Rust prevention performance management system

Abstract

To provide a rust prevention performance management system for managing rust prevention performance of an insulative coated film provided on a surface of a metal product, capable of accurately detecting a sign of abnormality of the rust prevention performance.SOLUTION: A rust prevention performance management system 100 is configured so that a metal product maker system 20 extracts a prescribed number of vehicle steel members for every production lot of a vehicle steel member 1, and applies a voltage between a surface of the insulative coated film and a steel plate 3 of the vehicle steel member, in a state of bringing a corrosion factor 5 into contact with the surface of the insulative coated film 4 of the vehicle steel member to measure a chronological change in current generated between them. A vehicle maker system 10 evaluates rust prevention performance of the insulative coated film for production lot, on the basis of position information indicating a position where measurement is executed on the vehicle steel member, and feature amounts including an inclination of a waveform of the current chronological change, the number of peaks, and a height of the peaks, then detects a sign of abnormality of the rust prevention performance on the basis of a shift of the rust prevention performance of the insulative coated film for each production lot, and then outputs a notification if the sign of the abnormality is detected.SELECTED DRAWING: Figure 4

Description

Technical Field

[0001] The present invention relates to a rust prevention performance management system for managing the rust prevention performance of an insulating coating film provided on the surface of a metal product.

Background Art

[0002] Conventionally, techniques for managing coating quality on objects to be coated such as automobile bodies are known. For example, Patent Document 1 discloses coating process control items (specifically, temperature and humidity inside coating equipment, flow rate and pressure of cleaning liquid, current value flowing through an electrodeposition coating apparatus, drying temperature, rotation speed of coating machine) and their time-varying change history, and quality control items (specifically, hue, gloss, smoothness, dust, cissing, unevenness, presence or absence of pinholes, etc.). A quality control system that learns the correlation between these is described.

Prior Art Literature

Patent Literature

[0003]

Patent Literature 1

Summary of the Invention

Problem to be Solved by the Invention

[0004] However, the conventional techniques as described above merely pre-learn the correlation between coating process control items, their time-varying change history and quality control items, and then manage the coating process to satisfy quality objectives, and do not manage the coating process based on the actual coating condition. Therefore, for example, even if any process is not managed as expected and the coating quality actually decreases, it is impossible to grasp this situation or detect a sign that the coating quality degradation will progress and lead to an abnormality.

[0005] The present invention was made to solve the problems of the prior art described above, and aims to enable accurate detection of signs of abnormal rust prevention performance in a rust prevention performance management system that manages the rust prevention performance of an insulating coating applied to the surface of a metal product. [Means for solving the problem]

[0006] To achieve the above objectives, the rust prevention performance management system of the present invention is a rust prevention performance management system for managing the rust prevention performance of an insulating coating applied to the surface of a metal product, comprising: a measurement unit that extracts a predetermined number of metal products for each manufacturing lot of metal products and measures the change in current over time between the surface of the insulating coating and the metal substrate of the metal product by applying a voltage to the surface of the insulating coating and the metal substrate of the metal product while a corrosive factor is in contact with the surface of the insulating coating of the metal product; an evaluation unit that evaluates the rust prevention performance of the insulating coating for each manufacturing lot based on position information indicating the location where the measurement was performed on the metal product and characteristic quantities including the slope of the waveform of the change in current over time, the number of peaks and the height of the peaks; an abnormality prediction unit that detects signs of abnormal rust prevention performance based on the trend of the rust prevention performance of the insulating coating for each manufacturing lot; and a notification unit that outputs a notification when an abnormality is detected.

[0007] In the present invention configured as described above, a voltage is applied between the surface of the insulating coating of a metal product and the metal substrate of the metal product while a corrosive factor is in contact with the surface of the insulating coating. The change in the current generated between the two over time is measured, and the rust prevention performance of the insulating coating for each manufacturing lot is evaluated based on positional information indicating the measurement location and characteristic quantities including the slope of the waveform of the change in current over time, the number of peaks, and the height of the peaks. This allows for accurate evaluation of the rust prevention performance, taking into account factors such as the number, size and type of defects, conductivity, film thickness, and insulation quality (film quality) that affect the rust prevention performance of the insulating coating. Furthermore, based on the changes in the rust prevention performance of the insulating coating for each manufacturing lot, which are evaluated with such high precision, signs of abnormal rust prevention performance are detected, allowing for accurate detection of signs that the deterioration of rust prevention performance is progressing and leading to abnormalities.

[0008] Furthermore, in the present invention, preferably, the measurement locations include the main surface, edge portion, and weld portion of the metal product. In the present invention configured in this way, the corrosion prevention performance can be evaluated with high accuracy, taking into account that the way characteristic quantities appear in the change of current over time differs depending on the measurement location.

