Steel surface conductive coating uniformity quantitative evaluation method based on SKP potential variation coefficient

Abstract

The invention relates to the technical field of quantitative evaluation of material surface conductive coating quality, in particular to a steel surface conductive coating uniformity quantitative evaluation method based on an SKP potential variation coefficient. The method comprises the following steps: firstly, cleaning the surface of a to-be-detected coating sample, and systematically planning at least 30 detection points; performing non-contact scanning by using a scanning Kelvin probe (SKP) to obtain a surface potential value of each point; calculating the ratio of the standard deviation to the average value of all the potential values to obtain a potential variation coefficient epsilon; and finally, objective quantitative rating is performed on the uniformity of the conductive coating according to a preset epsilon value grading standard. According to the method, traditional subjective and qualitative uniformity evaluation is converted into objective and quantitative analysis, the method has the advantages of high detection efficiency, no damage, capability of accurately positioning a defect area and wide applicable conductive coating types, and a reliable basis is provided for conductive coating process optimization and product quality control.

Description

Technical Field

[0001] This invention relates to the field of quantitative evaluation technology of conductive coating quality on material surfaces, specifically a quantitative evaluation method for the uniformity of conductive coatings on steel surfaces based on the SKP potential variation coefficient. Background Technology

[0002] Coating steel surfaces is a key process for improving their corrosion resistance, aesthetics, and extending service life. Coating uniformity refers to the consistency of the coating's thickness, chemical composition, and microstructure distribution, directly determining the coating's barrier protection effect, adhesion, and the reliability of the final product. Poor uniformity can lead to areas that are too thin, becoming rapid channels for corrosive media penetration and causing premature corrosion of the substrate; conversely, areas that are too thick may cause increased internal stress, decreased adhesion, or appearance defects such as sagging and orange peel. Therefore, accurate and efficient assessment of coating uniformity is a core element in controlling product quality and optimizing process parameters.

[0003] Currently, the conventional methods used in the industry to assess the uniformity of coatings on steel plate surfaces mainly rely on physical thickness measurement or local component analysis. These methods generally have limitations and are difficult to implement for rapid, quantitative, and comprehensive evaluation of the overall coating uniformity. Specifically: 1. Visual inspection and optical microscopy observation: This method is the most direct and can detect obvious macroscopic defects such as missed coating, bubbles, and wrinkles. However, the results are highly dependent on the operator's experience, are subjective, and cannot identify microscopic thickness changes, microcracks, and gradient distribution of chemical composition. It is a qualitative or semi-qualitative evaluation with poor repeatability and comparability.

[0004] 2. Single-point thickness measurement methods: such as magnetic thickness measurement and eddy current thickness measurement, can accurately measure the coating thickness at a specific point and are easy to operate. However, these methods can only provide data for discrete points, the number of sampling points is limited and the location selection is random, and they cannot fully reflect the continuous distribution of the coating on the entire surface. They are prone to missing local non-uniform areas and it is difficult to make a quantitative judgment on the overall attribute of "uniformity".

[0005] 3. Destructive Cross-Sectional Analysis: This method involves cutting the sample, creating a polished cross-section, and then observing and measuring it using a metallographic microscope or scanning electron microscope. While this method can obtain precise information on coating thickness, interfacial bonding, and microstructure, the sample preparation process is complex and time-consuming, and it causes permanent damage to the sample. Therefore, it is not suitable for online testing, large-scale screening, or non-destructive evaluation of finished products.

[0006] 4. Compositional distribution analysis techniques: such as X-ray fluorescence spectrometry or electron probe microscopy, can analyze the two-dimensional distribution of elements in the coating. However, these devices are usually expensive, not sensitive to the analysis of light elements, and have slow measurement speeds and complex data interpretation. They focus more on the qualitative or semi-quantitative distribution of chemical components rather than directly and comprehensively characterizing the "functional uniformity" that is directly related to the protective performance of the coating.

[0007] In recent years, scanning Kelvin probe (SKP) technology has been explored for its non-contact, non-destructive, and high spatial resolution characteristics, and has been used to study the corrosion initiation points, defects, and failure processes of conductive coatings. This technique, by measuring the local work function (correlation potential-dependent) of the material surface, can sensitively reflect the differences in the microscopic electrochemical state of the conductive coating / metal interface. However, in existing studies, SKP technology is mostly used for qualitative observation of potential distribution images or for mechanistic analysis of specific defect points. A systematic method has not yet been developed to transform the massive potential data obtained from SKP scanning into a concise, objective, and comparable quantitative index for rapid classification and quality evaluation of the overall uniformity of conductive coatings.

