Method for evaluating salt spray resistance of aluminum-magnesium-manganese color coated steel plate

Abstract

The invention discloses a method for evaluating the salt spray resistance of an aluminum-magnesium-manganese color coated steel plate, and relates to the technical field of corrosion testing of metal materials, and the method comprises the following steps: testing a sample to obtain microscopic morphology quantitative data and electrochemical impedance spectroscopy fitting parameters; and establishing a mapping association relationship between the two to form multi-source corrosion feature fusion data. And based on the fused data, constructing a corrosion resistance degradation curve taking corrosion time as a horizontal axis and multi-dimensional corrosion characteristics as a longitudinal axis, and extracting characteristic evolution rates and inflection points from the corrosion resistance degradation curve so as to evaluate different failure stages of the coating. And according to the change of the fusion data in the salt mist circulation, predicting the cycle period number required by the coating to reach a preset failure threshold value. And integrating all data, curves and prediction results to generate a structured comprehensive evaluation report. According to the method, deep correlation of corrosion multi-source information and early quantitative prediction of the service life are realized.

Description

Technical Field

[0001] This invention belongs to the field of metal material corrosion testing technology, specifically a method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates. Background Technology

[0002] Aluminum-magnesium-manganese color-coated steel sheets are widely used in building envelope systems, and their salt spray corrosion resistance is crucial for evaluating coating quality and service life. Currently, the evaluation of the salt spray resistance of this type of material mainly relies on standard salt spray tests, assessing the protection level by visually inspecting the coating for blistering, rust area, or corrosion spread width at the etched lines. This is a macroscopic and lagging method. In recent years, electrochemical impedance spectroscopy has been introduced, providing a more sensitive quantitative means by monitoring changes in parameters such as coating resistance and capacitance to reflect the degradation of coating barrier performance. Simultaneously, microscopic characterization techniques such as scanning electron microscopy are used to observe the morphology of the corroded coating, such as the formation and propagation of cracks and pores.

[0003] Existing technical solutions have significant shortcomings. Microscopic morphology observation and electrochemical testing are usually conducted independently, resulting in separate analytical results. Morphological data are mostly limited to qualitative or semi-quantitative descriptions, making it difficult to establish a direct, quantitative correspondence with electrochemical parameters reflecting interfacial reaction kinetics. This leads to explanations of corrosion mechanisms remaining at a superficial level, failing to deeply integrate features from different dimensions to accurately characterize the physicochemical processes of coating failure. Both macroscopic level assessments and single electrochemical parameter tracking focus on describing the already occurred corrosion state or determining the test endpoint. These methods lack the ability to extract early characteristics from the dynamic evolution process and cannot effectively identify the critical inflection point of accelerated performance degradation. Therefore, it is difficult to reliably and quantitatively predict the long-term service life of coatings based on limited experimental data. Summary of the Invention

[0004] This invention aims to solve at least one of the technical problems existing in the prior art;

[0005] Therefore, this invention proposes a method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates, comprising:

[0006] Experiments were conducted on aluminum-magnesium-manganese color-coated steel plates to obtain quantitative results of microstructure data and fitting parameters of electrochemical impedance spectroscopy data.

[0007] Establish a mapping relationship between the quantification results of the micromorphology data and the fitting parameters of the electrochemical impedance spectroscopy data to form multi-source corrosion characteristic fusion data;

[0008] Based on the multi-source corrosion feature fusion data, a corrosion resistance degradation curve is constructed with corrosion time as the horizontal axis and multi-dimensional corrosion features as the vertical axis.

[0009] The rate parameters and inflection point information of corrosion characteristics over time are extracted from the corrosion resistance degradation curve to evaluate different failure stages of the coating.

[0010] Based on the changes in the multi-source corrosion feature fusion data during multiple salt spraying and drying cycles, the number of cycles required for the aluminum-magnesium-manganese color-coated steel plate to reach the preset failure threshold is predicted.

[0011] By integrating the quantification results of the microstructure data, the fitting parameters of the electrochemical impedance spectroscopy data, the corrosion resistance degradation curve, and the cycle number prediction results, a structured comprehensive evaluation report on salt spray resistance is generated.

[0012] Furthermore, the experiment on the aluminum-magnesium-manganese color-coated steel plate sample to obtain the quantitative results of microstructure data and the fitting parameters of electrochemical impedance spectroscopy data includes:

[0013] A standard-sized aluminum-magnesium-manganese color-coated steel plate was prepared as a test sample, and the coating surface of the test sample was pretreated to remove surface contaminants.

[0014] The pretreated test sample is placed in a controlled salt spray corrosion test chamber, and periodic salt spraying and drying cycles are performed according to the preset accelerated corrosion test specifications.

[0015] After each salt spray and drying cycle, the test sample is taken out and the microscopic morphology data of the surface of the test sample is collected using a non-contact three-dimensional surface morphology instrument.

[0016] Feature extraction is performed on the micromorphological data to identify and quantify the area and depth distribution of corrosion product areas, blistering areas, and coating peeling areas on the coating surface.

