Ni60 alloy has the characteristics of high hardness, strong wear resistance and corrosion resistance, but its high crack sensitivity seriously restricts its engineering application process. Aiming at preparing crack-free Ni60 alloy cladding layer on the surface of 316L stainless steel, the author proposed a multivariate regression prediction method with laser power, scanning speed and powder feeding rate as input, crack density, dilution rate and forming coefficient as optimization targets, and used this method to optimize the cladding process parameters; single-layer single-pass and single-layer multi-pass laser cladding tests were carried out without preheating, and then the test results were analyzed, the influence of parameters was summarized, and the microstructure and microhardness of the cladding layer were studied. The results show that increasing laser power and reducing powder feeding rate and scanning speed can reduce crack density, increasing powder feeding rate and reducing laser power and scanning speed can reduce dilution rate, increasing scanning speed and powder feeding rate and reducing laser power can increase forming coefficient; the best cladding process parameter combination is laser power 1405 W, scanning speed 5.7 mm/s, powder feeding rate 0.4 r/min, and overlap rate 50%; the obtained cladding layer has a gradually decreasing grain size from the bonding area to the surface, and the microstructure shows a transition from planar crystals, dendrites to equiaxed crystals, and the cladding layer has a good metallurgical bond with the substrate; the microhardness of the Ni60 alloy cladding layer is 641~739 HV, and the hardness of the 316L stainless steel substrate is 219~231 HV. The hardness of the cladding layer is about 2.8~3.4 times that of the substrate, and the surface strengthening effect is obvious.

1 Introduction

Laser cladding is an advanced and efficient technology for strengthening and repairing the surface of metal parts. This technology effectively controls the process parameters such as laser power, scanning speed, powder feeding rate, scanning path, etc. to achieve a stable metallurgical bond between the cladding material and the substrate, thereby forming a coating with excellent performance, effectively improving the quality and performance of the substrate surface, and extending the service life of parts. This technology has been widely used in aerospace, military, metallurgy, petrochemical and other fields, creating great economic value.

316L stainless steel is widely used in the manufacture of parts for mechanical equipment in many industries due to its excellent corrosion resistance and good mechanical properties. In order to enhance the wear resistance and corrosion resistance of the parts before service, or to repair the damaged surface after service, people usually use laser cladding remanufacturing technology to clad the surface of 316L stainless steel substrate with high-temperature alloy powder. Ni60 alloy has the advantages of wear resistance, corrosion resistance, high temperature resistance, impact resistance, etc., and its price is lower than other high-temperature alloys. If the alloy powder can be widely used in the preparation of surface coatings of metal parts, it can not only extend the service life of the parts, but also reduce production costs. However, from the existing theoretical and practical research, Ni60 alloy has high crack sensitivity and is prone to cracks during cladding, which seriously restricts its engineering application process. How to improve the laser cladding process to obtain a Ni60 coating without crack defects and meeting the cladding preparation conditions is an urgent problem to be solved.

