Surface coating process detection method and device for steel pipe production

Abstract

The invention relates to the technical field of steel pipe detection, and discloses a surface coating process detection method and device for steel pipe production, and the method comprises the steps: obtaining surface topography data and temperature distribution data; performing roughness analysis according to the surface topography data to obtain roughness parameters; performing wetting simulation according to the roughness parameter and a preset initial condition to obtain permeation data and a stress distribution result; parameter optimization is carried out according to the permeation data and the stress distribution result, the spraying pressure adjustment amount and the spraying angle adjustment amount are obtained through calculation, and then optimal spraying parameters are obtained; inputting the temperature distribution data into a thermal stress distribution model to obtain thermal stress distribution data, and performing process optimization according to the thermal stress distribution data to obtain optimal curing parameters; and processing the coating on the surface of the steel pipe according to the optimal curing parameters and the optimal spraying parameters. The method has the following effect that the detection precision of the surface coating of the steel pipe can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of steel pipe detection, and particularly to a method and device for detecting the surface coating process in steel pipe production. Background Art

[0002] The quality of the surface coating process of steel pipes directly affects their corrosion resistance, service life, and safety, especially when used under extreme environmental conditions. With the development of industrial technology, the application fields of steel pipes are constantly expanding, and the requirements for the quality of the surface coating of steel pipes are also increasing day by day. Developing a method and device that can automatically and accurately detect the quality of the surface coating of steel pipes is particularly crucial. This new detection method should not only be able to identify parameters such as the uniformity and thickness of the coating, but also be able to monitor and feedback the results in real time on a high-speed production line.

[0003] One of the existing methods for detecting the surface coating of steel pipes is to use optical measurement technology. First, a set of high-resolution cameras or laser scanners are installed at specific positions on the production line to capture the image information of the steel pipe surface. Then, these image data are processed by computer vision algorithms to extract the characteristic parameters related to the coating, such as color, texture, and reflectivity. Then, these characteristic parameters are compared and analyzed with the pre-set standard values to determine whether the coating meets the requirements. If any non-compliant situation is found, the system will immediately trigger an alarm and record the specific location of the defect for subsequent processing. In addition, X-ray fluorescence analysis (XRF) or energy-dispersive spectroscopy (EDS) is also used to further determine the coating composition to ensure that its chemical composition meets the specifications.

[0004] However, this method has some obvious limitations. First, optical measurement is easily affected by external factors. For example, changes in lighting conditions lead to unstable image quality, which in turn affects the accuracy of the detection results. For steel pipes with complex geometric shapes, especially those with threads or other surface structures, the existing technology cannot fully cover all areas, resulting in ineffective detection of the coating in some parts and low detection accuracy of the surface coating process of steel pipes. Summary of the Invention

[0005] The present invention provides a method and device for detecting the surface coating process in steel pipe production to improve the detection accuracy of the surface coating of steel pipes.

[0006] In a first aspect, to solve the above technical problems, the present invention provides a method for detecting the surface coating process in steel pipe production, including: Obtaining surface topography data and temperature distribution data; Performing roughness analysis based on the surface topography data to obtain roughness parameters; Perform wetting simulation based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results; Perform parameter optimization based on the penetration data and the stress distribution results, calculate the spray pressure adjustment amount and the spray angle adjustment amount, and then obtain the optimal spray parameters; Input the temperature distribution data into the thermal stress distribution model to obtain thermal stress distribution data, and perform process optimization based on the thermal stress distribution data to obtain the optimal curing parameters; Process the surface coating of the steel pipe according to the optimal curing parameters and the optimal spray parameters.

[0007] In an alternative embodiment, the performing roughness analysis based on the surface topography data to obtain roughness parameters includes: Perform multi-scale segmentation on the surface topography data to obtain sub-regions of different scales; Calculate the root mean square roughness through the following formula: where, represents the root mean square roughness; Calculate the average roughness through the following formula: where, represents the average roughness, represents the evaluation length, represents the position at the height difference, represents the differential symbol; Calculate the average peak-to-valley height according to the following formula: where, represents the total number of sub-regions, represents the sub-region number, represents the maximum height of the sub-region No., represents the lowest height of the sub-region No., represents the average peak-to-valley height, represents the maximum, represents the minimum;

[0008] In an alternative embodiment, the performing wetting simulation based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results includes: Obtain the physical properties of the coating; The initial conditions include an initial thickness and a contact angle; Perform contact angle correction according to the roughness parameter to obtain a corrected contact angle; Among them, the corrected contact angle is calculated by the following formula: Among them, represents the corrected contact angle, represents the correction coefficient, represents the average roughness, represents the average peak-to-valley height; Perform finite element simulation according to the corrected contact angle, the initial thickness, and the physical properties of the coating to obtain penetration data and stress distribution results.

