit for receiving signals from a transmitting device.

[0060] The processor 92 reads and executes software (computer programs) from the memory 93 to perform the processing of the device described in the above embodiment. The number of processors 92 is not limited to one, and multiple processors 92 may be provided. As an example of the processor 92, one of a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an FPGA (Field-Programmable Gate Array), a DSP (Demand-Side Platform), and an ASIC (Application Specific Integrated Circuit) may be used, or multiple processors may be used in parallel.

[0061] The memory 93 is configured with a volatile memory, a nonvolatile memory, or a combination thereof. The memory 93 is not limited to one, and multiple memories may be provided. The volatile memory may be, for example, a RAM (Random Access Memory) such as a DRAM (Dynamic Random Access Memory) or an SRAM (Static Random Access Memory). The nonvolatile memory may be, for example, a ROM (Random Only Memory) such as a PROM (Programmable Random Only Memory) or an EPROM (Erasable Programmable Read Only Memory), a flash memory, or an SSD (Solid State Drive).

[0062] The memory 93 is used to store one or more instructions. Here, the one or more instructions are stored as a group of software modules in the memory 93. The processor 92 can perform the processes described in the above embodiments by reading and executing the group of software modules from the memory 93.

[0063] The memory 93 may include memory built into the processor 92 in addition to memory provided outside the processor 92. The memory 93 may also include storage located away from the processors constituting the processor 92. In this case, the processor 92 can access the memory 93 via an I / O (Input / Output) interface.

[0064] As described above, one or more processors included in each device in the above-described embodiments execute one or more programs including instructions for causing a computer to execute the algorithms described using the drawings. This processing enables the signal processing method described in each embodiment to be realized.

[0065] The program includes instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more functions described in the embodiments. The program may be stored in a non-transitory computer-readable medium or a tangible storage medium. By way of example and not limitation, computer-readable media or tangible storage media include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technologies, CD-ROM, digital versatile disk (DVD), Blu-ray® disk or other optical disk storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device. The program may also be transmitted on a transitory computer-readable medium or communication medium. By way of example and not limitation, transitory computer-readable media or communication media include electrical, optical, acoustic, or other forms of propagated signals.

[0066] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above. Various modifications that can be understood by a person skilled in the art can be made to the configuration and details of the present disclosure within the scope of the disclosure. [Explanation of symbols]

[0067] 100 Deterioration prediction device 101 Ratio regression equation calculation section 200 Deterioration Prediction Device 201 First test environment deterioration rate calculation unit 202 Deterioration rate regression equation calculation unit 203 Actual environment deterioration rate calculation unit 204 First accelerated deterioration magnification calculation unit 205 Accelerated deterioration magnification regression equation calculation unit 206 Second accelerated deterioration magnification calculation unit 207 Second test environment degradation rate calculation unit 208 Physical property value prediction unit

Claims

1. a ratio regression equation calculation unit that calculates a regression equation of a ratio in which a predetermined deterioration accelerating factor and the explanatory physical property value are explanatory variables and the ratio is a target variable, based on a ratio in each of the resin materials between the deterioration rate when the deterioration accelerating factor is a predetermined value and a target deterioration rate, which is the deterioration rate when the deterioration accelerating factor is a predetermined predicted target value, for a physical property value that is a deterioration evaluation target in a plurality of types of resin materials, and based on explanatory physical property values ​​in each of the resin materials other than the physical property value that is the deterioration evaluation target, Deterioration prediction device.

2. The method further includes a regression equation in which a set value of the degradation accelerating factor in an accelerated degradation test for the resin material to be evaluated and the explanatory physical property value of the resin material to be evaluated are substituted for the explanatory variables of the regression equation of the ratio, and a physical property value calculation unit that calculates the physical property value of the degradation evaluation target for the resin material to be evaluated at the predicted target value of the degradation accelerating factor, using elapsed time as an explanatory variable, based on the degradation rate of the resin material to be evaluated at the set value of the degradation accelerating factor. The deterioration prediction device according to claim 1 .

3. a degradation rate regression equation calculation unit that calculates a regression equation for each of the resin materials, with the degradation acceleration factors as explanatory variables and the degradation rate as a response variable, based on conditions for the plurality of degradation acceleration factors and degradation rates for the plurality of types of resin materials under the conditions for the plurality of degradation acceleration factors; a target deterioration rate calculation unit that calculates the target deterioration rate by substituting the predicted target value into the deterioration acceleration factor of the regression equation, The deterioration prediction device according to claim 1 or 2.

4. The physical property value of the degradation evaluation target includes at least one of Charpy impact strength, gloss, or tensile strength. The deterioration prediction device according to any one of claims 1 to 3.

5. The descriptive physical property values ​​include at least one of density, water absorption, mold shrinkage, deflection temperature under load, coefficient of linear expansion, dielectric constant, or melt flow rate; The deterioration prediction device according to any one of claims 1 to 4.

6. The deterioration-accelerating factor is any one of temperature, water vapor pressure, and ultraviolet ray irradiance. The deterioration prediction device according to any one of claims 1 to 5.

7. Regarding the deterioration rates of physical properties that are the object of deterioration evaluation in a plurality of types of resin materials, with a predetermined deterioration accelerating factor as an explanatory variable, a regression equation of the ratio is calculated based on the ratio in each of the resin materials between the deterioration rate when the deterioration accelerating factor is a predetermined value and the target deterioration rate, which is the deterioration rate when the deterioration accelerating factor is a predetermined predicted target value, and on explanatory physical property values ​​in each of the resin materials other than the physical property value that is the object of deterioration evaluation, with the deterioration accelerating factor and the explanatory physical property value as explanatory variables and the ratio as a target variable. A deterioration prediction method executed by a deterioration prediction device.

8. Regarding the deterioration rates of physical properties that are the object of deterioration evaluation in a plurality of types of resin materials, with a predetermined deterioration accelerating factor as an explanatory variable, a regression equation of the ratio is calculated based on the ratio in each of the resin materials between the deterioration rate when the deterioration accelerating factor is a predetermined value and the target deterioration rate, which is the deterioration rate when the deterioration accelerating factor is a predetermined predicted target value, and on explanatory physical property values ​​in each of the resin materials other than the physical property value that is the object of deterioration evaluation, with the deterioration accelerating factor and the explanatory physical property value as explanatory variables and the ratio as a target variable. A program that makes a computer do something.

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