The process parameters are crucial to the macro and micro forming quality of the cladding layer. Many domestic and foreign scholars are committed to the optimization of laser cladding process parameters. Guo et al. prepared Inconel 625 coating on the surface of 30CrMo steel plate by laser cladding, focusing on the influence of powder feed rate, scanning rate and laser power on the surface waviness and effective height of the cladding layer, and analyzed the coating quality using X-ray diffractometer, optical microscope, etc. Han et al. studied the effects of different laser cladding process parameters on the structure and properties of composite materials, and successfully prepared a Ni-Cr-Ti-B4C mixed powder alloy coating on a Q235 low-carbon steel substrate. Li et al. studied the influence of laser cladding process parameters on the dilution rate of Ni60 alloy powder on an H13 steel substrate, and concluded that the dilution rate is positively correlated with laser power and scanning speed, and negatively correlated with powder feeding voltage. Shayanfar et al. conducted a study on cladding Inconel 625 powder on an ASTM A592 steel substrate. In order to optimize and predict the quality of the cladding layer, they used a regression method to obtain the relationship between the geometry of the cladding layer and the process parameters, and obtained the optimal cladding process parameters. Zhou Jianbo et al. used a single factor experiment to study the influence of cladding parameters on the crack rate and structure of Ni60/WC coating by changing the laser power, scanning speed, overlap rate and powder feeding amount in turn, and analyzed the phase composition and element distribution of the coating with the help of energy spectrometer and X-ray diffractometer. In view of the problem that Ni60A laser cladding layer is prone to cracks, Wu Zupeng used a preheating and heat preservation process to reduce the temperature gradient of the cladding layer, thereby reducing the crack sensitivity of the cladding layer. Li Qi et al. conducted a study on the mechanism of crack generation in nickel-based alloy laser cladding layer. They believed that when the thermal stress caused by the difference in physical properties between the cladding layer material and the base material reaches the fracture strength of the material, the cladding layer is very likely to undergo brittle fracture. Sun et al. used laser cladding process to prepare nickel-based alloy coating on automotive mold steel parts, analyzed the influence of process parameters on the microstructure of the coating, and obtained the optimal cladding process parameters. Peng Yaojun et al. used the orthogonal test method to optimize the process and study the microstructure and properties of Stellite6 coating, and observed that the microstructure of the cladding layer was mainly plane crystals, dendrites, and equiaxed crystals. Wu et al. prepared Ni60A-25%WC alloy cladding layer on A3 steel plate, and optimized the cladding process parameters by response surface method. The results show that the effect of laser cladding process parameters on dilution rate is more significant than that on unit effective area. Huang Haibo et al. clad Ni60 alloy powder on the surface of 45 steel substrate, analyzed the main factors affecting cracks and coating thickness by orthogonal test method, and conducted range analysis on the test results to obtain the optimal process parameters. Cao Qiang et al. studied the influence of process parameters on single-pass cladding efficiency and cladding layer aspect ratio in order to solve the problem of laser cladding Ni60A forming efficiency, and realized the prediction and optimization of cladding process parameters by grey correlation analysis multi-objective optimization method. Liu et al. obtained the optimal laser cladding process parameters by combining response surface methodology with experiments. They established a multi-objective optimization model for laser cladding process parameters with laser power, scanning speed and preheating temperature as input factors and coating height, thickness and hardness as target responses. They also used the optimized process parameters to prepare nickel-based alloy coatings on preheated copper alloy substrates.

The crack sensitivity of the cladding layer has always been a problem that has troubled researchers at home and abroad, and has largely limited the application scope of laser cladding technology. Although researchers have taken a variety of measures to eliminate cracks while optimizing process parameters, such as preheating and heat preservation [18], coordinated electromagnetic composite fields, and even creating a vacuum environment for cladding, this undoubtedly increases the equipment burden, cladding cost and operation complexity. At present, there is no complete and unified evaluation index for the quality evaluation of laser cladding layers. The quality of the cladding layer is a problem of multiple influencing factors and multiple quality indicators. The existing process parameter optimization methods mostly take the geometric morphology of the cladding layer as the optimization target, such as melting height, melting depth, etc., or take a single crack rate or dilution rate as the optimization target. However, the achievement of a single indicator does not mean that other indicators can meet the standards. Therefore, the author adopts a method combining orthogonal experiments with theoretical predictions, takes multiple quality indicators of the cladding layer as the optimization target, and proposes a multi-objective optimization method with laser power, scanning speed and powder feeding rate as process parameter input factors, and crack density, dilution rate and forming coefficient of the cladding layer as quality target responses. In order to reduce costs and improve process efficiency, the author carried out laser cladding orthogonal tests and multivariate regression prediction analysis without preheating, using 316L stainless steel as the substrate and Ni60 alloy powder as the cladding material. The aim was to obtain the optimal cladding process parameter combination for Ni60 alloy coating without crack defects and promote the engineering application of Ni60 alloy in laser cladding green remanufacturing.

2 Stress in the cladding layer and control method

Laser cladding is a rapid heating and cooling process. In this process, the cladding layer generates internal stress due to external constraints. When the internal stress exceeds the yield strength of the cladding layer, cracks are generated. The residual internal stresses that cause cracks mainly include thermal stress, constraint stress and structural stress, among which thermal stress has the greatest impact on cracks.

The thermal expansion coefficients of the cladding layer and the base material are different. Therefore, the thermal stress generated by the inconsistent thermal expansion rate and contraction rate between the layers is: See formula (1) in the figure.
Where: Ec and Es are the elastic moduli of the cladding layer and the substrate respectively; α c and α s are the thermal expansion coefficients of the cladding layer and the substrate respectively; hc and hs are the thickness of the cladding layer and the substrate respectively, in mm; ΔT is the difference between the temperature of the cladding layer after solidification and the room temperature; υ is the Poisson’s ratio.
The cooling rates of different parts of the cladding layer are different. The parts with fast cooling rates will be constrained by the surrounding tissues and produce constraint stress. The calculation formula of constraint stress is: See formula (2) in the figure.
Where: Tt is the temperature gradient; B is the liquidus temperature of the cladding material, in °C; B0 is the substrate temperature; μ is the laser absorptivity; P is the laser power, in W; K is the thermal conductivity of the cladding material.
At the same time, there are also constraint stress σ3 in the cladding layer caused by the thermal expansion of the material melted in the molten pool and the constraint of the surrounding cold matrix, as well as internal stress σ4 caused by the uneven transformation of the organizational structure during the cladding process. Therefore, the total residual stress in the cladding layer can be expressed as: See formula (3) in the figure.

