JP5921989B2 - Product evaluation method - Google Patents

Description

  The present invention relates to a method for evaluating a product. In particular, the present invention relates to a method for evaluating the state of a product based on the results obtained in an accelerated degradation test of a simulated sample.

  A product is usually configured by combining a plurality of parts. For example, a tire is configured by combining parts such as a tread, a sidewall, a bead, and a carcass. Depending on the use, the state of some or all of these parts may change over time. This change affects product performance. Various studies have been conducted on methods for predicting this change from the viewpoint of improving product quality. Examples of this study are disclosed in Japanese Unexamined Patent Application Publication Nos. 2002-192924 and 2004-047295.

  Products with usage history may be collected from the market and the status of each part may be investigated. For example, when a tread of a tire is an object to be investigated, physical properties such as hardness, elongation at break, tensile stress at break, predetermined elongation tensile stress, and complex elastic modulus are measured using a sample collected from the tread. Based on the measured values obtained for each physical property, the tread state is evaluated.

  Exploring the cause of the product state change can contribute to improving the quality of the product. From this point of view, a test may be performed using a simulated sample to attempt to reproduce changes that have occurred in the product. Usually, an accelerated deterioration test is performed as a test for reproduction. Examples of this accelerated deterioration test are disclosed in Japanese Patent Laid-Open Nos. 2001-296235 and 09-133611. In the accelerated deterioration test, for example, in order to reproduce the state change of the tread, a simulation sample formed from a rubber composition equivalent to the rubber composition forming the tread is used.

JP 2002-192924 A JP 2004-047295 A JP 2001-296235 A JP 09-133611 A

  FIG. 17 shows an example in which a thermal deterioration test is adopted as the accelerated deterioration test. This thermal deterioration test is performed in accordance with JIS-K 6257. In this test, the temperature for deterioration is set to 80 ° C., and the hardness, complex elastic modulus, elongation at break, tensile stress at break, and tensile stress at 100% elongation (hereinafter also referred to as 100% modulus). ) And the change in swelling rate over time. The results of this tracking are plotted in FIG. In FIG. 17, the plot indicated by reference sign P1 represents the rate of change in hardness. The plot indicated by the symbol P2 represents the rate of change of the complex elastic modulus. The plot indicated by reference sign P3 represents the rate of change in elongation at break. The plot indicated by the symbol P4 represents the rate of change in tensile stress at the time of cutting. The plot indicated by the symbol P5 represents the rate of change of 100% modulus. And the plot shown with the code | symbol P6 represents the change rate of swelling rate. In this thermal degradation test, four types of “80 ° C. 4 days”, “80 ° C. 7 days”, “80 ° C. 14 days”, and “80 ° C. 21 days” are adopted as degradation conditions. In the figure, “untreated” represents the characteristic of the simulated sample before deterioration.

  In FIG. 17, the change rate of the physical property of the collected tire tread with respect to the physical property of the tread of a new tire corresponding to the recovered tire (hereinafter also referred to as a product change rate) is indicated by a dotted line. In FIG. 17, the dotted line L1 represents the product change rate of hardness. The dotted line L2 represents the product change rate of the complex elastic modulus. A dotted line L3 represents a product change rate of elongation at cutting. The dotted line L4 represents the product change rate of the tensile stress at the time of cutting. A dotted line L5 represents a product change rate of 100% modulus. A dotted line L6 represents the product change rate of the swelling rate.

  As shown in the figure, the time until the change rate of the physical property confirmed in the simulated sample reaches the product change rate varies depending on the physical property to be selected. It is difficult to comprehensively reproduce the deterioration state of the recovered tire using a plurality of physical properties as indices. The probability that the state of the deteriorated sample matches the state of the recovered tire is low, and it is difficult to determine whether the state of the deteriorated sample and the state of the recovered tire are similar. As described above, even if an accelerated deterioration test is performed under various conditions, it is difficult to determine the conditions for reproducing the product state.

  An object of the present invention is to provide an evaluation method for ascertaining conditions under which a change in the state of a product can be reproduced.

The product evaluation method according to the present invention is a method for evaluating the history received by a product having one or more parts by tracking the characteristics of a deteriorated sample obtained by degrading a simulated sample. This evaluation method is adopted for the above characteristic tracking. The element type representing this characteristic is represented by an element n using a natural number n of 1 to N, and the condition type for the deterioration is represented by a natural number of 1 to M. When expressed in condition m using m,
(1) preparing a target product with history and a reference product without history;
(2) A step of obtaining an actual sample from the parts of the target product and obtaining a measured value n _f of an element n representing the characteristics of the actual sample;
(3) corresponding to the component of the products to obtain a reference sample from the part of the reference product, obtaining a measurement value n _b of element n representing the characteristic of the reference sample step,
(4) obtaining a reference change rate _{S n} represented by the ratio _(n f / _{n b)} for the measurement value _{n b} of said measured values _{n f,}
(5) to produce a simulated sample of a material equivalent material used as the reference sample to obtain a measured value n _o elements n representing the characteristic of the simulated sample step,
(6) A step of artificially degrading the simulated sample under the condition m to produce a degraded sample, and obtaining a measured value n _am of an element n representing the characteristics of the degraded sample;
(7) obtaining a control change rate R _{(m) n} represented by the ratio _(n am / _{n o)} for the measurement value _{n o} of the measurement value _{n am,}
And (8) determining the similarity between the state of the target product and the state of the deteriorated sample using the reference change rate _Sn and the control change rate R (m) _n .

ここで、上記根二乗平均誤差関数α（ｍ）のβ（ｍ）_ｎは、上記対照変化率Ｒ（ｍ）_ｎ及び上記参照変化率Ｓ_ｎの乖離の程度を表す乖離関数である。 Preferably, in the product evaluation method, in the step of determining the similarity,
Using the root mean square error function α (m) represented by the following formula (1), a condition m that minimizes the root mean square error function α (m) can be determined.

Here, β (m) _n of the root mean square error function alpha (m) is a divergence function representing the degree of divergence of the control change rate R (m) _n and the reference change rate S _n.

この乖離関数β（ｍ）_ｎのｌ（ｎ）は、要素ｎについて得られる対照変化率Ｒ（ｍ）_ｎ及び参照変化率Ｓ_ｎの、冪数である。そして、上記模擬試料を人工的に劣化させて得られる較正試料の特性を表す要素ｎの計測値がｎ_ｃとされ、この計測値ｎ_ｃの、この模擬試料の特性を表す要素ｎの計測値ｎ_ｏに対する比率により較正変化率が表されたとき、
上記較正試料の特性を表す要素ｎの較正変化率が１よりも大きい場合において、この較正試料の特性を表す要素ｎのうち、この較正変化率が１．２以上１．６以下の範囲にある要素ｎ１を基準とし、この較正変化率が１．１に満たない要素ｎ２について、この要素ｎ２の較正変化率の冪乗が上記要素ｎ１の較正変化率よりも大きくなるように、この要素ｎ２についての冪数ｌ（ｎ２）は決められる。上記較正試料の特性を表す要素ｎの較正変化率が１よりも小さい場合においては、この較正試料の特性を表す要素ｎのうち、この較正変化率が０．２以上０．６以下の範囲にある要素ｎ１を基準とし、この較正変化率が０．７よりも大きな要素ｎ２について、この要素ｎ２の較正変化率の冪乗が上記要素ｎ１の較正変化率よりも小さくなるように、この要素ｎ２についての冪数ｌ（ｎ２）は決められる。 Preferably, in the method for evaluating the performance of this product, the divergence function beta (m) _n is represented by the following equation (2) using the above control change rate R (m) _n and the reference change rate S _n.