[0009] Furthermore, in the present invention, preferably, when the abnormality prediction detection unit detects an abnormality in rust prevention performance, it estimates the cause of the abnormality based on an abnormality cause estimation model that pre-associates location information and feature quantities with the cause of the rust prevention performance abnormality, and the location information and feature quantities at the time the abnormality prediction was detected. In the present invention configured in this way, the cause of an anomaly is estimated based on the positional information and characteristic quantities related to the time-dependent change of the current that was actually measured, so the cause of the anomaly can be estimated with high accuracy.

[0010] Furthermore, in the present invention, preferably, when the abnormality prediction detection unit detects an abnormality in rust prevention performance, it acquires production control items for the metal product or insulating coating related to the estimated cause of the abnormality prediction, and the notification unit outputs the acquired production control items along with a notification. In this configuration, the present invention outputs production control items related to the estimated cause of the abnormality, thereby informing the user of the production control items that need to be addressed to prevent the occurrence of abnormalities.

[0011] Furthermore, in the present invention, preferably, the evaluation unit evaluates the rust prevention performance using a risk score, the higher the risk of abnormal rust prevention performance, and the abnormality prediction detection unit calculates the average value of the risk score in past manufacturing lots and detects an abnormality when the deviation of the risk score from the average value is greater than or equal to a predetermined value and the risk score is greater than or equal to a prediction threshold. In the present invention configured in this way, rust prevention performance can be quantitatively evaluated using a risk score, and signs of abnormalities can be accurately detected based on the changes in the risk score.

[0012] In a preferred example of the present invention, the metal product is a steel component for a vehicle. [Effects of the Invention]

[0013] According to the present invention, in a rust prevention performance management system for managing the rust prevention performance of an insulating coating applied to the surface of a metal product, it is possible to accurately detect signs of abnormal rust prevention performance. [Brief explanation of the drawing]

[0014] [Figure 1] This block diagram shows the schematic configuration of a rust prevention performance management system according to an embodiment of the present invention. [Figure 2] This block diagram shows the functional configuration of the processor and memory of a vehicle manufacturer's system according to an embodiment of the present invention. [Figure 3] This figure shows an example of a measuring device for a rust prevention performance management system according to an embodiment of the present invention. [Figure 4] This flowchart shows the processes performed by the rust prevention performance management system according to an embodiment of the present invention. [Figure 5] This is an explanatory diagram illustrating an example of the change in current over time between the surface of an insulating coating and a metal product, and characteristic quantities in that change over time, in an embodiment of the present invention. [Figure 6] This table shows examples of the numerical ranges for each characteristic quantity when the rust prevention performance of the insulating coating film is in the normal, early signs of abnormality, and abnormal states, respectively, according to embodiments of the present invention. [Figure 7] This is an explanatory diagram showing an example of the progression of the rust prevention performance of the insulating coating film for each manufacturing lot in an embodiment of the present invention. [Figure 8] This table shows an example of production control item data in an embodiment of the present invention, in which the production process, the cause of abnormalities or signs of abnormalities, the performance of the insulating coating affecting it, the data trends, production control items, and output notifications are related to each other. [Modes for carrying out the invention]

[0015] Hereinafter, a rust prevention performance management system according to an embodiment of the present invention will be described with reference to the accompanying drawings. An embodiment in which the rust prevention performance management system according to the present invention is applied to a vehicle part (steel member for vehicles) as a metal product will be described below.

[0016] [System Configuration] First, the configuration of the rust prevention performance management system according to the present embodiment will be described with reference to FIG. 1 to FIG. 3. FIG. 1 is a block diagram showing a schematic configuration of the rust prevention performance management system according to the present embodiment. FIG. 2 is a block diagram showing a functional configuration of a processor and a memory of a vehicle manufacturer system according to the present embodiment. FIG. 3 is a diagram showing an example of a measuring device of the rust prevention performance management system according to the present embodiment.

[0017] As shown in FIG. 1, the rust prevention performance management system 100 includes a vehicle manufacturer system 10 used in a vehicle manufacturer (automobile manufacturer), and a metal product manufacturer system 20 used in a metal product manufacturer that manufactures metal products used for vehicles. In the rust prevention performance management system 100, these systems 10 and 20 are connected via a communication line 30.

[0018] In the present embodiment, the rust prevention performance management system 100 is a system for managing the rust prevention performance of an insulating coating film (insulating surface treatment film such as a resin coating film (electrodeposition coating film)) provided on the surface of a metal product used by a user, specifically a steel member for vehicles used in various parts constituting a vehicle. Although only one metal product manufacturer system 20 is shown in FIG. 1, a plurality of metal product manufacturer systems 20 may actually be provided.

[0019] Specifically, the vehicle manufacturer system 10 and the metal product manufacturer system 20 each have information processing devices 11 and 21, respectively. Each of these information processing devices 11 and 21 mainly comprises a processor 11a and 21a, such as a CPU, which executes various processes; a memory 11b and 21b (such as ROM, RAM, or hard disk) that stores programs to be executed by the processors 11a and 21a, and various data necessary for the execution of these programs; a communication device 11c and 21c for communication via a communication line 30; an input device 11d and 21d, such as a mouse, keyboard, or touch panel, for inputting various information into the information processing devices 11 and 21; and an output device 11e and 21e, such as a display, speaker, or printer, for outputting various information from the information processing devices 11 and 21. The metal product manufacturer system 20 also has a measuring device 22. The measuring device 22 is a device for measuring characteristic quantities necessary for evaluating the rust prevention performance of insulating coatings applied to steel components for vehicles, and functions as a measuring unit.

