Sliding bearing laser cladding quality visual detection method based on infrared thermal imaging

Abstract

The invention relates to the technical field of surface defect detection, in particular to a sliding bearing laser cladding quality visual detection method based on infrared thermal imaging. The method comprises the following steps: firstly, acquiring infrared thermal imaging data, calculating a temperature field coupling parameter, constructing a real-time monitoring graph according to the temperature field coupling parameter, and determining an infrared abnormal point on the sliding bearing; if the infrared abnormal point is located in the first machining area, cladding data, element diffusion data and heat conduction data of the infrared abnormal point are obtained and processed, and a first defect parameter is obtained; if the infrared abnormal point is located in a second processing area, obtaining a second defect parameter according to different weight coefficients; and constructing a comprehensive defect parameter according to the first and second defect parameters, and judging whether the sliding bearing sample has defects or not.

Description

Technical Field

[0001] The present invention relates to the technical field of surface defect detection, and specifically to a method for visual detection of the quality of laser cladding of sliding bearings based on infrared thermal imaging. Background Technique

[0002] Traditional quality detection methods such as metallographic section analysis and hardness gradient testing require destructive sampling, and there are problems of strong hysteresis and inability to provide real-time feedback; while contact temperature measurement such as thermocouples is limited to single-point measurement and it is difficult to capture the full-field temperature distribution and transient thermodynamic characteristics of the cladding layer.

[0003] As a core means of material testing and analysis, optical detection technology can non-contact obtain information on material composition, structure and surface state through the interaction of bands such as sub-millimeter waves, infrared light, visible light and ultraviolet light with materials. For example, infrared thermal imaging can quantify the temperature field distribution of materials, ultraviolet fluorescence spectroscopy can identify organic pollutants, and visible light interference technology can accurately detect surface topography. Due to the advantages of high sensitivity, fast response and multi-parameter synchronous acquisition, such methods are widely used in fields such as industrial manufacturing and aerospace. With the advantages of non-contact and full-field temperature measurement, infrared thermal imaging technology has been introduced into the field of cladding surface defect detection in recent years.

[0004] Therefore, a method for visual detection of the quality of laser cladding of sliding bearings based on infrared thermal imaging is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for visual detection of the quality of laser cladding of sliding bearings based on infrared thermal imaging to improve the accuracy of quality detection of the surface of sliding bearings. First, obtain infrared thermal imaging data and calculate the temperature field coupling parameters on the surface of the sliding bearing sample, construct a real-time monitoring map according to the temperature field coupling parameters and determine the infrared abnormal points on the sliding bearing; segment according to the stress distribution data and lubricating film pressure distribution data on the surface of the sliding bearing to obtain the first processing area and the second processing area; if the infrared abnormal point is in the first processing area, obtain the cladding data, element diffusion data and heat conduction data of the infrared abnormal point and process them to obtain the first defect parameter; if the infrared abnormal point is in the second processing area, obtain the second defect parameter according to different weight coefficients; construct a comprehensive defect parameter according to the first and second defect parameters and judge whether there are defects in the sliding bearing sample.

[0006] To achieve the above object, the present invention provides the following technical solution: A method for visual detection of the quality of laser cladding of sliding bearings based on infrared thermal imaging, including: Obtain data of infrared thermal imaging at different angles and calculate the temperature field coupling parameters on the surface of the sliding bearing sample, construct a real-time monitoring map according to the temperature field coupling parameters; determine the infrared abnormal points on the sliding bearing according to the temperature field coupling parameters and the abnormal threshold; Furthermore, three high-frame-rate infrared thermal imagers are arranged in a 120° circular distribution around the sliding bearing to cover the full-view angle of the cladding area, and multi-view synchronous acquisition is carried out to obtain a set of thermal imaging data; the set of thermal imaging data is preprocessed to obtain a calibrated set of thermal imaging data; a temperature field coupling parameter is constructed based on the calibrated thermal imaging data, and the calculation formula is: ; Among them, represents the calculation formula of the temperature field coupling parameter, represents the weight coefficient of the thermal stress term, represents the temperature gradient, represents the material elastic modulus, represents the thermal expansion coefficient, represents the local temperature difference, represents the material thermal conductivity; represents the weight coefficient of the phase change term, represents the temperature rise rate, represents the phase change time of the molten pool, represents the specific heat capacity of the material; represents the weight coefficient of the molten pool flow term, represents the viscosity of the molten metal, represents the flow velocity of the molten pool surface.

