Optimization of Multi-Objective Process Parameters and Performance Analysis of High-Speed Laser Cladding of TC4/AISI431 Composite Coatings

Abstract

The authors of this paper investigated the process parameters of high-speed laser cladding of TC4/AISI431 composite coatings on the surface of C45 steel, choosing laser power, scanning speed, and TC4 addition as the experimental factors, and porosity, microhardness, and corrosion resistance as the target indices. A regression model was established based on the response surface methodology BBD, and the reliability of the model was analyzed using an ANOVA. Then, the WOA was used for multi-objective optimization. The optimal parameter set was determined as follows: a laser power of 5315 W, a scanning speed of 378 mm/s, and a TC4 addition of 3.6%. The microstructure and surface elemental composition of the coating were analyzed. The results showed that the porosity reduced by 60% and that the corrosion resistance improved by 79.98%, while the microhardness remained essentially unchanged.

1. Introduction

In the field of modern industrial manufacturing, the optimization of material surface properties remains a key research direction. Although the traditional means of surface treatment, such as electroplating and thermal spraying, can improve the surface properties of material to a certain extent, problems such as an insufficiently strong bonding force, an uneven coating quality, or a non-environmentally friendly process are often faced [1,2]. The emergence of high-speed laser cladding technology has provided an efficient and precise method for material surface modification [3,4]. High-speed laser cladding has a faster scanning speed than conventional laser cladding, and it can refine the grains, thereby improving the coating hardness and abrasion resistance [5]; additionally, high-speed processing can reduce the heat input and thermal deformation of the substrate, which is important for precision parts.

As a medium-carbon structural steel, C45 steel is widely used in the manufacture of various mechanical components due to its cost-effectiveness and good overall mechanical properties. However, in some special working environments, such as in corrosive media or under highly abrasive working conditions, the surface of C45 steel is prone to corrosion and abrasion, which affects the service life and overall performance of the components. The application of composite coatings presents an effective optimization approach to enhance the surface properties of substrate materials [6,7]. AISI431 stainless steel has the advantages of high strength, high ductility, and good corrosion resistance, as well as good cutting and welding performance; it is widely used in spindles, nuts and bolts, piston rods, high-pressure pumps, and other key industrial components. TC4 titanium alloy possesses excellent strength, a light weight, and corrosion resistance [8,9,10], and it is widely used in aerospace, biomedical, and other fields. Both of these are ideal materials for improving the surface quality of substrates. In order to improve the quality defects of a TC4 titanium alloy fusion coating, Qin Jingpeng et al. [11] proposed a hybrid multi-objective optimization model based on RSM-NSGA-II. They selected the laser power, powder feeding speed, and scanning speed as the experimental factors, and the dilution rate, aspect ratio, and microhardness as the response factors. They carried out single-pass fusion coating experiments to analyze the optimization of the coating microstructure, and they found that the optimization of the coating parameters improved the various responses. However, the aforementioned study only focused on single-pass laser cladding. In the actual production process, the cladding layer is in a multi-pass lap state; therefore, the performance of the single-pass cladding layer will differ from that in actual applications. Aiming to prepare a crack-free Ni60 alloy cladding layer on the surface of stainless steel, Lian, Guofu et al. [12] proposed a multivariate regression prediction method, with the laser power, powder feeding speed, and scanning speed as the inputs and the crack density, dilution rate, and shaping coefficient as the optimization objectives, and the surface strengthening effect of the coating was improved. The optimization objectives of the above studies essentially focused on the dilution rate, coating aspect ratio, and other (mechanical) parameters, while few relevant studies have focused on optimizing the corrosion resistance, even though the corrosion resistance of the coating and the mechanical properties have a greater correlation.

Fused coatings of titanium alloy and iron-based materials are prone to the formation of intermetallic compounds, which can lead to cracks; thus, different additions of TC4 powders are chosen as a reference in this study. In addition, it has been proven that the interactions between laser power and scanning speed significantly affect cracks [13,14]. AISI431 stainless steel powder plus TC4 powder is chosen as the composite coating material because AISI431 is widely used and has a low cost, and the Ti and Al elements in TC4 are corrosion-resistant; furthermore, the addition of a small amount of TC4 is intended to improve the corrosion resistance of the coating without producing cracks.