The discrepancy function β _{(m) n} of l (n) is the control rate of change R _{(m) n} and a reference change rate _{S n} obtained for element n, is a power number. Then, the measurement value of the element n representing the characteristic of the calibration samples obtained by artificially degrade the simulated sample is the n _c, the measured value n _c, the measured value of the element n representing the characteristic of the simulated sample when the calibration rate of change represented by the ratio n _o,
When the calibration change rate of the element n representing the characteristics of the calibration sample is greater than 1, the calibration change rate of the elements n representing the characteristics of the calibration sample is in the range of 1.2 to 1.6. With respect to element n2 with respect to element n1, such that the power of the calibration change rate of element n2 is greater than the calibration change rate of element n1 for element n2 with a calibration change rate of less than 1.1. The power of 1 (n2) is determined. When the calibration change rate of the element n representing the characteristics of the calibration sample is smaller than 1, the calibration change rate of the element n representing the characteristics of the calibration sample is in the range of 0.2 to 0.6. This element n2 is set so that the power of the calibration change rate of the element n2 is smaller than the calibration change rate of the element n1 with respect to the element n2 with respect to a certain element n1. The power l (n2) for is determined.

Preferably, in the method for evaluating the performance of this product, the divergence function beta (m) _n is expressed by the following equation (3) by using the above control change rate R (m) _n and the reference change rate S _n.
β (m) _n = R (m) _n −S _n (3)

  Preferably, in this product evaluation method, the maximum value N of the natural numbers n representing the element types is 3 or more.

  Preferably, in this product evaluation method, the maximum value M of the natural numbers m representing the type of the condition is 3 or more.

  According to the evaluation method of the present invention, the conditions that can reproduce the state change of the product can be determined efficiently.

FIG. 1 is a flowchart showing an evaluation method according to an embodiment of the present invention. FIG. 2 is a cross-sectional view showing a part of a pneumatic tire on which the evaluation method of FIG. 1 is implemented. FIG. 3 is a graph showing a reference radar chart representing the tread state of the tire shown in FIG. FIG. 4 is a graph showing a control radar chart showing the state of a deteriorated sample obtained by degrading a simulated sample under condition 3 in Table 3. FIG. 5 is a graph showing a control radar chart showing the state of a deteriorated sample obtained by degrading a simulated sample under condition 5 in Table 3. FIG. 6 is a graph showing a control radar chart showing the state of a deteriorated sample obtained by degrading a simulated sample under condition 7 in Table 3. FIG. 7 is a graph showing a control radar chart showing the state of a deteriorated sample obtained by degrading a simulated sample under condition 9 in Table 3. FIG. 8 is a graph showing a control radar chart showing the state of a deteriorated sample obtained by degrading a simulated sample under condition 11 in Table 3. FIG. 9 is a graph showing a control radar chart showing the state of the deteriorated sample obtained by degrading the simulated sample under condition 13 in Table 3. FIG. 10 is a graph showing a control radar chart showing the state of the deteriorated sample obtained by degrading the simulated sample under the condition 15 in Table 3. FIG. 11 is a graph showing a control radar chart showing the state of a deteriorated sample obtained by degrading a simulated sample under condition 18 in Table 3. FIG. 12 is a graph showing a control radar chart showing the state of a deteriorated sample obtained by degrading a simulated sample under the condition 20 in Table 3. FIG. 13 is a graph showing the characteristics of a calibration sample obtained by degrading a simulated sample under conditions 1 to 4 in Table 3. FIG. 14 is a graph showing the calculation result of the root mean square average error function α (m). FIG. 15 is a graph showing other characteristics of the calibration sample obtained by degrading the simulated sample under conditions 1 to 4 in Table 3. FIG. 16 is a graph showing a calculation result of the root mean square error function α (m) obtained by the evaluation method according to another embodiment of the present invention. FIG. 17 is a graph showing an example of the acceleration test result.

  Hereinafter, the present invention will be described in detail based on preferred embodiments with appropriate reference to the drawings.

  Shown in the flowchart of FIG. 1 is a method for evaluating the history received by a product having one or more parts by tracking the characteristics of a degraded sample obtained by degrading a simulated sample. is there.

This evaluation method is preparation step (STEP1), a step of investigating the characteristics of the actual sample (STEP2), a step of investigating the properties of the reference sample (STEP3), reference changing rate calculating a _{S n} (STEP4), the simulated sample The process of investigating the characteristics of the product (STEP 5), the process of investigating the characteristics of the deteriorated sample (STEP 6), the process of calculating the control change rate R (m) _n (STEP 7) Is included (STEP 8).

In this specification, the preparation process (STEP 1) to the similarity determination process (STEP 8) will be described in order, but the present invention is not limited to this order. After the step of investigating the characteristics of the actual sample (STEP 2), the step of investigating the characteristics of the reference sample (STEP 3), the step of investigating the characteristics of the simulated sample (STEP 5), and the step of investigating the characteristics of the deteriorated sample (STEP 6) calculating a reference change rate _{S n} (STEP4) and control the rate of change R _{(m) n} calculating a (STEP7) may be performed. After the step of investigating the characteristics of the simulated sample (STEP 5) and the step of investigating the characteristics of the deteriorated sample (STEP 6), the step of investigating the characteristics of the actual sample (STEP 2) and the step of investigating the characteristics of the reference sample (STEP 3) May be implemented. The order of each step in the present invention is appropriately determined in consideration of the setup for evaluation.

  In the preparation step (STEP 1), the target product and the reference product are prepared. In this evaluation method, the history of changing the characteristics of all or some of the parts of the product is the object of evaluation. A product with this history is a target product, and a product without this history is a reference product. For example, when the evaluation method of the present invention targets a usage history by a consumer, a product with this usage history is a target product, and a product without this usage history is a reference product. When the evaluation method of the present invention targets the storage history, the product with the storage history is the target product, and the product without the storage history is the reference product. In this specification, a tire is taken up as a product to be evaluated, but the product to be used in the present invention is not limited to this tire.

  FIG. 2 shows a pneumatic tire 2 collected from the market. This tire 2 is for passenger cars. In FIG. 2, the vertical direction is the radial direction of the tire 2, the horizontal direction is the axial direction of the tire 2, and the direction perpendicular to the paper surface is the circumferential direction of the tire 2. In FIG. 2, an alternate long and short dash line CL represents the equator plane of the tire 2. The shape of the tire 2 is symmetrical with respect to the equator plane except for the tread pattern.

  The tire 2 includes a tread 4, a sidewall 6, a bead 8, a carcass 10, a belt 12, a band 14, an inner liner 16, and a chafer 18. The tire 2 is a product having a large number of parts such as a tread 4 and a sidewall 6.

  Although not shown, the tread 4 of the tire 2 is chipped. In other words, the tire 2 receives a history of causing the tread 4 to chip while being used while being mounted on a vehicle. In the present specification, the tire 2 shown in FIG. 2 is a target product. Although not shown, a new tire corresponding to the recovered tire 2 and having no use history is a reference product. In the preparation step (STEP 1) of this evaluation method, the recovered tire 2 and a new tire corresponding to the recovered tire 2 are prepared. Note that no chipping has occurred in the tread of a new tire. This chipping is a word representing a damaged state of the tire 2. A state in which the tread surface is damaged by running and a part of the tread 4 is chipped off as a thin piece is called chipping. This chipping is confirmed by visually observing the appearance of the tire 2.