[0020] Next, as shown in Figure 2, in the vehicle manufacturer system 10, the processor 11a functions as an evaluation unit, an anomaly prediction detection unit, and a notification unit. Specifically, the evaluation unit evaluates the rust prevention performance of the insulating coating for each manufacturing lot based on measurement data received from the metal product manufacturer system 20 for each of several parts of the vehicle. The anomaly prediction detection unit detects signs of anomalies in rust prevention performance based on the trend of rust prevention performance of the insulating coating for each manufacturing lot, and estimates the cause of the anomaly based on the measurement data. The notification unit outputs a notification via the output device 21e when an anomaly is detected.

[0021] Furthermore, the vehicle manufacturer system 10 stores an abnormality cause estimation model, production management item data, and evaluation result data in memory 11b. The abnormality cause estimation model is a model used by the abnormality prediction detection unit when estimating the cause of an abnormality based on measurement data. The production management item data is data that associates the cause of an abnormality in rust prevention performance, the production management item related to the cause of the abnormality, and the content of the notification corresponding to the production management item. The evaluation result data is data accumulated from the evaluation unit's evaluation of the rust prevention performance of the insulating coating applied to the vehicle steel components for each manufacturing lot.

[0022] Next, as shown in Figure 3, in the vehicle steel member 1, an insulating coating 4 is provided on the surface of the steel plate 3 (metal substrate) on which the chemical conversion coating 2 is formed. The measuring device 22 applies a voltage between the surface of the insulating coating 4 and the steel plate 3 while a corrosion factor 5 (an electrolyte material containing water, a supporting electrolyte such as sodium chloride, and clay minerals such as kaolinite) is in contact with the surface of the insulating coating 4, and measures the change in current between them over time.

[0023] Specifically, the measuring device 22 includes a container 40, an electrode 41, and a power supply 42.

[0024] The container 40 is placed on the insulating coating 4 of the vehicle's steel member 1 via a sealing material 43 to prevent liquid leakage. The corrosive factor 5 is contained within the container 40 and in contact with the surface of the insulating coating 4. The shape and material of the container 40 are not particularly limited; for example, it can be formed into a cylindrical or polygonal shape using a resin material such as acrylic resin or epoxy resin.

[0025] The sealing material 43 is, for example, a sheet-like sealing material made of silicone resin, and when the container 40 is placed on the vehicle steel member 1, it can improve the adhesion between the container 40 and the insulating coating 4 and fill the gap between them. In this way, leakage of corrosive factors 5 from between the container 40 and the insulating coating 4 can be effectively suppressed.

[0026] The electrode 41 is for applying a voltage between the steel plate 3 and the surface of the insulating coating 4, and is positioned on the insulating coating 4 side of the vehicle steel member 1 such that at least its tip is embedded in the corrosion factor 5 in the container 40 and in contact with the corrosion factor 5. As the electrode 41, electrodes commonly used in electrochemical measurements can be used, specifically, for example, carbon electrodes, platinum electrodes, etc.

[0027] The power supply 42 is connected to the electrode 41 and the steel plate 3 via wiring 44, and applies a voltage between the electrode 41 and the steel plate 3. Simultaneously, the power supply 42 measures the change in current flowing between the electrode 41 and the steel plate 3 over time in response to the applied voltage. The application of voltage and measurement of current by this power supply are controlled by the information processing device 21, and the measured data is output from the power supply to the information processing device 21. The measurement data may be data plotting the detected current value against time, or, if a gradually increasing voltage is applied, data plotting the detected current value against the applied voltage value. In addition to the detected current value, applied voltage value, and measurement time, the measurement data also includes information identifying the manufacturing lot of the vehicle steel member 1 on which the measurement was performed and the measurement location on the vehicle steel member 1.

[0028] [System Processing] Next, with reference to Figure 4, the processes performed in the rust prevention performance management system 100 described above will be explained. Figure 4 is a flowchart showing the processes executed by the rust prevention performance management system 100 according to this embodiment. In explaining the flowchart in Figure 4, although the processes included in this flowchart are actually executed by the information processing device 11 of the vehicle manufacturer system 10 (typically the processor 11a within the information processing device 11) and the information processing device 21 of the metal product manufacturer system 20 (typically the processor 21a within the information processing device 21), for convenience, they will be explained as being executed by the vehicle manufacturer system 10 and the metal product manufacturer system 20.

