Laser Cladding of AZ61 Magnesium Alloy Reinforced with Inconel 625 Particle: A Grey Relational Analysis Approach

Abstract

This study investigates the optimization of laser cladding parameters to enhance the surface properties of AZ61 magnesium alloy using Inconel 625 powder reinforcement. Due to the alloy’s inherent limitations such as low wear and corrosion resistance, surface modification through laser cladding offers a promising solution for improving functional performance. An L16 orthogonal array based on the Taguchi method facilitated the evaluation of four process parameters namely laser power, scanning speed, powder feed rate, and gas flow rate. The measured responses included microhardness, dilution rate, and wear volume. Analysis of variance and signal-to-noise ratios indicated that laser power significantly influenced microhardness (84.42%), scanning speed affected dilution rate (87.13%), and powder feed rate predominantly impacted wear volume (89.42%). Grey Relational Analysis identified the optimal parameter combination that achieved maximum hardness, minimum wear, and minimal dilution. The optimized settings produced a low prediction error of 1.89% in the grey relational grade. These findings confirm the effectiveness of Grey Relational Analysis based multi-response optimization in enhancing surface quality and wear resistance of magnesium alloys, making the process well-suited for lightweight applications in automotive and aerospace components.

Keyword:
Laser Cladding; Magnesium alloy; Coatings; Inconel; Optimization

1. Introduction

In recent years, industries are constantly increasing their efforts to improve the mechanical properties, corrosion and wear resistant properties with the application of automotive, aerospace and marine components to withstand harsh conditions. Magnesium alloys are increasingly utilized in industries due to their low density, high specific strength, and favorable strength-to-weight ratio¹-³. These alloys whether cast or wrought, contribute exceptional strength to weight ratios in many engineering applications. Magnesium alloys are extensively applied across industries such as automotive, defense, electronics, aerospace, biomedical, manufacturing, and green energy, owing to their lightweight design (up to 70% lighter than stainless steel and 33% lighter than aluminum), ease of processing, high damping capacity, and cost-effectiveness. However, unlike stainless steel and aluminum, magnesium alloys exhibit lower intrinsic corrosion resistance, which necessitates the use of protective coatings, alloying strategies, or surface treatments for reliable service performance. While magnesium alloys have several desirable benefits and features, less is known about their wear behavior when compared to other materials. Wear, in its broadest definition, can be thought of as the process by which the surface of one component degrades due to its continual friction against another⁴-⁶.

Wear properties are highly significant for magnesium alloys, particularly in automotive applications such as gearbox housings, engine blocks, steering wheels, seat frames, and transmission casings. Surface modification is essential to enhance the wear and corrosion resistance of magnesium alloys, making them viable for these components where material reactivity presents a critical challenge⁷-⁹. Many researchers enhanced the functionality and durability of magnesium alloys by modifying the material surface using various reinforcements to improve mechanical properties, wear and corrosion behaviors. In automotive applications, where high level of corrosion resistance and wear resistance are required, iron-based alloys and nickel-based alloys are frequently utilized in laser cladding (LC) methods as base material. Hard chrome coatings are increasingly prohibited because they contain hexavalent chromium (Cr6+), which the World Health Organization classifies as both carcinogenic and dangerous to the environment¹⁰,¹¹. It is imperative to transition towards more environmentally benign technologies over traditional hard chrome coatings, which are chrome-faced, to enhance both energy efficiency and product performance.

Surface modification is essential for magnesium alloys due to their inherent limitations such as high chemical reactivity, low corrosion resistance, and poor wear performance. These drawbacks restrict their broader application in aggressive environments, particularly in automotive, aerospace, and marine sectors. Historically, surface engineering methods like Plasma Electrolytic Oxidation, cold spray, and Electroless Nickel Plating have been utilized to enhance the corrosion and wear resistance of magnesium components. Although effective, these techniques often rely on environmentally hazardous materials, such as hard chrome coatings, which contain carcinogenic hexavalent chromium compounds. As a more sustainable alternative, laser cladding has gained prominence due to its ability to create dense, metallurgically bonded, and porosity-free coatings with superior mechanical integrity. Unlike conventional methods, laser cladding enables precise, layer-by-layer deposition, leading to crack-free surfaces with enhanced durability¹²-¹⁴. Among the materials used, Inconel 625, a nickel-based superalloy, is notable for its excellent corrosion resistance, thermal stability, and low porosity. However, achieving consistent coating quality depends on the precise control of parameters such as laser power (LP), scanning speed (SS), powder feed rate (PFR), and gas flow (GF) all of which collectively influence the final surface characteristics¹⁵,¹⁶.

Despite extensive research on laser cladding of steels and Ni-based alloys, studies involving Inconel 625 reinforcement on magnesium alloys, particularly AZ61, remain limited. Previous works have focused on optimizing parameters for systems such as Ni60A with tungsten carbide on Ni60A with 25% tungsten carbide on 42CrMo¹⁷, Inconel 718 powder on medium‑carbon 45 steel¹⁸, Ni60 on Q235 steel¹⁹, Nickel coating on AISI 410 stainless steel²⁰ and high-entropy alloys such as FeCuNiCrAl on Q235 steel²¹, often using Taguchi or hybrid optimization techniques. However, there is a clear research gap in applying such optimization frameworks to lightweight magnesium-based substrates. Furthermore, very few studies have employed multi-response optimization techniques such as Grey Relational Analysis (GRA) for laser cladding on AZ61 alloy with Inconel 625 reinforcement.