The calculation of the total residual stress in the cladding layer is affected by multiple parameters. The high temperature and instantaneous nature of the cladding process make it very difficult to collect data such as the molten pool temperature and the physical and chemical properties of the material in real time, so theoretical calculations are difficult. At present, researchers mostly use Von-Mises equivalent stress for finite element simulation calculations. Figure 1 is a cloud diagram of the Von-Mises stress field of the cladding layer obtained based on finite element calculation under the conditions of laser power of 1500 W and scanning speed of 6 mm/s. The stress components in the X, Y, and Z directions at the node A in the middle of the cladding layer are shown in Figure 2.

At t = 2.6 s, the heat reaches point A, the cladding layer is heated, and the stress in the three directions at this point begins to change sharply, as shown in Figure 2. Since the cladding layer has the smallest size in the Z direction and is sensitive to heat, the stress rises fastest and reaches the stress peak at t = 3.0 s; then, due to the decrease in the temperature of the surface layer of the cladding layer, the stress gradually decreases. At this time, the internal stress is mainly composed of constraint stress and tissue stress, so the internal stress in the Z direction is the smallest. The internal stress in the Y direction is mainly composed of thermal stress and constraint stress, and reaches a peak at t = 3.7 s, and then gradually decreases as the temperature decreases. Since the Y-direction width of the cladding layer is much smaller than the X-direction length, the internal stress in the Y direction is smaller than the internal stress in the X direction. The X-direction inward stress is mainly thermal stress and constraint stress. Since the amount of powder accumulation in this direction is large, the temperature drops and the stress release is slow. Therefore, the stress level in this direction drops slightly after reaching the peak value, and then rises again under the action of the constraint stress, and remains high during the condensation stage. It can be seen that the X-direction inward stress is the main cause of cracks in the single-pass cladding process.
From the stress curve, it can be seen that the cladding layer is mainly affected by tensile stress and has a tendency to crack. Cracks can be effectively controlled by controlling process parameters, which is essentially to control the energy in the laser cladding process. The relationship between process parameters and laser energy is usually expressed by specific energy Es, mass energy Em and line energy EL. The expressions of the above three parameters are as follows: See formulas (4)-(6) in the figure.
Where: P is laser power, unit is W; D is spot diameter, unit is mm; Vt is powder feeding rate, unit is r/min; Vs is scanning speed, unit is mm/s. It can be seen that laser cladding power, powder feeding amount and scanning speed affect cracking sensitivity by affecting heat input.

3 Experimental materials and methods

3.1 Experimental system and materials

The laser cladding test equipment mainly includes LDF-3000-60 semiconductor laser, water cooler (Sanhe Tongfei Refrigeration Co., Ltd.), KUKA six-axis robot, coaxial fiber laser cladding head and RC-PGF-D intelligent gas-borne powder feeder. The test platform is shown in Figure 3. The shielding gas and powder feeding gas are both high-purity argon gas, with a shielding gas flow rate of 20 L/min and a powder feeding gas flow rate of 6 L/min. The spot diameter is fixed at 3 mm. A heat shield is placed at the bottom of the substrate to reduce the cooling rate of the cladding layer and the substrate.

The substrate material is 316L stainless steel, and its size is 200 mm× 100 mm× 10 mm. Before the test, the substrate surface was fully polished with sandpaper, and then the substrate surface was cleaned with anhydrous ethanol to remove oil, impurities, etc. on the substrate surface. The cladding layer material is Ni60 high-temperature cemented carbide powder, and the powder is dried before use. The chemical composition of 316L stainless steel and Ni60 alloy powder is shown in Table 1 and Table 2 respectively.

In the actual test, the powder feeding rate of the airborne powder feeder is achieved by adjusting the speed of the powder feeding disk, and the adjustment range of the turntable speed is 0~5.8 r/min. For the convenience of actual operation, the author uses the speed of the powder feeding disk to represent the powder feeding rate. In order to facilitate the comparison with other scholars’ related research, the corresponding relationship between the rotating speed of the turntable and the amount of powder output per unit time is given through actual measurement, as shown in Figure 4.

