Parameter optimization of laser cladding for Stelcar powder coatings on AZ61 magnesium alloy

ABSTRACT

The primary objective of this study is to enhance surface coating characteristics and reduce the dilution rate of AZ61 magnesium alloy coated with Stelcar alloy powder through laser cladding. A Taguchi (L16) orthogonal experimental design employed to analyze the effects of scanning speed, laser power, powder feed rate, and gas flow on wear volume, dilution rate and micro-hardness. Signal-to-noise ratios were calculated for each parameter to identify their individual effects on the responses. The findings indicated that powder feed rate predominantly influenced wear volume, accounting for 88.18% of its variation, while scanning speed has the highest influence on dilution rate (73.20%), and laser power significantly affected micro-hardness (84.60%). The optimized processing parameters were identified as a scanning speed of 11 mm/s, a laser power of 1.3 kW, a powder feed rate of 40 g/min, and a gas flow rate of 380 L/h. These parameters yielded a minimum wear volume of 0.8427 mm³, a dilution rate of 18.21%, and a maximum micro-hardness of 678.07 HV. This study utilized grey relational analysis to determine the optimum processing parameters, which simultaneously reduced wear volume, minimized dilution rate and enhanced micro-hardness.

Keywords:
AZ61 magnesium alloy; Laser cladding; Stelcar alloy powder; Dilution; GRA

1. INTRODUCTION

In current scenario, the automotive sectors has experienced a revolution that has driven the demand for novel lightweight materials to meet the industrial needs. AZ61 magnesium alloys are promising structural materials widely used in the automotive and aerospace industries because they are 65% lighter than titanium (Ti) alloys and 35% lighter than aluminum (Al) alloys [¹,²,³]. Components such as engine blocks, transmission casings, steering wheel frames, seat frames and other automotive parts are increasingly being fabricated using AZ61 magnesium alloys. However, poor corrosion resistance, wear resistance, and high-temperature stability limit their industrial applications [⁴,⁵,⁶].

Surface modification techniques, such as laser cladding, have emerged as an effective solution to overcome these limitations, significantly enhancing the functional properties of magnesium alloys. Laser Cladding (LC) is a promising surface modification technique that offers numerous advantages over traditional coating methods like electroplating, thermal spraying, and physical vapor deposition. Conventional approaches often encounter challenges such as inadequate adhesion, high porosity, and limited control over coating thickness. In contrast, laser cladding provides precise control during the deposition process and enables the integration of reinforcing particles into the substrae [⁷,⁸,⁹].

Laser cladding is an advanced surface modification technique that utilizes a high-energy laser beam to melt and apply coating materials on the surface of the substrate. This process results in a coating that exhibits superior metallic bonding and enhanced properties. The primary benefits of laser cladding coatings include minimal dilution rates, limited heat-affected zones, and outstanding mechanical and physical characteristics [¹⁰]. Additionally, surface modification via laser cladding improves wear resistance, corrosion resistance and hardness while extending the lifespan of components in demanding applications. LC involves complex interactions among the laser, powder, substrate and processing parameters. The choice of parameters significantly impacts coating formation and quality. Depending on the specific application requirements, the parameters may vary. In industrial settings, multiple objectives must be considered, necessitating an optimal setup of parameters to achieve the desired coating performance [¹¹].

Several researchers have recently investigated laser cladding formation processes and coating quality, with findings summarized below. LU et al. [¹²] optimized laser cladding parameters for Ni60 coating on a Q235 substrate and studied the parametric influence on the coating’s microstructure and properties to enhance its friction and wear resistance. Similarly, KUMAR et al. [¹³] examined mechanical characterization in laser-cladded Ti6Al4V alloy with cBN and improved the properties and performance of the composite clad coating. XI et al. [¹⁴] investigated the geometric characteristics of multi-layer cladding of YCF102 and analyzed track offset, track overlap, and layer thickness by optimizing the laser cladding parameters. MA et al. [¹⁵] used a multi-objective quantum-behaved particle swarm optimization algorithm to model and optimize laser cladding, improving the properties and efficiency of composite coatings. FAN and ZHANG [¹⁶] explored laser cladding of 15MnNi4Mo steel with Co-based powder containing 40% WC, determining the impact of processing parameters on cladding layer dimensions, dilution rate, and hardness through a single-factor experiment and orthogonal experimental design.

Stelcar alloy powder, a mixture of carbide particles and nickel or cobalt-based powders, is frequently used in laser cladding due to its ability to resist corrosion, wear, high hardness, and high-temperature strength [¹⁷]. This research explores the influence of selected processing parameters on the properties of Stelcar alloy powder coatings using orthogonal experimental design and grey relational analysis. While current research on laser cladding has predominantly focused on the geometric characteristics of the cladding layer, studies that simultaneously examine the influence of processing parameters on multiple objectives are relatively rare. Specifically, there is a notable gap in multi-objective optimization methods that take into account wear volume, dilution rate, and micro-hardness.