[0007] Furthermore, the specific steps to determine the infrared abnormal points include obtaining the data on the real-time monitoring distribution map, and if it exceeds the abnormal threshold, it is divided into an abnormal area; calculating the geometric center of the abnormal area to obtain the infrared abnormal points, and further obtaining a set of infrared abnormal points.

[0008] According to the stress distribution data and lubricating film pressure distribution data on the surface of the sliding bearing in the historical data, segmentation is carried out to obtain a first processing area and a second processing area; The specific steps to obtain the first processing area and the second processing area include: Obtaining the point cloud data of the surface of the sliding bearing by three-dimensional scanning or CAD model export; Obtaining the distribution of the stress on the surface of the sliding bearing under the working state in the historical data, and dividing it into a first stress area and a second stress area according to the stress gradient; Obtaining the lubricating film pressure distribution recorded by the oil film pressure sensor in the historical data, and dividing it into a first pressure area and a second pressure area according to the pressure gradient; If the position on the surface of the sliding bearing is in the first stress area and in the first pressure area, it is marked as the first processing area; otherwise, it is marked as the second processing area.

[0009] If the infrared anomaly point is in the first processing area, obtain the cladding data, element diffusion data, and heat conduction data of the infrared anomaly point and process them to obtain the first defect parameter; if the infrared anomaly point is in the second processing area, obtain the second defect parameter according to different weight coefficients. Further, the calculation formula for the first defect parameter is: ; Where, represents the first defect parameter, represents the weight coefficient of the cladding term, represents the weight coefficient of the element diffusion term, represents the weight coefficient of the thermodynamics term, represents the effective energy density, reflecting the laser energy input efficiency, represents the expected energy efficiency, The peak temperature of the molten pool, directly measured by infrared thermal imaging, represents the ideal molten pool temperature, represents the temperature rise rate, represents the temperature rise rate threshold, represents the standard deviation of element concentration. The concentration of elements on the surface of the sliding bearing can be obtained through EPMA / EDS line scanning, represents the maximum concentration deviation threshold, represents the interfacial equivalent thermal conductivity, represents the material thermal conductivity, represents the temperature gradient, represents the temperature gradient threshold.

[0010] Further, the calculation formula for the second defect parameter also includes the cladding term, the element diffusion term, and the thermodynamics term, and the weight coefficients are 、 and .

[0011] Construct a comprehensive defect parameter based on the first and second defect parameters and determine whether there are defects in the sliding bearing sample.

[0012] Further, the weight of the comprehensive defect parameter is obtained according to the ratio of the areas of the first processing area and the second processing area to the total area. The calculation formula for the comprehensive defect parameter is: ; Where, represents the comprehensive defect parameter of the sliding bearing, represents the area of the first processing area, represents the area of the second processing area, represents the weight strengthening factor of the first area. Dynamically amplify the weight of the first processing area through the area ratio to ensure that its importance dominates the comprehensive parameter, represents the number of infrared abnormal points in the first processing area, represents the first defect parameter of the th infrared abnormal point in the first processing area, represents the number of infrared abnormal points in the second processing area, represents the second defect parameter of the th infrared abnormal point in the second processing area.

[0013] Furthermore, mark the positions of the infrared abnormal points on all the produced sliding bearings in the established three-dimensional model of the sliding bearing, and use visualization software to display the positions where all the infrared abnormal points are located.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. First, through the data acquisition and processing of infrared thermal imaging from multiple angles, ensure the full coverage of the cladding area, and avoid information loss caused by a single perspective; introduce multiple key physical quantities such as temperature gradient and thermal stress, construct a temperature field coupling parameter, quantify the thermodynamic state of the cladding layer, and accurately evaluate the cladding quality; initially identify infrared abnormal points through an abnormal threshold, providing data support for the subsequent accurate evaluation of the process quality of the sliding bearing.