[0008] In summary, existing technologies lack an effective means to objectively grade and evaluate the uniformity of conductive coatings on steel surfaces through non-destructive, rapid, and full-field scanning, and to quantify and standardize measurement results. This results in reliance on experience-based judgments or incomplete test data in production quality control, process comparison, and product performance prediction. Summary of the Invention

[0009] To overcome the shortcomings of existing technologies, this invention provides a quantitative evaluation method for the uniformity of conductive coatings on steel surfaces based on the SKP potential variation coefficient, which realizes objective, rapid, and non-destructive quantitative evaluation and classification of the uniformity of conductive coatings.

[0010] To achieve the above objectives, the present invention employs the following technical solution: A quantitative evaluation method for the uniformity of conductive coatings on steel surfaces based on the SKP potential variation coefficient specifically includes the following steps: 1. Sample preparation: Select the conductive coating sample of the steel to be tested and perform surface cleaning treatment.

[0011] 2. Detection point planning: Set up at least 30 detection points on the sample surface.

[0012] 3. Potential Scanning: Using a Scanning Kelvin Probe (SKP) system, a scan is performed at the detection points to measure and obtain the surface potential value E at each detection point. i .

[0013] 4. Uniformity Quantification Calculation: Based on the surface potential value E of all detection points iCalculate the potential variation coefficient ε of the sample surface. The formula for calculating the potential variation coefficient ε is: Where: S is the standard deviation of the potential values ​​at all detection points; This is the average value of the potential values ​​at all detection points; The formula for calculating S is: In the formula: n is the total number of detection points, n≥30; 5. Uniformity evaluation: The uniformity level of the conductive coating on the steel surface is evaluated based on the value of the potential variation coefficient ε. When ε≤5%, the conductive coating is evaluated as having excellent uniformity; When 5% < ε ≤ 10%, the conductive coating is evaluated as having good uniformity. When 10% < ε ≤ 20%, the uniformity of the conductive coating is rated as average. When ε>20%, the uniformity of the conductive coating is considered poor.

[0014] Furthermore, in step 1, the surface cleaning treatment is a degreasing treatment.

[0015] Furthermore, in step 2, for samples with regular shapes, rectangular grids are used to divide the detection points with a grid spacing of 5~10mm; for samples with irregular shapes, polar coordinates with the sample center as the origin or a uniform distribution principle are used to plan the detection points.

[0016] Furthermore, in step 3, the probe diameter of the scanning Kelvin probe is 150 μm or 500 μm, the distance between the probe and the sample surface is controlled at 30~100 μm, and the scanning horizontal and vertical steps are both 10~40 μm / point.

[0017] Furthermore, step 3 also includes performing density measurement of detection points on the preset defect area or edge area on the sample surface, and incorporating this data into the calculation in step 4).

[0018] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention achieves objectivity and standardization of evaluation results, significantly improving comparability. It transforms traditional experience-based visual observation or discrete single-point thickness measurement into a calculable and comparable numerical index (ε) based on full-field potential data. This index eliminates the bias of subjective human judgment, providing a unified and quantifiable benchmark for comparing the quality of conductive coatings from different batches, processes, or suppliers, greatly enhancing the scientific rigor and impartiality of quality control.

[0019] 2. Improved comprehensiveness and accuracy of detection, avoiding local misjudgments. Through systematic detection point planning (such as grid method, polar coordinate method) combined with key marking and intensive scanning of potential defect areas, this invention ensures that sampling points can fully cover the main areas and structurally weak areas of the sample. This design enables the method to accurately capture local potential anomalies caused by uneven conductive coating thickness, micro-defects, or compositional differences, overcoming the "generalization" problem that may be caused by random sampling or single-point measurement, thus providing a more realistic and reliable overall evaluation of the uniformity of the conductive coating.

