This paper explores the optimization of process parameters for preparing Mo2FeB2 coating on 45 steel surface by laser cladding technology. The orthogonal experiment was designed by Taguchi method, and laser power, scanning speed and powder feeding rate were selected as process parameters. The dilution rate and microhardness were used as response targets for evaluating the forming quality. The relationship between process parameters and response targets was studied by signal-to-noise ratio and variance analysis. The process parameters were optimized by combining grey correlation analysis, and the best process parameter combination was obtained: laser power 5 000 W, scanning speed 13.6 mm/s, powder feeding rate 15 g/s. The laser cladding experiment was carried out using this parameter combination. The results showed that compared with the results obtained by the orthogonal experiment, both response targets were improved, and the coating quality was significantly improved, which verified the reliability of the Taguchi-grey correlation method.
Laser cladding is a surface modification technology that uses high-energy-density laser beam irradiation to melt powder particles and micro-melt on the substrate surface, and form a metallurgical bond after cooling and solidification. This process involves nonlinear coupling of light, powder and gas, but it is difficult to directly describe the relationship between each process parameter and the quality of the cladding layer, so it is usually necessary to optimize the process parameters.
Zhao Jianfeng et al. used the grey correlation analysis method to optimize the process parameters of roller laser cladding Ni-WC25. The results showed that the dilution rate of the cladding layer obtained after parameter optimization was reduced by 10.07%, the hardness was increased by 13.45%, and the wear rate was reduced by 38.46%. Yang Kaixin et al. optimized the process parameters of laser cladding Fe06-15%TiC layer based on Taguchi-grey correlation method, and obtained a smaller dilution rate and a higher grey correlation degree. The cladding layer had no obvious defects. Lian et al. used Taguchi experiment combined with grey correlation analysis to improve the process parameters of laser cladding preparation of W6Mo5Cr4V2 coating, and found that the overlap rate had the most significant effect on the surface flatness of the coating, and the laser power had the greatest effect on the cladding efficiency, and the error between the predicted value and the experimental result was controlled within 1%. Yu et al. used Taguchi grey correlation method to optimize the parameters of laser cladding Fe313 on S55C steel substrate. The results showed that the grey correlation value increased by 0.153, and the optimized cladding layer was significantly better than other coatings in morphology and microstructure. It can be seen that Taguchi design method can quickly and accurately optimize single-objective response parameters with a small number of experiments. For multi-objective optimization problems, combined with grey correlation for comprehensive analysis, the significance of the relationship between factors can be effectively determined.
On the other hand, Mo2FeB2-based cermets use cheap iron-based alloys as bonding phases. Due to their high hardness, excellent wear and corrosion resistance, and good high-temperature oxidation resistance, they are widely used in the field of high-performance coatings. At present, there are few studies on the preparation of Mo2FeB2 coatings by laser cladding.
Based on this, this study intends to laser clad Mo2FeB2 coating on the surface of 45 steel, adopts Taguchi method to design orthogonal experiment, performs signal-to-noise ratio and variance analysis on experimental data, and optimizes process parameters by combining grey correlation analysis method, selects the best process parameter combination, compares predicted values with experimental values, characterizes the microstructure and composition of the prepared coating, verifies the reliability of Taguchi-grey correlation method, in order to provide reference for extending the service life of 45 steel and expanding the application of ternary borides in the industrial field.
1 Experiment
1.1 Materials
In this paper, 45 steel round hollow rods with a diameter of 200 mm and a wall thickness of 8 mm are used as the substrate. Before the experiment, the substrate surface is polished with sandpaper and cleaned with alcohol to remove impurities to improve its surface quality. The cladding powder is Mo2FeB2 prepared by vacuum induction gas atomization method, and the particles with a particle size of 48~106 μm are obtained by sieving the powder. Table 1 shows the chemical composition of the substrate material and powder.
After cladding, the sample was cut perpendicular to the scanning direction, with a size of 45 mm×15 mm×8 mm. After grinding and polishing, the cladding layer and the substrate were etched with K3[Fe(CN)6]4+NaOH+H2O etching solution (volume ratio of 1:1:10) and 4% volume fraction nitric acid alcohol, respectively, for 10 s, and then cleaned with alcohol and blown dry for use.
1.2 Experimental equipment and characterization method
The experimental equipment is LYH3050L ultra-high-speed laser cladding machine tool, the protective gas is argon, the flow rate is 10 L/min, the synchronous powder feeding method is adopted, and the laser beam spot is a rectangular spot of 1 mm×20 mm.
