Prediction device, prediction method, and prediction program

Abstract

The invention relates to a prediction device, a prediction method, and a prediction program. The prediction device is provided with a prediction unit that calculates a first ratio in a first period and a second ratio in a second period for each phase constituting the plating layer on the basis of each content of a plurality of components contained in the plating layer, and calculates a first corrosion rate for each phase on the basis of the corrosion sequence for each phase, the first ratio for each phase, and the first corrosion rate for each phase. The period length of a first period until a part of the corrosion of the plating layer reaches the base steel is calculated, and the period length of a second period following the first period is calculated on the basis of the corrosion order of each phase, the second proportion of each phase, and the second corrosion rate of each phase. The sum of the duration of the first period and the duration of the second period is calculated as a prediction result of corrosion resistance of the plating layer.

Description

Technical Field

[0001] This invention relates to a prediction device, a prediction method, and a prediction program.

[0002] This application is based on and claims priority to Japanese Special Purpose No. 2023-186777, filed in Japan on October 31, 2023, the contents of which are incorporated herein by reference. Background Technology

[0003] Steel corrodes upon contact with the atmosphere, therefore, anti-corrosion treatments such as plating are necessary for its long-term use. Plating-treated steel is less expensive than stainless steel. In recent years, from a carbon neutrality perspective, life-cycle costs have become a focus, leading to increased attention on highly corrosion-resistant coated steel sheets that provide long-term corrosion protection. Among these highly corrosion-resistant coated steel sheets are zinc-aluminum-magnesium (Zn-Al-Mg) alloy coated steel sheets (see Patent Documents 1 and 2).

[0004] Existing technical documents Patent documents Patent Document 1: Japanese Patent No. 7136351 Patent Document 2: Japanese Patent No. 7040695 Summary of the Invention

[0005] The problem that the invention aims to solve Coated steel sheets are required to meet various properties such as corrosion resistance, sacrificial corrosion resistance, machinability, abrasion resistance, and color. However, these properties are evaluated independently by each steel company, rather than by a unified standard. Therefore, end-users sometimes experience confusion regarding the selection of the optimal coated steel sheet based on these properties.

[0006] Furthermore, these properties are highly dependent on the alloy composition of the coating. That is, the coating properties depend on the substances (phases) formed by the combination of elements of each metallic component and their content. For example, a Zn-Al coating contains both Zn and Al phases. Here, if the Al content increases, the Al phase in the coating increases. As a result, the coating properties become closer to those of Al, and the corrosion resistance of the coating improves. On the other hand, the superior sacrificial corrosion resistance of the Zn phase decreases in coatings with less Zn phase.

[0007] Furthermore, knowing the corrosion resistance of galvanized steel sheets allows end-users to choose the optimal sheet based on their lifecycle cost. Additionally, understanding the corrosion resistance of galvanized steel sheets reduces confusion regarding maintenance and replacement frequency. Therefore, many users are interested in the corrosion resistance (lifespan, durability) of galvanized steel sheets.

[0008] To improve the accuracy of predicting the corrosion resistance of coatings, it is preferable to calculate the corrosion rate based on pre-obtained exposure test results and corrosion acceleration results for various coated steel sheets. However, in recent years, coatings have contained not only Al and Zn, but sometimes also Mg. Since the corrosion resistance of Al, Zn, and Mg varies, predicting the corrosion resistance of coatings has become more difficult. Thus, there is a problem that the accuracy of predicting the corrosion resistance of coatings cannot be improved.

[0009] In view of the above, the object of the present invention is to provide a prediction device, prediction method and prediction program that can improve the accuracy of predicting the corrosion resistance of coatings.

[0010] Methods for solving problems One aspect of the present invention is a prediction device comprising a prediction unit that, based on the content of various components contained in the coating, calculates a first proportion of each phase constituting the coating in a first period and a second proportion in a second period; calculates the duration of the first period until a portion of the corrosion of the coating reaches the base steel based on the corrosion sequence of each phase, the first proportion of each phase, and the first corrosion rate of each phase; calculates the duration of the second period following the first period based on the corrosion sequence of each phase, the second proportion of each phase, and the second corrosion rate of each phase; and calculates the sum of the duration of the first period and the duration of the second period as a prediction result of the corrosion resistance of the coating.

[0011] One aspect of the present invention is a prediction method performed by the aforementioned prediction device, comprising the following steps: calculating, based on the content of each of the various components contained in the coating, a first proportion of each phase constituting the coating in a first period and a second proportion in a second period; calculating, based on the corrosion sequence of each phase, the first proportion of each phase, and the first corrosion rate of each phase, the duration of the first period up to when a portion of the corrosion of the coating reaches the base steel; calculating, based on the corrosion sequence of each phase, the second proportion of each phase, and the second corrosion rate of each phase, the duration of the second period following the first period; and calculating, as a prediction result of the corrosion resistance of the coating, the sum of the duration of the first period and the duration of the second period.

[0012] One aspect of the present invention is a prediction program for causing a computer to perform the following steps: based on the content of each of the various components contained in the coating, calculating a first proportion of each phase constituting the coating in a first period and a second proportion in a second period; based on the corrosion sequence of each phase, the first proportion of each phase, and the first corrosion rate of each phase, calculating the duration of the first period until a portion of the corrosion of the coating reaches the base steel; based on the corrosion sequence of each phase, the second proportion of each phase, and the second corrosion rate of each phase, calculating the duration of the second period following the first period; and calculating the sum of the duration of the first period and the duration of the second period as a prediction result of the corrosion resistance of the coating.