[0020] 3. This invention provides a highly efficient and non-destructive method for on-site or online evaluation. Compared to cross-sectional analysis methods that require damaging samples, or expensive and cumbersome component analysis methods (such as XRF and electron probe microanalysis), the SKP technology employed in this invention has the advantages of being non-contact, requiring no electrolyte, and enabling rapid scanning. Combined with the efficient quantification algorithm of this method, evaluation results can be obtained quickly without damaging the coating, providing a feasible technical path for real-time quality monitoring on the production line or rapid screening of large batches of products.

[0021] 4. A clear grading and evaluation standard has been established, providing stronger guidance. This invention not only provides quantitative indicators but also establishes a four-level evaluation standard (e.g., excellent, good, average, and poor) corresponding to the indicator ε value. This standard directly links abstract numerical values ​​with specific quality levels, enabling technicians to intuitively and quickly judge the quality status of conductive coatings and make process decisions, greatly enhancing the practicality and guiding value of the method. Detailed Implementation

[0022] This invention discloses a method for quantitatively evaluating the uniformity of conductive coatings on steel surfaces based on the SKP potential variation coefficient. Those skilled in the art can refer to this document and appropriately modify the process parameters to achieve the desired result. It should be particularly noted that all similar substitutions and modifications are obvious to those skilled in the art and are considered to be included in this invention. The methods and applications of this invention have been described through preferred embodiments, and those skilled in the art can clearly modify or appropriately change and combine the methods and applications described herein without departing from the content, spirit, and scope of this invention to realize and apply the technology of this invention.

[0023] A quantitative evaluation method for the uniformity of conductive coatings on steel surfaces based on the SKP potential variation coefficient specifically includes the following steps: 1. Sample preparation: Select representative conductive coating samples, ensuring that the surface is free of obvious mechanical damage, bubbles, oxidation products, etc., and degrease them for later use.

[0024] 2. Detection Point Planning: For regular samples, divide the detection area and use a 10mm×10mm grid to divide the detection points, with the spacing appropriately increased to 5mm in the edge areas. For irregular samples, set at least 30 detection points with the sample center as the origin, according to the principle of polar coordinates or uniform distribution, to cover the main area of ​​the sample.

[0025] 3. Potential scanning: Place the sample after the above treatment on the SKP system test platform. The sample is connected to the electrochemical workstation through conductive adhesive, and the sample level is adjusted.

[0026] The probe diameter is 150 μm or 500 μm, and the distance between the probe and the sample surface is controlled between 30 and 100 μm. A probe distance of less than 30 mm is not suitable, as this can easily cause the probe to touch the sample and damage it. Therefore, the probe distance should be as close to the sample as possible without touching it, so that a larger feedback signal can be obtained, improving the sensitivity of the characterization.

[0027] The scan begins at the lower left of the sample and proceeds from left to right and from bottom to top. The horizontal and vertical step sizes are both 10–40 μm / point. After the potential scan is completed, the potential data within the scanned area are obtained.

[0028] 4. Record the potential data E at all detection points. i Simultaneously mark the sample edges, center, and areas that may have defects (such as bubbles or scratches) to facilitate subsequent analysis.

[0029] 5. Uniformity quantification calculation: This involves quantifying the uniformity of the conductive coating by calculating the dispersion of the potential at all detection points. ×100% In the formula, ε is the potential variation coefficient, which represents the magnitude of the potential dispersion level relative to the average potential level. The larger ε is, the greater the potential dispersion relative to the average value (the stronger the potential fluctuation); the smaller ε is, the more concentrated the potential data (the potential value is relatively stable).

[0030] S is the standard deviation of the potential. In the formula: The average potential of all detection points ; n is the total number of detection points (n≥30).

[0031] 6. Uniformity evaluation: The uniformity of the samples is compared based on the ε value. The larger the ε value, the worse the uniformity of the conductive coating, and the smaller the ε value, the better the uniformity of the conductive coating.

[0032] (1) When ε≤5%, the conductive coating has excellent uniformity and highly consistent potential distribution.

[0033] (2) When 5% < ε ≤ 10%, the uniformity is good and the local differences are small.

[0034] (3) When 10% < ε ≤ 20%, the uniformity is generally good, and there are obvious local differences; (4) When ε>20%, the uniformity is poor and the quality of the conductive coating is unstable.