Therefore, the authors of this paper examine the high-speed laser cladding process, using C45 steel as the substrate; AISI431 stainless steel powder plus TC4 powder as the composite coating material; and porosity, microhardness, and corrosion resistance as the optimization objectives. The aim is to prepare high-quality composite coatings, which will not only help to improve the reliability of medium-carbon structural steel parts in harsh working environments but will also be of great significance in promoting the practical application of high-speed laser cladding technology in more industries.

2. Experimental Materials and Methods

2.1. Coating Equipment and Materials

Coating experiments were conducted using a 6000 W fiber laser (RFL-A6000D/B/20/A/W/800, Wuxi Rui Ke Fiber Technology Limited Liability Company, Wuxi, China). The coaxial powder delivery method was employed, and the powder spray head was aimed at the surface of the substrate, with an out-of-focus distance of 15 mm and a laser spot size of 3 mm. Argon gas was used as the protective gas, with a purity greater than 99% and a gas flow rate of 15 L/min lap. The lap rate was 70%. Figure 1 presents a schematic diagram of the principle of laser cladding.

A C45 steel plate was used as the experimental substrate, with a length of 150 mm, a width of 150 mm, and a height of 20 mm. A milling machine was used to mill off the surface of the substrate oxidation layer. Then, alcohol was used to clean the surface, and it was blow dried with a hair dryer. The cladding materials were Rockit 431SR powder (particle size: 53~180 μm) and TC4 powder (particle size: 45~90 μm) (Höganäs, SE-263 83 Höganäs, Sweden), which were dried in an oven at 70 °C for 8 h before cladding. The chemical compositions of the powders are summarized in

Table 1. Chemical composition table of coating materials (wt.%).

431SR	Fe 76~78	C 0.18	Cr 16.5	Ni 1.75	Other <5
TC4	Fe 0.15	Ti 90	Al 5.5	V 3.5	Other <1

.

2.2. Experimental Methods

The Box–Behnken Design model of response surface methodology was used to design the experiment. To design an experimental scheme of three factors and three levels, laser power (X₁), scanning speed (X₂), and TC4 addition (X₃) were selected as the experimental factors. The experimental factors and levels are shown in

Table 2. Experimental factors modified during the laser cladding operation and selected levels.

Factors and Levels	Laser Power/W	Scanning Speed (mm/s)	Amount of TC4 Addition/%
−1	4000	300	0
0	5000	350	2.5
1	6000	400	5

. Porosity (Y₁), microhardness (Y₂), and corrosion resistance (Y₃) were selected as the evaluation indices.

2.2.1. Porosity

Due to the rapid heating and cooling characteristics of laser cladding [15,16,17], coatings are prone to porosity and cracks. In terms of mechanical properties, the presence of large pores reduces the service life of the coating, and stress concentrations are more likely to occur when subjected to external forces, which leads to the emergence and expansion of cracks. In terms of corrosion resistance, pores provide a channel for the penetration of corrosive media, making it easier for pitting corrosion to occur on the surface of the coating, thus reducing its protective effects.

In order to facilitate a metallographic analysis after coating, the coating was cut into 5 mm × 5 mm × 10 mm cubes using wire cutting, and the surface of the coating was sanded and polished. The macroscopic morphology of the coating surface was observed using an Olympus GX41(Olympus (China) Co., Ltd., Xuhui District, Shanghai, China) Compact Inverted Metallurgical Microscope. To further examine the porosity of the samples under different process parameters, the pore area was calculated from the image using the gray-scale method in the image analysis software ImageJ 1.54f; then, the ratio of the pore area and the coating area was calculated to determine the porosity. Figure 2 shows the pore topography, and

Table 3. Experimental results: the process parameters and evaluation metrics corresponding to various samples.

