Optimization of Laser Cladding Parameters for High-Entropy Alloy-Reinforced 316L Stainless-Steel via Grey Relational Analysis

Abstract

Laser cladding technology serves as a pivotal technique in industrial production, especially in the realms of additive manufacturing, surface enhancement, coating preparation, and the repair of part surfaces. This study investigates the influence of metal powder composition and processing parameters on laser cladding coatings utilizing the Taguchi orthogonal experimental design method. To optimize the laser cladding parameters, multi-response grey relational analysis (GRA) was employed, aiming to improve both the microhardness and the overall quality of the coatings. The optimal parameter combinations identified through GRA were subsequently validated through experimental tests. The results reveal that the microhardness and quality of the coatings are substantially influenced by several critical factors, including the powder feed rate, laser power, high-entropy alloy (HEA) addition rate, scanning speed, and substrate tilt angle. Specifically, the powder feed rate exerts the most significant effect on the microhardness, dilution rate, and average contact angle. In contrast, laser power primarily impacts the mean contact angle difference. The HEA addition rate notably affects the mean contact angle difference, while the scanning speed affects the microhardness and the substrate tilt angle influences the average contact angle. The results of the validation experiment showed a deviation of only 0.95% from the predicted values, underscoring the efficacy of the grey relational analysis (GRA) in optimizing the laser cladding process parameters. The methodology presented in this paper can be applied to determine the ideal processing parameters for multi-response laser cladding processes, encompassing applications such as surface peening and surface repair.

1. Introduction

Laser cladding is an advanced technology utilized for surface strengthening and repair. In this process, a laser creates a metallurgically bonded, dense coating on the surface of a substrate, enhancing properties such as hardness, abrasion resistance, corrosion resistance, and oxidation resistance [1,2,3,4,5,6,7]. As a consequence, laser cladding technology has seen widespread application in specialized sectors, such as aerospace, biomedical fields, and power transmission [8,9,10,11]. High-entropy alloys are composed of various metallic elements with small differences in mixing enthalpies and atomic sizes [12], requiring rapid cooling rates during the preparation process. Among existing methods for coating preparation, metal matrix composite coatings produced via electrochemical deposition are significantly affected by the deposition rates of constituent elements. This dependency leads to reduced processing efficiency, instability of the coatings, and increased susceptibility to secondary pollution [13]. Thermal spraying methods for composite coatings are susceptible to microcracks and other defects, which can be mitigated by increasing the temperature; however, this approach leads to higher energy consumption [14]. Vapor-phase deposition techniques, particularly magnetron sputtering, demand precise control over coating thickness and operational parameters, making them economically costly [15]. In contrast, laser cladding technology provides rapid cooling and shaping, high bonding strength with the substrate [16], and stable properties of the resulting composite coatings. Consequently, employing laser cladding technology for preparing metal matrix composite coatings holds considerable promise.

Laser cladding is a complex procedure, with the quality of the resulting coating primarily controlled by process parameters, including the HEA addition rate, laser power, scanning speed, powder feed rate, and substrate tilt angle [2,3,4]. Numerous studies have analyzed the interrelationships between process parameters and the quality of coatings produced by laser cladding. For instance, Tan et al. examined the interactions between laser power, powder composition, and substrate tilt using analytical models and deposition profile measurements. Their results indicated that the substrate tilt angle significantly affects the laser energy density and powder flow distribution on the tilted substrate [2]. Additionally, Zhang et al. systematically investigated the impact of scanning speed on the microstructure and mechanical properties of samples produced by laser melting FeCoNiAlTi HEA powder mixed with 316L stainless-steel powder [3]. Kahya et al. employed laser cladding technology to prepare 316L stainless-steel and investigated the effects of laser power, scanning speed, and powder feed rate on the material’s microstructure and mechanical properties, determining that the deposition rate was directly proportional to the energy density but inversely proportional to the powder feed rate [4]. Wang et al. analyzed the effects of process parameters on the width and height of the cladding layer using orthogonal tests. The results showed that the width of the cladding layer was primarily influenced by laser power, followed by powder feed rate and scanning speed [5]. Yang et al. explored the relationship between laser cladding process parameters and the geometrical characteristics of a single-track cladding layer using a regression analysis model, establishing a simplified relationship between the process parameters, molten pool characteristics, and the quality of laser-clad Ti-1023 titanium alloy [6]. To address the challenge of achieving both high strength and high ductility in the additive manufacturing of metal matrix composites, Zhang et al. innovatively selected FeCoNiCr HEA as the reinforcing phase to improve the properties of 316L stainless-steel processed by laser powder bed melting. This approach successfully enhanced the strength, ductility, and corrosion resistance of the stainless-steel composites, demonstrating that high-performance HEA particles with lattice structures and elemental types similar to those of the stainless-steel matrix have significant potential as a reinforcing phase [7].

Most of the aforementioned studies primarily focus on analyzing the influence of specific process parameters on the quality of coatings prepared by laser cladding. However, in practical industrial production, the application of laser cladding technology for additive manufacturing or surface coatings on non-horizontal substrates presents an unavoidable challenge. Currently, laser cladding frequently presents various defects during the fabrication of high-entropy alloys, which poses challenges for industrial-scale production [17]. Nevertheless, numerous studies have demonstrated that the morphology, microstructure, and properties of high-entropy alloys are significantly affected by the processing parameters employed during laser cladding [18]. Therefore, optimizing these parameters has the potential to substantially enhance the characteristics of the resulting high-entropy alloys. When laser cladding processes involve tilted substrates, it is crucial to consider the synergistic effects of metal powder composition, laser power, scanning speed, and powder feed rate on the quality of the coatings. Addressing these industrial challenges requires studying and predicting the optimal process parameters for laser cladding to achieve multi-objective optimization of coating quality. In this study, grey relational analysis (GRA) is employed to effectively optimize process parameters across multiple objectives, in contrast to previous methods like ANOVA, which are confined to single-objective optimization [19]. Additionally, single-track laser cladding layers are beneficial for analyzing the cladding process and investigating coating morphology.

This study aims to investigate the effects of the compositional and processing parameters of HEA-added metal powders on coatings prepared by single-track laser cladding. Utilizing multi-response grey relational analysis (GRA), this study seeks to identify the laser cladding processing parameters that optimize microhardness and ensure high coating quality.

2. Materials and Methods

The substrate was made of Q235 steel with dimensions of 100 mm × 50 mm × 5 mm. The elemental composition of Q235 steel is detailed in

Table 1. Chemical composition of Q235 substrate (wt.%).

C	Si	Mn	S	P	Fe
≤0.22	≤0.35	≤1.4	≤0.050	≤0.045	Bal.

