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Article

Comprehensive Investigation of Hardness, Wear and Frictional Force in Powder Metallurgy Engineered Ti-6Al-4V-SiCp Metal Matrix Composites

by
Adithya Hegde
1,
Rajesh Nayak
1,*,
Gururaj Bolar
1,*,
Raviraj Shetty
1,
Rakesh Ranjan
2 and
Nithesh Naik
1
1
Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Udupi 576104, Karnataka, India
2
Department of Information Technology, ABES Engineering College, Ghaziabad 201009, Uttar Pradesh, India
*
Authors to whom correspondence should be addressed.
J. Compos. Sci. 2024, 8(2), 39; https://doi.org/10.3390/jcs8020039
Submission received: 4 November 2023 / Revised: 15 November 2023 / Accepted: 13 December 2023 / Published: 23 January 2024
(This article belongs to the Special Issue Metal Composites)
Browse Figures
Versions Notes

Abstract

:
Metal matrix composites (MMCs) have achieved significant attention in engineering applications because of their exceptional properties, like increased strength-to-weight ratiosand resistance to wear. However, their manufacturing processes pose challenges for industries, such as oxidation, porosity, and chemical reactions. To address these challenges, this study investigates the processing and sintering (500 °C) of Ti-6Al-4V-SiCp composites and their mechanical properties, particularly hardness, wear and frictional force using a statistical approach. The main objective of this research is to identify optimal processing conditions for Ti-6Al-4V-SiCp composites that yield maximum hardness, minimal wear and frictional force. Thisstudy varies three key parameters, namely compaction pressure (Ton/sq.inch), SiC (wt.%), and PVA binder (wt.%) using Taguchi’s design of experiments (TDOE). Further, the response surface methodology (RSM) is used to develop second-order models to predict the output values under different processing conditions, by correlating with the values obtained from TDOE. The results indicate that the most significant influence on the output is exerted by SiC (wt.%), followed by PVA binder (wt.%) and compaction pressure (Ton/sq.inch). To achieve higher hardness with minimal wear and frictional force during processing, SiCp (15 wt.%), compaction pressure (4 Ton/sq.inch), and PVA binder (3 wt.%) arerecommended. Finally, microstructural analysis using (SEM) scanning electron microscope images, optical macrographs and (AFM) atomic force microscopy revealed that the inclusion of 15 wt.% SiCp resulted in improved hardness, wear and frictional force compared to 20 wt.% SiCp. In conclusion, this study provides valuable insights into optimizing the processing parameters of Ti-6Al-4V-SiCp samples, enabling the production of materials with enhanced hardness and wear resistance.

1. Introduction

Metal matrix composites (MMCs) represent a novel class of materials wherein the properties of a metal matrix are enhanced through the incorporation of a ceramic reinforcement [1,2,3,4,5,6,7]. By integrating metallic matrices (known for their high ductility and high toughness) along with ceramic material reinforcements (possessing high strength and high modulus), MMCs exhibit improved compression and shear strength, elevated working temperature, enhanced electrical conductivity, density, and coefficient of thermal expansion [8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33]. Consequently, MMCs have found widespread application in high-performance fields, such as recent advancements in the automotive sector and aircraft engines. The inclusion of ceramics as either fiber or particle reinforcement within the titanium alloy matrix leads to enhanced mechanical, thermal, and tribological properties [34]. Continuous fibers notably augment strength in the direction of fiber alignment, particularly at high temperatures [35]. The interaction between ceramics like silicon carbide and titanium results in the formation of brittle silicide phases, TiCx, and Ti5Si3Cx [36]. Presently, silicon carbide particles are commonly employed to reinforce titanium alloys and titanium aluminide matrix phases [37].
Titanium matrix composites (TMCs) with discontinuous or particle reinforcement are relatively easier to manufacture and exhibit nearly isotropic characteristics. As a result, particulate-reinforced TMCs have garnered significant interest for structural applications [38]. Extensive research has been dedicated to expanding the scope of applications and production methods for these materials. Among the ceramics investigated as reinforcements for titanium alloys are titanium carbide (TiC), titanium dioxide (TiO2), titanium boride (TiB) and titanium diboride (TiB2), as well as silicon dioxide (SiO2), silicon carbide (SiC) and silicon nitride (SiN). SiC has emerged as the most desirable material for reinforcement [39]. The mechanical properties of MMCs, such as a high strength-to-weight ratio and thermal–electrical conductivity, can be further improved by increasing the density of produced TMCs [40].
Powder metallurgy (PM) stands out as a highly versatile manufacturing technique, as it enables the creation of intricately shaped, high-quality products with tight tolerances. PM facilitates the production of near-net-shaped, highly functional components that are less susceptible to faults or porosity [41]. To study the manufacturing processes comprehensively, statistical techniques like Taguchi’s design of experiments are employed. This statistical method involves the creation of mathematical models through experimental testing to predict potential outcomes based on input factors [42,43]. Analysis of variance (ANOVA) is employed to examine the contribution percentage of process input factors [44,45]. In this context, response surface methodology is a widely used experimental design for optimization, while evaluating the effects of multiple parameters and the effect of their interactions on multiple response (output) parameters [46,47].
In this field despite of extensive research efforts, several critical knowledge gaps persist. One aspect is the exploration of alternative ceramic reinforcements beyond the widely utilized silicon carbide (SiC), including titanium carbide (TiC) and titanium dioxide (TiO2). Investigating their material properties and integration feasibility within composites is crucial for diversifying available materials. Additionally, there is a need for comprehensive studies optimizing powder metallurgy (PM) parameters for Ti-6Al-4V-SiCp composites, and a deeper understanding of how compression pressure, SiC content, and PVA binder Content interact to influence hardness. This research aims to address these gaps while striving to enhance the performance and application scope of these advanced composites. The present work aims to process Ti-6Al-4V-SiCp composites using the powder metallurgy technique and investigate the influence of input process parameters, such as compression pressure (MPa), SiC content (wt.%), and PVA binder content (wt.%), on hardness through the combination of TDOE and RSM.

