Coating adhesive force testing method based on pressure-displacement curve

Abstract

The invention discloses a coating adhesion testing method based on a pressure-displacement curve, which belongs to the technical field of valve coating detection, and comprises the following steps: applying pressure to a detection head through a loading mechanism to obtain a pressure measurement value, and recording a displacement measurement value of the detection head through a displacement measurement mechanism, generating a pressure displacement curve, sending the curve to a data processing unit for preprocessing, generating preprocessed curve data, performing feature point identification and extraction, generating a feature point data set for analysis and calculation, and generating an adhesive force value of the coating. The high-precision sensor is used for collecting pressure and displacement data in real time, the intelligent feature point recognition technology is adopted, the valve coating adhesive force can be rapidly and accurately tested on the industrial site, and the requirements for timeliness and convenience of valve coating adhesive force detection on the industrial site are met.

Description

Technical Field

[0001] This invention relates to the field of valve coating testing technology, and in particular to a coating adhesion testing method based on pressure-displacement curves. Background Technology

[0002] In industrial production, valves are key components for controlling fluid flow, and the adhesion of the coating on the valve surface has a crucial impact on the valve's corrosion resistance, service life, and operational stability. Coating adhesion is one of the most critical performance indicators for evaluating coating quality, and its test results are an important basis for coating process development, production quality control, and equipment safety assessment.

[0003] In existing technologies, there are various methods for testing coating adhesion, such as scratch tests and pull-out tests. While more sophisticated instrumental tests, such as nanoindentation or microindentation tests, can obtain the relationship curve between applied force and indentation depth, in practical applications, usually only the maximum load value on the curve is considered as the primary basis for judging adhesion, or a qualitative or semi-quantitative assessment is performed through subsequent observation of the indentation morphology. These methods are relatively simple in data acquisition and processing, focusing on obtaining the limiting state parameters at which the coating fails.

[0004] However, the aforementioned scratch and pull-out tests have many problems in practical applications. Scratch tests require professional operators and the results are greatly affected by subjective factors, making them difficult to perform accurately on complex valve surfaces. Pull-out tests usually require large equipment, making them difficult to conduct on-site, and can cause some damage to the valve, making them unsuitable for some valves that are already installed and in use. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a coating adhesion testing method based on pressure-displacement curves. It employs high-precision sensors to collect pressure and displacement data in real time, along with intelligent feature point recognition technology. This method enables rapid and accurate testing of valve coating adhesion in industrial settings, is easy to operate, causes minimal damage to the valve body, and meets the timeliness and convenience requirements for valve coating adhesion testing in industrial settings.

[0006] The above objectives can be achieved through the following approach:

[0007] A method for testing coating adhesion based on a pressure-displacement curve includes: applying pressure to a detection head using a loading mechanism to obtain pressure measurements; recording displacement measurements of the detection head using a displacement measuring mechanism; generating a pressure-displacement curve based on the pressure and displacement measurements and sending it to a data processing unit; preprocessing the pressure-displacement curve to generate preprocessed curve data; identifying and extracting feature points based on the preprocessed curve data to generate a feature point dataset; and analyzing and calculating the feature point dataset to generate a coating adhesion value.

[0008] Optionally, the generated pressure-displacement curve includes: controlling the loading mechanism to move the detection head at a preset constant rate to generate a controlled displacement process; acquiring pressure measurement values ​​through a pressure sensor during the controlled displacement process, and simultaneously acquiring displacement measurement values ​​through the displacement measurement mechanism, and generating a set of synchronous data pairs by combining the pressure measurement values ​​and the displacement measurement values; performing projection mapping based on the synchronous data pairs to generate a series of data points in a two-dimensional coordinate system; and performing interpolation fitting processing based on the series of data points to form a pressure-displacement curve and transmitting it to the data processing unit.

[0009] Optionally, generating preprocessed curve data includes: acquiring and integrating ambient temperature parameters measured by a temperature sensor and mechanical clearance parameters obtained through equipment calibration to generate calibration parameters; using the calibration parameters to perform zero-point error correction on the pressure-displacement curve to generate corrected curve data; and performing digital filtering on the corrected curve data to generate preprocessed curve data.

[0010] Optionally, the generation of the feature point dataset includes: calculating the instantaneous slope based on the preprocessed curve data, identifying the point where the instantaneous slope is first significantly lower than a preset reference stiffness value, and generating an elastic limit point; detecting a plateau or fluctuation region in the preprocessed curve data where the pressure growth rate slows down significantly, and generating a plastic yield point; monitoring the peak point in the preprocessed curve data before the pressure value drops sharply, and generating a critical failure point; and combining the elastic limit point, the plastic yield point, and the critical failure point to generate a feature point dataset.

[0011] Optionally, the method further includes: capturing images of coating surface morphology changes in real time; synchronously associating the images of coating surface morphology changes with the corresponding pressure-displacement curves in time to construct an associated dataset; performing damage event process analysis based on the associated dataset to identify the time point and displacement value of the damage event, and using it to assist in verifying or correcting the feature point dataset.

[0012] Optionally, the adhesion value of the generated coating includes: extracting the critical pressure value and critical displacement value based on the elastic limit point to generate a key parameter set; the microprocessor in the data processing unit calculates the adhesion value of the coating in combination with the key parameter set.

[0013] Optionally, the method further includes: selecting multiple detection points on the coating surface and obtaining a corresponding set of adhesion evaluation results; performing statistical calculations based on the adhesion evaluation results to obtain the mean, standard deviation, and coefficient of variation; and performing joint analysis based on the mean, standard deviation, and coefficient of variation to obtain a comprehensive evaluation result of the valve coating adhesion.