. The high-entropy alloy (FeCuNiCrAl) powder employed in the laser cladding process was supplied by Willary New Materials Technology Co., Ltd. (Xuzhou, China), featuring a particle size range of 45–105 µm. The elemental composition of the FeCuNiCrAl HEA is presented in

Table 2. Chemical composition of FeCuNiCrAl HEA (wt.%).

Fe	Ni	Cr	Al	Cu
22.15	22.41	19.72	10.39	Bal.

, and Figure 1 illustrates the microstructure of the HEA powder. Additionally, the 316L stainless-steel powder used in the laser cladding process was provided by Lianhong New Material Science and Technology Co., Ltd. (Tengzhou, China), with a particle size range of 50–150 µm. Both the HEA powder and the 316L stainless-steel powder met the feeding specifications required by the laser cladding system. The elemental composition of 316L stainless-steel is provided in

Table 3. Chemical composition of 316L stainless-steel (wt.%).

C	Si	Mn	S	P	Cr	Ni	Mo	Fe
<0.03	<1.00	<2.00	<0.03	<0.045	<18	<14	<3	Bal.

.

The laser cladding system utilized in this study, as depicted in Figure 2, consisted of an industrial robot laser cladding integrated system (HWL-RF2000W, Huawei Laser, Dongguan, China), a control system (PLC, Mitsubishi, Japan), and pneumatic powder feeding equipment (HW-05SF, Huawei Laser, Dongguan, China). Throughout the laser cladding process, the metal powder was protected by argon gas.

Prior to the laser cladding, the Q235 substrate was polished to remove the oxide layer, followed by rinsing and drying with anhydrous ethanol. The metal powder was dried under vacuum at 100 °C for two hours before use. During the laser cladding process, the laser was focused on the molten pool, causing the melted metal powder to form a metallurgical bond with the substrate material upon cooling [20]. Post-cladding, the samples were cut, mounted, ground, and polished for evaluation.

The morphological characteristics of the coatings were examined using an optical microscope (Axio Lab. A1, Carl Zeiss AG, Oberkochen, Baden-Württemberg, Germany). The Vickers hardness of the coatings was measured from the fusion zone towards the coating direction using an HXS-1000A microhardness tester (load: 3 N, dwell time: 10 s). Measurements were taken at 0.1 mm intervals between consecutive points, and the averaged measurements were used to characterize the Vickers hardness of the laser fusion coating. The phase composition and crystal structure of the coating were analyzed using an X-ray single crystal diffractometer (XtaLAB Synergy Custom, Rigaku, Tokyo, Japan).

In this study, the Taguchi orthogonal method was employed to design the single-track laser cladding experiment. Five process parameters were evaluated: HEA addition rate, laser power, scanning speed, powder feed rate, and substrate tilt angle. Given that each process parameter has four levels, a full factorial experiment would necessitate 4⁵ = 1024 different combinations. However, by using the Taguchi orthogonal design, only 16 runs were required, significantly reducing the number of experiments needed and conserving both manpower and resources [21]. The Taguchi orthogonal array is a balanced design that allows each factor to be evaluated independently.

Table 4. Orthogonal experimental design.

Processing Parameter	Notation	Unit	Levels
			1	2	3	4
HEA Addition Rate	A	%	0	5	10	15
Laser Power	B	W	750	1100	1450	1800
Scanning Speed	C	mm/s	5	7	9	11
Powder Feed Rate	D	r/min	2	5	8	11
Substrate Tilt Angle	E	°	0	10	20	30

presents the factors and their corresponding levels used in the Taguchi orthogonal experimental design.

The key responses in this study included microhardness (H), dilution rate (λ), average contact angle ($\overline{a}$), and mean difference of contact angles (Δa). These parameters are represented by Equations (1)–(3), respectively [22,23,24].

$$\mathsf{\lambda }={\mathrm{A}}_2/\left({\mathrm{A}}_1+{\mathrm{A}}_2\right)$$

(1)

$$\overline{a}={\displaystyle \frac{\left(a_1+a_2\right)}{2}}$$

(2)

$$\mathsf{\Delta }a={\displaystyle \frac{\left|a_1-a_2\right|}{2}}$$

(3)

where ${\mathrm{A}}_2$ is the area of the substrate melted during the laser cladding process in the center cross-section, while ${\mathrm{A}}_1$ is the area of the cladding layer obtained in the center cross-section. The parameters $a_1$ and $a_2$ are the contact angles on the left and right sides of the sample in the center cross-section, respectively (Figure 3).

The signal-to-noise ratio (SNR or S/N) is a crucial parameter for predicting optimization results in data analysis, as it evaluates the error between the actual response and the expected estimate [25]. The response is considered the signal, while the irregular fluctuations that occur with responses are regarded as noise. The SNR quantifies the relationship between the noise and the signal, thereby enabling a quantitative evaluation of the response effect [26].

The signal-to-noise ratio for microhardness (H) was calculated by Equation (4). For microhardness (H), a higher value was preferable [27].

$$\mathrm{S}/\mathrm{N}=10\log {\displaystyle \frac{1}{\mathrm{n}}}\mathsf{\Sigma }{\mathrm{y}}_{\mathrm{i}}^2$$

(4)

The signal-to-noise ratios for the dilution rate (λ), the average contact angle ($\overline{a}$), and the mean difference of contact angles ($\mathsf{\Delta }a$) were calculated by Equation (5). For these parameters, lower values were preferable [28,29,30].

$$\mathrm{S}/\mathrm{N}=-10\log {\displaystyle \frac{1}{\mathrm{n}}}\mathsf{\Sigma }{\mathrm{y}}_{\mathrm{i}}^2$$

(5)

where n is the number of experiments in this study, which is 2, and y_i represents the different values of the response target, which are the results of the experiments [27].

Converting the experimental results into corresponding signal-to-noise ratios was pivotal for optimizing the process parameters. Higher signal-to-noise ratios indicated superior outcomes for microhardness (H), dilution rate (λ), average contact angle ($\overline{a}$), and mean difference of contact angles ($\mathsf{\Delta }a$). Therefore, the optimal process parameter level was determined based on achieving the highest signal-to-noise ratio [25,26]. However, multi-objective process parameter optimization posed greater complexity compared to single-objective optimization.

A higher signal-to-noise ratio for one performance characteristic might correlate with a lower signal-to-noise ratio for another. Consequently, an overall assessment of the signal-to-noise ratio was essential to optimize multiple performance characteristics [31]. To address this challenge, this study employed the grey relational analysis (GRA) method to facilitate multi-objective optimization using the Taguchi orthogonal method, aiming to optimize multiple quality characteristics of laser cladding coatings. In GRA, the S/N ratio was initially normalized to a range from zero to unity. Subsequently, the grey relational coefficient was derived from the normalized S/N ratio to illustrate the relationship between the desired and actual S/N ratios. The grey relational grade was then calculated by aggregating the weighted grey relational coefficients associated with each performance characteristic. The comprehensive assessment of the multiple characteristics relied on the grey relational grade, thereby simplifying the optimization of complex characteristics into the optimization of a single grey relational grade [32].

