A method for optimizing laser cladding process parameters of a flange inner wall

Abstract

The present application relates to the technical field of flange laser cladding, in particular to a laser cladding process parameter optimization method for flange inner wall, the laser cladding process parameter optimization method for flange inner wall provided by the present application introduces a least square method model containing time-varying weight coefficient on the basis of traditional response surface method, and a dynamic response model is constructed based on this, while the optimal process parameter combination can be obtained, the interaction influence of each parameter in the optimal process parameter combination and the change trend of the interaction influence of each parameter with time are considered to dynamically adjust the process parameters, compared with the traditional response surface method, the interaction weight coefficients of each parameter in the model can be dynamically adjusted with time, and the accuracy of the laser cladding parameter optimization for the flange is improved. An efficient and reliable remanufacturing solution is provided for the repair and reinforcement of the flange in the deep-sea high-pressure and high-corrosion environment, and has important engineering application value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flange laser cladding, and particularly relates to a laser cladding process parameter optimization method for a flange inner wall. BACKGROUND

[0002] The flange component in the field of deep sea and oil field is subjected to high pressure, high corrosion medium and complex stress for a long time, and needs to prepare a corrosion-resistant cladding layer on the flange base material. The commonly used cladding technology is laser cladding technology. The laser cladding technology rapidly heats and melts the alloy powder or ceramic powder and the surface of the base body under the action of a laser beam, and a surface coating with extremely low dilution rate and metallurgical bonding with the base material is formed after the beam is removed, thereby significantly improving the surface performance of the base body. The technology is an advanced green laser remanufacturing technology with high energy density, high machining precision, wide material selection range and good interface metallurgical bonding, and provides technical support for the cladding repair of parts under high temperature and harsh conditions, and can well solve the problems of high-temperature alloy hot working deformation and metallurgical bonding.

[0003] The laser cladding process involves many process parameters, and the laser cladding effect needs to be changed by adjusting these parameters during the machining process, so that the final cladding layer meets the ASME chemical composition standard. In the prior art, the response surface method (RSM) is usually used to optimize and adjust the process parameters of laser cladding. For example, the general steps of the traditional response surface method disclosed in the Chinese patent with the publication number CN119623071A are described. The response surface method usually selects several process parameters of the laser cladding process and the quality evaluation index parameters of the cladding layer as response variables to establish a regression model equation and draw a surface graph. Then, the optimal process parameter combination of laser cladding is obtained by observing the surface graph. However, the traditional response surface method does not consider the interaction between the process parameters and the influence of the interaction between the process parameters changing over time, and the optimal process parameter combination obtained is not adjusted according to the above influences. The traditional response surface method lacks the ability to adjust the parameters in the process parameter combination in real time, so that the accuracy of the optimal process parameter combination obtained according to the traditional response surface method is limited. SUMMARY

[0004] The technical solution adopted by the present application to solve its technical problems is to provide a laser cladding process parameter optimization method for a flange inner wall, which comprises the following steps:

[0005] S1, input the chemical composition mass percentage (wt%) of the welding wire / welding powder of the laser cladding into the JmatPro software, calculate the melting point of the welding wire / welding powder material, and prepare for the cladding work;

[0006] S2. Based on the calculated melting point and empirical thermal input parameters, select the initial range of values ​​for the pipe inner wall cladding process parameters, which include A, B, and C.

[0007] S3. Within the initial range of values ​​for each process parameter, select three values ​​as representative values ​​for that process parameter. Let the three representative values ​​for the three process parameters be as follows: , , K can take the values ​​A, B, and C, and then the three representative values ​​can be combined to form a variety of experimental schemes.

[0008] S4. Import the actual values ​​of each process parameter and the cladding layer quality evaluation index parameter values ​​corresponding to all test schemes generated in S3 into the least squares model, and simultaneously introduce the interaction weight coefficient. This is used to dynamically describe the relationship between the interaction of parameter pairs formed by the interaction of various process parameters as the cladding time t changes.

