Enhanced mechanical and wear characteristics of AZ61/Si₃N₄ composites through stir casting technique and RSM modeling

ABSTRACT

Research has been conducted regarding the influence of Si₃N₄ micro-particle reinforcement with alloy on the mechanical and wear properties of AZ61/Si₃N₄ composites. The stir casting technique has been used to create AZ61/Si₃N₄ composites. Particles of Si₃N₄ with sizes between 15 to 40 μm and weight percentages of 4, 8, and 12 were mechanically injected into molten AZ61 alloy in an argon gas atmosphere and stirred at 400 rpm. Hardness and impact were shown to be increased gradually with the addition of 4wt.%–12wt.% Si₃N₄ reinforcement to the composites. Experiments were carried out using a Pin-on Disc tribometer at ambient temperature to simulate the wear rate. To enhance the predictability of wear rate and streamline the tests, a 3-level CCD utilizing RSM was devised. The created model accurately predicted the wear rate with a 95% level of confidence, and its overall validity was confirmed using analysis of variance.

Keywords
Analysis of variance; AZ61; Optimization; Response surface methodology; Silicon nitride; Tribological properties; Wear rate

1. INTRODUCTION

Composites are compound materials that differ from alloys in that they maintain their fundamental characteristics while also keeping their strengths. This allows them to capitalize on their strengths rather than their weaknesses, which is a significant advantage over alloys. Grain boundary, dispersion strengthening, and solid solution strengthening were employed to enhance the tribological and mechanical characteristics of the composites [¹, ²]. Metal matrix composites are a technique for improving material properties. Magnesium matrix composites (MMCs) have one kind of Magnesium alloy serving as the matrix phase [³]. Common ceramic reinforcements, such as Al₂O₃ and Si₃N₄, are also included in these Magnesium alloys for extra strength. The material characteristics are improved by the incorporation of Si₃N₄ particle [⁴]. Magnesium composite and other advanced metal matrix composites are used extensively in marine, ground, and air transportation. The development of MMCs, with magnesium as the most researched material, is the answer to this problem. The features, particularly its potential for lightweight and usability in cutting-edge applications, align with the objectives of our work and make it suitable for use in the transportation and biomedical sectors [⁵,⁶,⁷,⁸]. Magnesium composites reinforced with silicon nitride (Si₃N₄) particles have improved yield strength, wear resistance, modulus of elasticity, and thermal expansion over unreinforced magnesium matrix alloy systems [⁹]. AZ61 Stringers frames, and overlapping structural sections are high-wear areas that might benefit from a reduction in component weight and universal aeronautical applicability. The applications of lightweight and wear-resistant materials in industrial areas can benefit greatly from these insights due to its better properties [¹⁰, ¹¹]. Bonding in Al₂O₃/Mg-Li composites comes through a reaction between Al₂O₃ and Li, hence it’s important that the reinforcement and matrix make good contact with one another [¹²]. The Mg/ Si₃N₄ Magnesium composite has superior sliding wear characteristics compared to the base metal [¹³]. Wear rate and wear volume loss are both increased in the extreme wear circumstances. Wear rates were shown to be greater under high loads and lower at rotational speeds during the experiments [¹⁴, ¹⁵].

The rate at which AA7075 wears rises with both sliding distance and load [¹⁶]. It was noted that lowering wear rate may be achieved by the production of a tribo-layer or mechanically mixed layer, which is influenced by the load, sliding speed, and reinforcing content [¹⁷, ¹⁸]. Depending on wear behavior and matrix bonding, the effect might be negative. The coefficient of friction is affected by factors including sliding speed, load, and reinforcement in dry sliding wear [¹⁹]. The friction coefficient and wear rate were found to be unaffected by the size and kind of the reinforcing section. Recent advancements in mathematical models allow us to characterize the most important result variables and establish the relationship between them and the input parameters with minimal experimentation. Several studies have shown that RSM is useful for creating an empirical technique and employing a statistically sound experimental design. Situations involving material property modeling and optimization can benefit from the application of the RSM technique. When several variables affect results, like in the creation of composite materials, production procedures, and performance reviews, RSM is especially helpful. This concept can be expanded to fields such as process parameter optimization in machining, welding, and other fabrication procedures, where it improves the accuracy of material behaviour predictions while lowering the number of experimental runs [²⁰,²¹,²²]. From the previous literatures very few works have synthesized AZ61 composites through liquid metallurgy process and their study on different properties was limited. The novelty of this study is the use of the stir casting method to synthesize the AZ61 reinforced with Silicon nitride, a combination that has not been extensively studied to better the mechanical and tribological properties. ANOVA was used for regression analysis to optimize and forecast the wear in composite and reduce the trials. RSM is utilized to study and optimize wear behavior, providing a methodical way to correlate wear parameters with composite performance. CCD RSM was used to simulate the wear rate. Thus, the tribological and mechanical characteristics of AZ61 can be improved by including Silicon nitride reinforcement in the composite.