No.	X1/W	X2 (mm/s)	X3/%	Y1/%	Y2/HV	Y3/Ω
S1	4000	350	0	0.26	727.6	2219
S2	6000	350	0	0.17	747.8	2470
S3	5000	300	0	0.24	683.6	2920
S4	5000	400	0	0.39	754.7	1540
S5	4000	300	2.5	0.21	529.1	4649
S6	6000	300	2.5	0.12	606.8	4421
S7	4000	400	2.5	0.07	702	3321
S8	6000	400	2.5	0.19	555.2	3707
S9	5000	350	2.5	0.08	638.1	4227
S10	5000	350	2.5	0.09	643.5	4793
S11	5000	350	2.5	0.06	649	4768
S12	5000	350	2.5	0.05	627.4	5089
S13	5000	350	2.5	0.12	632.7	4354
S14	4000	350	5	0.05	369.8	6789
S15	6000	350	5	0.13	338.5	6042
S16	5000	300	5	0.27	336.6	6305
S17	5000	400	5	0.07	367.4	7091

shows the results of the porosity calculation.

2.2.2. Microhardness

The surface hardness of a coating serves as a critical indicator for evaluating a material’s resistance to localized plastic deformation. Materials with higher hardness typically possess finer and more homogeneous microstructures, which contribute to enhanced corrosion resistance [18,19,20]. This improved hardness prevents the formation of wear-induced surface defects, thereby eliminating potential sites for corrosive media accumulation and long-term degradation. The surface hardness of the coating is determined using an HV-1000Z-type(Dongguan Kedi Instrument Co., Ltd., Dongguan, Guangdong, China) Vickers hardness tester, with an applied force of 30 N, a holding time of 10 s, and an interval of 0.2 mm, with a total of six points measured to determine the average value. Figure 3 shows the average microhardness of each sample, and the values are shown in Table 3, from which it is observed that, with the increase in TC4 addition, the overall hardness of the coating shows a decreasing trend. This is because the hardness of the TC4 material itself is lower than that of the AISI431 martensitic stainless steel. Additionally, the opposite result is observed in individual samples, such as S3 and S7, which indicates that the other process parameters (laser power and scanning speed) also have an influence on the hardness factor. Therefore, by increasing the amount of corrosion-resistant TC4 added to the coating, it is possible to optimize the process parameters in order to maintain a certain hardness value, thereby improving the corrosion resistance of the coating.

2.2.3. Electrochemical Corrosion Resistance

A three-electrode system electrochemical workstation, model CHI660f (Shanghai Chenhua Instrument Co., Ltd., Yangpu District, Shanghai, China), was employed. The reference electrode was a saturated calomel electrode, the auxiliary electrode was a platinum sheet electrode, and the measurement solution was sodium chloride with a mass fraction of 3.5%. The sample was welded with copper wires and epoxy resin sealing, which revealed the surface of the test coatings. An ohmmeter was used to measure the conductivity of the test surface and the copper wire between the test surface, as well as to determine whether the conductor was conductive before electrochemical testing. During the test, the sample was immersed in the solution until the open-circuit voltage was stabilized, with a scanning voltage range of ±0.7 V, a sampling frequency of 10 Hz, and a scanning speed of 1 mV/s. Figure 4 shows the polarization curves of the samples. The self-corrosive current density (I_corr), self-corrosive potential (E_corr), and polarization resistance (R_P) could be obtained via extrapolation of the Tafel method. The self-corrosive potential is the horizontal coordinate, and the self-corrosion current density is the vertical coordinate. The polarization resistance formula is shown in Equation (1) [21,22,23]:

$${\mathrm{R}}_{\mathrm{P}}={\displaystyle \frac{{\mathsf{\beta }}_{\mathrm{a}}{\mathsf{\beta }}_{\mathrm{c}}}{{\mathsf{\beta }}_{\mathrm{a}}+{\mathsf{\beta }}_{\mathrm{c}}}}·{\displaystyle \frac{1}{{\mathrm{I}}_{\mathrm{corr}}}}$$

(1)

Here, β_a is the slope of the Tafel anodic polarization curve, and β_c is the slope of the Tafel cathodic polarization curve. The polarization resistance R_P is the transition resistance between the electrode and the electrolyte; a higher R_P value means higher corrosion resistance [24,25]. Thus, the evaluation of the corrosion resistance of the indices is based on the polarization resistance. The results of the polarization curve analysis are shown in Table 3. It can be observed that the corrosion resistance obtained from the formula is proportional to the TC4 content.