[0017] Electrochemical impedance spectroscopy (EIS) data of the test sample during the corrosion process are collected synchronously, and equivalent circuit fitting is performed on the EIS data to obtain coating resistance, charge transfer resistance, and coating capacitance parameters, which are used as fitting parameters for the EIS data.

[0018] The process involves placing the pretreated test sample in a controlled salt spray corrosion test chamber and performing periodic salt spraying and drying cycles according to a preset accelerated corrosion test specification, including:

[0019] A 5% sodium chloride solution was prepared as the corrosive medium, and the pH of the corrosive medium was adjusted to the neutral range.

[0020] The temperature of the controlled salt spray corrosion test chamber is maintained at a constant temperature, and the humidity of the controlled salt spray corrosion test chamber is controlled to reach saturation humidity;

[0021] The cycle structure of the salt spraying and drying cycle is set, wherein the duration of the salt spraying stage is set to a fixed duration, and the duration of the drying stage is set to another fixed duration.

[0022] During the salt spraying stage, the corrosive medium is uniformly sprayed onto the surface of the test sample through an atomizing nozzle;

[0023] During the drying stage, spraying is stopped and air circulation is maintained inside the controlled salt spray corrosion test chamber to allow the surface of the test sample to dry gradually.

[0024] Repeat the salt spraying stage and the drying stage until the preset total number of cycles is reached or the test termination condition is met.

[0025] Furthermore, a non-contact three-dimensional surface topography instrument is used to collect microscopic morphology data of the surface of the test sample, including:

[0026] Under standard lighting conditions, the test sample is placed horizontally on the sample stage of the non-contact three-dimensional surface profilometer.

[0027] Adjust the scanning parameters of the non-contact three-dimensional surface profilometer to ensure that its vertical resolution is sufficient to identify micron-level coating thickness variations and corrosion pit depths;

[0028] The entire exposed surface of the test sample is scanned line by line to obtain surface point cloud data containing height information;

[0029] The acquired surface point cloud data is denoised and smoothed to eliminate abnormal data points caused by environmental vibration or light source interference.

[0030] The processed surface point cloud data is reconstructed into a three-dimensional digital surface model, which fully represents the three-dimensional morphology of the surface of the test sample after corrosion.

[0031] Furthermore, feature extraction is performed on the microstructure data to identify and quantify the area and depth distribution of corrosion product regions, blistering regions, and coating peeling regions on the coating surface, including:

[0032] Calculate the height deviation of each point on the surface relative to the original uncorroded reference plane from the three-dimensional digital surface model;

[0033] Based on the sign and magnitude of the height deviation, the surface area is classified into corrosion product accumulation area, blistering and protrusion area, coating peeling and depression area, and unchanged area.

[0034] For the area where the corrosion products accumulate, the distribution area is statistically analyzed, and the average height deviation of all points within the area where the corrosion products accumulate is calculated as the average thickness of the accumulation.

[0035] For the bubbly protrusion area, its distribution area is statistically analyzed, and the height difference between the highest point in the bubbly protrusion area and the surrounding normal coating is calculated as the maximum bubbling height.

[0036] For the coating peeling depression area, its distribution area is counted, and the height difference between the lowest point in the coating peeling depression area and the surrounding normal coating is calculated as the maximum peeling depth.

[0037] Generate a quantitative statistical table containing the area, average thickness, maximum height, and maximum depth of various defect regions.

[0038] Furthermore, electrochemical impedance spectroscopy (EIS) data of the test sample during the corrosion process are simultaneously acquired, and equivalent circuit fitting is performed on the EIS data to obtain coating resistance, charge transfer resistance, and coating capacitance parameters, including:

[0039] During a specific stage of the salt spray and drying cycle, the test sample is connected to the electrochemical workstation as a working electrode.

[0040] A saturated calomel electrode is configured as a reference electrode, and a platinum electrode is configured as an auxiliary electrode, and both are immersed in the corrosive medium together with the working electrode.

[0041] Within a set frequency range, a small-amplitude sinusoidal AC potential disturbance is applied to the working electrode, and its current response is measured.

[0042] Record the impedance magnitude and phase angle at different frequencies, and plot them as electrochemical impedance spectroscopy.

[0043] An equivalent circuit model capable of simulating the corrosion process of the coated metal system is selected, and the electrochemical impedance spectroscopy is fitted using the nonlinear least squares method.

[0044] The values ​​of the coating resistance, the charge transfer resistance, and the coating capacitance can be directly read from the fitting results.

[0045] Furthermore, a mapping relationship is established between the quantification results of the microstructure data and the fitting parameters of the electrochemical impedance spectroscopy data to form multi-source corrosion characteristic fusion data, including:

[0046] The quantification results of the micromorphological data from the same corrosion time point are time-stamped with the fitting parameters of the electrochemical impedance spectroscopy data.

[0047] Construct a multi-dimensional feature vector, the elements of which include the area of ​​the corrosion product accumulation region, the area of ​​the blistering and protruding region, the area of ​​the coating peeling and depression region, the value of the coating resistance, the value of the charge transfer resistance, and the value of the coating capacitance.

[0048] The elements in the multi-dimensional feature vector are standardized to eliminate scale differences caused by different physical units.