2. Materials and Methods/Methodology

In this experimentation, Ti-6Al-4V (titanium alloy) and SiC (silicon carbide) with a particle size of 100 µm along with binder powder (PVA) polyvinyl alcohol have been sourced from Paraswamani Metals, Mumbai. The chemical composition of titanium alloy and silicon carbide are presented in Table 1 as well as in Table 2. A double action split-die has been designed for processing of titanium silicon carbide composites. The matrix and reinforcement powders are mixed along with PVA binder and are ball milled for 1 h to achieve homogenous mixing of materials. Further, the mixture is compacted using modified compacting machine setup. Figure 1 portrays the Flowchart of the experimentation process. The factors and levels selected for compacting the powders are presented in Table 3. The green compacted samples are then sintered at 500 °C for 2 h in a muffle furnace. The samples are characterized for Brinell hardness value using Analog B 3000 (H) Hardness Testing Equipment. Indentation load (10 kgf) with dwell time (20 s) to obtain indentation on the surface using steel ball of diameter (10 mm). The hardness is found out using equation given below.
H B W = 2 P π D ( D − ( D 2 − d 2 )
where P is the applied force (kgf); D is the diameter of the indenter ball in mm; d is the mean diameter of the indentation in mm (it ismeasured twice, typically left–right and top–bottom).
Contact area information is calculated using the given equation
A = π * D 2 2
where D is the diameter of the spherical indenter.
Further, for measuring wear and frictional force, specimens of 8 mm diameter with 30 mm length were chosen as per ASTM G99-05 standards. Input parameters such as sliding velocity (1 m/s), load (10 N) and sliding distance (1000 m) are taken constant. The pins were made even with P-1200 grade abrasive paper to ensure they had the sameroughness. The test duration, weight to apply, sliding speed, and sliding distance by conducting a pilot study. An accurate electronic balance is used to measure the pin’s starting weight (at least 0.0001 g). After putting the pin in the holder, weight is added using a lever. Once the test is conducted, the pin is weighed again with the same balance and weight lost is calculated. The test has been conducted three times to make sure the consistency of the results. The specific rate of wear (K) was calculated using below equation.
K = ∆ W ρ L D
Further, Taguchi’s L27 orthogonal array is formulated using MINITAB 15 to optimize the input process parameters. Finally, RSM (Table 4) is used to establish pragmatic interaction among the process parameters selected. In this study, sets of experiments are conducted in accordance with the experimental design matrix and sorted using the standard ordering.

3. Results and Discussion

After homogeneous distribution of silicon carbide (SiC) reinforcement with a titanium alloy (Ti-6Al-4V) matrix during processing, a TDOE and RSM approach for predicting hardness, wear and frictional force has been discussed.

