Temperature gradient calculation method and system for surface temperature field of laser cladding molten pool

Abstract

The invention provides a temperature gradient calculation method and system for a surface temperature field of a laser cladding molten pool, and relates to the technical field of laser cladding molten pool monitoring, and the calculation method comprises the steps: monitoring a transient temperature field of a molten pool at a point B at a certain moment in a laser cladding process, and calculating a transient temperature gradient G1 of the point B; monitoring the temperature field change of the molten pool at the point B from the generation moment t0 to the disappearance moment t2 in the laser single-channel cladding process, and calculating the average temperature gradient G2 of the point B; and monitoring the temperature field change of the B-point molten pool in the multi-path molten pool heating and cooling process in the laser single-layer cladding process, and calculating the total average temperature gradient G3 of the B point. According to the method, the cooling rate and the solidification process of a molten pool are effectively predicted by calculating the transient temperature gradient (G1, G2 and G3) of the point in the single-pass cladding process, the continuous time of the single-pass cladding process and the continuous time of the single-layer cladding process, so that the parameter optimization process of the laser cladding process is guided, and the forming quality of a cladding layer is effectively controlled.

Description

Technical Field

[0001] This invention relates to the field of laser cladding molten pool temperature field monitoring technology, and in particular to a method and system for calculating the temperature gradient of the surface temperature field of a laser cladding molten pool. Background Technology

[0002] Laser cladding technology is an advanced surface modification process that uses a high-energy laser beam to melt and rapidly solidify cladding materials onto a substrate surface. It is widely used in aerospace, automotive, and machinery manufacturing industries to improve the wear resistance, corrosion resistance, and fatigue resistance of parts.

[0003] During laser cladding, the temperature gradient of the molten pool directly affects the cooling rate, microstructure, defect formation, and bonding strength between the cladding layer and the substrate, and is a key factor affecting the quality of the cladding layer.

[0004] Currently, the measurement and calculation of the molten pool temperature gradient are relatively rare in the field of laser cladding technology. Traditional contact measurement methods such as thermocouples are difficult to obtain the temperature distribution on the molten pool surface in real time and accurately, and are easily affected by high-temperature environments and the cladding process. Although thermal imagers can obtain the temperature distribution on the molten pool surface, the analysis of the molten pool temperature field characteristics is limited to the cooling rate and peak temperature, making it difficult to intuitively reflect the changes in the molten pool temperature gradient under the cumulative thermal effects of multiple layers and channels in laser cladding. Finally, most existing temperature gradient calculation methods rely on simplification assumptions and numerical simulations, which cannot fully reflect the complex heat conduction phenomena in the actual cladding process, resulting in significant deviations between the calculated results and reality. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for calculating the temperature gradient of the surface temperature field of a laser cladding molten pool, in order to solve the technical problems in the prior art where the temperature gradient measurement method of a laser cladding molten pool is difficult to obtain the surface temperature distribution of the molten pool in real time and has a large measurement deviation. To address the aforementioned problems, the first objective of this invention is to provide a method for calculating the temperature gradient of the surface temperature field of a laser cladding pool, the calculation method comprising: Step S1: Monitor the transient temperature field of the molten pool at point B at a certain moment during the laser cladding process, and calculate the transient temperature gradient G1 at point B; Step S2: Monitor the temperature field change of the molten pool at point B from the time of generation t0 to the time of disappearance t2 during the single-pass laser cladding process, and calculate the average temperature gradient G2 at point B; Step S3: Monitor the temperature field change of the molten pool at point B during the multi-stage heating and cooling process of the laser monolayer cladding process, and calculate the average temperature gradient G3 at point B.

[0006] Furthermore, in step S1, monitoring the transient temperature field of the molten pool at point B at a certain moment during the laser cladding process and calculating the transient temperature gradient G1 at point B specifically includes the following steps: Step S 11 Find the highest temperature point of the molten pool at the midpoint B of the nth pass. The temperature at this point is the transient peak temperature of the molten pool at point B. Set the highest color grade temperature as the transient peak temperature of point B. Step S 12 Temperature color gradation processing was performed on the experimental data to obtain a temperature field cloud map; Step S 13 The central peak temperature point E and the edge point F of the molten pool along the overlapping direction are selected as data sampling points.

[0007] Furthermore, in step S2, the monitoring of the single-pass laser cladding process specifically includes the following steps: Step S 21 When the laser scans the nth channel, the molten pool at point B is generated first, then reaches its maximum size, then gradually decreases until it disappears. Step S 22 The temperature gradient within the molten pool at point B during single-pass laser cladding is calculated using the temperature change diagram of the line profile.

[0008] Furthermore, in step S 22 In this process, the temperature gradient within the molten pool at point B during laser single-pass cladding is calculated using a line profile temperature change diagram. Specifically, this includes: Step S 221 : Calculate the temperature and coordinates of 20 points on a straight line on the online profile, set the starting point to the peak temperature point E, and the ending point to the edge temperature point F; Step S 222 : Calculate the distance d between the peak temperature point E of the molten pool and the edge temperature point F of the molten pool; Step S 223 The 20 points are represented as Avg1 to Avg20, and the temperature changes of Avg1 to Avg20 are observed through the line profile temperature change graph.

[0009] Furthermore, in step S 223 The observation of temperature changes of Avg1 to Avg20 through line profile temperature change graphs specifically includes: Step S 2231 Observe the temperature change graph of the described line profile, at T e -T f The time corresponding to =0 is denoted as t0, T e The time when the peak temperature is reached is recorded as time t1, and the second time is T. e -T f=0 is denoted as time t2; Step S 2232 Extract the temperature and coordinates of Avg1 to Avg20 within the time interval t0 to t2, and calculate the average temperature gradient of point B within the time interval t0 to t2.