Based on the available literature, laser cladding has been established as a viable technique for surface modifications, known for its ability to enhance mechanical properties, wear resistance, and corrosion resistance. However, limited research exists on using Stelcar alloy powder for laser cladding of magnesium alloys. Moreover, no published reports address the optimization of wear resistance, dilution rate, and hardness of AZ61 magnesium alloy through laser cladding with Stelcar alloy powder.

This study aims to examine the influence of various processing parameters, including scanning speed, laser power, powder feed rate and gas flow on the surface modification of magnesium alloy substrates. The findings offer a theoretical foundation for multi-objective optimization in laser cladding, allowing for the prediction and control of outcomes such as reduced wear volume, minimized dilution rate, and enhanced micro-hardness. By adjusting processing parameters, researchers can achieve optimal coating performance tailored to specific engineering requirements. This comprehensive approach aims to contribute significantly to the advancement of laser cladding techniques and their practical applications in engineering fields.

2. MATERIALS AND METHODS

The AZ61 magnesium alloy is used in this study with the composition of magnesium (Mg) – 92%, aluminum (Al) – 5.80 to 7.20%, zinc (Zn) – 0.40 to 1.50%, manganese (Mn) – 0.15%, silicon (Si) – 0.10%, copper (Cu) – 0.050%, nickel (Ni) – 0.0050% and iron (Fe) – 0.0050%. The alloy was sourced in plate form from Vision Castings and Alloys, Hyderabad. Prior to laser cladding, the alloy underwent a series of pre-treatments to ensure a clean and uniform substrate, essential for optimal adhesion of the cladding layer. These pre-treatments removed the contaminants and oxides from the surface, followed by light polishing to achieve a smooth finish. Surface roughness measurements were conducted to assess initial surface conditions, providing baseline data for evaluating the effects of laser cladding on surface modifications. To prevent contamination and oxidation, the AZ61 specimens were stored in a controlled, low-humidity environment. Micro structural and elemental composition analyses of the AZ61 alloy were performed by using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDAX), as illustrated in Figure 1 (a & b) [¹⁸].

Figure 1
(a) SEM image of AZ61 Mg alloy, (b) EDAX of AZ61 Mg alloy, (c) SEM image of Stelcar powder, (d) EDAX of Stelcar powder.

The Stelcar alloy powder used in this study was procured from Zhangzhou Xiangcheng Yuteng Ceramic Products Co., Ltd, China. Its composition included tungsten carbide (WC) – 68.5%, cobalt (Co) – 12.5%, nickel (Ni) – 7.5%, chromium (Cr) – 4.2%, carbon (C) – 3.8%, iron (Fe) – 2.5% and Molybdenum (Mo) – 1.0%. The powder had a particle size distribution ranging from 50 to 120 microns. To ensure uniformity and address any agglomerations or irregularities, the powder was subjected to a 10-minute controlled milling process at 400 rpm in a planetary ball mill. During the laser cladding process, a specialized powder feeder was employed to maintain consistent and controlled powder flow. Prior to its use, the morphology and chemical composition of the Stelcar alloy powder were verified by using SEM and EDAX, as depicted in Figure 1 (c & d). This verification step ensured the powder’s quality and consistency, critical for effective surface modification [¹⁹].

The laser cladding system utilized in this study, depicted in Figure 2, was configured with several critical control components, including the laser generator, a powder feeding mechanism, a deposition system, and a PLC (Programmable Logic Controller) computer system. The cladding process was performed using a continuous wave fiber laser with a maximum laser power of 4 kW and a laser wavelength of 1070 nm. The system was equipped with a high-precision nozzle to deliver the Stelcar alloy powder, and the laser beam was focused on the substrate to achieve efficient melting and bonding. Prior to commencing the laser cladding process, the substrate surface preparation was done with great precision. This preparation involved a thorough cleaning of the AZ61 magnesium alloy surface by using acetone, followed by rinsing with alcohol, and a careful drying process to ensure no residual contaminants [²⁰,²¹,²²]. Initial images of the substrate surface were captured before treatment to document its pre-cladding condition, as shown in Figure 3(a).

Figure 2
Experimental setup of laser cladding machine.

Figure 3
(a) Uncoated AZ61 Mg alloy, (b) Coated AZ61 Mg alloy.

The laser cladding process itself is highly intricate and influenced by numerous parameters. This research specifically focuses on four critical variables: scanning speed, laser power, powder feed rate, and gas flow. Each of these variables was examined at four distinct levels. A four-factor, four-level orthogonal array design was employed to systematically explore these factors and their interactions [²³,²⁴,²⁵]. This design approach, detailed in Table 1, allowed for an efficient experimental setup that minimized the number of trials while maximizing the information obtained. As a result, the L16 (4^4) Taguchi orthogonal array was selected, encompassing a total of 16 experimental runs, as outlined in Table 2. Post-laser cladding, images were again taken to illustrate the modifications on the substrate surface, as shown in Figure 3(b).