3.2 Analysis method

3.2.1 Crack density

Crack density represents the crack sensitivity under different process parameters, which is defined as the number of cracks per unit length, and the expression is (7). In the formula: Re is the crack density, the unit is mm-1; C is the number of cracks; L is the cladding length, the unit is mm.

3.2.2 Dilution rate
The dilution rate indicates the degree of melting of the matrix material, and its expression is (8). Where: φ is the dilution rate; H is the melting height, in mm; h is the melting depth, in mm. If the dilution rate is too small, it indicates that the metallurgical bonding effect is poor and the coating is easy to peel off; if the dilution rate is too large, it means that there are more matrix components entering the cladding layer, the cladding layer has poor performance and has a tendency to crack. Therefore, it is necessary to reasonably control the size of the dilution rate.

3.2.3 Forming coefficient
The forming coefficient is a parameter that comprehensively considers the width of the cladding layer and the melting depth of the matrix. It can better characterize the quality of the cladding layer. The calculation formula of the forming coefficient is (9).
Where: η is the forming coefficient; W is the melting width, in mm. A large forming coefficient indicates that the forming quality of the cladding layer is relatively excellent.

3.2.4 Range
Range analysis can determine the primary and secondary factors that affect the test results. The larger the range value, the greater the influence of the factor on the test results, and it is the primary factor. Otherwise, it is the secondary factor. Let Sij be the sum of the results of factor i at level j, as shown in formula (10) in the figure.
Where: vij is the test result of factor i at level j; m is the number of levels. The range calculation formula is: see formula (11) in the figure. Where: Di is the range; n is the number of factors.

3.2.5 Regression optimization
Regression orthogonal test design can establish a regression equation with high accuracy and good statistical properties based on less test data to solve the test data optimization problem. Use orthogonal test data to establish a regression equation, as shown in formula (13) in the figure. Where: mindex is the number of evaluation indicators; ω i is the weight value; xi a and xi b are the upper and lower limits of different test factors respectively; f0 is the optimal value of the evaluation index.

4 Single-layer single-pass cladding test and result analysis
4.1 The first orthogonal test
The single-layer single-pass test is the basis for implementing regional laser cladding. The purpose of this test is to find out the main factors affecting the quality of the cladding layer, analyze the influence of single factors on the quality of the cladding layer, preliminarily determine the value range of process parameters, and provide guidance for the selection and optimization of process parameters in the second orthogonal test.
The test selected three cladding parameters, laser power P, scanning speed Vs and powder feeding rate Vt, as influencing factors. Each factor was set to 4 levels, namely, the laser power was 1000, 1200, 1400, and 1600 W, the scanning speed was 4, 5, 6, and 7 mm/s, and the powder feeding rate was 1.0, 1.2, 1.4, and 1.6 r/mm. The L16 (45) type orthogonal table design test scheme was adopted to obtain a total of 16 groups of test parameters from A1 to A16. According to the test parameters, the laser cladding orthogonal test was carried out to obtain 16 alloy cladding layers with a length of 40 mm. The cladding layer was subjected to color flaw detection, and the crack density of the cladding layer under each process was obtained according to the number of cracks in each cladding layer, as shown in Figure 5. The crack density was analyzed by range analysis, and the results are shown in Table 3. It can be seen from Table 3 that the range of laser power and powder feeding rate is basically the same, and the range of scanning speed is second. It can be seen that in single-pass cladding, the influence of laser power and powder feeding rate on the crack density of the cladding layer is greater than that of scanning speed. Therefore, it can be given priority to control the crack density of the cladding layer by changing the laser power and powder feeding rate.