[0010] Furthermore, in step S2, calculating the average temperature gradient G2 at point B specifically includes the following steps: Step S 23 The data recorded during the time period t0 to t2 is divided into x groups according to time, with each group containing 20 points of temperature and coordinates. When the peak temperature is reached, the highest temperature point is at T. e Point; when at the lowest temperature, the lowest temperature point is at point F; Step S 24 For each set of data, extract the maximum and minimum temperature values ​​from the 20 temperature points in that set, and calculate the difference between them. Step S 25 The actual temperature gradient of the data set is obtained by calculating the ratio of the temperature difference to the corresponding distance and then multiplying it by the scaling factor k. Step S 26 Calculate the temperature gradient of x groups within the time interval t0 to t2, and average the temperature gradient of these x groups to obtain the average temperature gradient G2 of point B within the time interval t0 to t2.

[0011] Furthermore, in step S3, the specific method for monitoring the temperature field change of the molten pool at point B during the multi-stage molten pool heating and cooling process in the laser monolayer cladding process is as follows: Based on step S2, the number of monitoring channels is increased from n to m, and the method for recording the average temperature gradient of each channel is the same as the method for calculating the average temperature gradient of a single channel described in step S2.

[0012] Furthermore, in step S3, the specific method for calculating the average temperature gradient G3 at point B is as follows: Step S 31 Record time t0 and time t2 for each pass to obtain the temperature change data of the linear profile for each pass during different time intervals from t0 to t2. Step S 32 Calculate the average temperature gradient for each pass and obtain the total average temperature gradient G3.

[0013] The second objective of this invention is to provide a laser cladding molten pool surface temperature field monitoring system, applied to the aforementioned laser cladding molten pool surface temperature gradient calculation method. The system includes a laser cladding experimental platform, an infrared thermal imager, and a data acquisition and analysis system, wherein: The experimental material of the laser cladding experimental platform is CoCrNi, and the substrate is made of 45 steel. The infrared thermal imager is used to record the spatial and spectral information of the molten pool and transmit the thermal image data to the data acquisition and analysis system. The data acquisition and analysis system is used to process the thermal image data recorded by the mid-infrared thermal imager.

[0014] Furthermore, the laser cladding experimental platform adopts a coaxial synchronous automatic powder feeding system, using nitrogen and argon as protective gases for powder feeding; the process parameters of the laser cladding experimental platform include a laser power of 400-1000W, a scanning speed of 400-1000mm / s, a scanning length of 8mm, and a scanning interval of 0.8mm.

[0015] Compared with the prior art, the present invention has significant advantages and beneficial effects, specifically reflected in the following aspects: 1. This invention provides a method for calculating the temperature gradient of the surface temperature field of a laser cladding molten pool. The calculation method includes: monitoring the transient temperature field of the molten pool at point B during the laser cladding process at a certain moment, and calculating the transient temperature gradient G1 at point B. In this step, by focusing on the transient temperature field and temperature gradient at a certain moment, the thermal state of the molten pool at a specific instant can be captured, which helps to understand the rapid dynamic changes of the molten pool, such as the transient thermal behavior during the formation and expansion of the molten pool at the moment of laser action, and provides data support for studying the transient physical phenomena of the molten pool; monitoring the temperature field change of the molten pool at point B from the generation time t0 to the disappearance time t2 during the single-pass laser cladding process, and calculating the average temperature gradient G2 at point B. In this step, by monitoring the temperature field change throughout the entire single-pass cladding process and calculating the average temperature gradient G2, the heat transfer and temperature evolution law of the molten pool during the single-pass cladding process can be comprehensively evaluated. This helps analyze the thermal cycling process of single-pass cladding, including the temperature change characteristics of the molten pool during heating, holding, and cooling stages, and the impact of these characteristics on the quality of the cladding layer. It monitors the temperature field changes of the molten pool at point B during the multi-pass heating and cooling process of laser single-layer cladding, calculating the total average temperature gradient G3 at point B. This step considers the interaction between the heating and cooling of multiple molten pools, and the calculated total average temperature gradient G3 reflects the overall thermal behavior of the molten pool at point B during single-layer cladding. In multi-pass cladding, the cladding layer of the previous pass has a thermal impact on subsequent passes. This step can reveal the temperature field change patterns under this multi-pass thermal effect, which is of great significance for understanding the interaction between cladding layers, the heat accumulation effect, and the formation mechanism of overall cladding quality. By calculating the temperature gradients at different levels (G1, G2, G3), more comprehensive and accurate information on the thermal state of the molten pool can be obtained. Correlating these temperature gradient indicators with the quality characteristics of the cladding layer can establish a more accurate quality assessment model, providing a more reliable basis for real-time monitoring and control of cladding quality.

[0016] 2. By constructing the correlation between transient and continuous temperature changes and the temperature gradient of the laser cladding process, this invention can realize the calculation of the molten pool temperature gradient based on thermal imaging data, avoiding the inaccuracy problem that may be caused by numerical simulation, and significantly improving the professionalism of thermal imagers in the analysis of the molten pool temperature field in the application of laser manufacturing.

[0017] 3. This invention uses a laser cladding molten pool surface temperature field monitoring system to perform real-time, non-destructive detection of the molten pool surface temperature field without contact with the molten pool. It can also quickly process and analyze temperature field data, avoiding interference and measurement errors to the high-temperature molten pool caused by traditional contact measurement methods. Attached Figure Description

[0018] Figure 1This is a flowchart illustrating the steps of the method for calculating the temperature gradient of the surface temperature field of the laser cladding pool in an embodiment of the present invention. Figure 2 This is a schematic diagram of the specific process of step S1 in an embodiment of the present invention; Figure 3 This is a schematic diagram of the specific process of step S2 in an embodiment of the present invention; Figure 4 This is a schematic diagram of the specific process of step S3 in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of the laser cladding weld pool surface temperature field monitoring system in an embodiment of the present invention; Figure 6 This is a schematic diagram of multi-channel laser cladding scanning in an embodiment of the present invention; Figure 7 This is a schematic diagram of the transient molten pool temperature gradient measurement at point B in an embodiment of the present invention; Figure 8 This is a schematic diagram of the molten pool line outline marking at point B in an embodiment of the present invention; Figure 9 This is a diagram showing the relationship between the molten pool morphology at point B and the temperature change of the line profile in an embodiment of the present invention. Figure 10 This is a thermal image of the moment when the transient temperature gradient at point B is at its highest in different channels according to an embodiment of the present invention.