3. Results

Table 5. Orthogonal experimental design parameters and responses.

Run	A (%)	B (W)	C (mm/s)	D (r/min)	E (°)	Parameter Combination	H (HV)	λ (%)	$\overline{\mathit{a}}$ (°)	Δa (°)
1	0	750	5	2	0	A1B1C1D1E1	439.87	38.32	24.26	0.634
2	0	1100	7	5	10	A1B2C2D2E2	181.60	15.92	51.85	0.600
3	0	1450	9	8	20	A1B3C3D3E3	364.09	27.24	57.76	26.464
4	0	1800	11	11	30	A1B4C4D4E4	194.13	18.65	63.27	4.236
5	5	750	7	8	30	A2B1C2D3E4	190.05	20.31	25.21	3.076
6	5	1100	5	11	20	A2B2C1D4E3	184.72	8.39	76.15	17.584
7	5	1450	11	2	10	A2B3C4D1E2	460.28	83.32	39.42	20.054
8	5	1800	9	5	0	A2B4C3D2E1	457.03	85.50	90.81	48.533
9	10	750	9	11	10	A3B1C3D4E2	203.30	6.55	59.06	1.460
10	10	1100	11	8	0	A3B2C4D3E1	257.83	21.93	52.39	1.029
11	10	1450	5	5	30	A3B3C1D2E4	407.13	36.07	33.94	2.513
12	10	1800	7	2	20	A3B4C2D1E3	424.47	86.27	17.95	4.194
13	15	750	11	5	20	A4B1C4D2E3	461.30	31.26	25.79	0.312
14	15	1100	9	2	30	A4B2C3D1E4	445.02	79.53	12.67	2.010
15	15	1450	7	11	0	A4B3C2D4E1	180.04	14.64	73.75	5.192
16	15	1800	5	8	10	A4B4C1D3E2	209.81	25.15	55.20	2.743

displays the 16 runs conducted in the Taguchi orthogonal experiment, detailing their respective process parameter configurations. Additionally, it presents the experimental data for microhardness (H), dilution rate (λ), average contact angle ($\overline{a}$), and mean difference of contact angles (Δa).

The optimal process parameter set optimized by GRA (A₄B₁C₂D₁E₄) was selected, with only the HEA addition rate being varied, to investigate its effect on the phase composition and crystal structure of the coating. The experimental design is shown in

Table 6. Single-factor experiment of high-entropy alloy (HEA) addition rate.

Processing Parameter	Notation	Unit	Levels
			1	2	3	4
HEA Addition Rate	A	%	0	5	10	15
Laser Power	B	W	750	750	750	750
Scanning Speed	C	mm/s	7	7	7	7
Powder Feed Rate	D	r/min	2	2	2	2
Substrate Tilt Angle	E	°	30	30	30	30

.

Figure 4 shows the X-ray diffraction (XRD) results of the laser cladding coatings as the HEA addition rate increases from 0% to 15%. The diffraction peak identification results indicated that four laser cladding layers are composed of a Laves phase, FCC phase, and BCC phase [33,34,35]. As one can see, the intensity of the diffraction peaks corresponding to the Laves phase and FCC phase (111) in the cladding coatings gradually strengthens with the increasing HEA addition rate, indicating a progressive increase in these phases. The lattice constants of each phase in the laser cladding coatings, determined using Equation (6), exhibit variation with different HEA addition rates. The calculated values are provided in

Table 7. Lattice constants of phases in the laser cladding coatings at different HEA addition rates.

Criterion	Phase	HEA Addition Rate
		0%	5%	10%	15%
Lattice constant (nm)	Laves	0.4891	0.4881	0.4885	0.4885
	FCC	0.4198	0.4206	0.4203	0.4203
	BCC	0.2883	0.2886	0.2888	0.2885

[36,37].

$$a={\displaystyle \frac{w}{2\mathrm{s}\mathrm{i}\mathrm{n}\mathsf{\theta }}}\sqrt{{\mathrm{h}}^2+{\mathrm{k}}^2+{\mathrm{l}}^2}$$

(6)

where $w$ represents the wavelength of the X-ray; h, k, and l are the Miller indices of the crystal plane; $\mathsf{\theta }$ is the diffraction angle; and a denotes the lattice constant.

The results indicate that as the HEA addition rate increases, the lattice constant of the Laves phase initially decreases and subsequently increases, while the FCC and BCC phases display an opposite trend. Specifically, at an HEA addition rate of 5%, the lattice constants of both the Laves and FCC phases reach their maximum values, whereas the lattice constant of the BCC phase peaks at a 10% HEA addition rate. This phenomenon suggests that within the 0% to 15% range of HEA addition, a diffusion process involving solute elements with varying atomic radii occurs between the Laves phase and the dual-phase solid solution of BCC and FCC, leading to lattice distortion in each phase [38,39]. Consequently, the variation in coating performance is determined by the combined effects of the increased presence of the Laves and FCC phases, as well as the associated lattice distortion.

3.1. Analysis of Microhardness

The highest microhardness among the 16 experiments was achieved in the 13th run, characterized by process parameters set to A₄B₁C₄D₂E₃ (HEA addition rate of 15%; laser power at 750 W; scanning speed of 11 mm/s; powder feed rate of 5 r/min; substrate tilt angle of 20°).

Table 8. Signal-to-noise ratio for microhardness.

Level	A	B	C	D	E
1	48.76	49.47	49.21	52.91	49.85
2	49.34	47.93	47.11	50.96	47.76
3	49.79	50.45	50.89	47.87	50.60
4	49.45	49.49	50.13	45.59	49.13
Delta	1.03	2.52	3.78	7.32	2.84
Rank	5	4	2	1	3

presents the signal-to-noise response of the microhardness data. The Delta values indicate that the order of factors affecting microhardness is as follows: powder feed rate > scanning speed > substrate tilt angle > laser power > HEA addition rate.

Figure 5 shows the main effects plot for the signal-to-noise ratio of microhardness. Within the figure, solid lines connect the mean values within each category, while a dashed reference line denotes the overall mean. The main effect of each categorical variable was assessed by comparing its solid plotted line with the dashed reference line. A horizontal solid line indicates the absence of a main effect for that parameter, whereas any deviation indicates its presence. A steeper slope suggests a stronger influence of the parameter on the results.