[0014] Optionally, the statistical calculation based on the adhesion evaluation results includes: obtaining the spatial coordinates of each detection point relative to the valve body reference point; associating the spatial coordinates with the corresponding comprehensive evaluation results to generate spatial distribution data; using the spatial distribution data to perform spatial interpolation calculations to generate a coating adhesion distribution heat map, and identifying weak areas of the coating based on the adhesion distribution heat map.

[0015] Optionally, the method further includes: associating the comprehensive evaluation results with the corresponding pressure-displacement curves and test timestamps, and storing them in the data storage module of the data processing unit to form historical data records; obtaining the valve's unique identifier, and performing retrieval and sorting in conjunction with the historical data records to construct a time series dataset of coating adhesion performance; performing trend comparison analysis based on the time series dataset to predict the coating life degradation trend, and generating maintenance warning information when the prediction result reaches a preset maintenance threshold.

[0016] Based on the same inventive concept, this invention also provides a coating adhesion testing system based on a pressure-displacement curve. The system includes: a curve data generation module, used to apply pressure to a detection head via a loading mechanism to obtain pressure measurements, and to record displacement measurements of the detection head via a displacement measurement mechanism; generating a pressure-displacement curve based on the pressure and displacement measurements and sending it to a data processing unit; a data preprocessing module, used to preprocess the pressure-displacement curve to generate preprocessed curve data; a feature point extraction module, used to identify and extract feature points based on the preprocessed curve data to generate a feature point dataset; and an adhesion analysis module, used by the data processing unit to analyze and calculate the feature point dataset to generate a coating adhesion value.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] 1. This invention, through high-precision pressure control and displacement measurement combined with scientific algorithms, can accurately calculate coating adhesion values, reduce human error, and improve the reliability and repeatability of test results. Compared with traditional testing methods, this device can obtain relatively consistent test results even when operated by different personnel.

[0019] 2. This invention, through in-depth analysis of the pressure-displacement curve, algorithmically identifies and extracts multiple feature points with clear physical significance, such as the elastic limit point, plastic yield point, and critical failure point. It surpasses the traditional evaluation mode that relies solely on a single destructive force, and can perform multi-dimensional quantitative characterization of the entire process of coating from elastic deformation to fracture failure, thereby providing a profound insight and comprehensive evaluation of coating failure mechanisms.

[0020] 3. The present invention, through the special conical shape design of the detection head, causes minimal damage to the valve coating during the testing process, and will not affect the normal use of the valve. For some critical valves or valves that have already been installed and put into use, the coating adhesion test can be completed without affecting their operation.

[0021] 4. The data processing unit of this invention can process and store test data in real time, which facilitates long-term tracking and analysis of valve coating adhesion data, provides strong data support for valve maintenance, helps to detect potential coating problems in a timely manner, take maintenance measures in advance, and reduce the risk of valve failure.

[0022] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of a coating adhesion testing method based on a pressure-displacement curve according to an embodiment of the present invention.

[0025] Figure 2 This is a feature point recognition and extraction diagram according to an embodiment of the present invention.

[0026] Figure 3 This is a spatial thermogram of coating adhesion according to an embodiment of the present invention.

[0027] Figure 4 This is a time series analysis diagram according to an embodiment of the present invention.

[0028] Figure 5 This is a schematic diagram of a coating adhesion testing system based on a pressure-displacement curve according to an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, 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.

[0030] Reference Figure 1 One embodiment of the present invention proposes a coating adhesion testing method based on pressure-displacement curves. By using high-precision sensors to collect pressure and displacement data in real time, and intelligent feature point recognition technology, the adhesion of valve coatings can be tested quickly and accurately in industrial settings. The method is simple to operate, causes minimal damage to the valve body, and meets the timeliness and convenience requirements for valve coating adhesion testing in industrial settings.

[0031] The method described in this embodiment specifically includes:

[0032] Pressure is applied to the detection head by a loading mechanism to obtain pressure measurement values, and displacement measurement values ​​of the detection head are recorded by a displacement measurement mechanism. A pressure-displacement curve is generated based on the pressure measurement values ​​and the displacement measurement values ​​and sent to the data processing unit.

[0033] Preprocessing is performed based on the pressure-displacement curve to generate preprocessed curve data;

[0034] Based on the preprocessed curve data, feature points are identified and extracted to generate a feature point dataset.

[0035] The data processing unit analyzes and calculates the feature point dataset to generate the adhesion value of the coating.

[0036] Specifically, a standardized mechanical loading and data analysis process is used to quantitatively evaluate the adhesion performance of coatings. The core of this process is to establish a mechanical response model of the coating under normal pressure, from initial contact, elastic deformation, plastic yielding, to final failure. First, a high-precision miniature electric actuator controls the pressure applied to the detection head using a loading mechanism to obtain pressure measurements. Simultaneously, a high-precision linear displacement sensor monitors the displacement change of the detection head in real time during loading, generating a pressure-displacement curve that fully describes this process, which is then sent to the data processing unit. The detection head employs a special conical design, minimizing damage to the valve coating during testing and ensuring normal valve operation. For critical valves or valves already in use, coating adhesion testing can be completed without affecting their operation. Subsequently, the pressure-displacement curve undergoes digital signal processing preprocessing to eliminate environmental and equipment-related errors and random noise, obtaining clean mechanical behavior curve data as the preprocessed curve data. Based on this, the algorithm automatically identifies and extracts key feature points on the curve representing critical transitions in the coating's mechanical state, resulting in a feature point dataset including elastic limit points, plastic yield points, and critical failure points. Finally, this dataset of physically significant feature points is combined with the entire pressure-displacement curve, and through mathematical calculations, the complex mechanical behavior process is ultimately transformed into a single numerical value representing the coating's adhesion strength to the substrate. This method automates and standardizes coating adhesion testing. Compared to traditional evaluation methods relying on single peak forces or subjective observation, this method can acquire and analyze the entire process of coating failure under stress. By accurately identifying key mechanical feature points and performing quantitative calculations, it enables rapid and accurate testing of valve coating adhesion in industrial settings with minimal damage to the valve body, meeting the timeliness and convenience requirements for valve coating adhesion testing in industrial environments. This provides a more scientific, reliable, and refined technical means for coating material research and development, production process optimization, and quality control.

