Material life forecasting method

Abstract

A method for predicting the lifespan of a material using material parameters and the following mathematical formula is introduced.

Description

Technology Field

[0001] The present invention relates to a method for predicting the lifespan of a material used as a vehicle part, etc., due to complex aging, and specifically, to a method for predicting the lifespan of a material using an equation that modifies the Weibull distribution (function) equation. Background Technology

[0003] The majority of vehicle parts are made using plastic materials. As these parts and materials age over time, quality issues arise when their performance falls below standard levels. To minimize quality problems, accurately predicting the lifespan of parts and materials under actual usage conditions is crucial, and many models have been proposed to predict material lifespan.

[0004] There are two main types of existing plastic aging models, as follows.

[0005] ① Time-Temperature Superposition (TTS)

[0006] TTS is a method that predicts lifespan by shifting the plastic aging property curves by temperature to form a single curve (since the material's lifespan can be considered exhausted when the property retention rate falls below a reference value, predicting properties is equivalent to predicting lifespan).

[0007] The criterion for shifting the plastic aging property curve according to temperature is called the Shift Factor, but there is a disadvantage that there is no standardized method to calculate the Shift Factor, so the predicted lifespan varies among users.

[0008] In addition, TTS assumes material aging caused by a single factor, so the usable temperature range is very narrow, and since it draws a single curve by shifting multiple curves, it is difficult to reflect the dispersion and deviation of material properties, so it cannot explain the concept of reliability.

[0009] ② Arrhenius Model (Model A)

[0010] There are linear Arrhenius and non-linear Arrhenius methods for predicting the lifespan of plastic materials by extrapolating the aging over time according to the Arrhenius formula.

[0011] The linear Arrhenius model is generally used and is relatively accurate and convenient for plastic thermal aging; however, its accuracy decreases for olefinic plastics, and it is difficult to apply to complex aging processes other than thermal aging.

[0012] Non-linear Arrhenius offers high accuracy and applicability to complex aging by deriving formulas through experiments for each aging mechanism and combining them; however, it is difficult to use in practice because it is inefficient due to the need to derive formulas through numerous experiments and the resulting complex model.

[0013] Referring to Figures 1a and 1b, it can be seen that while the prediction of thermal aging of nylon using Model A is relatively accurate, the prediction of complex aging of nylon differs significantly from the actual values.

[0015] Existing models have the problem of being unable to predict lifespan based on complex aging mechanisms as described above. Furthermore, accurate methods for predicting the physical properties and lifespan of plastic materials under various vehicle environmental conditions have not yet been established, and methods for predicting the lifespan of components made of these materials have not been proposed.

[0017] The matters described above as background technology are intended only to enhance understanding of the background of the present invention and should not be construed as an acknowledgment that they constitute prior art already known to those skilled in the art. Prior art literature

[0019] KR 10-2001583 B The problem to be solved

[0020] The present invention is proposed to solve these problems and aims to provide a method for accurately predicting the lifespan of plastic materials and parts under actual usage conditions (complex thermal and chemical environments). means of solving the problem

[0022] A material life prediction method according to the present invention for achieving the above objective

[0023] An aging step in which a material specimen is immersed in an immersion solution and heated to age it;

[0024] It includes a material parameter measurement step of measuring the material property retention rate relative to the initial material properties from a material specimen that has completed aging and defining it as a material parameter; and a derivation step of deriving the aging time at which the material property retention rate becomes less than or equal to a reference value by substituting the material parameter into the following mathematical formula 1.

[0025] [Mathematical Formula 1] is as follows.

[0026]

[0027] (y is the property retention rate, x is the aging time, θ is the scale parameter, β is the shape parameter, and γ is the material parameter.)

[0028] The physical property measured in the material parameter measurement stage may be tensile strength.

[0029] The immersion solution used in the aging stage may be an antifreeze containing either propylene glycol or ethylene glycol.

[0030] The aging step may be a step of aging the material specimen by immersing it in an immersion solution for 24 to 72 hours and heating the immersion solution to 100°C to 135°C. Effects of the invention

[0032] According to the material life prediction method of the present invention, it is possible to accurately predict not only the lifespan of materials used as parts for vehicles, but also the lifespan of actual parts under complex aging environments, thereby aiding in quality control of vehicles and reducing unnecessary costs. Brief explanation of the drawing

[0034] Figure 1a is a graph of material life prediction in a thermal aging environment according to the Arrhenius prediction model. Figure 1b is a graph of material life prediction in a complex aging environment according to the Arrhenius prediction model. FIG. 2 is a flowchart illustrating an embodiment of the material life prediction method of the present invention. Specific details for implementing the invention

[0035] Hereinafter, specific details for solving the aforementioned objectives and problems will be explained in detail with reference to the attached drawings. Meanwhile, regarding the understanding of the present invention, if a detailed description of known technology in the same field does not help in understanding the core content of the invention, such description will be omitted. Furthermore, the technical concept of the present invention is not limited thereto and can be modified and implemented in various ways by those skilled in the art.

