Method for estimating stress characteristics of tempered glass and method for creating a model for estimating stress characteristics

Abstract

To efficiently obtain stress properties of tempered glass.SOLUTION: The present invention discloses a method for estimating stress properties of tempered glass. Based on part of a plurality of stress properties of a compressive stress layer 2 formed by multiple rounds of ion exchange treatment, the other stress properties are estimated. The method for estimating stress properties includes a sampling step S4, a prediction model preparation step S5, a measurement step S7, and an estimation step S8. The other stress properties include depth DOC of the compressive stress layer 2.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a method for estimating stress characteristics of tempered glass having a compressive stress layer and a method for creating a model for estimating stress characteristics. [Background technology]

[0002] Devices such as mobile phones (especially smartphones), tablet computers, digital cameras, in-vehicle instrument panel devices, touch panel displays, and contactless power supply devices are becoming increasingly popular. Ion-exchanged tempered glass is used in electronic devices for these applications. In recent years, tempered glass has also been increasingly used in the exterior components of digital signage, pointing devices, smartphones, and other devices.

[0003] Tempered glass has a compressive stress layer formed on its surface by ion exchange treatment, which suppresses the formation and propagation of cracks on the surface and provides high strength. The strength of tempered glass can be improved by adjusting the formation mode of such a compressive stress layer. Therefore, when controlling the quality of tempered glass or developing it, it is necessary to accurately measure the stress characteristics of the compressive stress layer.

[0004] Patent Document 1 discloses a surface stress measuring device that measures the stress and depth of a compressive stress layer by utilizing the optical waveguiding effect of the compressive stress layer. This surface stress measuring device includes a light supplying member that introduces monochromatic light into the surface layer of tempered glass, a light extracting member that emits the light that has propagated within the surface layer of the glass to the outside of the glass, and a light converting member that separates the light emitted from the light extracting member into two light components that vibrate parallel and perpendicular to the interface between the glass and the light extracting member, and converts these light components into bright line trains (see the claims in the document).

[0005] This surface stress measuring device can determine the difference in the positions of bright line rows associated with two types of light components extracted from glass, and from this difference in the positions of the bright line rows, it is possible to determine the difference in the surface refractive index associated with the two types of light components for glass. The compressive stress on the surface of the glass can then be measured from this difference in surface refractive index. Furthermore, the depth (thickness) of the compressive stress layer can be measured from the number of bright line rows (see column 4, page 2 and column 5, page 3 of the same document).

[0006] Patent Document 2 discloses a stress measurement device that uses scattered laser light to measure the stress distribution in tempered glass. According to this stress measurement device, a deflection phase variable member is used to vary the polarization phase difference of the laser light by one or more wavelengths relative to the wavelength of the laser light, and an imaging element is used to capture multiple images of the scattered light emitted when the varied laser light is incident on the tempered glass. A calculation unit then measures the periodic brightness change of the scattered light using the multiple captured images, calculates the phase change of the brightness change, and calculates the stress distribution in the depth direction from the surface of the tempered glass based on the phase change (see claim 1 in the same document). This stress measurement device can measure the stress inside chemically strengthened glass regardless of the refractive index distribution. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Publication No. 53-136886 [Patent Document 2] International Publication No. 2018 / 056121 Summary of the Invention [Problem to be solved by the invention]

[0008] In recent years, lithium aluminosilicate (LAS) tempered glass has been attracting attention due to its high surface stress and the ability to create a deep stress layer.

[0009] When producing this tempered glass, for example, the glass to be tempered is immersed in a high-temperature molten salt containing NaNO3 to perform a first chemical tempering treatment. This causes ion exchange between the Li ions in the glass to be tempered and the Na ions in the molten salt. Since Na ions easily diffuse into tempered glass, they are introduced deeper into the tempered glass from its surface.

[0010] The tempered glass is then immersed in a molten salt containing KNO3 for a second chemical tempering treatment. This exchanges the K ions with the Li ions or Na ions contained in the tempered glass. This results in the formation of a compressive stress layer with a large compressive stress due to the K ions in a surface region shallower than the compressive stress layer due to the Na ions formed in the first ion exchange.

[0011] The refractive index of glass decreases when Li ions in the glass are ion-exchanged with Na ions in the molten salt, and increases when Na or Li ions in the glass are ion-exchanged with K ions in the molten salt. That is, the refractive index of the surface region of the glass that has been exchanged with K ions is higher than that of the non-ion-exchanged portion of the glass. On the other hand, the refractive index of the deeper region that has been exchanged with Na ions is lower than that of the non-ion-exchanged portion of the glass, resulting in a state in which the refractive index and stress are not proportional.

[0012] For this reason, the stress measurement device utilizing the surface optical waveguide effect disclosed in Patent Document 1 can measure the stress value and stress distribution of the compressive stress layer caused by K ions, but cannot measure the stress characteristics in the deeper compressive stress region caused by Na ions.

[0013] On the other hand, the stress measurement device disclosed in Patent Document 2 can measure the stress characteristics of the stress layer in a deeper range. However, with this stress measurement device, the beam diameter of the laser light becomes the resolution in the depth direction, and the value is, for example, about 10 μm. Therefore, it was not possible to measure the stress value with high accuracy in a shallow region from the surface of the glass to the interior (for example, a region within 10 μm from the surface).

[0014] For these reasons, in order to measure the stress characteristics from the surface to the deep interior regions of lithium aluminosilicate (LAS) tempered glass that has undergone ion exchange between Li ions and Na ions, and between Na ions and K ions, it has been necessary to use both the stress measuring device disclosed in Patent Document 1 and the stress measuring device disclosed in Patent Document 2. As a result, measuring the stress characteristics of a large number of tempered glasses during quality control and development of tempered glass has required a significant amount of time. In addition, it has become necessary to prepare multiple types of measuring devices in the manufacturing process, which has increased equipment costs.

[0015] The present invention has been made in view of the above circumstances, and has as its technical object to efficiently obtain the stress characteristics of tempered glass. [Means for solving the problem]

[0016] The present invention is intended to solve the above-mentioned problems, and provides a stress characteristic estimation method for tempered glass, which estimates other stress characteristics based on some of the stress characteristics in a compressive stress layer formed by multiple ion exchange treatments. The method includes: a sampling step of acquiring the plurality of stress characteristics in a sample tempered glass having the compressive stress layer as training data; a prediction model creation step of creating a prediction model showing a relationship between the some of the stress characteristics and the other stress characteristics based on the training data using a calculation processing device; a measurement step of acquiring the some of the stress characteristics in the tempered glass to be estimated having the compressive stress layer as input data for the prediction model; and an estimation step of inputting the input data acquired by the measurement step into the prediction model and acquiring output data related to the other stress characteristics using the calculation processing device, wherein the other stress characteristics include a depth of curvature (DOC) of the compressive stress layer.

[0017] According to this configuration, by inputting input data of some of the stress characteristics of the tempered glass to be estimated, which are acquired in the measurement process, into the prediction model, it is possible to accurately estimate the depth of the compressive stress layer, which is another stress characteristic. This significantly reduces the work time required to measure the stress characteristics of a large number of tempered glasses. Therefore, it is possible to efficiently perform strength inspections and strength analyses of a large number of tempered glasses.

[0018] In this method, the target tempered glass and the sample tempered glass may be in the form of a plate or sheet having a surface, and the partial stress characteristics may include a diffusion depth (DOL) from the surface of K ions introduced by the ion exchange treatment.

[0019] The tempered glass may also have a tensile stress layer at a central position in the thickness direction of the tempered glass, and the some stress characteristics may include a maximum compressive stress value (CS) in the compressive stress layer, and the other stress characteristics may include a maximum tensile stress value (CT) in the tensile stress layer.

[0020] The sampling step includes a first sampling step of acquiring, as first teacher data, the plurality of stress characteristics of the sample tempered glass that has undergone a first ion exchange treatment among the plurality of ion exchange treatments, and a final sampling step of acquiring, as final teacher data, the plurality of stress characteristics of the sample tempered glass that has undergone a final ion exchange treatment among the plurality of ion exchange treatments. The prediction model creation step creates the prediction model based on the first teacher data and the final teacher data. The measurement step includes a first measurement step of acquiring, as first input data for the prediction model, the some of the stress characteristics of the tempered glass to be estimated that has undergone a first ion exchange treatment among the plurality of ion exchange treatments, and a final measurement step of acquiring, as final input data for the prediction model, the some of the stress characteristics of the tempered glass to be estimated that has undergone a final ion exchange treatment among the plurality of ion exchange treatments. The estimation step includes inputting the first input data and the final input data acquired by the measurement step into the prediction model, and acquiring output data related to the other stress characteristics.

[0021] In this method, the multiple ion exchange treatments may be two ion exchange treatments.

[0022] In this method, in the estimation process, a regression equation and its constants as the prediction model can be obtained by regression analysis using some of the stress characteristics as explanatory variables and the other stress characteristics as dependent variables.

[0023] In the method, the regression equation may be a linear equation. In this case, when the maximum compressive stress value (CS) in the compressive stress layer and the diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment are the explanatory variables, and the depth (DOC) of the compressive stress layer is the target variable, the regression equation may include the following equation (1): DOC=aCS+bDOL+c (1) Here, a to c are constants.