[0009] Introduced interaction weight coefficient The mathematical expression is:

[0010]

[0011] In the formula These are the initial interaction weight coefficients for each parameter pair. The interaction range of each parameter with respect to time t, The initial interaction weight coefficients of each parameter pair and the interaction range of each parameter pair at time t were obtained through previous orthogonal single-factor pre-experiment calibration.

[0012] S5. Select one process parameter from the three process parameters. This process parameter can be chosen from A, B, and C. Based on this process parameter, group all parameter combinations containing the same process parameter in all test schemes into the same dataset. Establish a three-dimensional coordinate system for this dataset. The vertical Z-axis of the three-dimensional coordinate system corresponds to the cladding layer quality evaluation index parameter. The other two coordinate axes of the three-dimensional coordinate system are the remaining two process parameters from A, B, and C, respectively. Input the parameter values ​​of each process parameter in each combination under this dataset into the coordinate system. Finally, draw a surface plot and obtain the optimal combination of laser process parameters on the surface.

[0013] S6. Through optimization and prediction using Design-Expert software, the coding values ​​corresponding to the optimal combination of process parameters are inferred, and finally the actual optimal parameters are obtained by interpolation.

[0014] Furthermore, the process of obtaining the initial interaction weight coefficients of each parameter pair and the interaction range of each parameter pair at time t through prior orthogonal single-factor pre-experiment calibration includes the following sub-steps:

[0015] S41. Using the L9(3²) orthogonal array, the interaction range of the three parameter pairs AB, AC, and BC at time t=0 and the initial interaction weight coefficient of each parameter pair are calculated by using the single-factor control logic of "fixing the third parameter and variating the interaction two parameters".

[0016] S42. Multi-time point sampling: At t=5min, 10min, 15min, 20min, 25min, and 30min, cladding is paused and the cladding thickness corresponding to each time point t is measured using a thickness gauge. Then, the cross-range of each parameter with respect to time point t is calculated. .

[0017] Furthermore, the quality evaluation index parameter value of the cladding layer is the cladding thickness.

[0018] Furthermore, A, B, and C in the cladding process parameters of the inner wall of the pipeline are: laser power, scanning speed, and powder feeding rate / wire feeding rate, respectively.

[0019] Furthermore, within the initial range of values ​​for each process parameter, three values ​​are selected as representative values ​​for that process parameter. Let the three representative values ​​for the three process parameters be as follows: , , K can take the values ​​A, B, and C. Combining these three representative values ​​into various experimental schemes involves the following sub-steps:

[0020] S31, , , The encoded values ​​are -1, 0, and 1, respectively. This represents taking the minimum value within the initial range of the corresponding process parameters. This represents taking the middle value within the initial range of the corresponding process parameters. This represents the maximum value within the initial range of the corresponding process parameters;

[0021] S32. Combine the three representative values ​​to obtain a total of 27 test schemes. Perform cladding work on the inner wall of the flange according to each test scheme. After the cladding of each test scheme is completed, use a thickness gauge to measure the cladding thickness corresponding to each test scheme.

[0022] S33. Draw a spatial matrix based on 27 test schemes and the corresponding cladding thicknesses for each of the 27 test schemes.

[0023] Furthermore, the step of drawing a spatial matrix based on 27 test schemes and the corresponding cladding thicknesses includes:

[0024] Draw a full factorial model of a cube with a side length of 2 in three-dimensional space, and set the coordinates of the center point of the cube to O. Mark the 27 experimental schemes in the form of coordinates in the cube.

[0025] Furthermore, the baseline process parameter in step S5 is the powder feeding rate / filament feeding rate.

[0026] Furthermore, based on the aforementioned empirical thermal input parameters, selecting the initial range of values ​​for the pipe inner wall cladding process parameters includes the following sub-steps:

[0027] S21. Knowing the melting point of the material, based on the empirical heat input parameters and the upper limit of the scanning speed corresponding to the flange, pre-select the corresponding process parameters for the melting point.

