A method and apparatus for testing the surface coating process in steel pipe production.

Abstract

This invention relates to the field of steel pipe inspection technology, and discloses a method and apparatus for inspecting the surface coating process of steel pipe production. The method includes acquiring surface morphology data and temperature distribution data; performing roughness analysis based on the surface morphology data to obtain roughness parameters; performing wetting simulation based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results; optimizing parameters based on the penetration data and stress distribution results to calculate the spraying pressure adjustment amount and spraying angle adjustment amount, thereby obtaining optimal spraying parameters; inputting the temperature distribution data into a thermal stress distribution model to obtain thermal stress distribution data, and optimizing the process based on the thermal stress distribution data to obtain optimal curing parameters; and processing the steel pipe surface coating according to the optimal curing parameters and the optimal spraying parameters. This method has the following advantages: it can improve the inspection accuracy of the surface coating of steel pipes.

Description

Technical Field

[0001] This invention relates to the field of steel pipe testing technology, and in particular to a method and apparatus for testing the surface coating process of steel pipe production. Background Technology

[0002] 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 are constantly expanding, and the requirements for the quality of steel pipe surface coatings are also increasing. Developing a method and device that can automatically and accurately detect the quality of steel pipe surface coatings is therefore crucial. This new detection method must not only be able to identify parameters such as coating uniformity and thickness, but also be able to monitor and provide feedback on the results in real time on high-speed production lines.

[0003] One existing method for inspecting steel pipe surface coatings involves optical measurement technology. First, a set of high-resolution cameras or laser scanners are installed at specific locations on the production line to capture images of the steel pipe surface. Next, computer vision algorithms process this image data to extract coating-related feature parameters, such as color, texture, and reflectivity. These feature parameters are then compared with pre-set standard values ​​to determine if the coating meets requirements. If any non-compliance is detected, the system immediately triggers an alarm and records the specific location of the defect for further processing. Furthermore, X-ray fluorescence analysis (XRF) or energy dispersive spectroscopy (EDS) is used to further determine the coating composition, ensuring its chemical composition conforms to specifications.

[0004] However, this method has some obvious limitations. First, optical measurements are susceptible to external factors, such as changes in lighting conditions, which can lead to unstable image quality and thus affect the accuracy of the detection results. For steel pipes with complex geometries, especially those with threads or other surface structures, existing technology cannot fully cover all areas, resulting in some parts of the coating not being effectively detected, leading to low accuracy in the surface coating process detection of steel pipes. Summary of the Invention

[0005] This invention provides a method and apparatus for inspecting the surface coating process in steel pipe production, so as to improve the inspection accuracy of the surface coating of steel pipes.

[0006] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a method for detecting the surface coating process in steel pipe production, comprising:

[0007] Acquire surface morphology data and temperature distribution data;

[0008] Roughness analysis is performed based on the surface morphology data to obtain roughness parameters;

[0009] Wetting simulation is performed based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results;

[0010] Based on the penetration data and the stress distribution results, the parameters are optimized to calculate the spraying pressure adjustment amount and the spraying angle adjustment amount, thereby obtaining the optimal spraying parameters;

[0011] The temperature distribution data is input into the thermal stress distribution model to obtain thermal stress distribution data, and the process is optimized based on the thermal stress distribution data to obtain the optimal curing parameters;

[0012] The coating on the steel pipe surface is processed according to the optimal curing parameters and the optimal spraying parameters.

[0013] In one optional implementation, the step of performing roughness analysis based on the surface topography data to obtain roughness parameters includes:

[0014] The surface topography data is segmented into multiple scales to obtain sub-regions of different scales.

[0015] The root mean square roughness is calculated using the following formula:

[0016]

[0017] in, Indicates the root mean square roughness;

[0018] The average roughness is calculated using the following formula:

[0019]

[0020] in, Indicates average roughness. Indicates the evaluation length. Indicates position The height difference at the location, Represents the differential symbol;

[0021] The average peak-to-valley difference is calculated using the following formula:

[0022]

[0023] in, This represents the total number of sub-regions. Indicates the sub-region number. express The maximum height of the work area express The lowest elevation in the cell block area This represents the average peak-to-valley difference. Indicates the maximum. Indicates the minimum;

[0024] The roughness parameters include the root mean square roughness, the average roughness, and the average peak-to-valley difference.

[0025] In one optional implementation, the step of performing wetting simulation based on the roughness parameter and preset initial conditions to obtain penetration data and stress distribution results includes:

[0026] Obtain the physical properties of the coating;

[0027] The initial conditions include initial thickness and contact angle;

[0028] The contact angle is corrected based on the roughness parameters to obtain the corrected contact angle;

[0029] The corrected contact angle is calculated using the following formula:

[0030]

[0031] in, Indicates the correction of the contact angle. This represents the correction factor. Indicates average roughness. Indicates the average peak-to-valley difference;

[0032] Finite element simulations were performed based on the modified contact angle, the initial thickness, and the physical properties of the coating to obtain penetration data and stress distribution results.