To bridge this gap, the present work aims to investigate and optimize the laser cladding parameters namely LP, SS, GF, and PFR for AZ61 magnesium alloy coated with Inconel 625 using a Taguchi L16 orthogonal array integrated with GRA. This study is among the first to apply GRA in this specific material system. The novelty of the work lies in its combination of lightweight substrate and high-performance reinforcement, analyzed through statistically rigorous optimization techniques. The outcomes are expected to provide both scientific insights and practical guidelines for enhancing wear resistance, hardness, and dilution control in surface-modified magnesium alloys, with potential applications in automotive and aerospace structural components.

2. Experimental Design

In this study, AZ61 magnesium alloy, commercially available, was chosen as the substrate material with the dimensions of 200 x 150x 15 mm. The base material composition of the AZ61 is provided in Table 1. Morphological analysis was conducted by using SEM combined along with EDX, as shown in Figures 1a and 1b. The SEM and EDX picture contains magnesium as the primary element. Additionally, the EDAX analysis identified aluminum and zinc as secondary constituents. The reinforcement of substrate surfaces is a critical consideration for enhancing material properties; hence, Inconel 625 was chosen as the reinforcing agent in this research. The high nickel content in Inconel 625 contributes to its exceptional strength and capacity to withstand elevated temperatures. Table 2 enumerates the elemental composition of the Inconel 625 alloy, while Figures 2a and 2b display the morphology, confirming the particle size and shape. The Inconel 625 powder purchased from China ranged in size of 40 to 120µ and was used as the coating particle stored in the feedstock for reinforcement.

Table 1
AZ61 magnesium alloy-Composition.

Specimen	Element (weight %)
	Magnesium	Aluminium	Zinc	Manganese	Silicon	Copper	Nickel	Iron
AZ61	93.0	5.80 - 7.20	0.40 - 1.50	0.15	0.10	0.05	0.005	0.005

Figure 1
(a) SEM picture of as cast AZ61 (b) EDX of as cast AZ61.

Table 2
Inconel 625 alloy Powder-Composition.

Coating Particle	Element (weight %)
	Nickel	Chromium	Molybdenum	Niobium	Aluminium	Iron	Copper
Inconel625	56	22	12	5	2	2	1

Figure 2
Inconel 625 - (a) SEM (b) EDAX.

The schematic layout of the laser cladding setup utilized in this research is shown in Figure 3, and its technical specifications are provided in Table 3. Proper preparation before conducting the laser cladding is highly essential to ensure optimal results. This preparation process includes thorough cleaning of the substrate surface with acetone to remove any grease or contaminants, rinsing it with alcohol to eliminate any residual acetone, and then drying it completely to prevent any interference during the cladding process²²-²⁴. Taking these steps helps to achieve a clean and stable surface, which is essential for the effectiveness and quality of the laser cladding.

Figure 3
Schematic layout of the laser cladding setup.

Table 3
Specifications of the laser cladding equipment.

Parameter	Specification
Laser Type	Continuous Wave Fiber Laser
Wavelength	1070 nm
Maximum Power	4 kW
Beam Delivery	Fiber Optic
Focus Spot Diameter	2 mm
Powder Feeding System	Coaxial Nozzle
Motion Control	CNC Table

Laser cladding is a complex process governed by multiple interdependent parameters. In this study, four primary process variables were investigated. These include LP, SS, GF, and PFR, each varied across four distinct levels as presented in Table 4. These parameters were selected because of their significant influence on melt pool behavior, material deposition, and surface quality. Laser power regulates the energy supplied and melting efficiency. Scanning speed controls the duration of laser interaction and thermal distribution. Gas flow rate ensures melt pool protection and assists in powder delivery. Powder feed rate determines the volume of material deposited and affects the coating thickness and uniformity. The selected parameter ranges were derived from preliminary experimental observations and previously reported studies. For instance, the laser power range of 1.4 to 1.7 kilowatts was chosen to achieve melt pool stability while preventing issues such as weak bonding at lower power or excessive dilution and coarse microstructure at higher levels, as supported by prior findings²⁵,²⁶. Similar considerations were made for choosing appropriate levels of scanning speed, gas flow, and powder feed rate in accordance with the thermal sensitivity of AZ61 magnesium alloy²⁷.

Table 4
Parametric level of Laser cladding process.