MATERIALS AND METHODS

The AZ61 alloy and its chemical structure is shown in Table 1 and Si₃N₄ particles in the 15–40 μm range were used to create the AZ61/Si₃N₄ particulate composites. AZ61 magnesium alloy was chosen because of its exceptional strength-to-ductility ratio, enhanced corrosion resistance from zinc and aluminium, and higher machinability. Stir casting is a practical method for producing high-quality composites with high degree of wettability and minimal particle agglomeration as because the temperature and stirring speed controlled throughout the casting process have a direct impact on the performance of the final composite. Stir casting, in which molten AZ61 alloy is mechanically stirred to combine its constituent components, was used to create the composite samples. Under an argon gas environment, Si₃N₄ powder is preheated in the heating vessel and then injected into the melts. The Si₃N₄ powder was introduced into the liquid metal in varying proportions of 0%, 4%, 8%, and 12% by weight. Maintaining 400 rpm stirring speed as it directly affects the uniform dispersion of the reinforcement particles inside the matrix, stirring speed is a crucial component in stir casting that enhances the composite’s qualities and 700°C furnace temperature, a uniform molten metal mixture was achieved within 30 minutes. Subsequently, the molten metal was discharged into the mold, allowed to cool and solidify, and then the cast was extracted. The specimens’ hardness was assessed following ASTM E18 guidelines, This tester featured a steel ball indenter measuring 1/16 inches in diameter and a red dial with dimensions of 10 × 10 × 10 mm which is shown in Figure 1. The impact test was performed as per ASTM E23 and the sample is shown in Figure 2. For wear analysis, ASTM G99 was followed, using cylindrical pin samples of 8 mm diameter and 30 mm height on a pin-on-disk apparatus.

Table 1
AZ61 chemical composition.

ELEMENTS	Al	Zn	Mn	Mg
AZ61 (wt.%)	5.7	1	0.6	Bal

Figure 1
Hardness sample of AZ61/Si₃N₄ composites.

Figure 2
Impact sample of AZ61/Si₃N₄ composites.

Figure 3 shows the flow chart of the experimental process and RSM. The weight percent of Si₃N₄ (X), the load (Y), and the sliding distance (Z) are the three variables that affect the specific wear rate. The Specific wear rate-value response surface is represented by Equations (1) and (2). (where ‘SWR’ is a function of the input parameters chosen).

(1) $$\text{SWR}=f(X;Y;Z)$$

(2) $$\text{SWR}={\text{b}}_0+\sum {\text{b}}_{\text{i}}{\text{x}}_{\text{i}}+\sum {\text{b}}_{\text{ii}}{x}_{\text{i}}{}^2+\sum {\text{b}}_{\text{ij}}{\text{x}}_{\text{i}}{\text{x}}_{\text{j}}+{\text{e}}_{\text{r}}$$

Figure 3
Flow chart of the experimental process and RSM.

The sliding distance, load, matrix composition, and reinforcing % all have a role in the wear rate. Key process parameters are presumed to be correct based on observations due to a lack of data. The silicon nitride weight %, applied load, and sliding distance have been identified as crucial process factors The wear conditions (load, sliding distance, and reinforcement %) were selected based on typical operational environments where the composites would be subjected to high friction and wear, in transportation applications. The Design Expert programme ran the statistical analysis and built the model. Table 2 detail the experimental setup and the values of the input parameters, respectively. An L18 array with three factors and three level was used for runs. A load range of 6–18 N, reinforcement percentages (4%, 8%, and 12%) and sliding distances of 500–1500 m were chosen to simulate real-world contact scenarios. Response surface approach in the form of a central composite design was applied as this model makes it possible to forecast the rate of wear under various circumstances and optimize the parameters to reduce wear on the composite materials.