[0033] In one optional implementation, the step of optimizing parameters based on the penetration data and the stress distribution results to calculate the spraying pressure adjustment and spraying angle adjustment, thereby obtaining the optimal spraying parameters, includes:

[0034] The spray pressure adjustment amount is calculated using the following formula:

[0035]

[0036] in, Indicates the amount of spray pressure adjustment. Indicates the pressure adjustment coefficient. This represents the area where the stress concentration region exceeds a preset stress threshold. This represents the total area of ​​the stress concentration region. Indicates the average stress. Indicates the preset stress threshold;

[0037] The distribution bias angle is obtained based on the permeability data;

[0038] The spray angle adjustment amount is calculated using the following formula:

[0039]

[0040] in, Indicates the amount of adjustment for the spraying angle. Indicates the angle adjustment coefficient. Indicates the angle of distribution bias;

[0041] The initial conditions are optimized based on the spraying pressure adjustment and the spraying angle adjustment to obtain the optimal spraying parameters.

[0042] In one optional implementation, the step of inputting the temperature distribution data into a thermal stress distribution model to obtain thermal stress distribution data, and then optimizing the process based on the thermal stress distribution data to obtain optimal curing parameters, includes:

[0043] Obtain curing process parameters;

[0044] The thermal anomaly area is obtained by determining the area based on the thermal stress distribution data and the preset thermal stress threshold.

[0045] 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.

[0046] The training process of the thermal stress distribution model includes:

[0047] The thermal stress distribution model is trained based on historical thermal stress distribution data and historical temperature data. When the loss function of the model is detected to be less than a preset threshold, the trained model is obtained.

[0048] The temperature distribution data is input into the trained model, and the thermal stress distribution data is output.

[0049] In an optional embodiment, before processing the coating on the steel pipe surface according to the optimal curing parameters and the optimal spraying parameters, the method further includes:

[0050] Stress simulation was performed based on the optimal curing parameters and the optimal spraying parameters to obtain the simulation standard deviation;

[0051] The uniformity index is obtained by calculating the simulated standard deviation.

[0052] Obtain the actual stress distribution and perform uniformity calculation based on the actual stress distribution to obtain the actual uniformity index;

[0053] The effectiveness index is calculated using the following formula based on the simulated uniformity index and the actual uniformity index:

[0054]

[0055] in, Indicators of effectiveness This represents the simulated uniformity index. Indicates the actual uniformity index;

[0056] If the validity index is greater than the preset validity threshold, proceed to the next steps;

[0057] When the effectiveness index is less than the preset effectiveness threshold, the optimal curing parameters and the optimal spraying parameters are re-optimized.

[0058] In an optional embodiment, after processing the coating on the steel pipe surface according to the optimal curing parameters and the optimal spraying parameters, the method further includes:

[0059] Obtain internal defect data;

[0060] The internal defect data is input into a pre-trained intensity prediction model to obtain a uniformity score;

[0061] When the uniformity score is less than a preset score threshold, a local recoating command is triggered to recoat the defective location.

[0062] Secondly, the present invention provides a surface coating process inspection device for steel pipe production, comprising:

[0063] The data acquisition module is used to acquire surface morphology data and temperature distribution data;

[0064] The roughness analysis module is used to perform roughness analysis based on the surface morphology data to obtain roughness parameters;

[0065] The wetting simulation module is used to perform wetting simulation based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results;

[0066] The parameter optimization module is used to optimize parameters based on the penetration data and the stress distribution results, calculate the spraying pressure adjustment amount and the spraying angle adjustment amount, and then obtain the optimal spraying parameters.

[0067] The process optimization module is used to input the temperature distribution data into the thermal stress distribution model to obtain thermal stress distribution data, and to optimize the process based on the thermal stress distribution data to obtain the optimal curing parameters.

[0068] 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.

[0069] Thirdly, the present invention also 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, wherein the processor executes the computer program to implement the surface coating process detection method for steel pipe production as described in any one of the above.

[0070] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the surface coating process inspection method for steel pipe production as described in any one of the above.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] (1) This invention performs corresponding preprocessing on the acquired surface morphology data and temperature distribution data to ensure the integrity and accuracy of the data. By cleaning, standardizing and verifying the surface morphology data, noise and outliers can be eliminated, improving the accuracy of roughness analysis; 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.

[0073] (2) Based on the surface morphology data, the present invention performs roughness analysis to obtain roughness parameters. Through this process, the system can more accurately describe surface features, providing reliable basic data for subsequent wetting simulation. The accurate calculation of roughness parameters helps to optimize spraying process parameters, thereby improving the quality and uniformity of the coating. In addition, standardized roughness parameters are easy to store and manage, supporting subsequent data query and analysis operations.