3.1. Hardness Analysis

Hardness is an important criterion to consider while fabricating the titanium composites. Figure 2 presents the experimental details of hardness of samples processed under differentSiC (wt.%), compression pressure (Ton/sq.inch), PVA binder (wt.%). From the figure (Figure 2), it is observed that the hardness value increased at 15 wt.% SiC, compared to 10 wt.% SiC and 20 wt.% SiC. The 15 wt.% SiC content plays a crucial role in enhancing the overall hardness as SiC is known for its hardness and strength. Its uniform distribution within the matrix acts as effective barriers to impede dislocation movements during deformation and thus increasing the strength and hardness of the composite. Comparatively, with lower SiC content (10wt.%), there is not enough reinforcement to significantly improve the mechanical properties. Conversely, a higher SiC content (20 wt.%) leads to particle agglomeration, reducing uniformity and causing stress concentration points that further lowers the hardness. Additionally, the 4 Ton/sq.inch compaction pressure helps in achieving optimal particle packing and reduced porosity, resulting in improved mechanical properties. Lower compaction pressures (3 Ton/sq.inch) may lead to incomplete particle bonding and porosity, whereas higher pressures (5 Ton/sq.inch) could cause particle fracture and damage, both negatively impacting the hardness. Lastly, the 3 wt.%PVA binder provides the necessary binding strength during the compaction process, aiding in maintaining the structural integrity of the composite. However, excessive binder content may lead to increased porosity and reduced hardness. The increase in hardness at 15 wt.% SiC is due to its uniform distribution within the matrix, impeding dislocation movement during deformation, while lower SiC content lacks sufficient reinforcement and higher SiC content leads to particle clustering, causing stress concentration points that reduce hardness. Thus, the observed combination of SiCwt.%, compaction pressure, and PVA binder wt.% demonstrates a balanced microstructure, contributing to the highest hardness observed in the MMC.
From Figure 3, i.e., main effects plot of hardness, it can be clearly observed that the selection of SiCp (15 wt.%), compaction pressure (4 ton/sq. inch) and PVA binder (3 wt.%) as the optimum combination of parameters to obtain the highest hardness (BHN) value during fabrication of Ti-6 Al-4V-SiCp composites.
ANOVA indicates that SiC (wt.%) is one of the prominent factors considered while fabricating Ti-6Al-4V-SiCp composites. Among the contribution percentage (p%) of the different selected factors (Table 5) for hardness, SiC (wt.%) (A) has the largest contribution of around 77.99%. It can be seen that PVA binder (C) (19.87%), compaction pressure (B) (1.85%), and interactions A × B, A × C, (0.22%, 0.15%) had less significance on hardness both statistically and physically. The interactions (B × C) do not provide statistical or physical significance to the hardness.
Equation (4) presents a second-order differential equation for hardness, which is expressed as a function of input processing parameters (SiC wt.%, compaction pressure and PVA wt.%).
Hardness (BHN) =39.2029+ 27.9847 A + 27.6647 B + 5.01471 C − 0.838824 A2 − 2.97059 B2 − 1.97059 C2 − 0.2 AB +0.15 AC − 0.25 BC
Analysis is carried out at a significance level of 5% while the level of confidence is at 95%. The results of ANOVA clearly indicate that the ‘F’ table value (Table 6) is less than the calculated value of ‘F’ (F(0.05,9,9) = 329.4). Thus, the developed equation is considered adequate.
For each of the response surfaces, contour and surface plots are plotted at different SiC (wt.%) and PVA binder (wt.%) with compression pressure (4 Ton/sq.inch) is plotted (Figure 4). These response contours and surface plot are used to predict hardness (BHN) at any area. It is clear from the figure that the maximum hardness ranges from 14 to 18 (wt.%) of SiC with 3–3.5 (wt.%) of PVA binder.
Figure 5 presents the microstructure of titanium composites under varying combination of input parameters. From the analysis, it was concluded that Ti-6Al-4V reinforced with 10 wt.% of SiCp had more void formation compared to 15 wt.% and 20 wt.% of SiCp, respectively. Further, 15 wt.% of SiCp forms an α-phase of titanium, which further increases the hardness compared to 20 wt.% of SiCp. Further, cracks are formed when SiCp wt.% is increased, because of the decrease in interlocking and bond strength.
The RSM predicted values of hardness (BHN) are verified by comparing with TDOE hardness (BHN) values. For 27 trials, the observed average error was 0.69%, which concludes that the estimation is accurate. Figure 6 presents the verification test results for hardness (BHN).