Figure 5 illustrates that the powder feed rate exerts the most significant effect on microhardness, consistent with the findings in Table 8. The optimal microhardness was achieved at a powder feed rate of 2 r/min. The powder feed rate also exhibited a substantial influence on microhardness in the investigation conducted by Dong et al. [40]. Increasing the powder feed rate during laser cladding facilitated greater entry of metal powder into the melt pool. Initially, lower feed rates contributed to strengthened composite coatings due to factors such as solution strengthening and dislocation strengthening, resulting in elevated microhardness [41]. However, further increments in the powder feed rate intensified shielding effects on the laser, resulting in thicker coatings, and reduced fusion depth [42]. Furthermore, the increased introduction of powder into the substrate reduces the energy input, which, in turn, diminishes the influx of elements into the melt pool [40,43]. Therefore, the microhardness is relatively low.

Similar patterns were observed for laser power, scanning speed, and substrate tilt angle. Initially, laser power decreased microhardness from 750 to 1100 W, possibly attributed to abrupt structural and compositional changes [44]. Subsequently, an increase in microhardness was observed from 1100 to 1450 W, stemming from enhanced melting and denser crystal growth. However, within the power range of 1450–1800 W, grain coarsening led to a reduction in microhardness [45].

Scanning speed influenced microhardness by regulating heat input and thermal effects. Low speeds (5 to 7 mm/s) resulted in reduced densification, coarser structures, and decreased microhardness. In contrast, intermediate speeds (7 to 9 mm/s) enhanced cooling rates, leading to increased microhardness due to thinner fused layers and higher levels of amorphous structure [46]. High speeds (9 to 11 mm/s) advanced solidification but could induce pore formation, thereby reducing coating strength.

Increasing the substrate tilt angle from 0 to 10° enhanced molten metal flow, prolonged melting times, and promoted grain coarsening, resulting in reduced hardness. Angles between 10 and 20° improved the adhesion of powder particles and reduced coating defects, thereby gradually increasing hardness [47]. However, tilt angles from 20 to 30° increased heat input, slowed cooling rates, and further coarsened the grain structure, leading to decreased coating hardness.

As illustrated in Figure 4, with increase in the HEA addition rate from 0% to 10%, there is a slight fluctuation in the proportion of the Laves, FCC, and BCC phases in the laser cladding coatings. The Vickers hardness of the BCC phase is higher than that of the FCC phase, and the presence of the Laves phase can enhance the microhardness of coating. The synergistic effects of these three phases lead to an overall increase in microhardness. However, when the HEA addition rate is elevated to 15%, the proportion of the BCC phase in the coating significantly decreases, resulting in a reduction in microhardness. Additionally, as indicated in Table 7, the variations in the lattice constants of the BCC, FCC, and Laves phases influence grain refinement strengthening, which subsequently contributes to the increase in microhardness.

Based on the findings from the above analyses, the optimal processing parameters for maximizing microhardness were as follows: 1450 W laser power, 9 mm/s scanning speed, 2 r/min powder feed rate, and 20° substrate tilt angle.

3.2. Analysis of Dilution Rate

The optimal dilution rate was observed in the 9th run with the process parameters set to A₃B₁C₃D₄E₂ (HEA addition of 10%; laser power of 750 W; scanning speed of 9 mm/s; powder feed rate of 11 r/min; substrate tilt angle of 10°).

Table 9. Signal-to-noise ratio for dilution rate.

Level	A	B	C	D	E
1	12.54	13.99	12.68	3.30	9.89
2	9.58	13.16	11.95	9.07	13.30
3	11.75	9.61	9.58	12.58	11.05
4	10.19	7.31	9.86	19.12	9.82
Delta	2.97	6.68	3.10	15.82	3.48
Rank	5	2	4	1	3

presents the signal-to-noise response data for the dilution rate. The Delta values indicate the order of priority for the factors as follows: powder feed rate > laser power > substrate tilt angle > scanning speed > HEA addition rate.

Figure 6 illustrates the main effect plot, highlighting that the powder feed rate significantly influenced the dilution rate of the coating. As the powder feed rate increased, there was a corresponding decrease in the dilution rate. This finding is consistent with previous study [43,48]. This phenomenon arose because higher rates of powder delivery introduced more metal powder into the melting process, which reduced the width and depth of substrate melting but increased the height of melting. Higher powder delivery rates could induce a “thermal shielding” effect on the substrate surface, thereby diminishing the energy input of the laser [49]. Firstly, this approach reduces the depth of the melting area [49]; conversely, it minimizes the ingress of elements into the substrate melting area, ultimately resulting in a decreased dilution rate [48].

Conversely, higher laser power was associated with an increased dilution rate. Elevated laser power expanded the width of the cladding layer, thereby enhancing the dilution rate. This phenomenon stemmed from the heightened temperature and increased energy absorption in the melt pool, which enlarged both the area of substrate melting and the amount of energy absorbed by the powder per unit time. Consequently, as the melting and solidification processes progressed, both the cladding width and dilution rate increased [50].

An increase in the substrate tilt angle from 0° to 10° significantly influenced the liquid-solidification process within the melt pool. This adjustment reduced both the height and depth of the melt gradually, altering the flow of liquid along the substrate surface due to gravitational effects, and ultimately decreasing the overall dilution rate [51]. As the substrate tilt angle increased from 10° to 30°, the width of the deposition layer progressively enlarged, accompanied by a greater offset in layering. Within this range, the dilution rate showed a positive correlation with the tilt angle.

Increasing the scanning speed from 5 mm/s to 9 mm/s resulted in a more pronounced reduction in melt height compared to melt depth, thereby increasing the dilution rate. Further increasing the scanning speed from 9 mm/s to 11 mm/s significantly decreased the interaction time between the laser beam, powder, and substrate surface per unit length. This reduction led to a decrease in the volume of melted powder, shallower depth of the melt pool, and subsequently lowered the dilution rate [52].

The XRD results in Figure 4 indicate that the trend in the FCC phase proportion aligns with the trend in dilution rate, implying that variations in dilution rate may be associated with the formation of the FCC phase and the phase transformation between FCC and BCC [53]. Furthermore, variations in the proportion of HEA powder added to the process could influence the overall density of metal powder, potentially leading to fluctuations in dilution rate.

These findings aligned with the ranking of factors based on the signal-to-noise ratio response presented in Table 9, indicating their respective levels of importance. According to the analyses, when focusing solely on minimizing dilution rate, the optimal processing parameters included a laser power of 750 W, scanning speed of 5 mm/s, powder feed rate of 11 r/min, exclusion of HEA powder, and a substrate tilt angle of 10°.