[0024] Furthermore, when the maximum compressive stress value (CS) in the compressive stress layer and the diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment are used as the explanatory variables, and the maximum value of the tensile stress (CT) is used as the objective variable, the regression equation may include the following equation (2): CT=dCS+eDOL+f (2) Here, d to f are constants.

[0025] In this method, the some stress characteristics may include a thickness (T) of the tempered glass, and when the maximum compressive stress value (CS) in the compressive stress layer, the diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment, and the thickness (T) of the tempered glass are the explanatory variables, and the depth (DOC) of the compressive stress layer is the target variable, the regression equation may include the following equation (4): DOC=aT+bCS+cDOL+d (4) Here, a to d are constants.

[0026] In this method, the part of stress characteristics may include a thickness (T) of the tempered glass, and when the maximum compressive stress value (CS) in the compressive stress layer, the diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment, and the thickness (T) of the tempered glass are the explanatory variables, and the maximum value of the tensile stress (CT) is the target variable, the regression equation may include the following equation (5): CT=eT+fCS+gDOL+h (5) Here, e to h are constants.

[0027] In the method, the plurality of ion exchange treatments are two ion exchange treatments, and among the two ion exchange treatments, a maximum compressive stress value (CS 1st ) and the diffusion depth (DOL) from the surface of the K ions introduced by the first ion exchange treatment 1st) is used as the explanatory variable, and the maximum compressive stress value (CS 2nd ) and the diffusion depth (DOL) from the surface of the K ions introduced by the second ion exchange treatment, which is the final treatment. 2nd ) is used as the explanatory variable, and a depth of a compressive stress layer (DOC) of the tempered glass after the second, final ion exchange treatment is used as the response variable, the regression equation may include the following equation (7): DOC=aCS 1st +bDOL 1st +cCS 2nd +dDOL 2nd +e ···(7) Here, a to e are constants.

[0028] In the method, the plurality of ion exchange treatments are two ion exchange treatments, and among the two ion exchange treatments, a maximum compressive stress value (CS 1st ) and the diffusion depth (DOL) from the surface of the K ions introduced by the first ion exchange treatment 1st ) is used as the explanatory variable, and the maximum compressive stress value (CS 2nd ) and the diffusion depth (DOL) from the surface of the K ions introduced by the second ion exchange treatment, which is the final treatment. 2nd ) is the explanatory variable, and the maximum value of tensile stress (CT) of the tempered glass after the second, final ion exchange treatment is the response variable, the regression equation may include the following equation (8): CT=fCS 1st +gDOL 1st +hCS 2nd +iDOL 2nd +j ···(8) Here, f~j are constants.

[0029] In this method, the part of stress characteristics includes a thickness (T) of the tempered glass, the plurality of ion exchange treatments are two ion exchange treatments, and a maximum compressive stress value (CS 1st ) and the diffusion depth (DOL) from the surface of the K ions introduced by the first ion exchange treatment 1st ) is used as the explanatory variable, and the maximum compressive stress value (CS 2nd ) and the diffusion depth (DOL) from the surface of the K ions introduced by the second ion exchange treatment, which is the final treatment. 2nd ) as the explanatory variable, a thickness (T) of the tempered glass as the explanatory variable, and a depth of a compressive stress layer (DOC) of the tempered glass after the second, final ion exchange treatment as the objective variable, the regression equation may include the following equation (10): DOC=aT+bCS 1st +cDOL 1st +dCS 2nd +eDOL 2nd +f ···(10) Here, a to f are constants.

[0030] In this method, the part of stress characteristics includes a thickness (T) of the tempered glass, the plurality of ion exchange treatments are two ion exchange treatments, and a maximum compressive stress value (CS 1st ) and the diffusion depth (DOL) from the surface of the K ions introduced by the first ion exchange treatment 1st ) is used as the explanatory variable, and the maximum compressive stress value (CS 2nd ) and the diffusion depth (DOL) from the surface of the K ions introduced by the second ion exchange treatment, which is the final treatment.2nd ) as the explanatory variable, a thickness (T) of the tempered glass as the explanatory variable, and a maximum value (CT) of the tensile stress of the tempered glass after the second, final ion exchange treatment as the objective variable, the regression equation may include the following equation (11): CT = gT + hCS 1st +iDOL 1st +jCS 2nd +kDOL 2nd +l ···(11) Here, g~l is a constant.

[0031] The regression equation may be a multi-order equation. In this case, when the maximum compressive stress value (CS) in the compressive stress layer and the diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment are the explanatory variables, and the depth (DOC) of the compressive stress layer is the response variable, the regression equation may include the following equation (13): DOC=a(CS+b) 2 +c(DOL+d) 2 +e (13) Here, a to e are constants.

[0032] In this method, when the maximum compressive stress value (CS) in the compressive stress layer and the diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment are used as the explanatory variables, and the maximum value of the tensile stress (CT) is used as the response variable, the regression equation may include the following equation (14): CT=f(CS+g) 2 +h(DOL+i) 2 +j (14) Here, f~j are constants.

[0033] In this method, the part of stress characteristics includes a thickness (T) of the tempered glass, the plurality of ion exchange treatments are two ion exchange treatments, and a maximum compressive stress value (CS1st ) and the diffusion depth (DOL) of the K ions introduced by the first ion exchange treatment from the surface. 1st ) is used as the explanatory variable, and the maximum compressive stress value (CS 2nd ) and the diffusion depth (DOL) from the surface of the K ions introduced by the second ion exchange treatment, which is the final treatment. 2nd ) as the explanatory variable, a thickness (T) of the tempered glass as the explanatory variable, and a depth of a compressive stress layer (DOC) of the tempered glass after the second, final ion exchange treatment as the objective variable, the regression equation may include the following equation (16): DOC=a(T+b) 2 +c(CS 1st +d) 2 +e(DOL 1st +f) 2 +g(CS 2nd +h) 2 +i(DOL 2nd +j) 2 +k (16) Here, a to k are constants.

[0034] In this method, the part of stress characteristics includes a thickness (T) of the tempered glass, the plurality of ion exchange treatments are two ion exchange treatments, and a maximum compressive stress value (CS 1st ) and the diffusion depth (DOL) from the surface of the K ions introduced by the first ion exchange treatment 1st ) is used as the explanatory variable, and the maximum compressive stress value (CS 2nd ) and the diffusion depth (DOL) from the surface of the K ions introduced by the second ion exchange treatment, which is the final treatment. 2nd) as the explanatory variable, a thickness (T) of the tempered glass as the explanatory variable, and a maximum value (CT) of the tensile stress of the tempered glass after the second, final ion exchange treatment as the objective variable, the regression equation may include the following equation (17): CT=l(T+m) 2 +n(CS 1st +o) 2 +p(DOL 1st +q) 2 +r(CS 2nd +s) 2 +t(DOL 2nd +u) 2 +v (17) Here, l to v are constants.

[0035] It should be noted that the constants (a to v) contained in the above formulas (1) to (17) may have the same symbols used in individual formulas, but each represents an independent, individual constant value for each formula, and similar constant symbols do not represent the same value.

[0036] In this method, the regression equation may include, as the explanatory variable, a product term (CS × DOL) of the maximum compressive stress value (CS) in the compressive stress layer and the diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment.

[0037] In this method, the tempered glass to be estimated contains NaO and LiO as a glass composition, and the compressive stress layer includes a compressive stress layer caused by K ions introduced by the ion exchange treatment and a compressive stress layer caused by Na ions introduced by the ion exchange treatment. In the sampling step, some of the stress characteristics can be measured by a surface stress meter that utilizes an optical waveguiding effect, and the other stress characteristics can be measured by a scattered light photoelastic stress meter.

[0038] The present invention is intended to solve the above-mentioned problems, and provides a method for creating a model for estimating stress characteristics of tempered glass, which creates a predictive model used to estimate stress characteristics in a compressive stress layer formed by multiple ion exchange treatments, wherein the predictive model is a model used to estimate other stress characteristics based on some of the multiple stress characteristics in the compressive stress layer, and is characterized by comprising: a sampling step for acquiring the multiple stress characteristics in a sample tempered glass having the compressive stress layer as training data; and a predictive model creation step for creating the predictive model showing the relationship between the some stress characteristics and the other stress characteristics using a calculation processing device based on the training data.

[0039] The present invention also provides a stress characteristic estimation method for estimating stress characteristics of tempered glass using a prediction model prepared in advance by the above-mentioned stress characteristic estimation model creation method, comprising: a measurement process for acquiring some of the stress characteristics of the tempered glass to be estimated, which has the compressive stress layer, as input data for the prediction model; and an estimation process for inputting the input data acquired by the measurement process into the prediction model and acquiring output data related to the other stress characteristics, wherein the other stress characteristics include a depth (DOC) of the compressive stress layer.