[0029] The flowchart shown in Figure 4 is performed periodically, for example, for each manufacturing lot of the vehicle steel component 1. A manufacturing lot generally refers to a unit of production or order. In this embodiment, a manufacturing lot can be defined by, for example, a shipping unit, a production unit, a manufacturing month / day, a lot of raw materials (e.g., paint), a point of change in raw materials, or a point of change in the process (e.g., when the cutting blade of the vehicle steel component 1 is replaced).

[0030] First, in step S201, the metal product manufacturer system 20 extracts a predetermined number of vehicle steel components 1 from each manufacturing lot. The number of components to be extracted is set appropriately based on the size of the manufacturing lot, the expected probability of abnormalities occurring, etc.

[0031] Next, in step S202, the metal product manufacturer system 20 uses a measuring device 22 to measure the change in current over time between the surface of the insulating coating 4 and the steel plate 3 by applying a voltage while the insulating coating 5 is in contact with the surface of the insulating coating 4 of the extracted vehicle steel member 1. The measurement is performed at locations including the main surface, edge, and welded parts of the vehicle steel member 1.

[0032] Next, in step S203, the metal product manufacturer system 20 transmits the measurement data obtained in step S202 to the vehicle manufacturer system 10 via the communication device 21c. Then, in step S101, the vehicle manufacturer system 10 receives the measurement data from the metal product manufacturer system 20 via the communication device 11c.

[0033] Next, in step S102, the vehicle manufacturer system 10 evaluates the rust prevention performance of the insulating coating 4 of the vehicle steel member 1 in the manufacturing lot from which the measurement data was obtained, based on the measurement data received in step S101, and stores the evaluation result data in the memory 11b.

[0034] Here, referring to Figure 5, we will explain the characteristic quantities included in the measurement data used to evaluate the rust prevention performance of the insulating coating 4 of the vehicle steel member 1. Figure 5 is an explanatory diagram showing an example of the change in current over time between the surface of the insulating coating 4 and the steel plate 3 of the vehicle steel member 1 when a voltage is applied between them, and the characteristic quantities in that change over time, in this embodiment.

[0035] If the insulating coating 4 is free of defects, when a gradually increasing DC voltage is applied between the electrode and the steel plate 3, almost no current flows until the voltage reaches the dielectric breakdown voltage. Once the voltage reaches the dielectric breakdown voltage, the current increases sharply. This indicates that the insulating coating 4 maintains its barrier performance against the corrosion factor 5 until the applied voltage reaches the dielectric breakdown voltage. Once the dielectric breakdown voltage is reached, the application of voltage promotes the penetration of the corrosion factor 5 into the insulating coating 4, causing the corrosion factor 5 to reach the surface of the steel plate 3 at the most vulnerable points of the insulating coating 4, such as areas with relatively few cross-linked resin structures. In other words, a sharp increase in the detected current value indicates that the corrosion-preventive performance of the insulating coating 4 has been lost due to the corrosion factor 5 reaching the surface of the steel plate 3.

[0036] On the other hand, if the insulating coating 4 has localized defects (for example, gas pins, welding spatter and slag, burrs, foreign matter such as iron powder, or unevenness on the surface of the steel plate 3), and there are localized areas where the effective thickness of the insulating coating 4 is small, then when a gradually increasing DC voltage is applied between the electrode and the steel plate 3, the current generated between them will show a change over time as shown in Figure 5, for example.

[0037] In other words, if a localized defect exists in the insulating coating 4, localized penetration of a corrosive factor 5 occurs at the location of the defect. When the corrosive factor 5 penetrates the insulating coating 4 at a certain defect location and reaches the steel plate 3, conduction occurs, and the current value increases instantaneously. At this point, if a voltage higher than the voltage at which electrolysis of water occurs is applied between the electrode and the steel plate 3, electrochemical reactions such as the electrolysis of water proceed on the surface of the steel plate 3 due to the conduction. As a result, the generated gas and electrolytic products accumulate in the defect, interrupting conduction and causing the current value to decrease. In other words, if a localized defect exists, the conduction at the defect location and the subsequent interruption cause peaks in the waveform of the time-dependent change data of the detected current value to appear, indicating instantaneous increases and decreases in the current value.

[0038] When multiple defects are present, a peak of several Np corresponding to the number of defects occurs, as shown in Figure 5. Furthermore, the current value at the peak, i.e., the peak height Ip, is related to the size of the conduction path through which the current flows, i.e., the size, type, and conductivity of the defect. Moreover, since conduction occurs at lower applied voltage values ​​in areas where the thickness of the insulating coating 4 is smaller due to defects, the applied voltage value that gives the peak is correlated with the thickness of the coating at the location of the defect.

[0039] Furthermore, if there are overall defects in the insulating coating 4 (for example, low crosslinking density due to insufficient catalyst in the paint, oxide film on the surface of the steel plate 3, etc.), corrosive factors 5 will penetrate the entire insulating coating 4 and reach the steel plate 3. As a result, a gradual increase in the current value occurs in the waveform of the time-dependent change data of the detected current value. In other words, the slope S of the waveform of the time-dependent change data when the detected current value increases is related to the insulating quality (film quality) of the insulating coating 4.