2.3. Regression Analysis and Significance Analyses

The experimental results are shown in Table 3, and the ANOVA results are shown in

Table 4. Variance analysis.

Index	Variation Source	Sum of Squares	Freedom	F Values	p Values
Y1	Model	0.15	9	32.90	<0.0001	Significant
	Residual	3.45 × 10−3	7	/	/
	Lack of Fit	4.5 × 10−4	3	0.2	0.8914	Not Significant
	Pure Error	3 × 10−3	4	/	/
	X1X2	0.011	1	22.37	0.0021	Significant
	X1X3	7.225 × 10−3	1	14.66	0.0065	Significant
	X2X3	0.031	1	62.14	0.0001	Significant
Y2	Model	3.332 × 105	9	290.39	<0.0001	Significant
	Residual	892.33	7	/	/
	Lack of Fit	600.72	3	2.75	0.1769	Not Significant
	Pure Error	291.61	4	/	/
	X1X2	12,600.06	1	98.84	<0.0001	Significant
Y3	Model	3.974 × 107	9	34.81	<0.0001	Significant
	Residual	8.879 × 105	7	/	/
	Lack of Fit	3.944 × 105	3	1.07	0.4572	Not Significant
	Pure Error	4.063 × 105	4	/	/
	X2X3	1.173 × 106	1	9.25	0.0188	Significant

.

Design-Expert 10 (v10.0.7) software was used to establish the multivariate nonlinear regression models of X₁, X₂, and X₃ on Y₁, Y₂, and Y₃, respectively, in order to determine whether the established models were reasonable and to perform a significance analysis. As can be seen in Table 4, the p-value of the three response models is less than 0.01, indicating that the regression model is significant, and the p-value of the misfit term is greater than 0.05, indicating that the model is highly accurate. The signal-to-message ratios (Adeq Precision) are 20.115, 49.795, and 19.595, respectively, which are all greater than 4, indicating that the model has sufficient effective signals and does not affect the precision.

For the significance test of the porosity Y₁ regression model, the regression equation is

$$\begin{gathered} {\mathrm{Y}}_1=0.08+0.0025{\mathrm{X}}_1-0.015{\mathrm{X}}_2-0.068{\mathrm{X}}_3+0{.052\mathrm{X}}_1{\mathrm{X}}_2+ \\ 0.043{\mathrm{X}}_1{\mathrm{X}}_3-0.088{\mathrm{X}}_2{\mathrm{X}}_3-0.011{\mathrm{X}}_1^{ 2}+0.079{\mathrm{X}}_2^{ 2}+0.084{\mathrm{X}}_3^{ 2} \end{gathered}$$

(2)

In Equation (2), it can be seen that X₁ has a quadratic relationship with Y₁ and that X₂ and X₃ have a quadratic relationship with Y₁, indicating that, when X₁ is fixed, the values of X₂ and X₃ can be optimized to minimize Y₁. In laser cladding coatings, the smaller the porosity, the higher the overall performance; thus, the smaller the value, the better.

For the significance test of the microhardness Y₂ regression model, the regression equation is

$$\begin{gathered} {\mathrm{Y}}_2=638.14-10.03{\mathrm{X}}_1+27.91{\mathrm{X}}_2-187.69{\mathrm{X}}_3-56{\mathrm{X}}_1{\mathrm{X}}_2- \\ 12.88{\mathrm{X}}_1{\mathrm{X}}_3-10.05{\mathrm{X}}_2{\mathrm{X}}_3-14.75{\mathrm{X}}_1^{ 2}-25.12{\mathrm{X}}_2^{ 2}-77.47{\mathrm{X}}_3^{ 2} \end{gathered}$$

(3)

As can be seen in Equation (3), X₁, X₂, X₃, and Y₂ are quadratic functions with a downward opening, indicating that their values can be optimized to maximize Y₂. The higher the hardness of the coating surface, representing the grain refinement, the less likely the occurrence of wear and tear resulting in surface gaps and the adherence of corrosive media.