4.2 Controlled single variable cladding test
Based on the results of the first orthogonal test, the controlled variable method was used to conduct the cladding test again, and the influence of the powder feeding rate, laser power and scanning speed on the crack density of the cladding layer was further analyzed, so as to determine the value range of the single factor.
Figure 6 (a) shows the influence of the powder feeding rate (0.5~1.0 r/min) on the crack density when the laser power is 1600 W and the scanning speed is 4 mm/s. It can be seen that when the powder feeding rate is less than 0.7 r/min, no cracks are generated. When the powder feeding rate reaches 0.8 r/min, the curve begins to turn and cracks begin to appear. The larger the powder feeding rate, the more cracks there are. When the laser power and scanning speed remain unchanged, that is, when the laser line energy is constant, the heat input per unit length remains unchanged, but the powder feeding rate increases, resulting in a decrease in mass energy, and the alloy powder cannot be fully melted, which leads to uneven organization to a certain extent, thus causing cracks.
Figure 6 (b) shows the effect of laser power (1300~1800 W) on crack density when the scanning speed is 4 mm/s and the powder feeding rate is 0.7 r/min. It can be seen that: as the laser power increases, the number of cracks gradually decreases. When the laser power reaches 1500 W, the cracks are almost invisible. As the laser power continues to increase, the cracks disappear. When the powder feeding rate and scanning speed remain unchanged, as the laser power increases, the applied energy increases, the energy absorbed by the cladding layer increases, the alloy powder is fully melted, and the cracks in the cladding layer are reduced.
Figure 6 (c) shows the effect of scanning speed (3-8 mm/s) on crack density when the powder feeding rate is 0.7 r/min and the laser power is 1600 W. It can be seen that the smaller the scanning speed, the fewer cracks in the cladding layer. When the scanning speed is as low as 4 mm/s, the cracks disappear. When the laser power and powder feeding rate remain unchanged, as the scanning speed increases, the laser line energy decreases, the laser energy absorbed by the cladding layer per unit time decreases, the cooling rate increases, the temperature gradient increases, the thermal stress increases, and the cracking tendency of the cladding layer increases. At the same time, the accelerated condensation rate may also lead to uneven organization, thereby increasing the probability of cracking.
According to the effect of a single variable on crack density, parameter adjustment is performed on the basis of the process parameters of the first orthogonal test, that is, the laser power is appropriately increased, the powder feeding rate and the scanning speed are reduced to reduce the crack sensitivity of the cladding layer.
4.3 Second orthogonal test
Based on the results of the first orthogonal test, the process parameters were re-determined and the second orthogonal test was carried out. In this test, the laser power was appropriately increased and the scanning speed and powder feeding rate were reduced, that is, the laser power was 1400, 1600, 1800, and 2000 W, the scanning speed was 3.0, 4.0, 5.0, and 6.0 mm/s, and the powder feeding rate was 0.4, 0.6, 0.8, and 1.0 r/min. The L16 (45) type orthogonal table design test scheme was adopted to obtain a total of 16 groups of test parameters from B1 to B16. According to the test scheme, the laser cladding test was carried out to obtain 16 alloy cladding layers. The cladding layer was subjected to a color flaw detection test. The flaw detection results showed that among the 16 groups of tests, only 6 groups of specimens had crack defects, and no cracks were generated in the other groups. Compared with the first orthogonal test, the crack situation of the cladding layer has been greatly improved, indicating that the process parameter values ​​of this test are within the range of the cladding layer that can be prepared.

The cladding layer was cut along the cross-sectional direction using a wire cutting machine, and then ground with 120, 400, 600, 800, 1000, and 1500 mesh SiC sandpaper in turn. After grinding, the surface of the cladding layer was corroded with 4% nitric acid ethanol solution. After the surface corrosion of the cladding layer was completed, the cross-sectional morphology of the specimen was observed using an optical microscope (as shown in Figure 7), and the data of the 16 cladding layers such as the molten width, molten depth, and molten height were measured. The dilution rate φ and the forming coefficient η were calculated according to equations (8) and (9).
Figure 8 shows the effect of laser power, scanning speed, and powder feeding rate on the dilution rate of the cladding layer. As can be seen from Figure 8 (a), the dilution rate is positively correlated with the laser power, and increases with the increase of laser power. This is because as the laser power increases, the heat absorbed by the substrate increases, the depth of the molten pool increases, and the dilution rate of the cladding layer increases. As can be seen from Figure 8 (b), the dilution rate first decreases slightly and then increases with the increase of the scanning speed, and the dilution rate is the lowest when the scanning speed is 4.0 mm/s. This shows that the change of scanning speed has a similar effect on the height and depth of the molten pool. As can be seen from Figure 8 (c), the dilution rate is negatively correlated with the powder feeding rate, that is, the dilution rate decreases with the increase of the powder feeding rate. This is because as the powder feeding rate increases, the amount of powder input into the molten pool increases, the energy absorbed by the powder increases, and the energy absorbed by the substrate decreases, thereby increasing the height of the cladding layer while reducing the melting depth of the substrate.
Figure 9 shows the effect of laser power, scanning speed, and powder feeding rate on the forming coefficient. As can be seen from Figure 9 (a), the forming coefficient is negatively correlated with the laser power, that is, the forming coefficient decreases with the increase of the laser power. This is because as the laser power increases, the width of the cladding layer almost no longer changes, but the energy density input to the substrate increases, the melting depth increases, and therefore the forming coefficient decreases. As shown in Figure 9 (b), the forming coefficient generally increases with the increase of scanning speed, and the forming coefficient decreases slightly when the scanning speed is 5.0 mm/s. This is mainly because as the scanning speed increases, the heat input to the substrate per unit time decreases, and the melting depth decreases. As shown in Figure 9 (c), the forming coefficient first increases and then decreases slightly with the increase of powder feeding rate, reaching the maximum when the powder feeding rate is 0.8 r/min. This is because the increase of powder feeding rate is conducive to the increase of melting width, while the laser energy is constant, resulting in limited melting depth of the substrate.