[0040] The present invention is intended to solve the above-mentioned problems, and provides a stress characteristic estimation method for tempered glass, which estimates other stress characteristics based on some of the stress characteristics of a compressive stress layer formed by multiple ion exchange treatments. The method includes an estimation step of estimating the other stress characteristics using a processing device capable of performing calculations using a prediction model that shows the relationship between the some stress characteristics and the other stress characteristics, and a measurement step of acquiring the some stress characteristics of the tempered glass to be estimated that has the compressive stress layer as input data for the prediction model, wherein the other stress characteristics include a depth of compressive stress layer (DOC), and the estimation step involves inputting the input data acquired by the measurement step into the prediction model and acquiring output data related to the depth of compressive stress (DOC) as the other stress characteristic by the processing device. [Effects of the Invention]

[0041] According to the present invention, the stress characteristics of tempered glass can be obtained efficiently. [Brief explanation of the drawings]

[0042] [Figure 1] FIG. 1 is a schematic view showing a cross section of tempered glass. [Figure 2] 1 is a graph showing a stress profile in the thickness direction of tempered glass. [Figure 3] 1 is a flowchart showing a method for manufacturing tempered glass. [Figure 4] 1 is a flowchart illustrating a method for estimating stress characteristics of tempered glass. DETAILED DESCRIPTION OF THE INVENTION

[0043] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the drawings. Figures 1 to 4 show an embodiment of a method for estimating stress characteristics of tempered glass according to the present invention.

[0044] Tempered glass 1 is an example of glass that is the target of estimation by the method for estimating stress characteristics of tempered glass according to the present invention. As shown in Fig. 1, tempered glass 1 is a plate- or sheet-shaped chemically strengthened glass that has been chemically strengthened by ion exchange. Tempered glass 1 has surfaces 1a and 1b, a compressive stress layer 2, and a tensile stress layer 3.

[0045] The thickness T of the tempered glass 1 may be determined arbitrarily, but is preferably 2.0 mm or less, more preferably 1.8 mm or less, 1.6 mm or less, 1.4 mm or less, 1.2 mm or less, 1.0 mm or less, 0.9 mm or less, 0.85 mm or less, and still more preferably 0.8 mm or less, and is preferably 0.03 mm or more, 0.05 mm or more, 0.1 mm or more, 0.15 mm or more, 0.2 mm or more, 0.25 mm or more, 0.3 mm or more, 0.35 mm or more, 0.4 mm or more, 0.45 mm or more, 0.5 mm or more, 0.6 mm or more, and still more preferably 0.65 mm or more.

[0046] The surfaces 1a, 1b of the tempered glass 1 include the main surface 1a and the end surface 1b, which are opposite surfaces. The compressive stress layer 2 is formed in a surface layer portion including the main surface 1a and the end surface 1b of the tempered glass 1. The compressive stress layer 2 includes a compressive stress layer caused by K ions introduced by ion exchange treatment and a compressive stress layer caused by Na ions introduced by ion exchange treatment. The compressive stress layer caused by K ions is formed at a relatively shallow position on and near the surfaces 1a, 1b of the tempered glass 1. The compressive stress layer caused by Na ions is formed at a deeper position than the compressive stress layer caused by K ions. The tensile stress layer 3 is formed at a deeper position than the compressive stress layer 2.

[0047] The stress profile (stress distribution) of the tempered glass 1 is obtained by measuring the stress in the depth direction (direction perpendicular to the main surface 1a) from the main surface 1a side, with compressive stress being a positive number and tensile stress being a negative number. The stress profile of the tempered glass 1 obtained in this manner is shown, for example, in FIG. 2. In the graph of FIG. 2, the vertical axis represents stress, and the horizontal axis represents the position (depth) in the thickness direction relative to one of the main surfaces 1a. In the graph of FIG. 2, positive values ​​of stress represent compressive stress, and negative values ​​of stress represent tensile stress. That is, the larger the absolute value of the stress in the graph of FIG. 2, the larger the stress. Note that FIG. 2 is an exaggerated conceptual diagram for ease of understanding, and the stress profile of the tempered glass 1 is not limited to this embodiment.

[0048] The stress profile of the tempered glass 1 includes, in order from the main surface 1a side in the depth direction (direction perpendicular to the main surface 1a), a first peak P1, a first bottom B1, a second peak P2, and a second bottom B2.

[0049] The first peak P1 is the position where the compressive stress has a maximum value, and is present on the main surface 1a. The compressive stress CS of the first peak P1 is 500 MPa or more, preferably 700 MPa to 900 MPa, and more preferably 750 MPa to 850 MPa.

[0050] In the first bottom B1, the stress gradually decreases in the depth direction from the first peak P1 and reaches a minimum value. In Figure 2, the stress CSb in the first bottom B1 is shown as a compressive stress (positive value), but it can also be a tensile stress (negative value). The lower the stress CSb in the first bottom B1, the lower the tensile stress CT in the second bottom B2, making the behavior at the time of failure slower.

[0051] The stress CSb in the first bottom B1 is preferably +100 MPa or less, more preferably +90 MPa or less, +80 MPa or less, +70 MPa or less, or +60 MPa or less. However, if the stress CSb of the first bottom B1 is too low, cracks may occur on the surface during the strengthening process, deteriorating visibility. The stress CSb of the first bottom B1 is preferably -50 MPa or more, more preferably -45 MPa or more, -40 MPa or more, -35 MPa or more, or -30 MPa or more. The stress CSb of the first bottom B1 may be 0 MPa or more to +65 MPa or less, or may be -30 MPa or more to less than 0 MPa. The depth DOLb of the first bottom B1 is preferably 0.5% to 12% of the thickness T, more preferably 1% to 7% of the thickness T. The depth DOLb of the first bottom B1 is approximately equal to or slightly deeper than the depth of the compressive stress layer 2 due to K ions, i.e., the diffusion depth DOL of the K ions introduced by ion exchange. More specifically, DOLb is located within a range of ±10 μm with respect to DOL.

[0052] At the second peak P2, the stress gradually increases in the depth direction from the first bottom B1 and reaches a maximum value. The stress CSp at the second peak P2 is a compressive stress. The compressive stress CSp at the second peak P2 is 15 MPa to 250 MPa, preferably 15 MPa to 240 MPa, 15 MPa to 230 MPa, 15 MPa to 220 MPa, 15 MPa to 210 MPa, 15 MPa to 200 MPa, 15 MPa to 190 MPa, 15 MPa to 180 MPa, 15 MPa to 175 MPa, 15 MPa to 170 MPa, 15 MPa to 165 MPa, 15 MPa to 160 MPa, or 18 MPa to 100 MPa, and more preferably 20 MPa to 80 MPa.

[0053] The depth DOLp of the second peak P2 is 4% to 20% of the thickness T, preferably 4% to 19%, 4% to 18.5%, 4% to 18%, 4% to 17.5%, or 4% to 17% of the thickness T, and more preferably 4.5% to 17%, 5% to 17%, 6% to 17%, 7.3% to 17%, or 8% to 15%.

[0054] The depth direction distance from the first bottom B1 to the second peak P2, i.e., DOLp-DOLb, is 3% or more of the thickness T, preferably 4% or more of the thickness T, and more preferably 5% to 13% of the thickness T.

[0055] In the second bottom B2, the stress gradually decreases in the depth direction from the second peak P2 and reaches a minimum value (maximum absolute value). The absolute value (maximum value) of the tensile stress CT of the second bottom B2 at the center position in the thickness direction of the tempered glass 1 is 70 MPa or less, preferably 65 MPa or less, 60 MPa or less, and more preferably 40 MPa to 55 MPa.

[0056] The product of the tensile stress CT and the thickness T of the second bottom B2 is preferably -70 MPa·mm or more, more preferably -65 MPa·mm or more, -60 MPa·mm or more, or -55 MPa·mm or more. Also, the product of the tensile stress CT and the thickness T of the second bottom B2 is preferably -5 MPa·mm or less, -10 MPa·mm or less, -15 MPa·mm or less, -20 MPa·mm or less, -25 MPa·mm or less, or -30·mmMPa or less.

[0057] Between the second peak P2 and the second bottom B2 is the zero stress point Z where the stress is zero. Normally, it is difficult for the depth of the zero stress point Z, i.e., the depth DOC of the compressive stress layer 2, to exceed 20% of the thickness T, and the physical limit is about 22%. However, in this embodiment, a DOC exceeding this limit can be obtained.

[0058] The greater the depth DOC of the zero stress point Z, the higher the strength against protrusion penetration, and is preferably 10% or more of the thickness T, 10.5% or more, 11% or more, 11.5% or more, 12% or more, 12.5% ​​or more, 13% or more, 13.5% or more, 14% or more, 14.5% or more, 15% or more, 15.5% or more, 16% or more, 16.5% or more, 17% or more, 17.5% or more, 18% or more, more preferably 18.5% or more, 19% or more, 19.5% or more, 20% or more, 20.5% or more, 21% or more, 21.5% or more, 22.0% or more, 22.5% or more, 23% or more, 23.5% or more, and most preferably 24% or more.

[0059] However, if the depth DOC of the zero stress point Z becomes excessively large, there is a risk of excessive tensile stress being generated in the first bottom B1 or the second bottom B2. Therefore, the depth DOC of the zero stress point Z is preferably 35% or less of the thickness T, 34.5% or less, 34% or less, 33.5% or less, 33% or less, 32.5% or less, 32% or less, 31.5% or less, 31% or less, 30.5% or less, 30% or less, 29.5% or less, 29% or less, 28.5% or less, 28% or less, and more preferably 27% or less.