[0019] Explanation of reference numerals in the attached figures: 1-Laser cladding weld pool surface temperature field monitoring system; 11-Laser cladding experimental platform; 12-Infrared thermal imager; 13-Acquisition and analysis system. Detailed Implementation

[0020] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0021] Please see Figure 1 As shown, this embodiment of the invention provides a method for calculating the temperature gradient of the surface temperature field of a laser cladding pool. The calculation method includes: Step S1: Monitor the transient temperature field of the molten pool at point B at a certain moment during the laser cladding process, and calculate the transient temperature gradient G1 at point B; In this step, by focusing on the transient temperature field and temperature gradient at a certain moment, the thermal state of the molten pool at a specific instant can be captured, which helps to understand the rapid dynamic changes of the molten pool, such as the transient thermal behavior during the formation and expansion of the molten pool at the moment of laser action, and provides data support for the study of transient physical phenomena of the molten pool.

[0022] Step S2: Monitor the temperature field change of the molten pool at point B from the time of generation t0 to the time of disappearance t2 during the single-pass laser cladding process, and calculate the average temperature gradient G2 at point B; In this step, by monitoring the temperature field changes throughout the entire single-pass cladding process and calculating the average temperature gradient G2, the heat transfer and temperature evolution of the molten pool during the single-pass cladding process can be comprehensively evaluated. This helps to analyze the thermal cycling process of single-pass cladding, including the temperature change characteristics of the molten pool during the heating, holding, and cooling stages, and the impact of these characteristics on the quality of the cladding layer.

[0023] Step S3: Monitor the temperature field change of the molten pool at point B during the multi-stage heating and cooling process of the laser single-layer cladding process, and calculate the total average temperature gradient G3 at point B.

[0024] This step considers the interaction between heating and cooling of the molten pool in multiple passes. The calculated total average temperature gradient G3 reflects the overall thermal behavior of the molten pool at point B during single-layer cladding. In multi-pass cladding, the cladding layer of the previous pass has a thermal influence on subsequent passes. This step can reveal the temperature field variation law under this multi-pass thermal effect, which is of great significance for understanding the interaction between cladding layers, the heat accumulation effect, and the formation mechanism of overall cladding quality.

[0025] Therefore, by calculating temperature gradients at different levels (G1, G2, G3), more comprehensive and accurate information on the thermal state of the cladding pool can be obtained. Correlating these temperature gradient indicators with the quality characteristics of the cladding layer allows for the establishment of a more accurate quality assessment model, providing a more reliable basis for real-time monitoring and control of cladding quality.

[0026] For more details, please refer to Figure 2 As shown, in one embodiment of the present invention, step S1, which involves monitoring the transient temperature field of the molten pool at point B during the laser cladding process and calculating the transient temperature gradient G1 at point B, specifically includes the following steps: Step S 11 : Obtain the transient peak temperature of the molten pool at the midpoint B of the nth pass, and set the highest color temperature to the transient peak temperature of the molten pool at point B. In this step, during the monitoring of the laser cladding process, the focus is first placed on the molten pool at the midpoint B of the nth pass. Infrared thermal imagers capture real-time temperature change data in this area. The data acquisition and analysis system 13 identifies the point in the molten pool where the temperature reaches its highest point, i.e., the highest temperature point of the molten pool at the midpoint B of the nth pass. This highest temperature is determined as the transient peak temperature of the molten pool at point B, and the highest color temperature is correspondingly set to this transient peak temperature in the temperature field data so that the peak temperature point can be visually represented in the subsequent temperature field cloud map.

[0027] This step allows for the accurate location of the highest temperature point of the molten pool at the midpoint B of the nth pass, and the determination of the transient peak temperature at the highest temperature point of the molten pool at the midpoint B. This process ensures the precise location and temperature measurement of the hottest spot in the molten pool, which is crucial for understanding the heat concentration area of ​​the molten pool and the potential range of high-temperature influence. It also helps in subsequent analysis of the solidification behavior of the molten pool, the formation mechanism of the cladding layer, and the possible location of defects.

[0028] Step S 12 Temperature color gradation processing was performed on the experimental data to obtain a temperature field cloud map; In this step, the raw thermal image data acquired by the infrared thermal imager 12 is input into the data acquisition and analysis system 13. The temperature color gradation processing algorithm is used in the data acquisition and analysis system 13 to map different temperature values ​​to corresponding colors in order to generate an intuitive temperature field cloud map.

[0029] For example, lower temperatures can be represented by blue tones, medium temperatures by yellow tones, and higher temperatures by red tones, and the color gradients can clearly reflect the gradual change in temperature.

[0030] Finally, by processing the color levels of the data for the entire molten pool region, a cloud map is formed that can intuitively display the temperature distribution in different areas of the molten pool, making it easier for researchers to observe and analyze the temperature field characteristics of the molten pool.

[0031] This step transforms complex temperature data into an intuitive and easy-to-understand temperature field contour map. Compared to the original numerical temperature data, the contour map more clearly displays the overall temperature distribution characteristics of the molten pool, including temperature variation trends, the distribution range of different temperature regions, and the shape and outline of the molten pool. This helps researchers quickly identify abnormal temperature regions and areas with drastic temperature gradient changes within the molten pool, providing a visual basis for further in-depth analysis and process optimization.

[0032] Step S 13 On the temperature field cloud map after color gradation processing, the central peak temperature point E and the edge of the molten pool along the overlapping direction F are selected as data sampling points.