For the significance test of the polarization corrosion resistance Y₃ regression model, the regression equation is

$$\begin{gathered} {\mathrm{Y}}_3=4646.2-42.25{\mathrm{X}}_1-329.5{\mathrm{X}}_2+2134.75{\mathrm{X}}_3+153.5{\mathrm{X}}_1{\mathrm{X}}_2- \\ 249.5{\mathrm{X}}_1{\mathrm{X}}_3+541,5{\mathrm{X}}_2{\mathrm{X}}_3-352.85{\mathrm{X}}_1^{ 2}-268.85{\mathrm{X}}_2^{ 2}+86.65{\mathrm{X}}_3^{ 2} \end{gathered}$$

(4)

In Equation (4), X₁, X₂, and Y₂ show a quadratic relationship with a downward opening, and X₃ and Y₃ show a quadratic relationship with an upward opening, indicating that, when X₃ is fixed, the values of X₁ and X₂ can be optimized to maximize Y₃. The larger the polarization resistance, the higher the corrosion resistance; thus, the larger the value, the better.

2.4. Effects of Interactions Between Factors on Experimental Indicators

As can be seen in Table 4, the interaction terms X₁X₂ on Y₁ and Y₂; X₂X₃ on Y₁ and Y₃; and X₁X₃ on Y₁ have significant effects (p < 0.05). The response surface analysis method is used to generate the response surface corresponding to the target indices using Design-Expert 10 (v10.0.7) software, as shown in Figure 5, Figure 6 and Figure 7.

As shown in Figure 5a, the optimal value of Y₁ is obtained when the TC4 content is 2.5%, the laser power is 5000 W, and the scanning speed is 350 mm/s. When the scanning speed is fixed, Y₁ shows a gradual decreasing trend with an increase in the laser power. A high-power laser can extend the cooling time of the molten pool, so the gas has more opportunities to escape, thus reducing the formation of porosity. When the laser power is fixed, Y1 shows a trend of first decreasing and then increasing with an increase in the scanning speed. The reason for this is that a lower scanning speed delays the residence time of the laser beam, thus increasing the melt pool volume. Although the solidification time of the melt pool is extended, it also allows more time for bubbles to remain in the melt pool. Faster scanning speeds result in insufficient melting of the substrate surface and input powder, which increases the porosity, so there is an intermediate value between the scanning speed and porosity that can be optimized. As shown in Figure 5b, when the scanning speed is 350 mm/s, the laser power is 5000 W, and the TC4 content is 2–3%, the optimal value of Y₁ is obtained. When the TC4 content is fixed, Y₁ shows a gradual decreasing trend with an increase in the laser power. When the laser power is fixed, Y₁ shows a trend of first decreasing and then increasing with an increase in the TC4 content. This is because TC4 is an α + β-type titanium alloy and can play a role in grain refinement [26]. A small addition can expel the gas from the molten pool more quickly, thus reducing the porosity; however, with an increase in the addition, the Ti elements in the high-temperature cladding process can easily react with the surrounding gases to produce oxides and hydrides, increasing the amount of gas generated. Even if the coating grain is refined, the gas cannot be discharged immediately. As shown in Figure 5c, when the laser power is 5000 W, the scanning speed is 350 mm/s, and the TC4 content is 2~3%, the optimal value of Y₁ is obtained.