Table 1
Laser cladding constraints with L16

LEVELS	SCANNING SPEED (mm/s)	LASER POWER (kW)	POWDER FEED RATE (g/min)	GAS FLOW (L/h)
1	8	1.1	30	380
2	9	1.2	35	390
3	10	1.3	40	400
4	11	1.4	45	410

Table 2
Expanded L16 of process parameters and output responses

TRIAL NO	SCANNING SPEED (mm/s)	LASER POWER (kW)	POWDER FEED RATE (g/min)	GAS FLOW (L/h)	WEAR VOLUME (mm3)	DILUTION RATE (%)	MICRO-HARDNESS (HV)
1	8	1.1	30	380	1.9428	22.73	578.90
2	9	1.1	35	390	1.5966	21.27	601.17
3	10	1.1	40	400	1.3322	18.08	606.63
4	11	1.1	45	410	1.3678	18.91	589.13
5	8	1.2	40	390	0.8322	21.07	599.80
6	9	1.2	45	380	1.2322	21.14	646.93
7	10	1.2	30	410	1.8322	18.46	613.22
8	11	1.2	35	400	1.7407	19.81	606.57
9	8	1.3	45	400	1.2949	21.41	671.27
10	9	1.3	40	410	0.8816	19.92	674.95
11	10	1.3	35	380	1.6169	16.73	658.33
12	11	1.3	30	390	1.8492	17.53	660.12
13	8	1.4	35	410	1.5649	24.86	643.71
14	9	1.4	30	400	1.7407	22.45	639.36
15	10	1.4	45	390	1.4254	19.32	649.92
16	11	1.4	40	380	0.9812	16.95	633.30

The thermal energy generated by the laser melts both the Stelcar powder and the magnesium substrate, facilitating alloying and mixing at the interface. This process results in the formation of a composite layer that exhibits significantly enhanced properties, including increased hardness, improved wear resistance, and superior corrosion resistance compared to the untreated magnesium surface. Additionally, any existing pores and voids in the uncoated substrate were effectively filled and repaired through the cladding process. Upon the completion of the laser cladding procedure, the 16 samples with surface modifications were subjected to rigorous analysis to evaluate their wear volume, dilution rate, and micro-hardness. These assessments provided insights into the clad properties, confirming the efficacy of the parameter optimization.

The wear characteristics of the laser-clad specimens were evaluated by using a pin-on-disc apparatus as illustrated in Figure 4. The dry sliding wear properties of the clad samples were analyzed in accordance with ASTM G99 standards. Prior to conducting the wear tests, specimens were precisely cut to dimensions of 10 mm in diameter and 30 mm in height from the modified surface specimens by using Wire Electrical Discharge Machining (WEDM). The prepared specimens were then tested against a counter surface comprising an EN 32 steel disc with a hardness rating of 60 HRC. To ensure accurate results, both the specimens and the steel disc surfaces were cleaned with acetone solvent before testing. The wear behavior of the laser-clad samples was investigated under an applied load of 20 N, with a sliding speed of 1.25 m/s, and over a sliding distance of 800 m. Post wear-testing, the surface morphologies of the clad specimens were analyzed by using SEM. This examination aimed at to identify and characterize the wear scars and underlying wear mechanisms [²⁶].

Figure 4
Pin on disc apparatus.

After completion of the laser cladding process, the samples were sectioned transversely along the single clad tracks to measure the dilution rate. This parameter is vital as it indicates the degree of mixing between the base AZ61 magnesium alloy and the Stelcar powder coating, which in turn has a significant impact on the clad layer’s properties such as corrosion resistance, wear resistance and hardness. The dilution rate is calculated by analyzing the cross-sectional areas of the clad samples. Specifically, it involves comparing the area of the substrate material that has been melted and mixed with the cladding material (AS) to the total area of the clad layer (AS + AC), where AC is the area of the cladding material. Image analysis software is used to identify and measure these specific areas of interest within the cross-sectional samples. The dilution rate (D) is mathematically expressed as D = (AS/(AS + AC)) × 100%. Here, AS represents the melted area of the substrate material, and AC represents the area of the clad material. An accurate determination and control of the dilution rate are essential for optimizing the laser cladding process, as this parameter directly influences the mechanical properties and microstructure of the clad layer [²⁷, ²⁸].

The micro-hardness assessment was conducted by using a Vickers hardness tester, following the guidelines specified in the ASTM E92 Standard. This analysis focused on the cross-sectional area of the samples. For the AZ61 magnesium alloy substrate that underwent laser cladding, the micro-hardness was measured with a 300-gram load and a 12-second dwell time. The hardness was measured along the cross-section, specifically from the surface moving inward towards the core of the treated AZ61 Mg alloy substrate, at 0.1 mm increments. These measurements provide a longitudinal profile of hardness variation from the clad surface through to the substrate [²⁹]. Figures 5(a) and 5(b) provide detailed representations of the testing procedure and the configuration of the micro-hardness testing apparatus, respectively.

Figure 5
(a) Test location of the vickers indentation, (b) Vickers micro-hardness tester.