Through the range analysis, it can be seen that among the three factors affecting the dilution rate, the range of the powder feeding rate is the largest, which is 0.26675, the range of the laser power is close to it, which is 0.26525, and the range of the scanning speed is very small, which is 0.0585. This shows that in single-pass cladding, the scanning speed has little effect on the dilution rate, and it can be given priority to adjust the dilution rate of the cladding layer by changing the powder feeding rate, and then consider the laser power. Among the three factors affecting the forming coefficient, the range of the laser power is the largest, which is 21.4175, the range of the scanning speed is the second largest, which is 19.41, and the range of the powder feeding rate is the smallest, which is 10.585. This shows that in single-pass cladding, the powder feeding rate, scanning speed, and laser power have an increasing influence on the forming coefficient. It is possible to give priority to adjusting the forming coefficient of the cladding layer by changing the laser power.
Cracks are important indicators for evaluating the quality of the macroscopic forming of the cladding layer. The dilution rate and forming coefficient are important indicators of whether the microscopic cladding layer structure and metallurgical bonding strength meet the requirements. The changes in process parameters have a coupling relationship on the three. Therefore, the author will use the multivariate linear regression model to predict the comprehensive quality of the cladding layer and optimize the parameters to obtain the best combination of cladding process parameters.
4.4 Prediction of cladding layer quality and process verification
According to the results of the second orthogonal test, the crack density, dilution rate and forming coefficient of the cladding layer are taken as the quality response targets of the cladding layer, and the laser power x, scanning speed x2 and powder feeding rate x3 are taken as input factors. The Box-Behnken Design method is used to obtain the multivariate linear regression model considering the crack density, dilution rate and forming coefficient of the cladding layer, that is, see formulas (14)-(16) in the figure.
Where: f1 (x), f2 (x) and f3 (x) are the regression equations of crack density, dilution rate and forming coefficient respectively.
Figure 10 is the normal distribution diagram of the residuals of the multivariate linear regression equation. The residuals of the three sets of regression equations are randomly distributed around the straight line. It can be seen that the three sets of equations have a relatively good fit to the random error, good adaptability, and can meet the needs of prediction. Figure 11 is a comparison of the predicted values ​​and experimental values ​​of the crack density, dilution rate and forming coefficient of the cladding layer. All the points in the figure are distributed near the straight line, indicating that the model prediction results are highly consistent with the experimental results and have good consistency. Although there are two data points with large offsets in the normal distribution diagram of the crack density residual shown in Figure 10 (a), the crack density test value in Figure 11 (a) is consistent with the predicted value. The value of the multivariate coefficient R2 is 0.9619, close to 1, and the coefficient of the crack density regression model is 0.0022, less than 0.05, indicating that these two points with large offsets do not affect the accuracy of the model. For the dilution rate and forming coefficient, the values ​​of the multivariate coefficient R2 are 0.9914 and 0.9892, respectively, and the regression model coefficients are 0.00017 and 0.000035, respectively. This shows that the constructed regression model has high adaptability and accuracy.
Finally, the crack density Re = 0, dilution rate φ = 20%, and forming coefficient η = 25 are selected as the cladding layer quality target, the crack density weight ω 1 = 0.5, the dilution rate weight ω 2 = 0.25, and the forming coefficient weight ω 3 = 0.25 are taken to establish the optimization objective function, which is shown in formula (17).

Taking the minimum value of the objective function as the optimization target, the particle swarm optimization algorithm is used to optimize and solve it, and the optimal process parameter combination that meets the cladding layer quality target is obtained: laser power P = 1405 W, scanning speed Vs = 5.7 mm/s, powder feeding rate Vt = 0.4 r/min. In order to verify the effectiveness and repeatability of the optimized process parameters, three single-pass cladding tests were carried out using the optimized parameters, and the surface and color flaw detection results of the cladding layer are shown in Figure 12. It can be seen that the surface of the cladding layer is smooth, without crack defects, and the process repeatability is good.