[0074] (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 liquid on surfaces with different roughness can be predicted. This simulation method not only improves computational efficiency but also enhances the reliability of the results. The visualization of the simulation results helps to intuitively understand the problems existing in the spraying process and provides a scientific basis for parameter optimization.

[0075] (4) Based on the penetration data and stress distribution results, the present invention optimizes the parameters to calculate the spraying pressure adjustment and spraying angle adjustment, thereby obtaining 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 accuracy and efficiency of spraying, ensuring the stability and consistency of coating quality.

[0076] (5) In this invention, the temperature distribution data is input into the thermal stress distribution model to obtain thermal stress distribution data, and the process is optimized based on the thermal stress distribution data to obtain the optimal curing parameters. The thermal stress distribution model can accurately predict changes in thermal stress during the curing process, thereby guiding the optimization of the curing process. The optimized curing parameters can effectively reduce internal stress in the coating, improve the adhesion and durability of the coating, and further enhance the overall processing quality.

[0077] (6) The present invention processes the coating on the surface 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 coating on the surface of the steel pipe. This integrated method not only improves the performance of the coating, but also enhances the automation and intelligence of the entire processing flow, ensuring high efficiency and high precision in coating processing. Attached Figure Description

[0078] Figure 1 This is a schematic diagram of a surface coating process testing method for steel pipe production provided in the first embodiment of the present invention;

[0079] Figure 2 This is a schematic diagram of a surface coating process testing device for steel pipe production provided in the second embodiment of the present invention. Detailed Implementation

[0080] 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, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0081] 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 are constantly expanding, and the requirements for the quality of steel pipe surface coatings are also increasing. Developing a method and device that can automatically and accurately detect the quality of steel pipe surface coatings is therefore crucial. This new detection method must not only be able to identify parameters such as coating uniformity and thickness, but also be able to monitor and provide feedback on the results in real time on high-speed production lines.

[0082] One existing method for inspecting steel pipe surface coatings involves optical measurement technology. First, a set of high-resolution cameras or laser scanners are installed at specific locations on the production line to capture images of the steel pipe surface. Next, computer vision algorithms process this image data to extract coating-related feature parameters, such as color, texture, and reflectivity. These feature parameters are then compared with pre-set standard values ​​to determine if the coating meets requirements. If any non-compliance is detected, the system immediately triggers an alarm and records the specific location of the defect for further processing. Furthermore, X-ray fluorescence analysis (XRF) or energy dispersive spectroscopy (EDS) is used to further determine the coating composition, ensuring its chemical composition conforms to specifications.

[0083] However, this method has some obvious limitations. First, optical measurements are susceptible to external factors, such as changes in lighting conditions, which can lead to unstable image quality and thus affect the accuracy of the detection results. For steel pipes with complex geometries, especially those with threads or other surface structures, existing technology cannot fully cover all areas, resulting in some parts of the coating not being effectively detected, leading to low accuracy in the surface coating process detection of steel pipes.

[0084] To solve the above problems, refer to Figure 1 The first embodiment of the present invention provides a method for detecting the surface coating process in steel pipe production, comprising the following steps:

[0085] S11, acquire surface morphology data and temperature distribution data;

[0086] S12, perform roughness analysis based on the surface morphology data to obtain roughness parameters;

[0087] S13, Wetting simulation is performed based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results;

[0088] S14, Based on the penetration data and the stress distribution results, the parameters are optimized to calculate the spraying pressure adjustment amount and the spraying angle adjustment amount, thereby obtaining the optimal spraying parameters;

[0089] S15, input the temperature distribution data into the thermal stress distribution model to obtain thermal stress distribution data, and optimize the process based on the thermal stress distribution data to obtain the optimal curing parameters;

[0090] S16, The coating on the surface of the steel pipe is processed according to the optimal curing parameters and the optimal spraying parameters.

[0091] In step S11, surface morphology data and temperature distribution data are acquired.

[0092] In one embodiment, a high-precision 3D scanner is used to scan the surface of the steel pipe, capturing microstructural information and thus obtaining surface morphology data. An infrared thermal imager can be used to quickly acquire temperature distribution images of the target area.

[0093] It is worth noting that the surface morphology data is represented in three-dimensional coordinates, describing the undulations of the microstructure on the steel pipe surface. Each data point includes horizontal and vertical coordinates, as well as height information, used for subsequent roughness analysis. The temperature distribution data is presented as a two-dimensional temperature field map, showing the temperature values ​​at different locations on the steel pipe surface.

[0094] In step S12, roughness analysis is performed based on the surface morphology data to obtain roughness parameters.