[0013] Invention Effects According to the present invention, the accuracy of predicting the corrosion resistance of coatings can be improved. Attached Figure Description

[0014] Figure 1 This is a diagram illustrating an example of the configuration of the prediction device in the implementation method.

[0015] Figure 2 This is a diagram illustrating an example of a model of the corrosion mode of the Zn-Al-Mg coating in the implementation method.

[0016] Figure 3 This is a diagram illustrating an example of the liquidus temperature of Zn-Al-Mg in the embodiment.

[0017] Figure 4 This is a diagram showing an example of the molecular weight and density of each phase in the embodiment.

[0018] Figure 5 This is a diagram of an example database representing the area ratios of each phase in an implementation scheme.

[0019] Figure 6 This is a flowchart illustrating an example of the process for calculating the attached weight of each phase in an implementation embodiment.

[0020] Figure 7 This is a diagram illustrating an example of the corrosion rate of each phase during the first period of an embodiment.

[0021] Figure 8 This is a flowchart illustrating an example of a process in which the length of the period during which a portion of the corrosion can be identified on the surface of the coating (the first period) and the length of the period after a portion of the corrosion reaches the interface between the coating and the steel plate (the second period) are calculated. Detailed Implementation

[0022] The embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0023] The following description uses a zinc (Zn)-aluminum (Al)-magnesium (Mg) coating as an example of a coating to illustrate the prediction device, prediction method, and prediction procedure. However, the coating in the embodiments is not limited to a Zn-Al-Mg coating.

[0024] Figure 1 This diagram illustrates an example configuration of the prediction device 1 in the embodiment. The prediction device 1 is an information processing device that predicts the corrosion resistance of a coating, such as a personal computer or a smartphone terminal. The prediction device 1 includes a storage device 11, a storage unit 12, a prediction unit 13, a display unit 14, and a communication unit 15.

[0025] Storage device 11 is a non-volatile recording medium (non-temporary recording medium). The prediction program is deployed from storage device 11 to storage unit 12.

[0026] The prediction unit 13 is, for example, a processor such as a CPU (Central Processing Unit). The prediction unit 13 is implemented as software by executing a prediction program that is loaded from the storage device 11 to the storage unit 12. The prediction program may also be recorded on a computer-readable recording medium. Computer-readable recording media include non-transitory recording media such as floppy disks, optical disks, ROMs (Read Only Memory), CD-ROMs (Compact Disc Read Only Memory), hard disks built into the computer system, and solid-state drives (SSDs).

[0027] The prediction unit 13 may also be implemented using hardware containing electronic circuits, such as LSI (Large Scale Integration circuit), ASIC (Application Specific Integrated Circuit), PLD (Programmable Logic Device), or FPGA (Field Programmable Gate Array).

[0028] The prediction unit 13 predicts the corrosion resistance of the coating based on the contents (input values) of Zn, Al, and Mg. For example, the prediction unit 13 calculates the proportion of each phase constituting the coating and predicts the service life (service years) of the coating based on the calculated proportions.

[0029] Display unit 14 includes a display device such as a liquid crystal display. Display unit 14 displays, for example, prediction results.

[0030] The communication unit 15 performs communication with an external communication device (not shown) via a communication line such as the Internet, LAN (Local Area Network), or WAN (Wide Area Network). The communication unit 15 may also send prediction results to that communication device (not shown), for example.

[0031] Next, the details of the prediction device, prediction method, and prediction procedure will be explained.

[0032] Ideally, the service life of galvanized steel sheets should be predicted based on a database of exposure test results. However, galvanized steel sheets used in the building materials industry are required to have a service life (corrosion resistance) of 30 to 50 years or more. Therefore, it is practically difficult to create a database of recently developed galvanized steel sheets. However, by accurately predicting the corrosion resistance of the coating based on the results (insights) of corrosion-promoting tests (e.g., composite cyclic corrosion tests) that have a certain correlation with exposure tests, it is possible to predict the corrosion resistance of galvanized steel sheets in exposure tests.

[0033] Therefore, the prediction device 1 predicts the corrosion resistance of the coating based on insights accumulated regarding corrosion promotion that is correlated to a certain extent with the exposure test. By changing the brine concentration and its circulation interval, the degree of promotion (corrosion rate) of the corrosion promotion test can be changed. Furthermore, if the corrosion rates of each substance (phase) constituting the coating can be obtained, the corrosion resistance of the coated steel sheet can be predicted.

[0034] Furthermore, prediction device 1 uses a corrosion model based on insights gained from numerous corrosion-promoting tests to predict the corrosion resistance of the coating. To calculate the corrosion resistance (service life) of the coating, it is necessary to incorporate insights into the corrosion model by thoroughly elucidating the corrosion pathway of the coating in theory. In this regard, the relationship between the corrosion model and corrosion-promoting tests, as well as the relationship between the corrosion model and exposure tests, has been fully confirmed.