[0049] The standardized multi-dimensional feature vectors are stored together with the corresponding corrosion time labels and cycle period labels to form the multi-source corrosion feature fusion data organized in time series.

[0050] Furthermore, based on the multi-source corrosion feature fusion data, a corrosion resistance degradation curve is constructed with corrosion time as the horizontal axis and multi-dimensional corrosion features as the vertical axis, including:

[0051] With corrosion time or number of cycles as independent variables, and each standardized feature element in the multi-dimensional feature vector as the dependent variable;

[0052] Plot a scatter plot of each feature element as a function of corrosion time;

[0053] Each scatter plot is fitted with a piecewise linear regression or nonlinear function to obtain multiple feature degradation curves.

[0054] All feature degradation curves are superimposed on the same coordinate system and labeled with legends to form the corrosion resistance degradation curve.

[0055] Furthermore, the rate parameters and inflection point information of corrosion characteristics evolving over time are extracted from the corrosion resistance degradation curve to evaluate different failure stages of the coating, including:

[0056] For each feature degradation curve, calculate its average slope in the initial, intermediate, and final stages, which serves as the degradation rate of the feature degradation curve at different times.

[0057] Detect points on each feature degradation curve where the slope changes significantly, mark these points as inflection points, and record their corresponding corrosion times;

[0058] By analyzing the distribution of inflection points of different degradation curves on the time axis, the main stage transition nodes of the coating failure process can be identified.

[0059] Based on the aforementioned stage transition nodes, the entire failure process of the coating is divided into the initial protection period, the defect initiation period, the rapid deterioration period, and the complete failure period.

[0060] Furthermore, based on the changes in multi-source corrosion characteristic fusion data across multiple salt spray and drying cycles, the number of cycles required for the aluminum-magnesium-manganese color-coated steel sheet to reach the preset failure threshold is predicted, including:

[0061] From the multi-source corrosion feature fusion data, select one or more key corrosion features as failure judgment indicators;

[0062] A specific failure threshold is set for each of the aforementioned failure determination indicators;

[0063] Based on the corrosion resistance degradation curve, a mathematical relationship model between each failure judgment index and corrosion time is established.

[0064] Substitute the failure threshold into the corresponding mathematical relationship model to solve for the predicted corrosion time required to reach the failure threshold;

[0065] The predicted corrosion time is divided by the total duration of a single salt spray and drying cycle to obtain the predicted number of cycle periods.

[0066] Furthermore, a structured comprehensive evaluation report on salt spray resistance performance is generated, including:

[0067] Create a data summary section in the report, listing in tabular form the quantitative results of micromorphological data obtained after all salt spraying and drying cycles and the fitting parameters of electrochemical impedance spectroscopy data;

[0068] Create a graph section in the report, insert the corrosion resistance degradation curve, and provide a textual description of the curve's characteristics;

[0069] Create a failure analysis section in the report, detailing the coating failure stages based on degradation rate and inflection point information, and listing the characteristics of each stage;

[0070] Create a prediction results section in the report and give the predicted number of cycles for the aluminum-magnesium-manganese color-coated steel plate to reach the preset failure threshold.

[0071] The report includes a conclusion section that, based on all data analysis results, qualitatively classifies and summarizes the salt spray resistance performance levels of the aluminum-magnesium-manganese color-coated steel sheets.

[0072] Compared with the prior art, the beneficial effects of the present invention are:

[0073] By establishing a mapping relationship between the quantitative results of microstructure and the fitting parameters of electrochemical impedance spectroscopy, the changes in physical structure such as coating porosity and cracks are quantitatively correlated with electrochemical responses such as interfacial resistance and capacitance. This technical solution achieves a deep integration of microscopic defects and electrochemical process parameters, enabling the assessment of coating failure states to no longer rely on isolated data from a single technical means. From the two essential dimensions of physics and electrochemistry, mutually corroborating and uniformly quantified characterization indicators are formed for the same corrosion stage, improving the accuracy of condition diagnosis and the depth of mechanism explanation.

[0074] This technical solution constructs corrosion resistance degradation curves based on multi-source fusion data and extracts rate parameters and inflection point information of corrosion characteristics evolving over time. It shifts the evaluation perspective from static result observation to dynamic process analysis, focusing on the kinetic characteristics of performance degradation and the critical points of stage transitions. This allows for the identification of different stages of coating progression from complete protection to performance degradation and then to failure based on early experimental data, and the establishment of extrapolation models based on early evolution patterns. This enables the quantitative prediction of the number of cycles required for the coating to reach a specific performance failure threshold before all salt spray cycles are completed, realizing a shift in evaluation mode from "post-event detection" to "early warning." Attached Figure Description

[0075] Figure 1 This is a step diagram of the method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates according to the present invention;

[0076] Figure 2 A flowchart for collecting microscopic morphology data;

[0077] Figure 3 A graph showing the capacitance variation and failure cycle prediction of aluminum-magnesium-manganese coatings;

[0078] Figure 4 A degradation curve of salt spray resistance of aluminum-magnesium-manganese color-coated steel sheet;

[0079] Figure 5 This is a fusion degradation curve of multi-source corrosion characteristics. Detailed Implementation