Levels	LP (kW)	SS (mm/s)	GF (L/h)	PFR (g/min)
1	1.4	8	380	15
2	1.5	9	390	20
3	1.6	10	400	25
4	1.7	11	410	30

To evaluate the effectiveness of the process, three measurable outcomes were considered. These are microhardness, dilution rate, and wear volume. These factors serve as indicators of surface strength, metallurgical bonding quality, and tribological durability. Conducting a complete factorial analysis for all parameter combinations would require 256 experiments. To reduce this effort while maintaining meaningful statistical evaluation, the Taguchi orthogonal design methodology was adopted. An L16 orthogonal array was implemented, yielding 16 experimental conditions as detailed in Table 5. The resulting cladded samples underwent thorough characterization and wear performance analysis. The data collected from these tests provided essential insights into how each process parameter influenced the overall quality and behavior of the cladded surfaces²⁸,²⁹.

Table 5
Measured output responses of laser cladded specimen.

Trial No	LP (kW)	SS (mm/s)	GF (L/h)	PFR (g/min)	Micro-hardness (HV)	Dilution Rate (%)	Wear Volume (mm3)
1	1.4	8	380	15	347.33	24.89	1.82
2	1.4	9	390	20	348.23	21.45	1.62
3	1.4	10	400	25	381.24	15.03	1.58
4	1.4	11	410	30	362.63	18.23	1.39
5	1.5	8	390	25	385.23	22.17	1.19
6	1.5	9	380	30	389.13	18.72	1.30
7	1.5	10	410	15	414.43	13.85	1.79
8	1.5	11	400	20	403.60	16.08	1.44
9	1.6	8	400	30	428.93	21.35	1.33
10	1.6	9	410	25	452.27	18.23	1.16
11	1.6	10	380	20	468.21	15.46	1.25
12	1.6	11	390	15	457.82	16.74	1.63
13	1.7	8	410	20	398.22	21.32	1.45
14	1.7	9	400	15	387.53	20.56	1.76
15	1.7	10	390	30	429.21	14.95	1.36
16	1.7	11	380	25	416.27	17.15	1.26

The micro-hardness of the laser cladded specimens was evaluated using a Vickers micro-hardness tester, selected for its precision and suitability in assessing surface modifications and thin coatings. This method is particularly effective in measuring hardness variations across laser-treated zones due to its micro-scale indentation capability. The testing was conducted at a load of 300 grams and a dwell time of 12 seconds. Micro-hardness values were measured along the cross-section of the cladded AZ61 magnesium alloy substrate at three distinct points spaced 0.1 mm apart, starting from the top surface and moving inward. The average of these measurements was calculated to enhance the accuracy and reliability of the results. Compared to alternative hardness testing methods such as Rockwell or Brinell, the Vickers method provides higher resolution and is better suited for characterizing microstructural gradients in laser-modified layers³⁰,³¹. The indentation layout and testing locations are shown in Figure 4a, while the experimental setup and specimen positioning are illustrated in Figure 4b. This setup ensured consistent, repeatable, and precise measurements, making Vickers hardness an ideal choice for evaluating laser cladding effectiveness.

Figure 4
(a) Vickers microhardness indentation-test location (b) Experimental setup of Vickers Microhardness Tester.

Laser cladded samples are meticulously segmented transversely in the cladded tracks. This step is essential for calculating the dilution, a key parameter that measures the degree of particle mixing between the substrate and the reinforcement. The extent of this mixing has a major impact on critical properties of the cladded layer, including hardness, resist against wear and corrosion resistance. The dilution rate is determined by investigating the cross-sectioned cladded samples. In this study, image analysis software was employed to systematically quantify the areas of melted substrate and cladding material. The dilution rate is mathematically defined by the equation D = (A_S / (A_S + A_C)) × 100%, where A_S denotes the substrate material’s area that has melted and assorted with the cladding particle and A_C denotes the area of the cladded material³². This analysis requires precise identification and measurement of these areas on the cross-sectional samples. Proper control ensures desirable mechanical goods and a favorable microstructure in the resultant clad layer. The calculated dilution rates were then used to assess the effectiveness of the laser cladding process and to make necessary adjustments for process optimization.

The features of wear behaviour of the laser-cladded composites were assessed in a pin-on- disc device, as depicted in Figure 5. The experimental procedure followed the ASTM G99 standard to evaluate the dry sliding wear behaviour. Wire Electrical Discharge Machining is chosen to prepare the samples having dimensions of 30 mm in height and 10 mm in diameter. During the wear testing, the samples were placed against EN 32 steel discs, which possess a hardness of 60 HRC. In order to verify accuracy and cleanliness, both samples and steel discs were thoroughly cleaned by using acetone before the wear test. The testing parameters included an applied load of 20 N, sliding speed of 1.25 m/s and the sliding distance of 800 m. The wear volume, which indicates the material loss due to friction or abrasion, was estimated by using the difference in volume of the specimens at the beginning and at the end of the wear tests. To gain insights into the wear mechanisms and to observe the wear scars on the worn surfaces, SEM was employed³³. Additionally, the output response of the modified AZ61 magnesium alloy composites with Inconel powders were investigated under various input parameters. The results of these investigations are summarized in Table 5, providing comprehensive data on the output responses of the modified composites.

Figure 5
Pin on disc apparatus.