[0060] In this embodiment, the tempered glass 1 also has a similar stress profile at the end surface 1b. That is, the stress profile of the tempered glass 1 includes a first peak at the end surface 1b where the compressive stress reaches a maximum value, a first bottom where the stress gradually decreases in the depth direction from the first peak to a minimum value, a second peak where the compressive stress gradually increases in the depth direction from the first bottom to a maximum value, and a second bottom where the tensile stress gradually decreases in the depth direction from the second peak to a minimum value, the compressive stress at the first peak being 500 MPa or more, the compressive stress at the second peak being 15 MPa to 250 MPa, and the second peak being present at a depth of 4% to 20% of the thickness T. The preferred range of the stress profile for the end surface 1b can also be similarly applied to the preferred range of the stress profile for the main surface 1a.

[0061] The stress and its distribution in the tempered glass 1 can be measured using, for example, a surface stress meter (FSM-6000LE) and a scattered light photoelastic stress meter (SLP-1000) manufactured by Orihara Manufacturing Co., Ltd., and a combined value can be used.

[0062] The tempered glass 1 configured as described above is produced by preparing a plate-shaped glass containing alkali metal oxides as a composition (hereinafter referred to as glass to be tempered) and subjecting this glass to a tempering treatment.

[0063] The glass to be tempered preferably contains, in mass %, 40% to 70% of SiO2, 10% to 30% of Al2O3, 30% to 3% of B2O, 5% to 25% of Na2O, 0% to 5.5% of K2O, 0.1% to 10% of Li2O, 0% to 6% of MgO, and 50% to 15% of P2O as a glass composition.

[0064] The composition of the tempering glass described above is an example, and any tempering glass having a known composition may be used as long as it is capable of chemical strengthening by ion exchange. The composition of the tempered glass obtained by subjecting the above-described tempering glass to ion exchange treatment will be the same as the composition of the tempering glass before the ion exchange treatment.

[0065] A method for producing the tempered glass 1 (tempered glass plate) having the above configuration will be described below.

[0066] As shown in FIG. 3, this method includes a preparation step S1, a first ion exchange step S21, and a second ion exchange step S22.

[0067] The preparation step S1 is a step of preparing glass to be tempered. In the preparation step S1, glass raw materials prepared to have the above-mentioned glass composition are charged into a continuous melting furnace, heated and melted at 1500°C to 1600°C, refined, and then fed into a forming device, where they are formed into a plate or the like, and slowly cooled, thereby producing glass to be tempered.

[0068] As a method for forming glass sheets, it is preferable to employ the overflow downdraw method. The overflow downdraw method is a method that can mass-produce high-quality glass sheets, can easily produce large glass sheets, and can also minimize scratches on the surface of the glass sheet. In the overflow downdraw method, for example, alumina or dense zircon is used as a constituent material of the formed body. The glass to be tempered according to the present invention has good compatibility with alumina and dense zircon, particularly alumina (the compositional components of the molten glass are less likely to react with the compositional components of the formed body, and bubbles, bumps, etc. are less likely to occur).

[0069] In addition to the overflow downdraw method, various other forming methods can be employed, such as the float method, the downdraw method (slot-down method, redraw method, etc.), the roll-out method, and the press method.

[0070] After or simultaneously with forming the glass to be tempered, the glass may be subjected to bending, if necessary. In addition, the glass may be subjected to processes such as cutting, drilling, surface polishing, chamfering, edge polishing, and etching, if necessary.

[0071] The dimensions of the glass to be tempered may be determined arbitrarily, but the thickness T is preferably 2.0 mm or less, more preferably 0.05 to 1.0 mm, and even more preferably 0.1 mm to 0.9 mm, 0.3 mm to 0.85 mm, or 0.5 mm to 0.8 mm.

[0072] In the first ion exchange step S21, the glass to be tempered is immersed in (contacted with) a treatment tank filled with a first molten salt containing Na ions and held at a predetermined temperature for a predetermined time, thereby performing an ion exchange treatment on the surface of the glass to be tempered. This causes ion exchange between Li ions in the glass to be tempered and Na ions in the first molten salt, introducing Na ions near the surface (main surface and edge face) of the glass to be tempered. Furthermore, Na ions in the glass to be tempered are ion exchanged with K ions in the first molten salt. As a result, a compressive stress layer 2 is formed in the surface layer of the glass to be tempered, and the glass to be tempered is strengthened.

[0073] In the first ion exchange step S21, the region into which Na ions are introduced into the glass to be tempered is preferably a region from the surface of the glass to a depth of 10% or more of the thickness T, more preferably a region from the surface of the glass to a depth of 12% or more, 14% or more, 15% or more, or 15% or more to 40% or less of the thickness T.

[0074] The first molten salt used in the first ion exchange step S21 is preferably a mixed salt of NaNO3 and KNO3. If the first molten salt contains K ions, it becomes easier to measure the compressive stress and its distribution in the surface region of the glass to be tempered after the first ion exchange step S21, making this suitable for quality control of the resulting tempered glass. The concentration of NaNO3 in the first molten salt is preferably higher than the concentration of KNO3 in the first molten salt, but this relationship is not limited to this. The concentration of NaNO3 in the first molten salt is preferably 50% or more by mass, and the concentration of KNO3 in the first molten salt is preferably less than 50% by mass. While not limited thereto, the concentration of NaNO3 in the first molten salt is preferably 100-20%, 100-30%, 100-40%, 100-50%, or 100-60% by mass, with the remainder being KNO3. When the first molten salt is a mixed salt of NaNO3 and KNO3, Na ions diffuse more easily into the tempered glass than K ions, and are therefore introduced into a deeper region from the surface of the tempered glass. Note that the first molten salt may contain only NaNO3 and not KNO3. Alternatively, the first molten salt may contain LiNO3.

[0075] The ion exchange treatment temperature in the first ion exchange step S21 is preferably 350 to 480° C., more preferably 360 to 430° C., even more preferably 370 to 400° C., or 370 to 390° C. The ion exchange treatment time in the first ion exchange step S21 is preferably 1 to 20 hours, more preferably 1.5 to 15 hours, and even more preferably 2 to 10 hours.

[0076] In the second ion exchange process S22, the glass to be tempered is immersed in a treatment tank filled with a second molten salt containing K ions and Li ions, and is held at a predetermined temperature for a predetermined time, thereby performing an ion exchange treatment on the surface of the glass to be tempered.

[0077] As a result, Li ions in the second molten salt undergo reverse ion exchange with Na ions in the glass to be tempered, causing at least a portion of the Na ions to be removed from the glass to be tempered. At the same time, K ions undergo ion exchange with Li ions or Na ions contained in the glass to be tempered, causing K ions to be introduced into the glass to a region shallower than 7% of the thickness T from the surface. In other words, the compressive stress formed in the surface layer of the glass to be tempered is alleviated by the reverse ion exchange, while the glass to be tempered is strengthened by the ion exchange, and high compressive stress is formed only in the surface vicinity of the surface layer.

[0078] In the second ion exchange step S22, the region where Na ions are removed from the glass to be tempered is preferably a region from the surface of the glass to a depth of 15% or less of the thickness T, more preferably a region from the surface of the glass to a depth of 14%, 13%, 12%, 11%, 10%, 1% to 10%, 2% to 10%, 3% to 10%, 4% to 10%, or 5% to 10% of the thickness T. In the second ion exchange step S22, the region where K ions are introduced into the glass to be tempered is preferably a region from the surface of the glass to a depth of 7% or less of the thickness T, more preferably a region from the surface of the glass to a depth of 6.5%, 6%, 5.5%, or 5% of the thickness T.

[0079] The second molten salt used in the second ion exchange step S22 is preferably a mixed salt of LiNO3 and KNO3. The concentration of LiNO3 in the second molten salt is preferably lower than the concentration of KNO3 in the second molten salt. Specifically, the concentration of LiNO3 in the second molten salt is preferably 0.1 to 5%, 0.2 to 5%, 0.3 to 5%, 0.4 to 5%, 0.5 to 5%, 0.5 to 4%, 0.5 to 3%, 0.5 to 2.5%, 0.5 to 2%, or 1 to 2% by mass. The concentration of KNO3 in the second molten salt is preferably 95 to 99.5%, 96 to 99.5%, 97 to 99.5%, 98 to 99.5%, 98 to 99.4%, 98 to 99.3%, 98 to 99.2%, 98 to 99.1%, or 98 to 99% by mass.

[0080] The concentration of Li ions in the second molten salt is preferably 100 mass ppm or more. In this case, the concentration of Li ions in the second molten salt is determined by multiplying LiNO3, expressed in mass%, by 0.101.

[0081] The ion exchange treatment temperature in the second ion exchange step S22 is preferably 350 to 480°C, more preferably 360 to 430°C, even more preferably 370 to 400°C, or 370 to 390°C. The ion exchange treatment time in the second ion exchange step S22 is preferably shorter than the ion exchange treatment time in the first ion exchange step S21. The ion exchange treatment time in the second ion exchange step S22 is preferably 0.2 hours or more, more preferably 0.3 to 2 hours, 0.4 to 1.5 hours, and even more preferably 0.5 to 1 hour.

[0082] The glass to be tempered that is immersed in the molten salt in each of the ion exchange steps S21 and S22 may be preheated to the temperature of the molten salt in the ion exchange treatment in each of the ion exchange steps S21 and S22, or may be immersed in each of the molten salts while still at room temperature (e.g., 1°C to 40°C).