[0080] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0081] See Figure 1This process involves acquiring quantitative results of microstructure data and fitting parameters for electrochemical impedance spectroscopy (EIS). A mapping relationship is established between the quantitative results of microstructure data and the fitting parameters of EIS, forming multi-source corrosion characteristic fusion data. Based on this multi-source corrosion characteristic fusion data, a corrosion resistance degradation curve is constructed with corrosion time as the horizontal axis and multi-dimensional corrosion characteristics as the vertical axis. Rate parameters and inflection point information of corrosion characteristics evolving over time are extracted from the corrosion resistance degradation curve to assess different failure stages of the coating. Based on the changes in the multi-source corrosion characteristic fusion data during multiple salt spray and drying cycles, the number of cycles required for the aluminum-magnesium-manganese color-coated steel plate to reach a preset failure threshold is predicted. Finally, the quantitative results of microstructure data, fitting parameters of EIS, corrosion resistance degradation curve, and cycle number prediction results are integrated to generate a structured comprehensive salt spray resistance performance evaluation report.

[0082] In one embodiment of the present invention, a standard-sized aluminum-magnesium-manganese color-coated steel plate test sample is prepared, and the coating surface of the aluminum-magnesium-manganese color-coated steel plate test sample is pretreated to remove surface contaminants. A 5% sodium chloride solution is prepared as the corrosive medium, and the pH of the corrosive medium is adjusted to a neutral range. The pretreated aluminum-magnesium-manganese color-coated steel plate test sample is placed in a controlled salt spray corrosion test chamber. The temperature of the controlled salt spray corrosion test chamber is controlled to be kept constant, and the humidity of the controlled salt spray corrosion test chamber is controlled to reach saturation humidity. A cyclical structure of salt spraying and drying is set. The duration of the salt spraying stage is set to a fixed duration, and the duration of the drying stage is set to another fixed duration. During the salt spraying stage, the corrosive medium is uniformly sprayed onto the surface of the aluminum-magnesium-manganese color-coated steel plate test sample through an atomizing nozzle. During the drying stage, spraying was stopped while maintaining air circulation within the controlled salt spray corrosion test chamber, allowing the surface of the aluminum-magnesium-manganese color-coated steel plate test sample to gradually dry. The salt spraying and drying stages were repeated. After each salt spraying and drying cycle, the aluminum-magnesium-manganese color-coated steel plate test sample was removed, and microscopic morphology data of the surface of the aluminum-magnesium-manganese color-coated steel plate test sample was collected using a non-contact three-dimensional surface profilometer. Feature extraction was performed on the microscopic morphology data to identify and quantify the area and depth distribution of corrosion product areas, blistering areas, and coating peeling areas on the coating surface. Electrochemical impedance spectroscopy data of the aluminum-magnesium-manganese color-coated steel plate test sample during the corrosion process were simultaneously collected, and equivalent circuit fitting was performed on the electrochemical impedance spectroscopy data to obtain coating resistance, charge transfer resistance, and coating capacitance parameters, which were used as fitting parameters for the electrochemical impedance spectroscopy data.

[0083] Optionally, the scanning parameters of the non-contact 3D surface profilometer are adjusted, including vertical resolution settings, to ensure the identification of micron-level coating thickness variations and corrosion pit depths. In practice, the feature extraction process involves calculating height deviations from the 3D digital surface model and classifying surface regions based on these deviations. It is understood that electrochemical impedance spectroscopy (EIS) data acquisition needs to be performed at specific corrosion stages, and the fitting process uses nonlinear least squares to obtain coating resistance, charge transfer resistance, and coating capacitance parameters. In some embodiments, the configuration and adjustment of the corrosive medium follow standard specifications to maintain a neutral pH range. Optionally, the salt spray and drying cycles are repeated until a preset number of cycles is reached or a termination condition is met. In practice, microscopic morphology data acquisition and EIS data acquisition are performed synchronously to ensure timestamp alignment. It is understood that the quantitative statistical table generated by feature extraction contains information on the area and depth distribution of corrosion product regions, blistering regions, and coating peeling regions, used for subsequent multi-source data fusion.

[0084] See Figure 2 In one embodiment of the present invention, the aluminum-magnesium-manganese color-coated steel plate test sample is placed horizontally on the sample stage of a non-contact three-dimensional surface profilometer under standard lighting conditions. The scanning parameters of the non-contact three-dimensional surface profilometer are adjusted to ensure that its vertical resolution is sufficient to identify micron-level coating thickness changes and corrosion pit depths. The entire exposed surface of the aluminum-magnesium-manganese color-coated steel plate test sample is scanned line by line to obtain surface point cloud data containing height information. The obtained surface point cloud data is denoised and smoothed to eliminate abnormal data points caused by environmental vibration or light source interference. The processed surface point cloud data is reconstructed into a three-dimensional digital surface model. The three-dimensional digital surface model fully characterizes the three-dimensional surface morphology of the aluminum-magnesium-manganese color-coated steel plate test sample after corrosion. The height deviation of each point on the surface relative to the original uncorroded reference plane is calculated from the 3D digital surface model. Based on the sign and magnitude of the height deviation, the surface area is classified into corrosion product accumulation area, blistering and protrusion area, coating peeling and depression area, and unchanged area. For corrosion product accumulation area, its distribution area is counted and the average height deviation of all points in the corrosion product accumulation area is calculated as the average accumulation thickness. For blistering and protrusion area, its distribution area is counted and the height difference between the highest point in the blistering and protrusion area and the surrounding normal coating is calculated as the maximum blistering height. For coating peeling and depression area, its distribution area is counted and the height difference between the lowest point in the coating peeling and depression area and the surrounding normal coating is calculated as the maximum peeling depth. A quantitative statistical table containing the area, average thickness, maximum height and maximum depth of each type of defect area is generated.