[0033] In this step, on the temperature field cloud map after color gradation processing, the peak temperature point E is determined as the center peak temperature point, centered at the highest temperature point of the molten pool at the midpoint B of the nth pass. Along the overlap direction, a representative point is selected at the edge of the molten pool as the molten pool edge point F. This point F should be located at the edge of the overlap area between the current molten pass and the previous cladding layer or substrate. The selection of these two data sampling points (points E and F) aims to provide key temperature data points for subsequent temperature gradient calculation. The transient temperature gradient G1 at point B is calculated using the temperature difference between these two points and their spatial relationship.

[0034] This step, by selecting the central peak temperature point E and the edge of the molten pool point F as data sampling points, provides a crucial data foundation for calculating the transient temperature gradient G1 at point B. For example, by measuring the temperature difference between points E and F and their spatial distance along the overlap direction, a relatively accurate local temperature gradient value can be obtained using the temperature gradient calculation formula (temperature difference divided by distance). This key-point-based temperature gradient calculation method can reflect the rate of temperature change of the molten pool in a specific direction (overlap direction), which is of great significance for studying the heat conduction process of the molten pool, the advancement speed of the solidification front, and the bonding quality between the cladding layer and the previous cladding layer or substrate.

[0035] Therefore, by monitoring and calculating the transient temperature field and temperature gradient at point B, the quality characteristics of the cladding layer can be indirectly assessed. For example, an abnormal temperature gradient may indicate quality problems such as internal stress concentration and crack initiation within the cladding layer. Based on these monitoring results, researchers can adjust laser cladding process parameters in a timely manner, such as laser power, scanning speed, and powder feed rate, to optimize the temperature field distribution and temperature gradient of the molten pool, thereby improving the quality and performance of the cladding layer, reducing defect generation, and achieving effective control and quality assurance of the laser cladding process.

[0036] like Figure 6 The diagram shows a multi-channel laser cladding scanning embodiment of the present invention. The sampling points for the molten pool temperature are set along the laser cladding overlap direction. An S-shaped reciprocating scanning path is used, and a total of eight channels are performed. The temperature field distribution at the midpoint B of the fourth channel is also collected.

[0037] like Figure 7 The diagram shows a schematic of the transient molten pool temperature gradient measurement at point B in this embodiment of the invention. First, the highest temperature point of the molten pool at the midpoint B of the fourth pass is found. This temperature is the transient peak temperature of the molten pool at point B. The highest color-gradient temperature is set as the transient peak temperature at point B. Then, the experimental data is processed using temperature color-gradient analysis to obtain a temperature field cloud map. The central peak temperature point E and the edge of the molten pool along the overlap direction point F are selected as data sampling points, where d is the distance between points E and F. The temperature represented by point E is expressed in T. e This indicates that the temperature represented by point F is expressed in T. f The transient temperature gradient G1 of the molten pool at point B can be obtained using the following formula.

[0038]

[0039] For more details, please refer to Figure 7 , Figure 8 As shown, in one embodiment of the present invention, step S2, monitoring the laser single-pass cladding process specifically includes the following steps: Step S 21 When the laser scans the nth pass, calculate the line profile length of the molten pool at point B; Step S 22 The temperature gradient within the molten pool at point B during single-pass laser cladding is calculated using the temperature change diagram of the line profile.

[0040] like Figure 8 The figure shown is a schematic diagram of the B-point molten pool outline marking in an example of the present invention. In this schematic diagram of the B-point molten pool outline marking, the temperature and coordinates of 20 points on a straight line can be counted. These 20 points are represented as Avg1-Avg20 respectively. In this schematic diagram of the B-point molten pool outline marking, all points have the same horizontal coordinate and the vertical coordinate increases sequentially. The starting point is set at the peak temperature point E, and the ending point is set at the molten pool edge temperature point F. The length of the outline is the distance d between the peak temperature point E and the molten pool edge temperature point F of the B-point molten pool. like Figure 9 The diagram shows the relationship between the molten pool morphology at point B and the temperature change of the line profile in an example of this invention. During the fourth laser scan, the molten pool at point B is generated first, reaching its maximum size when the laser scan reaches point B, and then gradually decreases until it disappears. The generation and disappearance of the molten pool represent the peak molten pool temperature T. e With the edge temperature T of the molten pool f From almost the same temperature to the largest difference and then back to almost the same temperature.

[0041] For more details, please refer to Figure 7 , Figure 8 As shown, in one embodiment of the present invention, in step S 22 In this process, the temperature gradient within the molten pool at point B during laser single-pass cladding is calculated using a line profile temperature change diagram. Specifically, this includes: Step S 221 : Calculate the temperature and coordinates of 20 points on a straight line on the online profile, set the starting point to the peak temperature point E, and the ending point to the edge temperature point F; Step S 222 : Calculate the distance d between the peak temperature point E of the molten pool and the edge temperature point F of the molten pool; Step S 223 The 20 points are represented as Avg1 to Avg20, and the temperature changes of Avg1 to Avg20 are observed through the line profile temperature change graph.

[0042] For more details, please refer to Figure 9 , Figure 10 As shown, in one embodiment of the present invention, in step S 223 The observation of temperature changes of Avg1 to Avg20 through line profile temperature change graphs specifically includes: Step S2231 Observe the temperature change graph of the described line profile, at T e -T f The time corresponding to =0 is denoted as t0, T e The time when the peak temperature is reached is recorded as time t1, and the second time is T. e -T f =0 is denoted as time t2; Step S 2232 Extract the temperature and coordinates of Avg1 to Avg20 within the time interval t0 to t2, and calculate the average temperature gradient of point B within the time interval t0 to t2.

[0043] To obtain the time interval from the formation to the disappearance of the molten pool at point B, and the time T within this interval... e With T f The changes need to be observed through a "line profile temperature change graph," at T e With T f Recording begins when the values ​​are almost identical, then start to show differences; let's denote this time as t0. e The time when the peak temperature is reached is recorded as time t1,T e With T f When the time is almost identical again, record it as time t2. Extract the temperature and coordinates of Avg1 to Avg20 within the time period from t0 to t2, and calculate the average temperature gradient G2 of point B within this time period.