3. RESULTS AND DISCUSSION

The optimization of processing parameters in the laser cladding process for Stelcar powder coatings on AZ61 magnesium alloy was conducted by using the signal-to-noise ratio technique combined with Taguchi analysis. This methodology is pivotal for refining data analysis to enhance mechanical properties by identifying optimal values [³⁰,³¹,³²]. The objective of this study focuses on the key responses such as wear volume, dilution rate, and micro-hardness of the laser-cladded surface. These responses are critical in determining the best process parameters, categorized by the principles of “larger is better” for micro-hardness and “smaller is better” for dilution rate and wear volume. The S/N ratio for these three characteristics was calculated by using the actual data derived from Table 2. The results of the S/N conversion, including the measured values for wear volume, dilution rate and micro-hardness, are detailed in Table 3. Subsequently, Analysis of Variance (ANOVA) was employed to discern the significant factors affecting wear volume, dilution rate and micro-hardness. The ANOVA was performed at a 95% confidence level, facilitated by Design of Experiments (DoE) methodologies.

Table 3
Results of S/N conversion for wear volume, dilution rate and micro-hardness

TRIAL NO.	WEAR VOLUME	S/N OF WEAR VOLUME	DILUTION RATE	S/N OF DILUTION RATE	MICRO-HARDNESS	S/N OF MICRO-HARDNESS
1	1.9428	−5.76856	22.73	−27.1320	578.90	55.2521
2	1.5966	−4.06392	21.27	−26.5553	601.17	55.5799
3	1.3322	−2.49139	18.08	−25.1440	606.63	55.6585
4	1.3678	−2.72045	18.91	−25.5338	589.13	55.4042
5	0.8322	1.59545	21.07	−26.4733	599.80	55.5601
6	1.2322	−1.81362	21.14	−26.5021	646.93	56.2171
7	1.8322	−5.25946	18.46	−25.3246	613.22	55.7523
8	1.7407	−4.81448	19.81	−25.9377	606.57	55.6576
9	1.2949	−2.24472	21.41	−26.6123	671.27	56.5379
10	0.8816	1.09457	19.92	−25.9858	674.95	56.5854
11	1.6169	−4.17366	16.73	−24.4699	658.33	56.3689
12	1.8492	−5.33968	17.53	−24.8756	660.12	56.3925
13	1.5649	−3.88973	24.86	−27.9100	643.71	56.1738
14	1.7407	−4.81448	22.45	−27.0243	639.36	56.1149
15	1.4254	−3.07874	19.32	−25.7201	649.92	56.2572
16	0.9812	0.16485	16.95	−24.5834	633.30	56.0322

3.1. Anova variance analysis

Analysis of variance was employed to identify the optimal parameters by analyzing the influence of the signalto-noise ratio table. To ensure the normality of the data, a normality test was conducted by using the Anderson–Darling (AD) test in Minitab 18. This test indicated a normal distribution when the p-value exceeded 0.05, according to references [³³, ³⁴]. Specifically, the AD test results showed that the p-values for the transformed S/N values of wear volume, dilution rate and micro-hardness were all greater than 0.05, as depicted in Figures 6 (a-c). Consequently, the normal distribution of these response variables was validated, allowing for the application of ANOVA.

Figure 6
Normality test for (a) S/N of wear volume, (b) S/N of dilution rate, (c) S/N of micro-hardness.

3.1.1. Analysis of variance on wear volume with signal-to-noise ratio

Table 4 presents the percentage contributions of various factors to the wear volume in the laser cladding process. The % of contribution follows as: powder feed rate contributes 88.18%, scanning speed contributes 4.64%, laser power contributes 3.50%, gas flow rate contributes 1.79%, and the error margin is 1.89%. From these results, it is evident that the powder feed rate plays a predominant role in influencing wear volume, accounting for a significant effect of 88.18%. This notable impact is specifically attributed because of the use of Stelcar powder, which demonstrated a higher effect on wear compared to other factors. Consequently, the findings underscore the critical importance of optimizing the powder feed rate to achieve desired wear resistance in the laser cladding process.

Table 4
S/N of wear volume ANOVA analysis

SOURCE	DF	SEQ SS	CONTRIBUTION	ADJ SS	ADJ MS	F-VALUE	P-VALUE
SS (mm/s)	3	0.08337	4.64%	0.08337	0.02779	2.46	0.240
LP (kW)	3	0.06294	3.50%	0.06294	0.02098	1.86	0.312
PFR (g/m)	3	1.58468	88.18%	1.58468	0.52823	46.71	0.005
GF (L/h)	3	0.03214	1.79%	0.03214	0.01071	0.95	0.517
Error	3	0.03393	1.89%	0.03393	0.01131
Total	15	1.79705	100.00%

Table 5 presents the average S/N ratios for wear volume for each experimental factor at various levels. The delta value, representing the difference between the highest and lowest S/N ratio for each factor, was calculated to rank the factors in ascending order of their influence. The results indicate that the powder feed rate exerts the most significant impact, with a delta of 5.39. This is followed by scanning speed, which has a delta of 1.35, laser power with a delta of 1.19 and gas flow with a delta of 0.90. Using the S/N ratio response table, a principal effects plot for wear volume was generated as shown in Figure 7. The plot highlights that the optimal parameters for minimizing wear volume and maximizing S/N ratios are achieved at the second level of scanning speed, second level of laser power, the third level of powder feed rate and the fourth level of gas flow. Notably, increasing the powder feed rate significantly reduces wear loss. This reduction can be attributed to the suppression of plastic deformation in the AZ61 magnesium alloy matrix due to the addition of Stelcar powder. The incorporation of Stelcar reinforcement particles enhances the hardness of the cladded structure and thereby diminishing wear loss. Stelcar also reduces the abrasive action by covering the matrix region that interacts with the counter surface, thereby limiting wear loss due to abrasive action [³⁵]. From the analysis presented in Figure 7, the optimal laser cladding parameters are identified as a scanning speed of 9 mm/s, laser power of 1.2 kW, a powder feed rate of 40 g/min and a gas flow of 410 L/h.