[0083] It is preferable to provide a cleaning step for cleaning the glass to be tempered that has been withdrawn from the molten salt between the first ion exchange step S21 and the second ion exchange step S22. By performing the cleaning, it becomes easier to remove deposits that have adhered to the glass to be tempered, and the ion exchange treatment can be performed more uniformly in the second ion exchange step S22.

[0084] Next, we will explain a method for estimating stress characteristics of the tempered glass 1. The method for estimating stress characteristics of tempered glass according to the present invention can estimate other stress characteristics by a prediction model based on some of the stress characteristics of the tempered glass 1 having a compressive stress layer 2 on the surface layer by multiple ion exchange treatments (two times in the above example).

[0085] As shown in Figure 4, this method is roughly divided into a model generation phase and a model utilization phase. After obtaining a predictive model, the processing in the model generation phase does not need to be repeated until the predictive model needs to be updated, for example, when manufacturing conditions such as glass composition or ion exchange conditions change. On the other hand, the processing in the model utilization phase can be repeatedly performed for quality control purposes, for example, in the manufacturing process of tempered glass products.

[0086] In the model generation phase, a sample tempered glass is produced by subjecting tempering glass having the same dimensions, shape, and composition as the tempered glass to be estimated to an ion exchange treatment under the same ion exchange conditions as those of the tempered glass to be estimated.

[0087] Model Generation The model generation phase includes a sample glass preparation step S3, a sampling step S4, and a prediction model creation step S5.

[0088] In the sample glass preparation step S3, a plurality of tempering glasses for producing sample tempered glasses are prepared.

[0089] The sampling process S4 includes multiple (N times: N is an integer of 2 or more) ion exchange processes S20-1 to S20-N performed on the tempering glass, and a first sampling process S4-1 to a final sampling process S4-N for measuring the stress characteristics of the sample tempered glass after each of the ion exchange processes S20-1 to S20-N.

[0090] In each of the sampling steps S4-1 to S4-N, multiple stress characteristics of the sample tempered glass are obtained as training data after each of the ion exchange steps S20-1 to S20-N. That is, multiple stress characteristics of the sample tempered glass after the first ion exchange step S20-1 are obtained as first training data in the first sampling step S4-1. Furthermore, after the second ion exchange step S20-2 is performed on this sample tempered glass, the stress characteristics are obtained as second training data in the second sampling step S4-2. This sampling is repeated, and the stress characteristics of the sample tempered glass after the final Nth ion exchange step S20-N are obtained as final training data in the final sampling step S4-N.

[0091] The number of ion exchange processes and samplings in the sampling step S4 is set to be the same as the number of ion exchange processes for producing the tempered glass to be estimated in the utilization phase, for example. That is, when the tempered glass to be estimated is produced by two ion exchange processes, the sample tempered glass is also produced by two ion exchange processes (a first ion exchange process S20-1 and a second ion exchange process S20-2) in the sampling step S4. In this case, two samplings (a first sampling process S4-1 and a second sampling process S4-2) are performed corresponding to each ion exchange process, and first teacher data and second teacher data, which are final teacher data, are obtained.

[0092] The stress characteristics included in the training data include CS, DOL, DOC, CT, CS80, and the thickness T of the tempered glass. Here, CS80 is the compressive stress value at a depth of 80 μm from the main surface of the sample tempered glass. Note that the training data is preferably converted in advance so that similar physical quantities have common units. For example, it is preferable to convert T, DOL, and DOC, which are data related to length (depth), into μm before use.

[0093] Of these stress properties, CS and DOL are measured, for example, using a surface stress meter (FSM-6000LE) manufactured by Orihara Manufacturing Co., Ltd. The FSM-6000LE can measure these stress properties using the optical waveguiding effect. Furthermore, DOC, CT, and CS80 are measured, for example, using a scattered light photoelastic stress meter (SLP-1000) manufactured by Orihara Manufacturing Co., Ltd. The thickness T can be measured after the final ion exchange process using, for example, a micrometer, a laser displacement meter, or other measuring device.

[0094] The measured training data is recorded on a recording medium or stored in a processing device capable of executing the prediction model creation step S5.

[0095] In the prediction model creation step S5, a prediction model for predicting stress characteristics is created by a calculation processing device based on the training data obtained in the sampling step S4. In the prediction model creation step S5, some or all of the training data acquired in the sampling step S4 is used. When some of the training data is used, for example, the first training data and the final training data may be used. Alternatively, only the final training data may be used. However, the prediction model may be created by adding other training data to the first training data and the final training data.

[0096] A commercially available computer can be used as the arithmetic processing device. Statistical analysis software is installed in the arithmetic processing device. The arithmetic processing device can create a regression equation, which is a prediction model, by regression analysis of the statistical analysis software. For example, JMP (registered trademark) manufactured by SAS Institute Inc. is preferably used as the statistical analysis software.

[0097] In the prediction model creation step S5, some of the stress characteristics included in the training data related to stress characteristics are used as explanatory variables, and the other stress characteristics are used as response variables. Specifically, CS, DOL, and / or T can be used as explanatory variables, and DOC, CT, and CS80 can be used as response variables.

[0098] The regression equation as a prediction model is expressed as a linear function (linear equation) of the above variables, for example, by the least squares method. For example, a regression equation created based only on the final training data is expressed by the following (1) to (3).

[0099] DOC=aCS+bDOL+c (1) CT=dCS+eDOL+f (2) CS80=gCS+hDOL+i (3) Here, a to i are positive or negative constants (the same applies below).

[0100] In another embodiment, the regression equation may be expressed by the following equations (4) to (6).

[0101] DOC=aT+bCS+cDOL+d (4) CT=eT+fCS+gDOL+h (5) CS80=iT+jCS+kDOL+l (6) Here, j to l are positive or negative constants (the same applies below).

[0102] In another embodiment, for example, when two ion exchange processes are performed, the regression equation created based on the first teacher data and the final teacher data (second teacher data) is expressed, for example, by the following equations (7) to (9).

[0103] DOC=aCS 1st +bDOL 1st +cCS 2nd +dDOL 2nd +e ···(7) CT=fCS 1st +gDOL 1st +hCS 2nd +iDOL 2nd +j ···(8) CS80=kCS 1st +lDOL 1st +mCS 2nd +nDOL 2nd +o ···(9) Here, m to o are positive or negative constants (the same applies below).

[0104] In the above equations (7) to (9), CS 1st is CS obtained after the first ion exchange treatment (first ion exchange step S20-1), and CS 2nd is the CS measured after the final ion exchange treatment (second ion exchange step) (hereinafter the same). 1st is the DOL measured after the first ion exchange treatment (first ion exchange step S20-1), and DOL 2nd is the DOL measured after the final ion exchange treatment (second ion exchange step) (the same applies below).

[0105] In another embodiment, the regression equation may be expressed by the following equations (10) to (12).

[0106] DOC=aT+bCS 1st +cDOL 1st +dCS 2nd +eDOL 2nd +f ···(10) CT = gT + hCS 1st +iDOL 1st +jCS 2nd +kDOL 2nd +l ···(11) CS80=mT+nCS 1st +oDOL 1st +pCS 2nd +qDOL 2nd +r ···(12) Here, p to r are positive or negative constants (the same applies below).

[0107] In another embodiment, the regression equation may be expressed as a multi-order function (multi-order equation). For example, a quadratic regression equation created based only on the final training data is expressed by the following equations (13) to (15).

[0108] DOC=a(CS+b) 2+c(DOL+d) 2 +e (13) CT=f(CS+g) 2 +h(DOL+i) 2 +j (14) CS80=k(CS+l) 2 +m(DOL+n) 2 +o ···(15)

[0109] In another embodiment, the regression equation (quadratic equation) may be expressed by the following equations (16) to (18).

[0110] DOC=a(T+b) 2 +c(CS 1st +d) 2 +e(DOL 1st +f) 2 +g(CS 2nd +h) 2 +i(DOL 2nd +j) 2 +k (16) CT=l(T+m) 2 +n(CS 1st +o) 2 +p(DOL 1st +q) 2 +r(CS 2nd +s) 2 +t(DOL 2nd +u) 2 +v (17) CS80=w(T+x) 2 +y(CS 1st +z) 2 +α(DOL 1st +β) 2 +γ(CS 2nd +δ) 2 +ε(DOL 2nd +ζ) 2 +η (18) Here, s to z and α to η are positive or negative constants.

[0111] It should be noted that the constants (a to z, α to η) contained in the above formulas (1) to (18) may have the same symbols used in individual formulas, but these represent individual constant values ​​that are calculated independently for each formula, and similar constant symbols do not represent the same value.

[0112] In addition to the above examples, the regression equation for the prediction model may be a polynomial (quadratic equation) including a product term of variables related to stress characteristics as an explanatory variable. The product term may be, for example, T × CS 1st ,T×DOL 1st ,T×CS 2nd ,T×DOL 2nd ,CS 1st ×DOL 1st ,CS 1st ×CS 2nd ,CS 1st ×DOL 2nd ,DOL 1st ×CS 2nd ,DOL 1st ×DOL 2nd ,CS 2nd ×DOL 2nd Examples include:

[0113] In the prediction model creation step S5, the validity of the prediction model created by the arithmetic processing device is verified (verification step). Specifically, the DOC (DOC 1st ,DOC 2nd ),CS(CS 1st ,CS 2nd ),CS80(CS80 1st ,CS80 2nd ) is compared with the data calculated by the prediction model. In the validation process, the coefficient of determination (R 2 The suitability of a predictive model is verified by the root mean square error (RMSE) and the coefficient of determination (COD). Here, the coefficient of determination represents the proportion of the total variation in the objective variable that can be explained by all explanatory variables, and is a value that indicates the goodness of fit between the regression equation and the training data. The closer the COD is to 1, the better.