[0035] Example 1: Uniformity evaluation of colored organic conductive coating on cold-rolled steel sheet This embodiment uses the method of the present invention to evaluate the cathodic protection conductive substrate for galvanic corrosion protection of three marine engineering steel pieces, specifically including the following steps: 1. Sample Preparation: Select three conductive coating samples with dimensions of 10cm × 5cm, numbered A1, A2, and A3. Ensure that the surface is free of obvious mechanical damage, bubbles, and oxidation products, and perform degreasing treatment on them.

[0036] 2. Detection point planning: Due to the regularity of the sample, a rectangular grid was used for division. The main grid spacing was 10mm, and the spacing was increased to 5mm in the four edge areas, resulting in a total of 35 effective detection points.

[0037] 3. SKP Potential Scan: Place the pre-treated sample on the SKP system testing platform. Connect the sample to the electrochemical workstation via conductive adhesive. Adjust the sample level, using a probe diameter of 150 μm and maintaining a probe distance of 50 μm from the sample surface. The scan begins from the lower left of the sample, proceeding from left to right and from bottom to top. Both the horizontal and vertical step sizes are 20 μm / point. After the potential scan is complete, obtain the potential data within the scanned area.

[0038] 4. Record the potential data E at all detection points. i Simultaneously mark the sample edges, center, and areas that may have defects (such as bubbles or scratches) to facilitate subsequent analysis.

[0039] 5. Data Processing and Evaluation: Calculate the average potential of each sample. Standard deviation S and potential variation coefficient ε. Evaluation is based on the following evaluation criteria: (1) When ε≤5%, the conductive coating has excellent uniformity and the potential distribution is highly consistent; (2) When 5%<ε≤10%, the uniformity is good and the local differences are small; (3) When 10%<ε≤20%, the uniformity is average and there are obvious local differences; (4) When ε>20%, the uniformity is poor and the quality of the conductive coating is unstable.

[0040] Table 1 Data from Example 1 As shown in Table 1, the ε values ​​of all three samples are less than 5, indicating excellent uniformity of the conductive coating. Furthermore, the ε value precisely distinguishes subtle differences in uniformity. Sample A3 has the lowest ε value among the three groups of samples, meaning that sample A3 exhibits the best uniformity of its conductive coating.

[0041] Example 2: Evaluation of the uniformity of the zinc coating on hot-dip galvanized steel sheets This embodiment evaluates the coating uniformity of irregularly shaped hot-dip galvanized parts and verifies the applicability of the method to metal coatings, specifically including the following steps: 1. Sample preparation: Select three irregular stamped parts that have undergone hot-dip galvanizing, numbered B1, B2, and B3. Perform degreasing and cleaning.

[0042] 2. Detection point planning: Taking the geometric center of the sample as the origin, 40 detection points are evenly set in the radial direction using polar coordinates, covering all functional surfaces including edges and grooves.

[0043] 3. Place the processed sample on the SKP system testing platform. Connect the sample to the electrochemical workstation using conductive adhesive. Adjust the sample level, using a probe diameter of 500 μm and maintaining a probe distance of 100 μm from the sample surface. Begin scanning from the lower left of the sample, proceeding from left to right and from bottom to top. The horizontal and vertical step sizes are both 40 μm / point. After the potential scan is complete, obtain the potential data within the scanned area.

[0044] 4. Record the potential data E at all detection points. i Simultaneously mark the sample edges, center, and areas that may have defects (such as bubbles or scratches) to facilitate subsequent analysis.

[0045] 5. Data Processing and Evaluation: Calculate the average potential of each sample. Standard deviation S and potential variation coefficient ε. Evaluation is based on the following evaluation criteria: (1) When ε≤5%, the conductive coating has excellent uniformity and the potential distribution is highly consistent; (2) When 5%<ε≤10%, the uniformity is good and the local differences are small; (3) When 10%<ε≤20%, the uniformity is average and there are obvious local differences; (4) When ε>20%, the uniformity is poor and the quality of the conductive coating is unstable.

[0046] Table 2 Data from Example 2 As shown in Table 2, when 5% < ε for samples B1 and B2 ≤ 10%, and 10% < ε for sample B3 ≤ 20%, sample B1 is rated as "good", sample B2 is rated as "good", and sample B3 is rated as "average".

[0047] The method of this invention has been successfully applied to irregular metal coated parts. The ε value effectively reflects the difference in coating uniformity among different samples. Sample B3 has relatively poor uniformity, which may be related to uneven flow of the plating solution.