[0009] In an alternative embodiment, parameter optimization is performed based on the penetration data and the stress distribution results, and a spray pressure adjustment amount and a spray angle adjustment amount are calculated, and then optimal spray parameters are obtained, including: The spray pressure adjustment amount is calculated by the following formula: Among them, represents the spray pressure adjustment amount, represents the pressure adjustment coefficient, represents the area of the stress concentration region that exceeds the preset stress threshold, represents the total area of the stress concentration region, represents the average stress, represents the preset stress threshold; Obtain the distribution deviation angle according to the penetration data; The spray angle adjustment amount is calculated by the following formula: Among them, represents the spray angle adjustment amount, represents the angle adjustment coefficient, represents the distribution deviation angle; Optimize the initial conditions according to the spray pressure adjustment amount and the spray angle adjustment amount to obtain optimal spray parameters.

[0010] In an alternative embodiment, the temperature distribution data is input into a thermal stress distribution model to obtain thermal stress distribution data, and process optimization is performed according to the thermal stress distribution data to obtain optimal curing parameters, including: Obtain curing process parameters; Perform area determination according to the thermal stress distribution data and a preset thermal stress threshold to obtain a thermal anomaly area; When the thermal anomaly area is greater than a preset thermal area threshold, the curing process parameters are optimized to obtain optimal curing parameters; Among them, the training process of the thermal stress distribution model includes: Training the thermal stress distribution model based on historical thermal stress distribution data and historical temperature data. When it is detected that the loss function of the model is less than a preset threshold, the trained model is obtained; Inputting the temperature distribution data into the trained model to output thermal stress distribution data.

[0011] In an alternative embodiment, before processing the steel pipe surface coating according to the optimal curing parameters and the optimal spraying parameters, it further includes: Performing stress simulation according to the optimal curing parameters and the optimal spraying parameters to obtain a simulation standard deviation; Performing uniformity calculation according to the simulation standard deviation to obtain a simulation uniformity index; Obtaining the actual stress distribution and performing uniformity calculation according to the actual stress distribution to obtain an actual uniformity index; Calculating an effectiveness index according to the simulation uniformity index and the actual uniformity index through the following formula: Among them, represents the effectiveness index, represents the simulation uniformity index, represents the actual uniformity index; When the effectiveness index is greater than a preset effectiveness threshold, continue with the subsequent steps; When the effectiveness index is less than a preset effectiveness threshold, re-optimize the optimal curing parameters and the optimal spraying parameters.

[0012] In an alternative embodiment, after processing the steel pipe surface coating according to the optimal curing parameters and the optimal spraying parameters, it further includes: Obtaining internal defect data; Inputting the internal defect data into a pre-trained strength prediction model to obtain a uniformity score; When the uniformity score is less than a preset score threshold, trigger a local recoating instruction to recoat the defective position.

[0013] In a second aspect, the present invention provides a surface coating process detection device for steel pipe production, including: A data acquisition module for acquiring surface topography data and temperature distribution data; A roughness analysis module for performing roughness analysis according to the surface topography data to obtain roughness parameters; A wetting simulation module, configured to perform wetting simulation according to the roughness parameter and preset initial conditions to obtain penetration data and stress distribution results; A parameter optimization module, configured to perform parameter optimization according to the penetration data and the stress distribution results, calculate a spray pressure adjustment amount and a spray angle adjustment amount, and further obtain optimal spray parameters; A process optimization module, configured to input the temperature distribution data into a thermal stress distribution model to obtain thermal stress distribution data, and perform process optimization according to the thermal stress distribution data to obtain optimal curing parameters; A result output module, configured to process the surface coating of the steel pipe according to the optimal curing parameters and the optimal spray parameters.

[0014] In a third aspect, the present invention further provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the surface coating process detection method for steel pipe production described in any one of the above is implemented.

[0015] In a fourth aspect, the present invention further provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the surface coating process detection method for steel pipe production described in any one of the above.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention performs corresponding preprocessing on the obtained surface topography data and temperature distribution data to ensure the integrity and accuracy of the data. By cleaning, standardizing, and validating the surface topography data, noise and outliers can be eliminated, and the accuracy of roughness analysis can be improved. At the same time, preprocessing the temperature distribution data can ensure the consistency of the input data for the subsequent thermal stress distribution model. This preprocessing process improves the data quality, making subsequent calculations and analyses more efficient and accurate.

[0017] (2) The present invention performs roughness analysis based on the surface topography data to obtain roughness parameters. Through this process, the system can more accurately describe the surface characteristics, providing reliable basic data for subsequent wetting simulation. The accurate calculation of roughness parameters helps to optimize the spray process parameters, thereby improving the quality and uniformity of the coating. In addition, the standardized roughness parameters are convenient for storage and management, supporting subsequent data query and analysis operations.

[0018] (3) The present invention performs wetting simulation based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results. By establishing a wetting simulation model, the penetration behavior and stress distribution of the liquid on surfaces with different roughness can be predicted. This simulation method not only improves the calculation efficiency but also enhances the reliability of the results. The visual display of the simulation results helps to intuitively understand the problems existing in the spraying process and provides a scientific basis for parameter optimization.

[0019] (4) The present invention optimizes parameters according to the penetration data and the stress distribution results, calculates the adjustment amounts of the spraying pressure and the spraying angle, and then obtains the optimal spraying parameters. Through the parameter optimization algorithm, the system can automatically adjust the spraying process parameters to achieve the best spraying effect. This optimization process significantly improves the spraying accuracy and efficiency, ensuring the stability and consistency of the coating quality.