[0015] 2. Utilize the historical stress distribution and lubricating film pressure distribution data to distinguish the first processing area with high stress and requiring strengthening from the second processing area of the non-bearing area. This can not only conduct key detection on key areas, help optimize subsequent process parameters, and ultimately enhance the operating reliability and service life of the sliding bearing.

[0016] 3. By separately extracting and processing the corresponding cladding data, element diffusion data, and heat conduction data of the infrared abnormal points in different processing areas, calculate the first and second defect parameters according to the characteristics of the first and second processing areas respectively, enabling accurate defect positioning and quantitative evaluation of the surface material properties of the sliding bearing; adopt the area ratio of the region and the dynamic weight factor, so that the comprehensive defect parameter can reflect the characteristics of different processing areas, improving the adaptability and accuracy of the detection method for defects under complex working conditions. Description of the Drawings

[0017] Figure 1 is the flow chart of the method for visual inspection of the laser cladding quality of the sliding bearing based on infrared thermal imaging provided by the embodiment of the present invention; Figure 2 is the flow chart of constructing the first processing area and the second processing area provided by the embodiment of the present invention; Figure 3 is the flow chart of constructing the comprehensive defect parameter provided by the embodiment of the present invention. Detailed Embodiment

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1: When a sliding bearing production company performs cladding processing on the surface of a sliding bearing, in order to detect whether there are defects in the processing results and meet the production quality requirements, the visualization detection method for the laser cladding quality of sliding bearings based on infrared thermal imaging provided by the present invention is introduced. The method flow is as Figure 1 shown, and the specific implementation is as follows: Obtain infrared thermal imaging data at different angles and calculate the temperature field coupling parameters on the surface of the sliding bearing sample, and construct a real-time monitoring map according to the temperature field coupling parameters; determine the infrared abnormal points on the sliding bearing according to the temperature field coupling parameters and the abnormal threshold; Further, three high-frame-rate infrared thermal imagers are arranged in a 120° circular distribution around the sliding bearing to ensure accurate coverage of the full-view angle of the cladding area, and multi-view synchronous acquisition is performed to obtain a set of thermal imaging data; Further, a calibration plate is used for multi-camera joint calibration to establish a three-dimensional coordinate system mapping relationship, map the thermal imaging data collected by the three high-frame-rate infrared thermal imagers onto the three-dimensional coordinate system, and eliminate the perspective distortion through a series of operations such as calibrating the radiation response curves of each camera with a blackbody radiation source and compensating for the angle-dependent emissivity change, etc., to obtain a calibrated set of thermal imaging data; Further, obtain the data in the calibrated set of thermal imaging data, and construct the thermal stress term, phase change term, and molten pool flow term, and further obtain the temperature field coupling parameters. The calculation formula is: ; Among them, represents the calculation formula of the temperature field coupling parameter, represents the weight coefficient of the thermal stress term. The higher the thermal stress term, the higher the risk of cracks, represents the temperature gradient, represents the material elastic modulus, represents the thermal expansion coefficient, represents the local temperature difference, represents the material thermal conductivity; represents the weight coefficient of the phase change term. A low value is likely to cause non-equilibrium phase change, represents the temperature rise rate, represents the molten pool phase change time, represents the material specific heat capacity; represents the weight coefficient of the molten pool flow term. A low value is not conducive to uniform diffusion. represents the viscosity of the molten metal represents the surface flow velocity of the molten pool.