  In the step of examining the characteristics of the actual sample (STEP 2), an actual sample is obtained from the parts of the target product, and the characteristics of the actual sample are investigated. As described above, chipping occurs in the tread 4 of the tire 2 that is the target product. In this evaluation method, an actual sample is produced from the tread 4 and measurement is performed on an element representing the characteristics of the actual sample.

  In the present specification, the tread 4 is treated as a part to be evaluated, but the object of the present invention is not limited to the tread 4 in which an abnormality is visually confirmed. In this evaluation method, the side wall 6 in which no abnormality is visually confirmed may be used as an evaluation target. The apex 20 of the bead 8 that is not exposed on the surface of the tire 2 may be an evaluation target.

  The tread 4 to be evaluated is made of a crosslinked rubber. The types of elements that represent the properties of crosslinked rubber include physical properties such as hardness, elongation at break, tensile stress at break, 100% modulus, complex elastic modulus, swelling rate, residual antioxidant, residual oil, total network Examples thereof include an index such as density, monosulfide network density, quantitative value of carbonyl group, and quantitative value of hydroxyl group. The measuring method of each element is as follows.

(A) The hardness is measured with a type A durometer in an environment of 23 ° C. in accordance with the provisions of “JIS-K6253”.

(B) Each of the elongation at break, the tensile stress at break and the predetermined tensile stress is measured in accordance with the provisions of “JIS-K6251”. The conditions are as follows.
Specimen shape = No. 4 dumbbell Environmental temperature = 23 ° C
Test machine = Toyo Seiki Seisakusho Co., Ltd. trade name "Strograph"
Tensile speed = 500mm / min

(C) The complex elastic modulus is measured in accordance with the regulations of “JIS-K6394”. The conditions are as follows.
Viscoelastic Spectrometer: Iwamoto Seisakusho's trade name “VES”
Initial strain: 10%
Dynamic strain: ± 5%
Frequency: 10Hz
Deformation mode: Tensile Measurement temperature: 30 ° C

(D) In order to obtain the swelling rate, an immersion test is performed in accordance with the provisions of “JIS-K6258”. The volume change rate of the test piece when immersed in toluene at 40 ° C. for 24 hours is expressed as a swelling rate. A smaller value indicates a higher crosslink density.

(E) The amount of residual anti-aging agent is the amount of anti-aging agent remaining on the test piece. In accordance with the provisions of “JIS-K6229”, an anti-aging agent is extracted from the test piece, and the amount of the extracted anti-aging agent is quantified. The larger the value, the greater the remaining amount.

(F) The amount of remaining oil is the amount of oil remaining on the test piece. Oil is extracted from the test piece in accordance with the provisions of “JIS-K6229”, and the amount of the extracted oil is quantified. The larger the value, the greater the remaining amount.

(G) The total network density νT and the monosulfide network density νM are obtained as follows. Samples with a thickness of 1.5 mm to 2.5 mm are prepared. This sample is subjected to Soxhlet extraction at 80 ° C. to 90 ° C. for 15 hours using acetone as a solvent. After extraction, the sample is vacuum dried at room temperature for over 1 hour. After vacuum drying, the specimen is cut into a size of about 2 mm in length and about 2 mm in width to prepare a test piece. The vertical length Wo, the horizontal length Do, and the height Ho of the test piece are accurately measured.

(G-1) A test piece is immersed in a mixed solution of toluene / THF (1/1 (volume ratio)) for 20 hours in a thermostat adjusted to 30 ° C. After immersion, the test piece is taken out, and the height Hs after swelling of the test piece is measured. About this test piece, the distortion amount (compression ratio (alpha)) with respect to a load is measured using a thermomechanical analyzer (TMA). Using this compression ratio α, the network density ν (× 10 ⁻⁴ mol / cm ³ ) is calculated from the following mathematical formula (i). This network density ν is referred to as a total network density νT.
ν = Vr ^1/3 × F × 10 ⁴ / (R × T × (α−α ⁻² ) × A) (i)
Here, Vr is the rubber volume fraction in the swollen sample. F is a load (mg). R is a gas constant (8.31). T is the absolute temperature (K). A is the cross-sectional area (Wo × Do) of the unswelled test piece. The rubber volume fraction Vr is calculated from the following mathematical formula (ii).
Vr = (Vo−Vf) / (Vs−Vf) (ii)
In the formula (ii), Vo is a volume before swelling (Wo × Do × Ho). Vs is the volume after swelling (Wo × Do × Hs ³ / Ho ² ). Vf represents the volume of the chemical contained in the rubber composition. When the rubber composition contains carbon black, zinc white and sulfur, the volume Vf is expressed by Vo × d (C / 1.85 + Z / 5.6 + S / 2.05). D is the specific gravity of the base rubber. C is the carbon black content (% by mass). Z is the content (mass%) of zinc white. S is the sulfur content (% by mass).

(G-2) The test piece prepared in (g) above is immersed in a solution of lithium aluminum hydride for 20 hours in a thermostat adjusted to 30 ° C. After immersion, the lithium aluminum hydride solution is replaced with a mixed solution of toluene / THF (1/1 (volume ratio)), and the test piece is immersed in the mixed solution for another 2 hours. Thereafter, the test piece is taken out, and the height Hs after swelling of the test piece is measured. About this test piece, the distortion amount (compression ratio (alpha)) with respect to a load is measured using a thermo mechanical analyzer (TMA). Using this compression ratio α, the network density ν (× 10 ⁻⁴ mol / cm ³ ) is calculated from the above formula (i). This network density ν is referred to as a monosulfide network density νM.

(I) In order to obtain quantitative values of carbonyl group and hydroxyl group, an infrared absorption spectrum of a test piece was obtained using a total reflection infrared spectrophotometer, and the polymer as an internal reference in this spectrum was obtained. derived from a methylene group (1445cm ^-1), and the absorption intensity of the carbonyl group as an indicator of degradation (1740 cm ^-1) and hydroxyl (3300 cm ^-1) is measured. The quantitative value of the carbonyl group is represented by the ratio of the absorption intensity of the carbonyl group to the absorption intensity of the methylene group. The quantitative value of the hydroxyl group is represented by the ratio of the absorption intensity of the hydroxyl group to the absorption intensity of the methylene group.

In the present invention, the type of element that is employed for characteristic tracking and represents this characteristic is represented as element n using a natural number n of 1 to N. Therefore, in the step (STEP 2) of investigating the characteristics of the actual sample, an actual sample is obtained from the parts of the target product, the element n representing the characteristics of the actual sample is investigated, and the measured value n _{f of the} element n is obtained. It is done.

In this embodiment, the element types employed for property tracking are hardness, complex modulus, elongation at break, tensile stress at break and 100% modulus. In this embodiment, the hardness is represented as “Element 1”. The complex elastic modulus is expressed as “Element 2”. The elongation at break is expressed as “Element 3”. The tensile stress at cutting is expressed as “Element 4”. The 100% modulus is represented as “element 5”. Therefore, in the process of investigating the characteristics of the actual sample (STEP2), characteristic of the actual samples from the tread 4 of the recovery tire 2 is examined, measured value 1 _f of "Element 1" is obtained. A measurement value 2 _f of “element 2” is obtained. A measured value 3 _f of “element 3” is obtained. A measurement value 4 _f of “element 4” is obtained. Then, the measurement value 5 _f of “element 5” is obtained.