[0028] The beneficial effects of this invention are as follows: The laser cladding process parameter optimization method for the inner wall of flanges provided by this invention introduces a least squares model with time-varying weight coefficients based on the traditional response surface methodology. A dynamic response model is then constructed based on this model. While obtaining the optimal combination of process parameters, it considers the interaction effects of each parameter in the optimal combination and the changing trend of these interaction effects over time to dynamically adjust the process parameters. Compared to the traditional response surface methodology, this method can dynamically adjust the interaction weight coefficients of each parameter in the model over time, improving the accuracy of optimizing the laser cladding parameters for flanges. It provides an efficient and reliable remanufacturing solution for flange repair and strengthening in deep-sea high-pressure and highly corrosive environments, and has significant engineering application value. Attached Figure Description

[0029] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0030] In the picture: Figure 1 A flowchart illustrating the steps of the laser cladding process parameter optimization method for the inner wall of a flange provided by the present invention;

[0031] Figure 2 This is a three-dimensional structural diagram of the laser cladding equipment involved in this invention;

[0032] Figure 3 This is a diagram of the full factorial model described in the embodiments of the present invention;

[0033] Figure 4 Two-dimensional surface contour plots were drawn for all test schemes when the powder feeding rate was 0.4 r / min in the implementation of this invention;

[0034] Figure 5 Three-dimensional surface plots were drawn for all test schemes when the powder feeding rate was 0.4 r / min in the embodiments of the present invention;

[0035] Figure 6This is a simulation parameter diagram of the predicted cladding thickness described in step S6 of this embodiment of the invention;

[0036] Figure 7 This is a superimposed map of the feasible region for predicting the cladding thickness as described in step S6.

[0037] Explanation of reference numerals in the attached drawings: 1. Laser cladding mechanism; 2. Motion mechanism; 3. Rotary positioner. Detailed Implementation

[0038] To make the technical problem to be solved, the technical solution, and the beneficial effects of this invention clearer, the invention will now be described in detail with reference to the accompanying drawings. This drawing is a simplified schematic diagram, illustrating only the basic aspects of the invention, and therefore only shows the components relevant to the invention. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0039] Please refer to Figure 2 The laser cladding equipment of the present invention includes a laser cladding mechanism 1, a motion mechanism 2 for controlling the movement of the laser cladding mechanism, a conveying mechanism for conveying heated cladding material, and a rotary positioner 3 for controlling the rotation of the flange, as well as a displacement sensor detection module and a cooling system, wherein the displacement sensor detection module, the cooling system and the conveying mechanism are not shown in the figure.

[0040] The laser cladding mechanism 1 includes a laser and a cladding head. The cladding head is a multi-beam laser inner wall cladding head. During cladding, the motion mechanism uses a cross shaft to control the vertical and horizontal displacement of the cladding head, while the rotary positioner 3 controls the rotation of the flange to cooperate with the cladding head for cladding. The conveying mechanism uses a multi-cylinder hot powder agitator / hot filament feeder (not shown in the figure), with corresponding process parameters of powder feeding rate / filament feeding rate. A displacement sensor detection module is clamped at the head of the cladding head for real-time detection of the cladding position.

[0041] This invention provides a method for optimizing laser cladding process parameters for the inner wall of a flange, the method comprising the following steps:

[0042] S1. Using JmatPro software, input the chemical composition mass percentage (wt%) of the welding wire / powder for laser cladding, calculate the melting point of the welding wire / powder material, and prepare for the cladding work.