[0040] The number, size, type, conductivity, film thickness, and film quality of the above-mentioned defects are all factors that affect the rust prevention performance of the insulating coating 4. Therefore, returning to Figure 4, in step S102, the vehicle manufacturer system 10 obtains the number of current value peaks Np, the peak height Ip, and the slope S of the waveform of the current change over time, which are related to these factors, from the measurement data received in step S101, as feature quantities that characterize the rust prevention performance of the insulating coating 4. Furthermore, since the likelihood of peaks occurring in the current differs depending on the location where the current change over time is measured (including the main surface, edge, and welded parts of the vehicle steel member 1), the vehicle manufacturer system 10 also obtains location information indicating the location where the current change over time was measured, from the measurement data, as a parameter for evaluating the rust prevention performance of the insulating coating 4.

[0041] The vehicle manufacturer system 10 then numerically evaluates the rust prevention performance of the insulating coating 4 of the vehicle steel component 1 in the manufacturing lot from which the measurement data was obtained, based on the location information and feature quantities obtained from the measurement data, and stores it in memory 11b. Specifically, the vehicle manufacturer system 10 uses pre-set conversion formulas and maps stored in memory 11b to convert each feature quantity obtained from the measurement data, namely the number of peaks Np of the current value, the peak height Ip, and the slope S of the waveform of the change in current over time, into a risk score Rs, which is a dimensionless number between 0 and 1.

[0042] Here, the risk score Rs is a numerical value representing the level of risk of abnormal rust prevention performance of the insulating coating 4, with a higher value indicating a higher risk of abnormality. For example, in this embodiment, a risk score Rs of less than 0.5 indicates normal rust prevention performance, a risk score Rs of 0.75 or higher indicates abnormal rust prevention performance, and a risk score Rs of 0.5 or higher but less than 0.75 indicates that the rust prevention performance has not yet reached abnormality but there are signs of an impending abnormality.

[0043] The vehicle manufacturer system 10 then calculates the average of the risk scores Rs derived from each feature as the overall risk score Rsi, and stores this overall risk score Rsi in memory 11b as the evaluation result of the rust prevention performance.

[0044] Here, with reference to Figure 6, the relationship between each characteristic of rust prevention performance and the risk score will be explained. Figure 6 is a table showing an example of the numerical range of each characteristic when the rust prevention performance of the insulating coating 4 in this embodiment is in the normal, early signs of abnormality, and abnormal state, respectively.

[0045] The table in Figure 6 shows the possible numerical ranges for the characteristic quantities (i.e., the number of current peaks Np, the peak height Ip, and the slope S of the waveform of the current change over time) at each measurement position for the corrosion prevention performance of the insulating coating 4, in each case: when the corrosion prevention performance of the insulating coating 4 is normal ("normal" (risk score Rs < 0.5)), when there are signs of an impending abnormality ("signs of abnormality" (risk score 0.5 ≤ Rs < 0.75)), and when the performance is abnormal ("abnormal" (risk score 0.75 ≤ Rs)).

[0046] In the example shown in Figure 6, for instance, if the corrosion prevention performance of the insulating coating 4 is normal, the number of peaks Np in the time-dependent change of current measured at the weld will be less than 4. If the corrosion prevention performance is showing signs of abnormality, the number of peaks Np in the time-dependent change of current measured at the weld will be 4 or more but less than 6. If the corrosion prevention performance is abnormal, the number of peaks Np in the time-dependent change of current measured at the weld will be 6 or more.

[0047] On the other hand, when the change in current over time is measured at the edge of the vehicle steel member 1, there are many areas near the tip of the edge where the film thickness is locally smaller compared to other locations. Therefore, even if there are no defects in the insulating coating 4, peaks tend to occur in the change in current over time. Consequently, the number of peaks Np corresponding to each state of rust prevention performance when the measurement location is the edge is larger than when the measurement location is the welded part.

[0048] Furthermore, when the change in current over time is measured on the main surface of the vehicle steel member 1, the surface of the steel plate 3 on the main surface is smoother and the film thickness tends to be more uniform compared to other locations, so peaks tend not to occur in the change in current over time. Therefore, the number of peaks Np corresponding to each state of rust prevention performance when the measurement location is the main surface is smaller than when the measurement location is a welded area.

[0049] In the example shown in Figure 6, there is no difference in the peak height Ip and the waveform slope S in the change of current over time depending on the measurement location. However, similar to the number of peaks Np, the range of these feature quantities may differ depending on the measurement location.