In the step of investigating the characteristics of the reference sample (STEP 3), a reference sample is obtained from the parts of the reference product corresponding to the parts of the target product, and the characteristics of the reference sample are investigated. As described above, in the step of examining the characteristics of the actual sample (STEP 2), the actual sample is produced from the tread 4 of the recovered tire 2. Therefore, in the step (STEP 3) of investigating the characteristics of the reference sample, the reference sample is produced from the tread of a new tire as the reference product. Then, element n representing the characteristic of the reference sample is examined, measured value n _b of the element n can be obtained.

As described above, in this embodiment, the element type for characteristic tracking is represented by the hardness represented by “element 1”, the complex elastic modulus represented by “element 2”, and “element 3”. Tensile elongation at break, tensile stress at break represented by “element 4” and 100% modulus represented by “element 5” are employed. Therefore, in the step (STEP 3) of investigating the characteristics of the reference sample, the measured value 1 _b of “element 1” is obtained by investigating the characteristics of the reference sample obtained from the tread of the new tire. A measurement value 2 _b of “element 2” is obtained. A measured value 3 _b of “element 3” is obtained. A measurement value 4 _b of “element 4” is obtained. A measurement value 5 _b of “element 5” is obtained.

In the step of calculating a reference change rate _{S n} (STEP4), the measurement value n elements n obtained by the measurement value _{n f} and the reference sample survey process elements n obtained in study process of a real sample (STEP2) (STEP3) _The reference change rate _Sn is calculated using _b . In this evaluation method, the reference change rate _{S n} is represented by the ratio _(n f / _{n b)} for the measurement value _{n b} of measurements _{n f.}

As described above, in this embodiment, the element type for characteristic tracking is represented by the hardness represented by “element 1”, the complex elastic modulus represented by “element 2”, and “element 3”. Tensile elongation at break, tensile stress at break represented by “element 4” and 100% modulus represented by “element 5” are employed. Therefore, in the step of calculating the reference change rate _{S n} (STEP4), the "Element 1", the reference change rate _{S 1} represented by the ratio ₍₁ f / _{1 b)} are obtained. For “element 2”, a reference change rate S ₂ represented by a ratio (2 _f / 2 _b ) is obtained. For “element 3”, a reference change rate S ₃ represented by a ratio (3 _f / 3 _b ) is obtained. For “element 4”, a reference change rate S ₄ represented by a ratio (4 _f / 4 _b ) is obtained. For “element 5”, a reference change rate S ₅ represented by a ratio (5 _f / 5 _b ) is obtained. Shown in Table 1 below, investigation step (STEP2) real sample is data obtained in step (STEP4) for calculating an investigation step (STEP3), and the reference change rate of the reference sample _{S n.}

In this evaluation method, using the reference change rate S _n, the radar chart may be created. In other words, the evaluation method uses a reference change rate S _n, may further comprise the step of creating a radar chart. The radar chart facilitates understanding of the state of the tread 4 of the collected tire 2. This radar chart is useful for comprehensively understanding the product state using a plurality of elements. In this evaluation method, a radar chart represented using reference change rate S _n is referred to as a reference radar charts.

FIG. 3 shows an example of a reference radar chart. In FIG. 3, a reference radar chart created using the reference change rates S ₁ , S ₂ , S ₃ , S ₄ and S ₅ is indicated by a solid line. A dotted line is a radar chart of a new tire. From FIG. 3, the tread 4 of the recovered tire 2 has a hardness represented by “element 1”, a complex elastic modulus represented by “element 2”, and a 100% modulus represented by “element 5”. It can be seen that the tensile elongation at break represented by “element 3” and the tensile stress at break represented by “element 4” are lower than those of the new tire.

In the step of investigating the characteristics of the simulated sample (STEP 5), a simulated sample is produced from a material equivalent to the material constituting the reference sample, and the characteristics of the simulated sample are investigated. As described above, the reference sample is made from a tread of a new tire. Therefore, in the step (STEP 5) of investigating the characteristics of the simulation sample of this embodiment, a rubber composition equivalent to the rubber composition forming the tread is prepared, and the simulation sample is prepared by crosslinking the rubber composition. . Then, element n representing the characteristic of the simulated sample is examined, measured value n _o of the element n can be obtained.

As described above, in this embodiment, the element type for characteristic tracking is represented by the hardness represented by “element 1”, the complex elastic modulus represented by “element 2”, and “element 3”. Tensile elongation at break, tensile stress at break represented by “element 4” and 100% modulus represented by “element 5” are employed. Therefore, in the step of investigating the characteristics of the simulated sample (STEP 5), the measured value _1o of “element 1” is obtained by investigating the characteristics of the simulated sample. A measurement value 2 _o of “element 2” is obtained. A measurement value 3 _o of “element 3” is obtained. A measurement value 4 _o of “element 4” is obtained. A measurement value 5 _o of “element 5” is obtained. Table 2 below shows data obtained in the simulation sample investigation process (STEP 5).

In the step of investigating the characteristics of the deteriorated sample (STEP 6), the deteriorated sample is produced by artificially degrading the simulated sample. In the present invention, various measures can be taken to degrade the simulated sample. For example, as a measure to artificially degrade a simulated sample made of crosslinked rubber,
(A) A simulated sample is placed in a test tank whose temperature is adjusted to 80 ° C.
(B) A simulated sample is placed in a test tank whose temperature is adjusted to 80 ° C. with 20% tensile strain applied.
(C) Using a device having a structure that reciprocates to draw a sine wave in a test chamber whose room temperature or temperature is adjusted to 80 ° C., the application of tensile strain and the release thereof are repeated for this simulated sample. The maximum value of tensile strain is 20%, the minimum value is 0%, and the frequency of reciprocating motion is 0.5 Hz.
(D) A simulated sample whose surface is wiped with acetone is placed in a test tank whose temperature is adjusted to 80 ° C. Then, the simulated sample is taken out every two days from the day when the simulated sample is placed in the test tank, and the surface of the simulated sample is further wiped with acetone.
(E) In accordance with JIS-K6259, a simulated sample is placed in a test tank with a temperature adjusted to 40 ° C. and an ozone concentration adjusted to 50 pphm with 20% tensile strain applied.
(F) In accordance with JIS-K6259, in a test tank adjusted to a temperature of 40 ° C. and an ozone concentration of 50 pphm, using a device having a structure that reciprocates to draw a sine wave, the application of tensile strain and its Release is repeated on this simulated sample. The maximum value of tensile strain is 20%, the minimum value is 0%, and the frequency of reciprocation is 0.5 Hz.
(G) A simulated sample is placed in a test tank of a xenon weather meter with 20% tensile strain applied, and the simulated sample is irradiated with ultraviolet rays at an irradiation intensity of 180 W / m ² .
Is mentioned.

  In this evaluation method, for example, the simulated sample may be deteriorated in combination by combining the measures (A) and (E). The simulated sample may be deteriorated in combination by combining the measures (C) and (E).

  In the present invention, the condition type for deterioration is represented by the condition m using a natural number m of 1 to M. In the step of investigating the characteristics of the deteriorated sample (STEP 6), the deteriorated sample is produced by artificially degrading the simulated sample under the condition m.