[0037] The feature of the present invention is that by incorporating a previously non-existent concept called material parameters into the Weibull distribution (function), the lifespan of the material can be accurately predicted in complex aging environments, and beyond the material, the lifespan of the component composed of the material can also be accurately predicted.

[0038] The material life prediction method according to the present invention comprises: an aging step (S100) in which a material specimen is immersed in an immersion solution and heated to age it; a material parameter measurement step (S200) in which the material property retention rate relative to the initial physical property is measured from the material specimen after aging is completed and defined as a material parameter; and a derivation step (S300) in which the material parameter is substituted into the following mathematical formula 1 to derive the aging time at which the material property retention rate becomes less than or equal to a reference value.

[0039] [Mathematical Formula 1] is as follows.

[0040]

[0041] (y is the property retention rate, x is the aging time, θ is the scale parameter, β is the shape parameter, and γ is the material parameter.)

[0042] Material parameters are constant values ​​measured through experiments on the material, having values ​​of 1 or less and 0 or greater. Material parameters are values ​​representing the retention rate of the material's major properties after aging compared to the initial state, after the material is immersed in an immersion solution under high temperature conditions (100°C to 135°C) for a short period (24 to 72 hours).

[0043] That is, the material parameter can be measured through an aging step (S100) in which a material specimen is immersed in an immersion solution and heated to age it; and a material parameter measurement step (S200) in which the material property retention rate relative to the initial physical property is measured from the material specimen after aging is completed and defined as the material parameter.

[0044] The immersion solution used for measuring material parameters may be an antifreeze containing either propylene glycol or ethylene glycol, but is not limited thereto. When selecting the immersion solution, it is desirable to select a solution that is present in the surrounding environment where the material is mainly placed.

[0045] In this specification, assuming that the materials are primarily placed in an antifreeze environment, antifreeze was selected as the immersion solution for the experiment.

[0046] Meanwhile, when measuring material parameters, it is desirable for the temperature to be 100°C or higher, as a lower temperature than 100°C slows down the chemical reaction rate and increases the measurement time of the material parameters. It is also desirable for the temperature to be 135°C or lower, as a higher temperature than 135°C may exceed the boiling point of the antifreeze solution, making it unsuitable for actual use conditions, and the antifreeze solution may vaporize, preventing accurate measurement of the material parameters.

[0047] It is not limited to the above temperatures, and the temperature conditions may vary depending on the immersion solution used.

[0048] In addition, when measuring material parameters, if the time is less than 24 hours, the immersion solution does not sufficiently penetrate into the material specimen, resulting in a significant measurement error of the material parameters; if it is more than 72 hours, the aging of the material specimen begins in earnest, so the aging properties are reflected rather than the intrinsic value of the material. Therefore, it is preferable to have a time of 24 to 72 hours, and 48 hours is the most desirable.

[0049] In addition, the concentration of the immersion solution should preferably be an aqueous solution of 25 to 75% by volume. This is because at concentrations greater than 25%, hydrolysis reactions by water are dominant, resulting in slower chemical aging; conversely, at concentrations less than 75%, the high concentration of chemicals in the immersion solution hinders uniform dispersion and chemical equilibrium within the specimen, which may lead to inaccurate measured material parameters. A immersion solution concentration of 50% is the most desirable.

[0050] Furthermore, when measuring material parameters, the measured property may be tensile strength. However, this is not limited to this; since the properties considered important differ depending on the material and the purpose of the component in which the material is utilized, material parameters can be tested by varying the properties even for the same material.

[0051] In this specification, material parameters were determined by measuring the retention rate of tensile strength, which is an important physical property of the materials.

[0053] After the material parameter measurement step (S200), the measured material parameters can be substituted into Equation 1 to model the material's property retention rate over time.

[0054] Mathematical formula 1 is as follows.

[0055]

[0056] (y is the property retention rate, x is the aging time, θ is the scale parameter, β is the shape parameter, and γ is the material parameter.)

[0057] Here, the scale parameter and shape parameter are values ​​that can be derived through linear regression, such as the least squares method or the maximum likelihood method, using actual experimental life data and predicted material life data. Since the actual experimental life data consists of values ​​already obtained by the material manufacturer or values ​​that can be acquired through experiments, and the predicted material life data can be derived through the TTS or A-model, which are material life prediction equations, the scale parameter and shape parameter can be calculated as described above.

[0059] Meanwhile, in the derivation step (S300), the material parameter, scale parameter, and shape parameter are given as constants, and thus the material property retention rate (y) according to the aging time (x) can be calculated, or the aging time (x) according to the material property retention rate (y) can be derived.

[0060] In particular, since the required property retention rate for each material varies depending on the material's purpose and usage environment, a reference value can be established to derive the aging time at which the retention rate falls below the reference value, thereby enabling the prediction of the material's lifespan.

[0062] Below, we introduce some experimental results that demonstrate that the material life prediction method according to the present invention matches actual experimental results.