[0037] Optionally, the generated pressure-displacement curve includes:

[0038] The loading mechanism is controlled to move the detection head at a preset constant rate, thereby generating a controlled displacement process;

[0039] Based on the pressure measurement value obtained by the pressure sensor during the controlled displacement process, and the displacement measurement value obtained by the displacement measuring mechanism simultaneously, a set of synchronous data pairs is generated by combining the pressure measurement value and the displacement measurement value;

[0040] Based on the synchronous data pair, a projection mapping is performed to generate a series of data points in a two-dimensional coordinate system;

[0041] Based on the series of data points, interpolation fitting is performed to form a pressure-displacement curve, which is then transmitted to the data processing unit.

[0042] Specifically, to generate a pressure-displacement curve that accurately reflects the mechanical behavior of the coating, a standardized testing procedure must first be executed. Upon arrival at the valve site, the device power is turned on, and the normal operation of each component is checked. A relatively flat and representative area on the valve coating surface is selected as the test point. Dust, oil, and other impurities on the test point surface are cleaned to ensure good contact between the test head and the coating surface. When the procedure is started, the test head is aligned with the test point, and the loading mechanism is activated. The miniature electric actuator is mounted on the main frame of the device, ensuring it is securely installed and moves smoothly. The control circuit of the electric actuator is connected to the microprocessor in the data processing unit to achieve precise control of the actuator. The loading speed and loading range are set through the data processing unit, thereby initiating a controlled displacement process. The selection of this constant rate is crucial, as it ensures the quasi-static characteristics of the loading process, eliminates interference from impact or acceleration changes on the measurement results, and thus guarantees the repeatability of the test and the comparability between different samples. Throughout the entire displacement process of the detection head's descent and contact with the coating, a pressure sensor mounted on the detection head measures the interaction force between the detection head and the coating in real time, i.e., the pressure measurement value. Simultaneously, a displacement measuring mechanism, such as a high-precision grating ruler or a linear variable differential transformer, accurately records the vertical displacement of the detection head relative to its initial position, i.e., the displacement measurement value. These two measurement channels are locked by a synchronous clock signal, ensuring that at any given time point, the acquired pressure and displacement measurements correspond precisely, forming a series of discrete synchronous data pairs. The conical detection head is mounted at the output end of the electric actuator, ensuring a secure installation and appropriate angle when in contact with the valve coating. Subsequently, these synchronous data pairs are projected and mapped into a two-dimensional coordinate system, with displacement as the abscissa and pressure as the ordinate, generating a series of data points. Finally, to obtain a continuous and smooth curve for subsequent analysis, interpolation fitting is performed on this series of data points. This typically employs algorithms such as cubic spline interpolation or polynomial fitting. This process not only connects discrete points but also smooths out random noise caused by electrical signals or minute vibrations to a certain extent, ultimately forming a complete and detailed pressure-displacement curve, which is then transmitted to the data processing unit. The entire method ensures the objectivity and accuracy of adhesion force measurement through automated data acquisition, processing, and analysis.

[0043] For example, consider the adhesion testing of an epoxy coating on the surface of an industrial valve. First, check that all components of the device are functioning properly. Select appropriate testing points and clean them. Install a rechargeable battery and connect the battery to the circuits of all electrical components to ensure a stable power supply. After preparation, align the testing head of the portable testing device with the area of ​​the valve coating to be tested. Activate the loading mechanism, controlling a miniature electric push rod to push the testing head into the coating at a constant rate of 0.5 mm / min. The pressure sensor and linear displacement sensor work synchronously, recording pressure and displacement data in real time, forming synchronous data pairs and mapping them to two-dimensional data points. A smooth pressure-displacement curve is generated through cubic spline interpolation and sent to the data processing unit. This method achieves synchronization and accuracy in data acquisition through high-precision sensors and a controllable loading mechanism, avoiding human error. The entire process is highly integrated, easy to operate, and suitable for industrial environments, providing strong support for quality control and maintenance decisions regarding valve coatings.

[0044] Optionally, the generation of preprocessed curve data includes:

[0045] The ambient temperature parameters measured by the temperature sensor and the mechanical clearance parameters obtained through equipment calibration are acquired and integrated to generate calibration parameters.

[0046] The zero-point error of the pressure-displacement curve is corrected using the calibration parameters to generate corrected curve data.

[0047] The corrected curve data is subjected to digital filtering to generate preprocessed curve data.

[0048] Specifically, firstly, the ambient temperature parameter, measured in real time by a temperature sensor, is acquired. This ambient temperature parameter reflects the thermodynamic conditions of the test site. Simultaneously, the mechanical clearance parameter, obtained through pre-shipment calibration, is acquired. This mechanical clearance parameter characterizes the inherent assembly clearance between the loading mechanism and the displacement measuring mechanism. The ambient temperature parameter and the mechanical clearance parameter are integrated to generate a comprehensive calibration parameter. This calibration parameter is used to correct the zero-point error of the original pressure-displacement curve. For calculating the corrected pressure value F' and the corrected displacement value d', we have:

[0049]

[0050] Where F represents the original pressure measurement; d represents the original displacement measurement; and F0 and d0 represent the zero-point error compensation amount determined by both temperature T and mechanical clearance G. This compensation amount is obtained through a pre-established error mapping table, which is obtained during equipment calibration by measuring outputs under different temperatures and no-load conditions. After performing this correction operation on all data points, corrected curve data is generated. Subsequently, digital filtering is performed on the corrected curve data, using a low-pass Butterworth filter with an adjustable cutoff frequency to eliminate high-frequency noise, generating the final pre-processed curve data. This pre-processing reduces the impact of environmental interference and inherent errors on the measurement data.