  In this evaluation method, the condition m is set based on measures for deterioration. For example, conditions for artificially degrading a simulated sample made of a crosslinked rubber can be set based on the measures shown in the above (A) to (G). Table 3 below shows the contents of the condition m for deterioration of the simulated sample, which is adopted in this embodiment.

In the step (STEP 6) of investigating the characteristics of the deteriorated sample, an element n representing the characteristics of the deteriorated sample obtained by deteriorating the simulated sample under the condition m is investigated, and a measured value n _{am of the} element n is obtained. . As described above, in this embodiment, the element type for characteristic tracking is represented by the hardness represented by “element 1”, the complex elastic modulus represented by “element 2”, and “element 3”. Tensile elongation at break, tensile stress at break represented by “element 4” and 100% modulus represented by “element 5” are employed. Therefore, in the step (STEP 6) of investigating the characteristics of the deteriorated sample, for example, by measuring the characteristics of the deteriorated sample obtained by degrading the simulated sample under the condition 1 in Table 3, the measured value 1 of “element 1” _a1 is obtained. A measured value 2 _a1 of “element 2” is obtained. A measurement value 3 _a1 of “element 3” is obtained. A measured value 4 _a1 of “element 4” is obtained. A measured value 5 _a1 of “element 5” is obtained. By investigating the characteristics of the deteriorated sample obtained by degrading the simulated sample under the condition 5 in Table 3, the measured value 1 _a5 of “element 1” is obtained. A measurement value 2 _a5 of “element 2” is obtained. A measurement value 3 _a5 of “element 3” is obtained. The measured value 4 _a5 of “element 4” is obtained. A measurement value 5 _a5 of “element 5” is obtained.

In the step (STEP 7) of calculating the control change rate R (m) _n , the measured value n _o of the element n obtained in the simulation sample investigation step (STEP 5) and the element n obtained in the degradation sample investigation step (STEP 6). A control change rate R (m) _n is calculated using the measured value n _am . The control rate of change R _{(m) n} is represented by the ratio of measured values _{n am} for measurement _{n o (n am / n o} ).

As described above, in this embodiment, the element type for characteristic tracking is represented by the hardness represented by “element 1”, the complex elastic modulus represented by “element 2”, and “element 3”. Tensile elongation at break, tensile stress at break represented by “element 4” and 100% modulus represented by “element 5” are employed. Therefore, in the step of calculating the control change rate R (m) _n (STEP 7), for example, based on the characteristics of the simulated sample and the characteristics of the deteriorated sample obtained by degrading the simulated sample under the condition 1 in Table 3, “ For element 1 ”, a control change rate R (1) ₁ expressed as a ratio (1 _a1 / 1 _o ) is obtained. For “element 2”, a control change rate R (1) ₂ represented by a ratio (2 _a1 / 2 _o ) is obtained. For “element 3”, a control change rate R (1) ₃ represented by a ratio (3 _a1 / 3 _o ) is obtained. For “element 4”, the control change rate R (1) ₄ represented by the ratio (4 _a1 / 4 _o ) is obtained. For “element 5”, a control change rate R (1) ₅ represented by a ratio (5 _a1 / 5 _o ) is obtained. Based on the characteristics of the simulated sample and the characteristics of the deteriorated sample obtained by degrading the simulated sample under the condition 5 in Table 3, for “Element 1”, the control change rate R represented by the ratio (1 _a5 / 1 _o ) (5) ₁ is obtained. For “element 2”, a control change rate R (5) ₂ represented by the ratio (2 _a5 / 2 _o ) is obtained. For “element 3”, a control change rate R (5) ₃ represented by a ratio (3 _a5 / 3 _o ) is obtained. For “element 4”, a control change rate R (5) ₄ represented by a ratio (4 _a5 / 4 _o ) is obtained. For “element 5”, the control change rate R (5) ₅ represented by the ratio (5 _a5 / 5 _o ) is obtained. The following Tables 4 to 7 show data obtained in the deterioration sample investigation step (STEP 6) and the control change rate R (m) _n calculation step (STEP 7).

In this evaluation method, a radar chart may be created using the control change rate R (m) _n . In other words, the evaluation method can further include a step of creating a radar chart using the control change rate R (m) _n . The radar chart facilitates understanding of the state of the deteriorated sample. This radar chart is useful for comprehensively understanding the state of a deteriorated sample using a plurality of elements. In this evaluation method, the radar chart expressed using the contrast change rate R (m) _n is referred to as a contrast radar chart.

Shown in FIG. 4 is an example of a control radar chart. In FIG. 4, the control radar chart of the deteriorated sample obtained by deteriorating the simulated sample under condition 3 in Table 3 is shown by a solid line. In this control radar chart, the control change rates R (3) ₁ , R (3) ₂ , R (3) ₃ , R (3) ₄ and R (3) ₅ of the deteriorated sample are plotted, and these are connected by a solid line. Is obtained. The dotted line is a radar chart of the simulated sample. From FIG. 4, when the simulated sample is deteriorated under the condition 3, the complex elastic modulus of “Element 2” and the 100% modulus of “Element 5” are higher than those of the simulated sample. It is confirmed that the elongation and the tensile stress at the time of cutting of “Element 4” are lower than those of the simulated sample. The hardness of “Element 1” is higher than that of the simulated sample, but the difference between the two is slight.

  In FIG. 5, the control radar chart of the deteriorated sample obtained by degrading the simulated sample under the condition 5 in Table 3 is shown by a solid line. From FIG. 5, when the simulated sample is deteriorated under the condition 5, the complex elastic modulus of “Element 2” and the 100% modulus of “Element 5” are higher than those of the simulated sample, and the hardness of “Element 1” and It is confirmed that the tensile elongation at the time of cutting of “Element 3” is lower than that of the simulated sample. The tensile stress during cutting of “Element 4” is almost the same as that of the simulated sample.

  In FIG. 6, the control radar chart of the deteriorated sample obtained by degrading the simulated sample under condition 7 in Table 3 is shown by a solid line. From FIG. 6, when the simulated sample is deteriorated under condition 7, only the hardness of “element 1” is lower than that of the simulated sample, but the complex elastic modulus of “element 2” and the tensile strength at the time of cutting of “element 3”. It is confirmed that the elongation, the tensile stress at break of “Element 4” and the 100% modulus of “Element 5” are almost the same as those of the simulated sample.

  In FIG. 7, the control radar chart of the deteriorated sample obtained by degrading the simulated sample under the condition 9 in Table 3 is shown by a solid line. From FIG. 7, when the simulated sample is deteriorated under condition 9, the hardness of “element 1”, the tensile elongation at the time of cutting of “element 3”, and the tensile stress at the time of cutting of “element 4” are lower than those of the simulated sample. The 100% modulus of “Element 5” is confirmed to be higher than that of the simulated sample. The complex elastic modulus of “Element 2” is almost the same as that of the simulated sample.

  In FIG. 8, the control radar chart of the deteriorated sample obtained by degrading the simulated sample under the condition 11 in Table 3 is shown by a solid line. From FIG. 8, when the simulated sample is deteriorated under the condition 11, the hardness of “element 1”, the complex elastic modulus of “element 2”, the tensile elongation at the time of cutting “element 3”, and at the time of cutting “element 4” It is confirmed that the tensile stress and the 100% modulus of “Element 5” are all lower than the simulated sample, and among these, the hardness reduction of “Element 1” is particularly significant.