Table 5
Response table for S/N of wear volume

LEVEL	SS (mm/s)	LP (kW)	PFR (g/m)	GF (L/h)
1	−2.58	−3.76	−5.30	−2.90
2	−2.40	−2.57	−4.24	−2.72
3	−3.75	−2.67	0.09	−3.59
4	−3.18	−2.90	−2.46	−2.69
Delta	1.35	1.19	5.39	0.90
Rank	2	3	1	4

Figure 7
S/N of wear volume with main effects plot.

3.1.2. Analysis of variance on dilution rate with signal-to-noise ratio

Analysis of variance was conducted to identify the significant factors affecting the dilution rate response and to quantify each factor’s percentage contribution. As illustrated in Table 6, scanning speed emerged as the most critical factor, contributing 73.20% to the dilution rate. This is followed by laser power, which accounts for 10.84%, powder feed rate at 8.07%, gas flow at 4.63%. Therefore, scanning speed is the most crucial control factor to consider in the laser cladding process for dilution rate optimization, with laser power, powder feed rate and gas flow also playing notable roles. Table 7 presents the ranking of these parameters based on their significance, with scanning speed being the primary factor, followed by laser power, powder feed rate and gas flow in that order. The main signal-to-noise effects ratio depicted in Figure 8 indicates that optimal values were achieved at the third level of scanning speed, third level of laser power, third level of powder feed rate and first level of gas flow. Specifically, from Figure 8, the optimal parameters for the laser cladding process were determined to be a scanning speed of 10 mm/s, a laser power of 1.3 kW, a powder feed rate of 40 g/min and a gas flow rate of 380 L/h.

Table 6
S/N of dilution rate ANOVA analysis

SOURCE	DF	SEQ SS	CONTRIBUTION	ADJ SS	ADJ MS	F-VALUE	P-VALUE
SS (mm/s)	3	56.325	73.20%	56.325	18.7749	22.50	0.015
LP (kW)	3	8.343	10.84%	8.343	2.7810	3.33	0.175
PFR (g/m)	3	6.211	8.07%	6.211	2.0703	2.48	0.238
GF (L/h)	3	3.560	4.63%	3.560	1.1868	1.42	0.390
Error	3	2.503	3.25%	2.503	0.8343
Total	15	76.942	100.00%

Table 7
Response table for S/N of dilution rate

LEVEL	SS (mm/s)	LP (kW)	PFR (g/m)	GF (L/h)
1	−27.03	−26.09	−26.09	−25.67
2	−26.52	−26.06	−26.22	−25.91
3	−25.16	−25.49	−25.55	−26.18
4	−25.23	−26.31	−26.09	−26.19
Delta	1.87	0.82	0.67	0.52
Rank	1	2	3	4

Figure 8
S/N of dilution rate with main effects plot.

The influence of scanning speed on the dilution rate during the laser cladding process is substantial. At lower scanning speeds, the laser or heat source remains longer on a given area of the substrate, causing excessive melting of both the substrate material and the Stelcar powder. This prolonged exposure results in a higher dilution rate due to the increased incorporation of substrate material into the molten pool [³⁶, ³⁷]. On the other hand, when the scanning speed exceeds 10 mm/s, the laser or heat source traverses the substrate surface more rapidly, thereby decreasing the interaction time between the heat source and the substrate. This reduced interaction time leads to less substrate material melting, consequently lowering the dilution rate. In this scenario, the molten pool is predominantly composed of the Stelcar powder with minimal incorporation of the substrate material. Initially, as the scanning speed increases from 8 to 10 mm/s, the dilution rate decreases. However, a significant increase in dilution rate is observed when the scanning speed surpasses 10 mm/s.

3.1.3. Analysis of variance on micro-hardness with signal-to-noise ratio

Table 8 presents the ANOVA results for the S/N ratio of micro-hardness measurements. The p-value associated with laser power was found to be 0.02, which is considerably below the commonly accepted significance threshold of 0.05. This result clearly indicates that laser power has a statistically significant impact on microhardness. The ANOVA table further reveals that laser power is the most influential factor on microhardness, accounting for 84.60% of the variation observed. This dominant contribution underscores the critical role of laser power in the laser cladding process for Stelcar powder coatings on AZ61 magnesium alloy. Following the laser power, the scanning speed emerges as the second most significant factor, although its influence is notably smaller, contributing 6.34% to the variation in micro-hardness. In comparison, the effects of powder feed rate and gas flow are minimal, with contribution percentages of 4.19% and 0.17%, respectively. The remaining 4.70% is attributed to experimental error. This analysis underscores that, although scanning speed affects micro-hardness, its impact is significantly less substantial compared to laser power. The relatively minor contributions of powder feed rate and gas flow suggest that these parameters have a negligible effect on micro-hardness under the conditions studied.