[0051] For example, consider the field test of valve coating adhesion at a power plant. Before the test, the operator turns on the portable testing device. The built-in temperature sensor monitors the ambient temperature in real time, which is 28°C. Simultaneously, the data processing unit reads the mechanical clearance parameter of 0.02mm, determined during the device's factory calibration, from its internal memory. These two parameters are input into the calibration algorithm to calculate the zero-point error compensation under the current conditions. During the test, the loading mechanism pushes the detection head into the coating at a constant rate. The pressure sensor and displacement sensor simultaneously collect data to form the original pressure-displacement curve. The data processing unit then applies the aforementioned calibration formula to compensate the original pressure and displacement measurements point by point, eliminating baseline shifts caused by temperature drift and mechanical clearance. Next, digital filtering is performed on the corrected curve data to remove high-frequency interference introduced by on-site electrical equipment, ultimately obtaining a smooth and accurate pre-processed curve for subsequent feature point identification and adhesion calculation. By incorporating ambient temperature and mechanical clearance parameters for comprehensive calibration, the impact of temperature changes and mechanical clearance on measurement accuracy during on-site testing is effectively reduced, improving the authenticity and reliability of the data. Digital filtering effectively suppresses various electromagnetic noises, resulting in clearer curve characteristics and facilitating accurate identification of key mechanical response points. This preprocessing method ensures high-quality analytical data even in complex industrial environments, laying a solid foundation for accurate assessment of coating adhesion and enhancing the practicality of the portable device and the reliability of the test results.

[0052] Optionally, the generated feature point dataset includes:

[0053] Based on the preprocessed curve data, the instantaneous slope is calculated, and the point where the instantaneous slope is first significantly lower than the preset reference stiffness value is identified to generate the elastic limit point.

[0054] The plateau or fluctuation region in the preprocessed curve data where the pressure growth rate slows down significantly is detected, and the plastic yield point is generated.

[0055] Monitor the peak point of the pressure value before a sudden drop in the preprocessed curve data to generate the critical failure point;

[0056] The elastic limit point, the plastic yield point, and the critical failure point are combined to generate a feature point dataset.

[0057] Specifically, the instantaneous slope is first calculated based on the preprocessed curve data. The instantaneous slope is defined as the ratio of the pressure change to the displacement change. For calculating the instantaneous slope K of the i-th data point... i ,have:

[0058]

[0059] Wherein, ΔP i Let ΔD be the pressure difference within a small window near that point. i This corresponds to the displacement difference within the window. The point where the instantaneous slope first significantly falls below a preset reference stiffness value is identified. This reference stiffness value is preset based on the coating material properties and equipment calibration. This point is identified and recorded as the elastic limit point, marking the beginning of the coating's transition from elastic deformation to plastic deformation. Next, a plateau or fluctuation region where the pressure increase rate significantly slows down is detected in the pre-processed curve data. This plateau or fluctuation region is characterized by the instantaneous slope values ​​of multiple consecutive data points approaching zero or fluctuating slightly around their mean. The starting point of this plateau or fluctuation region is identified and a plastic yield point is generated, indicating that the coating has undergone significant plastic flow. Subsequently, the peak point before the pressure value drops sharply in the pre-processed curve data is monitored. This peak point corresponds to the maximum pressure value in the entire curve, i.e., the critical failure point, representing the point where the coating is about to fail under the maximum pressure it withstands. Figure 2 As shown in the figure, the triangle represents the elastic limit point, the turning point where the coating transitions from elastic deformation to plastic deformation, and the instantaneous slope first decreases significantly; the rectangle represents the plastic yield point, the starting point where the coating exhibits significant plastic flow; and the pentagram represents the critical failure point, the peak point where the coating is about to fail under maximum pressure. Finally, the pressure values, displacement values, and corresponding instantaneous slope values ​​of these three key feature points—the elastic limit point, the plastic yield point, and the critical failure point—are combined to generate the feature point dataset, providing complete feature input for subsequent adhesion calculations.

[0060] For example, consider the adhesion test of polyurethane coating on the surface of a valve in a chemical plant. After obtaining the pre-processed pressure-displacement curve, the data processing unit first calculates the instantaneous slope at each point on the curve, with the window size set to 5 data points. The calculated instantaneous slope sequence is compared with a preset reference stiffness value of 200 N / mm / s, identifying the first point where the instantaneous slope value is consistently lower than the reference value, and marking it as the elastic limit point. Next, the algorithm scans the middle section of the curve and finds that the pressure value fluctuates only within a small range in a displacement interval of about 0.1 mm, with its average instantaneous slope value close to zero. The starting point of this interval is determined as the plastic yield point. The scan continues to the later part of the curve, locating the point of maximum pressure. After this maximum point, the pressure drops rapidly, so this peak point is marked as the critical failure point. Finally, all the data from these three feature points, including the pressure of 20 N and displacement of 0.05 mm at the elastic limit point, the pressure of 45 N and displacement of 0.15 mm at the plastic yield point, and the pressure of 60 N and displacement of 0.22 mm at the critical failure point, are combined to generate a feature point dataset and output. By employing instantaneous slope analysis and feature point identification processes, the critical states of coatings at different mechanical stages can be accurately captured, thus comprehensively characterizing their deformation and failure behavior. Identifying the elastic limit point helps assess the initial yield strength of the coating, the plastic yield point clarifies the transition of the material into plastic flow, and the critical failure point records the maximum load-bearing capacity. This multi-feature point joint analysis method improves the comprehensiveness and accuracy of adhesion assessment, overcomes the limitations of single failure point criteria, and provides a more reliable scientific basis for the quality control and life prediction of valve coatings.