  In FIG. 9, the control radar chart of the deteriorated sample obtained by degrading the simulated sample under the condition 13 in Table 3 is shown by a solid line. From FIG. 9, when the simulated sample was deteriorated under condition 13, the hardness of “Element 1” and the tensile elongation at break of “Element 3” were lower than those of the simulated sample, and the complex elastic modulus of “Element 2” It is confirmed that the tensile stress at the time of cutting of “Element 4” and the 100% modulus of “Element 5” are higher than those of the simulated sample.

  In FIG. 10, the control radar chart of the deteriorated sample obtained by deteriorating the simulated sample under the condition 15 in Table 3 is shown by a solid line. From FIG. 10, when the simulated sample is deteriorated under the condition 15, the hardness of the “element 1”, the tensile elongation at the time of cutting of the “element 3” and the tensile stress at the time of cutting of the “element 4” are lower than those of the simulated sample. It is confirmed that the complex elastic modulus of “Element 2” and the 100% modulus of “Element 5” are higher than those of the simulated sample.

  In FIG. 11, the control radar chart of the deteriorated sample obtained by degrading the simulated sample under the condition 18 in Table 3 is shown by a solid line. From FIG. 11, when the simulated sample is deteriorated under the condition 18, the hardness of “Element 1”, the tensile elongation at the time of cutting of “Element 3”, and the tensile stress at the time of cutting of “Element 4” are lower than those of the simulated sample. It is confirmed that the complex elastic modulus of “Element 2” and the 100% modulus of “Element 5” are higher than those of the simulated sample. The decrease in the hardness of “Element 1” is remarkable.

  In FIG. 12, the control radar chart of the deteriorated sample obtained by deteriorating the simulated sample under the condition 20 in Table 3 is shown by a solid line. From FIG. 12, it is confirmed that when the simulated sample is deteriorated under the condition 20, the hardness of the “element 1” and the tensile stress at the time of cutting of the “element 4” are lower than those of the simulated sample. The complex elastic modulus of “Element 2”, the tensile elongation at break of “Element 3” and the 100% modulus of “Element 5” are almost the same as those of the simulated sample.

In step (STEP 8) of determining the similarity of this evaluation method, using the reference change rate S _n and control the rate of change R (m) _n, similarity to the state of the products and the state of deterioration sample is determined . In other words, a deteriorated sample in a state similar to the state of the target product is selected from deteriorated samples obtained by deteriorating the simulated sample under various conditions. As a result, the deterioration condition (condition m) corresponding to the history received by the target product can be determined.

  In this evaluation method, a radar chart can be used in determining similarity. For example, the control radar chart shown in FIGS. 4 to 12 and the reference radar chart shown in FIG. 3 are compared, and a deteriorated sample in a state similar to the state of the target product is selected. By using a radar chart, not only numerical values but also morphological differences can be easily determined, so application of this radar chart can contribute to the efficiency of similarity determination. Since the state of the sample can be comprehensively understood based on a plurality of factors, the application of this radar chart can suppress the difference between the determined condition m and the history received by the target product. This evaluation method can contribute to ascertaining the condition m for reproducing the state change of the product.

  In this evaluation method, the root mean square error function α (m) represented by the following formula (1) can be used in the step of determining similarity (STEP 8).

In the root mean square error function α (m), the beta (m) _n is a deviation function representing the degree of divergence of the control change rate R (m) _n and a reference change rate S _n. The discrepancy function beta _{(m) n} is, for example, represented by the following equation (2) using a control rate of change R _{(m) n} and a reference change rate _{S n.}
β (m) _n = R (m) _n −S _n (2)

In the step of determining the similarity (STEP 8), a root mean square error function α (m) is calculated. More specifically, the root mean square error function for each of the conditions 1 to 20 using the reference change rate S _n shown in Table 1 and the control change rate R (m) _n shown in Tables 4 to 7. α (m) is calculated.

  The root mean square error function α (m) is a numerical evaluation of the difference between the state of the target product and the state of the deteriorated sample. The small root mean square error function α (m) indicates that the difference between the state of the target product and the state of the deteriorated sample is small, in other words, the state of the deteriorated sample is close to the state of the target product. On the other hand, the large root mean square error function α (m) indicates that the difference between the state of the target product and the state of the deteriorated sample is large, in other words, the state of the deteriorated sample is far from the state of the target product.

  In the step of determining similarity (STEP 8), a condition m that minimizes the root mean square error function α (m) is determined. More specifically, in the root mean square error function α (m) calculated for each of the conditions 1 to 20, the root mean square error function α (m) representing the minimum value is selected. The condition m is determined based on the selected root mean square error function α (m). In this evaluation method, the similarity between the state of the target product and the state of the deteriorated sample is quantitatively determined using numerical values. The application of the root mean square error function α (m) can easily contribute to the determination of similarity. Since the determination of similarity is supported by numerical values, the reliability of the condition m determined by this evaluation method is high. According to this evaluation method, the conditions for reproducing the state change of the product can be appropriately determined.

By the way, Table 8 below shows the hardness (“element 1”), complex elastic modulus (“" of the deteriorated sample obtained by degrading the simulated sample under the condition 4 of Table 3 (heating at 80 ° C. for 3 weeks). The rate of change of element 2 ") and 100% modulus (" element 5 ") is shown. In Table 8, the rate of change in hardness corresponds to R (4) ₁ in Table 4. The rate of change of the complex elastic modulus corresponds to R (4) ₂ in Table 4. The rate of change of 100% modulus corresponds to R (4) ₃ in Table 4.

In recovering the tire 2 (products) shown in Figure 2, the hardness of the tread 4 is 70 (corresponding to Table 1 of the measured value 1 _f). The hardness of the tread of a new tire as a reference product is 65 (corresponding to the measurement value 1 _b in Table 1). Therefore, the amount of change in the hardness of the tread 4 is 5, the reference change rate S ₁ in this case is 1.08. This reference change rate S ₁ is comparable to the change rate of the hardness shown in Table 8.

  Hardness, complex elastic modulus, and 100% modulus are all physical properties obtained by imparting strain to a sample. Although the hardness is a physical property similar to the complex elastic modulus and 100% modulus, when the degree of change is represented by the rate of change, as shown in Table 8 above, the rate of change in hardness is Small compared to complex elastic modulus and 100% modulus change rate. However, the amount of change in hardness in the product is “5”, and the degree of change as hardness is significant. Therefore, in this evaluation method, elements that do not sufficiently reflect the degree of change in the rate of change even though the degree of substantial change is large are similar to this element, and the rate of change Weighting may be performed so that the rate of change is similar to that of other elements whose degree is sufficiently reflected. The similarity of the elements may be determined based on whether or not there is a relationship between the elements, and there is no particular limitation when determining the similarity.

In this evaluation method, when weighting is performed, in the root mean square error function α (m) represented by the above formula (1), the divergence function β (m) _n represented by the above formula (2) Using the rate R (m) _n and the reference change rate S _n , the deviation function β (m) _n represented by the following formula (3) is substituted. In this divergence function β (m) _n , l (n) is a natural number represented by 1, 2, 3,. The l (n) is the control rate of change R _{(m) n} and a reference change rate _{S n} obtained for element n, is a power number.