[0020] Furthermore, by measuring the stress-strain curve through the tensile experiment of the raw material, the slope of the elastic section is the elastic modulus of the material; the coefficient of thermal expansion can be obtained according to the expansion amount of the sliding bearing with temperature change; the thermal conductivity of the material can be obtained according to the thermal diffusion efficiency, density and specific heat capacity of the sliding bearing; the temperature rise rate can be obtained according to the time series data of the infrared thermal imager; the phase change time of the molten pool can be obtained according to differential scanning calorimetry; the viscosity of the molten metal can be obtained through the melting data of the raw material, and the surface flow velocity of the molten pool can be obtained by collecting and processing the data on the surface of the sliding bearing by a high-speed camera.

[0021] Furthermore, after calculating the temperature field coupling parameters of each point on the three-dimensional coordinate system, a real-time monitoring map of the surface of the sliding bearing on the three-dimensional coordinate system can be obtained.

[0022] Through the detection of multiple instruments, full coverage of the cladding area is achieved, ensuring that the temperature field information at each angle can be accurately collected, thereby greatly improving the comprehensiveness and accuracy of data collection; by constructing the temperature field coupling parameters through the thermal stress term, phase change term and molten pool flow term, the thermal state of the surface of the sliding bearing can be quantitatively evaluated. As a comprehensive index, the temperature field coupling parameters help to timely detect problems such as excessive temperature gradient and excessive thermal stress, so as to predict the risk of defects such as possible cracks, non-equilibrium phase change or uneven flow.

[0023] Furthermore, first, according to the data in the historical cladding operation, the abnormal threshold of the temperature coupling parameter is obtained. If the temperature coupling parameter is greater than the abnormal threshold, it indicates that there may be an abnormality at the point corresponding to the temperature coupling threshold; the essence of the real-time monitoring map is a surface function. Obtain the data of all points on the real-time distribution map, and the points exceeding the abnormal threshold form an abnormal area. Calculate the geometric center of the abnormal area and mark it as an infrared abnormal point. Further, an infrared abnormal point set can be obtained, and all possible abnormal positions on the surface of the sliding bearing after laser cladding processing can be marked. Table 1 shows the detection results of the temperature field coupling parameters of some infrared abnormal points.

[0024] Table 1. Temperature field coupling parameters of some infrared abnormal points ; When the temperature coupling parameter at a certain point on the real-time monitoring graph exceeds the abnormal threshold, the position where defects may exist can be accurately located; after regarding the real-time detection graph as a surface function, all points exceeding the abnormal threshold are extracted and an abnormal area is formed, and then the geometric center of the abnormal area is calculated and marked as the infrared abnormal point, which can achieve accurate and automated positioning of the abnormal area, not only improving the accuracy and real-time performance of abnormal detection, but also providing data support for subsequent repair, process adjustment and preventive maintenance, effectively improving the processing quality of the sliding bearing.

[0025] Segment according to the stress distribution data and lubricating film pressure distribution data on the surface of the sliding bearing in the historical data to obtain the first processing area and the second processing area. The process is as Figure 2 shown; Furthermore, the first processing area generally corresponds to positions such as high-stress bearing areas and areas that require strengthened coatings, and the second processing area generally corresponds to positions such as non-bearing areas. By segmenting the surface of the sliding bearing, different analysis methods can be realized for different positions; Furthermore, first, three-dimensional scanning or CAD model export is used to obtain the point cloud / mesh data of the surface of the sliding bearing; Furthermore, under the actual working state of the sliding bearing, stress value data at each position on the surface is obtained through methods such as strain gauges, embedded sensors or finite element simulations; then the original data is cleaned, smoothed and normalized to ensure that the data has high precision and consistency; then the stress difference between adjacent areas, that is, the stress gradient, is calculated to reflect the local stress concentration or uniform distribution situation, and then according to the change situation of the stress gradient, the surface of the sliding bearing is divided into two areas, namely the first stress area and the second stress area.

[0026] Furthermore, lubricating film pressure distribution data is collected under actual working conditions using an oil film pressure sensor, and the oil film pressure values at different positions on the surface of the sliding bearing are recorded; then the collected pressure data is denoised and standardized to ensure that the data can truly reflect the oil film state; then the pressure change rate between different positions, that is, the pressure gradient, is calculated to reveal the unevenness of the oil film pressure distribution, and then according to the distribution characteristics of the pressure gradient, the surface of the sliding bearing is divided into the first pressure area and the second pressure area.