[0095] In one embodiment, the surface topography data is segmented at multiple scales to obtain sub-regions of different scales;

[0096] The root mean square roughness is calculated using the following formula:

[0097]

[0098] in, Indicates the root mean square roughness;

[0099] The average roughness is calculated using the following formula:

[0100]

[0101] in, Indicates average roughness. Indicates the evaluation length. Indicates position The height difference at the location, Represents the differential symbol;

[0102] The average peak-to-valley difference is calculated using the following formula:

[0103]

[0104] in, This represents the total number of sub-regions. Indicates the sub-region number. express The maximum height of the work area express The lowest elevation in the cell block area This represents the average peak-to-valley difference. Indicates the maximum. Indicates the minimum;

[0105] The roughness parameters include the root mean square roughness, the average roughness, and the average peak-to-valley difference.

[0106] It's worth noting that multi-scale segmentation is a technique used to analyze surface morphology. First, the raw surface morphology data is preprocessed, including outlier removal and smoothing, to ensure the accuracy of subsequent analysis. Several analysis scales are then selected. Each scale corresponds to a specific spatial resolution or wavelength range; smaller scales focus on microscopic details, while larger scales emphasize macroscopic structure. 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 surface roughness characteristics can be comprehensively understood from different scale perspectives.

[0107] It's worth noting that root mean square (RMS) roughness describes the standard deviation of surface height variation. A higher RMS roughness value indicates a more irregular and undulating surface; a lower RMS roughness value indicates a relatively flat surface. Mean roughness represents the arithmetic mean of the absolute values ​​of the surface profile relative to the centerline. A higher mean roughness value indicates a rougher surface overall; conversely, a lower mean roughness value indicates a smoother surface. Mean peak-to-valley difference reflects the average difference between the peaks and troughs of the surface. A higher mean peak-to-valley difference value indicates significant height variation on the surface, i.e., distinct peaks and valleys; a lower mean peak-to-valley difference value indicates a relatively flat surface with smaller peak-to-valley differences.

[0108] It is worth noting that the height difference refers to the position. The height difference between the point and the reference line. The reference line is chosen as the average line of the profile.

[0109] In step S13, wetting simulation is performed based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results.

[0110] In one implementation, the physical properties of the coating are obtained;

[0111] The initial conditions include initial thickness and contact angle;

[0112] The contact angle is corrected based on the roughness parameters to obtain the corrected contact angle;

[0113] The corrected contact angle is calculated using the following formula:

[0114]

[0115] in, Indicates the correction of the contact angle. This represents the correction factor. Indicates average roughness. Indicates the average peak-to-valley difference;

[0116] Finite element simulations were performed based on the modified contact angle, the initial thickness, and the physical properties of the coating to obtain penetration data and stress distribution results.

[0117] It is worth noting that the physical properties of the coating are obtained from the manufacturer's specifications. Initial conditions can be set by repeatedly measuring performance data at different thicknesses and contact angles, and collecting experimental data to help determine the preset values ​​for the initial thickness and contact angle. For example, the optimal initial thickness and contact angle can be determined by measuring coating adhesion, abrasion resistance, corrosion resistance, and other performance indicators at different thicknesses and contact angles. 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 coating penetration and the resulting stress distribution. This is based on the given roughness parameters and initial conditions.

[0118] It is worth noting 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 formula parameters.

[0119] In step S14, parameters are optimized based on the penetration data and stress distribution results to calculate the spraying pressure adjustment amount and spraying angle adjustment amount, thereby obtaining the optimal spraying parameters.

[0120] In one embodiment, the spray pressure adjustment amount is calculated using the following formula:

[0121]

[0122] in, Indicates the amount of spray pressure adjustment. Indicates the pressure adjustment coefficient. This represents the area where the stress concentration region exceeds a preset stress threshold. This represents the total area of ​​the stress concentration region. Indicates the average stress. Indicates the preset stress threshold;

[0123] The distribution bias angle is obtained based on the permeability data;

[0124] The spray angle adjustment amount is calculated using the following formula:

[0125]

[0126] in, Indicates the amount of adjustment for the spraying angle. Indicates the angle adjustment coefficient. Indicates the angle of distribution bias;

[0127] The initial conditions are optimized based on the spraying pressure adjustment and the spraying angle adjustment to obtain the optimal spraying parameters.

[0128] It's worth noting that the spraying pressure adjustment represents the pressure value that needs to be increased or decreased; a positive value indicates increased pressure, and a negative value indicates decreased pressure. The pressure adjustment coefficient (dimensionless) is 0.15 MPa·mm² / N for aluminum alloy coatings. The area of ​​the stress exceeding the limit is obtained through X-ray residual stress detection. The total area of ​​the stress concentration region is taken as 80% of the critical structural area. The unit of the measured average stress is MPa. A preset stress threshold is used, such as 620 MPa for IN718 alloy coatings. The angle adjustment amount, for example, +3° means the spray gun needs to be deflected 3 degrees clockwise. The angle sensitivity coefficient (dimensionless) is determined through deposition efficiency experiments (e.g., the value range is 0.25-0.35 for plasma spraying). The distribution deviation angle is 8°.