[0020] (5) The present invention inputs the temperature distribution data into a thermal stress distribution model to obtain thermal stress distribution data, and optimizes the process according to the thermal stress distribution data to obtain the optimal curing parameters. Through the thermal stress distribution model, the thermal stress changes occurring during the curing process can be accurately predicted, thereby guiding the optimization of the curing process. The optimized curing parameters can effectively reduce the internal stress in the coating, improve the adhesion and durability of the coating, and further enhance the overall processing quality.

[0021] (6) The present invention processes the surface coating of the steel pipe according to the optimal curing parameters and the optimal spraying parameters. By comprehensively applying the optimal spraying parameters and curing parameters, the system can achieve high-quality processing of the surface coating of the steel pipe. This integrated method not only improves the performance of the coating but also enhances the automation level and intelligence degree of the entire processing flow, ensuring the high efficiency and high precision of the coating processing. Description of the Drawings

[0022] Figure 1 is a schematic flow chart of a method for detecting the surface coating process of steel pipe production provided by the first embodiment of the present invention; Figure 2 is a schematic structural diagram of a device for detecting the surface coating process of steel pipe production provided by the second embodiment of the present invention. Detailed Embodiments

[0023] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0024] The quality of the surface coating process of steel pipes directly affects their corrosion resistance, service life, and safety, especially when used in extreme environmental conditions. With the development of industrial technology, the application fields of steel pipes have been continuously expanding, and the requirements for the quality of the surface coating of steel pipes have also been increasing. Developing a method and device that can automatically and accurately detect the quality of the surface coating of steel pipes is particularly crucial. This new detection method should not only be able to identify parameters such as the uniformity and thickness of the coating but also be able to monitor and feedback results in real-time on high-speed production lines.

[0025] One of the existing methods for detecting the surface coating of steel pipes is to use optical measurement technology. First, a set of high-resolution cameras or laser scanners are installed at specific positions on the production line to capture the image information of the steel pipe surface. Then, these image data are processed through computer vision algorithms to extract coating-related characteristic parameters such as color, texture, and reflectivity. Next, these characteristic parameters are compared and analyzed with the preset standard values to determine whether the coating meets the requirements. If any non-compliant situation is found, the system will immediately trigger an alarm and record the specific location of the defect for subsequent processing. In addition, X-ray fluorescence analysis (XRF) or energy-dispersive spectroscopy (EDS) is used to further determine the coating composition to ensure that its chemical composition meets the specifications.

[0026] However, this method has some obvious limitations. First, optical measurement is easily affected by external factors. For example, changes in lighting conditions lead to unstable image quality, which in turn affects the accuracy of the detection results. For steel pipes with complex geometric shapes, especially those with threads or other surface structures, the existing technology cannot fully cover all areas, resulting in ineffective detection of the coating on some parts and low detection accuracy of the surface coating process of steel pipes.

[0027] To solve the above problems, referring to Figure 1 , the first embodiment of the present invention provides a method for detecting the surface coating process of steel pipe production, including the following steps: S11, obtaining surface topography data and temperature distribution data; S12, performing roughness analysis based on the surface topography data to obtain roughness parameters; S13, performing wetting simulation based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results; S14, performing parameter optimization based on the penetration data and the stress distribution results, calculating the spraying pressure adjustment amount and spraying angle adjustment amount, and then obtaining the optimal spraying parameters; S15, inputting the temperature distribution data into the thermal stress distribution model to obtain thermal stress distribution data, and performing process optimization based on the thermal stress distribution data to obtain the optimal curing parameters; S16. Process the surface coating of the steel pipe according to the optimal curing parameters and the optimal spraying parameters.

[0028] In step S11, obtain the surface topography data and the temperature distribution data.

[0029] In one implementation, use a high-precision 3D scanner to scan the surface of the steel pipe, which can capture the microscopic structure information of the surface, so as to obtain the surface topography data. Use an infrared thermal imager to quickly obtain the temperature distribution image of the target area.

[0030] It should be noted that the surface topography data is represented in the form of three-dimensional coordinates, describing the undulation of the microscopic structure of the steel pipe surface. Each data includes the horizontal and vertical coordinates as well as the height information. It is used for subsequent roughness analysis. The temperature distribution data is presented as a two-dimensional temperature field map, showing the temperature values at different positions on the steel pipe surface.

[0031] In step S12, perform roughness analysis based on the surface topography data to obtain roughness parameters.

[0032] In one implementation, perform multi-scale segmentation on the surface topography data to obtain sub-regions of different scales; Calculate the root mean square roughness through the following formula: Among them, represents the root mean square roughness; Calculate the average roughness through the following formula: Among them, represents the average roughness, represents the evaluation length, represents the position the height difference at, represents the differential symbol; Calculate the average peak-to-valley height according to the following formula: Among them, represents the total number of sub-regions, represents the sub-region number, represents the maximum height of the th sub-region, represents the minimum height of the th sub-region, represents the average peak-to-valley height, represents the maximum, represents the minimum; Wherein the roughness parameters include the root mean square roughness, the average roughness, and the average peak-to-valley height.