Table 8
S/N of micro-hardness ANOVA analysis

SOURCE	DF	SEQ SS	CONTRIBUTION	ADJ SS	ADJ MS	F-VALUE	P-VALUE
SS (mm/s)	3	874.8	6.34%	874.8	291.61	1.35	0.406
LP (kW)	3	11676.5	84.60%	11676.5	3892.17	18.00	0.020
PFR (g/m)	3	578.9	4.19%	578.9	192.97	0.89	0.536
GF (L/h)	3	22.9	0.17%	22.9	7.65	0.04	0.989
Error	3	648.9	4.70%	648.9	216.29
Total	15	13802.1	100.00%

Table 9 provides the average signal-to-noise ratios for micro-hardness measurements at various factor levels. The delta value, representing the range of means, indicates the significance of each parameter, with a ranking system where a rank of 1 denotes the most influential parameter based on the calculated mean table.

Table 9
Response table for S/N of micro-hardness

LEVEL	SS (mm/s)	LP (kW)	PFR (g/m)	GF (L/h)
1	55.88	55.47	55.88	55.97
2	56.12	55.80	55.95	55.95
3	56.01	56.47	55.96	55.99
4	55.87	56.14	56.10	55.98
Delta	0.25	1.00	0.23	0.04
Rank	2	1	3	4

Notably, the data in Table 8 highlights laser power as the most significant factor impacting the micro-hardness among the parameters studied. Figure 9 illustrates the main effects plot, which shows the relationship between laser power and micro-hardness. An increase in laser power results in a significant enhancement in micro-hardness up to a power level of 1.3 kW. Beyond this threshold, the micro-hardness begins to decline due to the formation of a larger melt pool on the substrate surface. This larger melt pool prolongs the cooling period, which leads to grain coarsening and reduces the hardness. Additionally, excessive melting can result in increased dilution, negatively impacting the clad layer’s microstructure and mechanical properties. At a laser power of 1.4 kW, a noticeable variation in hardness across different regions of the cladded surface is observed, indicating uneven heat distribution. Optimal hardness values are achieved at the second level of scanning speed, third level of laser power, fourth level of powder feed rate, and third level of gas flow, according to the S/N ratio effect plot. From Figure 9, the optimal parameters for achieving the best micro-hardness are identified as scanning speed 9 mm/s, laser power 1.3 kW, powder feed rate 45 g/min and gas flow rate 400 L/h.

Figure 9
S/N of micro-hardness with main effects plot.

3.2. Grey relational analysis (gra) of mechanical responses

This study focuses on optimizing the laser cladding process for Stelcar powder coating on AZ61 magnesium alloy, aiming at to enhance the micro-hardness while concurrently reducing both the dilution rate and wear volume. Traditional methods like the Taguchi orthogonal experiment design and Analysis of Variance are typically limited in optimizing a single objective at a time [³⁸, ³⁹]. In this research, optimal processing parameter settings for achieving reduced wear volume, minimal dilution rate and superior hardness were identified in various experimental runs, as detailed in Table 2. However, to effectively address the multi-objective nature of this optimization problem, the GRA was employed. This approach allows for the simultaneous consideration of multiple responses by transforming the optimization criteria into a single relational grade using the Grey Relational Grade (GRG) [⁴⁰, ⁴¹]. The detailed steps involved in the GRA data processing are outlined below.

3.2.1. S/N ratio normalization and deviation

GRA employed in this study, the initial step involves normalizing and transforming the experimental data to fall within the range of 0 to 1. This normalization is crucial for ensuring comparability across different data sets. For this analysis, we adopt the “higher-is-better” criterion for micro-hardness, as higher values indicate superior cladding performance. Conversely, for the dilution rate and wear volume, the “lower-the-better” criterion is applied since lower values correspond to better cladding outcomes [⁴²]. Therefore, achieving higher micro-hardness, along with reduced dilution rates and wear volumes, is deemed desirable in this context. To normalize the micro-hardness values, Equation (1) is used. Similarly, the normalization of the dilution rate and wear volume is carried out by using Equation (2). Following normalization, the deviation sequences are calculated and the resulting values are presented in Table 10.