[0050] Returning to Figure 4, in step S103 following step S102, the vehicle manufacturer system 10 detects an abnormality or a precursor to an abnormality in rust prevention performance based on the trend of the evaluation results of the rust prevention performance of the insulating coating 4 for each manufacturing lot, which is stored in memory 11b. Specifically, the vehicle manufacturer system 10 calculates the average value of the overall risk score Rsi (rust prevention performance) for each past manufacturing lot, and if the deviation of the overall risk score Rsi from the average value is greater than or equal to a predetermined value, and the overall risk score Rsi is greater than or equal to a predetermined abnormality precursor threshold, then a precursor to an abnormality is detected. The average value of the overall risk score Rsi may be the arithmetic mean of the overall risk score Rsi for each past manufacturing lot, or it may be a moving average for each manufacturing lot over a predetermined period or a predetermined number of manufacturing lots. Furthermore, if the overall risk score Rsi is greater than or equal to a predetermined abnormality threshold, then an abnormality is detected.

[0051] Here, with reference to Figure 7, an example of detecting an abnormality in the rust-preventive performance of the insulating coating 4 will be explained. Figure 7 is an explanatory diagram showing an example of the progression of the rust-preventive performance of the insulating coating 4 for each manufacturing lot in this embodiment.

[0052] In Figure 7, the solid line shows the trend of the overall risk score Rsi (corrosion prevention performance) for each manufacturing lot, and the dashed line shows the moving average of the overall risk score Rsi. In addition, the abnormal threshold (e.g., 0.75) and the abnormal warning threshold (e.g., 0.5) for the overall risk score Rsi are shown by dotted lines.

[0053] As shown by the moving average line (dashed line) in Figure 7, the overall risk score Rsi may also change gradually as the temperature of components, paint, water washing, furnace, etc., in the manufacturing process of the insulating coating 4 changes with the seasons. However, such changes do not cause any abnormality in the rust prevention performance of the insulating coating 4, so if the deviation of the overall risk score Rsi from the moving average is less than a predetermined value, the vehicle manufacturer system 10 does not determine it to be a sign of an abnormality. Furthermore, even if the deviation of the overall risk score Rsi from the moving average is greater than or equal to a predetermined value, if the overall risk score Rsi is less than the abnormality prediction threshold, the rust prevention performance of the insulating coating 4 is maintained, and the vehicle manufacturer system 10 still does not determine it to be a sign of an abnormality.

[0054] On the other hand, in the manufacturing lot Lx shown in Figure 7, if the deviation of the overall risk score Rsi from the moving average is greater than or equal to a predetermined value and the overall risk score Rsi is greater than or equal to the abnormality prediction threshold, the vehicle manufacturer system 10 determines that an abnormality has appeared.

[0055] Furthermore, if the overall risk score Rsi is above the abnormality threshold, the vehicle manufacturer system 10 determines that an abnormality has occurred, regardless of the deviation of the overall risk score Rsi from the moving average.

[0056] Returning to Figure 4, in step S104, following step S103, the vehicle manufacturer system 10 determines whether or not an abnormality or a precursor to an abnormality in the rust prevention performance was detected in step S103. If no abnormality or precursor to an abnormality is detected (step S104: No), i.e., if the rust prevention performance is normal, the vehicle manufacturer system 10 terminates the process.

[0057] On the other hand, if an abnormality or sign of an abnormality in rust prevention performance is detected (step S104: Yes), in step S105, the vehicle manufacturer system 10 uses the abnormality cause estimation model stored in memory 11b to estimate the cause of the abnormality or sign of an abnormality corresponding to the location information and feature quantities of the measurement data received in step S101 for the manufacturing lot in which the abnormality or sign of an abnormality in rust prevention performance was detected.

[0058] The anomaly cause estimation model takes location information and features of measurement data when an anomaly or anomaly precursor is detected as input and outputs the cause of the detected anomaly or anomaly precursor. This anomaly cause estimation model is pre-built using machine learning algorithms such as random forest, using location information and each feature of measurement data when an anomaly or anomaly precursor occurred in the rust prevention performance of the insulating coating 4, along with the cause of the anomaly or anomaly precursor at that time, as training data from measurement data accumulated in the past, and is stored in memory 11b.

[0059] Furthermore, in step S105, the vehicle manufacturer system 10 refers to the production control item data stored in memory 11b and obtains the production control items for the vehicle steel member 1 or insulating coating 4 related to the estimated cause of the abnormality or sign of abnormality. Next, in step S106, the vehicle manufacturer system 10 determines whether the production control item obtained in step S105 is a production control item of the vehicle manufacturer or the metal product manufacturer. If it is a production control item of the vehicle manufacturer, the vehicle manufacturer system 10 outputs a notification via its output device 11e that an abnormality or sign of abnormality has been detected and the production control item related to the cause of the abnormality or sign of abnormality. If it is a production control item of the metal product manufacturer, the vehicle manufacturer system 10 transmits a notification via the communication device 11c to the metal manufacturer system 20 that an abnormality or sign of abnormality has been detected and the production control item related to the cause of the abnormality or sign of abnormality. The metal manufacturer system 20 receives a notification from the vehicle manufacturer system 10 via the communication device 21c, and in step S204, the received notification is output by the output device 21e.