3.2. Wear Analysis

Wear in materials refers to the gradual loss of material due to the mechanical action of another surface rubbing against it. The wear rate (mg/min) of composite sample during Pin on Disc Wear Testing process can be significantly influenced by several processing parameters such as SiC wt.%, compaction pressure (Ton/sq.inch) and PVA binder wt.%. The SiC wt.% affects the atomic structure of the composite, where increasing the SiC wt.% can lead to better dispersion of the particles of reinforcement, increased hardness and stiffness of the composite, and reduced material loss due to wear. Figure 7 presents the experimental results of wear (mg/m) under constant load (10 N) sliding velocity (1 m/s), along with sliding distance (1000 m). A PVA binder content of 3 wt.% results in minimal wear due to reduced porosity in titanium silicon carbide composites, while 4 wt.% yields intermediate wear, and 5 wt.% leads to elevated wear levels, correlating with increased porosity as PVA evaporates during the powder metallurgy process, leaving behind pores that influence wear resistance. At the micro level, when SiC content is lower (10 wt.%), the composite has less reinforcement, making it more susceptible to wear as the softer titanium matrix is exposed. Similarly, at higher compaction pressures (5 Ton/sq.inch), the higher densification of the composite might lead to increased wear, as the interfaces between the Ti-SiC particles become more prone to damage due to the higher contact stresses during sliding. However, at the intermediate SiC content (15 wt.%), the microstructure is optimized, providing a balanced combination of reinforcement and matrix properties. This leads to improved wear resistance, as the SiC particles can act as load-bearing elements, distributing the applied load and reducing wear on the titanium matrix. Additionally, a moderate compaction pressure (4 Ton/sq.inch) creates a well-bonded structure with fewer defects, resulting in better wear resistance compared to lower compaction pressures. The wear rate during Pin on Disc Wear Testing is significantly influenced by SiC wt.%, compaction pressure, and PVA binder wt.%; higher SiC wt.% enhances dispersion, hardness, and stiffness, reducing wear, while lower SiC content makes the composite more prone to wear; increased compaction pressure may lead to higher wear due to increased contact stresses, but at an intermediate SiC content of 15 wt.%, a balanced microstructure improves wear resistance by distributing load through SiC particles, and moderate compaction pressure reduces defects, enhancing wear resistance compared to lower pressures.
Figure 8 presents the worn surface of Ti6Al4V-SiCp composites after the experimentation. In the composite specimen with 10 wt.% SiC, a surface with visible signs of wear is observed. The wear patterns are more pronounced compared to the samples with higher SiC content. The wear mechanisms in this sample are dominated by abrasive wear, where the softer titanium matrix is subjected to localized abrasion from the harder SiC particles. Similarly, the composite sample with 20 wt.% SiC shows a distinctive wear pattern compared to the other two samples. With the highest SiC content, this sample exhibits a more textured and rough surface due to the increased presence of hard SiC particles. However, despite the higher SiC content, wear at this level is not high compared to the 10 wt.% SiC sample. The SiC particles play a crucial role in enhancing wear resistance, but an excessively high SiC content leads to increased abrasive wear and potential particle agglomeration. However, the composite specimen with 15 wt.% SiC exhibits a more uniform and smoother surface compared to the other samples. At this intermediate SiC content, the wear behavior shows a transition from predominantly abrasive wear to a combination of abrasive and adhesive wear. The SiC particles are load-bearing elements which reduce the wear on the titanium matrix, resulting in a more balanced wear profile.
Figure 9 presents the atomic force microscope (AFM) image of theworn surface with different SiC wt.%. For the sample with 10 wt.% SiC, the AFM image displays a surface with noticeable wear tracks, increased surface roughness, and abrasive wear debris.The presence of harder SiC particles likely caused localized ploughing and scratching of the titanium matrix, leading to the formation of wear tracks. The surface roughness looks pronounced compared to samples with higher SiC content, indicating higher wear rates and a less uniform surface. Moreover, the AFM images suggest the presence of wear debris and possible particle agglomeration, shedding light on the dominant wear mechanisms at 10 wt.% SiC. Further, for the sample with 20 wt.% SiC, the AFM image exhibits distinct wear patterns due to an increased presence of SiC particles. The higher SiC content leads to a textured surface, with possible indications of SiC particle agglomeration. AFM analysis provides detailed information about the distribution and arrangement of SiC particles on the surface, which influences the wear behavior. However, despite the higher SiC content, the AFM image shows relatively fewer wear features compared to the 10 wt% SiC sample, suggesting improved wear resistance achieved with 20 wt.% SiC samples. Finally, the AFM image of 15 wt.% SiC sample reveals a smoother and more controlled surface. The wear behavior at this intermediate SiC content achieves a balance between abrasive and adhesive wear mechanisms, resulting in a relatively more uniform wear profile and reduced surface roughness. The presence of SiC particles contribute to load-bearing and wear reduction effects, resulting in shallower wear tracks and less pronounced surface features in the AFM image.
The main effects plot (Figure 10) for wear indicates that the selection of SiCp (15 wt.%), compaction pressure (4 ton/sq. inch) and PVA binder (3 wt.%) results in the best combination of input parameters to derive least wear values during processing of titaniumcomposites.
Results of ANOVA deduce that SiC (wt.%) is a prominent parameter which needs to be taken into consideration while processing titanium composites. Among the percentage of contribution (P%) of the various parameters (Table 7) for wear, SiC (A) has the largest contribution of around 76.99%. It can be seen that PVA binder (C) (p = 19.76%), compaction pressure (B) (p = 2.072%), and interactions A × B, A × C, (p = 0.101%, 0.899%) had less physical and statistical significance on wear. The interactions (B × C) are not of any physical or statistical significance.
Equation (5) presents the second-order differential equation for representing the wear (mg/min) which can also be given as a function of input processing parameters such as (A) SiC (wt.%), (B) compression pressure (Ton/sq. inch) and (C) PVA binder (wt.%).
Wear (mg/min) = 0.0473 − 0.00370192 A − 0.00424972 B − 0.00126972 C + 0.000109133 A2 + 0.000478329 B2 + 0.000178329 C2 + 0.00000093099 A × B + 0.00000293 A × C + 0.000009654 B × C
Analysis is carried outat a significance level of 5% while the level of confidence is at 95%. The results of ANOVA clearly indicate that the ‘F’ table value (Table 8) is less than the calculated value of ‘F’ (F(0.05,9,9) = 109.2). Thus, the developed equation is considered adequate.
For each of the response surfaces, contour and surface plots are plotted at different SiC (wt.%) and PVA binder (wt.%) with compression pressure (4 Ton/sq.inch) is plotted (Figure 11). These response contours and surface plot are used to predict wear(mg/min) at any area. It is clear from the figure that the minimum wear ranges from 16 to 18 (wt.%) of SiC with 3.5–5 (wt.%) of PVA binder.
The RSM predicted values of wear (mg/min) are verified by comparing with TDOE values. For 27 trials, the observed average error was 1.21%, which concludes that the estimation is clearly accurate. Figure 12 presents the verification test results for wear(mg/min).