[0061] Optionally, the method further includes:

[0062] Real-time capture of images showing changes in the surface morphology of the coating;

[0063] The images of the surface morphology changes of the coating are correlated with the corresponding pressure-displacement curves in time to construct a correlated dataset.

[0064] Based on the associated dataset, the process of the destructive event is analyzed to identify the time point and displacement value of the destructive event, and this is used to assist in verifying or correcting the feature point dataset.

[0065] Specifically, during the process of the loading mechanism applying pressure to the detection head and recording displacement, a high-resolution miniature camera captures images of coating surface morphology changes in real time, with the acquisition frequency synchronized with the pressure-displacement data acquisition. The images of coating surface morphology changes are temporally correlated with the corresponding pressure-displacement curves, specifically by assigning the same timestamp to each frame and each set of pressure-displacement data, thus constructing a correlated dataset containing visual information and mechanical data. Based on this correlated dataset, damage event process analysis is performed. The analysis first compares the image sequence to identify key frames where the coating first cracks, peels, or detaches, extracting the corresponding time point and the displacement value of the detection head at that time. This visually identified damage event time point and displacement value are compared with the feature point dataset previously generated through curve analysis. If the difference between the displacement value of the critical damage point in the feature point dataset and the visually identified displacement value exceeds a preset difference threshold, the visual recognition result is used to correct the feature point dataset. That is, the displacement value of the critical damage point in the feature point dataset is updated with the visually identified displacement value, and the coating adhesion value is recalculated, ensuring consistency between mechanical analysis and visual observation. The preset difference threshold is obtained by comparing the images of coating damage captured by the camera with the pressure-displacement curve to identify the critical failure point through multiple tests. The displacement difference between the two at the time of failure point identification is statistically analyzed, and the average difference in multiple tests is added to twice the standard deviation.

[0066] For example, during the test, a high-resolution miniature camera continuously captured images of the contact area between the detection head and the coating at a rate of 100 frames per second. At a certain point during loading, the data processing unit analyzed the video stream in real time using an image recognition algorithm. It detected a micro-crack on the coating surface, immediately recorded the timestamp of that frame as 0.3 seconds, and read the displacement value recorded by the displacement sensor as 0.18 mm. This visual event was automatically correlated with the pressure-displacement curve. It was found that at the curve position corresponding to this time point, the pressure value was still in the rising phase and had not yet reached its peak. Compared to the previously identified critical failure point displacement of 0.23 mm based on the curve slope, it was determined that the visual event occurred earlier. Therefore, a correction procedure was initiated, correcting the critical failure point displacement value in the feature point dataset to 0.18 mm, and recalculating the coating adhesion value accordingly. Subsequently, when the pressure continued to increase to 0.23 mm, the image showed large-area peeling of the coating, verifying that the initial crack was indeed the starting point of failure. By introducing synchronous correlation analysis of visual monitoring and mechanical data, multi-dimensional perception and cross-verification of the coating failure process are achieved, improving the accuracy of feature point identification, especially the location of critical failure points, and enhancing the reliability and scientific nature of test results. It is particularly suitable for evaluating coating adhesion under complex working conditions, providing a more solid technical guarantee for the safe operation of valves.

[0067] Optionally, the adhesion value of the generated coating includes:

[0068] Based on the critical pressure and critical displacement values ​​extracted at the elastic limit point, a key parameter set is generated.

[0069] The microprocessor in the data processing unit calculates the adhesion value of the coating by combining the key parameter set.

[0070] Specifically, firstly, the critical pressure and critical displacement values ​​are extracted based on the elastic limit point. The elastic limit point is identified by calculating the instantaneous slope of the preprocessed curve data. This elastic limit point marks the transition of the coating from elastic deformation to plastic deformation. The corresponding critical pressure and critical displacement values ​​are directly read from this elastic limit point. The microprocessor in the data processing unit then generates a set of key parameters and calculates the coating's adhesion value. For calculating the coating's adhesion value P, we have:

[0071]

[0072] Where k is the material property coefficient, in mm. -1 F was obtained by calibrating different coating materials in the laboratory. e This is the critical pressure value at the elastic limit point, expressed in Newtons (D). e The critical displacement value at the elastic limit point is expressed in millimeters, and A is the effective contact area between the detection head and the coating, expressed in square millimeters, calculated from the geometric dimensions of the detection head. This formula reflects the energy density accumulated in the coating during the elastic deformation stage and is used to scientifically characterize the adhesion strength of the coating. The entire calculation process ensures that all physical quantities are dimensionlessly consistent and that the operations conform to objective laws.