[0060] Here, with reference to Figure 8, the causes of abnormalities or signs of abnormalities, production control items, and output notifications will be explained. Figure 8 is a table showing an example of production control item data in which the production process, the causes of abnormalities or signs of abnormalities, the performance of the insulating coating 4 that is affected, the data trends, production control items, and output notifications are related to each other. As shown in Figure 8, the production control item data stores the following in relation to each other: the production process (e.g., "materials"), the cause of the abnormality or the precursor of the abnormality (e.g., "large surface irregularities of the steel plate"), the performance of the insulating coating 4 affected by the abnormality or the precursor of the abnormality (film thickness or film quality), the changes that appear in the data (overall risk score) when an abnormality or the precursor of an abnormality exists (e.g., short-term periodic fluctuations associated with variations between manufacturing lots (affecting within a single manufacturing lot), long-term periodic fluctuations affected across manufacturing lots, and unexpected fluctuations caused by factors other than the manufacturing lot (e.g., environmental factors such as abnormal weather, mechanical factors such as production line stoppages or machine maintenance, and other human factors)), the production control item related to the cause of the abnormality or the precursor of the abnormality (e.g., "surface roughness"), and the content to be notified when an abnormality or the precursor of an abnormality is detected, such as matters to be addressed in the production control item related to the cause of the abnormality or the precursor of an abnormality (e.g., "Please check the surface roughness"). The vehicle manufacturer system 10 obtains production control items and notifications corresponding to the cause of the abnormality or abnormality precursor estimated in step S105 from this production control item data, and outputs a notification via the output device 11e.

[0061] Returning to Figure 4, after step S106, the vehicle manufacturer system 10 terminates the process.

[0062] [Differentiation] Next, further modifications of embodiments of the present invention will be described. First, in the embodiment described above, the measuring device 22 was explained to measure the change in current over time between the surface of the insulating coating 4 and the steel plate 3 by applying a voltage between them while the corrosive factor 5 is in contact with the surface of the insulating coating 4. However, it is also possible to measure the change in voltage over time between the surface of the insulating coating 4 and the steel plate 3 by applying a current between them. In this case, the vehicle manufacturer system 10 can evaluate the rust prevention performance of the insulating coating 4 for each manufacturing lot based on the position information and characteristic quantities including the slope of the waveform of the voltage change over time, the number of peaks, and the height of the peaks.

[0063] Furthermore, in the embodiment described above, the vehicle manufacturer system 10 determined that an abnormality was present when the deviation of the overall risk score Rsi from the moving average of the overall risk score Rsi was greater than or equal to a predetermined value and the overall risk score Rsi was greater than or equal to an abnormality prediction threshold. However, the system may also detect an abnormality when the rate of change of the overall risk score Rsi in the most recent predetermined number of manufacturing lots is greater than or equal to a predetermined value.

[0064] Furthermore, in the embodiments described above, it was explained that a manufacturing lot can be defined by a unit separated by a shipping unit, a production unit, a manufacturing month / day, a lot of raw materials (e.g., paint), a point of change in raw materials, a point of change in the process (e.g., when the cutting blade of the vehicle steel member 1 is replaced), etc. However, the rust prevention performance of the insulating coating 4 may be individually evaluated for each manufacturing lot separated by multiple criteria such as points of change in raw materials and points of change in the process, and abnormal signs may be detected, causes estimated, and notifications made accordingly. Alternatively, abnormal signs may be detected, causes estimated, and notifications made comprehensively based on the evaluation of rust prevention performance in each manufacturing lot separated by multiple criteria.

[0065] Furthermore, in the embodiment described above, the vehicle manufacturer system 10 calculates the average value of the overall risk score Rsi (rust prevention performance) for each past manufacturing lot, and determines that an abnormality has been detected when the deviation of the overall risk score Rsi from the average value is greater than or equal to a predetermined value, and the overall risk score Rsi is greater than or equal to a predetermined abnormality prediction threshold. However, it is also possible to determine that an abnormality has been detected when the deviation of the overall risk score Rsi from the maximum value (worst value) of the overall risk score Rsi for each past manufacturing lot is greater than or equal to a predetermined value, and the overall risk score Rsi is greater than or equal to a predetermined abnormality prediction threshold.

[0066] Furthermore, if it is determined that there is a high risk of abnormalities occurring due to unforeseen fluctuations such as environmental factors like extreme weather, mechanical factors such as production line shutdowns or machine maintenance, or other human factors, the number of vehicle steel components 1 extracted for each manufacturing lot may be increased.

[0067] Furthermore, although the above-described embodiment shows an example of applying the present invention to a vehicle (specifically, a steel member 1 for a vehicle), the present invention is applicable to various metal products (for example, building materials and home appliances) that have an insulating coating 4 on their surface.

[0068] [Mechanism of Action and Effects] Next, the operation and effects of the rust prevention performance management system 100 according to the embodiments of the present invention described above and modified embodiments thereof will be explained.