[0035] Figure 2 This is a diagram (cross-sectional view) illustrating an example of a corrosion model (corrosion model) of the Zn-Al-Mg coating in the embodiment. Coating 101 is a Zn-Al-Mg coating. Coating 101 is present on the surface of steel 102, thereby providing sacrificial corrosion resistance relative to steel 102. For example, the extent of the ternary eutectic structure is shown in coating 103-1.

[0036] During the corrosion periods, including Phase I (first period) and Phase II (second period) as illustrated below, the coating 101 (Zn-Al-Mg coating) corrodes depending on the usage environment.

[0037] Phase I: The period during which the coating corrodes from the surface of the coating.

[0038] Phase II: Part of the corrosion of the coating reaches the interface between the coating and the steel, during the period when the coating corrodes while simultaneously providing sacrificial corrosion protection to the base steel. Corrosion protection is applied to the steel plate until the coating, which provides sacrificial corrosion protection to the base steel, disappears.

[0039] The end of Phase II marks the end of the coating's service life. At the end of Phase II, the pitted red rust caused by corrosion on the steel becomes visually identifiable from the coating's surface. Specifically, the prediction device 1 calculates the number of cycles in which the coating's adhering weight on the steel plate surface decreases during the corrosion-promoting test, and the number of cycles in which the coating's adhering weight or thickness, assigned as an input value (initial value), eventually becomes zero, as the coating's service life (corrosion resistance).

[0040] The reason for dividing the corrosion period (service life) into two phases, Phase I and Phase II, is that in Phase I, the corrosion rate of the phase constituting the coating is inherent. In contrast, in Phase II, corrosion of the coating is promoted by applying corrosion protection to the steel plate (base steel). Therefore, in Phase II, a modified corrosion rate (second corrosion rate) for each phase is needed, obtained by multiplying the corrosion rate of Phase I (first corrosion rate) by the promotion rate.

[0041] The prediction device 1 calculates the corrosion rate of each phase constituting the coating for each period of Phase I and Phase II based on the coating composition (input value). The prediction device 1 calculates the overall corrosion resistance (lifetime) of the coating by summing the service life corresponding to the corrosion rates of each phase.

[0042] Figure 3 This is a graph (liquidline diagram) showing an example of the liquidus temperature of Zn-Al-Mg in the embodiment (Reference 1: P. Liang, T. Tarfa, JA Robinson, et al., “Experimental investigation and thermodynamic calculation of the Al±Mg±Zn system,” Thermochimica Acta 314 (1998) 87-110).

[0043] Figure 3 The liquidus lines in the recorded liquidus diagram are equivalent to contour lines on a map. For example, Figure 3The thick liquidus line in the recorded liquidus diagram corresponds to the valley floor on the map. The thick liquidus line represents the solidification process of the Zn-Al-Mg alloy. That is, in... Figure 3 In the illustrated liquidus diagram, when a predetermined position is assigned to the prediction device 1 as the input value (initial value) of each content of Zn-Al-Mg, the Zn-Al-Mg alloy solidifies in such a way that the temperature becomes lower along the coarse liquidus line close to that position.

[0044] <input> Information about the coating of the object whose corrosion resistance (service life) is calculated is assigned to the prediction unit 13. Specifically, in order to confirm the change in corrosion resistance caused by the compositional differences of the Zn-Al-Mg coating, the composition of Al and Mg is assigned to the prediction unit 13 as "at%" or "mass%". The total percentage of Zn, Al, and Mg is 100%. Therefore, it can be expressed as "(Zn at%, Al at%, Mg at%) = (100 - xy, x, y)". The atomic weights of Zn, Al, and Mg are Zn = 65.38, Al = 26.98, and Mg = 24.305. Using these, the "mass%" equivalent to "at%" is calculated.

[0045] Furthermore, the densities of each component (Zn, Al, Mg) are assigned to the prediction unit 13. The prediction unit 13 calculates the theoretical density "ρ" based on the densities of each component (Zn, Al, Mg). Here, the density of Zn is "ρZn = 7.14 g / cm³". 3 The density of Al is ρAl = 2.7 g / cm³. 3 The density of Mg is ρMg = 1.738 g / cm³. 3 The prediction department 13 calculates the theoretical density "ρ" based on the density of each component and "mass%".

[0046] In addition to the composition of the coating being predicted, the coating thickness (in μm) and the coating adhesion weight also need to be assigned to the prediction unit 13. The coating adhesion weight is, for example, 5~100 μm or 20~630 g / m². 2 Approximately. When converting between plating thickness and adhesion weight, the plating thickness multiplied by the theoretical density "ρ" becomes the adhesion weight. Alternatively, the plating adhesion weight divided by the specific gravity gives the plating thickness [μm].

[0047] In the prediction calculations for the implementation method, a value related to the adhesion weight of the plating is used. Here, "plating adhesion weight = ρL g / m 2 "The prediction section 13 is assigned. For example, in the case of 100% Zn plating, it is equivalent to 100 g / m." 2 The plating thickness is approximately 14 μm (100 ÷ 7.12 ≈ 14 μm).

[0048] The thinner the coating, the shorter its service life. With a thicker coating, even with significant corrosion, the sufficient thickness relative to the corrosion results in a longer Phase I "τ1" and a delayed onset of Phase II "τ2". Conversely, with a thinner coating, Phase I "τ1" is shorter and Phase II "τ2" begins earlier.