Table 2
Process parameters for input levels.

S.NO.	FACTOR	SYMBOL	UNIT	LEVELS
				−1	0	1
1	Si3N4 Reinforcement	X	%	4	8	12
2	Load	Y	N	6	12	18
3	Sliding Distance	Z	m	500	1000	1500

RESULTS AND DISCUSSIONS

Hardness test

Three AZ61/Si₃N₄ composite samples were subjected to a Rockwell hardness test with a 100 kg load. The findings were determined for AZ61 alloy and its composites with varying Si₃N₄ weight percentages. Figure 4 shows how the amount of tiny silicon nitride particles used in the casting process affected the composites’ final hardness. As a result of the stirring action uniformly dispersing the reinforcement particles in the molten metal, composite with a higher weight percentage of Si₃N₄ exhibited a 33.33% increase in hardness compared to the base alloy. Pre-heating the reinforcement enhances the bonding between both the reinforcement and the matrix.

Figure 4
Hardness variation in different wt.% of Si₃N₄.

Impact test

Figure 5 shows a plot of the impact strengths of the three specimens. Toughness quantifies the amount of energy a material can absorb before fracturing, and impact test serves as definitive high strain rate assessment for this purpose. Through the introduction of 12wt.% Si₃N₄ reinforcement, along with a reasonable level of reinforcement and even distribution, the composite’s impact strength increased by 123%. At a greater concentration of reinforcement, the matrix and reinforcement bonding joined in the Izod impact test. The reinforcement increased the material’s toughness and decreased the chance of brittle fracture and hence the impact properties were positively impacted.

Figure 5
Impact energy variation in different wt.% of Si₃N₄.

RSM and ANOVA

Table 3 detail the experimental design and the values of the output parameters, respectively. RSM was used to investigate and evaluate the empirical model’s preliminary results, which showed the positive reaction. The relationship between the dependent response and the components was examined using linear, 2FI, and quadratic models. Once the most significant variables have been identified, a conclusive model can be formulated. Table 4 displays the wear rate ANOVA findings. The constructed model may be tested with the help of ANOVA. For the purpose of demonstrating the significance of all significant components and the interactions that exist between them, the ANOVA statistic is applied [²³, ²⁴]. Model terms are statistically significant when their “P-value” is less than 0.0500. X, Z, XY, XZ, YZ, X2, Y2 are all crucial model terms. Statistical insignificance of the model terms is shown by values larger than 0.1000. Our model might benefit from removing a number of superfluous models (apart from those needed to enable hierarchy). The determination coefficient, or R², measures how well a model fits the data. The model’s strong significance is demonstrated by its high significance determination coefficients (R² = 0.8859, Adjusted R² = 7575, and Predicted R² = 0.1385). The Model F-value of 52.15 indicates that the model is statistically significant. The 0.02 percent possibility that a big “Model F-Value” occurs owing to noise is just that, a chance.

Table 3
Experimental design.

RUN	RESPONSE: WEAR RATE × 10−4(mm3/Nm)	RANK
1	0.876	16
2	2.966	9
3	9.532	1
4	0.596	18
5	2.076	11
6	1.861	12
7	2.963	10
8	4.78	3
9	1.254	14
10	1.6	13
11	0.869	17
12	1.163	15
13	6.976	2
14	4.215	4
15	3.996	5
16	3.777	6
17	3.257	8
18	3.578	7

Table 4
ANOVA table.

MODEL	SS	df	MS	F-VALUE	P-VALUE	REMARKS
	79.89	9	8.88	6.90	0.0061	significant
X-Reinforcement of Si3N4	8.63	1	8.63	6.71	0.0321
Y-Load	9.31	1	9.31	7.23	0.0275
Z-Sliding distance	0.5343	1	0.5343	0.4153	0.5373
XY	0.6481	1	0.6481	0.5038	0.4980
XZ	11.33	1	11.33	8.81	0.0179
YZ	5.90	1	5.90	4.59	0.0645
X2	43.49	1	43.49	33.80	0.0004
Y2	2.20	1	2.20	1.71	0.2276
Z2	2.50	1	2.50	1.94	0.2012
Residual	10.29	8	1.29
Lack of Fit	10.17	5	2.03	52.15	0.0041	significant
Pure Error	0.1171	3	0.0390
Cor Total	90.18	17