[0044] For more details, please refer to Figure 9 As shown, in one embodiment of the present invention, step S2, calculating the average temperature gradient G2 at point B, specifically includes the following steps: Step S 23 The data recorded during the time period t0 to t2 is divided into x groups according to time. Each group has 20 points with temperature and coordinates. When the temperature is at its peak, the highest temperature point is at point E; when the temperature is at its lowest, the lowest temperature point is at point F. Step S 24 For each set of data, extract the maximum and minimum temperature values ​​from the 20 temperature points in that set, and calculate the difference between them. Step S 25 The actual temperature gradient of the data set is obtained by calculating the ratio of the temperature difference to the corresponding distance and then multiplying it by the scaling factor k. Step S 26 Calculate the temperature gradient of x groups within the time interval t0 to t2, and average the temperature gradient of these x groups to obtain the average temperature gradient G2 of point B within the time interval t0 to t2.

[0045] Specifically, in this embodiment, such as Figure 9The following is a detailed implementation process for calculating the temperature gradient at point B during the time interval t0 to t2 in an embodiment of the present invention: In this experiment, the data recorded during the time period t0 to t2 were divided into 37 groups according to time, with each group containing the temperature and coordinates of 20 points. At the peak temperature, the highest temperature point was at point E, and the lowest temperature point was at point F. However, at other times, the highest and lowest temperature points did not necessarily correspond to points E and F. Furthermore, since the shape of the molten pool changes, d also changes accordingly. Therefore, in order to obtain a more accurate average temperature gradient, step S is required. 23 -S 26 and step S 3。

[0046] For each set of data, first extract the maximum and minimum temperature values ​​from the 20 temperature points in that set and calculate the difference between them; then calculate the distance between the coordinates corresponding to these two temperature points. Since the coordinate distance and the actual distance need to be converted, a scaling factor k (k = coordinate distance / actual distance) is set. In this experiment, the scaling factor k is 56.6. By calculating the ratio of the temperature difference to the corresponding distance and then multiplying it by 56.6, the average actual temperature gradient G2 of that set of data can be obtained.

[0047] More specifically, in one embodiment of the present invention, in step S3, the specific method for monitoring the temperature field change of the molten pool at point B during the multi-stage molten pool heating and cooling process in the laser monolayer cladding process is as follows: Based on step S2, the number of monitoring channels is increased from n to m, and the method for recording the average temperature gradient of each channel is the same as the method for calculating the average temperature gradient of a single channel described in step S2.

[0048] Specifically, in this embodiment, according to step S 23 -S 26 The preferred processing method is n=4, m=8. Calculate the temperature gradient of 37 groups within the time period t0 to t2, and average the temperature gradient of these 37 groups. The result is the average temperature gradient G2 of point B within the time period t0 to t2.

[0049] It should be noted that, based on the above processing method, manually calculating the average temperature gradient is quite troublesome. To facilitate the calculation, the experimental data in Excel is imported into MATLAB software, the code is compiled and the calculation is performed, which can obtain the average temperature gradient of each group and the average temperature gradient of 37 groups.

[0050] For more details, please refer to Figure 4 As shown, in one embodiment of the present invention, step S3, calculating the total average temperature gradient G3 at point B, specifically includes the following steps: Step S 31Record time t0 and time t2 for each pass to obtain the linear profile temperature change data for each pass during different time intervals from t0 to t2. Step S 32 Calculate the average temperature gradient for each pass and obtain the total average temperature gradient G3.

[0051] like Figure 10 The image shown is a thermal image of the moment when the transient temperature gradient at point B is highest during different passes in an embodiment of the present invention. Since each pass of laser scanning heats point B, a temperature difference exists between points E and F. Therefore, point B will have an average temperature gradient during each pass of laser scanning.

[0052] based on Figure 9 The method described above, in order to obtain the average temperature gradient at the midpoint B of the fourth pass, modifies the monitoring from the fourth pass to eight passes, and records the average temperature gradient for each pass using the same method as... Figure 9 The method for calculating the single-channel average temperature gradient is the same.

[0053] Specifically, firstly, the time t0 and t2 of each track are recorded to obtain the temperature change data of the line profile of each track at different time intervals from t0 to t2; finally, the average temperature gradient of each track is calculated to obtain the total average temperature gradient G3.

[0054] It is important to emphasize that, because the distance between the laser and the midpoint B of the fourth pass is different in each pass, the temperatures of points E and F in the molten pool at the midpoint B of the fourth pass will also be different at each moment. This will result in different time intervals for generating temperature gradients, i.e., the time intervals t0 to t2 are different for each pass. Therefore, the number of sets of temperature change data for the obtained line profile will be different. In this experiment, the number of sets for the first to eighth passes are 19, 22, 32, 38, 37, 31, 24, and 21, respectively. Therefore, the code needs to be modified when performing calculations in MATLAB software.

[0055] Please see Figure 5 As shown, this embodiment of the invention also provides a laser cladding molten pool surface temperature field monitoring system 1, applied to the temperature gradient calculation method for the laser cladding molten pool surface temperature field described above. The laser cladding molten pool surface temperature field monitoring system 1 includes a laser cladding experimental platform 11, an infrared thermal imager 12, and a data acquisition and analysis system 13, wherein: The experimental material of the laser cladding experimental platform 11 is CoCrNi, with a particle size of 53-105μm. The substrate is made of No. 45 steel and the substrate size is 70mm×30mm×8mm. The infrared thermal imager 12 is used to record the spatial and spectral information of the molten pool and transmit the thermal image data to the data acquisition and analysis system 13; The data acquisition and analysis system 13 is used to process the thermal image data recorded by the infrared thermal imager 12. The data acquisition and analysis system 13 can record the temperature and morphological changes of the molten pool within the image range during the complete laser cladding process. The software in the data acquisition and analysis system 13 can adjust the color temperature to obtain a suitable molten pool temperature field cloud map, and can also measure the size of the molten pool and calibrate the temperature of the molten pool.