[0073] For example, consider the adhesion test of the polytetrafluoroethylene (PTFE) coating on the surface of a ball valve from a certain company. After obtaining the pre-processed curve data and identifying the elastic limit point, the data at that point is automatically extracted, yielding a critical pressure value of 35 Newtons and a critical displacement value of 0.12 mm. The data processing unit then retrieves the pre-stored PTFE material property coefficient of 0.85 mm. -1 Based on the conical detection head with a cone angle of 60 degrees and an indentation depth of 0.12 mm, the effective contact area was calculated to be 0.8 square millimeters. Substituting these parameters into the calculation formula, the adhesion value of the coating was calculated. The result is rounded to two decimal places; this value represents the adhesion strength of the coating at that test point. By focusing on the elastic limit, a key mechanical characteristic, the calculation effectively captures the maximum load-bearing capacity of the coating before irreversible deformation occurs, thus accurately reflecting the bond strength between the coating and the substrate. The use of energy density for characterization ensures the calculation results have clear physical meaning and good comparability. This method is suitable for evaluating the adhesion performance of coatings with different thicknesses and mechanical response characteristics, providing a more scientific and reliable basis for the quality control and life assessment of valve coatings.

[0074] Optionally, the method further includes:

[0075] Multiple test points were selected on the coating surface, and a set of corresponding adhesion evaluation results were obtained;

[0076] Statistical calculations were performed based on the adhesion evaluation results to obtain the mean, standard deviation, and coefficient of variation.

[0077] A comprehensive evaluation result of the valve coating adhesion is obtained by combining the mean value, the standard deviation, and the coefficient of variation.

[0078] Specifically, firstly, multiple test points are selected on the valve coating surface. The selection of test points should cover typical areas of the valve, including the upstream side, downstream side, and edge areas, ensuring representative spatial distribution. At each test point, tests are performed using the aforementioned method, obtaining a corresponding set of adhesion evaluation results. This set of adhesion evaluation results includes the adhesion values ​​of the coating at each test point. Based on this set of adhesion evaluation results, statistical calculations are performed, first calculating the average value. For calculating the average value μ, we have:

[0079]

[0080] Where n represents the total number of detection points; P i Let represent the adhesion value of the coating at the i-th test point. Then, to calculate the standard deviation σ, we have:

[0081]

[0082] This reflects the degree of dispersion of the values ​​at each detection point relative to the average value; then, for calculating the coefficient of variation (CV), we have:

[0083]

[0084] The coefficient of variation (CV) is used to measure the relative dispersion of data to eliminate the influence of dimensions. A joint analysis based on the mean, standard deviation, and CV is performed. If the mean is higher than a preset acceptable threshold and the CV is lower than a preset uniformity threshold, the coating adhesion is considered generally acceptable and uniformly distributed. The preset acceptable threshold is based on historical test data or industry standards, statistically analyzing the adhesion strength of different types of coatings, such as epoxy, polyurethane, and polytetrafluoroethylene, on typical substrates, and taking the minimum allowable adhesion value of this type of coating under normal construction conditions. If the mean is acceptable but the CV exceeds the standard, the coating is considered to have local defects; if the mean is unacceptable, the coating adhesion is directly considered unacceptable. This yields a comprehensive evaluation result of the valve coating adhesion, including overall strength level and uniformity assessment.

[0085] For example, consider the comprehensive evaluation of the epoxy coating adhesion on the surface of a gate valve in a chemical plant. Operators first selected eight testing points on the valve surface: four on the main valve body, two on the valve cover, and two on the valve stem protective coating. Using a portable testing device, each point was tested sequentially, yielding a set of coating adhesion values ​​of 52 MPa, 48 MPa, 55 MPa, 50 MPa, 45 MPa, 51 MPa, 38 MPa, and 53 MPa. The data processing unit calculated the average value μ to be 49 MPa, the standard deviation σ to be 5.2 MPa, and the coefficient of variation CV to be 0.106, rounded to three decimal places. Comparing the average value with the preset acceptable threshold of 45 MPa and the coefficient of variation with the uniformity threshold of 0.15, the average value was found to be within the acceptable range, and the coefficient of variation was lower than the uniformity threshold. Therefore, a comprehensive evaluation result was generated: the overall adhesion strength of the valve coating is good and uniformly distributed, meeting the usage requirements. Through multi-point sampling and statistical analysis, the overall adhesion of the valve coating can be comprehensively and objectively evaluated, avoiding the randomness and limitations of single-point testing. The average value calculation provides an assessment of the overall strength level, while the standard deviation and coefficient of variation analysis reveal the uniformity and consistency of the coating, effectively identifying local defects or construction quality issues. This comprehensive evaluation method provides a scientific basis for valve coating quality acceptance and maintenance decisions, and is particularly suitable for coating condition assessment of large valves and critical equipment, improving the reliability of test results and their engineering guidance value.

[0086] Optionally, the statistical calculation based on the adhesion assessment results includes:

[0087] Obtain the spatial coordinates of each detection point relative to the valve body reference point;

[0088] The spatial location coordinates are correlated with the corresponding comprehensive evaluation results to generate spatial distribution data;

[0089] Spatial interpolation calculations are performed using the spatial distribution data to generate a heat map of coating adhesion distribution, and weak areas of the coating are identified based on the adhesion distribution heat map.

[0090] Specifically, firstly, the spatial coordinates of each detection point relative to a reference point on the valve body are obtained. This reference point is typically chosen as the structural center of the valve or the center of the flange connection surface. The three-dimensional coordinates of each detection point are measured using a laser rangefinder or a structured light 3D scanner. The spatial coordinates are then correlated with the corresponding comprehensive evaluation results, establishing a mapping relationship between each coordinate point and its coating adhesion value, generating spatial distribution data containing both spatial location and mechanical performance data. Spatial interpolation calculations are then performed using this spatial distribution data, employing the Kriging interpolation algorithm to generate a heat map of the coating adhesion distribution. The estimated adhesion value P at the interpolation point (x, y, z) is calculated. (x，y，z) ,have:

[0091]