3.3. Analysis of Average Contact Angle

The 14th experimental run produced the smallest mean contact angle, indicating optimal powder-substrate “wettability” under those conditions [54]. The process parameters for this run were set as A₄B₂C₃D₁E₄ (HEA addition ratio of 15%; laser power of 1100 W; scanning speed of 9 mm/s; powder feed rate of 2 r/min; substrate tilt angle of 30°).

Table 10. Signal-to-noise ratio for average contact angle.

Level	A	B	C	D	E
1	−33.31	−29.85	−32.7	−26.69	−34.65
2	−34.19	−32.09	−31.19	−33.07	−34.12
3	−31.38	−33.78	−32.97	−33.12	−31.54
4	−30.62	−33.78	−32.64	−36.61	−29.18
Delta	3.57	3.93	1.78	9.92	5.47
Rank	4	3	5	1	2

presents the signal-to-noise response data for the average contact angle. The Delta value reveals the priority order of factors as follows: powder feed rate > substrate tilt angle > laser power > HEA addition ratio > scanning speed.

The main effect plot (Figure 7) illustrates that the powder feed rate exerts the most significant influence on the average contact angle of the coating. The impact of the powder feed rate on the average contact angle contrasted with that of the dilution rate. Increasing the powder feed rate from 2 to 5 r/min enhanced the volume of powder beam melting, thereby enlarging the area of the cladding layer and resulting in a continuous rise in the average contact angle. Within the 5–8 r/min range, the average contact angle stabilized due to constraints within the process window. From 8 to 11 r/min, higher powder feed rates increased the cladding layer height, further elevating the contact angle.

As the substrate tilt angle increased from 0° to 30°, the downward flow of liquid in the molten pool along the substrate surface intensified due to gravity, leading to a decrease in the average contact angle influenced by the Marangoni effect [55].

The laser energy input also significantly affected the flight speed and temperature of molten droplets. Raising the laser power from 750 W to 1450 W facilitated easier spreading of molten metal droplets, resulting in an increased contact angle of the coating. The contact angle reached its peak at 1450 W and remained relatively stable between 1450 W and 1800 W [56].

An increase in the HEA proportion from 0% to 5% reduced the wettability of the metal powder due to the addition of aluminum, which resulted in a higher average contact angle. However, as the HEA proportion rose from 5% to 15%, the synergistic effects of elements such as Fe, Cu, Ni, Cr, and Al altered the solid–liquid interface adsorption behavior and microstructural formation mechanism of the coating. Specifically, the atomic radii of the aforementioned metal elements differ, and as the HEA addition rate increases from 5% to 15%, more solute elements separate from the BCC and FCC dual-phase structure and precipitate to form the Laves phase, which might enhance the interface wettability, consequently decreasing the average contact angle [57,58].

The main effect plot indicated that scanning speed fluctuated around the reference dashed line, suggesting it had minimal influence on the average contact angle.

The analyses were consistent with the prioritization of factors based on the signal-to-noise ratio response in Table 10. For optimizing the average contact angle, the optimal processing parameters were determined to be a laser power of 750 W, scanning speed of 7 mm/s, powder feed rate of 2 r/min, HEA addition ratio of 15%, and a substrate tilt angle of 30°.

3.4. Analysis of Mean Difference of Contact Angles

The 13th experimental run exhibited the smallest mean difference in contact angles, indicating optimal consistency in the coating. The process parameters were configured as A₄B₁C₄D₂E₃ (15% HEA addition ratio; 750 W laser power; 11 mm/s scanning speed; 5 r/min powder feed rate; 20° substrate tilt angle).

Table 11. Signal-to-noise ratio for mean difference of contact angles.

Level	A	B	C	D	E
1	−8.15	0.26	−9.43	−10.15	−11.08
2	−23.61	−6.69	−8.02	−6.79	−8.41
3	−6.00	−19.20	−17.88	−11.81	−13.92
4	−4.75	−16.87	−7.17	−13.76	−9.09
Delta	18.86	19.46	10.71	6.97	5.51
Rank	2	1	3	4	5

presents the signal-to-noise response data for the mean difference in contact angles. The Delta values indicate the prioritization of factors as follows: laser power > HEA addition rate > scanning speed > powder feed rate > substrate tilt angle.

The main effect plot (Figure 8) illustrates that laser power has the greatest influence on the mean difference of contact angles in the coating. As the laser power increased from 750 W to 1450 W, the contact angle difference amplified. This effect was attributed to the higher laser power, which intensified energy input and consequently led to greater variation in the mean difference of contact angles. Further increasing the laser power from 1450 W to 1800 W enhanced the energy input, promoted complete melting of the metal powder, and improved the fluidity of the molten pool. This resulted in a slight reduction in the mean difference in contact angle due to surface tension [59].

The mean difference of contact angles followed an increasing-then-decreasing trend with rising HEA addition rates, mirroring the effect observed in average contact angle variations influenced by HEA proportions. At 0%–5% HEA addition rates, the introduction of aluminum reduced the wettability of the metal powder and the mobility of the molten pool, thereby increasing the mean difference of contact angles. Conversely, in the 5% to 15% range, synergistic effects between HEA and 316L hybrid powder components improved wettability and solid–liquid interfacial adsorption behavior, leading to a decrease in the mean difference of contact angles [60].

Increasing the scanning speed from 5 mm/s to 7 mm/s resulted in negligible change in the mean difference of contact angles. Within the range of 7 mm/s to 9 mm/s, higher scanning speeds reduced the laser energy density, weakened the flow capacity of the liquid molten cladding layer, and consequently accelerated the increase in the mean difference of contact angles. Despite the consistent amount of metal powder melting per unit time, scanning speeds from 9 mm/s to 11 mm/s significantly reduced the area of the cladding layer, leading to a sharp decline in the contact angle difference [61].

An increase in the powder feed rate from 2 r/min to 5 r/min escalated the amount of powder beam melting and expanded the area of the fused cladding layer, thereby increasing the mean difference in contact angles. Within the range of 5 r/min to 11 r/min, higher feed rates raised the height of the molten cladding layer, altering the distribution of surface tension effects. As the height increased, surface tension exerted a significant influence on the morphology of the melted cladding layer, resulting in a decrease in the mean difference in contact angles [62]. Importantly, higher feed rates led to more pronounced attenuation of powder interaction with the laser beam, which slowed the reduction trend of the contact angle difference in the 8 r/min to 11 r/min region, as evidenced in the main effects plot.

From the main effect plot, it was evident that the substrate tilt angle fluctuated around the reference dashed line, indicating minimal impact on the mean difference of contact angles.