[0085] In practice, denoising and smoothing are performed using specific filtering algorithms, and the reconstruction of the 3D digital surface model is achieved through triangular meshing. It can be understood that the original uncorroded reference plane is obtained by scanning the surface of a control sample from the same batch that has not undergone corrosion testing. In some embodiments, the accuracy of the surface point cloud data acquisition is determined by the vertical resolution and lateral scanning step size of the non-contact 3D surface profilometer. Optionally, a quantitative statistical table is generated in spreadsheet format, explicitly recording the area and average thickness of each corrosion product accumulation region, the area and maximum blister height of each blistering protrusion region, and the area and maximum peeling depth of each coating peeling depression region.

[0086] In one embodiment of the present invention, during a specific stage of the salt spray and drying cycle, an aluminum-magnesium-manganese color-coated steel plate sample is connected to an electrochemical workstation as the working electrode. A saturated calomel electrode is configured as a reference electrode, and a platinum electrode is configured as an auxiliary electrode. Both electrodes are immersed in the corrosive medium. A small-amplitude sinusoidal AC potential perturbation is applied to the working electrode within a set frequency range, and its current response is measured. The impedance modulus and phase angle at different frequencies are recorded to plot an electrochemical impedance spectroscopy. An equivalent circuit model capable of simulating the corrosion process of the coated metal system is selected, and the electrochemical impedance spectroscopy is fitted using the nonlinear least squares method. The values ​​of coating resistance, charge transfer resistance, and coating capacitance are directly read from the fitting results. In some embodiments, the set frequency range covers a wide frequency band from high to low frequency, for example, from 100 kHz to 10 mHz, and the amplitude of the sinusoidal AC potential perturbation is typically 10 mV. In practice, the quantification results of microscopic morphology data from the same corrosion time point are time-stamped with the fitting parameters of electrochemical impedance spectroscopy data to construct a multi-dimensional feature vector. The elements of the multi-dimensional feature vector include the area of ​​the corrosion product accumulation region, the area of ​​the blistering and protruding region, the area of ​​the coating peeling and depression region, the value of the coating resistance, the value of the charge transfer resistance, and the value of the coating capacitance. Each element in the multi-dimensional feature vector is standardized to eliminate the scale differences caused by different physical dimensions. The standardized multi-dimensional feature vector is stored together with the corresponding corrosion time label and cycle label to form multi-source corrosion feature fusion data organized in time series.

[0087] In some embodiments, the equivalent circuit model is chosen as an R(QR)(QR) structure to simulate the corrosion process between coating defects and the metal substrate, and nonlinear least squares fitting is implemented using dedicated electrochemical software. Optionally, the multi-source corrosion feature fusion data is stored in matrix form, where each row of the matrix corresponds to a time point, and each column corresponds to a standardized feature element. In specific implementations, corrosion time tags and cycle period tags are extracted from the log records of salt spray tests.

[0088] See Figure 3 This is a graph showing the capacitance change and failure cycle prediction of aluminum-magnesium-manganese (AMgM) coatings. It illustrates the trend of coating capacitance changes with cycle time in an accelerated salt spray corrosion test on AMgM color-coated steel plates, and uses this data to predict failure. The abstract concept of "salt spray resistance" is transformed into a quantifiable coating capacitance change curve, intuitively reflecting the entire process of the coating from intact to failed. The slope and inflection points of the curve directly correspond to the degradation rate and stage transition of the coating's protective capability, providing an objective basis for material performance classification. By setting a preset failure threshold, the number of cycle times required to reach that threshold can be directly read from the curve, achieving life prediction. This provides crucial data support for maintenance cycle planning, material selection, and cost control in engineering applications. As a key parameter in electrochemical impedance spectroscopy, coating capacitance can be time-stamped with microscopic morphology data to form multi-source corrosion characteristic fusion data.

[0089] In one embodiment of the present invention, using corrosion time or cycle number as independent variables and each standardized feature element in the multi-dimensional feature vector as the dependent variable, a scatter plot of the change of each feature element with corrosion time is plotted. Piecewise linear regression or nonlinear function is used to fit curves to each scatter plot to obtain multiple feature degradation curves. All feature degradation curves are superimposed on the same coordinate system and labeled with legends to jointly constitute the corrosion resistance degradation curve. For each feature degradation curve, the average slope of the feature degradation curve in the initial stage, intermediate stage and final stage is calculated as the degradation rate of the feature degradation curve in different periods. Points on each feature degradation curve where the slope changes significantly are detected and marked as inflection points. The corrosion time corresponding to the inflection points is recorded. The distribution of inflection points of different feature degradation curves on the time axis is analyzed to identify the main stage transition nodes of the coating failure process. According to the stage transition nodes, the entire failure process of the coating is divided into the initial protection period, the defect initiation period, the rapid deterioration period and the complete failure period.

