JP-7856320-B2 - Method and system for predicting the durability of coatings under actual exposure.

JP7856320B2JP 7856320 B2JP7856320 B2JP 7856320B2JP-7856320-B2

Inventors

田峰良子
鈴木裕之
仲沢憲幸

Assignees

日本ペイント・インダストリアルコーティングス株式会社

Dates

Publication Date: 20260511
Application Date: 20231211

Claims (6)

A method for predicting the actual exposure durability of a coating film obtained by applying a coating composition, using machine learning techniques, A method for predicting the durability of a coating film under actual exposure, comprising a predetermined artificial intelligence model that takes as input (a) data on paint information of the paint composition and data on the properties of the coating film obtained by applying the paint composition before actual exposure, and (b) data on at least one of the following: exposure time of the coating film, environmental information of the exposure of the coating film, installation angle, installation orientation of the coated object on which the coating film is formed, and evaluation results of an accelerated test of the coating film, and outputs the result of the durability of the coating film under actual exposure, wherein the computer inputs (a) data on paint information of the paint composition and data on the properties of the coating film obtained by applying the paint composition before actual exposure, and (b) data on at least one of the following: exposure time, environmental information of the exposure, installation angle, installation orientation of the coated object, and evaluation results of an accelerated test, thereby calculating and predicting the durability of the coating film under actual exposure, the method comprising a durability prediction step for actual exposure.
The input to the artificial intelligence model includes (a) data on paint information of the paint composition and data on the properties of the coating film obtained by applying the paint composition before actual exposure, and (b) at least the exposure time of the coating film. The method for predicting the durability of a coating film under actual exposure according to claim 1, wherein in the step of predicting durability under actual exposure, (a) data on the coating information of the coating composition and data on the properties of the coating film obtained by applying the coating composition before actual exposure, and (b) at least the exposure time are input.
The input to the artificial intelligence model includes (a) data on paint information of the paint composition and data on the properties of the coating film obtained by applying the paint composition before actual exposure, and (b) data on at least the exposure time of the coating film and environmental information of the exposure of the coating film. The method for predicting the durability of a coating film under actual exposure according to claim 1 or 2, wherein in the step of predicting the durability under actual exposure, (a) data on the coating information of the coating composition and data on the properties of the coating film obtained by applying the coating composition before actual exposure, and (b) data on at least the exposure time and the environmental information of the exposure.
The method for predicting the durability of a coating film under actual exposure, according to claim 1 or 2, wherein the coating information data includes at least information regarding the formulation of the coating composition.
The method for predicting the durability of a coating under actual exposure according to claim 1 or 2, wherein the environmental information of the coating's exposure includes at least one of the following: latitude, solar radiation, ultraviolet radiation, temperature, precipitation, relative humidity, wind speed, amount of sea salt particles adhering to the coating, and amount of sulfur oxides adhering to the coating.
A system for predicting the actual exposure durability of a coating film obtained by applying a paint composition, using machine learning techniques, A system for predicting the durability of a coating film under actual exposure, comprising a predetermined artificial intelligence model that takes as input (a) data on paint information of the paint composition and data on the properties of the coating film obtained by applying the paint composition before actual exposure, and (b) data on at least one of the following: exposure time of the coating film, environmental information of the exposure of the coating film, installation angle, installation orientation of the coated object on which the coating film is formed, and evaluation results of an accelerated test of the coating film, and outputs the result of the durability of the coating film under actual exposure, wherein the computer calculates and predicts the durability of the coating film under actual exposure by inputting (a) data on paint information of the paint composition and data on the properties of the coating film obtained by applying the paint composition before actual exposure, and (b) data on at least one of the following: exposure time, environmental information of the exposure, installation angle, installation orientation of the coated object, and evaluation results of an accelerated test, and further comprising a durability prediction unit for actual exposure of a coating film.

Description

This invention relates to a method and system for predicting the durability of a coating film obtained by applying a coating composition under actual exposure. Various paint compositions are applied to the walls and roofs of buildings such as houses and office buildings to maintain their quality and appearance under conditions of exposure to wind, rain, and direct sunlight. Such paint compositions require durability against wind, rain, and sunlight. Therefore, various methods have been proposed to predict the degree of aging changes in the properties of coating films. For example, Patent Document 1 proposes using an artificial intelligence model that uses paint information as an explanatory variable and the evaluation of an item as an objective variable, employing machine learning techniques to predict accelerated test results as one of the item evaluations. Such machine learning techniques are said to enable rapid prediction. Japanese Patent Publication No. 2023-021558 This is a flowchart of a method for predicting the durability of a coating film under actual exposure according to one embodiment of the present invention. The embodiments of the present invention will be described in detail below with reference to the drawings. <Method for predicting the durability of coatings under actual exposure> Figure 1 is a flowchart of a method for predicting the durability of a coating film against actual exposure according to one embodiment of the present invention. Hereinafter, with reference to Figure 1, an embodiment of a method for predicting the durability of a coating film against actual exposure obtained by applying a paint composition, using machine learning techniques, will be described as an example. Note that the method for predicting the durability of a coating film against actual exposure according to this embodiment can, for example, be performed using the coating film durability prediction system according to one embodiment of the present invention, which will be described later. As shown in Figure 1, in this embodiment, first, relationship data is prepared showing the relationship between (a) data on paint information of the paint composition and data on the properties of the coating film obtained by applying the paint composition before actual exposure, and (b) data showing the relationship between at least one of the following: exposure time of the coating film, environmental information of the coating film exposure, installation angle and orientation of the coated object on which the coating film is formed, and the evaluation results of the accelerated test of the coating film, and the durability results of the coating film after actual exposure (Step S101). The "paint information data of the paint composition" in (a) above refers to the paint information data of the paint composition in its initial state. Here, it is preferable that the "paint information data of the paint composition" in (a) above includes at least information regarding the formulation of the paint composition. Specifically, this information may be the names of the raw materials (also called compound names) of the paint composition and their respective amounts. In lieu of or in addition to the names of the raw materials, data on the product name (product number) may also be used. Furthermore, the color of the resulting coating film (L * value, a * value, b * value, spectral reflectance spectrum) and the amount applied may also be included in this data. Examples of shape include the shape of the colorant (spherical, flaky, fibrous, etc.), average primary particle diameter, average secondary particle diameter, average dispersed particle diameter, particle size distribution, aspect ratio, and thickness. Examples of chemical properties include molecular weight, molecular weight distribution, discoloration temperature, and reactivity. Furthermore, the "pre-exposure property information data" in (a) above may be data on one or more of the color and gloss of the obtained coating film before actual exposure. Color can be measured using color systems such as the L *, a *, and b * values in the L * a * b * color space (JIS Z_8781-4 (2013)), the X-Y-Z color system, the R-G-B color system, the Yxy color system, the Hunter L-a-b color system, the L * -C * -h * color system, and the Munsell color system. Color can be measured using known color measurement methods. For example, using the commercially available CM-512m3 from Konica Minolta, Inc., the L * , a * , and b * values can be measured by irradiating the light source from angles of 25°, 45°, and 75°, with the light-receiving part perpendicular to the coating film set to 0°. Alternatively, it can be measured using the X-Rite MA68II (manufactured by X-Rite Corporation). The measurement angle can be adjusted as appropriate depending on the purpose or the equipment used. Other arbitrary indicators can also be used. Furthermore, any arbitrary index can be used, such as reflection spectral da