3.3. Frictional Force Analysis

Frictional force is a measure of the resistance encountered when two surfaces slide against each other. The frictional force (N) of composite sample during the Pin on Disc Wear Testing process can be significantly influenced by several processing parameters such as SiC wt.%, compaction pressure (Ton/sq.inch) and PVA binder wt.%. Frictional force, on the other hand, is a measure of the resistance encountered when two surfaces slide against each other. In the Ti6Al4V-SiCp composites, frictional force is influenced by the interfacial behavior between the TiSiC particles and the titanium matrix. At higher SiC contents (20 wt.%), there isa higher tendency for agglomeration of SiC particles, leading to uneven distribution and increased frictional resistance during sliding. Similarly, at higher binder content (5 wt.% PVA), excess binder material coats the SiC particles, reducing their direct interaction with the titanium matrix and increasing the contact area between the binder and the counter-surface, resulting in higher frictional force. Conversely, at intermediate SiC content (15 wt.%) and moderate PVA binder content (3 wt.%), the composite exhibits the best balance between load-bearing capacity, interfacial adhesion, and wear resistance. This combination could reduce the frictional force during sliding due to optimized microstructural features and better load distribution, resulting in lower frictional losses. The presence of SiC particles as reinforcement enhances the hardness as well as resistance to wear of the composite. Further, the moderate compaction pressure promotes a well-bonded microstructure with a balanced distribution of SiC particles, minimizing defects and ensuring effective load transfer. Finally, the appropriate PVA binder content allows for good interfacial adhesion between the particles and the titanium matrix, leading to efficient stress transfer and reduced frictional losses. Figure 13 presents the experimental results of frictional force (N) under constant sliding velocity (1 m/s), load (10 N) and sliding distance (1000 m). Thus, the frictional force during Pin on Disc Wear Testing in Ti6Al4V-SiCp composites is significantly affected by SiC content and PVA binder content; higher SiC content leads to particle agglomeration and increased friction, while higher PVA binder content reduces SiC-titanium matrix interaction, resulting in greater friction, but at an intermediate SiC content and moderate PVA binder content, a well-balanced microstructure reduces frictional force through optimized load distribution and interfacial adhesion, minimizing frictional losses.
The main effects plot (Figure 14) for frictional force indicates that the selection of SiCp (15 wt.%), compaction pressure (4 ton/sq. inch) and PVA binder (3 wt.%) results in the best combination of input parameters to derive least frictional force values during processing of titaniumcomposites.
Results of ANOVA deduce that SiC (wt.%) is a prominent factor to be taken into consideration while processing titanium composites. Among the percentage of contribution (P percent) of the various factors (Table 9) for wear, SiC (A) has the largest contribution of around 64.75%. It can be seen that PVA binder (C) (p = 21.58%), compaction pressure (B) (p = 12.64%), and interactions A × B, A × C, (p = 0.654%, 0.244%) had less statistical and physical significance on wear. The interactions (B × C) are not of any physical or statistical significance.
Equation (6) presents the second-order differential equation for representing the frictional force(N) which can also be expressed as a function of input processing factors such as (A) SiC (wt.%), (B) compression pressure (Ton/sq. inch) and (C) PVA binder (wt.%).
Frictional force (N) = 123.463 − 0.769095 A − 15.5701 B − 1.63011 C + 0.00392352 A2 + 0.652198 B2 − 0.147802 C2 + 0.00124176 A × B + 0.00324176 A × C + 0.556044 B × C
Analysis is carried outa significance level of 5% while the level of confidence is at 95%. The results of ANOVA clearly indicate that the ‘F’ table value (Table 10) is less than the calculated value of ‘F’ (F(0.05,9,9) = 54.6). Thus, the developed equation is considered adequate.
For each of the response surfaces, contour and surface plots are plotted at different SiC (wt.%) and PVA binder (wt.%) with compression pressure (4 Ton/sq.inch) is plotted (Figure 15). These response contours and surface plot are used to predict frictional force(N) at any area. It is clear from the figure that the minimum frictional force ranges from 14 to 18 (wt.%) of SiC with 4.5–5 (wt.%) of PVA binder.
The RSM predicted values of frictional force(N) are verified by comparing with TDOE values. For 27 trials, the observed average error was 0.47%, which concludes that the estimation is very accurate. Figure 16 presents the verification test results for frictional force(N).