[0043] S2. Based on the calculated melting point and empirical thermal input parameters, select the initial range of values ​​for the pipe inner wall cladding process parameters, which include: laser power, scanning speed, powder feeding rate / wire feeding rate;

[0044] Based on the aforementioned empirical thermal input parameters, the initial range of values ​​for the pipe inner wall cladding process parameters is selected, including the following sub-steps:

[0045] S21. Knowing the melting point of the material, based on the empirical heat input parameters and the upper limit of the scanning speed corresponding to the flange, pre-select the corresponding process parameters for the melting point.

[0046] Among them, the empirical thermal input parameter = laser power / scanning speed, and the powder feeding rate or wire feeding rate is estimated according to the corresponding empirical formula within the range:

[0047] The empirical formula corresponding to the wire feeding rate is: ;

[0048] The empirical formula corresponding to the powder delivery speed is: .

[0049] Specifically, the values ​​of the empirical thermal input parameters are determined by combining the melting point calculated in step S1 with actual production requirements, and the upper limit of the scanning speed corresponding to the flange is also determined based on actual production requirements. Where... The wire feeding rate is (mm / min). The powder feeding rate is (r / min). Laser power (W); Energy absorption efficiency (usually taken as 0.3-0.6); The diameter of the cladding flange is in mm. To account for the density of the cladding metal, the powder feeding rate of the multi-cylinder hot powder agitator in this application is 1 r / min, which corresponds to 42.5 g / min, meaning that the multi-cylinder hot powder agitator feeds 42.5 g of powder per revolution.

[0050] S3. Within the initial range of values ​​for each process parameter, select three values ​​as representative values ​​for that process parameter. Let the three representative values ​​for the three process parameters be as follows: , , K can take the values ​​A, B, and C, and then the three representative values ​​can be combined to form a variety of experimental schemes.

[0051] Three values ​​are selected from the initial range of each process parameter as representative values ​​for that parameter. Let the three representative values ​​for the three process parameters be as follows: , , K can take the values ​​A, B, and C. Combining these three representative values ​​into various experimental schemes involves the following sub-steps:

[0052] S31, , , The encoded values ​​are -1, 0, and 1, respectively. This represents taking the minimum value within the initial range of the corresponding process parameters. This represents taking the middle value within the initial range of the corresponding process parameters. This represents the maximum value within the initial range of the corresponding process parameters;

[0053] Specifically, with The encoded values ​​of (max, mid, min) are -1, 0, and 1, which represent the row header. Table 1 is created using the types of process parameters represented as column headings.

[0054] S32. Combine the three representative values ​​to obtain a total of 27 test schemes. Perform cladding work on the inner wall of the flange according to each test scheme. After the cladding of each test scheme is completed, use a thickness gauge to measure the cladding thickness corresponding to each test scheme.

[0055] Specifically, in this embodiment, during laser cladding, a magnetic induction thickness gauge is used for laser cladding of different base materials, while an ultrasonic thickness gauge is used for laser cladding of the same base material. The specific model of the thickness gauge is selected according to the actual situation. In this embodiment, the conveying mechanism is a multi-cylinder hot powder agitator, and the corresponding process parameter is the powder feeding rate (r / min).

[0056] Table 1. Actual values ​​of three process parameters and their corresponding coded values.

[0057]

[0058] S33. Draw a spatial matrix based on the 27 test schemes and the corresponding cladding thicknesses:

[0059] A cube with a side length of 2 is drawn in three-dimensional space as a full factorial model, with the center point of the cube at coordinate O and the coordinates of point O being (0,0,0). The 27 experimental schemes are then labeled in coordinate form within the cube. For ease of labeling, the coordinate values ​​of each experimental scheme are replaced with coded values. (The full factorial model diagram of the cube is attached.) Figure 3 (As shown).

[0060] S4. Import the actual values ​​of each process parameter and cladding thickness corresponding to all the test schemes generated in S3 into the least squares model, and simultaneously introduce the interaction weight coefficient. This is used to dynamically describe the interaction between the pairs of parameters formed by the interaction of laser power (A), powder feed rate / wire feed rate (B), and scanning speed (C) as a function of cladding time t. The formula of the least squares model is:

[0061] ,

[0062] In the formula: Let be the response variable for cladding thickness; ε represent various errors. Here, x represents the coefficient estimate, and x represents the factor code.