[0056] Specifically, in the technical solution of this embodiment, a laser cladding experimental platform 11 is first set up. The experimental material is CoCrNi with a particle size of 53-105μm, and the substrate is made of No. 45 steel with dimensions of 70mm×30mm×8mm. The substrate is fixed in a suitable position, and the parameters of the laser cladding equipment, such as laser power and scanning speed, are adjusted to prepare for subsequent laser cladding operations.

[0057] During laser cladding, an infrared thermal imager comes into play. It is aimed at the molten pool area and, by detecting the infrared radiation emitted by the object, acquires thermal radiation information from the molten pool surface, thus reflecting the spatial and spectral information of the molten pool. This information is recorded in real time and transmitted to the data acquisition and analysis system in the form of thermal image data.

[0058] After receiving thermal image data from the infrared thermal imager, the data acquisition and analysis system processes the data using appropriate data processing algorithms. This includes preprocessing the data to remove noise interference, thereby improving the accuracy and usability of the data. Then, the thermal image data is further analyzed to calculate key information such as the temperature field and temperature gradient on the surface of the molten pool.

[0059] In this embodiment, a CoCrNi-2%MoSi2 mixed coating is clad on the surface of No. 45 steel using a laser cladding experimental platform 11. The spatial and spectral information of the molten pool is recorded using an infrared thermal imager 12. Then, the thermal image data recorded by the infrared thermal imager 12 is processed using a data acquisition and analysis system 13 to prepare for calculating the total average temperature gradient G3 of the midpoint B of the fourth pass.

[0060] After the laser cladding experiment begins, the infrared thermal imager 12 starts recording. Based on the dynamic changes of the molten pool and experimental requirements, the exposure time is adjusted within the range of 10-5000 μs to obtain optimal image quality and signal-to-noise ratio. The infrared thermal imager 12 continuously records thermal image data at a maximum frame rate of 180 fps, ensuring that it can capture the rapid changes and subtle dynamic characteristics of the molten pool during laser scanning, and completely record the spatial information (such as the shape, size, and location of the molten pool) and spectral information (the distribution of thermal radiation intensity at different wavelengths) of the molten pool.

[0061] The recorded thermal image data is transmitted to the data acquisition and analysis system 13 in real time. During transmission, the integrity and accuracy of the data are ensured to avoid data loss or corruption. After receiving the thermal image data, the data acquisition and analysis system 13 performs preliminary processing, such as data format conversion and image preprocessing (denoising, correction, etc.), to prepare for subsequent in-depth analysis.

[0062] Therefore, the infrared thermal imager 12 can acquire the thermal radiation information of the molten pool surface in real time and non-contactly, thereby obtaining the spatial distribution of the temperature field on the molten pool surface. Compared with traditional contact measurement methods, this avoids interference with the molten pool, and can more accurately reflect the true temperature field state, providing a more reliable data foundation for subsequent process optimization and quality control.

[0063] Based on accurate temperature field data, the data acquisition and analysis system 13 can further calculate the temperature gradient.

[0064] Real-time monitoring and analysis of the temperature field and temperature gradient provide insights into the thermal state changes of the molten pool under different laser cladding process parameters. Based on the monitoring results, process parameters such as laser power, scanning speed, and overlap ratio can be adjusted in a targeted manner to obtain a more ideal cladding layer quality, such as a more uniform composition distribution and fewer defects, thereby optimizing the laser cladding process.

[0065] It is important to explain that temperature gradient is one of the key factors affecting the quality of cladding layer. Understanding temperature gradient information helps to understand the heat transfer in the molten pool, and further analyze the formation process of solidification structure. This is of great significance for predicting and controlling the microstructure and properties of cladding layer.

[0066] More specifically, in a preferred embodiment of the present invention, the laser cladding experimental platform 11 adopts a coaxial synchronous automatic powder feeding system, and uses nitrogen and argon as protective gases for powder feeding.

[0067] The coaxial synchronous automatic powder feeding system is the core of the laser cladding experimental platform. This system ensures that the powder material is stably and accurately delivered to the molten pool area during the laser cladding process, and the powder feeding path is coaxial with the laser beam path, achieving synchronous operation. At the same time, nitrogen and argon are used as protective gases for powder feeding, and the protective gases are delivered to the molten pool together with the powder.

[0068] When laser cladding is initiated, the coaxial synchronous automatic powder feeding system uniformly and stably delivers CoCrNi powder (particle size 53-105μm) to the molten pool according to preset parameters. Nitrogen and argon act as protective gases, forming a protective gas flow around the powder to isolate it from impurities such as oxygen and moisture in the outside air. This prevents adverse reactions such as oxidation and moisture absorption during the transportation and cladding process, ensuring the chemical stability and purity of the powder. This allows the powder to integrate into the molten pool in an ideal state and participate in the cladding process.

[0069] Throughout the laser cladding process, the infrared thermal imager 12 continuously monitors the molten pool, acquiring its thermal radiation information to reflect the spatial and spectral information of the temperature field on the molten pool surface. The thermal image data is then transmitted to the data acquisition and analysis system 13. The data acquisition and analysis system 13 processes and analyzes this data, calculating key information such as the temperature field and temperature gradient on the molten pool surface, thereby gaining a deeper understanding of the thermal behavior during the cladding process.

[0070] Understandably, a coaxial synchronous automatic powder feeding system can precisely control powder delivery, ensuring uniform powder distribution in the molten pool and forming a uniform cladding layer. This reduces defects caused by uneven powder distribution, such as porosity and inclusions. Simultaneously, the protective effects of nitrogen and argon prevent powder oxidation, ensuring the purity of the cladding layer composition, improving its density, hardness, and wear resistance, and ultimately enhancing the overall cladding quality.

[0071] More specifically, in another embodiment of the present invention, the process parameters of the laser cladding experimental platform 11 include a laser power of 400-1000W, a scanning speed of 400-1000mm / s, a scanning length of 8mm, and a scanning spacing of 0.8mm.