4. Conclusions

The values of hardness, wear and frictional force of the Ti-6Al-4V-SiCp composite specimen under various processing conditions using TDOE and RSM are studied. The conclusions drawn based on the results are as follows:
  • The SiC (wt.%) is the dominant parameter for the increase in hardness, wear and frictional force, followed by PVA binder (wt.%) and compression pressure (Ton/sq.inch).
  • During the fabrication of the Ti-6Al-4V-SiCp composite specimen, to achieve maximum hardness and minimal wear and frictional force, the factors SiCp (15 wt.%), compaction pressure (4 ton/sq. inch) and PVA binder (3 wt.%) are preferred.
  • From the obtained data, a response surface model of the second order has been created for all the output parameters. Given that the projected and measured values are rather close, it can be used to accurately predict the hardness, wear and frictional force of Ti-6Al-4V-SiCp composite specimens as they are processed.
  • Further, microstructural analysis reveals that 15(wt.%) of SiCp has resulted in the formation of the α-phase of titanium and silicon carbide, which caused an increase in hardness values compared to 20 (wt.%) of SiCp. With the intermediate SiC content (15 wt.%), the microstructure is optimized, providing a balanced combination of reinforcement and matrix properties reducing the wear and frictional force.
  • From atomic force microscopy, it is observed that the 15 wt.% SiC sample reveals a smoother and more controlled surface. The wear behavior at this intermediate SiC content achieves a balance between abrasive and adhesive wear mechanisms, resulting in a relatively more uniform wear profile and reduced surface roughness.
  • The findings highlight that in industrial applications involving Ti-6Al-4V-SiCp composites, optimizing the SiC content (15 wt.%), compaction pressure (4 ton/sq. inch), and PVA binder (3 wt.%) can lead to superior hardness, reduced wear, and frictional force, enhancing the overall performance and durability of such composite materials.
  • For potential future research, exploring advanced microstructural analyses and surface characterization techniques could further refine our understanding of the relationships between composition, microstructure, and mechanical properties, allowing for even more precise control and tailoring of composite materials for specific industrial applications.