As shown in Figure 6, when the TC4 content is 2.5%, the laser power is low, and the line speed is high, the optimal value of Y₂ is obtained. When the scanning speed is fixed, Y₂ shows a downward trend with an increase in laser power. This is because, with the increase in laser power, the dilution rate also increases, resulting in more base material being mixed into the coating, thus affecting the hardness. When the laser power is fixed, Y₂ increases with an increase in the scanning speed. This is because the fast scanning speed causes the laser to stay on the substrate for a short period of time, resulting in an increase in the cooling rate of the molten pool. The crystal nucleation rate is much higher than the growth rate, making the crystals more refined and thus increasing the hardness.

As shown in Figure 7, the optimum value of Y₃ is obtained when the laser power is 5000 W, the scanning speed is slow, and the TC4 addition is large. When the TC4 content is fixed, Y₃ decreases with an increase in the scanning speed. When the scanning speed is fixed, Y₃ increases with an increase in the TC4 content. Because TC4 is a good corrosion-resistant material, in which Ti and Al elements can form a dense oxide film on the material surface to improve the polarization resistance, a lower scanning speed and powder addition will increase the coating intake ratio during fusion coating.

Figure 8 shows the normal distribution of the residuals of the response targets. The residuals of the response values corresponding to different experimental parameters are all approximately distributed around a straight line, indicating that the residual distribution conforms to the normal distribution. This suggests that the model fits well to the treatment of random errors.

3. WOA Multi-Objective Process Parameter Optimization

The whale optimization algorithm (WOA) is a new intelligent optimization algorithm that achieves a balance between the global search ability and adaptivity of whales through the strategy of individual collaboration and target tracking in whale foraging behavior. The principle of the algorithm is shown in Figure 9.

The distance and position vectors between individuals in the WOA are, respectively,

$$\left\{\begin{array}{c}\mathrm{D}=\mathrm{C}x\left(t\right)-x\left(t\right)\\ x\left(t+1\right)=x\left(t\right)-\mathrm{AD}\end{array}\right.$$

(5)

where t is the number of previous iterations; D is the distance vector between the current whale and a random whale; x(t) is the position vector; x(t) is the position vector of the best solution obtained so far; and A and C are the coefficients, with A = 2a(t) × r₁(t) and C = 2r₂(t). The convergence factor a decreases linearly from 2 to 0 throughout the iteration, and r₁ and r₂ are random vectors in [0,1].

There are two main mechanisms for whale predation in the WOA: encircling predation and bubble net predation. Therefore, according to the probability p of choosing bubble net predation or contraction encirclement, the position update formula is as follows:

$$x\left(t+1\right)=\left\{\begin{array}{ll}x\left(t\right)-\mathrm{AD}& p\le 0.5\\ \mathrm{D} \prime \mathit{exp}(\mathrm{BL})\mathit{cos}(2\mathsf{\pi }\mathrm{L})+x\left(t\right)& p\ge 0.5\end{array}\right.$$

(6)

where D′ is the distance vector between the current searching individual and the current optimal solution; B is the spiral shape parameter; L is a random number uniformly distributed in [−1,1]; and p is the probability of the predation mechanism, a random number with a value domain of [0,1].

As the number of iterations t increases, parameter A and convergence factor a gradually decrease, and, if |A| ≥ 1, then the whale population gradually surrounds the current optimal solution and is in the local optimal search stage. To ensure that all whales can be fully searched, the WOA updates the position according to the distance between the whales to achieve the purpose of a random search. Therefore, when |A| ≥ 1, the searching individuals will swim to random whales, thus obtaining the optimal solution:

$$\left\{\begin{array}{c}\mathrm{D}″=\mathrm{C}{x}_{rand}\left(t\right)-x\left(t\right)\\ x\left(t+1\right)=x_{rand}\left(t\right)-\mathrm{AD}\end{array}\right.$$

(7)

Here, D″ is the distance vector between the current searching individual and a random individual, and x_rand(t) is the current position of the random individual.