[0090] Optionally, inflection point detection is achieved by analyzing the changes in the first or second derivative of the feature degradation curve, and the identification of stage transition nodes is based on statistical clustering of multiple inflection point times. It can be understood that the construction and parameter extraction of the corrosion resistance degradation curve provide a visual and quantitative basis for evaluating the coating failure stages. Refer to Table 1, which shows an exemplary feature degradation curve parameter table.

[0091] Table 1: Stage Division and Parameter Table of Characteristic Degradation Curve

[0092]

[0093] In practical implementation, piecewise linear regression fitting is achieved by minimizing the sum of squared residuals, while nonlinear function fitting can be performed using exponential or polynomial models. In some embodiments, the superposition of corrosion resistance degradation curves is accomplished using a multi-axis plotting tool, and the inflection point times are recorded in tabular form. Optionally, the failure stages are divided based on the dense regions of inflection point times on the time axis and the coordinated changing trends of the slopes of different characteristic degradation curves. It can be understood that the extraction of average slope and inflection point information supports the phased assessment of coating life.

[0094] See Figure 4 This is a salt spray resistance degradation curve of an aluminum-magnesium-manganese color-coated steel plate. It integrates microscopic morphology characteristics with electrochemical impedance spectroscopy parameters, visually demonstrating the entire process of coating failure from intact to complete failure, possessing significant engineering and scientific value. Aligning microscopic morphology data and electrochemical parameters on the same time axis verifies the mechanism that "coating failure is a synergistic degradation of electrochemical performance and physical structure." The inflection point of the curve clearly indicates that the sudden drop in coating resistance is the direct cause of failure, subsequently triggering substrate corrosion and physical defects. This provides a clear direction for optimizing coating formulations and improving initial protective performance. It provides a quantitative basis for material selection; by comparing the degradation curves of different coatings, materials with superior salt spray resistance can be selected. It provides a data foundation for lifespan prediction; combined with preset failure thresholds, the lifespan of the coating under actual working conditions can be predicted, guiding maintenance cycle planning.

[0095] In one embodiment of the present invention, one or more key corrosion features are selected as failure criteria from multi-source corrosion feature fusion data, and a specific failure threshold is set for each failure criterion. A mathematical relationship model between each failure criterion and corrosion time is established based on the corrosion resistance degradation curve. The failure threshold is then substituted into the corresponding mathematical relationship model to solve for the predicted corrosion time required to reach the failure threshold. The predicted corrosion time is divided by the total duration of a single salt spray and drying cycle to obtain the predicted number of cycle periods. In some embodiments, the mathematical relationship model can be a linear regression model or an exponential decay model, and the solution for the predicted corrosion time involves inverse function calculation.

[0096] In practice, the report includes a data summary section that lists the quantitative results of microscopic morphology data and fitting parameters of electrochemical impedance spectroscopy data obtained after all salt spraying and drying cycles in tabular form. It also includes a curve graph section with a corrosion resistance degradation curve and a textual description of the curve characteristics. Furthermore, the report includes a failure analysis section that details the coating failure stages based on degradation rate and inflection point information and lists the characteristics of each stage. Finally, the report includes a prediction results section that provides a predicted number of cycles required for the aluminum-magnesium-manganese color-coated steel plate to reach the preset failure threshold. Finally, the report includes a conclusion section that qualitatively classifies and summarizes the salt spray resistance performance level of the aluminum-magnesium-manganese color-coated steel plate based on all data analysis results.

[0097] In some embodiments, the salt spray resistance performance level can be classified based on the predicted number of cycles and the rate of development of the failure stage. Optionally, the report is generated in electronic document format, with each chapter arranged in a fixed order, and text descriptions, charts, and data mutually referencing each other. In specific implementations, the preset failure threshold can be set with reference to relevant product standards or historical data, for example, using a coating resistance drop to 50% of the initial value or the area of ​​corrosion products reaching 5% of the total surface area as a failure criterion. It can be understood that the structured comprehensive salt spray resistance performance evaluation report integrates all analytical results from raw data to predicted conclusions.

[0098] See Figure 5 This is a multi-source corrosion characteristic fusion degradation curve, which intuitively shows the synergistic changes of various key performance indicators of aluminum-magnesium-manganese color-coated steel plates over time during salt spray cycling tests. It is a core visualization result for salt spray resistance performance evaluation. The inflection point of the curve clearly indicates that the rapid decrease in coating resistance is the direct cause of failure, which then triggers substrate corrosion and physical defects. This provides a clear direction for optimizing coating formulations and improving initial protective performance. It provides a quantitative basis for material selection; by comparing the degradation curves of different coatings, materials with better salt spray resistance can be selected. It provides a data foundation for life prediction; combined with preset failure thresholds, the service life of the coating under actual working conditions can be predicted, guiding maintenance cycle planning.