[0063] The experiment was conducted by immersing the material in antifreeze and then measuring the property retention rate after heating. In other words, aging proceeded under thermal, chemical, and combined aging conditions.

[0065] Experimental Example 1: PA66-GF30 Complex Aging

[0067] 1) Aging tests were conducted at different temperatures (120℃, 130℃, 140℃) and times (48, 120, 216, 360, 500, 1000 hr), and tensile strength was measured as a physical property. The results were graphed as follows (P represents the physical property, and P0 represents the initial physical property value).

[0068] PA66-GF30 Complex Aging Experiment

[0069] 2) The experimental values ​​above were overlapped to derive the graph below, and a lifespan prediction graph was derived based on this graph. It is as follows.

[0070] Overlap graph (Red Dot) and prediction graph (Black Dot)

[0071] 3) The shape parameter (β), scale parameter (θ), and material parameter (γ) are as follows.

[0072] Shape parameter (β), scale parameter (θ), material parameter (γ)

[0075] Experimental Example 2: PA12 Complex Aging

[0077] 1) Aging tests were conducted at different temperatures (100℃, 110℃, 120℃, 130℃) and times (120, 240, 360, 500, 1000hr), and tensile strength was measured as a physical property. The results were graphed as follows (P represents the physical property, and P0 represents the initial physical property value).

[0078] PA12 Complex Aging Experiment

[0079] 2) The experimental values ​​above were overlapped to derive the graph below, and a lifespan prediction graph was derived based on this graph. It is as follows.

[0080] Overlap graph (Red Dot) and prediction graph (Black Dot)

[0081] 3) The shape parameter (β), scale parameter (θ), and material parameter (γ) are as follows.

[0082] Shape parameter (β), scale parameter (θ), material parameter (γ)

[0084] Through the above experimental examples, it was confirmed that the predicted value according to the present invention matches the actual value well.

[0086] Below, we explain that the present material life prediction method can predict the lifespan of components made of the material, going beyond just the material itself.

[0087] This invention can be utilized to predict not only materials but also the lifespan of parts, and has the advantage of very high accuracy.

[0089] Experimental Example 3: Prediction of Component Life

[0091] 1) When the evaluation test values ​​for the material and the evaluation test values ​​for the part are combined, the following graph can be obtained.

[0092] Evaluation experimental values ​​of materials and components

[0093] 2) If the graphs combined as above are linked to overlap as much as possible as shown below, the following graph can be obtained.

[0094] Linkage between material and component evaluation test values

[0095] 3) If the existing Weibull survival function without material parameters is used, a life prediction graph as shown below can be obtained.

[0096] Existing Weibull survival function graph

[0097] 4) If you use a function with applied material parameters, you can obtain a life prediction graph like the one below.

[0098] Function graph with applied material parameters

[0099] 5) As a result, when the method according to the present invention is applied, it can be confirmed that the experimental values ​​and predicted values ​​match better than the existing model.

[0101] The material life prediction method according to the present invention has the advantage of having no limitations on the aging mechanisms to which it can be applied and high prediction accuracy compared to existing aging models. In other words, it solves the problem that existing models have a fixed operating temperature range or can only be applied to a single aging mechanism, and even then, their accuracy is not high.

[0102] The material parameters, which are a new concept of the present invention, can be quantitatively obtained through simple experiments, and through this, material properties and lifespan can be predicted using a simple formula.

[0103] Furthermore, while many conventional models predicted lifespans limited to the material, the present invention has the effect of providing high accuracy in prediction results not only for the material but also for the parts made of the material.

[0104] By utilizing the present invention, it is possible to propose a guaranteed lifespan for new materials prior to mass production for which field data is currently insufficient, thereby enabling robust design to extend the lifespan of the material.

[0106] Although specific embodiments of the present invention have been illustrated and described, it will be obvious to those skilled in the art that the present invention can be modified and changed in various ways without departing from the technical spirit of the invention as provided by the following claims. Explanation of the symbols

[0108] S100: Aging stage S200: Material parameter measurement step S300: Derivation stage

Claims

Claim 1 A method for predicting material life comprising: an aging step of immersing a material specimen in an immersion solution and heating it to age it; a material parameter measurement step of measuring the property retention rate relative to the initial property from a material specimen after aging is complete and defining it as a material parameter; and a derivation step of deriving the aging time at which the material's property retention rate becomes less than or equal to a reference value by substituting the material parameter into the following mathematical formula 1. [Mathematical Formula 1] (y is the property retention rate, x is the aging time, θ is the scale parameter, β is the shape parameter, and γ is the material parameter.) Claim 2 A method for predicting material life according to claim 1, characterized in that the physical property measured in the material parameter measurement step is tensile strength. Claim 3 A method for predicting material life according to claim 1, characterized in that the immersion solution used in the aging step is an antifreeze containing either propylene glycol or ethylene glycol. Claim 4 A method for predicting material life according to claim 1, wherein the aging step is a step of immersing a material specimen in an immersion solution for 24 to 72 hours and heating the immersion solution to 100°C to 135°C to age the material.

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