[0092] Among them, w i The weighting coefficients are obtained using the Kriging interpolation algorithm. This algorithm first quantifies spatial autocorrelation based on the spatial coordinates and measured adhesion values ​​of each detection point using a semi-variogram model, characterizing the degree of mutual influence between data points at different locations. Then, it solves the Kriging equations, calculating the optimal weighting coefficients for each known point to the current interpolation point under the conditions of unbiasedness and minimum estimation variance. Weak areas of the coating are identified based on the coating adhesion distribution heatmap. Specifically, an adhesion threshold is set, marking continuous areas in the heatmap below this threshold as weak areas, and calculating their area proportion and spatial distribution characteristics. The adhesion threshold is a critical value set based on historical data or engineering experience. Figure 3 As shown in the figure, the color gradient from dark to light represents the adhesion distribution from low to high; the dots represent the actual test points; the dashed lines represent the adhesion threshold boundary, marking weak areas; and the figure visually displays the adhesion distribution of the coating on the valve surface.

[0093] For example, inspectors first determine twenty test points on the valve surface and use a 3D scanner to obtain the precise coordinates of each test point relative to the valve flange center. After completing the adhesion test at all points, the coordinate data and test results are correlated and input into data processing software. The software uses a Kriging interpolation algorithm to generate a complete heat map of the adhesion distribution on the valve surface. The adhesion distribution heat map visually displays the distribution of adhesion from 55 MPa to 35 MPa using a gradient of color depth. A continuous area on the lower right side of the valve body is automatically identified, with adhesion values ​​all below the set adhesion threshold of 40 MPa, and is marked as a weak area in the valve coating. Simultaneously, calculations show that this weak area accounts for 8% of the total tested area, mainly distributed at the junction of the valve's flow-facing surface and the substrate weld. Through the fusion analysis of spatial coordinate measurements and adhesion data, a visualized spatial characterization of the valve coating performance is achieved, intuitively displaying the overall condition and local characteristics of the adhesion distribution. Spatial interpolation calculations transform discrete point data into continuous distribution information, improving the comprehensiveness and accuracy of the assessment. This provides precise guidance for targeted maintenance and repair of valves and effectively solves the technical challenge of fully understanding the spatial distribution characteristics of coatings using traditional methods.

[0094] Optionally, the method further includes:

[0095] The comprehensive evaluation results are associated with the corresponding pressure-displacement curves and test timestamps, and stored in the data storage module of the data processing unit to form historical data records;

[0096] Obtain the valve's unique identifier and combine it with the historical data records for retrieval and sorting to construct a time-series dataset of coating adhesion performance;

[0097] Based on the time series dataset, a trend comparison analysis is performed to predict the coating life degradation trend, and maintenance warning information is generated when the prediction result reaches the preset maintenance threshold.

[0098] Specifically, the comprehensive evaluation results are first correlated with the corresponding pressure-displacement curve data and test timestamps. The test timestamps are automatically generated by a clock. All this data is stored in the data storage module of the data processing unit, forming a complete historical data record. A unique valve identifier is obtained, which is affixed to the valve body in the form of a QR code or RFID tag, containing information such as the valve model, factory number, and installation location. The historical data records are then retrieved and sorted according to the test time sequence to construct a time-series dataset of coating adhesion performance. This dataset contains the coating adhesion values ​​at different time points and their corresponding test conditions. Trend comparison analysis is performed based on the time-series dataset, and a linear regression model is used to predict the coating life degradation trend. When the prediction result reaches a preset maintenance threshold, a protective warning message containing the valve identifier, predicted remaining life, and recommended maintenance measures is automatically generated. The preset maintenance threshold is a lower limit for the adhesion value set based on the coating life prediction model and historical performance degradation data, combined with the valve operating environment such as corrosion, temperature, and pressure. Figure 4 As shown, the horizontal dashed line represents the preset maintenance threshold, the diagonal dashed line represents the trend prediction line based on linear regression, and the diamond shape indicates the area that needs maintenance.

[0099] For example, consider the long-term monitoring of a control valve at the outlet of a reactor in a chemical plant. This valve is equipped with an RFID tag, identified as RV-2022-087. A comprehensive test was conducted quarterly for the past three years, and twelve test records are stored in the data storage module. After this test, all historical data records for the valve were retrieved and sorted by time to construct a time-series dataset. Trend analysis shows that the coating adhesion value has gradually decreased from an initial 52 MPa to the current 43 MPa. Linear regression prediction indicates that, following this trend, the valve coating adhesion will fall below the maintenance threshold of 40 MPa in seven months. A maintenance warning is immediately generated, indicating that the valve coating performance is continuously deteriorating, recommending coating repair or replacement within six months, and providing a detailed performance degradation report and prediction basis. By establishing a long-term performance monitoring database and trend analysis model, scientific prediction and proactive maintenance of the valve coating status are achieved. The time-series analysis method can clearly show the degradation pattern of coating performance, identify potential risks in advance, and avoid sudden failure accidents. The establishment of an early warning mechanism transforms traditional periodic maintenance into predictive maintenance based on actual conditions, improving equipment reliability and service life, and effectively reducing the risk of unplanned downtime and maintenance costs.

[0100] Based on the same inventive concept, such as Figure 5 As shown, the present invention also provides a coating adhesion testing system based on a pressure-displacement curve, the system comprising:

[0101] The curve data generation module is used to apply pressure to the detection head through the loading mechanism to obtain pressure measurement values, and to record the displacement measurement values ​​of the detection head through the displacement measurement mechanism. Based on the pressure measurement values ​​and the displacement measurement values, a pressure-displacement curve is generated and sent to the data processing unit.

[0102] The data preprocessing module is used to preprocess the pressure-displacement curve to generate preprocessed curve data.

[0103] The feature point extraction module is used to identify and extract feature points based on the preprocessed curve data to generate a feature point dataset.