[0063] Introduced interaction weight coefficient The mathematical expression is:

[0064]

[0065] In the formula These are the initial interaction weight coefficients for each parameter pair. The interaction range of each parameter with respect to time t, .

[0066] Introducing interaction weight coefficients The least squares model was then obtained as follows:

[0067]

[0068] In this model, Y represents the quality evaluation index of the cladding layer (cladding thickness), and β is the regression coefficient to be determined.

[0069] The corresponding cross-weighting coefficients between the laser power (A), powder feed rate / filament feed rate (B), and scanning speed (C) are used to determine the pairwise interaction weights. , and The mathematical expressions are as follows:

[0070]

[0071]

[0072]

[0073] The range of t in the formula is (0 < t ≤ 30 min) to ensure that it covers the total time of a single cladding operation on the inner wall of the flange. , , , , and The specific values ​​were obtained through prior orthogonal single-factor pilot experiments. The result was obtained by substituting 27 experimental schemes and then calculating using the least squares method.

[0074] The initial interaction weight coefficients of each parameter pair and the interaction range of each parameter pair at time t are obtained through the calibration of the previous orthogonal single-factor pilot experiment, including the following sub-steps:

[0075] S41. Using an L9(3²) orthogonal array (3 factors, 3 levels, 9 trials per group), the interaction range of the three parameter pairs AB, AC, and BC at time t=0 and the initial interaction weight coefficients of each parameter pair are calculated by using the single-factor control logic of "fixing the third parameter and variating the interaction two parameters".

[0076] Table 2 L9(3²) orthogonal array

[0077]

[0078] The following is a table showing the range of the interactions of the three parameter pairs AB, AC, and BC at t=0, as well as the initial interaction weight coefficients for each parameter pair.

[0079] Table 3. Cladding thickness corresponding to various laser powers and powder feeding rates at a scanning speed C=1000 mm / min.

[0080]

[0081] Table 4. Cladding thickness corresponding to various laser powers and scanning speeds when the powder feeding rate B is 0.4 r / min.

[0082]

[0083] Table 5. Clad thickness corresponding to various powder feeding rates and scanning speeds when the laser power is 2000W.

[0084]

[0085] Specifically, the data in Tables 3, 4 and 5 combined are the process parameters and cladding thicknesses corresponding to all the test schemes generated in step S3 above.

[0086] Based on the data in Tables 3, 4, and 5, the range of the three interaction groups when t = 0 was calculated. , , Then, based on the range of the three sets of interactions, the initial interaction weight coefficients of each parameter pair are calculated using a normalization formula. , , (Multiply by 0.8 to reserve space for iterative optimization):

[0087] The normalization formula is:

[0088] in, .

[0089] S42, Multi-time-node sampling: At t=5min, 10min, 15min, 20min, 25min, and 30min (t≤30min as specified in the coverage document), cladding is paused and the cladding thickness corresponding to each t is measured using a thickness gauge. Then, the cross-range at that time t is calculated. , where at t=0, the values ​​of each interaction range are calculated in step S41.

[0090] The calculation formula is:

[0091]

[0092] In the formula The value of t represents the average change per minute, and the range of t is (0→30min). The calculation formula is:

[0093]

[0094] The following is a table showing the corresponding data for calculating the interaction range at time t for the three interaction groups AB, AC, and BC, respectively.