The results from the aforementioned analyses corroborated the prioritization of factors as indicated by the signal-to-noise ratio response in Table 11. Considering solely the mean difference of contact angles, the optimal processing parameters identified were as follows: 750 W laser power, 11 mm/s scanning speed, 5 r/min powder feed rate, and an HEA addition rate of 15%.

3.5. Multi-Response Grey Relational Analysis

The objective of this study was to enhance both the microhardness and the quality of laser cladding layers simultaneously. Since the optimal outcomes for microhardness, dilution rate, average contact angle, and mean difference of contact angles were observed under different combinations of process parameters, further exploration was necessary for multi-response optimization. To achieve higher microhardness and improved quality of the cladding layer, multi-response grey relational analysis was employed to ascertain the optimal process parameters.

Developed by Julong Deng in the 1980s, grey relational analysis effectively integrates multiple objectives into a unified metric, addressing complex multi-response problems. This method offers an efficient approach to optimizing multiple parameters to achieve an optimal solution that meets diverse objectives [63,64]. Grey relational analysis involves three main steps [63,64,65,66]. The first step is data normalization, which adjusts for differences in units and ranges of the original data. Normalized data are expressed as values between 0 and 1. In this study, microhardness (H) was normalized using Equation (7), where larger values indicate better performance. The dilution rate (λ), the average contact angle ($\overline{a}$), and the mean difference of contact angles (Δa) were normalized by Equation (8), where smaller values indicate superior performance.

$$Y_i\left(k\right)={\displaystyle \frac{X_i\left(k\right)-\mathit{min}{X}_i\left(k\right)}{\mathit{max}{X}_i\left(k\right)-\mathit{min}{X}_i\left(k\right)}}$$

(7)

$$Y_i\left(k\right)={\displaystyle \frac{\mathit{max}{X}_i\left(k\right)-X_i\left(k\right)}{\mathit{max}{X}_i\left(k\right)-\mathit{min}{X}_i\left(k\right)}}$$

(8)

where k denotes the kth response (k = 1, 2) in this study; i represents the ith experimental data (i = 1, 2, 3, …, 16); Y_i(k) signifies the normalized response data; X_i(k) denotes the original response data; min X_i(k) and max X_i(k) indicate the minimum and maximum values of X_i(k).

The second step involves calculating the grey relational coefficient for each response using Equation (9).

$${GRC}_i\left(k\right)={\displaystyle \frac{{\mathsf{\Delta }}_0\left(min\right)+\xi ·{\mathsf{\Delta }}_0\left(max\right)}{{\mathsf{\Delta }}_{0i}\left(k\right)+\xi ·{\mathsf{\Delta }}_0\left(max\right)}}$$

(9)

where GRC_i(k) represents the grey relational coefficient of the kth response in the ith run; ∆_0i(k) indicates the deviation between the normalized value Y_i(k) and the reference sequence {X₀} = {1, 1, 1, 1}. The deviation is calculated as ∆_0i(k) = $\left|Y_0\left(k\right)-Y_i\left(k\right)\right|$; ∆_0min and ∆_0max denote the minimum and maximum value of ∆_0i(k), respectively; $\xi$ is the distinguishing coefficient with $\xi$ ∈ [0, 1]. A value of 0.5 is selected for ξ to ensure a moderate distinguishing effect and stability [67].

Following the normalization of microhardness, dilution rate, average contact angle, and mean difference of contact angles, the results of grey relational generation (Y) and their deviations (∆₀) are presented in

Table 12. Normalized experimental data and corresponding deviations.

Run	Y(H)	∆0(H)	Y(λ)	∆0(λ)	$\mathbf{Y}(\overline{\mathit{a}}$)	∆0$(\overline{\mathit{a}}$)	$\mathbf{Y}(\mathsf{\Delta }\mathit{a}$)	∆0$(\mathsf{\Delta }\mathit{a}$)
1	0.92381	0.07619	0.60226	0.39774	0.85170	0.14830	0.99332	0.00668
2	0.00555	0.99445	0.88331	0.11669	0.49864	0.50136	0.99403	0.00597
3	0.65438	0.34562	0.74153	0.25847	0.42301	0.57700	0.45766	0.54234
4	0.05010	0.94990	0.84818	0.15182	0.35247	0.64754	0.91863	0.08138
5	0.03559	0.96441	0.82811	0.17190	0.83960	0.16040	0.94268	0.05732
6	0.01664	0.98336	0.97742	0.02259	0.18761	0.81239	0.64182	0.35818
7	0.99637	0.00363	0.03764	0.96236	0.65775	0.34225	0.59059	0.40941
8	0.98482	0.01518	0.01004	0.98996	0	1	0	1
9	0.08270	0.91730	1	0	0.40630	0.59370	0.97619	0.02381
10	0.27658	0.72342	0.80803	0.19197	0.49171	0.50829	0.98513	0.01487
11	0.80740	0.19260	0.62986	0.37014	0.72783	0.27218	0.95436	0.04564
12	0.86905	0.13095	0	1	0.93246	0.06755	0.91950	0.08050
13	1	0	0.69009	0.30991	0.83219	0.16781	1	0
14	0.94212	0.05788	0.08532	0.91468	1	0	0.96479	0.03521
15	0	1	0.89962	0.10038	0.21841	0.78159	0.89880	0.10120
16	0.10585	0.89416	0.76663	0.23338	0.45573	0.54427	0.94959	0.05041

. In the final step, multi-response grey relational calculations are performed using Equation (10).

$${GRG}_i={\displaystyle \frac{1}{n}}\sum _{k=1}^n{GRC}_i\left(k\right)$$

(10)

where GRG_i represents the grey relational grade (GRG) for the ith run and n is the number of responses, which in this study is 2.

Table 13. GRCs and GRG for each experimental run.

Run	GRC(H)	GRC(λ)	$\mathbf{GRC}(\overline{\mathit{a}}$)	$\mathbf{GRC}(\mathsf{\Delta }\mathit{a}$)	GRG	Rank
1	0.86777	0.55695	0.77125	0.98682	0.80883	2
2	0.33457	0.81078	0.49932	0.98820	0.70690	8
3	0.59128	0.65922	0.46426	0.47969	0.54251	15
4	0.34485	0.76708	0.43572	0.86003	0.64119	11
5	0.34143	0.74416	0.75711	0.89715	0.71920	7
6	0.33707	0.95678	0.38098	0.58263	0.58204	14
7	0.99280	0.34191	0.59365	0.54981	0.59483	13
8	0.97053	0.33558	0.33333	0.33333	0.45854	16
9	0.35278	1.00000	0.45717	0.95455	0.73666	4
10	0.40869	0.72258	0.49589	0.97112	0.69312	9
11	0.72192	0.57462	0.64752	0.91635	0.73350	5
12	0.79246	0.33333	0.88099	0.86132	0.72156	6
13	1.00000	0.61735	0.74871	1.00000	0.84899	1
14	0.89625	0.35344	1.00000	0.93421	0.79775	3
15	0.33333	0.83281	0.39014	0.83167	0.63556	12
16	0.35864	0.68178	0.47880	0.90841	0.64853	10

displays the grey relational coefficients (GRCs) and grey relational grades (GRG) for each run, where higher values indicate greater preference.