[0027] Furthermore, if the position on the surface of the sliding bearing is in the first stress area and in the first pressure area, it is marked as the first processing area; otherwise, it is marked as the second processing area.

[0028] By finely analyzing the stress distribution and oil film pressure distribution on the surface of the sliding bearing in the historical data, the surface of the workpiece is divided into different processing areas, realizing the precise division of the surface of the sliding bearing, ensuring that key areas can be more strictly monitored and targeted process treatment can be carried out, providing data support for subsequent calculation of comprehensive defect parameters.

[0029] If the infrared anomaly point is in the first processing area, obtain the cladding data, element diffusion data, and heat conduction data of the infrared anomaly point and process them to obtain the first defect parameter; if the infrared anomaly point is in the second processing area, obtain the second defect parameter according to different weight coefficients. Furthermore, the calculation formula for the first defect parameter is: ; Wherein, represents the first defect parameter, represents the weight coefficient of the cladding term, represents the weight coefficient of the element diffusion term, represents the weight coefficient of the thermodynamics term, represents the effective energy density, reflecting the laser energy input efficiency, represents the expected energy efficiency, the peak temperature of the molten pool, directly measured by infrared thermal imaging, represents the ideal molten pool temperature, represents the temperature rise rate, represents the temperature rise rate threshold, represents the standard deviation of element concentration. The concentration of elements on the surface of the sliding bearing can be obtained through EPMA / EDS line scanning, represents the maximum concentration deviation threshold, represents the interface equivalent thermal conductivity, represents the material thermal conductivity, represents the temperature gradient, represents the temperature gradient threshold.

[0030] Furthermore, the effective energy density can be calculated based on the laser power and the surface absorptivity of the sliding bearing; the maximum concentration deviation threshold can be set according to the allowable deviation degree of the material; the interface equivalent thermal conductivity can be obtained through an infrared thermal imager and a finite element inversion algorithm.

[0031] By organically combining multiple key parameters such as the laser energy input efficiency, the peak temperature of the molten pool, the temperature rise rate, the element concentration distribution, the interface thermal conductivity, and the temperature gradient through weight coefficients, the synergistic effects of various influencing factors during the laser cladding process can be comprehensively reflected, ensuring a more comprehensive and accurate evaluation of the material properties and achieving accurate identification of surface defects of the sliding bearing.

[0032] Furthermore, the calculation formula for the second defect parameter also includes the cladding term, the element diffusion term, and the thermodynamics term, and their weight coefficients are respectively , and , and the calculation formula for the second defect parameter is: ; Among them, represents the second defect parameter, represents the weight coefficient of the cladding item, represents the effective energy density, reflecting the laser energy input efficiency, represents the desired energy efficiency, The peak temperature of the molten pool, directly measured by infrared thermal imaging, represents the ideal molten pool temperature, represents the temperature rise rate, represents the temperature rise rate threshold, represents the weight coefficient of the element diffusion term, represents the standard deviation of element concentration. The concentration of elements on the surface of the sliding bearing can be obtained through EPMA / EDS line scanning, represents the maximum concentration deviation threshold, represents the weight coefficient of the thermodynamic term, represents the interface equivalent thermal conductivity, represents the thermal conductivity of the base material, represents the temperature gradient, represents the temperature gradient threshold.

[0033] For the characteristics of the second processing area, different weight coefficients are used to reflect the actual working conditions, making the parameter calculation more in line with the material behavior under low load or non-bearing conditions in this area, so as to obtain a more accurate evaluation of material properties.

[0034] Construct a comprehensive defect parameter based on the first and second defect parameters and judge whether there are defects in the sliding bearing sample. The process of constructing the comprehensive defect parameter is as Figure 3 shown.