3. Result and Analysis

Signal-to-noise ratio technique is adopted to locate the optimal processing constraints by laser cladding process with Taguchi analysis. The signal-to-noise ratio is a crucial tool in optimizing data analysis for enhancing the mechanical properties by improving the optimum values³⁴,³⁵. The purpose of this task is to estimate the input responses of the laser-cladded surface, emphasizing the importance of criteria where "smaller is better" for wear volume and dilution rate, whereas "larger is better" for the micro-hardness. To calculate the S/N ratio, we considered three categories of characteristics such as output responses by utilizing the data derived from Table 5. The outcome of the S/N conversion and the observed values for output responses are tabulated as given in Table 6. Afterward, the Analysis of Variance (ANOVA) technique was performed to investigate the various parameters influencing output responses. During the ANOVA process, a confidence level of 95% was used, supported by Design of Experiments methodology.

Table 6
Results of S/N conversion for output responses.

Trial No.	Micro-hardness	Dilution rate	Wear volume	S/N of micro-hardness	S/N of dilution rate	S/N of wear volume
1	347.33	24.89	1.82	50.81	-27.92	-5.18
2	348.23	21.45	1.62	50.84	-26.63	-4.18
3	381.24	15.03	1.58	51.62	-23.54	-3.97
4	362.63	18.23	1.39	51.19	-25.22	-2.84
5	385.23	22.17	1.19	51.71	-26.92	-1.48
6	389.13	18.72	1.30	51.80	-25.45	-2.27
7	414.43	13.85	1.79	52.35	-22.83	-5.08
8	403.60	16.08	1.44	52.12	-24.13	-3.15
9	428.93	21.35	1.33	52.65	-26.59	-2.47
10	452.27	18.23	1.16	53.11	-25.22	-1.26
11	468.21	15.46	1.25	53.41	-23.78	-1.95
12	457.82	16.74	1.63	53.21	-24.48	-4.24
13	398.22	21.32	1.45	52.00	-26.58	-3.21
14	387.53	20.56	1.76	51.77	-26.26	-4.89
15	429.21	14.95	1.36	52.65	-23.49	-2.65
16	416.27	17.15	1.26	52.39	-24.69	-2.01

3.1. ANOVA variance analysis

To identify the optimal data influenced by the S/N ratio, an ANOVA was conducted. The Anderson-Darling (AD) normality test was conducted in Minitab 18 to evaluate data normality. Here, this test is crucial for confirming whether the data follow a normal distribution, which is a prerequisite for ANOVA. The p-value, more than 0.05 depicts a normal distribution³⁶,³⁷. In this study, the AD test was applied to the transformed S/N values of output responses. All the resulting p-values for each parameter were more than 0.05, as illustrated in Figures 6a-c.

Figure 6
Normality test for S/N of output responses (a) Micro-hardness, (b) Dilution rate, (c) Wear volume.

These results confirmed the normality of the distributions for these responses. Having established that the data were normally distributed, we proceeded with the ANOVA to further analyze the influence of various factors on the S/N ratio. The ANOVA results provided insights into the significance of different parameters, allowing us to determine the optimal conditions for improving the material properties under study.

3.1.1. Influence of the control variables on micro-hardness

Table 7 presents the ANOVA outcome for the S/N ratio of micro-hardness. The p-value for LP was 0.001, which is notably lower than the 0.05 threshold, indicating a strong statistical impact on the micro-hardness. From Table 7, it is evident that the laser power has a predominant influence on micro-hardness, holding for 84.42% of the total contribution. Following the LP, SS emerges as the next significant factor, holding the second most substantial influence on micro-hardness. The contribution percentages of the other process parameters are SS (13.80%), PFR (0.58%), and GF (0.47%). The error percentage is relatively minimal at 0.72%. These results suggest that while laser power is the primary determinant of micro-hardness, the scanning speed also plays a notable role, albeit to a lesser extent. Gas flow and powder feed rate have relatively minor impacts.

Table 7
ANOVA analysis of micro-hardness.

Source	DF	Adj SS	Adj MS	F-Value	P-Value	Seq SS	Contribution
LP	3	17131.4	5710.45	116.58	0.001	17131.4	84.42%
SS	3	2800.4	933.47	19.06	0.019	2800.4	13.80%
GF	3	96.0	32.02	0.65	0.632	96.0	0.47%
PFR	3	118.2	39.41	0.80	0.569	118.2	0.58%
Error	3	147.0	48.98			147.0	0.72%
Total	15					20293.0	100.00%

Table 8 represents the average S/N ratio of micro-hardness for every factor at various levels. The delta value indicates the maximum mean, signifying the most influential parameters, with rank 1 being the most significant based on the calculated means. According to Table 8, laser power emerges as the most important factor compared to other parameters.

Table 8
Response table for micro-hardness.