[0049] Furthermore, in Zn-Al-Mg coatings with a thickness of 20 μm, there is a tendency for stage II "τ2" to begin when the overall coating has corroded by 30%. In Zn-Al-Mg coatings with a thickness of 30 μm, there is a tendency for stage II "τ2" to begin when the overall coating has corroded by 50%. Therefore, the proportion of the coating at which stage II "τ2" begins (=1-G) needs to be input into the prediction unit 13 as the proportion of the coating corroded in stage I "τ1" "G=0.0~1.0". Assuming that the coating structure is homogeneous, Equations (1) and (2) hold true based on the proportion "G" and the coating adhesion weight "ρL".

[0050] TM1=ρL G ……(1) TM2=ρL (1-G) ……(2) Here, "TM1" represents the weight of the coating that was corroded in period I "τ1" (corrosion reduction). "TM2" represents the weight of the coating that was corroded in period II "τ2" (corrosion reduction).

[0051] The corrosion resistance of a coating varies depending on the proportions of the constituent phases. Increasing the proportion of the highly corrosion-resistant phase (the phase with a slow corrosion rate) extends the coating's lifespan. Furthermore, the corrosion resistance discussed here refers to the corrosion reduction (loss) of the coating itself, excluding the steel. This is distinct from the effect of corrosion protection achieved by replacing the steel with a coating (sacrificing corrosion protection) and is considered true corrosion resistance.

[0052] When the coating contains Zn, Al, and Mg, the phases constituting the coating can be classified into six types. Furthermore, this classification (from the first to the sixth phase) is based on the solidification process of the Zn, Al, and Mg alloy. Therefore, the range (input range) of each Zn, Al, and Mg content is limited. For example, the input range for Zn is "25~85 at%", the input range for Al is "10~73 at%", and the input range for Mg is "2~18%".

[0053] The following are examples of the definitions of each phase.

[0054] The first phase, "Pure Al phase", contains trace amounts (content "~16.5 at%) of Zn and Al.

[0055] The second phase, “Al-Zn(α) phase”, is a mixed phase of Al and Zn phases (Zn content is 16.5 at%).

[0056] The third phase, “Zn-Al(β) phase”, is a mixture of Zn and Al phases (Zn content is 59 at%).

[0057] The fourth phase, "Pure_Zn(η) phase", is a phase containing trace amounts (content "~16.5 at%) of Al and Zn (Zn content is 98.4~100 at%).

[0058] The fifth phase, the "MgZn2 phase," is a single phase (grain) that forms simultaneously with the formation of the Pure Al phase or initially from the liquid phase. Typically, the size of the "MgZn2 phase" is greater than 1 μm.

[0059] The sixth phase, the "ternary eutectic structure," is a mixed structure composed of fine Pure Al, fine Pure Zn, and fine MgZn2 particles, formed through a ternary eutectic reaction. Its overall composition is "Zn-9at%Al-5at%Mg".

[0060] These definitions clarify the atomic composition of each phase, thus enabling the theoretical calculation of the molecular weight and density of each phase.

[0061] Figure 4 This is a diagram showing an example of the molecular weight and density of each phase in the embodiment. Given the composition of the coating and the molecular weight of each constituent phase, if the volume ratio of each phase is determined, the weight and number of moles of each phase constituting the coating can be calculated.

[0062] Figure 5 This is a diagram illustrating an example of a database representing the area ratios of each phase in an implementation scheme. Figure 5 A portion of the database is shown in tabular form. The database contains measured results of the correspondence between the components of Al-Zn-Mg and the area fraction SP (≈ volume fraction VP) of each phase. The prediction unit 13 uses the database, the composition of the coating, and the molecular weight of each phase to calculate the weight and molar number of each phase constituting the coating.

[0063] In order to make Figure 5 The database shown was created in conjunction with... Figure 2The Zn-Al-Mg coating illustrated is the same type of coating. The fabricated coating was subjected to EPMA (Electron Probe Micro Analyzer) cross-sectional observation. The proportions of each phase were calculated by classifying them as values ​​close to the composition values ​​of each phase, and the area fraction SP (≈ volume fraction VP) of each phase was calculated based on the calculated proportions.

[0064] In the fabrication of the plated steel sheets used to create the database, a hot-dip galvanizing simulator manufactured by our company is used. This simulator is capable of performing complete nitrogen replacement through experimentation. Within this simulator, a series of plating operations can be performed under oxygen-free conditions (less than 5 ppm), and the temperature during the process can also be monitored.

[0065] The plating bath uses metal with a purity of over 99%, and a Zn-Al-Mg alloy is manufactured through vacuum melting. The base steel sheet used for plating is a 1.2mm cold-rolled steel sheet (JIS G 3141) of general structural steel. To suppress the reactivity between iron (Fe) and the plating layer, Cr (0.1g / m³) is sequentially vapor-deposited onto the plating surface after thorough degreasing, cleaning, and pickling. 2 "and "Ni: 0.1g / m 2 ".

[0066] Using this as the base plate, the base plate was heated to 800°C at a rate of 10°C / second. Then, 20% H2-N2 gas was sprayed onto the base plate and held for 60 seconds to perform surface reduction. After cooling the base plate to the plating bath temperature, it was immersed in the plating bath for 1 second. After removing the plate from the plating bath, it was wiped with N2 gas, and the coating thickness was uniformly controlled to 25 μm. The flow rate of the sprayed N2 gas was controlled so that the time from the melting point to 380°C (solidification completion temperature) was 20 seconds. The plating bath temperature was the melting point (= the liquidus temperature described in Reference 1 above) + 20°C.