[0069] First, the metal product manufacturer system 20 applies a voltage between the surface of the insulating coating 4 and the steel plate 3 of the vehicle steel member 1 while a corrosion factor 5 is in contact with the surface of the insulating coating 4 of the vehicle steel member 1, and measures the change in current between them over time. The vehicle manufacturer system 10 evaluates the rust prevention performance of the insulating coating 4 for each manufacturing lot based on positional information indicating the measurement location and characteristic quantities including the slope of the waveform of the change in current over time, the number of peaks, and the height of the peaks. This allows for an accurate evaluation of the rust prevention performance, taking into account factors such as the number, size and type of defects, conductivity, film thickness, and insulation quality (film quality) that affect the rust prevention performance of the insulating coating 4. Based on the changes in the rust prevention performance of the insulating coating 4 for each manufacturing lot, which have been evaluated with such high accuracy, the system detects signs of abnormal rust prevention performance, thus enabling accurate detection of signs that the deterioration of rust prevention performance is progressing and leading to abnormalities.

[0070] Furthermore, since the measurement locations for the temporal change in current include the main surface, edge, and welded parts of the vehicle's steel member 1, the corrosion prevention performance can be evaluated with high accuracy, taking into account that the way characteristic quantities appear in the temporal change of current differs depending on the measurement location.

[0071] Furthermore, when the vehicle manufacturer system 10 detects an indication of an abnormality in rust prevention performance, it estimates the cause of the abnormality based on an abnormality cause estimation model that pre-associates location information and feature quantities with the causes of the abnormality in rust prevention performance, and the location information and feature quantities at the time the abnormality indication was detected. Therefore, it can estimate the cause of the abnormality indication based on the location information and feature quantities related to the time-dependent changes in the current that were actually measured, and can estimate the cause of the abnormality indication with high accuracy.

[0072] Furthermore, when the vehicle manufacturer system 10 detects an indication of an abnormality in rust prevention performance, it acquires production control items related to the estimated cause of the abnormality and outputs the acquired production control items along with a notification. By outputting production control items related to the estimated cause of the abnormality, the system can inform the user of the production control items that need to be addressed to prevent the occurrence of the abnormality.

[0073] Furthermore, the vehicle manufacturer system 10 evaluates rust prevention performance using a risk score, the higher the risk of abnormal rust prevention performance, and calculates the average value of the risk score in past manufacturing lots. If the deviation of the risk score from the average value is greater than a predetermined value and the risk score is greater than or equal to a warning threshold, it detects a warning sign of an abnormality. Thus, rust prevention performance can be quantitatively evaluated using the risk score, and warning signs of abnormalities can be detected with high accuracy based on the trend of the risk score. [Explanation of Symbols]

[0074] 1. Steel components for vehicles 3 steel plate 4. Insulating coating 10. Vehicle Manufacturer System 20 Metal Products Manufacturer System 11, 21 Information Processing Equipment 100 Rust Prevention Performance Management System

Claims

1. A rust prevention performance management system for managing the rust prevention performance of an insulating coating applied to the surface of a metal product, A measuring unit that extracts a predetermined number of the metal products from each manufacturing lot of the metal products, and measures the change in current over time between the surface of the insulating coating and the metal substrate of the metal product by applying a voltage to the surface of the insulating coating and the metal substrate of the metal product while a corrosive factor is in contact with the surface of the insulating coating of the metal product, An evaluation unit that evaluates the rust prevention performance of the insulating coating for each manufacturing lot based on positional information indicating the location where the measurement was performed on the metal product, and characteristic quantities including the slope, number of peaks, and peak height of the waveform of the change in the current over time, An abnormality prediction unit detects signs of abnormality in the rust prevention performance based on the changes in the rust prevention performance of the insulating coating for each manufacturing lot, A notification unit that outputs a notification when the aforementioned signs of abnormality are detected, A rust prevention performance management system equipped with this system.

2. The location where the measurement is performed includes the main surface, edge, and welded portion of the metal product. The rust prevention performance management system according to claim 1.

3. When the abnormality prediction detection unit detects an abnormality in the rust prevention performance, it estimates the cause of the abnormality based on an abnormality cause estimation model that pre-associates the location information and feature quantities with the cause of the rust prevention performance abnormality, and the location information and feature quantities at the time the abnormality prediction was detected. The rust prevention performance management system according to claim 1 or 2.

4. When the abnormality prediction detection unit detects an abnormality in the rust prevention performance, it acquires the production control items of the metal product or the insulating coating film related to the estimated cause of the abnormality prediction, The notification unit outputs the acquired production management items together with the notification. The rust prevention performance management system according to claim 3.

5. The evaluation unit evaluates the rust prevention performance using a risk score, the higher the risk of the rust prevention performance becoming abnormal, and The abnormality prediction detection unit calculates the average value of the risk score in past manufacturing lots, and detects the abnormality prediction when the deviation of the risk score from the average value is greater than or equal to a predetermined value and the risk score is greater than or equal to the prediction threshold. The rust prevention performance management system according to claim 1 or 2.

6. The aforementioned metal product is a steel component for a vehicle. The rust prevention performance management system according to claim 1 or 2.

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