Level	LP	SS	GF	PFR
1	51.12	51.79	52.10	52.04
2	52.00	51.88	52.10	52.09
3	53.09	52.51	52.04	52.21
4	52.20	52.23	52.16	52.07
Delta	1.98	0.71	0.12	0.17
Rank	1	2	4	3

Figure 7 depicts the main effect plot, revealing that an increase in laser power substantially enhances micro-hardness. The observations revealed that a substrate coated with Inconel 625 achieves greater hardness when compared to an unreinforced AZ61 Mg alloy. It is also observed that micro-hardness increases with the rise in laser power up to 1.6 kW; beyond this point, it starts to decline due to the formation of a larger melt pool on the substrate surface. The extended cooling period associated with a larger melt pool leads to a reduction in micro-hardness³⁸. At 1.7 kW, there is a notable change in hardness over different sections of the cladded surface. The optimum values, as per the S/N ratio effect plot, are arrived at the third level of LP, the third level of scanning speed, the fourth level of gas flow, and the third level of powder feed rate. Specifically, the optimal parameters are LP - 1.6 kW, SS - 10 mm/s, GF - 410 L/h and PFR - 25 g/min. These results are in agreement with the findings of Gao et al.²¹, who reported enhanced surface hardness in Inconel-cladded layers due to refined microstructure and metallurgical bonding. Similar trends were also observed by Yang et al.¹⁸ in Inconel 718 cladding systems.

Figure 7
Main effects plot for micro-hardness.

3.1.2. Effect of the control variables on dilution rate

ANOVA analysis was carried out to find out the key factors influencing the rate of dilution and to calculate the % contribution of each factor. The results, summarized in Table 9, indicate that scanning speed is the highly influential parameter contributing 87.13% to the rate of dilution, 7.74% to laser power, 2.21% to gas flow and 1.14% to powder feed rate. Therefore, controlling the scanning speed is crucial for managing the dilution rate during the laser cladding process, with gas flow, laser power and powder feed rate also playing significant roles.

Table 9
ANOVA analysis of dilution rate.

Source	DF	Adj SS	Adj MS	F-Value	P-Value	Seq SS	Contribution
LP	3	11.598	3.8661	4.33	0.130	11.598	7.74%
SS	3	130.513	43.5042	48.76	0.005	130.513	87.13%
GF	3	3.303	1.1011	1.23	0.433	3.303	2.21%
PFR	3	1.707	0.5690	0.64	0.640	1.707	1.14%
Error	3	2.677	0.8922			2.677	1.79%
Total	15					149.798	100.00%

Table 10 provides a ranking of all these factors based on their impact, with scanning speed ranked first, laser power second, gas flow third, and powder feed rate fourth. The primary S/N effects ratio shown in Figure 8 reveals that the optimum conditions are observed at the second level of laser power, the third level of scanning speed, the fourth level of gas flow and the third level of powder feed rate. Specifically, the optimum parameters identified are LP - 1.5 kW, SS - 10 mm/s, GF - 410 L/h and PFR - 25 g/min.

Table 10
Response table for dilution rate.

Level	LP	SS	GF	PFR
1	-25.83	-27.00	-25.46	-25.37
2	-24.83	-25.89	-25.38	-25.28
3	-25.02	-23.41	-25.13	-25.09
4	-25.25	-24.63	-24.96	-25.19
Delta	1.00	3.59	0.50	0.28
Rank	2	1	3	4

Figure 8
Main effects plot for dilution rate.

The scanning speed has a pronounced effect on the rate of dilution throughout the process. At low scanning speeds, either the laser or heat source lingers longer over a specific substrate area which causes extensive melting of both substrates and Inconel 625 powder. Such prolonged exposure leads to a high dilution rate as more substrate material is incorporated into the molten pool³⁹. In contrast, at high scanning speeds (exceeding 10 mm/s), the heat source or laser traverses over the surface of the substrate rapidly, reducing the time of interaction between the substrate and the heat source. This quick movement results in less substrate melting, thereby decreases the dilution rate. Under these conditions, the molten pool is mainly composed of the Inconel 625 powder having an inclusion of minimum substrate material. Initially, the decrease in dilution rate was observed when the scanning speed raises from 8 to 10 mm/s, but it shows a significant increase once the speed surpasses 10 mm/s.

3.1.3. Influence of various control variables on wear volume

Table 11 illustrates the percentage contribution of various factors on wear volume. The results suggest that the powder feed rate is the highly influential factor, accounting for 70.70% of the overall contribution. Laser power follows with a 19.71% contribution, while the gas flow rate and scanning speed contribute 4.23% and 1.40%, respectively. The error value is recorded at 3.97%. The data clearly shows that wear volume is predominantly influenced by the powder feed rate, particularly when using Inconel 625 powder. This factor alone impacts wear volume by 70.70%, underscoring its critical role in the wear process. Therefore, among all factors analyzed, the powder feed rate of Inconel 625 powder exhibits the greatest effect on wear, emphasizing its substantial influence.

Table 11
ANOVA analysis of wear volume.