[0067] In the vapor deposition process, the formation of an intermetallic compound layer containing Fe and Al between the plating bath and the coated steel sheet is suppressed. Therefore, the composition of the coating and the plating bath is approximately the same, eliminating the influence of Fe. However, since the coating is an extremely thin film, if the coated steel sheet is immersed in the plating bath, the diffusion of Fe into the coating is hindered, but Fe diffuses into the plating bath immediately after the plating process. Therefore, these traces (Ni layer, Cr layer) are almost entirely absent from the coating, and the interfacial alloy layer is virtually non-existent. Thus, the influence of the plating alloy layer on the base steel can be minimized.

[0068] The 20-second cooling rate refers to the typical time (20 seconds) from immersion in the plating bath to reaching the upper roller. To prevent the plating bath from winding onto the upper roller, the temperature of the steel sheet is adjusted by gas blowing during the plating solidification process, ensuring that the steel sheet passes through only after complete solidification. Additionally, a 20mm square piece of plating sheet is cut from the center of the steel sheet. The steel sheet is then vertically embedded with resin and ground.

[0069] EPMA was used to observe the cross-section of the coating on the ground coated steel sheet. This confirmed that the Al-Fe alloy layer remained near the interface, the composition of the plating bath became the coating composition, and the coating thickness. Scanning Electron Microscope (SEM) images of the field of view confirming the coating thickness were obtained. For the locations where SEM images were obtained, Zn, Al, and Mg composition mapping images (EPMA mapping images) were obtained using EPMA. Based on this, the area fraction of each phase was determined from a database.

[0070] Typically, the coating solidifies from the surface, resulting in a final solidified portion near the interface between the steel substrate and the coating. Consequently, a microstructure with compositional deviations is formed in the horizontal cross-section of the coating. Therefore, the area fraction of each phase is measured from the cross-sectional direction of the coating.

[0071] To confirm the area fraction of each phase, the coating was examined using a scanning electron microscope at 500x magnification, and the area fraction of each phase was repeatedly measured until the total field of view of the coating reached 100,000 μm. A local image of the coating was cut out from the EPMA image and divided into a 5 μm × 5 μm grid.

[0072] Phases containing Al and Zn, with Zn content below 16.5 at% and other components content below 5 at%, are classified as the first phase, "Pure Al phase".

[0073] The phase containing Al and Zn, with a Zn content of 16.5–37.75 at%, was classified as the second phase, “Al-Zn(α) phase”.

[0074] The phase containing Zn and Al, with a Zn content of 37.75–59 at%, was classified as the third phase, “Zn-Al(β) phase”.

[0075] The phases containing Zn and Al, with Zn content ranging from 59 to 98.4 at%, were classified as the third phase, "Zn-Al(β) phase," and the fourth phase, "Pure_Zn(η) phase."

[0076] The phase with a Mg content of 33.3 at% (±3 at%) and a Zn content of 66.6 at% (±3%) is classified as the fifth phase, "MgZn2 phase" (single phase).

[0077] The phase containing Zn, Al, and Mg, with Zn content above 80 at%, Mg content between 1 and 10 at%, and Al content less than 5 to 15 at%, was classified as the sixth phase, "ternary eutectic structure".

[0078] Thus, in most cases, phases can be classified based on Zn composition. Exceptions include the interface between the coating and steel, and the resin interface, which are sometimes classified as "other." Therefore, the area fraction of each phase can be determined within the range outside of those classified as "other."

[0079] In the following text, the subscripts indicate the phase number (nth phase). For example, area fraction "SP1" indicates the area fraction of the first phase. For example, volume fraction "VP2" indicates the volume fraction of the second phase.

[0080] The area fractions of SP1~SP6 (≈ volume fractions VP1~VP6) can be confirmed in the database. Therefore, based on the total volume of each phase constituting the coating "V=V1+V2+V3+V4+V5+V6" and the density of the nth phase "ρ", n "Corresponding volume fraction" VP n The prediction unit 13 calculates the volume "V" of the nth phase as in equation (3). n ".

[0081] V n =VP n ÷100÷V ……(3) Based on the density "ρ" of the nth phase n "and the volume of the nth phase" V n “The prediction section 13 calculates the content (by weight) of each phase in the coating as in formula (4)”m n ".

[0082] m n =V n ×ρ n ……(4) In addition, the total content (by weight) of each phase in the coating “M” is shown in Equation (5).

[0083] M = m1 + m2 + m3 + m4 + m5 + m6 ……(5) When displayed as "Mass%", the prediction unit 13 will display the content (by weight) of each phase in "m". n "Convert to the content (by weight) of each phase as shown in equation (6)" MS n (Mass% display).

[0084] MS n =m n / M×100 ……(6) Prediction unit 13 measures the content (by weight) of each phase using MS n Multiply by the total attached weight of the coating, ρL, and then calculate the weight of the nth phase contained in the coating, RM, as in Equation (7). n ".

[0085] RM n =ρL×MS n ……(7) Furthermore, the prediction unit 13 can also be based on the weight "RM" of the nth phase. n "and molecular weight "N" n ” calculate the number of moles of the nth phase “mol” as in equation (8). n ".