Determination of the Optimal Solution

In order to obtain the optimal set of process parameters, with the goal of reducing porosity to improve surface microhardness and corrosion resistance, as well as combining the boundary conditions of each experimental factor, the constraints are derived as follows:

$$\left\{\begin{array}{c}\min {\mathrm{Y}}_1\\ \max {\mathrm{Y}}_2\\ \max {\mathrm{Y}}_3\\ \mathrm{s}.\mathrm{t}.\left\{\begin{array}{c}4000 \mathrm{W}\le {\mathrm{X}}_1\le 6000 \mathrm{W}\\ 300 \mathrm{mm}/\mathrm{s}\le {\mathrm{X}}_2\le 400 \mathrm{mm}/\mathrm{s}\\ 0 \%\le {\mathrm{X}}_3\le 5 \%\end{array}\right.\end{array}\right.$$

(8)

The initial population number is set to 100, and the iteration number is 500; Figure 10 shows the optimized Pareto solution set. The solution results in the following optimal parameter set: a laser power of 5315 W, a scanning speed of 378 mm/s, and a TC4 addition of 3.6%.

4. Results and Discussion

4.1. EDS Surface Scanning Results of the Coating Surface

The surface of a coating serves as the functional working interface where wear and corrosion preferentially initiate. Accordingly, energy-dispersive X-ray spectroscopy (EDS) elemental mapping was conducted on the coatings’ surfaces to investigate their elemental composition distribution. The corresponding results are presented in Figure 11 and

Table 5. Elemental content.

Elemental	Line Type	wt/%	wt/% Sigma	At/%
Al	K	0.16	0.02	0.32
Ti	K	3.33	0.06	3.80
V	K	0.20	0.06	0.21
Cr	L	18.59	0.38	19.57
Fe	L	76.55	0.38	75.00
Ni	L	1.17	0.10	1.09
Total		100.00		100.00

. The results indicate that Fe elements are predominantly concentrated within the dendrite cores, while Ti elements are mainly distributed in the interdendritic regions, with other elements exhibiting relatively uniform distribution. The elements that account for the largest proportion of the content, from highest to lowest, are Fe, Cr, and Ti. Among these, when the Cr and Ti elements come into contact with the air, CrO₃ and TiO₂ can form on the surface of the coatings, thereby creating a dual-layer antioxidant effect [27]. This is why corrosion resistance improves with the addition of TC4 material.

4.2. Parameter Optimization Experiment Comparison

To ensure accuracy, the optimized parameter set was subjected to five repetitions of the experiment, and the results are shown in Figure 12 and

Table 6. Optimization results for process parameters using WOA algorithm (five repeated trials).

No.	Y1/%	Y2/HV	Y3/Ω
S1	0.01	702.0	13,912
S2	0.02	721.1	11,127
S3	0.02	727.6	13,780
S4	0.03	708.3	11,499
S5	0.02	714.6	13,494

.

The results showed average values of 0.02% porosity, 714.72 HV hardness, and 12,762.4 Ω polarization resistance. Compared to the S1–17 controls with optimal parameters of 0.05% porosity, 754.7 HV hardness (without TC4), and 7091 Ω polarization resistance, the optimized parameters achieved a 60% reduction in porosity while maintaining hardness and increasing polarization resistance by 79.98%. TC4 addition improved corrosion resistance and reduced porosity, but concentrations exceeding 3.6% decreased hardness and increased porosity. Considering TC4’s high material cost, parameter optimization is essential for cost-effective performance enhancement of laser-clad coatings.

5. Conclusions

This study employed a Box–Behnken Design (BBD) within the response surface methodology to optimize the laser cladding process for TC4/AISI431 composite coatings on C45 steel substrates. Key process parameters—laser power, scanning speed, and TC4 content—were selected as independent variables, while porosity, microhardness, and corrosion resistance served as response variables.

The investigation revealed significant interactive effects across all process parameters on the target properties. Through Whale Optimization Algorithm (WOA) analysis, the optimal parameter combination was determined to be laser power of 5315 W, scanning speed of 378 mm/s, and TC4 content of 3.6%. Validation was performed by comparing three optimal response values extracted from 17 experimental trials with the optimized parameter set. The optimized coating demonstrated a 60% reduction in porosity while maintaining comparable hardness, along with a 79.98% improvement in polarization resistance (corrosion performance).