5 Single-layer multi-pass cladding test and result analysis
5.1 Overlap rate test
After obtaining the optimized process parameters of high-quality cladding layer through single-layer single-pass test, single-layer multi-pass cladding test was then carried out to achieve large-area cladding of Ni60 alloy powder on 316L stainless steel substrate. Overlap rate is a key parameter affecting the roughness of multi-pass cladding layer. Therefore, under the conditions of laser power P = 1405 W, scanning speed Vs = 5.7 mm/s, and powder feeding rate Vt = 0.4 r/min, the designed overlap rates are 30%, 35%, 40%, 45% and 50%, and the overlap offsets are 2.1, 1.95, 1.8, 1.65, and 1.5 mm, respectively, and single-layer multi-pass cladding tests are carried out. The total number of cladding passes is 10, and the cladding length is 40 mm. The five groups of cladding layers obtained from the experiment and their color flaw detection results are shown in Figure 13. It can be seen that the laser cladding process parameters obtained by the multivariate regression quality prediction method can produce a cladding layer with a smooth surface and no cracks.

Figure 14 shows the cross-sectional morphology of the cladding layer under different overlap rates. It can be seen that: when the overlap rate is 30%, 35% and 40%, the overlap area between the passes is small, and the melt filling between the passes is insufficient, resulting in an uneven surface of the cladding layer; when the overlap rate reaches 45%, the surface quality of the cladding layer is improved, with slight unevenness; when the overlap rate is 50%, the surface of the cladding layer tends to be flat and can meet the requirements for surface flatness. Therefore, when the overlap rate is 45%, the surface of the cladding layer can basically meet the flatness requirements; when the overlap rate is 50%, the flatness of the single-layer multi-pass cladding layer is better.

Figure 15 (b) shows the Ni60 alloy cladding layer prepared on a 316L stainless steel substrate by Lin Yinghua et al. after applying electromagnetic composite field (EMCF) during laser cladding. Figure 15 (a) shows the cladding layer prepared without applying electromagnetic composite field. They mainly used electric field and magnetic field to generate induced Lorentz force and directional Lorentz force in the laser molten pool area to affect the fluid flow inside the molten pool and enhance the equivalent buoyancy of the particles. Li Qi first preheated the substrate to 300 ℃ and 500 ℃ respectively, and then laser clad Ni60 powder. After cladding, the cladding sample was placed in an incubator for 1 h. The cladding layer obtained is shown in Figure 16. In comparison, applying electromagnetic composite field in the laser cladding process puts forward higher requirements on the type and cost of cladding equipment, and the process is more complicated; the method of preheating the substrate to about 500 ℃ for laser cladding and then keeping it warm can be realized for parts with small volume and regular shape, but it has certain limitations for parts with large volume and irregular structure, and it has high requirements for heating, insulation equipment and handling tools; while the process optimization method of orthogonal test combined with quality prediction proposed by the author does not require preheating treatment, is easy to implement, and the obtained cladding layer is more uniform and the surface is smoother.

5.2 Microstructure analysis
The microstructure of the cross section of the cladding layer was observed by an inverted metallographic microscope. Figure 17 shows the organizational structure diagrams of the bonding zone, middle and top of the cladding layer respectively. It can be seen that the molten pool presents a typical rapid solidification characteristic organization. From the bonding zone to the surface of the cladding layer, the grain size gradually decreases, and the organizational morphology shows a transition from planar crystals, dendrites to equiaxed dendrites. As shown in Fig. 17 (a), a planar crystal thin layer appears in the junction area between the substrate and the cladding layer. This is because there is a rapid cooling and heating effect during the cladding process. In the junction area near the substrate and the cladding layer, the heat at the bottom of the molten pool is quickly extracted through the substrate, the temperature gradient is large, and the composition is undercooled, which is conducive to the growth of fine planar crystals. The appearance of the planar crystal thin layer indicates that the Ni60 alloy powder and the 316L stainless steel substrate form a dense metallurgical bond. Dendrites perpendicular to the bonding surface appear above the planar crystal thin layer. This is because the temperature gradient near the substrate is large, the cooling rate is fast, and the grains preferentially grow epitaxially along the reverse direction with the largest heat loss rate after nucleation. In addition, under a negative temperature gradient, the cladding layer crystals grow in a dendritic manner. As shown in Fig. 17 (b) and (c), under a faster cooling rate and molten pool stirring, the microstructure in the middle and top of the cladding layer is composed of fine and uniform equiaxed crystals and equiaxed dendrites. This is because the heat dissipation form at the top has changed under the combined effect of the convective heat dissipation caused by the protective gas and the conductive heat dissipation of the solidified cladding layer, resulting in the transformation of the dendrites to equiaxed dendrites in the middle and top of the cladding layer.