[0104] The adhesion analysis module is used by the data processing unit to analyze and calculate the adhesion value of the coating in conjunction with the feature point dataset.

[0105] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0106] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for testing coating adhesion based on pressure-displacement curves, characterized in that, The method includes: Pressure is applied to the detection head by a loading mechanism to obtain pressure measurement values, and displacement measurement values ​​of the detection head are recorded by a displacement measurement mechanism. A pressure-displacement curve is generated based on the pressure measurement values ​​and the displacement measurement values ​​and sent to the data processing unit. Preprocessing is performed based on the pressure-displacement curve to generate preprocessed curve data; Based on the preprocessed curve data, feature points are identified and extracted to generate a feature point dataset. The data processing unit analyzes and calculates the feature point dataset to generate the adhesion value of the coating.

2. The coating adhesion testing method based on pressure-displacement curves according to claim 1, characterized in that, The generated pressure-displacement curve includes: The loading mechanism is controlled to move the detection head at a preset constant rate, thereby generating a controlled displacement process; Based on the pressure measurement value obtained by the pressure sensor during the controlled displacement process, and the displacement measurement value obtained by the displacement measuring mechanism simultaneously, a set of synchronous data pairs is generated by combining the pressure measurement value and the displacement measurement value; Based on the synchronous data pair, a projection mapping is performed to generate a series of data points in a two-dimensional coordinate system; Based on the series of data points, interpolation fitting is performed to form a pressure-displacement curve, which is then transmitted to the data processing unit.

3. The coating adhesion testing method based on pressure-displacement curves according to claim 1, characterized in that, The generated preprocessed curve data includes: The ambient temperature parameters measured by the temperature sensor and the mechanical clearance parameters obtained through equipment calibration are acquired and integrated to generate calibration parameters. The zero-point error of the pressure-displacement curve is corrected using the calibration parameters to generate corrected curve data. The corrected curve data is subjected to digital filtering to generate preprocessed curve data.

4. The coating adhesion testing method based on pressure-displacement curves according to claim 1, characterized in that, The generated feature point dataset includes: Based on the preprocessed curve data, the instantaneous slope is calculated, and the point where the instantaneous slope is first significantly lower than the preset reference stiffness value is identified to generate the elastic limit point. The plateau or fluctuation region in the preprocessed curve data where the pressure growth rate slows down significantly is detected, and the plastic yield point is generated. Monitor the peak point of the pressure value before a sudden drop in the preprocessed curve data to generate the critical failure point; The elastic limit point, the plastic yield point, and the critical failure point are combined to generate a feature point dataset.

5. The coating adhesion testing method based on pressure-displacement curves according to claim 1, characterized in that, The method further includes: Real-time capture of images showing changes in the surface morphology of the coating; The images of the surface morphology changes of the coating are correlated with the corresponding pressure-displacement curves in time to construct a correlated dataset. Based on the associated dataset, the process of the destructive event is analyzed to identify the time point and displacement value of the destructive event, and this is used to assist in verifying or correcting the feature point dataset.

6. The coating adhesion testing method based on pressure-displacement curves according to claim 4, characterized in that, The adhesion values ​​of the generated coating include: Based on the critical pressure and critical displacement values ​​extracted at the elastic limit point, a key parameter set is generated. The microprocessor in the data processing unit calculates the adhesion value of the coating by combining the key parameter set.

7. The coating adhesion testing method based on pressure-displacement curves according to claim 6, characterized in that, The method further includes: Multiple test points were selected on the coating surface, and a set of corresponding adhesion evaluation results were obtained; Statistical calculations were performed based on the adhesion evaluation results to obtain the mean, standard deviation, and coefficient of variation. A comprehensive evaluation result of the valve coating adhesion is obtained by combining the mean value, the standard deviation, and the coefficient of variation.

8. The coating adhesion testing method based on pressure-displacement curves according to claim 7, characterized in that, The statistical calculations based on the adhesion assessment results include: Obtain the spatial coordinates of each detection point relative to the valve body reference point; The spatial location coordinates are correlated with the corresponding comprehensive evaluation results to generate spatial distribution data; Spatial interpolation calculations are performed using the spatial distribution data to generate a heat map of coating adhesion distribution, and weak areas of the coating are identified based on the adhesion distribution heat map.

9. The coating adhesion testing method based on pressure-displacement curves according to claim 7, characterized in that, The method further includes: The comprehensive evaluation results are associated with the corresponding pressure-displacement curves and test timestamps, and stored in the data storage module of the data processing unit to form historical data records; Obtain the valve's unique identifier and combine it with the historical data records for retrieval and sorting to construct a time-series dataset of coating adhesion performance; Based on the time series dataset, a trend comparison analysis is performed to predict the coating life degradation trend, and maintenance warning information is generated when the prediction result reaches the preset maintenance threshold.

10. A coating adhesion testing system based on a pressure-displacement curve, applied to a coating adhesion testing method based on a pressure-displacement curve as described in any one of claims 1-9, characterized in that, The system includes: The curve data generation module is used to apply pressure to the detection head through the loading mechanism to obtain pressure measurement values, and to record the displacement measurement values ​​of the detection head through the displacement measurement mechanism. Based on the pressure measurement values ​​and the displacement measurement values, a pressure-displacement curve is generated and sent to the data processing unit. The data preprocessing module is used to preprocess the pressure-displacement curve to generate preprocessed curve data. The feature point extraction module is used to identify and extract feature points based on the preprocessed curve data to generate a feature point dataset. The adhesion analysis module is used by the data processing unit to analyze and calculate the adhesion value of the coating in conjunction with the feature point dataset.

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