[0114] In the model utilization phase, tempered glass to be estimated is produced by subjecting tempered glass to multiple ion exchange treatments. In this case, part of the stress characteristics of the tempered glass is measured after each ion exchange treatment, and other stress characteristics are estimated from the part of the stress characteristics based on the prediction model created in the prediction model creation step S5.

[0115] As shown in FIG. 4, the model use phase includes an estimation target glass preparation step S6, a measurement step S7, and an estimation step S8.

[0116] In the estimation target glass preparation step S6, a plurality of tempering glasses for producing tempered glass to be estimated are prepared.

[0117] The measurement step S7 includes multiple (N times: N is an integer greater than or equal to 2) ion exchange steps S200-1 to S200-N and multiple (N times) measurement steps S7-1 to S7-N performed after each ion exchange step S200-1 to S200-N. In the measurement step S7, a portion of the stress characteristics of the tempered glass is measured after each ion exchange step S200-1 to S200-N, and the measured stress characteristics are used as input data for the prediction model. For example, a portion of the stress characteristics of the tempered glass after the first ion exchange step S200-1 is measured in the first measurement step S7-1. The data related to the portion of the stress characteristics measured in the first measurement step S7-1 becomes first input data for the prediction model. When the second ion exchange step S200-2 is performed, a portion of the stress characteristics of the tempered glass thereafter is measured in the second measurement step S7-2 to obtain second input data. This measurement is repeated, and a part of the stress characteristics of the tempered glass after the final ion exchange step S200-N is performed is measured in a final measurement step S7-N to obtain final input data.

[0118] In the following description, the measuring step S7 and the estimating step S8 will be described in the case where tempered glass to be estimated is produced by two ion exchange steps. In this case, the second measuring step is the final measuring step.

[0119] In the first measurement step S7-1, after the first ion exchange step S200-1 (before the second ion exchange step), which is the initial ion exchange treatment, the CS of the tempered glass to be estimated is measured using, for example, a surface stress meter (FSM-6000LE) manufactured by Orihara Manufacturing Co., Ltd. 1st ,DOL 1st is measured as the first input data for the prediction model.

[0120] In the final measurement process, after the second ion exchange process, which is the final ion exchange process, the CS of the tempered glass to be estimated is measured using a surface stress meter (FSM-6000LE) manufactured by Orihara Seisakusho, for example. 2nd ,DOOL 2nd is measured as the final input data for the predictive model.

[0121] When T is used as a variable in the prediction model, the thickness of the tempered glass to be estimated is measured after the second ion exchange process (final ion exchange process) using a measuring device such as a micrometer, a laser displacement meter, or other measuring device. The measured data is sent to or input into a processing device capable of executing the estimation process S8.

[0122] In the estimation step S8, input data relating to the stress characteristics measured in the measurement step S7 is introduced into the prediction model. As the arithmetic processing device that executes the estimation step S8, a computer in which the above-mentioned prediction model is pre-installed and which is capable of executing calculations using a regression equation is used. That is, as the arithmetic processing device, the prediction model may be installed and used in a computer different from the computer that executed the prediction model creation step S5. Also, as the arithmetic processing device, the computer that executed the prediction model creation step S5 may be used. The arithmetic processing device calculates the first input data (CS 1st ,DOL 1st ) and final input data (CS 2nd ,DOL 2nd ), and in some cases measured T, are input into the predictive model to calculate output data related to other stress properties (DOS, CT, CS80).

[0123] According to the above-described method for estimating stress characteristics of tempered glass according to the present embodiment, among the multiple stress characteristics (CS, DOL, DOC, CT, CS80, T) of the tempered glass to be estimated, some of the stress characteristics (CS, DOL and / or T) are measured, and the acquired input data is input into a prediction model in the estimation step S8, thereby making it possible to accurately estimate other stress characteristics (DOC, CT, CS80). This significantly reduces the work time required to measure the stress characteristics of a large number of tempered glasses. Therefore, it becomes possible to efficiently perform strength inspections and strength analyses of a large number of tempered glasses. Furthermore, it becomes unnecessary to install multiple types of measuring devices in the tempered glass manufacturing process, thereby reducing equipment costs.

[0124] The present invention is not limited to the configuration of the above-described embodiment, nor is it limited to the above-described effects. The present invention can be modified in various ways without departing from the spirit of the present invention.

[0125] In the above embodiment, the prediction model creation step S5 is exemplified, in which regression analysis is performed using DOS, CT, and CS80 as the dependent variables, but the present invention is not limited to this configuration. It is possible to create a prediction model using not only CS80 (the stress value at a depth of 80 μm from the surface of the tempered glass) but also a stress value at any depth position or its depth as the dependent variables (for example, the stress value CSp of the second peak P2 in the stress profile and its depth DOLp, etc.).

[0126] In the above embodiment, an example of creating a prediction model by regression analysis is shown, but the present invention is not limited to this. A prediction model may be created using machine learning (deep learning) or other techniques.

[0127] In the above embodiment, each step may be performed by a different business operator, or may be performed by a single business operator. For example, the steps in the model creation phase (steps S3 to S5) and the steps in the model utilization phase (steps S6 to S8) may be performed by different business operators. [Example]

[0128] The tempered glass according to the present invention will be described below based on examples. Note that the following examples are merely illustrative and the present invention is not limited to the following examples in any way.

[0129] The samples were prepared as follows. First, a tempered glass plate was prepared for ion exchange treatment. The tempered glass plate had a glass composition, in mass %, of 51.6% SiO2, 27.9% Al2O3, 0.3% BO3, 0.6% KO, 7.5% Na2O, 3.3% Li2O, 0.3% MgO, 58.4% PO, and 0.1% SnO.

[0130] Glass raw materials were prepared to obtain the above composition and melted in a platinum pot at 1600°C for 21 hours. The resulting molten glass was then cast from a refractory molding using the overflow downdraw method. The glass ribbon thus formed was cut to a predetermined size to obtain a plurality of glass plates to be tempered as test pieces. Glass plates to be tempered with different thicknesses were prepared. The thicknesses of the glass plates to be tempered were 0.55 mm, 0.7 mm, and 0.8 mm.

[0131] Next, the glass to be tempered was immersed in a molten salt bath to undergo ion exchange treatment in a first ion exchange step and a second ion exchange step, thereby obtaining a tempered glass sheet.

[0132] In the first ion exchange process, a molten salt with a weight concentration ratio of KNO3 to NaNO3 of 70%:30% was used for chemical strengthening of a 0.55 mm thick tempered glass sheet, and a molten salt with a weight concentration ratio of KNO3 to NaNO3 of 40%:60% was used for chemical strengthening of a 0.7 mm and 0.8 mm thick tempered glass sheet.

[0133] The ion exchange treatment temperature of the molten salt in the first ion exchange step was 380°C. The ion exchange treatment time in the first ion exchange step was divided into two periods, 90 minutes and 120 minutes, for tempered glass sheets having a thickness of 0.55 mm. The ion exchange treatment time in the first ion exchange step was 180 minutes for tempered glass sheets having a thickness of 0.7 mm. The ion exchange treatment time in the first ion exchange step was 210 minutes for tempered glass sheets having a thickness of 0.8 mm.

[0134] In the second ion exchange step, the weight concentration ratio of KNO3 to LiNO3 in the molten salt was 99(%):1(%). The ion exchange treatment temperature of the molten salt in the second ion exchange step was 380°C. The ion exchange treatment time in the second ion exchange step was 45 minutes.

[0135] In Example 1, in order to create the regression equations (4) to (6) in the above embodiment, the stress characteristics CS, DOL, CT, CS80, and T of a tempered glass sheet after the second ion exchange process were measured. A surface stress meter (FSM-6000LE) and a scattered light photoelastic stress meter (SLP-1000) manufactured by Orihara Manufacturing Co., Ltd. were used to measure the stress characteristics. Using the measurement data as final training data, statistical analysis software JMP (registered trademark) was used to create a prediction model corresponding to the regression equations (4) to (6) in the above embodiment based solely on this final training data using a calculation processing device.

[0136] The measured CS and DOL were substituted into the created regression equation, and the output (estimated) DOC, CT, and CS80 were compared with the measured DOC, CT, and CS80. Based on the estimated and measured values ​​of the stress characteristics, the coefficient of determination (R 2 ) and root mean square error (RMSE) were calculated.

[0137] In Example 2, in order to create a prediction model corresponding to the regression equations (10) to (12) in the above embodiment, necessary teacher data (first teacher data and final teacher data) related to stress characteristics were acquired by a method similar to that of Example 1. Using a method similar to that of Example 1, a prediction model corresponding to the regression equations (10) to (12) in the above embodiment was created based on the acquired teacher data. Thereafter, similar to Example 1, a coefficient of determination (R 2 ) and root mean square error (RMSE) were calculated.