Table 10
Results of data normalization and GRC calculation

TRIAL NO.	S/N OF WEAR VOLUME		S/N OF DILUTION RATE		S/N OF MICRO-HARDNESS
	NORMALIZED	GRC	NORMALIZED	GRC	NORMALIZED	GRC
1	0.0000	0.3333	0.2620	0.4039	0.0000	0.3333
2	0.3117	0.4208	0.4416	0.4724	0.2319	0.3943
3	0.5498	0.5262	0.8339	0.7507	0.2887	0.4128
4	0.5177	0.5090	0.7319	0.6509	0.1065	0.3588
5	1.0000	1.0000	0.4662	0.4836	0.2176	0.3899
6	0.6398	0.5813	0.4576	0.4796	0.7083	0.6315
7	0.0996	0.3570	0.7872	0.7015	0.3573	0.4376
8	0.1820	0.3794	0.6212	0.5689	0.2881	0.4126
9	0.5834	0.5455	0.4244	0.4648	0.9617	0.9288
10	0.9555	0.9183	0.6076	0.5603	1.0000	1.0000
11	0.2934	0.4144	1.0000	1.0000	0.8270	0.7429
12	0.0843	0.3532	0.9016	0.8356	0.8456	0.7641
13	0.3403	0.4311	0.0000	0.3333	0.6748	0.6059
14	0.1820	0.3794	0.2964	0.4154	0.6295	0.5744
15	0.4659	0.4835	0.6814	0.6108	0.7394	0.6574
16	0.8658	0.7884	0.9729	0.9487	0.5664	0.5355

Larger the better option:

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

Smaller the better option:

$$Y_i(p)=\frac{ma{x}_i(p)-X_i(p)}{max{X}_i(p)-min{X}_i(p)}$$ (2)

3.2.2. Grey relation coefficient (GRC) calculation

After normalizing the Signal-to-Noise ratio, the GRC is computed by using Equation (3):

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

In this equation, GRCi(p) represents the grey relational coefficient for the p-th parameter in the i-th experimental run, where p can take the values 1, 2, or 3, and i ranges from 1 to 16. The term Δ_i(p) is the difference between 1 and the normalized value of the p-th response in the i-th run, expressed as Δ_i(p) = 1 – Y_i(p). Here, Δ_max(p) and Δ_min(p) denote the maximum and minimum values of Δ_i(p) for the p-th response across all runs, respectively. The distinguishing coefficient δ is a parameter that ranges from 0 to 1, with a common choice being 0.5 for balancing the comparability and distinguishability of the parameters [⁴³, ⁴⁴]. By computing the GRC, the significance of each process parameter can be evaluated, aiding in the optimization of laser cladding conditions for Stelcar powder coatings on AZ61 magnesium alloy. This methodology ensures a robust analysis of the influence of different parameters, facilitating improved coating quality and performance.

3.2.3. Grey relational grade (GRG) calculation

The GRG value is determined by using Equation (4), which assigns equal importance to micro-hardness, dilution rate and wear volume responses:

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

Tables 10 and 11 display the normalized values, GRC, GRG and S/N of GRG for all experimental runs. Subsequently, an ANOVA was performed, with Table 12 presenting the analysis of input variables such as laser power percentage (51.18%), powder feed rate (38.78%), scanning speed (5.26%) and gas flow rate (3.92%), influencing the overall grey relational grade. Table 11 provides the overall ranking of GRG across all runs, with the 10th run exhibiting parameters are 9 m/s of SS, 1.3 kW of LP, 40 g/m of PFR and 410 L/h of GF. The Anderson-Darling test was employed to assess normality, as illustrated in Figure 10. Additionally, Figure 11 presents the S/N of the GRG with main effects plots and Table 13 shows the response table for S/N of GRG.

Table 11
Results of GRG and S/N conversion

TRIAL NO.	GREY RELATIONAL GRADE	S/N OF GRG	RANK
1	0.3568	−8.95036	16
2	0.4292	−7.34778	15
3	0.5632	−4.98636	9
4	0.5063	−5.91264	10
5	0.6245	−4.08920	6
6	0.5642	−4.97204	8
7	0.4987	−6.04339	11
8	0.4536	−6.86620	14
9	0.6464	−3.79022	5
10	0.8262	−1.65826	1
11	0.7191	−2.86419	3
12	0.6509	−3.72928	4
13	0.4568	−6.80582	12
14	0.4564	−6.81343	13
15	0.5839	−4.67323	7
16	0.7575	−2.41179	2

Table 12
S/N of GRG ANOVA analysis

SOURCE	DF	SEQ SS	CONTRIBUTION	ADJ SS	ADJ MS	F-VALUE	P-VALUE
SS (mm/s)	3	0.013268	5.26%	0.013268	0.004423	6.07	0.086
LP (kW)	3	0.129140	51.18%	0.129140	0.043047	59.07	0.004
PFR (g/m)	3	0.097848	38.78%	0.097848	0.032616	44.76	0.005
GF (L/h)	3	0.009883	3.92%	0.009883	0.003294	4.52	0.124
Error	3	0.002186	0.87%	0.002186	0.000729
Total	15	0.252325	100.00%

Figure 10
Normality test for S/N of GRG.

Figure 11
S/N of GRG with main effects plot.

Table 13
Response table for S/N of GRG

LEVEL	SS (mm/s)	LP (kW)	PFR (g/m)	GF (L/h)
1	−5.909	−6.799	−6.384	−4.800
2	−5.198	−5.493	−5.971	−4.960
3	−4.642	−3.010	−3.286	−5.614
4	−4.730	−5.176	−4.837	−5.105
Delta	1.267	3.789	3.098	0.814
Rank	3	1	2	4

3.3. Experimental validation

The final step involves validating the experiment and evaluating the performance characteristics. After determining the optimal input parameter values, a validation experiment is conducted using the same setup with these parameters to measure the output responses. Table 14 presents the predicted and observed response values, along with their respective percentage errors during the validation of the developed model. The maximum error recorded is 3.27%, demonstrating a strong correlation between the predicted and observed values.