[0095] Table 6. AB Cross-Range Values

[0096]

[0097] Table 7. AC Interaction Range Values

[0098]

[0099] Table 8. BC Cross-Range Values

[0100]

[0101] S5. Select one process parameter from the three process parameters. This process parameter is the powder feeding rate / wire feeding rate. Based on the powder feeding rate / wire feeding rate, group all parameter combinations containing the same powder feeding rate / wire feeding rate in all test schemes into the same dataset. Establish a three-dimensional coordinate system for this dataset. The vertical Z-axis of the three-dimensional coordinate system corresponds to the cladding thickness. The other two axes of the three-dimensional coordinate system are the scanning speed and the laser power, respectively. Input the parameter values ​​of each process parameter in each combination under this dataset into the coordinate system. Finally, draw a surface plot. The highest point of the surface is the highest point of the cladding thickness at this scanning speed. Obtain the laser process parameter combination (laser power, scanning speed, powder feeding rate / wire feeding rate) corresponding to the cladding thickness on the surface.

[0102] Specifically, the cladding thickness in step S5 can be replaced with other cladding layer quality evaluation index parameters, such as dilution rate. The process parameters used as a reference in step S5 can also be laser power or scanning speed. When laser power is used as a reference, the other two axes of the three-dimensional coordinate system are scanning speed and powder feeding rate / wire feeding rate, respectively; when scanning speed is used as a reference, the other two axes of the three-dimensional coordinate system are laser power and powder feeding rate / wire feeding rate, respectively.

[0103] S6. Finally, the optimal process parameter combination is calculated by using Design-Expert software for optimization and prediction. The actual optimal parameters are then obtained by interpolation.

[0104] Specifically, the predicted optimal combination of process parameters (laser power, scanning speed, powder feed rate / wire feed rate, powder feed rate in this embodiment) is as follows: Figure 6 and Figure 7 As shown, Figure 6 The optimal process parameter combination shown, calculated using interpolation, yielded the following actual optimal parameters: laser power (A) = 1957W; powder feeding rate (B) = 0.48156 r / min; scanning speed (C) = 700 mm / min, corresponding to a cladding thickness of approximately 2.06652 mm. The flange clad under this parameter combination, after precision machining and non-destructive testing, met the requirements of relevant standards such as ASME BPVC IX in terms of composition and performance.

[0105] The laser cladding process parameter optimization method for flange inner walls provided by this invention introduces a least squares model with time-varying weight coefficients based on the traditional response surface methodology. A dynamic response model is then constructed based on this model. This method not only obtains the optimal combination of process parameters but also considers the interaction effects of each parameter in the optimal combination and the changing trend of these interactions over time, allowing for dynamic adjustment of the process parameters. Compared to the traditional response surface methodology, this method can dynamically adjust the interaction weight coefficients of each parameter in the model over time, improving the accuracy of laser cladding parameter optimization for flanges. It provides an efficient and reliable remanufacturing solution for flange repair and strengthening in deep-sea high-pressure and highly corrosive environments, and has significant engineering application value.