[0138] In Example 3, a prediction model corresponding to the regression equations (16) to (18) in the above embodiment was created using a method similar to that of Example 1. Thereafter, similar to Example 1, the coefficient of determination (R 2 ) and root mean square error (RMSE) were calculated.

[0139] In Example 4, a prediction model was created by adding product terms of each variable to the regression equations (7) to (9) in the above embodiment using the same method as in Example 1. The added product terms were T × CS 1st ,T×DOL 1st ,T×CS 2nd ,T×DOL 2nd ,CS 1st ×DOL 1st ,CS 1st ×CS 2nd ,CS 1st ×DOL 2nd ,DOL 1st ×CS 2nd ,DOL 1st ×DOL 2nd ,CS 2nd ×DOL 2nd Then, similarly to Example 1, the coefficient of determination (R 2 ) and root mean square error (RMSE) were calculated.

[0140] The verification results of Examples 1 to 4 are shown in Tables 1 and 2. [Table 1] [Table 2]

[0141] As shown in Tables 1 and 2, the stress characteristics estimated by the prediction model showed a high correlation with the actually measured stress characteristics. Therefore, the present invention makes it possible to estimate the stress characteristics (DOC, CT, CS80) with high accuracy. [Explanation of symbols]

[0142] 1. Tempered glass 1a Main surface of tempered glass 1b Tempered glass edge 2 Compressive stress layer 3 Tensile stress layer CS: Maximum compressive stress value of the compressive stress layer CT Maximum tensile stress DOC Depth of compressive stress layer S4 Sampling process S5 Prediction model creation process S7 Measurement process S8 Estimation process T Tempered glass thickness

Claims

1. A method for estimating stress characteristics of lithium aluminosilicate tempered glass, which estimates other stress characteristics based on some stress characteristics among multiple stress characteristics in a compressive stress layer formed by multiple ion exchange treatments, the tempered glass has a tensile stress layer at a central position in a thickness direction of the tempered glass, a sampling step of acquiring the plurality of stress characteristics of a sample tempered glass having the compressive stress layer as training data; a prediction model creation step of creating, by a calculation processing device, a prediction model indicating a relationship between the part of stress characteristics and the other stress characteristics based on the teacher data; a measuring step of acquiring stress characteristics of the portion of the tempered glass to be estimated, the tempered glass having the compressive stress layer, as input data for the prediction model; an estimation step of inputting the input data acquired in the measurement step into the prediction model and acquiring output data relating to the other stress characteristics by the arithmetic processing device, The tempered glass to be estimated and the sample tempered glass are in a plate or sheet shape having a surface, The plurality of stress characteristics include a maximum compressive stress value (CS) in the compressive stress layer, a diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment, a depth (DOC) of the compressive stress layer, a maximum value (CT) of tensile stress in the tensile stress layer, a compressive stress value (CS80) at a position 80 μm deep from the surface of the sample tempered glass, and a thickness (T) of the tempered glass, The part of stress characteristics includes a maximum compressive stress value (CS) in the compressive stress layer, a diffusion depth (DOL) from the surface of K ions introduced by the ion exchange treatment, and a thickness (T) of the tempered glass; In the estimation step, a regression equation and its constants as the prediction model are obtained by performing a regression analysis using the part of stress characteristics as explanatory variables and the other stress characteristics as objective variables; The regression equation is a linear equation, the multiple ion exchange treatments are two ion exchange treatments, The maximum compressive stress value (CS) in the compressive stress layer of the tempered glass after the first ion exchange treatment and before the second ion exchange treatment, which is the final treatment, of the two ion exchange treatments. １ｓｔ ) and the diffusion depth (DOL) from the surface of the K ions introduced by the first ion exchange treatment １ｓｔ ) are the explanatory variables, The maximum compressive stress value (CS ２ｎｄ ) and the diffusion depth (DOL) from the surface of the K ions introduced by the second ion exchange treatment, which is the final time. ２ｎｄ ) are the explanatory variables, When the depth of compressive stress layer (DOC) of the tempered glass after the second ion exchange treatment, which is the final treatment, is used as the objective variable, The method for estimating stress characteristics of tempered glass, wherein the regression equation includes the following equation (7): DOC=aCS １ｓｔ +bDOL １ｓｔ +cCS ２ｎｄ +dDOL ２ｎｄ +e ・・・（７） Here, a to e are constants.

2. A method for estimating stress characteristics of lithium aluminosilicate tempered glass, which estimates other stress characteristics based on some stress characteristics among multiple stress characteristics in a compressive stress layer formed by multiple ion exchange treatments, the tempered glass has a tensile stress layer at a central position in a thickness direction of the tempered glass, a sampling step of acquiring the plurality of stress characteristics of a sample tempered glass having the compressive stress layer as training data; a prediction model creation step of creating, by a calculation processing device, a prediction model indicating a relationship between the part of stress characteristics and the other stress characteristics based on the teacher data; a measuring step of acquiring stress characteristics of the portion of the tempered glass to be estimated, the tempered glass having the compressive stress layer, as input data for the prediction model; an estimation step of inputting the input data acquired in the measurement step into the prediction model and acquiring output data relating to the other stress characteristics by the arithmetic processing device, The tempered glass to be estimated and the sample tempered glass are in a plate or sheet shape having a surface, The plurality of stress characteristics include a maximum compressive stress value (CS) in the compressive stress layer, a diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment, a depth (DOC) of the compressive stress layer, a maximum value (CT) of tensile stress in the tensile stress layer, a compressive stress value (CS80) at a position 80 μm deep from the surface of the sample tempered glass, and a thickness (T) of the tempered glass, The part of stress characteristics includes a maximum compressive stress value (CS) in the compressive stress layer, a diffusion depth (DOL) from the surface of K ions introduced by the ion exchange treatment, and a thickness (T) of the tempered glass; In the estimation step, a regression equation and its constants as the prediction model are obtained by performing a regression analysis using the part of stress characteristics as explanatory variables and the other stress characteristics as objective variables; The regression equation is a linear equation, the multiple ion exchange treatments are two ion exchange treatments, The maximum compressive stress value (CS) in the compressive stress layer of the tempered glass after the first ion exchange treatment and before the second ion exchange treatment, which is the final treatment, of the two ion exchange treatments. １ｓｔ ) and the diffusion depth (DOL) from the surface of the K ions introduced by the first ion exchange treatment １ｓｔ ) are the explanatory variables, The maximum compressive stress value (CS ２ｎｄ ) and the diffusion depth (DOL) from the surface of the K ions introduced by the second ion exchange treatment, which is the final time. ２ｎｄ ) are the explanatory variables, When the maximum value (CT) of the tensile stress of the tempered glass after the second ion exchange treatment, which is the final treatment, is used as the objective variable, The method for estimating stress characteristics of tempered glass, wherein the regression equation includes the following equation (8): CT=fCS １ｓｔ +gDOL １ｓｔ +hCS ２ｎｄ +iDOL ２ｎｄ +j ・・・（８） Here, f to j are constants.

3. A method for estimating stress characteristics of lithium aluminosilicate tempered glass, which estimates other stress characteristics based on some stress characteristics among multiple stress characteristics in a compressive stress layer formed by multiple ion exchange treatments, the tempered glass has a tensile stress layer at a central position in a thickness direction of the tempered glass, a sampling step of acquiring the plurality of stress characteristics of a sample tempered glass having the compressive stress layer as training data; a prediction model creation step of creating, by a calculation processing device, a prediction model indicating a relationship between the part of stress characteristics and the other stress characteristics based on the teacher data; a measuring step of acquiring stress characteristics of the portion of the tempered glass to be estimated, the tempered glass having the compressive stress layer, as input data for the prediction model; an estimation step of inputting the input data acquired in the measurement step into the prediction model and acquiring output data relating to the other stress characteristics by the arithmetic processing device, The tempered glass to be estimated and the sample tempered glass are in a plate or sheet shape having a surface, The plurality of stress characteristics include a maximum compressive stress value (CS) in the compressive stress layer, a diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment, a depth (DOC) of the compressive stress layer, a maximum value (CT) of tensile stress in the tensile stress layer, a compressive stress value (CS80) at a position 80 μm deep from the surface of the sample tempered glass, and a thickness (T) of the tempered glass, The part of stress characteristics includes a maximum compressive stress value (CS) in the compressive stress layer, a diffusion depth (DOL) from the surface of K ions introduced by the ion exchange treatment, and a thickness (T) of the tempered glass; In the estimation step, a regression equation and its constants as the prediction model are obtained by performing a regression analysis using the part of stress characteristics as explanatory variables and the other stress characteristics as objective variables; The regression equation is a linear equation, the multiple ion exchange treatments are two ion exchange treatments, The maximum compressive stress value (CS) in the compressive stress layer of the tempered glass after the first ion exchange treatment and before the second ion exchange treatment, which is the final treatment, of the two ion exchange treatments. １ｓｔ ) and the diffusion depth (DOL) from the surface of the K ions introduced by the first ion exchange treatment １ｓｔ ) are the explanatory variables, The maximum compressive stress value (CS ２ｎｄ ) and the diffusion depth (DOL) from the surface of the K ions introduced by the second ion exchange treatment, which is the final time. ２ｎｄ ) are the explanatory variables, The thickness (T) of the tempered glass is the explanatory variable, When the depth of compressive stress layer (DOC) of the tempered glass after the second ion exchange treatment, which is the final treatment, is used as the objective variable, The method for estimating stress characteristics of tempered glass, wherein the regression equation includes the following equation (10): DOC=aT+bCS １ｓｔ +cDOL １ｓｔ +dCS ２ｎｄ +eDOL ２ｎｄ +f ・・・（１０) Here, a to f are constants.