[0033] It should be noted that multi-scale segmentation is a technique for analyzing surface topography. First, the original surface topography data is preprocessed, including operations such as removing outliers and smoothing, to ensure the accuracy of subsequent analysis. Several analysis scales are selected. Each scale corresponds to a specific spatial resolution or wavelength range. Smaller scales focus on microscopic details, while larger scales pay more attention to macroscopic structures. The entire surface is segmented according to the selected scales, generating multiple sub-regions. For each sub-region, different filters (such as Gaussian filters) can be applied to extract information at a specific scale. In this way, the roughness characteristics of the surface can be comprehensively understood from different scale perspectives.

[0034] It should be noted that the root mean square roughness describes the standard deviation of the surface height fluctuations. A higher root mean square roughness value means the surface is more irregular and has greater undulations; a lower root mean square roughness value indicates that the surface is relatively flat. The average roughness represents the arithmetic mean of the absolute values of the surface profile relative to the center line. A higher average roughness value indicates that the surface is rougher overall; conversely, it means the surface is smoother. The average peak-to-valley height reflects the average difference between the surface peaks and valleys. A higher average peak-to-valley height value means there are significant height variations on the surface, that is, there are obvious peaks and valleys; a lower average peak-to-valley height value indicates that the surface is relatively flat with small peak-to-valley differences.

[0035] It should be noted that the height difference refers to the height difference of the position from the reference line. The reference line is selected as the average line of the profile.

[0036] In step S13, a wetting simulation is performed according to the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results.

[0037] In one implementation, the physical properties of the coating are obtained; The initial conditions include the initial thickness and the contact angle; The contact angle is corrected according to the roughness parameters to obtain a corrected contact angle; Wherein, the corrected contact angle is calculated by the following formula: Wherein, represents the corrected contact angle, represents the correction coefficient, represents the average roughness, represents the average peak-to-valley height; Perform a finite element simulation based on the corrected contact angle, the initial thickness, and the physical properties of the coating to obtain penetration data and stress distribution results.

[0038] It should be noted that the physical properties of the coating are obtained from the manufacturer's instructions at the time of factory shipment; the initial conditions can be set by measuring the performance data at different thicknesses and contact angles through multiple experiments and collecting the experimental data to help determine the preset values of the initial thickness and contact angle; for example, the optimal initial thickness and contact angle are determined according to the performance indicators such as the coating adhesion, wear resistance, and corrosion resistance at different thicknesses and contact angles. The wetting simulation uses existing finite element analysis (FEA) tools, which can handle complex geometries and material properties, simulate the behavior of liquids on solid surfaces (i.e., the wetting process), and predict the penetration of the coating and the resulting stress distribution. It is carried out based on the given roughness parameters and initial conditions.

[0039] It should be noted that the role of finite element analysis is to predict the coating thickness uniformity and internal stress distribution by establishing a three-dimensional model and inputting the above-mentioned formulation parameters.

[0040] In step S14, parameter optimization is performed according to the penetration data and the stress distribution results, the spraying pressure adjustment amount and the spraying angle adjustment amount are calculated, and then the optimal spraying parameters are obtained.

[0041] In one implementation, the spraying pressure adjustment amount is calculated by the following formula: Where, represents the spraying pressure adjustment amount, represents the pressure adjustment coefficient, represents the area of the stress concentration region exceeding the preset stress threshold, represents the total area of the stress concentration region, represents the average stress, represents the preset stress threshold; Obtain the distribution deviation angle according to the penetration data; The spraying angle adjustment amount is calculated by the following formula: Where, represents the spraying angle adjustment amount, represents the angle adjustment coefficient, represents the distribution deviation angle; Optimize the initial conditions according to the spraying pressure adjustment amount and the spraying angle adjustment amount to obtain the optimal spraying parameters.

[0042] It should be noted that the spray pressure adjustment amount represents the pressure value that needs to be increased or decreased. A positive value indicates an increase in pressure, and a negative value indicates a decrease in pressure. The pressure adjustment coefficient (dimensionless), for an aluminum alloy coating, takes a value of 0.15 MPa·mm² / N. The area of the stress exceeding standard region is obtained through X-ray residual stress detection. The total area of the stress concentration region takes 80% of the key structural region. The unit of the measured average stress is MPa. The preset stress threshold, for example, for an IN718 alloy coating, takes a value of 620 MPa. The angle adjustment amount, for example, a calculated value of +3° means the spray gun needs to deflect 3 degrees clockwise. The angle sensitivity coefficient (dimensionless) is determined through a deposition efficiency experiment (for example, the value range for plasma spraying is 0.25 - 0.35). The distribution deviation angle takes a value of 8°.

[0043] It should be noted that the coating formulation parameters include composition and ratio: such as the ratio of epoxy resin to curing agent (for example, 60% epoxy resin, 30% curing agent), pigment content (5%), solvent type, etc. These are all key factors affecting the coating performance. These parameters determine the basic physical and chemical properties of the coating, such as hardness, flexibility, adhesion, etc.