[0035] Furthermore, the weights are designed according to the area ratios of the first and second processing areas to the total area. The calculation formula of the comprehensive defect parameter is: ; Among them, represents the comprehensive defect parameter of the sliding bearing, represents the area of the first processing area, represents the area of the second processing area, represents the weight strengthening factor of the first area, dynamically amplifying the weight of the first processing area through the area ratio to ensure that its importance dominates the comprehensive parameter, represents the number of infrared abnormal points in the first processing area, represents the th first defect parameter of the represents the number of infrared abnormal points in the second processing area, represents the The second defect parameter of an infrared anomaly point.

[0036] Furthermore, if the comprehensive defect parameter of the sliding bearing exceeds the preset quality threshold, it indicates that the sliding bearing sample has defects. Table 2 shows the comprehensive defect parameters of some sliding bearing samples and the data on whether their quality is qualified. When the comprehensive defect parameter is between 0 and 8, it is a high-quality product; when it is between 8 and 10, it is a medium-quality product; and when it is greater than 10, it is a non-conforming product.

[0037] Table 2. Comprehensive defect parameters and their quality ; By assigning a higher weight to the first processing area, it is possible to more sensitively capture abnormal conditions in the key area and ensure that the high-risk area dominates the comprehensive evaluation; by integrating multiple key indicators such as the cladding item, element diffusion item, and thermodynamics item, the local material properties under different working conditions are uniformly quantified, enabling the overall metallurgical state to be comprehensively and objectively reflected, providing sufficient data basis for defect determination; by dynamically weighting the first and second defect parameters according to the area ratio and regional weight strengthening factor of different processing areas, the metallurgical state and potential defect risks on the surface of the sliding bearing are comprehensively reflected.

[0038] Mark the positions of the infrared anomaly points on all produced sliding bearings in the established three-dimensional model of the sliding bearing, and use visualization software to display the positions where all infrared anomaly points are located.

[0039] Calculate the temperature field coupling parameter using multi-angle infrared thermal imaging data and construct a real-time monitoring map to promptly detect temperature anomaly points and achieve early warning of defects; divide the surface of the sliding bearing into the first processing area (high stress, area requiring strengthening coating) and the second processing area (non-bearing area) using historical stress distribution and lubricating film pressure data, and then adopt different data processing strategies for different areas to ensure key monitoring of high-risk areas; for infrared anomaly points in different areas, obtain cladding data, element diffusion data, and heat conduction data respectively, construct the first and second defect parameters, and then fuse them into a comprehensive defect parameter to comprehensively reflect the surface metallurgical state and processing quality and achieve quantitative defect judgment.

[0040] Example 2: When a company was repairing the surface of a sliding bearing, in order to detect the quality of the surface repair of the sliding bearing, the visualization detection method for laser cladding quality of the sliding bearing based on infrared thermal imaging provided by the present invention was introduced. The specific implementation method is as follows: Obtain data of infrared thermal imaging from different angles and calculate the temperature field coupling parameter on the surface of the sliding bearing sample, and construct a real-time monitoring map according to the temperature field coupling parameter; determine the infrared anomaly points on the sliding bearing according to the temperature field coupling parameter and the anomaly threshold; Further, three high-frame-rate infrared thermal imagers are arranged in a 120° circular distribution around the sliding bearing to cover the full-view perspective of the cladding area, and multi-view synchronous acquisition is performed to obtain a set of thermal imaging data; the set of thermal imaging data is preprocessed to obtain a calibrated set of thermal imaging data; a temperature field coupling parameter is constructed based on the calibrated thermal imaging data, and the calculation formula is: ; where, represents the calculation formula of the temperature field coupling parameter, represents the weight coefficient of the thermal stress term, represents the temperature gradient, represents the material elastic modulus, represents the coefficient of thermal expansion, represents the local temperature difference, represents the material thermal conductivity; represents the weight coefficient of the phase change term, represents the temperature rise rate, represents the melting pool phase change time, represents the specific heat capacity of the material; represents the weight coefficient of the melting pool flow term, represents the viscosity of the molten metal, represents the flow velocity on the surface of the melting pool.