Then the microstructure and chemical composition of the middle part of the Ni60 cladding layer were observed and analyzed. The microstructure of the Ni60 cladding layer sample is shown in Figure 18 (a), which is mainly composed of black block structure, dark gray dendrite structure and light gray matrix structure. The energy dispersive X-ray spectrometer (EDS) was used to analyze the components of A, B, and C in Figure 18 (a), and the EDS spectra of these three places are shown in Figures 18 (b) to (d). It can be seen that: the block structure at A contains a large amount of Cr, which is a segregated structure of Cr elements; the dark gray dendrite structure at B contains more Cr, Ni, Fe, etc., which is judged to be an intergranular eutectic hard phase; the light gray matrix at C contains a large amount of Ni elements and a small amount of Cr and Fe elements, which is judged to be a solid solution dominated by Ni.

In order to identify the phases in the cladding layer, X-ray diffraction (XRD) analysis was performed on the middle part of the cladding layer, and the XRD phase analysis results are shown in Figure 19. By comparative analysis, it can be seen that the phases in the cladding layer are mainly γ-Ni solid solution, M23C6, M7C3 (M=Fe, Ni, Cr), C0.055Fe1.945, CrB, NiB2, Ni31Si12, etc. The diffraction peak of γ-Ni solid solution in the cladding layer is the strongest. This is because when the molten pool solidifies, the Ni atoms at the bottom of the molten pool form solid solution nuclei. During the growth of the nuclei, they need to absorb the molten Ni atoms in the molten pool, so a large amount of Ni-rich γ-solid solution is formed in the fusion zone. There are complex hard phases in the cladding layer, such as M23C6, M7C3 (M=Fe, Ni, Cr), C0.055Fe1.945, CrB, etc. These hard phases are scattered, which increases the hardness and brittleness of the cladding layer.

5.3 Microhardness analysis
The cross-sectional hardness of the cladding layer was measured using a micro Vickers hardness tester, with a load of 4.9 N and a loading time of 15 s. The transverse microhardness and longitudinal microhardness obtained are shown in Figure 20 and Figure 21, respectively. Each hardness value in the figure is the average hardness of the three adjacent measuring points. As can be seen from Figure 20, the minimum transverse microhardness of the cross-sectional area of ​​the cladding layer is 670 HV, the maximum is 760 HV, and the hardness has an increasing trend along the cladding direction. As can be seen from Figure 21, the longitudinal microhardness of the cross-sectional area of ​​the cladding layer is evenly distributed in the middle of the coating, with a minimum value of 641 HV and a maximum value of 739 HV, and there is a slight upward trend, which is due to the formation of chromium carbide. The hardness of 316L stainless steel substrate is in the range of 219~231 HV, and the hardness of Ni60 alloy cladding layer is about 2.8~3.4 times of the hardness of 316L stainless steel substrate.

6 Conclusion

The experiment of preparing Ni60 nickel-based cladding layer by single-layer single-pass laser cladding on 316L stainless steel surface was carried out. The multivariate linear regression model was constructed by combining orthogonal test method with controlled variable test method, and then the process parameters were optimized by particle swarm optimization algorithm. The optimal process parameter combination of crack-free cladding layer was obtained as follows: laser power P = 1405 W, scanning speed Vs = 5.7 mm/s, powder feeding rate Vt = 0.4 r/min.

The single-layer multi-pass cladding test under different overlap rates was carried out using the best process parameter combination. The results showed that the cladding layer surface was flat under 50% overlap rate, and the inter-pass organization was well connected, which met the requirements for the macroscopic morphology of the cladding layer surface when large-area cladding was performed on the surface of the part.
The microstructure of the cladding layer was observed by metallographic microscope. It was found that from the bonding zone to the surface of the cladding layer, the organization morphology showed a transition from planar crystals, dendrites to equiaxed dendrites. The microstructure of the middle and upper part of the cladding layer was composed of small and disorderly equiaxed crystals and equiaxed dendrites. The cladding layer showed typical rapid solidification organization characteristics, and the Ni60 alloy powder formed a dense metallurgical bond with the 316L stainless steel matrix.

From the flaw detection map and cross-sectional morphology of the cladding layer, it can be found that the cladding layer has no obvious crack defects and the cladding layer has high quality. The measurement of its microhardness shows that the hardness of the Ni60 alloy cladding layer is about 2.8 to 3.4 times that of the 316L stainless steel substrate, and the substrate surface strengthening effect is significant.