[0044] It should be noted that the preset threshold of the internal stress concentration region represents the set maximum allowable internal stress (for example, 10 MPa), which is used as a standard to evaluate whether the coating is qualified. If the simulation results show that some regions exceed this threshold, process parameter adjustment is required to avoid problems such as coating cracking or peeling.

[0045] It should be noted that the generation of new coating process parameters is an iterative optimization process. The initial parameters (such as the original spray pressure is 0.3 MPa and the angle is 60 degrees) are adjusted (to 0.25 MPa and 65 degrees), and calculations are made according to the given adjustment amount. The optimization process is the initial amount plus the adjustment amount.

[0046] In step S15, the temperature distribution data is input into the thermal stress distribution model to obtain thermal stress distribution data, and process optimization is performed according to the thermal stress distribution data to obtain the optimal curing parameters.

[0047] In one implementation, curing process parameters are obtained; Area determination is performed according to the thermal stress distribution data and a preset thermal stress threshold to obtain the thermal anomaly area; When the thermal anomaly area is greater than the preset thermal area threshold, the curing process parameters are optimized to obtain the optimal curing parameters; Among them, the training process of the thermal stress distribution model includes: The thermal stress distribution model is trained based on historical thermal stress distribution data and historical temperature data. When it is detected that the loss function of the model is less than the preset threshold, the trained model is obtained; Input the temperature distribution data into the trained model to output the thermal stress distribution data.

[0048] In one implementation, the thermal stress distribution model uses a long short-term memory neural network and is trained based on a large amount of historical thermal stress distribution data and corresponding temperature data. These data include temperature fields under different conditions, material properties (such as thermal expansion coefficient, elastic modulus, etc.), and the finally calculated thermal stress distribution.

[0049] In one implementation, the stepwise temperature increase scheme is adopted to optimize the curing process parameters, that is, the curing process is divided into multiple temperature platforms, and each platform maintains a constant temperature for a period of time to fully release the thermal stress. The new curing process scheme is implemented through a process control system.

[0050] In step S16, process the surface coating of the steel pipe according to the optimal curing parameters and the optimal spraying parameters.

[0051] In one implementation, before processing the surface coating of the steel pipe according to the optimal curing parameters and the optimal spraying parameters, it further includes: Conduct stress simulation according to the optimal curing parameters and the optimal spraying parameters to obtain the simulation standard deviation; Conduct uniformity calculation according to the simulation standard deviation to obtain the simulation uniformity index; Obtain the actual stress distribution, and conduct uniformity calculation according to the actual stress distribution to obtain the actual uniformity index; Calculate the effectiveness index according to the simulation uniformity index and the actual uniformity index through the following formula: where, represents the effectiveness index, represents the simulation uniformity index, represents the actual uniformity index; When the effectiveness index is greater than the preset effectiveness threshold, continue with the subsequent steps; When the effectiveness index is less than the preset effectiveness threshold, re-optimize the optimal curing parameters and the optimal spraying parameters.

[0052] It should be noted that if the effectiveness index is greater than the preset effectiveness threshold, it indicates that there is a large deviation between the simulation and the actual situation, and the optimization parameters need to be readjusted; if the effectiveness index is less than or equal to the preset threshold, the simulation results are considered reliable and the subsequent coating processing steps can be continued.

[0053] In one implementation, after processing the steel pipe surface coating according to the optimal curing parameters and the optimal spraying parameters, the following steps are further included: Obtain internal defect data; Input the internal defect data into a pre-trained strength prediction model to obtain a uniformity score; When the uniformity score is less than a preset score threshold, trigger a local recoating instruction to recoat the defective position.

[0054] In one implementation, the strength prediction model uses a support vector machine. By obtaining data on surface cleanliness, roughness, coating thickness, and thermal stress distribution, an initial dataset is formed for training. After training, the collected internal defect data is input into the strength prediction model, which can predict the coating uniformity and overall strength based on historical data and material science principles. The uniformity score reflects the consistency of the coating across the entire surface and potential weak points.

[0055] It should be noted that triggering the local recoating instruction for additional coating treatment at the detected specific defective positions compensates for the deficiencies in the original coating, ensuring the integrity and reliability of the entire coating system.

[0056] In summary, the present invention discloses a method for detecting the surface coating process of steel pipe production, aiming to improve the detection accuracy of the steel pipe surface coating quality. The method first obtains the topography data and temperature distribution data of the steel pipe surface, and conducts roughness analysis by analyzing the surface topography data to obtain roughness parameters including root mean square roughness, average roughness, and average peak-to-valley height. These parameters help to comprehensively understand the characteristics of the steel pipe surface microstructure and provide basic data for subsequent wetting simulation. Then, based on the roughness parameters and preset initial conditions (such as initial thickness and contact angle), wetting simulation is carried out to calculate the penetration data and stress distribution results. To ensure that the coating can adhere evenly to the steel pipe surface, the spraying parameters are optimized according to the penetration data and stress distribution results to determine the optimal spraying pressure and angle adjustment amount, thereby ensuring the coating uniformity and adhesion. In addition, the present invention also introduces a thermal stress distribution model, inputs the temperature distribution data of the steel pipe surface into the model to obtain the thermal stress distribution, and optimizes the curing process parameters accordingly to avoid the risk of coating defects or failures caused by excessive thermal stress.