The analysis of the signal-to-noise (S/N) data in

Table 14. Mean of S/N (GRG) for each processing parameter.

Processing Parameter	Notation	Levels				Absolute Value Difference	Rank
		1	2	3	4
HEA Addition Rate	A	−3.51	−4.71	−2.84	−2.77	1.94	2
Laser Power	B	−2.20	−3.22	−4.11	−4.31	2.11	1
Scanning Speed	C	−3.25	−3.16	−4.18	−3.25	1.01	4
Powder Feed Rate	D	−2.79	−3.48	−3.78	−3.79	1	5
Substrate Tilt Angle	E	−3.93	−3.49	−3.57	−2.84	1.09	3

reveals that laser power exhibits the largest absolute difference, underscoring its substantial impact on both microhardness and cladding layer quality. Utilizing Table 14, optimal processing parameter settings were identified by selecting the highest value in each category. The optimal combination of parameters identified was A₄B₁C₂D₁E₄ (15% HEA addition rate; 750 W laser power; 7 mm/s scanning speed; 2 r/min powder feed rate; 30° substrate tilt angle). Since this specific parameter setting was not included in the original 16 runs, a validation experiment was conducted to verify the efficacy of these processing parameters.

3.6. Processing Parameters Optimization and Experimental Validation

The GRG prediction was employed to forecast the outcomes for the parameter setpoint (A₄B₁C₂D₁E₄) prior to conducting the experiment. This predictive calculation was derived using Equation (11).

$${GRG}_p=\overline{GRG}+\sum _{j=1}^r(\overline{GR{G}_j}-\overline{GRG})$$

(11)

where GRG_p is the predicted value of GRG for the selected set; $\overline{GRG}$ represents the overall mean of the GRG; r is the number of processing parameters, which is 5 in this study; and $\overline{GR{G}_j}$ denotes the average GRG value at the selected level for the jth processing parameter.

Table 15. Comparison of optimization and experimental validation results.

Output	Best Parameter Set fromOrthogonal Design	GRA Prediction	Validation on GRA Prediction
Parameter Set	A4B1C4D2E3	A4B1C2D1E4	A4B1C2D1E4
H	461.30 HV	-	549.14 HV
λ	0.31	-	0.536
$\overline{a}$	25.79°	-	16.16°
Δa	0.312	-	1.69
GRG	0.84899	0.94316	0.93427

compares the optimal outcomes derived from the 16 orthogonal experiments, the predictions from grey relational analysis (GRA), and the results of the validation experiment. Specifically, it compares the best results achieved using configuration A₄B₁C₄D₂E₃ in the orthogonal experimental design with the optimal parameter settings identified through GRA (A₄B₁C₂D₁E₄). The microhardness increased significantly from 461.30 HV to 549.14 HV, marking a notable 19.04% improvement. Moreover, the average contact angle decreased impressively from 25.79° to 16.16°, representing a substantial 37.34% reduction. While there was a slight increase in both the dilution rate and the mean difference in contact angles, their impact on the quality of the coating remained negligible.

The Vickers hardness of the Q235 substrate measures 160 HV, while pure 316L stainless-steel registers 200 HV. In contrast, the laser cladding coating achieved a remarkable 549.14 HV—nearly 3.5 times the hardness of the Q235 substrate and 2.75 times that of pure 316L stainless-steel. This finding underscored the potential of the coating for creating robust and wear-resistant surfaces. The average contact angle of the laser cladding coating, measured at 16.16° under a 30° substrate tilt angle, indicated outstanding coating performance alongside its impressive hardness characteristics. In conclusion, the laser cladding technology employed in this study proves highly suitable for repairing and reinforcing parts with intricate geometries and specific tilt angles, offering substantial economic advantages in practical manufacturing applications.

Additionally, the discrepancy between the grey relational grade (GRG) prediction from Equation (11) and the experimental validation was less than one percent (0.95%), demonstrating the high accuracy of multi-response optimization via grey relational analysis. The methods proposed in this study could also assess uneven weighting distributions for metrics such as microhardness, dilution rate, average contact angle, and mean difference of contact angles. For instance, to achieve a laser melting coating with optimal dilution rate properties, the weighting factor in the GRA could be assigned as 60% for dilution rate, 20% for microhardness, and 10% for both the average contact angle and the mean difference of contact angles. Furthermore, this multi-response optimization approach using GRA could guide the design of large-scale experiments, which was crucial given the high cost and time requirements of actual experimentation. It could evaluate various responses, including aspect ratio, surface roughness, wear resistance, and cladding efficiency. Since GRA was designed for multi-response optimization, it could simultaneously optimize five or six of these responses.

Figure 9 shows the cross-sectional microscopic morphology of the laser cladding coatings prepared using the best combination of process parameters from the orthogonal design and the optimized parameter combination from the GRA. The figure illustrates that, compared to the optimal orthogonal parameter combination (a), the optimal GRA parameter combination (b) produced coatings that were devoid of pores and inclusions. The coating and the substrate formed a smooth and natural metallurgical bond, with the substrate being flat and free of undercuts. The surface of the coating was more uniform, rendering it more suitable for multi-pass laser cladding processes with overlapping joints. Additionally, Figure 9a shows that the ratio of the heat-affected zone depth to the cladding layer thickness (D/T) was 1.98, and the height-to-width ratio (H/W) was 0.158. In contrast, Figure 9b reveals that for the coating produced using the grey relational optimization parameters, the D/T value was 0.98 and the H/W value was 0.097. Generally, smaller D/T and H/W values are preferred [68,69]. In the grey relational optimization parameter group, the values of D/T and H/W were reduced by 25.8% and 38.6%, respectively, compared to those in the optimal parameter group identified from the orthogonal design. The lateral extensibility of single-pass coatings is enhanced as the H/W value decreases. Laser cladding parameters optimized via GRA are superior, resulting in laser cladding coatings that exhibit greater lateral extension, fuller morphologies, and smoother fusion with the substrate, ultimately leading to a reduction in the H/W value of the coating [69]. The D/T value quantitatively characterizes the extent of thermal impact on the substrate during processing. As the D/T value increases, the heat-affected zone in the composite coating enlarges, increasing the likelihood of defects such as fine cracks, microcracks, and penetrating cracks, which significantly threaten the coating’s service life. Due to the superior parameters identified by GRA, the heat input during the laser cladding process is more concentrated within the coating, reducing the thermal impact on the substrate and resulting in a lower D/T value in the metal matrix composite coating [68]. These results demonstrate the exceptional quality of the laser cladding coating produced using the optimal processing parameters derived from grey relational analysis.