Source	DF	Adj SS	Adj MS	F-Value	P-Value	Seq SS	Contribution
LP	3	0.138635	0.046212	4.97	0.110	0.138635	19.71%
SS	3	0.009811	0.003270	0.35	0.793	0.009811	1.40%
GF	3	0.029746	0.009915	1.07	0.480	0.029746	4.23%
PFR	3	0.497179	0.165726	17.82	0.020	0.497179	70.70%
Error	3	0.027901	0.009300			0.027901	3.97%
Total	15					0.703271	100.00%

Table 12 displays the average S/N ratio of wear volume for every parameter across different levels. Here, the value of delta, indicating the differences between the highest and the lowest S/N ratio for each parameter, was utilized to rank the parameters in their influencing order. The findings reveal that the powder feed rate has the highest impact with a delta of 2.664, followed by laser power with a delta of 1.566, gas flow with a delta of 0.768, and scanning speed with a delta of 0.351. From the S/N ratio response table, the principal effects plot for wear volume was arrived and is depicted in Figure 9. This plot indicates that the optimum settings for high S/N ratios and minimal wear volume are achieved with the third level of laser power, the fourth level of scanning speed, the first level of gas flow, and the third level of powder feed rate. An important observation from the data is that the increase the powder feed rate considerably decreases wear loss. Such reduction is likely due to the addition of Inconel 625, which limits the plastic deformation in this AZ61 matrix. The reinforcement particles from Inconel 625 enhance the hardness of the cladded structure and thereby reducing wear loss. Furthermore, Inconel 625 minimizes interaction with the counter surface across the cladded structure and covers the matrix region, which directly engages with the counter surface, mitigating the abrasive action of the hard counter surface⁴⁰. From Figure 9, the optimal parameters are identified as 1.6 kW of laser power, 11 mm/s of scanning speed, 380 L/h of gas flow and 25 g/min powder feed rate. The observed decrease in wear volume with increased powder feed rate correlates with the outcomes reported by Lu et al.¹⁹ and Lian et al.²³, where dense particle reinforcement led to lower wear losses in nickel-based laser coatings.

Table 12
Response table for wear volume.

Level	LP	SS	GF	PFR
1	-4.044	-3.087	-2.852	-4.846
2	-2.996	-3.148	-3.139	-3.122
3	-2.478	-3.413	-3.621	-2.182
4	-3.192	-3.062	-3.098	-2.560
Delta	1.566	0.351	0.768	2.664
Rank	2	4	3	1

Figure 9
Main effects plot for Wear volume.

3.2. Grey relational analysis

GRA was chosen as the optimization technique in this study due to its proven effectiveness in handling complex multi-response problems, particularly in manufacturing and surface engineering processes. Unlike traditional single-response optimization methods such as ANOVA or regression analysis, GRA allows simultaneous consideration of multiple conflicting output responses, such as maximizing hardness while minimizing both dilution and wear volume, without requiring extensive datasets or probabilistic assumptions. Furthermore, GRA is computationally straightforward, interpretable, and robust even when the sample size is small, which is often the case in experimental design studies using Taguchi orthogonal arrays⁴¹,⁴². Compared to advanced methods like TOPSIS or response surface methodology, GRA is less sensitive to data distribution assumptions and offers quicker convergence toward optimal settings⁴³,⁴⁴. Several recent works in laser cladding and additive manufacturing have validated GRA’s accuracy and practicality for optimizing surface properties, making it a suitable and justified choice for the current investigation involving AZ61 magnesium alloy reinforced with Inconel 625⁴⁴.

3.2.1. S/N ratio normalization and deviation

In GRA, the initial step is to normalize and deviate experimental data to make the range 0-1. In this analysis, higher micro-hardness suggests better cladding performance, hence "higher-is-better" is chosen for hardness. Lower dilution rate and wear volume signifies higher cladding performance, hence "lower-the-better" is chosen⁴⁵. In the work at hand, having a high micro-hardness, reduced dilution rate and wear volume are all attractive qualities. Therefore, normalized values of micro-hardness are derived by Equation 1, and normalized values of wear volume and dilution rate are calculated by Equation 2 and the values of deviation sequence are shown in the Table 13.

Table 13
Values of Normalization and GRC.

Trial No.	S/N of Micro-hardness		S/N of Dilution Rate		S/N of Wear Volume
	Normalized	GRC	Normalized	GRC	Normalized	GRC
1	0.0000	0.3333	0.0000	0.3333	0.0000	0.3333
2	0.0074	0.3350	0.3116	0.4207	0.2997	0.4166
3	0.2805	0.4100	0.8931	0.8239	0.3561	0.4371
4	0.1266	0.3641	0.6033	0.5576	0.6485	0.5872
5	0.3135	0.4214	0.2464	0.3988	0.9532	0.9143
6	0.3458	0.4332	0.5589	0.5313	0.7823	0.6967
7	0.5551	0.5292	1.0000	1.0000	0.0323	0.3407
8	0.4655	0.4833	0.7980	0.7123	0.5730	0.5394
9	0.6750	0.6061	0.3207	0.4240	0.7373	0.6556
10	0.8681	0.7913	0.6033	0.5576	1.0000	1.0000
11	1.0000	1.0000	0.8542	0.7742	0.8549	0.7751
12	0.9140	0.8533	0.7382	0.6564	0.2816	0.4104
13	0.4210	0.4634	0.3234	0.4249	0.5568	0.5301
14	0.3326	0.4283	0.3922	0.4513	0.0905	0.3547
15	0.6774	0.6078	0.9004	0.8338	0.6941	0.6204
16	0.5703	0.5378	0.7011	0.6259	0.8407	0.7584

Larger-the-better option:

$Y_i\left(p\right)=\frac{X_i\left(p\right)-min{X}_i\left(p\right)}{max{X}_i\left(p\right)-min{X}_i\left(p\right)}$ …(1)

Smaller-the-better option:

$Y_i\left(p\right)=\frac{max{X}_i\left(p\right)-X_i\left(p\right)}{max{X}_i\left(p\right)-min{X}_i\left(p\right)}$ …(2)

3.2.2. Grey relation co-efficient (GRC) calculation

After the S/N ratio has been normalized, the GRC is evaluated by using the following Equation 3.