[0099] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates, characterized in that, include: Experiments were conducted on aluminum-magnesium-manganese color-coated steel plates to obtain quantitative results of microstructure data and fitting parameters of electrochemical impedance spectroscopy data. Establish a mapping relationship between the quantification results of the micromorphology data and the fitting parameters of the electrochemical impedance spectroscopy data to form multi-source corrosion characteristic fusion data; Based on the multi-source corrosion feature fusion data, a corrosion resistance degradation curve is constructed with corrosion time as the horizontal axis and multi-dimensional corrosion features as the vertical axis. The rate parameters and inflection point information of corrosion characteristics over time are extracted from the corrosion resistance degradation curve to evaluate different failure stages of the coating. Based on the changes in the multi-source corrosion feature fusion data during multiple salt spraying and drying cycles, the number of cycles required for the aluminum-magnesium-manganese color-coated steel plate to reach the preset failure threshold is predicted. By integrating the quantification results of the microstructure data, the fitting parameters of the electrochemical impedance spectroscopy data, the corrosion resistance degradation curve, and the cycle number prediction results, a structured comprehensive evaluation report on salt spray resistance is generated.

2. The method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates as described in claim 1, characterized in that, The test was conducted on the aluminum-magnesium-manganese color-coated steel plate sample to obtain the quantitative results of microstructure data and the fitting parameters of electrochemical impedance spectroscopy data, including: A standard-sized aluminum-magnesium-manganese color-coated steel plate was prepared as a test sample, and the coating surface of the test sample was pretreated to remove surface contaminants. The pretreated test sample is placed in a controlled salt spray corrosion test chamber, and periodic salt spraying and drying cycles are performed according to the preset accelerated corrosion test specifications. After each salt spray and drying cycle, the test sample is taken out and the microscopic morphology data of the surface of the test sample is collected using a non-contact three-dimensional surface morphology instrument. Feature extraction is performed on the micromorphological data to identify and quantify the area and depth distribution of corrosion product areas, blistering areas, and coating peeling areas on the coating surface. Electrochemical impedance spectroscopy (EIS) data of the test sample during the corrosion process are collected synchronously, and equivalent circuit fitting is performed on the EIS data to obtain coating resistance, charge transfer resistance, and coating capacitance parameters, which are used as fitting parameters for the EIS data. The process involves placing the pretreated test sample in a controlled salt spray corrosion test chamber and performing periodic salt spraying and drying cycles according to a preset accelerated corrosion test specification, including: A 5% sodium chloride solution was prepared as the corrosive medium, and the pH of the corrosive medium was adjusted to the neutral range. The temperature of the controlled salt spray corrosion test chamber is maintained at a constant temperature, and the humidity of the controlled salt spray corrosion test chamber is controlled to reach saturation humidity; The cycle structure of the salt spraying and drying cycle is set, wherein the duration of the salt spraying stage is set to a fixed duration, and the duration of the drying stage is set to another fixed duration. During the salt spraying stage, the corrosive medium is uniformly sprayed onto the surface of the test sample through an atomizing nozzle; During the drying stage, spraying is stopped and air circulation is maintained inside the controlled salt spray corrosion test chamber to allow the surface of the test sample to dry gradually. Repeat the salt spraying stage and the drying stage until the preset total number of cycles is reached or the test termination condition is met.

3. The method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates as described in claim 2, characterized in that, Microscopic morphology data of the surface of the test sample were acquired using a non-contact three-dimensional surface profilometer, including: Under standard lighting conditions, the test sample is placed horizontally on the sample stage of the non-contact three-dimensional surface profilometer. Adjust the scanning parameters of the non-contact three-dimensional surface profilometer to ensure that its vertical resolution is sufficient to identify micron-level coating thickness variations and corrosion pit depths; The entire exposed surface of the test sample is scanned line by line to obtain surface point cloud data containing height information; The acquired surface point cloud data is denoised and smoothed to eliminate abnormal data points caused by environmental vibration or light source interference. The processed surface point cloud data is reconstructed into a three-dimensional digital surface model, which fully represents the three-dimensional morphology of the surface of the test sample after corrosion.

4. The method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates as described in claim 3, characterized in that, Feature extraction was performed on the microstructure data to identify and quantify the area and depth distribution of corrosion product regions, blistering regions, and coating peeling regions on the coating surface, including: Calculate the height deviation of each point on the surface relative to the original uncorroded reference plane from the three-dimensional digital surface model; Based on the sign and magnitude of the height deviation, the surface area is classified into corrosion product accumulation area, blistering and protrusion area, coating peeling and depression area, and unchanged area. For the area where the corrosion products accumulate, the distribution area is statistically analyzed, and the average height deviation of all points within the area where the corrosion products accumulate is calculated as the average thickness of the accumulation. For the bubbly protrusion area, its distribution area is statistically analyzed, and the height difference between the highest point in the bubbly protrusion area and the surrounding normal coating is calculated as the maximum bubbling height. For the coating peeling depression area, its distribution area is counted, and the height difference between the lowest point in the coating peeling depression area and the surrounding normal coating is calculated as the maximum peeling depth. Generate a quantitative statistical table containing the area, average thickness, maximum height, and maximum depth of various defect regions.