Author Contributions

Conceptualization, A.H. and R.S.; methodology, A.H., R.S. and G.B.; software, A.H., R.S., R.N., N.N., G.B. and R.R.; validation, R.S., R.N. and N.N.; formal analysis, A.H. and R.S.; investigation, A.H., R.S., R.N., N.N. and G.B.; resources, A.H., R.S., R.N. and N.N.; data curation, A.H., R.S. and N.N.; writing—original draft preparation, A.H. and R.S.; writing—review and editing, R.S., N.N. and G.B.; visualization, A.H., R.S., R.N., R.R. and N.N.; supervision, R.S. and R.N.; project administration, N.N. and G.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Flowchart of processing and characterization of titanium silicon carbide composite.
Figure 1. Flowchart of processing and characterization of titanium silicon carbide composite.
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Figure 2. Variation inhardness (BHN) under varying SiCp (wt.%) and PVA binder (wt.%) with compaction pressure; (a) 3 Ton/sq.inch; (b) 4 Ton/sq.inch; (c) 5 Ton/sq.inch.
Figure 2. Variation inhardness (BHN) under varying SiCp (wt.%) and PVA binder (wt.%) with compaction pressure; (a) 3 Ton/sq.inch; (b) 4 Ton/sq.inch; (c) 5 Ton/sq.inch.
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Figure 3. Main effects plot for hardness (BHN).
Figure 3. Main effects plot for hardness (BHN).
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Figure 4. Hardness contour plot and surface plot in SiC (wt.%)-PVA binder (wt.%) at 4 Ton/sq.inch.
Figure 4. Hardness contour plot and surface plot in SiC (wt.%)-PVA binder (wt.%) at 4 Ton/sq.inch.
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Figure 5. Microscopic images of processed Ti-6Al-4V-SiCp samples with (a) SiC wt.% (10 wt.%), (b) SiC wt.% (15 wt.%), and (c) SiC wt.% (20 wt.%) while PVA binder wt.% (3wt.%) and compression pressure (4 Ton/sq. inch) are kept constant.
Figure 5. Microscopic images of processed Ti-6Al-4V-SiCp samples with (a) SiC wt.% (10 wt.%), (b) SiC wt.% (15 wt.%), and (c) SiC wt.% (20 wt.%) while PVA binder wt.% (3wt.%) and compression pressure (4 Ton/sq. inch) are kept constant.
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Figure 6. Verification of RSM prediction of hardness (BHN).
Figure 6. Verification of RSM prediction of hardness (BHN).
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Figure 7. Variation inwear (mg/min) under varying SiCp (wt.%) and PVA binder (wt.%) with compaction pressure; (a) 3 Ton/sq.inch; (b) 4 Ton/sq.inch; (c) 5 Ton/sq.inch.
Figure 7. Variation inwear (mg/min) under varying SiCp (wt.%) and PVA binder (wt.%) with compaction pressure; (a) 3 Ton/sq.inch; (b) 4 Ton/sq.inch; (c) 5 Ton/sq.inch.
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Figure 8. Optical macrograph of worn surface of Ti-6Al-4V-SiCp composites under different processing conditions (a) SiC wt.%(10 wt.%), (b) SiC wt.% (15 wt.%), and (c) SiC wt.% (20 wt.%) at PVA binder wt.% (3 wt.%) and compression pressure (4 Ton/sq. inch) kept constant.
Figure 8. Optical macrograph of worn surface of Ti-6Al-4V-SiCp composites under different processing conditions (a) SiC wt.%(10 wt.%), (b) SiC wt.% (15 wt.%), and (c) SiC wt.% (20 wt.%) at PVA binder wt.% (3 wt.%) and compression pressure (4 Ton/sq. inch) kept constant.
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Figure 9. Atomic force microscopic images of worn surface of the composite specimen with; (a) SiC wt.%(10 wt.%), (b) SiC wt.% (15 wt.%), and (c) SiC wt.% (20 wt.%) at PVA binder wt.% (3 wt.%) and compression pressure (4 Ton/sq. inch) kept constant.
Figure 9. Atomic force microscopic images of worn surface of the composite specimen with; (a) SiC wt.%(10 wt.%), (b) SiC wt.% (15 wt.%), and (c) SiC wt.% (20 wt.%) at PVA binder wt.% (3 wt.%) and compression pressure (4 Ton/sq. inch) kept constant.
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Figure 10. Main effects plot for wear (mg/min).
Figure 10. Main effects plot for wear (mg/min).
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Figure 11. Contour plot and surface plot for wear (mg/min).
Figure 11. Contour plot and surface plot for wear (mg/min).
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Figure 12. Verification of RSM prediction of wear.
Figure 12. Verification of RSM prediction of wear.
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Figure 13. Variation inhardness (BHN) under varying SiCp (wt.%) and PVA binder (wt.%) with compaction pressure; (a) 3 Ton/sq.inch; (b) 4 Ton/sq.inch; (c) 5 Ton/sq.inch.