[0129] It is worth noting that coating formulation parameters include components and ratios: such as the ratio of epoxy resin to curing agent (e.g., 60% epoxy resin, 30% curing agent), pigment content (5%), and solvent type, all of which are key factors affecting coating performance. These parameters determine the basic physicochemical properties of the coating, such as hardness, flexibility, and adhesion.

[0130] It is worth noting that the preset threshold for the stress concentration area represents the maximum allowable internal stress (e.g., 10 MPa), serving as a standard for evaluating the coating's quality. If simulation results show that certain areas exceed this threshold, process parameters need to be adjusted to avoid problems such as coating cracking or peeling.

[0131] It is worth noting that the generation of new coating process parameters is an iterative optimization process. Initial parameters (such as the original spraying pressure of 0.3 MPa and angle of 60 degrees) are adjusted (becoming 0.25 MPa and 65 degrees), and calculations are performed based on the given adjustment amounts. The optimization process involves adding the initial values ​​to the adjustment values.

[0132] In step S15, the temperature distribution data is input into the thermal stress distribution model to obtain thermal stress distribution data, and the process is optimized based on the thermal stress distribution data to obtain the optimal curing parameters.

[0133] In one implementation, curing process parameters are obtained;

[0134] The thermal anomaly area is obtained by determining the area based on the thermal stress distribution data and the preset thermal stress threshold.

[0135] 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.

[0136] The training process of the thermal stress distribution model includes:

[0137] The thermal stress distribution model is trained based on historical thermal stress distribution data and historical temperature data. When the loss function of the model is detected to be less than a preset threshold, the trained model is obtained.

[0138] The temperature distribution data is input into the trained model, and the thermal stress distribution data is output.

[0139] In one embodiment, the thermal stress distribution model employs 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. This data includes temperature fields under different conditions, material properties (such as coefficient of thermal expansion, elastic modulus, etc.), and the ultimately calculated thermal stress distribution.

[0140] In one embodiment, the curing process parameters are optimized using a stepped heating scheme, which divides the curing process into multiple temperature plateaus, each of which is maintained at a constant temperature for a period of time to fully release thermal stress. The new curing process scheme is implemented through a process control system.

[0141] In step S16, the coating on the steel pipe surface is processed according to the optimal curing parameters and the optimal spraying parameters.

[0142] In one embodiment, before processing the coating on the steel pipe surface according to the optimal curing parameters and the optimal spraying parameters, the method further includes:

[0143] Stress simulation was performed based on the optimal curing parameters and the optimal spraying parameters to obtain the simulation standard deviation;

[0144] The uniformity index is obtained by calculating the simulated standard deviation.

[0145] Obtain the actual stress distribution and perform uniformity calculation based on the actual stress distribution to obtain the actual uniformity index;

[0146] The effectiveness index is calculated using the following formula based on the simulated uniformity index and the actual uniformity index:

[0147]

[0148] in, Indicators of effectiveness This represents the simulated uniformity index. Indicates the actual uniformity index;

[0149] If the validity index is greater than the preset validity threshold, proceed to the next steps;

[0150] When the effectiveness index is less than the preset effectiveness threshold, the optimal curing parameters and the optimal spraying parameters are re-optimized.

[0151] It is worth noting 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 subsequent coating processing steps can continue.

[0152] In one embodiment, after processing the coating on the steel pipe surface according to the optimal curing parameters and the optimal spraying parameters, the method further includes:

[0153] Obtain internal defect data;

[0154] The internal defect data is input into a pre-trained intensity prediction model to obtain a uniformity score;

[0155] When the uniformity score is less than a preset score threshold, a local recoating command is triggered to recoat the defective location.

[0156] In one implementation, the strength prediction model employs a support vector machine (SVM). An initial dataset is generated by acquiring data on surface cleanliness, roughness, coating thickness, and thermal stress distribution, which is then used for training. After training, collected internal defect data is input into the strength prediction model. This model can predict the uniformity and overall strength of the coating based on historical data and materials science principles. The uniformity score reflects the consistency of the coating across the entire surface and potential weak points.

[0157] It is worth noting that triggering a local touch-up command involves applying additional coating to the specific defect location detected, in order to compensate for the deficiencies in the original coating and ensure the integrity and reliability of the entire coating system.