[0048] Example 3: Uniformity evaluation of zinc-based composite coating (zinc-aluminum-magnesium) This embodiment evaluates zinc-aluminum-magnesium composite coatings with higher corrosion resistance requirements, demonstrating the sensitivity of the method to multi-element composite systems.

[0049] step: 1. Sample preparation: Select two zinc-aluminum-magnesium coated steel plates from the same batch of the same process, numbered C1 and C2, with a size of 15cm×10cm.

[0050] 2. Inspection point planning: A 10mm×10mm grid is used, and a total of 50 inspection points are selected in the central area of ​​the plate and the edge area near the weld.

[0051] 3. SKP potential scan: Parameters are the same as in Example 1.

[0052] 4. Data processing and evaluation: Same as in Example 1.

[0053] result: Sample C1: =720mV, S=32mV, ε=4.44%, rated as "excellent".

[0054] Sample C2: =705mV, S=52mV, ε=7.38%, rated as "good".

[0055] Analysis: The ε value of sample C2 was significantly higher than that of C1. SKP potential distribution cloud map analysis revealed a distinct low-potential region (dark spot) at the edge of sample C2. Subsequent destructive cross-sectional SEM-EDS analysis confirmed that this low-potential region corresponded to a lower local enrichment level of aluminum and magnesium, leading to differences in electrochemical activity within this micro-region. This demonstrates that the method of this invention can sensitively capture differences in "functional uniformity" caused by uneven element distribution in composite coatings.

[0056] Comparative experiment: The methods of this invention, magnetic thickness measurement (representing conventional thickness measurement), and micro-area X-ray fluorescence spectroscopy (representing component analysis) were used for detection and comparison.

[0057] 1. Detection objective: To assess the uniformity of the edge region of sample C2.

[0058] 2. Operation and Results: As described in Example 3, the method of this invention involves scanning 50 points across the entire field, taking approximately 30 minutes in total. It directly yields a quantitative conclusion of ε=7.38% and a "good" grade, and accurately locates abnormal areas using a potential cloud map.

[0059] Magnetic thickness measurement: Thickness was measured at 20 randomly selected points on the sample surface. Data showed an average thickness of 18.5 μm and a standard deviation of 1.8 μm, indicating acceptable thickness uniformity. However, this method completely failed to specifically detect edge anomaly areas, as the thickness in these areas did not show significant abnormalities.

[0060] Micro-area XRF surface scanning: Elemental surface distribution analysis was performed on suspected areas, with the total time for equipment warm-up and scanning exceeding 2 hours. The results confirmed locally low concentrations of Al and Mg, perfectly matching the low SKP potential region of this method. However, XRF equipment is expensive, resulting in high detection costs, slow speed, and limited quantitative accuracy for the light element Mg.

[0061] Through the above comparative experiments, and by comprehensively evaluating the performance differences between this invention and existing mainstream technologies (magnetic thickness measurement and micro-area XRF method), the following conclusions can be clearly drawn: In terms of detection efficiency, the method of this invention can complete the full-field potential scan and data analysis of a sample in about 30 minutes, providing a complete evaluation of uniformity. While the magnetic thickness measurement method has a fast single-point measurement speed, it is limited by discrete point sampling (usually only about 20 points), making it difficult to fully reflect the overall uniformity. Its effective information acquisition efficiency is actually moderate. Micro-area XRF analysis has the lowest efficiency because of the long equipment warm-up time and slow surface scanning speed, with the analysis time for a single sample usually exceeding 2 hours.

[0062] In terms of detection properties and safety, both the method of this invention and the micro-area XRF method are non-destructive and non-contact measurements that will not cause any physical damage or contamination to the coating surface; while the magnetic thickness measurement method is a contact measurement, which carries the risk of the probe scratching the soft coating and has limitations when detecting high-value or smooth surfaces.

[0063] In terms of information dimension and depth, the method of this invention uniquely and directly reflects the "electrochemical functional uniformity" of the conductive coating, that is, the surface potential distribution that is directly related to corrosion resistance. The magnetic thickness measurement method can only provide the single parameter of physical thickness and cannot detect the non-uniformity of composition or microstructure. The micro-area XRF method focuses on the distribution of elemental chemical composition. Although it can reveal compositional non-uniformity, it has no direct quantitative relationship with the functional protective performance of the coating.