[0086] mol n =RM n / N n ……(8) The total number of moles “MOL” is shown in equation (9).

[0087] MOL=mol1+mol2+mol3+mol4+mol5+mol6…(9) Prediction unit 13 can also calculate the molar percentage "mol%" of the nth phase as in equation (10). n ".

[0088] mol% n =mol n / MOL ……(10) Thus, the prediction unit 13 calculates the proportion of each phase in the coating. Based on the proportion of each phase, the prediction unit 13 uniformly disperses each phase in the corrosion mode model of the coating. The prediction unit 13 calculates the corrosion reduction in period I "τ1" and period II "τ2" respectively. From this, the corrosion resistance (service life) of the coating can be determined. Here, it is important to note the order in which each phase corrodes.

[0089] In exposure tests and corrosion-promoting tests (compound cyclic corrosion tests), the phases begin to corrode in order of lower corrosion potential. Specifically, the corrosion sequence (corrosion order) is confirmed through cross-sectional observation of coatings in numerous corrosion tests conducted by our company, following the order of the third phase "Zn-Al(β) phase", the sixth phase "ternary eutectic structure", the fifth phase "MgZn2 phase", the fourth phase "Pure_Zn(η) phase", the second phase "Al-Zn(α) phase", and the first phase "Pure_Al phase".

[0090] Next, an example of the operation of the prediction device 1 will be explained.

[0091] Figure 6 This is a flowchart illustrating an example of the process for calculating the adhesion weight of each phase in the embodiment. Since the phases corrode in the order described above, the proportion of phases with lower corrosion potentials increases in phase I "τ1". In phase II "τ2", the proportion of phases with higher corrosion potentials increases. The prediction unit 13 calculates the proportion of each phase in the adhesion weight "TM1" of the coating corroded in phase I "τ1" and the proportion of each phase in the adhesion weight "TM2" of the coating corroded in phase II "τ2" as follows.

[0092] In the following examples, "SUM" represents the total weight. The total weight is substituted into the total weight "SUM" according to the processing details in each step. The total weight "SUM" is used to determine whether it marks the end of Phase I "τ1". That is, the total weight "SUM" is used to determine whether corrosion has reached the boundary between the coating corroded in Phase I "τ1" and the coating corroded in Phase II "τ2". Here, the end time of Phase I "τ1" is determined based on the proportion "G" of the coating corroded in Phase I "τ1".

[0093] The prediction unit 13 initializes the total weight "SUM" (variable) with the adhesion weight of the third phase (step S101). The prediction unit 13 determines whether the adhesion weight "TM1" of the coating corroded in the first period minus the total weight "SUM" is less than or equal to 0 (step S102).

[0094] If the weight of the coating "TM1" - the total weight "SUM" of the coating etched in the first period is less than or equal to 0 (step S102: Yes), the prediction unit 13 calculates the weight of the coating "TM2" of the coating etched in the second period. Here, the weight "TM2" is "TM2 = SUM - TM1 + RM6 + RM5 + RM4 + RM2 + RM1" (step S103-1).

[0095] If the adhesion weight "TM1" - total weight "SUM" of the etched coating during the first period exceeds 0 (step S102: No), the prediction unit 13 selects a phase for each iteration from step S106 onwards, following the order of fifth phase → fourth phase → second phase → first phase. The prediction unit 13 then calculates the adhesion weight "RM" of the selected nth phase. n The weight of the first phase attached to the total weight "SUM" is added to the total weight "SUM" (step S104). The prediction unit 13 determines whether to add the weight of the first phase attached to the total weight "SUM" to the total weight "SUM" (step S105).

[0096] If it is determined that the weight of the phases other than the first phase is added to the total weight "SUM" (step S105: Yes), the prediction unit 13 determines whether the total weight "SUM" - the weight of the coating "TM1" etched in the first period is less than or equal to 0 (step S106). If it is determined that the total weight "SUM" - the weight of the coating "TM1" etched in the first period is greater than or equal to 0 (step S106: No), the prediction unit 13 returns the process to step S104.

[0097] If the total weight "SUM" - the adhesion weight "TM1" of the coating corroded in the first period is determined to be 0 or less (step S106: Yes), the prediction unit 13 returns the process to step S103. Here, if the adhesion weight "RM6" of the sixth phase is added to the total weight "SUM" in step S104, the adhesion weight "TM2" is "TM2=SUM-TM1+RM5+RM4+RM2+RM1" ​​(step S103-2). If the adhesion weight "RM5" of the fifth phase is added to the total weight "SUM" in step S104, the adhesion weight "TM2" is "TM2=SUM-TM1+RM4+RM2+RM1" ​​(step S103-3). If the adhesion weight "RM4" of the fourth phase is added to the total weight "SUM" in step S104, the adhesion weight "TM2" is "TM2=SUM-TM1+RM2+RM1" ​​(step S103-4). In step S104, when the attached weight "RM2" of the second phase is added to the total weight "SUM", the attached weight "TM2" is "TM2=SUM-TM1+RM1" ​​(steps S103-5).

[0098] If it is determined that the attachment weight "RM1" of the first phase is added to the total weight "SUM" (step S105: Yes), the prediction unit 13 returns the process to step S103. Here, the attachment weight "TM2" is "TM2=SUM-TM1" (steps S103-6).