  In this evaluation method, in the step of determining similarity (STEP 8), the power l (n) is determined as follows. By taking measures for deterioration (for example, the above-mentioned measures (A) to (G)) on the simulated sample, the change with time of the element n is tracked. When the object of evaluation is made of a polymer material such as a crosslinked rubber, it is preferable to heat the simulated sample in a test tank as a measure for deterioration from the viewpoint of simplicity and excellent reproducibility. When the object of evaluation is a part made of a crosslinked rubber such as the tread 4 of the tire 2, a measure for heating the simulated sample at 80 ° C. (the measure (A) described above) is preferable. When it is selected that the simulated sample is degraded in the measure (A), for example, the simulated sample is artificially degraded under the conditions 1 to 4 shown in Table 3 to obtain a calibration sample, and the characteristics of the calibration sample are investigated. Is done. From the viewpoint of a large degree of change and excellent reproducibility, condition 4 in Table 3 is preferable.

By examining the properties of the calibration samples measurements n _c elements n representative of the characteristics can be obtained. In this evaluation method, the measured value n _c, ratio measured value n _o elements n representing the characteristic of the simulated sample, expressed as a calibration rate of change of the elements n.

  In this evaluation method, when the calibration change rate of the element n representing the characteristic of the calibration sample is larger than 1, the calibration change rate of the element n representing the characteristic of the calibration sample is 1.2 or more and 1.6. Based on the element n1 in the following range, the power of the calibration change rate of the element n2 is larger than the calibration change rate of the element n1 for the element n2 whose calibration change rate is less than 1.1. The power l (n2) for this element n2 is determined. From the viewpoint that accurate determination is possible, among the elements n representing the characteristics of the calibration sample, the element n1 whose calibration change rate is in the range of 1.2 to 1.6 and closest to 1.4 is the reference. Preferably it is done.

FIG. 13 shows the characteristics of the calibration sample obtained by degrading the simulated sample under conditions 1 to 4 in Table 3. FIG. 13 shows hardness (hereinafter also referred to as element 1), complex elastic modulus (hereinafter also referred to as element 2), and 100% modulus (hereinafter referred to as element 5) obtained under each condition. Also referred to as calibration rate of change). This calibration change rate is synonymous with the reference change rate _Sn and the control change rate R (m) _n described above. In the graph shown in FIG. 13, the vertical axis represents the calibration change rate. The horizontal axis corresponds to time. FIG. 13 shows a change with time in the calibration change rate of the element 1, the element 2 and the element 5. In FIG. 13, a solid line LH indicates the time-dependent change in the calibration change rate of the element 1. What is indicated by the solid line LE is the change with time of the calibration change rate of the element 2. What is indicated by a solid line LM is a change with time in the calibration change rate of the element 5.

  As shown in FIG. 13, the calibration change rate of element 1, the calibration change rate of element 2, and the calibration change rate of element 5 are all greater than one. That is, FIG. 13 shows an example in which the calibration change rate of the element n representing the characteristics of the calibration sample is larger than 1.

  As is clear from FIG. 13, in the calibration sample obtained under the conditions 2 to 4, among the elements 1, 2 and 5, the change rate of the element 2 is in the range of 1.2 to 1.6 and 1 .4 is the element closest to .4 (corresponding to the element n1 described above), and the element 1 is an element whose change rate is less than 1.1 (corresponding to the element n2 described above). Therefore, with the element 2 as a reference, the power l (1) of the element 1 is adjusted so that the power of the change rate of the element 1 is larger than that of the element 2.

  In FIG. 13, the transition of the power of the change rate of the element 1 when the power 1 (1) is 5 is indicated by a dotted line LH5, and the power of the element 1 when the power 1 (1) is 10 is shown. The transition of the power of the change rate is indicated by a dotted line LH10. As can be seen from FIG. 13, in condition 4, when the power l (1) is set to 10, a power of the change rate of element 1 larger than the change rate of element 2 is obtained. Therefore, in the example shown in FIG. 13, the power l (1) of element 1 is set to 10. In this embodiment, elements other than this element 1, that is, the power l (2) of element 2, the power l (3) of element 3, the power l (4) of element 4, and the power of element 5 The power l (5) is set to 1.

  FIG. 14 shows the result of calculating the root mean square error function of conditions 1 to 20 using the divergence function represented by the above formula (3), where the power l (1) of element 1 is 10. ing. In FIG. 14, the vertical axis represents the value of the root mean square error function. The horizontal axis represents each condition. As shown in FIG. 14, the root mean square error function of condition 3 is the smallest. That is, the state of the tread 4 of the recovered tire 2 shown in FIG. 2 is close to the state of a deteriorated sample obtained by heating the simulated sample at 80 ° C. for 2 weeks. In other words, a state close to the state of the tread 4 of the recovered tire 2 can be reproduced by heating the simulated sample at Condition 2, ie, at 80 ° C. for 2 weeks. Thus, in this evaluation method, the determination of the similarity between the state of the target product and the state of the deteriorated sample is quantitatively determined using numerical values. By applying a power of l (n), similarity can be easily and accurately determined. Moreover, since the determination of similarity is supported by numerical values, the reliability of the condition m determined by this evaluation method is high. According to this evaluation method, the conditions for reproducing the state change of the product can be appropriately determined.

  As described above, this evaluation method is used for characteristic tracking, and the type of element representing this characteristic is represented as element n using a natural number n of 1 to N. This evaluation method can effectively contribute to ascertaining conditions for reproducing a change in the state of a product when three or more element types are targeted. Therefore, the maximum value N of the natural numbers n representing the element type is preferably 3 or more.

  As described above, in this evaluation method, the condition type for deterioration is represented by the condition m using a natural number m of 1 to M. This evaluation method can effectively contribute to ascertaining the conditions for reproducing the state change of the product in the case of targeting three or more condition types. Accordingly, it is preferable that the maximum value M of the natural number m representing this type of condition is 3 or more.

  Depending on the element to be selected, unlike the example shown in FIG. 13, the change rate of the element obtained by deterioration may be smaller than 1. In this case, the power l (n) is determined as follows.

  In this evaluation method, when the calibration change rate of the element n representing the characteristics of the calibration sample is smaller than 1, the calibration change rate of the element n representing the characteristics of the calibration sample is 0.2 or more and 0.6. The element n1 in the following range is used as a reference so that the power of the calibration change rate of the element n2 is smaller than the calibration change rate of the element n1 for the element n2 whose calibration change rate is greater than 0.7. The power l (n2) for this element n2 is determined. From the viewpoint that accurate determination is possible, among the elements n representing the characteristics of the calibration sample, the element n1 whose calibration change rate is in the range of 0.2 to 0.6 and closest to 0.4 is the reference. Preferably it is done.

  FIG. 15 shows the characteristics of the calibration sample obtained by degrading the simulated sample under conditions 1 to 4 in Table 3. FIG. 15 plots the rate of change in calibration for each element of the residual oil amount, the residual anti-aging agent amount, and the swelling rate obtained under each condition. In the graph shown in FIG. 15, the vertical axis represents the calibration change rate. The horizontal axis corresponds to time. FIG. 15 shows changes over time in the amount of residual oil, the amount of residual anti-aging agent, and the swelling rate. In FIG. 15, what is indicated by a solid line LA is a change with time in the calibration change rate of the residual oil amount. What is indicated by a solid line LO is a change with time of the calibration change rate of the amount of the remaining anti-aging agent. What is indicated by a solid line LS is a change with time in the calibration change rate of the swelling rate.