3.7. Economic, Energy, and Sustainability Analyses

Thermal spray is a traditional technique for producing metal matrix composite coatings. Among its methods, plasma spray has become particularly notable for its ability to enhance the surfaces of substrates or components in demanding and corrosive environments. This technique is distinguished by its high deposition rates and the flexibility to achieve varied coating properties through modifications in processing parameters and ionic characteristics [70,71]. This method draws a parallel with the emerging laser cladding technology. Consequently, this study will compare the economic and energy consumption aspects of both methods during the preparation phase, aiming to demonstrate the cost-effectiveness and sustainability potential of laser cladding coating preparation optimized through grey relational analysis (GRA).

A total of 1000 samples of metal matrix composite coatings from Wuhan, China, were analyzed. Wang et al. [72] conducted a comprehensive examination of the plasma spray process for preparing composite coatings. Therefore, this study compared it with the laser cladding process utilizing the optimal parameters identified through GRA.

In the first step, the labor time costs were analyzed. Calculations based on the time required for multi-pass spraying and the number of passes revealed that plasma spray technology averages approximately 3.17 s for a single pass of about 60 mm, with an additional 254 s allocated to preheating and preparation tasks. In contrast, laser cladding technology using the optimal parameters requires about 0.86 s for the same pass, with an additional 241 s for preheating and preparation. Consequently, the production of 1000 composite coating samples via plasma spraying technology necessitated 71.44 working hours, whereas laser cladding required 67.18 working hours.

In the subsequent phase, the analysis focused on energy costs. Wang et al. demonstrated that the energy consumption for the optimal performance of plasma spray coating was 3.86 × 10⁴ kJ for 25 passes. According to this estimation, the energy consumption for a single-pass process is approximately 0.43 kW·h. In this study, the power consumption of each subsystem involved in the laser cladding process was systematically assessed by referencing the nominal power ratings provided on the equipment nameplates. This analysis determined that the energy consumption for a single-pass operation is approximately 0.29 kW·h. As a result, 430 kW·h of electrical energy was consumed to produce 1000 composite coating samples using plasma spraying technology, whereas laser cladding technology required 290 kW·h.

The third step entails an assessment of sustainability impacts. Carbon emission factors for electricity exhibit considerable variation based on the geographic location and the electricity generation conditions of each country. In this study, we selected the permanent members of the United Nations Security Council as the focus of our analysis and calculated the carbon emissions associated with the production of 1000 composite coating samples in China, the United Kingdom, France, the United States, and the Russian Federation. The detailed findings are presented in

Table 16. Carbon emissions associated with the production of 1000 composite coating samples in selected countries.

Technical Categories	China/kg	The United Kingdom/kg	France/kg	The United States/kg	The Russian Federation/kg
Laser cladding	161.59	61.21	11.88	108.21	93.32
Plasma spray	239.60	90.76	17.62	160.45	138.37

. Carbon emission factor data were derived from sources such as Climate Transparency (2022 Report), UK Govt—Defra/BEIS (2022), Association of Issuing Bodies (AIB) 2022, US Env Protection Agency (EPA), and other pertinent reports [73,74,75,76].

In summary, the production of 1000 metal matrix composite coating samples utilizing laser cladding technology optimized by GRA achieves a reduction in labor time by 4.26 h and a decrease in electricity consumption by 140 kW·h compared to traditional thermal spraying technology. The associated reduction in CO₂ emissions ranges from 5.74 kg to 78.01 kg, depending on the electricity generation conditions and the geographic location in the respective countries. Therefore, the application of laser cladding technology for composite coating preparation offers significant advantages in terms of economic cost-effectiveness and sustainability.

4. Conclusions

This study employs an orthogonal experimental design to systematically investigate the impact of varying the HEA addition rate, laser power, scanning speed, powder feed rate, and substrate tilt angle on the microhardness, dilution rate, average contact angle, and mean difference of contact angles in the single-track laser cladding layer. Optimal parameter combinations were identified through signal-to-noise ratio calculations and grey relational analysis (GRA), which were subsequently validated by experimental verification. Based on this investigation, the following conclusions can be drawn:

• The analysis of microhardness as the sole response variable indicates a notable decrease with higher powder feed rates. Moreover, variations in laser power, scanning speed, and substrate tilt angle initially lead to a reduction in microhardness, followed by subsequent improvements and subsequent declines.
• The analysis of the dilution rate indicates a significant decrease with higher powder feed rates and an increase with greater laser power. As scanning speed increases, the dilution rate initially rises and then declines. Conversely, as the substrate tilt angle increases, the dilution rate first decreases and then rises. Regarding the average contact angle, there is a progressive increase with higher powder feed rates, while an increase in substrate tilt angle leads to a decrease. The addition of HEA initially raises the average contact angle and subsequently reduces it, whereas higher laser power consistently enhances it. The mean difference in contact angles shows an initial sharp increase followed by a slight decrease with increasing laser power. Increases in the HEA addition rate, scanning speed, and powder feed rate result in an initial rise followed by a decline in the mean difference of contact angles.
• Grey relational analysis effectively discerns the processing parameters that optimize coating quality. The optimal combination comprises a 15% HEA addition rate, 750 W laser power, 7 mm/s scanning speed, 2 r/min powder feed rate, and a 30° substrate tilt angle. Coatings produced under these conditions exhibit markedly enhanced microhardness and average contact angle compared to those achieved using the optimal parameters identified in the orthogonal test.
• The validation experiment, conducted using optimal parameter settings identified through grey relational analysis, demonstrates a minimal error of 0.95% relative to the predicted value, underscoring the efficacy of GRA in optimizing laser cladding parameters. The preparation of laser cladding coatings utilizing the optimal parameters derived through GRA demonstrates enhanced economic efficiency and superior environmental sustainability compared to conventional methods. The methodologies presented in this study can also be applied to optimize laser processing parameters in selective laser melting of titanium alloys and laser powder bed fusion of aluminum alloys. These methodologies offer a robust framework for advancing laser cladding technology in industrial applications, thereby facilitating the fabrication of wear-resistant coatings and the enhancement or repair of component surfaces. Specifically, laser cladding coatings optimized through GRA can be used for corrosion resistance treatment of ships in the maritime industry, surface enhancement of specialized parts in the aerospace industry, and service protection of cooling tower components in the power industry.