[0099] <Corrosion Rate> The prediction unit 13 calculates the service life of the coating based on the adhered weight "TM1" and corrosion rate "r1" in phase I "τ1" and the adhered weight "TM2" and corrosion rate "r2" in phase II "τ2". Hereinafter, the corrosion rate of the nth phase in phase I "τ1" will be denoted as "r1". n The corrosion rate of the nth phase in phase II "τ2" is labeled as "r2". n ".

[0100] In corrosion acceleration tests (compound cyclic corrosion tests), the corrosion rate is the corrosion reduction per specified cycle. Therefore, the units for the corrosion rates "r1" and "r2" of the coating are "g / m". 2 / cycle". In exposure tests, the corrosion rates of the coating, "r1" and "r2", are measured in g / m³. 2 " / year (day, month)" etc. The corrosion rates of the coating, "r1" and "r2", vary depending on the corrosion environment of the corrosion promotion test or exposure test.

[0101] Furthermore, by rapidly cooling the alloy composition of each phase, a single-phase metal test piece was fabricated without causing compositional inhomogeneity. Various corrosion tests were then conducted on this fabricated metal test piece to determine the corrosion rate of each phase.

[0102] Figure 7 This is a graph illustrating an example of the corrosion rates of each phase during the first period in the embodiment. In the combined cyclic corrosion test (JIS H 8502 JASO M609-91), the corrosion rates are as follows... Figure 7 As shown. In corrosion rate "r2", the rate of corrosion promotion depends on the composition of the steel plate. In the case of using general mild steel in the test, in the composite cyclic corrosion test (JIS H8502 JASO M609-91), the corrosion rate "r2" is mostly 30 to 50 times that of the corrosion rate "r1".

[0103] Figure 8 This is a flowchart illustrating an example of a process in which the length "TIME1" of the period during which a portion of the corrosion can be confirmed on the surface of the coating (the first period) and the length "TIME2" of the period after a portion of the corrosion reaches the interface between the coating and the steel plate (the second period) are calculated. Figure 8 The steps S201 to S206 illustrated are as follows: Figure 6 The steps S101 to S106 illustrated are the same.

[0104] If the weight of the coating deposited during the first period "TM1" minus the total weight "SUM" is less than or equal to 0 (step S202: Yes), the prediction unit 13 calculates the weight of the coating deposited during the second period "TM2" (step S203-1). Additionally, the prediction unit 13 calculates the period length "TIME1" of the first period and the period length "TIME2" of the second period. Here, "TIME1 = SUM / r13". Furthermore, "TIME2 = (SUM - TM1) / r23 + RM6 / r26 + RM5 / r25 + RM4 / r24 + RM2 / r22 + RM1 / r21" (step S207-1).

[0105] If the total weight "SUM" - the weight "TM1" of the coating corroded in the first period is determined to be 0 or less (step S206: Yes), the prediction unit 13 returns the process to step S203. Furthermore, the prediction unit 13 calculates the period length "TIME1" of the first period and the period length "TIME2" of the second period. Here, when the weight "RM6" of the sixth phase is added to the total weight "SUM" in step S204, "TIME1 = RM3 / r13 + (SUM - RM3) / r16". Also, "TIME2 = (SUM - TM1) / r26 + RM5 / r25 + RM4 / r24 + RM2 / r22 + RM1 / r21" (step S207-2). When the weight "RM5" of the fifth phase is added to the total weight "SUM" in step S204, "TIME1 = RM3 / r13 + RM6 / r16 + (SUM - RM3 - RM6) / r15". Additionally, "TIME2 = (SUM - TM1) / r25 + RM4 / r24 + RM2 / r22 + RM1 / r21" (step S207-3). In step S204, when the attached weight of the fourth phase "RM4" is added to the total weight "SUM", "TIME1 = RM3 / r13 + RM6 / r16 + RM5 / r15 + (SUM - RM3 - RM6 - RM5) / r14". Additionally, "TIME2 = (SUM - TM1) / r24 + RM2 / r22 + RM1 / r21" (step S207-4). In step S204, when the attached weight of the second phase "RM2" is added to the total weight "SUM", "TIME1 = RM3 / r13 + RM6 / r16 + RM5 / r15 + RM4 / r14 + (SUM - RM3 - RM6 - RM5 - RM4) / r12". Additionally, “TIME2=(SUM-TM1) / r22+RM1 / r21” (step S207-5).

[0106] If it is determined that the attached weight "RM1" of the first phase should be added to the total weight "SUM" (step S205: Yes), the prediction unit 13 returns the process to step S203. Furthermore, the prediction unit 13 calculates the duration "TIME1" of the first period and the duration "TIME2" of the second period. Here, "TIME1 = RM3 / r13 + RM6 / r16 + RM5 / r15 + RM4 / r14 + RM2 / r12 + (SUM - RM3 - RM6 - RM5 - RM4 - RM2) / r11". Additionally, "TIME2 = (SUM - TIME1) / r21" (steps S207-6).

[0107] Thus, the prediction unit 13 calculates the period "TIME1" of period I "τ1" and "TIME2" of period II "τ2". The prediction unit 13 calculates the corrosion resistance (durability years) of the coating "TIME=TIME1+TIME2".