Figure 13. Variation inhardness (BHN) under varying SiCp (wt.%) and PVA binder (wt.%) with compaction pressure; (a) 3 Ton/sq.inch; (b) 4 Ton/sq.inch; (c) 5 Ton/sq.inch.
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Figure 14. Main effects plot for frictional force (N).
Figure 14. Main effects plot for frictional force (N).
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Figure 15. Contour plot and surface plot for frictional force(N).
Figure 15. Contour plot and surface plot for frictional force(N).
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Figure 16. Verification of RSM prediction of frictional force (N).
Figure 16. Verification of RSM prediction of frictional force (N).
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Table 1. Ti-6Al-4V constituents [48].
Table 1. Ti-6Al-4V constituents [48].
ConstituentsVAlOFeNCHYTi
wt.%46.10.110.160.010.020.00110.0013Bal
Table 2. SiC constituents [48].
Table 2. SiC constituents [48].
CompoundSiAl2O3CFe2O3SiO2CaOSPSiC
wt.%1.430.241.180.670.80.150.050.33Bal
Table 3. TDOE factors and levels.
Table 3. TDOE factors and levels.
Levels(A)
SiC (wt.%)		(B)
Compression Pressure (Ton/sq.inch)		(C)
PVA Binder (wt.%)
11033
21544
32055
Table 4. RSM factors and levels.
Table 4. RSM factors and levels.
Levels(A)
SiC (wt.%)		(B)
Compression Pressure (Ton/sq.inch)		(C)
PVA Binder (wt.%)
11033
22055
Table 5. ANOVA (analysis of variance) for SN ratios.
Table 5. ANOVA (analysis of variance) for SN ratios.
SourceDeg. FSeq. SSAdj. SSAdj. MSFpp%
A25.039015.039012.519501035.670.00077.99
B20.112441.284030.0562223.110.0001.85
C21.284031.284030.64202263.910.00019.87
A × B40.029690.029690.007423.050.0840.22
A × C40.019750.019750.004942.030.1830.15
B × C40.001300.001300.000330.130.9650.009
Residual error80.019460.019460.00243
Total266.50569
Table 6. ANOVA for response function of the hardness.
Table 6. ANOVA for response function of the hardness.
SourceDeg.FSeq. SSAdj. MSFP
Regression94991.39554.599329.40.000
Residual Error915.151.684
Total181.89670
Table 7. ANOVA (analysis of variance) for SN ratios.
Table 7. ANOVA (analysis of variance) for SN ratios.
SourceDeg.FSeq. SSAdj. SSAdj. MSFpp%
A20.0000740.0000740.000037882.990.00076.99
B20.0000020.0000020.00000123.800.0002.072
C20.0000190.0000190.000010226.420.00019.76
A × B40.0000000.0000000.0000001.160.3970.101
A × C40.0000020.0000020.00000010.300.0030.899
B × C40.0000000.0000000.0000000.880.5160.076
Residual Error80.0000000.0000000.000000
Total260.000098
Table 8. ANOVA for response function of the wear.
Table 8. ANOVA for response function of the wear.
SourceDeg.FSeq. SSAdj. MSFP
Regression90.0000720.000008109.200.000
Residual Error90.0000010.000000
Total180.000073
Table 9. ANOVA (analysis of variance) for SN ratios.
Table 9. ANOVA (analysis of variance) for SN ratios.
SourceDeg.FSeq. SSAdj. SSAdj. MSFPP%
A27.565717.565713.78286844.610.00064.75
B21.477151.477150.73858164.910.00012.64
C22.522322.522321.26116281.580.00021.58
A × B40.152840.152840.038218.530.0060.654
A × C40.057190.057190.014303.190.0760.244
B × C40.028560.028560.007141.590.2660.121
Residual Error80.035830.035830.00448
Total2611.8396
Table 10. ANOVA for response function of the frictional force (N).
Table 10. ANOVA for response function of the frictional force (N).
SourceD.FSeq. SSAdj. MSFP
Regression947.69285.299254.600.000
Residual Error90.87350.0971
Total18
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Hegde, A.; Nayak, R.; Bolar, G.; Shetty, R.; Ranjan, R.; Naik, N. Comprehensive Investigation of Hardness, Wear and Frictional Force in Powder Metallurgy Engineered Ti-6Al-4V-SiCp Metal Matrix Composites. J. Compos. Sci. 2024, 8, 39. https://doi.org/10.3390/jcs8020039

AMA Style

Hegde A, Nayak R, Bolar G, Shetty R, Ranjan R, Naik N. Comprehensive Investigation of Hardness, Wear and Frictional Force in Powder Metallurgy Engineered Ti-6Al-4V-SiCp Metal Matrix Composites. Journal of Composites Science. 2024; 8(2):39. https://doi.org/10.3390/jcs8020039

Chicago/Turabian Style

Hegde, Adithya, Rajesh Nayak, Gururaj Bolar, Raviraj Shetty, Rakesh Ranjan, and Nithesh Naik. 2024. "Comprehensive Investigation of Hardness, Wear and Frictional Force in Powder Metallurgy Engineered Ti-6Al-4V-SiCp Metal Matrix Composites" Journal of Composites Science 8, no. 2: 39. https://doi.org/10.3390/jcs8020039

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