[0057] Furthermore, by segmenting the surface topography data at different scales, multiple sub-regions can be obtained, and each sub-region represents the characteristics of the surface at a specific scale. This not only helps to capture the multi-level information of surface roughness but also comprehensively evaluates the surface quality from macro to micro. It should be noted that parameters such as root mean square roughness, average roughness, and average peak-to-valley height used in the roughness analysis process of the present invention act together to evaluate the quality of the surface coating and provide a scientific basis. For example, the root mean square roughness describes the standard deviation of surface height changes and reflects the overall fluctuation of the surface; the average roughness quantifies the average smoothness of the surface; while the average peak-to-valley height measures the distance between the highest and lowest points on the surface and is a key indicator for evaluating the difference between surface peaks and valleys.

[0058] Furthermore, in terms of optimizing the spraying parameters, the present invention adopts a pressure adjustment strategy based on stress analysis. First, it evaluates the area ratio of the stress concentration region exceeding the threshold value and the gap between the average stress level and the preset threshold value to determine the magnitude of the spraying pressure to be adjusted. This method helps to avoid coating defects caused by excessive local stress and ensures the uniformity and durability of the coating. At the same time, according to the distribution deviation angle in the penetration data, the optimal adjustment angle of the spray head relative to the workpiece is determined. This process takes into account the natural distribution tendency of the coating material on the surface to ensure the best coverage effect and adhesion, effectively meets the spraying requirements of complex-shaped workpieces, and improves the spraying efficiency and quality.

[0059] In addition, the present invention also uses historical thermal stress distribution data and temperature data to train a thermal stress distribution model. The model determines the area by comparing the current thermal stress distribution data with a preset thermal stress threshold value and identifies the existing thermal anomaly regions. Once the detected thermal anomaly area exceeds the set threshold value, it indicates that there are risks or deficiencies in the existing curing process, and the curing process parameters need to be adjusted. This process not only effectively prevents coating defects or failures caused by excessive thermal stress but also ensures the best state of coating quality and durability. In addition, the present invention also designs a feedback mechanism to perform stress simulation and calculate the effectiveness index before actual processing. When the effectiveness index is less than the preset threshold value, the optimal curing parameters and optimal spraying parameters are optimized again to ensure that the quality of the final coating meets the expected standards.

[0060] In summary, the surface coating process detection method for steel pipe production proposed by this method can improve the accuracy of surface coating process detection for steel pipe production.

[0061] Referring to Figure 2 , the second embodiment of the present invention provides a surface coating process detection device for steel pipe production, including: A data acquisition module for acquiring surface topography data and temperature distribution data; A roughness analysis module for performing roughness analysis based on the surface topography data to obtain roughness parameters; A wetting simulation module for performing wetting simulation based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results; A parameter optimization module for performing parameter optimization based on the penetration data and the stress distribution results, calculating the spray pressure adjustment amount and the spray angle adjustment amount, and further obtaining the optimal spray parameters; A process optimization module for inputting the temperature distribution data into a thermal stress distribution model to obtain thermal stress distribution data, and performing process optimization based on the thermal stress distribution data to obtain the optimal curing parameters; A result output module for processing the surface coating of the steel pipe according to the optimal curing parameters and the optimal spray parameters.

[0062] Preferably, the data acquisition module is used to: Acquire surface topography data and temperature distribution data.

[0063] Preferably, the roughness analysis module is used to: Perform roughness analysis based on the surface topography data to obtain roughness parameters, including: Perform multi-scale segmentation on the surface topography data to obtain sub-regions of different scales; Calculate the root mean square roughness through the following formula: where, represents the root mean square roughness; Calculate the average roughness through the following formula: where, represents the average roughness, represents the evaluation length, represents the position at the height difference, represents the differential symbol; Calculate the average peak-to-valley difference according to the following formula: where, represents the total number of sub-regions, represents the sub-region number, represents the maximum height of the th sub-region, represents the lowest height of the Denotes the average peak-to-valley difference, Denotes the maximum, Denotes the minimum; Wherein the roughness parameters include the root mean square roughness, the average roughness, and the average peak-to-valley difference.

[0064] Preferably, the wetting simulation module is configured to: Perform wetting simulation according to the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results, including: Obtain the physical properties of the coating; The initial conditions include the initial thickness and the contact angle; Perform contact angle correction according to the roughness parameters to obtain the corrected contact angle; Wherein, the corrected contact angle is calculated by the following formula: Wherein, Denotes the corrected contact angle, Denotes the correction coefficient, Denotes the average roughness, Denotes the average peak-to-valley difference; Perform finite element simulation according to the corrected contact angle, the initial thickness, and the physical properties of the coating to obtain penetration data and stress distribution results.