[0108] As described above, the prediction unit 13 (proportion calculation unit) calculates the proportion (first proportion) of each phase constituting the coating in a first period and the proportion (second proportion) of each phase constituting the coating in a second period based on the content of each of the various components contained in the coating. The prediction unit 13 (first period calculation unit) calculates the duration of the first period until a portion of the coating corrosion reaches the base steel based on the corrosion sequence of each phase, the first proportion of each phase, and the first corrosion rate of each phase. The prediction unit 13 (second period calculation unit) calculates the duration of the second period following the first period based on the corrosion sequence of each phase, the second proportion of each phase, and the second corrosion rate of each phase. The second corrosion rate is a rate obtained by correcting the first corrosion rate based on the corrosion promotion of the coating caused by corrosion protection of the base steel. The prediction unit 13 (prediction result calculation unit) calculates the sum of the duration of the first period and the duration of the second period as the prediction result of the coating's corrosion resistance.

[0109] Thus, the prediction unit 13 calculates the duration of the corrosion period (service life) based on the corrosion sequence, proportion, and corrosion rate of each phase. This improves the accuracy of predicting the corrosion resistance of the coating. End users of the coated steel sheet can effectively select materials based on the prediction results. Furthermore, it shortens the period until the adoption of the coated steel sheet is approved, thereby saving labor costs.

[0110] Thus, the prediction unit 13 can also divide the corrosion period (service life) into period I and period II. That is, the prediction unit 13 can also calculate the corrosion period in period I and the corrosion period in period II separately. As a result, the accuracy of predicting the corrosion resistance of the coating can be further improved.

[0111] (Modified Example) To improve the accuracy of predicting the corrosion resistance (lifespan) of coatings, actual data from corrosion tests can be incorporated into the prediction results. By using a correction function "f" to combine the predicted lifespan "TIME" with the lifespan "TIME'" (actual data) obtained from corrosion tests (TIME'=f(TIME)), higher prediction accuracy can be achieved.

[0112] The following are examples of data points obtained in the corrosion test.

[0113] • (Zn, Al, Mg) = (80, 13, 7) at%, plating thickness 20 μm, red rust formation cycle = 210 • (Zn, Al, Mg) = (80, 17, 8) at%, plating thickness 20 μm, red rust formation cycle = 240 • (Zn, Al, Mg) = (70, 22, 8) at%, plating thickness 20 μm, red rust formation cycle = 300 • (Zn, Al, Mg) = (70, 24, 10) at%, plating thickness 20 μm, red rust formation cycle = 300 • (Zn, Al, Mg) = (52, 34, 13) at%, plating thickness 20 μm, red rust formation cycle = 840 • (Zn, Al, Mg) = (25, 73, 2) at%, plating thickness 20 μm, red rust formation cycle = 840 The number of data points can be further increased through corrosion tests, and the prediction unit 13 uses methods such as machine learning (e.g., supervised learning) based on a large number of data points to correct the prediction results of the number of service years.

[0114] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the specific configuration is not limited to these embodiments, and also includes designs that do not depart from the spirit of the present invention.

[0115] Explanation of symbols 1: Prediction device; 11: Storage device; 12: Storage unit; 13: Prediction unit; 14: Display unit; 101: Plating; 102: Steel; 103: Plating.

Claims

1. A prediction device, wherein, The coating includes a prediction unit that, based on the content of various components contained in the coating, calculates a first proportion of each phase constituting the coating in a first period and a second proportion in a second period; calculates the duration of the first period until a portion of the corrosion of the coating reaches the base steel based on the corrosion sequence of each phase, the first proportion of each phase, and the first corrosion rate of each phase; calculates the duration of the second period following the first period based on the corrosion sequence of each phase, the second proportion of each phase, and the second corrosion rate of each phase; and calculates the sum of the duration of the first period and the duration of the second period as a prediction result of the corrosion resistance of the coating.

2. The prediction device according to claim 1, wherein, The second corrosion rate is obtained by modifying the first corrosion rate based on the corrosion promotion of the coating caused by the anti-corrosion treatment of the base steel.

3. A prediction method, executed by a prediction device, wherein, Includes the following steps: Based on the content of each of the various components contained in the coating, the first proportion of each phase constituting the coating in the first period and the second proportion in the second period are calculated. Based on the corrosion sequence of each phase, the first proportion of each phase, and the first corrosion rate of each phase, the duration of the first period until a portion of the corrosion of the coating reaches the base steel is calculated. Based on the corrosion sequence of each phase, the second proportion of each phase, and the second corrosion rate of each phase, the duration of the second period following the first period is calculated. The sum of the duration of the first period and the duration of the second period is used as the predicted result of the corrosion resistance of the coating.

4. A prediction procedure, wherein, To enable the computer to perform the following steps: Based on the content of each of the various components contained in the coating, the first proportion of each phase constituting the coating in the first period and the second proportion in the second period are calculated. Based on the corrosion sequence of each phase, the first proportion of each phase, and the first corrosion rate of each phase, the duration of the first period until a portion of the corrosion of the coating reaches the base steel is calculated. Based on the corrosion sequence of each phase, the second proportion of each phase, and the second corrosion rate of each phase, the duration of the second period following the first period is calculated. The sum of the duration of the first period and the duration of the second period is used as the predicted result of the corrosion resistance of the coating.

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