$GR{C}_i\left(p\right)=\frac{{\Delta }_{min}\left(p\right)+\delta {\Delta }_{max}\left(p\right)}{{\Delta }_i\left(p\right)+\delta {\Delta }_{max}\left(p\right)}$ …(3)

The grey relational coefficient, denoted as GRC_i(p), corresponds to the response of the p_th parameter (where p = 1,2,3) in the i_th experimental run (where i = 1, 2, 3, …, 16). The term Δi​(p) represents the differences between 1 and the normalized value of the p_th response in the i_thrun, calculated using the formula Δi(p) = 1 – Yi(p). Here, Δmax(p) and Δmin(p) are the higher and lower values of Δi(p)for all responses of the p_th parameter, respectively. The distinguishing coefficient δ ranges between 0 and 1, with a common choice being 0.5⁴⁶,⁴⁷.

3.2.3. Grey relational grade (GRG) calculation

The value of GRG is evaluated by using Equation 4. This equation provides equal weightage to the output responses.

$GR{C}_i=\frac{1}{n}{{\displaystyle \sum }}_{p=1}^{n}GR{C}_i\left(p\right)$ …(4)

Tables 13 and 14 present the computed values for normalization, GRC, and GRG for all experimental runs. Table 14 also includes the GRG ranking and identifies the 11th experimental run as the optimal parameter setting, corresponding to 1.6 kW laser power, 10 mm/s scanning speed, 380 L/h gas flow rate, and 20 g/min powder feed rate. Using these results, ANOVA was carried out to assess the influence of each process parameter on the overall GRG. The ANOVA results, summarized in Table 15, show that laser power contributes 50.43%, scanning speed 29.59%, powder feed rate 13.68%, and gas flow rate 4.41% to the total variation in GRG. Furthermore, the response values for GRG at different parameter levels are reported in Table 16. The normality of residuals was verified using the Anderson–Darling test, as illustrated in Figure 10. Additionally, Figure 11 displays the S/N ratio of the GRG along with the corresponding main effects plots.

Table 14
Values of GRG and S/N conversion.

Run No.	GRG	S/N of GRG	Rank order
1	0.3333	-9.54243	16
2	0.3908	-8.16177	15
3	0.5570	-5.08298	10
4	0.5029	-5.96973	12
5	0.5782	-4.75839	8
6	0.5537	-5.13412	11
7	0.6233	-4.10643	6
8	0.5783	-4.75668	7
9	0.5619	-5.00718	9
10	0.7830	-2.12520	2
11	0.8498	-1.41398	1
12	0.6400	-3.87612	5
13	0.4728	-6.50630	13
14	0.4115	-7.71351	14
15	0.6874	-3.25637	3
16	0.6407	-3.86722	4

Table 15
ANOVA analysis of GRG.

Source	DF	Adj SS	Adj MS	F-Value	P-Value	Seq SS	Contribution
LP	3	0.140139	0.046713	26.69	0.012	0.140139	50.43%
SS	3	0.082213	0.027404	15.66	0.025	0.082213	29.59%
GF	3	0.012260	0.004087	2.34	0.252	0.012260	4.41%
PFR	3	0.038000	0.012667	7.24	0.069	0.038000	13.68%
Error	3	0.005250	0.001750			0.005250	1.89%
Total	15					0.277862	100.00%

Table 16
Response table for GRG.

Level	LP	SS	GF	PFR
1	-7.189	-6.454	-4.989	-6.310
2	-4.689	-5.784	-5.013	-5.210
3	-3.106	-3.465	-5.640	-3.958
4	-5.336	-4.617	-4.677	-4.842
Delta	4.084	2.989	0.963	2.351
Rank	1	2	4	3

Figure 10
Normality test of GRG.

Figure 11
Main effects plot for GRG.

3.3. GRG optimum condition validation

GRA identified the optimal laser cladding parameters with LP at 1.6 kW, SS at 10 mm/s, GF at 410 L/h, and PFR at 25 g/min. A confirmation test was conducted using these settings, and the results showed close alignment between predicted and experimental responses. As summarized in Table 17, the experimental values for microhardness (172.1 HV), dilution rate (29.0%), and wear volume (2.61 mm³) closely matched the predicted values (174.5 HV, 28.4%, and 2.56 mm³, respectively), with a maximum deviation of less than 2.11%. The predicted Grey Relational Grade (GRG) was 0.782, while the experimental GRG was 0.767, yielding a minimal error of 1.89%. This strong agreement validates the effectiveness of the multi-response optimization strategy.