[0064] Regarding the ability to locate defects or abnormal areas, the method of this invention can intuitively and accurately locate abnormal potential areas through SKP potential distribution cloud maps, thereby guiding subsequent process improvements or key monitoring; magnetic thickness measurement is a random single-point measurement, which is very easy to miss local defects; although micro-area XRF method can locate anomalies through composition surface distribution maps, its analysis is usually carried out in specific areas where problems are suspected, rather than a full-field screening.

[0065] In terms of quantitative output and practicality of results, the outstanding advantage of the method of this invention lies in its ability to directly output a comprehensive quantitative index—the coefficient of variation of potential ε—and provide a clear quality grade evaluation (such as excellent, good, etc.). The results are objective, intuitive, and easy to use for quality judgment and comparison. Magnetic thickness measurement can calculate the average thickness and standard deviation, but its statistical significance is limited to physical thickness. The main output of micro-area XRF method is a qualitative or semi-quantitative elemental distribution image, which is difficult to simplify into a unified quantitative score for rapid comparison.

[0066] Finally, in terms of equipment cost and technology accessibility, the SKP equipment required by this invention has a significantly lower cost than large-scale micro-area XRF analytical instruments, and has better cost-effectiveness and broader industrial application potential; although magnetic thickness gauges have the lowest equipment cost and are the most widely used, the information dimensions and depth they provide are limited, and they cannot meet the high-standard quantitative evaluation requirements for uniformity.

[0067] In summary, the method of this invention demonstrates significant advantages and technological advancements over traditional thickness measurement and composition analysis methods in terms of detection efficiency, information relevance, defect location capability, intuitive quantitative output, and overall cost-effectiveness. It successfully achieves a leap from indirect, partial, and qualitative assessment of conductive coating uniformity to direct, comprehensive, and quantitative evaluation.

[0068] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A quantitative evaluation method for the uniformity of conductive coatings on steel surfaces based on the SKP potential variation coefficient, characterized in that, Specifically, the steps include the following: 1) Sample preparation: Select the conductive coating sample of the steel to be tested and perform surface cleaning treatment; 2) Detection point planning: Set up at least 30 detection points on the sample surface; 3) Potential scanning: Using a scanning Kelvin probe (SKP) system, a scan is performed at the detection points to measure and obtain the surface potential value E at each detection point. i ; 4) Uniformity quantification calculation: based on the surface potential value E of all detection points. i Calculate the potential variation coefficient ε of the sample surface. The formula for calculating the potential variation coefficient ε is: ×100% Where: S is the standard deviation of the potential values ​​at all detection points; This is the average value of the potential values ​​at all detection points; The formula for calculating S is: In the formula: n is the total number of detection points, n≥30; 5) Uniformity evaluation: The uniformity level of the conductive coating on the steel surface is evaluated based on the value of the potential variation coefficient ε. When ε≤5%, the conductive coating is evaluated as having excellent uniformity; When 5% < ε ≤ 10%, the conductive coating is evaluated as having good uniformity. When 10% < ε ≤ 20%, the uniformity of the conductive coating is rated as average. When ε>20%, the uniformity of the conductive coating is considered poor.

2. The method for quantitatively evaluating the uniformity of conductive coatings on steel surfaces based on the SKP potential variation coefficient as described in claim 1, characterized in that, In step 1), the surface cleaning treatment is a degreasing treatment.

3. The method for quantitatively evaluating the uniformity of conductive coatings on steel surfaces based on the SKP potential variation coefficient as described in claim 1, characterized in that, In step 2), for samples with regular shapes, rectangular grids are used to divide the detection points with a grid spacing of 5-10 mm; for samples with irregular shapes, polar coordinates with the sample center as the origin or uniform distribution principle are used to plan the detection points.

4. The method for quantitatively evaluating the uniformity of conductive coatings on steel surfaces based on the SKP potential variation coefficient as described in claim 1, characterized in that, In step 3), the diameter of the scanning Kelvin probe is 150 μm or 500 μm, the distance between the probe and the sample surface is controlled at 30~100 μm, and the scanning horizontal and vertical steps are both 10~40 μm / point.

5. The method for quantitatively evaluating the uniformity of conductive coatings on steel surfaces based on the SKP potential variation coefficient as described in claim 1, characterized in that, Step 3) also includes performing density measurement of detection points in the preset defect area or edge area on the sample surface, and this data is included in the calculation in step 4).

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