Table 14
Experimental validation and comparison of the optimized setup

INPUT/OUTPUT	10th RUN IN L16DESIGN	PREDICTED OPTIMAL SET	VALIDATION
Scanning Speed (mm/s)	9	11	11
Laser Power (kW)	1.3	1.3	1.3
Powder Feed Rate (g/m)	40	40	40
Gas Flow Rate (L/h)	410	380	380
Wear volume (mm3)	0.8816	–	0.8427
Dilution rate (%)	19.92	–	18.21
Micro-hardness (HV)	674.95	–	678.07
GRG	0.8262	0.8900	0.8573

4. WEAR MECHANISM

The analysis of wear mechanisms revealed that increasing the powder feed rate has several effects on the wear behavior of Stelcar-coated AZ61 magnesium alloy. Specifically, it was observed that higher feed rates led to a reduction in wear volume and improved wear resistance. This improvement was attributed to the mitigation of plastic deformation and the reinforcement of the AZ61 matrix, which helps in protecting it against wear. Figure 12(a) visually demonstrates that as the powder feed rate increases, the coatings become thicker, consequently decreasing the wear rate. Notably, the presence and influence of tungsten carbide particles on worn surfaces, showing minimal plastic deformation of the magnesium matrix and grooved lines caused by interaction with the hard counter surface.

Figure 12
SEM morphology of worn surfaces (a) Low Wear Loss, (b) High Wear Loss.

At higher powder feed rates of Stelcar particles, phenomena such as plastic drift, crater formation and ploughing marks were significantly reduced. This suggests that composite coatings with higher Stelcar particle content provide enhanced protection to the softer magnesium-based alloy, resulting in reduced wear and fewer deep plough grooves. However, Figure 12(b) introduces another aspect of the wear mechanism, indicating the formation of a tribo layer when the powder feed rate exceeds 40 g/min. While generally beneficial, the effectiveness of this layer is limited by the aggregation of Stelcar particles within the matrix phase. The presence of isolated patches of the tribo layer on the worn surface suggests an optimal concentration of Stelcar particles for achieving the best tribological performance [³⁷].

5. CONCLUSIONS

This study employed Taguchi orthogonal design to investigate the impact of various processing parameters (scanning speed, laser power, powder feed rate and gas flow) on the wear volume, dilution rate and microhardness of laser-cladded Stelcar coatings on AZ61 magnesium alloy. S/N ratio conversion and multiobjective GRA were employed to optimize for minimum wear volume, minimum dilution rate and maximum micro-hardness. The experimental validation of the optimized processing parameters demonstrated the practical applicability of this approach.

1. In the wear volume analysis, powder feed rate was identified as the predominant parameter, followed by scanning speed, laser power and gas flow. Increasing powder feed rate up to 40 g/min reduced wear volume, yet further increases resulted in higher wear volumes due to potential porosity, insufficient melting, inadequate bonding and undesirable phase formation.

2. In the analysis of dilution rate, the order of influential parameters was scanning speed, laser power, powder feed rate and gas flow. Scanning speed emerged as the most critical factor affecting dilution rate, with significant variations observed particularly around 8-9 mm/s and a notable increase at 10 mm/s. Control of substrate melting and reduction of dilution were achieved through incremental adjustments in powder feed rate, although higher rates led to increased dilution due to molten pool interactions and material ejection.

3. In the micro-hardness analysis, the sequence of processing parameters in order of significance was found to be laser power, scanning speed, powder feed rate and gas flow. Laser power demonstrated a pronounced effect on micro-hardness, showing improvement with increasing power up to 1.3 kW. Beyond this point, hardness begins to decline due to the formation of a larger melt pool on the substrate surface.

4. For overall optimization using GRA, the priority sequence of processing parameters was laser power, powder feed rate, scanning speed and gas flow. Laser power and powder feed rate were statistically significant in influencing the outcomes of minimum wear volume, minimum dilution rate and maximum micro-hardness. Predicted optimal parameters were SS = 10 mm/s, LP = 1.3 kW, PFR = 40 g/min and GF = 380 L/h, which were validated experimentally and found to align closely with expected results.

5. The calculated GRG indicated a low error rate of 3.27%, validating the effectiveness of this method in optimizing coating properties and cladding dilution rates under optimized conditions.

6. This research shows that laser cladding with Stelcar alloy powder can greatly improve the wear resistance, corrosion resistance, and hardness of AZ61 magnesium alloy. These improvements make the alloy more suitable for demanding applications in the automotive and aerospace industries. The optimized cladding parameters provide a practical guide for industries to achieve high-quality coatings tailored to specific needs.

6. BIBLIOGRAPHY

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Publication Dates

• Publication in this collection
  27 Jan 2025
• Date of issue
  2024

History

• Received
  09 Sept 2024
• Accepted
  02 Dec 2024