[0158] In summary, this invention discloses a method for inspecting the surface coating process in steel pipe production, aiming to improve the accuracy of surface coating quality inspection. The method first acquires the morphology and temperature distribution data of the steel pipe surface. Roughness analysis is performed by analyzing the surface morphology data to obtain roughness parameters, including root mean square roughness, average roughness, and average peak-to-valley difference. These parameters help to comprehensively understand the characteristics of the microstructure of the steel pipe surface, providing basic data for subsequent wetting simulation. Next, wetting simulation is performed based on the roughness parameters and preset initial conditions (such as initial thickness and contact angle) to calculate penetration data and stress distribution results. To ensure uniform adhesion of the coating to the steel pipe surface, the spraying parameters are optimized based on the penetration data and stress distribution results to determine the optimal spraying pressure and angle adjustment, thereby ensuring coating uniformity and adhesion. Furthermore, this invention introduces a thermal stress distribution model, inputting the temperature distribution data of the steel pipe surface into the model to obtain the thermal stress distribution, and optimizing the curing process parameters accordingly, thereby avoiding the risk of coating defects or failure due to excessive thermal stress.

[0159] Furthermore, by segmenting the surface morphology data at different scales, multiple sub-regions can be obtained, each representing the surface characteristics at a specific scale. This not only helps capture multi-level information about surface roughness but also enables a comprehensive assessment of surface quality from macro to micro. It is worth noting that the root mean square roughness, average roughness, and average peak-to-valley difference parameters used in the roughness analysis process of this invention work together to evaluate the quality of the surface coating, providing a scientific basis. For example, the root mean square roughness describes the standard deviation of surface height variation, reflecting the overall surface fluctuation; the average roughness quantifies the average smoothness of the surface; and the average peak-to-valley difference measures the distance between the highest and lowest points of the surface, serving as a key indicator for evaluating the differences between surface peaks and troughs.

[0160] Furthermore, in optimizing spraying parameters, this invention employs a pressure adjustment strategy based on stress analysis. First, it assesses the proportion of areas exceeding a threshold value in stress concentration regions and the difference between the average stress level and a preset threshold. This determines the required adjustment of the spraying pressure. This method helps avoid coating defects caused by excessive local stress, ensuring coating uniformity and durability. Simultaneously, based on the distribution bias angle in the penetration data, the optimal adjustment angle of the nozzle relative to the workpiece is determined. This process considers the natural distribution tendency of the coating material on the surface to ensure optimal coverage and adhesion, effectively addressing the spraying needs of complex-shaped workpieces and improving spraying efficiency and quality.

[0161] In addition, this invention utilizes historical thermal stress distribution data and temperature data to train a thermal stress distribution model. This model determines the area by comparing the current thermal stress distribution data with a preset thermal stress threshold, identifying areas of thermal anomaly. If the detected thermal anomaly area exceeds the set threshold, it indicates a risk or deficiency in the existing curing process, requiring adjustment of the curing process parameters. This process not only effectively prevents coating defects or failures caused by excessive thermal stress but also ensures optimal coating quality and durability. Furthermore, this invention incorporates a feedback mechanism: stress simulation is performed before actual processing to calculate an effectiveness index. When the effectiveness index is less than a preset threshold, the optimal curing and spraying parameters are re-optimized, ensuring that the final coating quality meets the expected standards.

[0162] In summary, the proposed method for detecting surface coating processes in steel pipe production can improve the accuracy of surface coating process detection in steel pipe production.

[0163] Reference Figure 2 The second embodiment of the present invention provides a surface coating process inspection device for steel pipe production, comprising:

[0164] The data acquisition module is used to acquire surface morphology data and temperature distribution data;

[0165] The roughness analysis module is used to perform roughness analysis based on the surface morphology data to obtain roughness parameters;

[0166] The wetting simulation module is used to perform wetting simulation based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results;

[0167] The parameter optimization module is used to optimize parameters based on the penetration data and the stress distribution results, calculate the spraying pressure adjustment amount and the spraying angle adjustment amount, and then obtain the optimal spraying parameters.

[0168] The process optimization module is used to input the temperature distribution data into the thermal stress distribution model to obtain thermal stress distribution data, and to optimize the process based on the thermal stress distribution data to obtain the optimal curing parameters.

[0169] 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.

[0170] Preferably, the data acquisition module is used for:

[0171] Obtain surface morphology data and temperature distribution data.

[0172] Preferably, the coarse analysis module is used for:

[0173] Roughness analysis is performed based on the surface morphology data to obtain roughness parameters, including:

[0174] The surface topography data is segmented into multiple scales to obtain sub-regions of different scales.

[0175] The root mean square roughness is calculated using the following formula:

[0176]

[0177] in, Indicates the root mean square roughness;

[0178] The average roughness is calculated using the following formula:

[0179]

[0180] in, Indicates average roughness. Indicates the evaluation length. Indicates position The height difference at the location, Represents the differential symbol;

[0181] The average peak-to-valley difference is calculated using the following formula:

[0182]

[0183] in, This represents the total number of sub-regions. Indicates the sub-region number. express The maximum height of the work area express The lowest elevation in the cell block area This represents the average peak-to-valley difference. Indicates the maximum. Indicates the minimum;

[0184] The roughness parameters include the root mean square roughness, the average roughness, and the average peak-to-valley difference.