  As shown in FIG. 15, the calibration change rate of the residual oil amount, the calibration change rate of the residual anti-aging agent amount, and the calibration change rate of the swelling rate are all smaller than 1. That is, FIG. 15 shows an example in which the calibration change rate of the element n representing the characteristics of the calibration sample is smaller than 1.

  As is clear from FIG. 15, in the calibration samples obtained under the conditions 2 to 4, among the remaining oil amount, residual anti-aging agent amount, and swelling factor, the remaining anti-aging agent has a calibration change rate of 0.2. An element in the range of 0.6 or less and closest to 0.4 (corresponding to the element n1 described above), and a residual oil amount whose calibration change rate is greater than 0.7 (in the element n2 described above) Equivalent). Therefore, the residual oil amount is adjusted so that the power of the calibration change rate of the residual anti-aging agent is smaller than the calibration change rate of the residual anti-aging agent with the residual anti-aging agent amount as a reference factor. .

  In FIG. 15, the transition of the power of the calibration change rate of the residual oil amount when the power of 4 is indicated by a dotted line LH4, and the calibration change rate of the residual oil amount when the power of 5 is set. The power transition is indicated by a dotted line LH5. As is apparent from FIG. 15, in conditions 2 and 3, when the number of powers is 4 or 5, the calibration change of the residual oil amount is larger than the calibration change rate of the residual anti-aging agent amount as the reference element. The power of rate is obtained. In condition 4, when the number of powers is set to 5, a power of the calibration change rate of the residual oil amount that is larger than the calibration change rate of the residual anti-aging agent amount as the reference element is obtained. Therefore, in the example shown in FIG. 15, the power of the residual oil amount is 4 or 5.

  FIG. 16 shows the calculation result of the root mean square error function obtained by the evaluation method according to another embodiment of the present invention. In this embodiment, the quantitative value of the carbonyl group, the quantitative value of the hydroxyl group, the total network density, the monosulfide network density, the residual anti-aging agent amount and the residual oil amount are the element types employed for property tracking. In this embodiment, the quantitative value of the carbonyl group is represented as “element 1”. The quantitative value of the hydroxyl group is expressed as “Element 2”. The total mesh density is expressed as “Element 3”. The monosulfide network density is expressed as “Element 4”. The amount of residual anti-aging agent is expressed as “Element 5”. The residual oil amount is represented as “element 5”.

  In this embodiment, a deteriorated sample is produced under the conditions shown in Table 9 below, and the characteristics of the deteriorated sample are investigated. Based on the result obtained in this investigation, the root mean square error function of conditions 1 to 11 is calculated using the divergence function expressed by the above-described equation (3). The calculation result is shown in FIG. In calculating the root mean square error function, the power l (5) of the element 5 is set to 5 based on the result shown in FIG. Elements other than this element 5, that is, the power l (1) of element 1, the power l (2) of element 2, the power l (3) of element 3, and the power l (4) of element 4 are: It is set to 1.

  As shown in FIG. 16, the root mean square error function of condition 3 is the smallest. That is, the state of the target product is close to the state of a deteriorated sample obtained by heating the simulated sample for 7 days at 80 ° C. with 20% tensile strain applied. In other words, a state close to the state of the product can be reproduced by heating the simulated sample for 7 days at 80 ° C. under condition 3, ie, with 20% tensile strain applied. Thus, in this evaluation method, the determination of the similarity between the state of the target product and the state of the deteriorated sample is quantitatively determined using numerical values. By applying a power of l (n), similarity can be easily and accurately determined. Moreover, since the determination of similarity is supported by numerical values, the reliability of the condition m determined by this evaluation method is high. According to this evaluation method, the conditions for reproducing the state change of the product can be appropriately determined.

  The method described above can also be applied to the evaluation of various products.

2 ... tyre 4 ... tread 6 ... side wall 8 ... bead 10 ... carcass 12 ... belt 14 ... band 16 ... inner liner 18 ... chafer 20 ...・ ・ Apex

Claims (6)

A method for evaluating a history received by a product having one or more parts by tracking characteristics of a deteriorated sample obtained by degrading a simulated sample, which is employed for tracking the characteristics described above. When the element type representing this characteristic is represented by an element n using a natural number n of 1 to N, and the condition type for deterioration is represented by a condition m using a natural number m of 1 to M,
Preparing a target product with history and a reference product without history,
Obtaining a real sample from the parts of the target product and obtaining a measured value n _f of an element n representing the characteristics of the real sample;
To obtain a reference sample from the part of the reference product corresponding to the component of the products to obtain a measurement value n _b of element n representing the characteristic of the reference sample,
Obtaining a reference change rate _{S n} represented by the ratio _(n f / _{n b)} for the measurement value _{n b} of said measured values _{n f,}
Producing a simulated sample from a material equivalent to the material constituting the reference sample and obtaining a measured value n _o of an element n representing the characteristics of the simulated sample;
A step of artificially degrading the mock sample under condition m to produce a deteriorated sample, and obtaining a measured value n _am of an element n representing the characteristics of the deteriorated sample;
It is obtaining a reference rate of change R _{(m) n} represented by the ratio _(n am / _{n o)} for the measurement value _{n o} of the measurement value _{n am,}
And a method for evaluating a product, comprising the step of determining the similarity between the state of the target product and the state of the deteriorated sample using the reference change rate _Sn and the control change rate R (m) _n . In the step of determining the similarity,
Using the root mean square error function α (m) represented by the following mathematical formula (1), a condition m that minimizes the root mean square error function α (m) is determined,

Β (m) _n of the root mean square error function alpha (m) is the divergence function representing the degree of divergence of the control change rate R (m) _n and the reference change rate S _n, according to claim 1 Product evaluation method. The divergence function β (m) _n is expressed by the following formula (2) using the reference change rate R (m) _n and the reference change rate S _n .

The discrepancy function beta _{(m) n} of l (n) is the control rate of change obtained for element n R _(m) of the _n and the reference change rate _{S n,} is a power number,
Measurement values of element n representing the characteristic of the calibration samples obtained by artificially degrade the simulated sample is the n _c, the measured value n _c, measured value n _o elements n representing the characteristic of the simulated sample When the rate of change in calibration is expressed as a ratio to
When the calibration change rate of the element n representing the characteristics of the calibration sample is greater than 1, the calibration change rate of the elements n representing the characteristics of the calibration sample is in the range of 1.2 to 1.6. With respect to element n2 with respect to element n1, such that the power of the calibration change rate of element n2 is greater than the calibration change rate of element n1 for element n2 with a calibration change rate of less than 1.1. The power of 1 (n2) is determined,
When the calibration change rate of the element n representing the characteristics of the calibration sample is smaller than 1, the calibration change rate of the elements n representing the characteristics of the calibration sample is in the range of 0.2 to 0.6. With reference to element n1, for this element n2, such that the power of the calibration change rate of element n2 is smaller than the calibration change rate of element n1 for element n2 with a calibration change rate greater than 0.7. The method for evaluating a product according to claim 2, wherein a power of 1 (n2) is determined. The divergence function beta (m) _n is an evaluation method of a product according to claim 2 represented by the following equation (3) by using the above control change rate R (m) _n and the reference change rate S _n.
β (m) _n = R (m) _n −S _n (3)   The method for evaluating a product according to any one of claims 1 to 4, wherein a maximum value N of natural numbers n representing the type of the element is 3 or more.   The method for evaluating a product according to any one of claims 1 to 5, wherein a maximum value M of natural numbers m representing the type of the condition is 3 or more.

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