5. The method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates as described in claim 4, characterized in that, Electrochemical impedance spectroscopy (EIS) data of the test sample during the corrosion process are simultaneously acquired, and equivalent circuit fitting is performed on the EIS data to obtain coating resistance, charge transfer resistance, and coating capacitance parameters, including: During a specific stage of the salt spray and drying cycle, the test sample is connected to the electrochemical workstation as a working electrode. A saturated calomel electrode is configured as a reference electrode, and a platinum electrode is configured as an auxiliary electrode, and both are immersed in the corrosive medium together with the working electrode. Within a set frequency range, a small-amplitude sinusoidal AC potential disturbance is applied to the working electrode, and its current response is measured. Record the impedance magnitude and phase angle at different frequencies, and plot them as electrochemical impedance spectroscopy. An equivalent circuit model capable of simulating the corrosion process of the coated metal system is selected, and the electrochemical impedance spectroscopy is fitted using the nonlinear least squares method. The values ​​of the coating resistance, the charge transfer resistance, and the coating capacitance can be directly read from the fitting results.

6. The method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates as described in claim 5, characterized in that, Establish a mapping relationship between the quantification results of the microstructure data and the fitting parameters of the electrochemical impedance spectroscopy data to form multi-source corrosion characteristic fusion data, including: The quantification results of the micromorphological data from the same corrosion time point are time-stamped with the fitting parameters of the electrochemical impedance spectroscopy data. Construct a multi-dimensional feature vector, the elements of which include the area of ​​the corrosion product accumulation region, the area of ​​the blistering and protruding region, the area of ​​the coating peeling and depression region, the value of the coating resistance, the value of the charge transfer resistance, and the value of the coating capacitance. The elements in the multi-dimensional feature vector are standardized to eliminate scale differences caused by different physical units. The standardized multi-dimensional feature vectors are stored together with the corresponding corrosion time labels and cycle period labels to form the multi-source corrosion feature fusion data organized in time series.

7. The method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates as described in claim 6, characterized in that, Based on the multi-source corrosion feature fusion data, a corrosion resistance degradation curve is constructed with corrosion time as the horizontal axis and multi-dimensional corrosion features as the vertical axis, including: With corrosion time or number of cycles as independent variables, and each standardized feature element in the multi-dimensional feature vector as the dependent variable; Plot a scatter plot of each feature element as a function of corrosion time; Each scatter plot is fitted with a piecewise linear regression or nonlinear function to obtain multiple feature degradation curves. All feature degradation curves are superimposed on the same coordinate system and labeled with legends to form the corrosion resistance degradation curve.

8. The method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates as described in claim 7, characterized in that, The rate parameters and inflection point information of corrosion characteristics evolving over time are extracted from the corrosion resistance degradation curve to evaluate different failure stages of the coating, including: For each feature degradation curve, calculate its average slope in the initial, intermediate, and final stages, which serves as the degradation rate of the feature degradation curve at different times. Detect points on each feature degradation curve where the slope changes significantly, mark these points as inflection points, and record their corresponding corrosion times; By analyzing the distribution of inflection points of different degradation curves on the time axis, the main stage transition nodes of the coating failure process can be identified. Based on the aforementioned stage transition nodes, the entire failure process of the coating is divided into the initial protection period, the defect initiation period, the rapid deterioration period, and the complete failure period.

9. The method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates as described in claim 8, characterized in that, Based on the changes in multi-source corrosion characteristic fusion data across multiple salt spray and drying cycles, the number of cycles required for aluminum-magnesium-manganese color-coated steel sheets to reach a preset failure threshold is predicted, including: From the multi-source corrosion feature fusion data, select one or more key corrosion features as failure judgment indicators; A specific failure threshold is set for each of the aforementioned failure determination indicators; Based on the corrosion resistance degradation curve, a mathematical relationship model between each failure judgment index and corrosion time is established. Substitute the failure threshold into the corresponding mathematical relationship model to solve for the predicted corrosion time required to reach the failure threshold; The predicted corrosion time is divided by the total duration of a single salt spray and drying cycle to obtain the predicted number of cycle periods.

10. The method for evaluating the salt spray resistance of aluminum-magnesium-manganese color-coated steel plates as described in claim 9, characterized in that, Generate a structured comprehensive evaluation report on salt spray resistance performance, including: Create a data summary section in the report, listing in tabular form the quantitative results of micromorphological data obtained after all salt spraying and drying cycles and the fitting parameters of electrochemical impedance spectroscopy data; Create a graph section in the report, insert the corrosion resistance degradation curve, and provide a textual description of the curve's characteristics; Create a failure analysis section in the report, detailing the coating failure stages based on degradation rate and inflection point information, and listing the characteristics of each stage; Create a prediction results section in the report and give the predicted number of cycles for the aluminum-magnesium-manganese color-coated steel plate to reach the preset failure threshold. The report includes a conclusion section that, based on all data analysis results, qualitatively classifies and summarizes the salt spray resistance performance levels of the aluminum-magnesium-manganese color-coated steel sheets.

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