[0185] Preferably, the wetting simulation module is used for:

[0186] Wetting simulation is performed based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results, including:

[0187] Obtain the physical properties of the coating;

[0188] The initial conditions include initial thickness and contact angle;

[0189] The contact angle is corrected based on the roughness parameters to obtain the corrected contact angle;

[0190] The corrected contact angle is calculated using the following formula:

[0191]

[0192] in, Indicates the correction of the contact angle. This represents the correction factor. Indicates average roughness. Indicates the average peak-to-valley difference;

[0193] Finite element simulations were performed based on the modified contact angle, the initial thickness, and the physical properties of the coating to obtain penetration data and stress distribution results.

[0194] Preferably, the parameter optimization module is used for:

[0195] Based on the penetration data and stress distribution results, parameter optimization is performed to calculate the spraying pressure adjustment and spraying angle adjustment, thereby obtaining the optimal spraying parameters, including:

[0196] The spray pressure adjustment amount is calculated using the following formula:

[0197]

[0198] in, Indicates the amount of spray pressure adjustment. Indicates the pressure adjustment coefficient. This represents the area where the stress concentration region exceeds a preset stress threshold. This represents the total area of ​​the stress concentration region. Indicates the average stress. Indicates the preset stress threshold;

[0199] The distribution bias angle is obtained based on the permeability data;

[0200] The spray angle adjustment amount is calculated using the following formula:

[0201]

[0202] in, Indicates the amount of adjustment for the spraying angle. Indicates the angle adjustment coefficient. Indicates the angle of distribution bias;

[0203] The initial conditions are optimized based on the spraying pressure adjustment and the spraying angle adjustment to obtain the optimal spraying parameters.

[0204] Preferably, the process optimization module is used for:

[0205] The temperature distribution data is input into the thermal stress distribution model to obtain thermal stress distribution data. Based on this thermal stress distribution data, the process is optimized to obtain the optimal curing parameters, including:

[0206] Obtain curing process parameters;

[0207] The thermal anomaly area is obtained by determining the area based on the thermal stress distribution data and the preset thermal stress threshold.

[0208] 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.

[0209] The training process of the thermal stress distribution model includes:

[0210] The thermal stress distribution model is trained based on historical thermal stress distribution data and historical temperature data. When the loss function of the model is detected to be less than a preset threshold, the trained model is obtained.

[0211] The temperature distribution data is input into the trained model, and the thermal stress distribution data is output.

[0212] Preferably, the result output module is used for:

[0213] The coating on the steel pipe surface is processed according to the optimal curing parameters and the optimal spraying parameters.

[0214] Preferably, before processing the coating on the steel pipe surface according to the optimal curing parameters and the optimal spraying parameters, the method further includes:

[0215] Stress simulation was performed based on the optimal curing parameters and the optimal spraying parameters to obtain the simulation standard deviation;

[0216] The uniformity index is obtained by calculating the simulated standard deviation.

[0217] Obtain the actual stress distribution and perform uniformity calculation based on the actual stress distribution to obtain the actual uniformity index;

[0218] The effectiveness index is calculated using the following formula based on the simulated uniformity index and the actual uniformity index:

[0219]

[0220] in, Indicators of effectiveness This represents the simulated uniformity index. Indicates the actual uniformity index;

[0221] If the validity index is greater than the preset validity threshold, proceed to the next steps;

[0222] When the effectiveness index is less than the preset effectiveness threshold, the optimal curing parameters and the optimal spraying parameters are re-optimized.

[0223] Preferably, after processing the coating on the steel pipe surface according to the optimal curing parameters and the optimal spraying parameters, the process further includes:

[0224] Obtain internal defect data;

[0225] The internal defect data is input into a pre-trained intensity prediction model to obtain a uniformity score;

[0226] When the uniformity score is less than a preset score threshold, a local recoating command is triggered to recoat the defective location.

[0227] It should be noted that the surface coating process inspection device for steel pipe production provided in this embodiment of the invention is used to execute all the process steps of the surface coating process inspection method for steel pipe production described in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0228] This 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, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the data acquisition module.

[0229] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0230] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0231] The processor can be a Central Processing Unit (CPU), or 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. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0232] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0233] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0234] 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 separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0235] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for detecting the surface coating process in steel pipe production, characterized in that, include: Acquire surface morphology data and temperature distribution data; Roughness analysis is performed based on the surface morphology data to obtain roughness parameters; Wetting simulation is performed based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results; Based on the penetration data and the stress distribution results, the parameters are optimized to calculate the spraying pressure adjustment amount and the spraying angle adjustment amount, thereby obtaining the optimal spraying parameters; The temperature distribution data is input into the thermal stress distribution model to obtain thermal stress distribution data, and the process is optimized based on the thermal stress distribution data to obtain the optimal curing parameters; The coating on the steel pipe surface is processed according to the optimal curing parameters and the optimal spraying parameters.