Table 17
Prediction and validation-experimental values.

Input / Output	11th run in L16 Design	Predicted optimal set	Validation
Laser Power	1.6	1.6	1.6
Scanning Speed	10	10	10
Gas Flow Rate	380	410	410
Powder Feed Rate	20	25	25
Micro-hardness	468.21HV	-	469.03HV
Dilution rate	15.46%	-	16.29%
Wear volume	1.25mm3	-	1.15mm3
GRG	0.8498	0.9049	0.8816

4. Wear Mechanism

Wear characteristics of the laser surface-modified AZ61 magnesium alloy were thoroughly evaluated through SEM analysis, focusing on worn surfaces exhibiting both high and low wear loss conditions, as illustrated in Figures 12a and 12b. Increasing the powder feed rate of Inconel 625 during laser cladding led to the formation of a thicker and denser coating layer, which effectively reduced the wear rate of the material. This improvement in wear resistance results from a combination of compositional enrichment and microstructural refinement.

Figure 12
SEM picture of worn surfaces a) Lower wear loss b) Higher wear loss.

The laser-cladded surface showed a significant increase in the surface concentration of nickel (from approximately 0.05 wt.% to 60.0 wt.%) and chromium (from approximately 0.02 wt.% to 21.0 wt.%), which are the primary constituents of Inconel 625. The corresponding EDX of the cladded surface is presented in Figure 13. Nickel contributes to enhanced ductility and work-hardening behavior, while chromium facilitates the formation of a stable and protective chromium oxide layer that suppresses adhesive and oxidative wear mechanisms⁴⁸. The enrichment in these elements improves surface hardness and thermal stability under tribological conditions. Additionally, the laser cladding process limits dilution from the AZ61 substrate, thereby reducing the upward diffusion of base metal elements such as aluminum and zinc. This reduction in contamination helps preserve the mechanical and chemical integrity of the cladded surface⁴⁹.

Figure 13
EDX of the cladded surface.

SEM micrographs revealed reduced plastic deformation, fewer craters, and less evidence of plowing marks on worn surfaces treated with higher powder feed rates. These features indicate improved mechanical stability and a shift toward a predominantly abrasive wear mechanism. The presence of hard intermetallic phases and uniformly distributed Inconel 625 particulates within the magnesium matrix contributes to the suppression of both abrasive and adhesive wear. As a result, wear resistance improves significantly due to the synergistic effects of Ni and Cr surface enrichment and fine microstructural features developed through rapid solidification during laser cladding⁴⁸,⁴⁹.

The findings further demonstrate that composite coatings containing higher concentrations of Inconel 625 particles offer enhanced protection to the relatively soft AZ61 alloy. This leads to minimal material removal and uniform abrasion features across the worn surface. As seen in Figure 12b, powder feed rates exceeding 25 g/min promote the formation of a tribological layer, which can serve as a solid lubricating film during sliding. However, excessive particle concentration causes localized clustering of Inconel 625 in the matrix, forming isolated patches that lead to surface inhomogeneity. These defects reduce wear stability and compromise practical applicability. Therefore, achieving an optimal concentration of Inconel 625 within the cladded layer is essential to maximize tribological performance while maintaining uniformity, hardness, and durability.

5. Conclusion

This study optimizes laser cladding parameters for AZ61 magnesium alloy reinforced with Inconel 625 using Taguchi-GRA methodology. The key findings are as follows:

1. Laser power emerged as the most influential factor for microhardness, contributing 84.42%. Increasing laser power up to 1.6 kW enhanced hardness due to improved melting and metallurgical bonding, while higher energy levels led to a decline in hardness from excessive melt pool formation.

2. Scanning speed significantly influenced dilution rate, contributing 87.13%. Faster scanning limited substrate melting, resulting in lower dilution, whereas slower speeds increased melt pool interaction with the substrate.

3. Powder feed rate dominated wear volume performance with a 70.70% contribution. Higher feed rates improved wear resistance through particle reinforcement, though excessive amounts introduced porosity and potential surface defects.

4. Grey Relational Analysis identified the optimal process conditions as a laser power of 1.6 kW, scanning speed of 10 mm/s, gas flow rate of 410 L/h, and powder feed rate of 25 g/min. Experimental validation confirmed this combination with a minimal prediction error of 2.33% in Grey Relational Grade.

5. The optimized laser cladding process significantly enhanced surface performance, offering improved hardness, reduced wear, and controlled dilution—attributes critical for structural applications.

6. These findings reinforce the suitability of the developed method for surface engineering in automotive and aerospace components, particularly where lightweight, high-durability materials are essential.

• Data Availability
  All data analyzed during this study are included in this published article.

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Edited by

• Associate Editor:
  José Daniel Biasoli de Mello.

• Editor-in-Chief:
  Luiz Antonio Pessan.

Data availability

All data analyzed during this study are included in this published article.

Publication Dates

• Publication in this collection
  05 Dec 2025
• Date of issue
  2025

History

• Received
  04 Mar 2025
• Reviewed
  29 Aug 2025
• Accepted
  26 Oct 2025