[0065] Preferably, the parameter optimization module is configured to: Perform parameter optimization according to the penetration data and the stress distribution results, calculate the spray pressure adjustment amount and the spray angle adjustment amount, and then obtain the optimal spray parameters, including: Calculate the spray pressure adjustment amount by the following formula: Wherein, Denotes the spray pressure adjustment amount, Denotes the pressure adjustment coefficient, Denotes the area of the stress concentration region exceeding the preset stress threshold, Denotes the total area of the stress concentration region, Denotes the average stress, Denotes the preset stress threshold; Obtain the distribution deviation angle according to the penetration data; Calculate the spray angle adjustment amount by the following formula: Wherein, Denotes the spray angle adjustment amount, Denotes the angle adjustment coefficient, Denotes the distribution deviation angle; Optimize the initial conditions according to the spraying pressure adjustment amount and the spraying angle adjustment amount to obtain the optimal spraying parameters.

[0066] Preferably, the process optimization module is configured to: Input the temperature distribution data into a thermal stress distribution model to obtain thermal stress distribution data, and perform process optimization according to the thermal stress distribution data to obtain the optimal curing parameters, including: Obtain the curing process parameters; Perform area determination according to the thermal stress distribution data and a preset thermal stress threshold to obtain the thermal anomaly area; When the thermal anomaly area is greater than a preset thermal area threshold, optimize the curing process parameters to obtain the optimal curing parameters; Wherein, the training process of the thermal stress distribution model includes: Train the thermal stress distribution model based on historical thermal stress distribution data and historical temperature data. When it is detected that the loss function of the model is less than a preset threshold, obtain the trained model; Input the temperature distribution data into the trained model and output the thermal stress distribution data.

[0067] Preferably, the result output module is configured to: Process the steel pipe surface coating according to the optimal curing parameters and the optimal spraying parameters.

[0068] Preferably, before processing the steel pipe surface coating according to the optimal curing parameters and the optimal spraying parameters, it further includes: Perform stress simulation according to the optimal curing parameters and the optimal spraying parameters to obtain the simulation standard deviation; Perform uniformity calculation according to the simulation standard deviation to obtain the simulation uniformity index; Obtain the actual stress distribution, and perform uniformity calculation according to the actual stress distribution to obtain the actual uniformity index; Calculate the effectiveness index according to the simulation uniformity index and the actual uniformity index through the following formula: Wherein, represents the effectiveness index, represents the simulation uniformity index, represents the actual uniformity index; When the effectiveness index is greater than a preset effectiveness threshold, continue with the subsequent steps; When the effectiveness index is less than a preset effectiveness threshold, re-optimize the optimal curing parameters and the optimal spraying parameters.

[0069] Preferably, after processing the surface coating of the steel pipe according to the optimal curing parameters and the optimal spraying parameters, the method further includes: Obtaining internal defect data; Inputting the internal defect data into a pre-trained strength prediction model to obtain a uniformity score; When the uniformity score is less than a preset score threshold, triggering a local recoating instruction to recoat the defective position.

[0070] It should be noted that a surface coating process detection device for steel pipe production provided by an embodiment of the present invention is used to execute all the process steps of a surface coating process detection method for steel pipe production in the above embodiment. The working principles and beneficial effects of the two correspond one by one, so they will not be elaborated here.

[0071] An embodiment of the present invention also provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data acquisition program. When the processor executes the computer program, it implements the steps in the above embodiments of the surface coating process detection method for steel pipe production, such as Figure 1 Step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above device embodiments, such as a data acquisition module.

[0072] Exemplarily, the computer program can be divided into one or more modules / units. The one or more modules / units are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the electronic device.

[0073] The electronic device can be a computing device such as a desktop computer, a notebook, a palm computer, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that the above components are only examples of the electronic device and do not constitute a limitation on the electronic device. It may include more or fewer components than the above, or combine some components, or different components. For example, the electronic device may further include input / output devices, network access devices, a bus, etc.

[0074] The so-called processor may be a Central Processing Unit (CPU), or may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor is the control center of the electronic device, and connects various parts of the entire electronic device through various interfaces and circuits.

[0075] The memory can be used to store the computer programs and / or modules. The processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory, and by calling the data stored in the memory. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as audio data, phone book, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0076] Among them, if the modules / units integrated in the electronic device are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0077] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0078] The specific embodiments described above have further elaborated on the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. It is particularly pointed out that for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A surface coating process detection method for steel pipe production, characterized in that: include: Obtain surface topography data and temperature distribution data; Performing roughness analysis according to the surface topography data to obtain roughness parameters; Performing wetting simulation according to the roughness parameter and the preset initial conditions to obtain penetration data and stress distribution results; Optimizing parameters according to the penetration data and the stress distribution results, calculating the spraying pressure adjustment amount and the spraying angle adjustment amount, and then obtaining the optimal spraying parameters; Inputting the temperature distribution data into a thermal stress distribution model to obtain thermal stress distribution data, and performing process optimization according to the thermal stress distribution data to obtain optimal curing parameters; The surface coating of the steel pipe is processed according to the optimal curing parameters and the optimal spraying parameters.