[0072] Preferably, the process parameters of the laser cladding experimental platform are set as follows: laser power is 450W, scanning speed is 800mm / s, powder feeding rate is 10 g / min, scanning length is 8mm, scanning interval is 0.8mm, and a total of eight scans are performed, that is, the cladding size is 8×5.6mm.

[0073] Therefore, by activating the laser cladding experimental platform 11, the cladding operation was carried out according to the optimized process parameters. The laser output power of 450W and scanned the substrate at a speed of 800mm / s along the set scanning trajectory (scanning length 8mm, scanning interval 0.8mm), while CoCrNi powder was automatically fed into the molten pool area at a rate of 10g / min. During the scanning process, each scanning pass was performed sequentially, completing a total of eight scans to form a cladding layer of the set size.

[0074] During the cladding process, the infrared thermal imager 12 records the spatial and spectral information of the molten pool in real time, acquires the thermal radiation information of the molten pool surface, and transmits this thermal image data to the data acquisition and analysis system 13. The data acquisition and analysis system 13 processes and analyzes the data, calculates key information such as the temperature field and temperature gradient of the molten pool surface, so as to evaluate and optimize the cladding process and quality in the future.

[0075] Therefore, while ensuring cladding quality, by rationally optimizing process parameters, such as setting a relatively high scanning speed (800 mm / s) and a moderate powder feeding rate (10 g / min), a faster cladding progress can be achieved, improving production efficiency. Simultaneously, the clearly defined scanning length (8 mm), scanning spacing (0.8 mm), and the planning of eight scanning passes make the cladding path clearer and more rational, reducing unnecessary operations and waiting time, further improving overall cladding efficiency.

[0076] By precisely setting parameters such as laser power and scanning speed, the molten pool can receive appropriate energy input, achieving a favorable melting and solidification state. This helps optimize the thermal behavior of the molten pool, such as controlling the temperature field distribution and temperature gradient, thereby influencing the solidification microstructure of the cladding layer. A suitable solidification microstructure is beneficial for improving the hardness, wear resistance, and other properties of the cladding layer, thus extending the product's service life.

[0077] More specifically, in one embodiment of the present invention, the infrared thermal imager 12 operates in the short-wavelength range of 900-1700nm, has a focal length of 30cm, a maximum frame rate of 180 fps, and an exposure time adjustable in the range of 10-5000μs.

[0078] The infrared thermal imager 12 operates in the short-wavelength range of 900-1700 nm. This wavelength range offers high sensitivity and resolution for the thermal radiation of molten metal pools, enabling more precise capture of thermal radiation information from the molten pool surface and thus achieving high-precision monitoring of the molten pool temperature field. Compared with traditional temperature measurement methods, it provides richer and more accurate temperature distribution information, offering more reliable data support for studying the thermal behavior of molten pools and optimizing cladding processes.

[0079] The infrared thermal imager 12 has a maximum frame rate of 180fps, enabling it to quickly capture transient changes in the molten pool during laser cladding. During laser cladding, the shape and temperature distribution of the molten pool change rapidly with the laser scan. High-speed thermal image recording ensures that this important dynamic information is not missed, allowing researchers to gain a more comprehensive understanding of the dynamic behavior of the molten pool, such as fluctuations and spatter. This provides a strong basis for further analysis of unstable factors in the cladding process and optimization of process parameters.

[0080] In this embodiment, the infrared thermal imager 12 is installed in a suitable position so that it can be clearly aimed at the molten pool area during the laser cladding process. Based on the size and distance of the molten pool, the focal length of the infrared thermal imager 12 is adjusted to 30cm to ensure accurate focusing on the molten pool surface and acquisition of a clear thermal image. Simultaneously, the operating wavelength range of the infrared thermal imager 12 is set to a short-wave range of 900-1700nm. This wavelength range is highly sensitive to the temperature monitoring of the molten pool of metallic materials and can effectively capture the thermal radiation information of the molten pool.

[0081] The exposure time is adjustable within the range of 10-5000 μs, allowing for flexible adjustment based on different molten pool conditions and experimental requirements. When the molten pool brightness is high, the exposure time can be shortened to avoid overexposure; conversely, when the molten pool brightness is low, the exposure time can be appropriately extended to improve the signal-to-noise ratio. This flexible exposure time adjustment function enhances the adaptability of the infrared thermal imager 12 to different experimental environments, ensures the quality and reliability of thermal image data, and helps improve the accuracy and stability of temperature measurements.

[0082] The 30cm focal length allows the thermal imager to accurately focus on the molten pool surface, acquiring clear spatial information about the molten pool. Combined with high-resolution thermal imaging technology, it is possible to precisely measure spatial parameters such as the size, shape, and position of the molten pool. This spatial information is crucial for studying the geometric changes of the molten pool, the formation process of the cladding layer, and its interaction with the substrate, and helps optimize the trajectory planning and process parameter settings for laser cladding.

[0083] More specifically, in one embodiment of the present invention, the infrared thermal imager 12 uses a short-wavelength high-dynamic indium gallium arsenide (InGaAs) infrared array with a resolution of 640H×512V pixels. This sensor is capable of high-precision thermal radiation detection of the molten pool surface. High resolution means that more and more detailed molten pool surface temperature information can be obtained, and the temperature change details of the molten pool can be distinguished more accurately in space. Compared with low-resolution sensors, the temperature at different locations in the molten pool can be measured more accurately, providing a richer and more accurate data foundation for subsequent analysis of the thermal behavior and temperature field distribution of the molten pool.

[0084] In this way, the short-wave high dynamic indium gallium arsenide (InGaAs) infrared array sensor is installed in the infrared thermal imager 12, and relevant technical means are used to ensure that the sensor is accurately matched with the optical system and data acquisition and analysis system 13 of the infrared thermal imager 12 so as to achieve normal operation.