2. The surface coating process testing method for steel pipe production according to claim 1, characterized in that, The roughness analysis based on the surface morphology data to obtain roughness parameters includes: The surface topography data is segmented into multiple scales to obtain sub-regions of different scales. The root mean square roughness is calculated using the following formula: in, Indicates the root mean square roughness; The average roughness is calculated using the following formula: in, Indicates average roughness. Indicates the evaluation length. Indicates position The height difference at the location, Represents the differential symbol; The average peak-to-valley difference is calculated using the following formula: in, This represents the total number of sub-regions. Indicates the sub-region number. express The maximum height of the work area express The lowest elevation in the cell block area This represents the average peak-to-valley difference. Indicates the maximum. Indicates the 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 testing method for steel pipe production according to claim 1, characterized in that, The step of 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 initial thickness and contact angle; The contact angle is corrected based on the roughness parameters to obtain the corrected contact angle; The corrected contact angle is calculated using the following formula: in, Indicates the correction of the contact angle. This represents the correction factor. Indicates average roughness. Indicates the average peak-to-valley difference; Finite element simulations were performed based on 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 testing method for steel pipe production according to claim 1, characterized in that, The step of optimizing parameters based on the penetration data and stress distribution results, calculating the spraying pressure adjustment and spraying angle adjustment, and then obtaining the optimal spraying parameters includes: The spray pressure adjustment amount is calculated using the following formula: in, Indicates the amount of spray pressure adjustment. Indicates the pressure adjustment coefficient. This represents the area where the stress concentration region exceeds a preset stress threshold. This represents the total area of ​​the stress concentration region. Indicates the average stress. Indicates the preset stress threshold; The distribution bias angle is obtained based on the permeability data; The spray angle adjustment amount is calculated using the following formula: in, Indicates the amount of adjustment for the spraying angle. Indicates the angle adjustment coefficient. Indicates the angle of distribution bias; The initial conditions are optimized based on the spraying pressure adjustment and the spraying angle adjustment to obtain the optimal spraying parameters.

5. The surface coating process testing method for steel pipe production according to claim 1, characterized in that, The process involves inputting the temperature distribution data into a thermal stress distribution model to obtain thermal stress distribution data, and then optimizing the process based on the thermal stress distribution data to obtain optimal curing parameters, including: Obtain curing process parameters; The thermal anomaly area is obtained by determining the area based on the thermal stress distribution data and the preset thermal stress threshold. 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. 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 the loss function of the model is detected to be less than a preset threshold, the trained model is obtained. The temperature distribution data is input into the trained model, and the thermal stress distribution data is output.

6. The surface coating process testing method for steel pipe production according to claim 1, characterized in that, Before processing the coating on the steel pipe surface according to the optimal curing parameters and the optimal spraying parameters, the method further includes: Stress simulation was performed based on the optimal curing parameters and the optimal spraying parameters to obtain the simulation standard deviation; The uniformity index is obtained by calculating the simulated standard deviation. Obtain the actual stress distribution and perform uniformity calculation based on the actual stress distribution to obtain the actual uniformity index; The effectiveness index is calculated using the following formula based on the simulated uniformity index and the actual uniformity index: in, Indicators of effectiveness This represents the simulated uniformity index. Indicates the actual uniformity index; If the validity index is greater than the preset validity threshold, proceed to the next steps; When the effectiveness index is less than the preset effectiveness threshold, the optimal curing parameters and the optimal spraying parameters are re-optimized.

7. The method for detecting the surface coating process in steel pipe production according to claim 1, characterized in that, After processing the coating on the steel pipe surface according to the optimal curing parameters and the optimal spraying parameters, the process further includes: Obtain internal defect data; The internal defect data is input into a pre-trained intensity prediction model to obtain a uniformity score; When the uniformity score is less than a preset score threshold, a local recoating command is triggered to recoat the defective location.

8. A surface coating process testing device for steel pipe production, characterized in that, include: The data acquisition module is used to acquire surface morphology data and temperature distribution data; The roughness analysis module is used to perform roughness analysis based on the surface morphology data to obtain roughness parameters; The wetting simulation module is used to perform wetting simulation based on the roughness parameters and preset initial conditions to obtain penetration data and stress distribution results; The parameter optimization module is used to optimize parameters based on the penetration data and the stress distribution results, calculate the spraying pressure adjustment amount and the spraying angle adjustment amount, and then obtain the optimal spraying parameters. The process optimization module is used to input the temperature distribution data into the thermal stress distribution model to obtain thermal stress distribution data, and to optimize the process based on the thermal stress distribution data to obtain the 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 includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the surface coating process inspection method for steel pipe production as described in any one of claims 1 to 7.

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, it controls the device containing the computer-readable storage medium to perform the surface coating process inspection method for steel pipe production as described in any one of claims 1 to 7.

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