[0041] Further, the specific steps for determining the infrared anomaly points include obtaining the data on the real-time monitoring distribution map, and dividing it into an abnormal area if it exceeds the anomaly threshold; calculating the geometric center of the abnormal area to obtain the infrared anomaly points, and further obtaining a set of infrared anomaly points.

[0042] Segment according to the stress distribution data and lubricating film pressure distribution data on the surface of the sliding bearing in the historical data to obtain a first processing area and a second processing area; Further, the specific steps for obtaining the first processing area and the second processing area include: Obtain the point cloud / mesh data on the surface of the sliding bearing by 3D scanning or CAD model export; Obtain the distribution of the stress on the surface of the sliding bearing under the working state in the historical data, and divide it into a first stress area and a second stress area according to the stress gradient; Obtain the lubricating film pressure distribution recorded by the oil film pressure sensor in the historical data, and divide it into a first pressure area and a second pressure area according to the pressure gradient; If the surface position of the sliding bearing is in the first stress area and in the first pressure area, it is marked as the first processing area; otherwise, it is marked as the second processing area.

[0043] If the infrared anomaly point is in the first processing area, obtain the cladding data, element diffusion data, and heat conduction data of the infrared anomaly point and process them to obtain the first defect parameter; if the infrared anomaly point is in the second processing area, obtain the second defect parameter according to different weight coefficients. Further, the calculation formula for the first defect parameter is: ; Where, represents the first defect parameter, represents the weight coefficient of the cladding term, represents the weight coefficient of the element diffusion term, represents the weight coefficient of the thermodynamics term, represents the effective energy density, reflecting the laser energy input efficiency, represents the desired energy efficiency, The peak temperature of the molten pool, directly measured by infrared thermal imaging, represents the ideal molten pool temperature, represents the temperature rise rate, represents the temperature rise rate threshold, represents the standard deviation of element concentration. The concentration of elements on the surface of the sliding bearing can be obtained through EPMA / EDS line scanning, represents the maximum concentration deviation threshold, represents the interface equivalent thermal conductivity, represents the thermal conductivity of the base material, represents the temperature gradient, represents the temperature gradient threshold.

[0044] Further, the calculation formula for the second defect parameter also includes the cladding term, element diffusion term, and thermodynamics term, and the weight coefficients are , and .

[0045] Construct a comprehensive defect parameter based on the first and second defect parameters and determine whether there are defects in the sliding bearing sample.

[0046] Further, the weights are obtained according to the ratio of the areas of the first processing area and the second processing area to the total area. The calculation formula for the comprehensive defect parameter is: ; Where, represents the comprehensive defect parameter of the sliding bearing, represents the area of the first processing area, represents the area of the second processing area, represents the weight strengthening factor of the first area. Dynamically amplify the weight of the first processing area through the area ratio to ensure that its importance dominates the comprehensive parameter, represents the number of infrared abnormal points in the first processing area, represents the first defect parameter of the th infrared abnormal point in the first processing area, represents the number of infrared abnormal points in the second processing area, represents the second defect parameter of the th infrared abnormal point in the second processing area. The detection results of some sliding bearing samples are shown in Table 3. When the comprehensive defect parameter is between 0 and 8, it is a high-quality product; when it is between 8 and 10, it is a medium-quality product; and when it is greater than 10, it is a non-conforming product.

[0047] Table 3. Detection Results of Some Sliding Bearing Samples ; Furthermore, mark the positions of the infrared abnormal points on all the produced sliding bearings in the established three-dimensional model of the sliding bearing, and use visualization software to display the positions where all the infrared abnormal points are located.

[0048] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Visual detection method of laser cladding quality of sliding bearing based on infrared thermal imaging, characterized in that: include: Acquire infrared thermal imaging data at different angles and calculate the temperature field coupling parameters on the surface of the sliding bearing sample, and construct a real-time monitoring map based on the temperature field coupling parameters; Determine the infrared abnormal points on the sliding bearing according to the temperature field coupling parameters and the abnormal threshold value; Segmenting the sliding bearing surface according to historical data to obtain a first processing area and a second processing area; If the infrared abnormal point is in the first processing area, the cladding data, element diffusion data and heat conduction data of the infrared abnormal point are obtained and processed to obtain the first defect parameter; if the infrared abnormal point is in the second processing area, the second defect parameter is obtained according to different weight coefficients; A comprehensive defect parameter is constructed based on the first and second defect parameters to determine whether the sliding bearing sample has a defect.