The cross-sectional morphology of the coating was observed using a ZEISS Axio Plan2 optical microscope (OM); the phase composition of the coating was identified using an X-Pert MPD X-ray diffractometer (XRD) equipped with a Cu target, with a voltage and current of 40 kV and 20 mA, respectively; the microstructure of the coating was observed using a Nova 400 Nano field emission scanning electron microscope (SEM), and the elemental composition of the cladding layer was analyzed using an attached INCA IE 350 PentaFET X-3 energy dispersive spectrometer (EDS); the microhardness of different regions from the coating to the substrate was measured using an HX-500 microhardness tester, with a load of 500 g and a holding time of 10 s. The hardness result was the average of multiple measurements.
1.3 Taguchi design method
Replace the rotation speed in the initial process parameters with the scanning speed, and the relationship between the two is shown in formula (1): See the formula in the figure.
Where: B is the scanning speed, mm/s; R is the rotation speed, r/h, the rotation speed is set to 62, 78, 93 r/h, and the corresponding scanning speed is 10.8, 13.6, 16.2 mm/s. In Taguchi design, the interaction between parameters is not considered. The levels of each factor are detailed in Table 2. Figure 1 shows different areas of the cladding layer cross section, including the cladding layer (CZ), molten pool (MZ), heat affected zone (HAZ) and substrate (SZ). The cladding layer width is recorded as W, the cladding layer height is H, and the molten pool depth is h. The dilution rate η can be calculated by formula (2): See the formula in the figure.
Where: A1 represents the cladding layer area, A2 represents the molten pool area, and the unit is mm2. In industrial applications, it is very important to reasonably control the dilution rate. If the value is too large, it may cause cracking and deformation of the coating, and if it is too small, it may cause poor metallurgical bonding between the coating and the substrate.
2 Results and discussion
2.1 Coating morphology analysis
Figure 2 shows the macroscopic morphology of the cladding layer obtained by 9 groups of different process parameters. It can be seen that the surface of the cladding layer is smooth and continuous as a whole, with a metallic luster, and no unmelted powder adhesion or macro cracks are observed, indicating that its quality is relatively good. The microscopic morphology of each cross-sectional layer is shown in Figure 3. The measurement data show that the maximum height of the coating can reach 1.047 mm (sample No. 7) and the minimum is 0.412 mm (sample No. 3). The microstructure shows that large-sized hard phase particles are evenly distributed, and no obvious defects such as cracks and pores are found. In addition, the boundary between the substrate and the molten pool is clear, indicating that the cladding process is relatively stable.
2.2 Response signal-to-noise ratio analysis
The signal-to-noise ratio (SNR) is an important indicator for measuring process robustness. It is used to evaluate the stability of each response and quantify the degree of closeness of each response to its ideal value. According to the research purpose, it can be divided into long-term characteristics (LTB) and short-term characteristics (STB). For dilution rate, a smaller value is more ideal and STB is suitable; for microhardness, a higher value is better, so LTB is used to calculate the signal-to-noise ratio, as shown in formula (3). The converted results are shown in Table 3.
Where: S/N is the signal-to-noise ratio; n is the total number of experiments, where n = 9; yi represents the experimental value of the i-th group under a certain target.
2.3 Analysis of variance of response signal-to-noise ratio
A variance analysis was performed on the dilution rate and microhardness signal-to-noise ratio, and the confidence interval was set to 95%. The results are shown in Table 4. According to the F distribution table, F0.05 (2, 2) = 19; F0.01 (2, 2) = 99. By comparison, the dilution rate signal-to-noise ratio is FB＞F0.01（2,2）, F0.05（2,2）＜FC＜FA＜F0.01（2,2）, indicating that B has a significant effect on the dilution rate, A and C have significant effects and the difference is not large, and the influence degree is ranked as follows: scanning speed＞laser power＞powder feeding rate. For the microhardness signal-to-noise ratio, FC＞F0.01（2,2）, F0.05（2,2）＜FB＜FA＜F0.01（2,2）, that is, C has a significant effect on the microhardness, A and B have significant effects, and the influence degree is ranked as follows: powder feeding rate＞laser power＞scanning speed.
Figure 4 shows the main effect diagrams of dilution rate and microhardness signal-to-noise ratio, respectively. It can be seen that for the dilution rate, as the laser power increases, the dilution first decreases and then increases. This is because in the initial stage, the substrate continuously absorbs energy, the molten pool area increases, and the dilution rate increases; when the power is increased to a certain extent, the molten pool area is stable, and the coating area continues to increase, resulting in a decrease in the dilution rate. With the increase of scanning speed, the dilution first decreases and then increases. This is because at a lower scanning rate, the input energy per unit time decreases, and the coating area decreases more than the molten pool, resulting in a decrease in the dilution rate; when the scanning speed is too high, the molten pool area decreases more than the coating, and the dilution rate increases. With the increase of powder feeding rate, the dilution rate continues to decrease. This is because as the powder supply increases, the absorbed energy increases, the coating area expands, and the shielding effect on the laser increases, the molten pool area decreases, and the dilution rate decreases.