The wear rate, expressed in terms of the X, Y, and Z variables, is:

Wear rate = +11.78110 – 2.10815 Reinforcement of Si₃N₄ – 0.034192 Load – 0.004482 Sliding distance – 0.011859 Reinforcement of Si₃N₄ * Load + 0.000595 Reinforcement of Si₃N₄ * Sliding distance – 0.000286 Load * Sliding distance + 0.115885 Reinforcement Si₃N₄² + 0.011578 Load² + 1.77676E-06 Sliding distance²

Evaluating the adequacy of the model

There are three procedures for verifying the accuracy of the data and the validity of the model. The data is being analyzed for normality, independence, and variance. The normality of the data may be examined by drawing a normal probability plot. Figure 6 displays the residual probability distributions in the normal distribution. The straight line of the residuals in the normal probability plot provides evidence that the error is normally distributed. Plotting the residual and run-order graph allows us to test for data independence. Data dependencies are revealed by a trend in the residuals against run order plot. Data independence is shown in Figure 7 by a plot of run order vs wear rate residuals. There is no discernible trend.

Figure 6
Normal probability graph.

Figure 7
Residual plot.

Analysis of wear rate

A perturbation plot summarizes impact of the relevant variables on single graph. The plot of the response vs one variable change, with all other variables held constant. The Design Expert chooses the point in the middle of all variables as the standard. Indicators of factor sensitivity include a factor’s curvature. Straight line indicates the variable is not sensitive to change. If there are more than two variables, the plot can help identify the ones with the most impact on the outcome. Figure 8 is a perturbation plot, with parameters centered on the wear rate. Figure 8 shows that when the fraction of silicon nitride increases (X), the wear rate goes down. After a certain load range, the wear rate rises as a function of the load (Y), indicating a negative relationship between the two variables. After an ultimate limit is reached, the stress may be great enough to erode the oxide layer, leading to surface wear. Interaction describes the relationship between the influence of two elements and the variation in those parameters. Two-way relationships may be understood with the use of plots. Two-parameter interactions on wear rate are shown in a three-dimensional (3D) in Figure 9. The influence of weight percent of Si₃N₄ and load on wear rate is seen in Figure 9(a). But when the percentage of Si₃N₄ in the material increases and the load rises from 6 to 18 N, the rate of wear decreases. It may occur because of an oxide layer forming on the pin and disc’s mating surfaces. Figure 9(b) displays the weight percent of Si₃N₄ vs the sliding distance interaction graph. The creation of an oxidative layer reduces the wear rate, and a higher percentage of Si₃N₄ in the material also helps reduce the wear rate as the sliding distance rises. A possible cause of the marginal increase in wear rate is delamination. As shown in Figure 9(c), the wear rate is affected by both the sliding distance and the load. The graph shows that the wear rate is lowest for a middle-ground combination of load and sliding distance. Due to delamination and oxidative layer depletion, wear rate increases with increasing load.

Figure 8
Perturbation plot.

Figure 9
Perturbation plot interaction plot weight percentage vs (a) load, (b) sliding distance, (c) load vs sliding distance.

CONCLUSIONS

Stir cast AZ61/Si₃N₄ composites with varying reinforcement was evaluated for their mechanical characteristics, and wear rate were modelled using the RSM method and validated. The key findings derived from this study are summarized as follows.

• Stir casting successfully created the AZ61 composites with silicon nitride and we were able to evenly disperse the reinforcing Si₃N₄ throughout the AZ61 alloy. Mechanical and tribological qualities were greatly improved by the material’s high density and lack of defects in the casting process.

• An inclusions of Si₃N₄ improved hardness and wear resistance without compromising impact strength. However, as the weight fraction increases, the composite exhibits increased hardness and wear resistance due to the higher reinforcement content.

• Composites reinforced with 12wt.% Si₃N₄ showed considerable improvements in its properties with higher improvement percentage in both hardness and impact strength.

• To minimize experimental circumstances and estimate the abrasive response of wear rate, a CCD using RSM was implemented. The entire model is verified with ANOVA, and the resulting model accurately predicts the wear rate to within 5%.

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Publication Dates

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

History

• Received
  26 Oct 2024
• Accepted
  09 Dec 2024