2. The method for visual detection of sliding bearing laser cladding quality based on infrared thermal imaging according to claim 1 is characterized in that: Three high-frame-rate infrared thermal imagers are distributed in a 120° ring around the sliding bearing to perform multi-view synchronous acquisition to obtain a thermal imaging data set. The thermal imaging data set is preprocessed to obtain a calibrated thermal imaging data set. The temperature field coupling parameters are constructed based on the calibrated thermal imaging data.

3. The method for visual detection of sliding bearing laser cladding quality based on infrared thermal imaging according to claim 1 is characterized in that: The specific steps of determining infrared anomaly points include obtaining data on the real-time monitoring distribution map, and dividing the data into abnormal areas if the data exceeds the abnormal threshold; calculating the geometric center of the abnormal area to obtain the infrared anomaly point, and further obtaining the infrared anomaly point set.

4. The method for visual detection of sliding bearing laser cladding quality based on infrared thermal imaging according to claim 1 is characterized in that: The specific steps of obtaining the first processing area and the second processing area include: 3D scanning or CAD model to export point cloud data of the sliding bearing surface; Obtain the distribution of stress on the surface of the sliding bearing under the working state of the sliding bearing in historical data, and divide the first stress area and the second stress area according to the stress gradient; Obtaining the lubricating film pressure distribution recorded by the oil film pressure sensor in the historical data, and dividing the first pressure area and the second pressure area according to the pressure gradient; If the surface position of the sliding bearing is in the first stress area and in the first pressure area, it is marked as the first processing area; otherwise, it is marked as the second processing area.

5. The method for visual detection of sliding bearing laser cladding quality based on infrared thermal imaging according to claim 1 is characterized in that: The calculation formula of the first defect parameter is: ； in, represents the first defect parameter, represents the weight coefficient of the cladding term, represents the weight coefficient of the element diffusion term, represents the weight coefficient of the thermodynamic term, is the effective energy density, represents the expected energy efficiency, Peak melt pool temperature, represents the ideal molten pool temperature, represents the rate of temperature rise, Indicates the temperature rise rate threshold, represents the standard deviation of element concentration, represents the maximum concentration deviation threshold, represents the interface equivalent thermal conductivity, represents the thermal conductivity of the material, represents the temperature gradient, Indicates the temperature gradient threshold.

6. The method for visual detection of sliding bearing laser cladding quality based on infrared thermal imaging according to claim 1 is characterized in that: The calculation formula of the second defect parameter also includes cladding term, element diffusion term and thermodynamic term, and the weight coefficients are , and .

7. The method for visual detection of sliding bearing laser cladding quality based on infrared thermal imaging according to claim 1 is characterized in that: The weight of the comprehensive defect parameter is obtained according to the ratio of the area of ​​the first processing area to the area of ​​the second processing area to the total area. The calculation formula of the comprehensive defect parameter is: ； in, Represents the comprehensive defect parameters of the sliding bearing, represents the area of ​​the first processing area, represents the area of ​​the second processing area, represents the first region weight enhancement factor, Indicates the number of infrared abnormal points in the first processing area, Indicates the first processing area The first defect parameter of an infrared abnormal point, Indicates the number of infrared abnormal points in the second processing area, Indicates the second processing area The second defect parameter of an infrared abnormal point.

8. The method for visual detection of sliding bearing laser cladding quality based on infrared thermal imaging according to claim 1 is characterized in that: The locations of infrared abnormal points on all produced sliding bearings are marked in the established three-dimensional model of sliding bearings, and the locations of all infrared abnormal points are displayed using visualization software.

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