For microhardness, microhardness is greatly affected by laser power. When the power is low, the grains grow fully and the hardness increases; excessive power causes the volatilization of alloy elements and excessive growth of grains, resulting in a decrease in hardness; the increase in scanning speed leads to insufficient formation of hard phases, thereby reducing the coating density and hardness. High powder feeding rate helps to form more fine solid solutions and hard phases, thereby improving the microhardness of the coating.
2.4 Grey correlation analysis
First, the signal-to-noise ratio results are normalized to a range between 0 and 1, and calculated by formula (4): see the formula in the figure.
Where: Xi is the normalized value of the signal-to-noise ratio of the i-th group under a certain target, Yi is the original signal-to-noise ratio; min(Yi) and max(Yi) are the minimum and maximum values of the signal-to-noise ratio in the target, respectively.
According to formula (5), the grey correlation coefficient (GRC) is calculated to measure the degree of closeness of each group of parameters to the ideal situation (reference value), that is: see the formula in the figure.
Where: ξi is the grey correlation coefficient of the i-th group under a certain target; xi0 is the reference value, which is 1; ξ is the discrimination coefficient, which is 0.5.
Finally, ξi is substituted into formula (5) to calculate the grey correlation degree (GRG) to determine the best process parameter combination, and the two response targets have the same weight, that is: see formula (6) in the figure.
Where: γi(k) is the gray correlation degree of the i-th group; m is the target number, and m = 2 in this study.
According to the gray correlation analysis results in Table 5, the optimal process parameter combination is A1B2C3: laser power 5500 W, scanning speed 13.6 mm/s, powder feeding rate 15 g/s (sample No. 5), which showed the highest gray correlation signal-to-noise ratio (S/N)γ in the experiment, showing excellent comprehensive performance.
The results of the variance analysis of the gray correlation signal-to-noise ratio are shown in Table 6. It can be seen from Table 6 that the influence of factors A, B, and C are all very significant, and the influence degree is ranked as follows: scanning speed> powder feeding rate> laser power. Further analysis of the mean GRG signal-to-noise ratio in Table 7 can determine that the optimal process parameter combination is A1B2C3 according to the level of the maximum average GRG signal-to-noise ratio in each factor: laser power 5000 W, scanning speed 13.6 mm/s, powder feeding rate 15 g/s.
2.5 Verification and prediction
The multi-objective optimization prediction formula is: see formula (7) in the figure.
Where: γp is the predicted value of GRG; γm is the average value of GRG; γj is the average value of GRG under the jth process parameter selected level; N is the total number of process parameters, where N = 3.
From the comparison between the verification experiment results and the orthogonal optimal results in Table 8, it can be seen that compared with the orthogonal experimental results, after parameter optimization, the dilution rate decreased by 1.2 percentage points and the microhardness increased by 7.05%. It can be seen that both response targets have improved. Only one-step analysis shows that the error between the gray correlation degree obtained in the verification experiment and the predicted value is only 2.48%, indicating that the gray correlation analysis optimization of multi-objectives has a strong predictive control ability and its reliability has been verified, which is consistent with the results reported in the literature [16].
2.6 Coating phase analysis
Figure 5 shows the XRD spectra of the verification sample and sample No. 5 prepared based on the optimized process parameters. As shown in Figure 5, in contrast, the two coatings have a higher content of Mo2FeB2 phase with different peak intensities, in addition to MoFe2B4, Mo2FeB4, Mo2Fe13B5 hard phases, carbides Cr3C2, Cr7C3, Fe2C, Fe7C3, binary borides Cr2B, Cr5B and Fe-Cr, Fe-Ni solid solutions. In the molten pool, due to the strong affinity between Cr and B elements, Cr2B and Cr5B3 phases are easily formed. Carbides Cr3C2 and Cr7C3 can be synthesized in situ, and the synthesis reaction becomes more intense with increasing temperature. The formation of these hard phases and carbides significantly enhances the performance of the coating, and this enhancement is mainly achieved through the second phase strengthening mechanism.