4. A method for estimating stress characteristics of lithium aluminosilicate tempered glass, which estimates other stress characteristics based on some stress characteristics among multiple stress characteristics in a compressive stress layer formed by multiple ion exchange treatments, the tempered glass has a tensile stress layer at a central position in a thickness direction of the tempered glass, a sampling step of acquiring the plurality of stress characteristics of a sample tempered glass having the compressive stress layer as training data; a prediction model creation step of creating, by a calculation processing device, a prediction model indicating a relationship between the part of stress characteristics and the other stress characteristics based on the teacher data; a measuring step of acquiring stress characteristics of the portion of the tempered glass to be estimated, the tempered glass having the compressive stress layer, as input data for the prediction model; an estimation step of inputting the input data acquired in the measurement step into the prediction model and acquiring output data relating to the other stress characteristics by the arithmetic processing device, The tempered glass to be estimated and the sample tempered glass are in a plate or sheet shape having a surface, The plurality of stress characteristics include a maximum compressive stress value (CS) in the compressive stress layer, a diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment, a depth (DOC) of the compressive stress layer, a maximum value (CT) of tensile stress in the tensile stress layer, a compressive stress value (CS80) at a position 80 μm deep from the surface of the sample tempered glass, and a thickness (T) of the tempered glass, The part of stress characteristics includes a maximum compressive stress value (CS) in the compressive stress layer, a diffusion depth (DOL) from the surface of K ions introduced by the ion exchange treatment, and a thickness (T) of the tempered glass; In the estimation step, a regression equation and its constants as the prediction model are obtained by performing a regression analysis using the part of stress characteristics as explanatory variables and the other stress characteristics as objective variables; The regression equation is a linear equation, the multiple ion exchange treatments are two ion exchange treatments, The maximum compressive stress value (CS) in the compressive stress layer of the tempered glass after the first ion exchange treatment and before the second ion exchange treatment, which is the final treatment, of the two ion exchange treatments. １ｓｔ ) and the diffusion depth (DOL) from the surface of the K ions introduced by the first ion exchange treatment １ｓｔ ) are the explanatory variables, The maximum compressive stress value (CS ２ｎｄ ) and the diffusion depth (DOL) from the surface of the K ions introduced by the second ion exchange treatment, which is the final time. ２ｎｄ ) are the explanatory variables, The thickness (T) of the tempered glass is the explanatory variable, When the maximum value (CT) of the tensile stress of the tempered glass after the second ion exchange treatment, which is the final treatment, is used as the objective variable, The method for estimating stress characteristics of tempered glass, wherein the regression equation includes the following equation (11): CT=gT+hCS １ｓｔ +iDOL １ｓｔ +jCS ２ｎｄ +kDOL ２ｎｄ +l ・・・(１１) Here, g to l are constants.

5. A method for estimating stress characteristics of lithium aluminosilicate tempered glass, which estimates other stress characteristics based on some stress characteristics among multiple stress characteristics in a compressive stress layer formed by multiple ion exchange treatments, the tempered glass has a tensile stress layer at a central position in a thickness direction of the tempered glass, a sampling step of acquiring the plurality of stress characteristics of a sample tempered glass having the compressive stress layer as training data; a prediction model creation step of creating, by a calculation processing device, a prediction model indicating a relationship between the part of stress characteristics and the other stress characteristics based on the teacher data; a measuring step of acquiring stress characteristics of the portion of the tempered glass to be estimated, the tempered glass having the compressive stress layer, as input data for the prediction model; an estimation step of inputting the input data acquired in the measurement step into the prediction model and acquiring output data relating to the other stress characteristics by the arithmetic processing device, The tempered glass to be estimated and the sample tempered glass are in a plate or sheet shape having a surface, The plurality of stress characteristics include a maximum compressive stress value (CS) in the compressive stress layer, a diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment, a depth (DOC) of the compressive stress layer, a maximum value (CT) of tensile stress in the tensile stress layer, a compressive stress value (CS80) at a position 80 μm deep from the surface of the sample tempered glass, and a thickness (T) of the tempered glass, The part of stress characteristics includes a maximum compressive stress value (CS) in the compressive stress layer, a diffusion depth (DOL) from the surface of K ions introduced by the ion exchange treatment, and a thickness (T) of the tempered glass; In the estimation step, a regression equation and its constants as the prediction model are obtained by performing a regression analysis using the part of stress characteristics as explanatory variables and the other stress characteristics as objective variables; The regression equation is a multi-order equation, the multiple ion exchange treatments are two ion exchange treatments, The maximum compressive stress value (CS) in the compressive stress layer of the tempered glass after the first ion exchange treatment and before the second ion exchange treatment, which is the final treatment, of the two ion exchange treatments. １ｓｔ ) and the diffusion depth (DOL) of the K ions introduced by the first ion exchange treatment from the surface １ｓｔ ) are the explanatory variables, The maximum compressive stress value (CS ２ｎｄ ) and the diffusion depth (DOL) from the surface of the K ions introduced by the second ion exchange treatment, which is the final time. ２ｎｄ ) are the explanatory variables, The thickness (T) of the tempered glass is the explanatory variable, When the depth of compressive stress layer (DOC) of the tempered glass after the second ion exchange treatment, which is the final treatment, is used as the objective variable, The method for estimating stress characteristics of tempered glass, wherein the regression equation includes the following equation (16): DOC=a(T+b) ２ +c(CS １ｓｔ +d) ２ +e(DOL １ｓｔ +f) ２ +g(CS ２ｎｄ +h) ２ +i(DOL ２ｎｄ +j) ２ +k ・・・(16) Here, a to k are constants.

6. A method for estimating stress characteristics of lithium aluminosilicate tempered glass, which estimates other stress characteristics based on some stress characteristics among multiple stress characteristics in a compressive stress layer formed by multiple ion exchange treatments, the tempered glass has a tensile stress layer at a central position in a thickness direction of the tempered glass, a sampling step of acquiring the plurality of stress characteristics of a sample tempered glass having the compressive stress layer as training data; a prediction model creation step of creating, by a calculation processing device, a prediction model indicating a relationship between the part of stress characteristics and the other stress characteristics based on the teacher data; a measuring step of acquiring stress characteristics of the portion of the tempered glass to be estimated, the tempered glass having the compressive stress layer, as input data for the prediction model; an estimation step of inputting the input data acquired in the measurement step into the prediction model and acquiring output data relating to the other stress characteristics by the arithmetic processing device, The tempered glass to be estimated and the sample tempered glass are in a plate or sheet shape having a surface, The plurality of stress characteristics include a maximum compressive stress value (CS) in the compressive stress layer, a diffusion depth (DOL) from the surface of the K ions introduced by the ion exchange treatment, a depth (DOC) of the compressive stress layer, a maximum value (CT) of tensile stress in the tensile stress layer, a compressive stress value (CS80) at a position 80 μm deep from the surface of the sample tempered glass, and a thickness (T) of the tempered glass, The part of stress characteristics includes a maximum compressive stress value (CS) in the compressive stress layer, a diffusion depth (DOL) from the surface of K ions introduced by the ion exchange treatment, and a thickness (T) of the tempered glass; In the estimation step, a regression equation and its constants as the prediction model are obtained by performing a regression analysis using the part of stress characteristics as explanatory variables and the other stress characteristics as objective variables; The regression equation is a multi-order equation, the multiple ion exchange treatments are two ion exchange treatments, The maximum compressive stress value (CS) in the compressive stress layer of the tempered glass after the first ion exchange treatment and before the second ion exchange treatment, which is the final treatment, of the two ion exchange treatments. １ｓｔ ) and the diffusion depth (DOL) from the surface of the K ions introduced by the first ion exchange treatment １ｓｔ ) are the explanatory variables, The maximum compressive stress value (CS ２ｎｄ ) and the diffusion depth (DOL) from the surface of the K ions introduced by the second ion exchange treatment, which is the final time. ２ｎｄ ) are the explanatory variables, The thickness (T) of the tempered glass is the explanatory variable, When the maximum value (CT) of the tensile stress of the tempered glass after the second ion exchange treatment, which is the final treatment, is used as the objective variable, The method for estimating stress characteristics of tempered glass, wherein the regression equation includes the following equation (17): CT=l(T+m) ２ +n(CS １ｓｔ +o) ２ +p(DOL １ｓｔ +q) ２ +r(CS ２ｎｄ +s) ２ +t(DOL ２ｎｄ +u) ２ +v ・・・(17) Here, l to v are constants.

7. The tempered glass to be estimated contains, as a glass composition, Na ２ O and Li ２ O and the compressive stress layer includes a compressive stress layer caused by K ions introduced by the ion exchange treatment and a compressive stress layer caused by Na ions introduced by the ion exchange treatment, 7. The method for estimating stress characteristics of tempered glass according to claim 1, wherein in the sampling step, the part of the stress characteristics is measured by a surface stress meter utilizing an optical waveguiding effect, and the other stress characteristics are measured by a scattered light photoelastic stress meter.

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