2. The surface coating process detection method for steel pipe production according to claim 1 is characterized in that: The performing roughness analysis according to the surface topography data to obtain roughness parameters includes: Performing multi-scale segmentation according to the surface morphology data to obtain sub-regions of different scales; The root mean square roughness is calculated by the following formula: in, represents the root mean square roughness; The average roughness is calculated by the following formula: in, represents the average roughness, Indicates the evaluation length, Indicates location The height difference, represents the differential symbol; The average peak-to-valley difference is calculated according to the following formula: in, represents the total number of sub-regions, Indicates the sub-area number, express The maximum height of the number area, express The minimum height of the trumpet area, represents the average peak-to-valley difference, Indicates the maximum, Indicates minimum; The roughness parameters include the root mean square roughness, the average roughness and the average peak-to-valley difference.

3. The surface coating process detection method for steel pipe production according to claim 1 is characterized in that: The wetting simulation is performed according to the roughness parameter and the preset initial conditions to obtain penetration data and stress distribution results, including: Obtain coating physical properties; The initial conditions include initial thickness and contact angle; Performing contact angle correction according to the roughness parameter to obtain a corrected contact angle; The corrected contact angle is calculated by the following formula: in, represents the corrected contact angle, represents the correction factor, represents the average roughness, represents the average peak-to-valley difference; Finite element simulation is performed according to the modified contact angle, the initial thickness and the physical properties of the coating to obtain penetration data and stress distribution results.

4. The surface coating process detection method for steel pipe production according to claim 1 is characterized in that: The parameter optimization is performed according to the penetration data and the stress distribution result, and the spraying pressure adjustment amount and the spraying angle adjustment amount are calculated to obtain the optimal spraying parameters, including: Calculate the spray pressure adjustment using the following formula: in, Indicates the spray pressure adjustment amount, Indicates the pressure adjustment factor, Indicates the area where the stress concentration region exceeds the preset stress threshold. represents the total area of ​​stress concentration region, represents the mean stress, represents a preset stress threshold; Obtaining a distribution deviation angle according to the penetration data; Calculate the spray angle adjustment using the following formula: in, Indicates the spray angle adjustment amount. represents the angle adjustment coefficient, Indicates the distribution deviation angle; The initial conditions are optimized according to the spraying pressure adjustment amount and the spraying angle adjustment amount to obtain optimal spraying parameters.

5. The surface coating process detection method for steel pipe production according to claim 1 is characterized in that: The step of inputting the temperature distribution data into a thermal stress distribution model to obtain thermal stress distribution data, and performing process optimization according to the thermal stress distribution data to obtain optimal curing parameters includes: Obtain curing process parameters; Performing area determination according to the thermal stress distribution data and a preset thermal stress threshold to obtain a thermal anomaly area; When the thermal abnormality area is greater than a preset thermal area threshold, the curing process parameters are optimized to obtain optimal curing parameters; The training process of the thermal stress distribution model includes: The thermal stress distribution model is trained based on the historical thermal stress distribution data and the historical temperature data, and when it is detected that the loss function of the model is less than a preset threshold, the trained model is obtained; The temperature distribution data is input into the trained model, and thermal stress distribution data is output.

6. The surface coating process detection method for steel pipe production according to claim 1 is characterized in that: Before processing the coating on the surface of the steel pipe according to the optimal curing parameters and the optimal spraying parameters, the method further includes: Perform stress simulation according to the optimal curing parameters and the optimal spraying parameters to obtain a simulation standard deviation; Perform uniformity calculation according to the simulated standard deviation to obtain a simulated uniformity index; Acquiring actual stress distribution, and performing uniformity calculation according to the actual stress distribution to obtain an actual uniformity index; The effectiveness index is calculated based on the simulated average index and the actual average index using the following formula: in, Represents the effectiveness index, represents the simulated uniform index, Indicates the actual average index; When the effectiveness index is greater than a preset effectiveness threshold, proceeding to subsequent steps; When the effectiveness index is less than a preset effectiveness threshold, the optimal curing parameters and the optimal spraying parameters are re-optimized.

7. The surface coating process detection method for steel pipe production according to claim 1 is characterized in that: After processing the coating on the surface of the steel pipe according to the optimal curing parameters and the optimal spraying parameters, the method further includes: Obtain internal defect data; Inputting the internal defect data into a pre-trained strength prediction model to obtain a uniformity score; When the uniformity score is less than a preset score threshold, a local re-coating instruction is triggered to re-coat the defective position.

8. A surface coating process detection device for steel pipe production, characterized in that: include: A data acquisition module, used to acquire surface morphology data and temperature distribution data; A roughness analysis module, used to perform roughness analysis according to the surface topography data to obtain roughness parameters; A wetting simulation module, used to perform wetting simulation according to the roughness parameter and the preset initial conditions to obtain penetration data and stress distribution results; A parameter optimization module, used to perform parameter optimization according to the penetration data and the stress distribution result, calculate the spraying pressure adjustment amount and the spraying angle adjustment amount, and then obtain the optimal spraying parameters; A process optimization module, used for inputting the temperature distribution data into a thermal stress distribution model to obtain thermal stress distribution data, and performing process optimization according to the thermal stress distribution data to obtain optimal curing parameters; The result output module is used to process the coating on the surface of the steel pipe according to the optimal curing parameters and the optimal spraying parameters.

9. An electronic device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the surface coating process detection method for steel pipe production according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the surface coating process detection method for steel pipe production according to any one of claims 1 to 7.

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