2.7 Coating microstructure analysis
The typical microstructures and EDS composition analysis results of the two coatings are shown in Figure 6 and Table 9, respectively. As shown in Figure 6, the coating structure is divided into gray, bright white, light and dark, and fishbone, feather, small fragments and other irregular structures, as well as the agglomeration of hard phase particles can be observed. Under laser irradiation, Mo2FeB2 powder decomposes, and some elements melted by heat on the substrate surface diffuse into the coating, and form hard phases of different forms after rapid solidification;
The molten pool has fluidity. Since the surface tension at the edge of the coating is greater than that inside, the inside expands to the edge during the cladding process, forming Marangoni convection. This fluidity makes the structure grow regularly along the convection and heat dissipation direction. The spacing between larger powders increases during the melting process, resulting in particle agglomeration.
In the samples prepared based on the optimal process parameters, fishbone and feather structures are more than sample 5. This may be because the higher laser power enhances the convection effect, promotes the reaction of more elements to form hard phases, and also improves the density of the coating. The agglomerated particle size is smaller than that of sample 5, which is related to the larger temperature gradient. This condition promotes the nucleation rate of grains and refines the grains.
Combining Table 9 and Figure 5, it can be seen that the Fe content in the dark structure (1, 5) is higher than that in the Mo element, and contains C and Cr elements. It is inferred that the structure is Mo2FeB2, MoFe2B4, Mo2Fe13B5, Cr3C2, Cr7C3, Fe2C, Fe7C3 and other phases; the Fe and Mo content of the light dark structure (2, 6) is similar, and the Cr element is higher. It is inferred that the structure is Mo2FeB2 CrB, Cr2B, Cr5B3 phase; the Mo element in the bright white structure (3, 7) is higher than that of the Fe element, and the structure is inferred to be Mo2FeB2, MoB2, Mo2B5 phase; the Mo element in the gray structure (4, 8) is very little, and there are a small amount of Ni and Cr elements. It is inferred that the structure is Fe-N, Fe-Cr solid solution and FeNi3, Fe3B, FeB and other phases.
2.8 Microhardness analysis
Figure 7 shows the microhardness changes of the two samples at the interface between the substrate and the coating. As can be seen from Figure 7, the overall microhardness shows a characteristic of gradually increasing from the substrate to the heat-affected zone and the coating, and no abnormal change in hardness value is observed. This is because the presence of hard phases such as Mo2FeB2, Cr3C2, Cr7C3 and solid solutions Fe-Cr and Fe-Ni in the coating significantly improves the hardness of the coating; in addition, the rapid heating and cooling characteristics of laser cladding contribute to grain refinement, thereby increasing the hardness. In the heat-affected zone, due to the diffusion and reaction between elements to form solid solutions, these solid solutions are evenly distributed in the structure, playing a role in solid solution strengthening, making the hardness of the heat-affected zone higher than that of the substrate.
The microhardness HV0.5 of the verification sample and the No. 5 sample substrate is 281.79 and 273.36 respectively, and the hardness of the coating is roughly 4.3 and 4.1 times that of the substrate. This shows that the coatings of both samples effectively improve the hardness of the substrate, and the strengthening effect of the verification sample is more significant. This may be due to the larger temperature gradient in the coating of the verification sample, which forms more uniformly distributed hard phases. In contrast, the agglomerated particle size in sample No. 5 is relatively larger, resulting in a slightly lower hardness.
Conclusion
1) Based on the response signal-to-noise ratio and variance analysis, the factors affecting the dilution rate are ranked in order of importance: scanning speed > laser power > powder feeding rate; while the order of influence on microhardness is: powder feeding rate > laser power > scanning speed.
2) After further optimization by gray correlation analysis, the best process parameter combination is laser power 5 000 W, scanning speed 13.6 mm/s, powder feeding rate 15 g/s. Under this parameter setting, the dilution rate of the verification sample is 12.3%, and the microhardness reaches 1 211.6HV0.5. In contrast, the optimal process parameters of the orthogonal experiment are set as laser power 5500 W, scanning speed 13.6 mm/s, and powder feeding rate 15 g/s. Under these parameters, the sample dilution rate is 13.5%, and the coating microhardness is 1131.85HV0.5. By comparison, the microhardness of the verification sample increased by 7.05%, and the dilution rate decreased by 1.2 percentage points, indicating that both response targets have been improved. In addition, the grey correlation error between the verification experiment and the predicted value is only 2.48%, which verifies the feasibility of the multi-objective optimization method based on grey correlation analysis.
3) The optimized cladding layer performs better in microstructure and microhardness, and significantly improves the substrate hardness, which verifies the reliability of the Taguchi-grey correlation method.