[0085] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of this disclosure, and all such changes and modifications will fall within the scope of protection of this invention.

Claims

1. A method for calculating the temperature gradient of the surface temperature field of a laser cladding molten pool, characterized in that, The calculation method includes: Step S1: Monitor the transient temperature field of the molten pool at point B at a certain moment during the laser cladding process, and calculate the transient temperature gradient G1 at point B; Step S2: Monitor the temperature field change of the molten pool at point B from the time of generation t0 to the time of disappearance t2 during the single-pass laser cladding process, and calculate the average temperature gradient G2 at point B; Step S3: Monitor the temperature field change of the molten pool at point B during the multi-stage heating and cooling process of the laser single-layer cladding process, and calculate the total average temperature gradient G3 at point B.

2. The method for calculating the temperature gradient of the surface temperature field of the laser cladding pool according to claim 1, characterized in that, In step S1, monitoring the transient temperature field of the molten pool at point B at a certain moment during the laser cladding process and calculating the transient temperature gradient G1 at point B specifically includes the following steps: Step S 11 : Obtain the transient peak temperature of the molten pool at the midpoint B of the nth pass, and set the highest color temperature to the transient peak temperature of the molten pool at point B; Step S 12 Temperature color gradation processing was performed on the experimental data to obtain a temperature field cloud map; Step S 13 The central peak temperature point E and the edge point F of the molten pool along the overlapping direction are selected as data sampling points.

3. The method for calculating the temperature gradient of the surface temperature field of the laser cladding pool according to claim 1, characterized in that, In step S2, the monitoring of the single-pass laser cladding process specifically includes the following steps: Step S 21 When the laser scan is performed on the nth pass, observe the changes in the molten pool at point B. Step S 22 The temperature gradient within the molten pool at point B during single-pass laser cladding is calculated using the temperature change diagram of the line profile.

4. The method for calculating the temperature gradient of the surface temperature field of the laser cladding pool according to claim 3, characterized in that, In step S 22 In this process, the temperature gradient within the molten pool at point B during laser single-pass cladding is calculated using a line profile temperature change diagram. Specifically, this includes: Step S 221 : Calculate the temperature and coordinates of 20 points on a straight line on the online profile, set the starting point to the peak temperature point E, and the ending point to the edge temperature point F; Step S 222 : Calculate the distance d between the peak temperature point E of the molten pool and the edge temperature point F of the molten pool; Step S 223 The 20 points are represented as Avg1 to Avg20, and the temperature changes of Avg1 to Avg20 are observed through the line profile temperature change graph.

5. The method for calculating the temperature gradient of the surface temperature field of the laser cladding pool according to claim 4, characterized in that, In step S 223 The observation of temperature changes of Avg1 to Avg20 through line profile temperature change graphs specifically includes: Step S 2231 Observe the temperature change graph of the described line profile, at T e -T f The time corresponding to =0 is denoted as t0, T e The time when the peak temperature is reached is recorded as time t1, and the second time is T. e -T f =0 is denoted as time t2; Step S 2232 Extract the temperature and coordinates of Avg1 to Avg20 within the time interval t0 to t2, and calculate the average temperature gradient of point B within the time interval t0 to t2.

6. The method for calculating the temperature gradient of the surface temperature field of the laser cladding pool according to claim 1, characterized in that, In step S2, calculating the average temperature gradient G2 at point B specifically includes the following steps: Step S 23 The data recorded during the time period t0 to t2 is divided into x groups according to time. Each group has 20 points with temperature and coordinates. When the temperature is at its peak, the highest temperature point is at point E; when the temperature is at its lowest, the lowest temperature point is at point F. Step S 24 For each set of data, extract the maximum and minimum temperature values ​​from the 20 temperature points in that set, and calculate the difference between them. Step S 25 The actual temperature gradient of the data set is obtained by calculating the ratio of the temperature difference to the corresponding distance and then multiplying it by the scaling factor k. Step S 26 Calculate the temperature gradient of x groups within the time interval t0 to t2, and average the temperature gradient of these x groups to obtain the average temperature gradient G2 of point B within the time interval t0 to t2.

7. The method for calculating the temperature gradient of the surface temperature field of the laser cladding pool according to claim 6, characterized in that, In step S3, the specific method for monitoring the temperature field change of the molten pool at point B during the multi-stage molten pool heating and cooling process in the laser monolayer cladding process is as follows: Based on step S2, the number of monitoring channels is increased from n to m, and the method for recording the average temperature gradient of each channel is the same as the method for calculating the average temperature gradient of a single channel described in step S2.

8. The method for calculating the temperature gradient of the surface temperature field of the laser cladding pool according to claim 7, characterized in that, In step S3, the specific method for calculating the average temperature gradient G3 at point B is as follows: Step S 31 Record time t0 and time t2 for each pass to obtain the temperature change data of the linear profile for each pass during different time intervals from t0 to t2. Step S 32 Calculate the average temperature gradient for each pass and obtain the total average temperature gradient G3.

9. A laser cladding weld pool surface temperature field monitoring system, characterized in that, The method for calculating the temperature gradient of the surface temperature field of the laser cladding molten pool as described in any one of claims 1-8, wherein the system comprises a laser cladding experimental platform, an infrared thermal imager, and a data acquisition and analysis system, wherein: The experimental material of the laser cladding experimental platform is CoCrNi, and the substrate is made of 45 steel. The infrared thermal imager is used to record the spatial and spectral information of the molten pool and transmit the thermal image data to the data acquisition and analysis system. The data acquisition and analysis system is used to process the thermal image data recorded by the infrared thermal imager.

10. The laser cladding molten pool surface temperature field monitoring system according to claim 9, characterized in that, The laser cladding experimental platform uses a coaxial synchronous automatic powder feeding system with nitrogen and argon as protective gases. The process parameters of the laser cladding experimental platform include a laser power of 400-1000W, a scanning speed of 400-1000mm / s, a scanning length of 8mm, and a scanning interval of 0.8mm.

Images