Claims

1. A method for optimizing laser cladding process parameters for the inner wall of a flange, characterized in that, The process includes the following steps: S1. Using JmatPro software, input the mass percentage of the chemical composition of the laser cladding wire / powder, and calculate the melting point of the wire / powder material; S2. Based on the calculated melting point and empirical heat input parameters, select the initial range of values ​​for the pipe inner wall cladding process parameters, which include A, B, and C. S3. Within the initial range of values ​​for each process parameter, select three values ​​as representative values ​​for that process parameter. Let the three representative values ​​for the three process parameters be as follows: , , K can be A, B, or C, and then the three representative values ​​are combined into various experimental schemes; S4. Import the actual values ​​of each process parameter and the values ​​of the cladding layer quality evaluation index parameter corresponding to all the test schemes generated in S3 into the least squares model. The cladding layer quality evaluation index parameter is the cladding thickness. At the same time, an interactive weighting coefficient is introduced. This is used to dynamically describe the relationship between the interaction of parameter pairs formed by the interaction of various process parameters as the cladding time t changes. Introduced interaction weight coefficient The mathematical expression is: ; In the formula These are the initial interaction weight coefficients for each parameter pair. The interaction range of each parameter with respect to time t, The initial interaction weight coefficients of each parameter pair and the interaction range of each parameter pair at time t were obtained through previous orthogonal single-factor pre-experiment calibration. The process of obtaining the initial interaction weight coefficients of each parameter pair and the interaction range of each parameter pair at time t through prior orthogonal single-factor pre-experiment calibration includes the following sub-steps: S41. Using the L9(3²) orthogonal array, the interaction range of the three parameter pairs AB, AC, and BC at time t=0 and the initial interaction weight coefficient of each parameter pair are calculated by using the single-factor control logic of "fixing the third parameter and variating the interaction two parameters". S42. Multi-time point sampling: At t=5min, 10min, 15min, 20min, 25min, and 30min, cladding is paused and the cladding thickness corresponding to each time point t is measured using a thickness gauge. Then, the cross-range of each parameter with respect to time point t is calculated. ; S5. Select one process parameter from the three process parameters, which may be A, B, or C. Based on this process parameter, group all parameter combinations containing the same process parameter in all test schemes into the same dataset. Establish a three-dimensional coordinate system for this dataset. The vertical Z-axis of the three-dimensional coordinate system corresponds to the cladding layer quality evaluation index parameter. The other two coordinate axes of the three-dimensional coordinate system are the other two process parameters. Input the parameter values ​​of each process parameter in each combination under this dataset into the coordinate system. Finally, draw a surface plot and obtain the optimal combination of laser process parameters on the surface. S6. Through optimization and prediction using Design-Expert software, the coding values ​​corresponding to the optimal combination of process parameters are inferred, and finally the actual optimal parameters are obtained by interpolation.

2. The method for optimizing laser cladding process parameters for the inner wall of a flange according to claim 1, characterized in that... In the cladding process parameters for the inner wall of the pipeline, A, B, and C are respectively: laser power, scanning speed, and powder feeding rate / wire feeding rate.

3. The method for optimizing laser cladding process parameters for the inner wall of a flange according to claim 2, characterized in that... Within the initial range of values ​​for each process parameter, three values ​​are selected as representative values ​​for that process parameter. Let the three representative values ​​for the three process parameters be... , , K can be A, B, or C. Then, the three representative values ​​are combined into various experimental schemes, including the following sub-steps: S31, , , The encoded values ​​are -1, 0, and 1, respectively. This represents taking the minimum value within the initial range of the corresponding process parameters. This represents taking the middle value within the initial range of the corresponding process parameters. This represents the maximum value within the initial range of the corresponding process parameters; S32. Combine the three representative values ​​to obtain a total of 27 test schemes. Perform cladding work on the inner wall of the flange according to each test scheme. After the cladding of each test scheme is completed, use a thickness gauge to measure the cladding thickness corresponding to each test scheme. S33. Draw a spatial matrix based on 27 test schemes and the corresponding cladding thicknesses for each of the 27 test schemes.

4. The method for optimizing laser cladding process parameters for the inner wall of a flange according to claim 3, characterized in that... The process of drawing a spatial matrix based on 27 test schemes and their corresponding cladding thicknesses includes: Draw a full factorial model of a cube with a side length of 2 in three-dimensional space, and set the coordinates of the center point of the cube to O. Mark the 27 experimental schemes in the form of coordinates in the cube.

5. The method for optimizing laser cladding process parameters for the inner wall of a flange according to claim 1, characterized in that... The baseline process parameters in step S5 are powder feeding rate / filament feeding rate.

6. The method for optimizing laser cladding process parameters for the inner wall of a flange according to claim 1, characterized in that... Based on the aforementioned empirical thermal input parameters, the preliminary range of values ​​for the pipeline inner wall cladding process parameters is selected, including the following sub-steps: S21. Knowing the melting point of the material, based on the empirical heat input parameters and the upper limit of the scanning speed corresponding to the flange, pre-select the corresponding process parameters for the melting point.

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