Arabinda Meher

The requirement for high-performance and energy-saving materials motivated the researchers to develop novel composite materials. This investigation focuses on utilizing aluminum alloy (A383) as the matrix material to produce hybrid metal matrix composites (HMMCs) incorporating boron carbide (B₄C) and multi-walled carbon nanotube (MWCNT) through a cost-effective stir casting technique. The synthesis of HMMCs involved varying the weight fractions of B₄C (2%, 4%, and 6%) and MWCNT (0.5%, 1%, and 1.5%). The metallographic study was carried out by field emission scanning electron microscopy (FESEM) mapped with EDS analysis. The results indicated a uniform dispersion and robust interfacial interaction between aluminum and the reinforced particles, significantly enhancing the mechanical properties. Micro-hardness and wear characteristics of the fabricated HMMCs were investigated using Vickers microhardness testing and the pin-on-disc tribometer setup. The disc is made of hardened chromium alloy EN 31 steel of hardness 62 HRC. The applied load was varied as 10N, 20N, 30N with a constant sliding speed of 1.5 m/s for different sliding distances. The micro-hardness value of composites reinforced with 1.5 wt% MWCNT and 6 wt% B₄C improved by 61% compared to the base alloy. Additionally, the wear resistance of the composite material improved with increasing reinforcement content. Incorporating 1.5% CNT and 6% B₄C as reinforcements results in the composite experiencing about a 40% reduction in wear loss compared to the unreinforced aluminum alloy matrix. Furthermore, the volumetric wear loss of the HMMCs was critically analyzed with respect to different applied loads and sliding distances. This research underscores the positive impact of varying the reinforcement content on the mechanical and wear properties of aluminum alloy-based hybrid metal matrix composites.

This study focuses on a comparative analysis of mechanical behavior and microstructural characteristics of Cu matrix (C87600) based hybrid composites reinforced with SiC-Gr_p and WC-Gr_p fabricated by the stir casting process. The graphite particle percentage was kept constant, whereas the content of SiC and WC in the respective composites was varied to analyze the mechanical properties of the fabricated composites. The morphological observation was carried out by field emission scanning electron microscope (FESEM), which revealed uniform dispersion of the reinforced particles in the hybrid composites. Clear phases of SiC and WC along with the Cu alloy were identified by the x-ray diffractometer (XRD). Further, a comparative study was conducted to analyze the mechanical behavior of the Cu-SiC-Gr and Cu-WC-Gr hybrid composites. With the addition of the hard ceramic materials, the tensile behavior and microhardness of both the Cu-based MMCs were improved. The WC-Gr reinforced composites exhibited higher mechanical properties than the SiC-Gr reinforced hybrid composites. Further, the fracture surfaces were also characterized to study the tensile behavior of the fabricated copper-based hybrid composites, which shows that ductile fracture was mainly associated with both hybrid composites.

The effective deployment of electrical energy has received attention because of its environmental implications. On the other hand, induction motors are the primary equipment used in many industries. Industrial facilities demand the maximum percentage of energy. This energy demand is determined by the operating circumstances imposed by the internal characteristics of the induction motor. Because internal parameters of an induction motor are not immediately measurable, they must be obtained through an identification process. This paper proposed an improved version of moth flame optimization (IMFO) for the efficient parameter estimation of induction motors. A steady-state equivalent circuit of the induction motor is employed for the simulation. The proposed technique handles the parameter estimation problem better than moth flame optimization (MFO), particle swarm optimization (PSO), the flower pollination algorithm (FPA), the tunicate swarm algorithm (TSA), and the sine cosine algorithm (SCA). The anticipated IMFO reduces the cost function by 49.38% as compared with the basic version of MFO.

Due to greater specific strength and stiffness than monolithic metal, aluminum matrix composite is in high demand. The present study investigated the interaction of borosilicate glass powder with the AA7075 aluminum alloy matrix. The lower density of borosilicate glass powder compared to AA7075 alloy makes the composite more appropriate for various industrial applications due to the increased material strength. This study uses the stir casting fabrication process to develop microsized borosilicate glass powder-reinforced aluminum matrix composites. The microstructure shows that borosilicate glass powder is near-uniformly distributed throughout the aluminum matrix. It is observed that the 9 wt (%) borosilicate glass powder-reinforced aluminum matrix composite increases the tensile strength by 40.71%, hardness by 21.21%, and impact strength by 34.37% compared to unreinforced aluminum AA7075 alloy. Pin-on-disc wear testing setup was used to determine the wear performance of the base AA7075 alloy and cast composites. The dry sliding wear test revealed that the glass powder-reinforced composites have better wear resistance properties compared to the unreinforced matrix due to the hard and rigid behavior of borosilicate glass reinforcement.

Machinability analyses of metal matrix composites are essential for manufacturing industries. The current study is focused on the mathematical modeling of the machinability of an in-situ synthesized RZ5-8 wt.% TiB₂ composite using the Taguchi design statistical tools and analysis of variance (ANOVA). Taguchi’s method indicates that the feed rate is the most influential parameter, followed by the depth of cut and cutting speed in determining the cutting force and surface roughness during the machining of the RZ5/8 wt.% TiB₂ composite. A regression analysis of the experimental data was carried out using ANOVA, and regression equations were established to estimate cutting force and surface roughness under different parametric conditions. The regression model was validated for other test conditions and the maximum deviation observed was ±10%. Main effects plots and response surface plots were developed to analyze the machining parameters’ individual and combined effects on the RZ5/8 wt.% TiB₂ composite’s machinability. The chip morphology and tool wear of the RZ5/8 wt.% TiB₂ composite were analyzed using FESEM under different machining conditions.

In the present study, the statistical analysis on tribological behavior of RZ5/TiB ₂ magnesium-based metal matrix composites is carried out using Taguchi design and analysis of variance (ANOVA) technique. Taguchi analysis using signal-to-noise ratio indicates that the sliding distance and wt.% TiB ₂ are the most significant factors in evaluating weight loss and coefficient of friction, respectively. The regression equation is formulated utilizing the ANOVA technique to study the output responses based on the input abrasive wear test experimental results. The regression equation is validated through a comprehensive study taking a series of abrasive wear tests and indicates the percentage deviation of regression modeling is in the range of ± 10%. The individual and combined effect of wear parameters on tribological behavior are investigated through the main effect plots and response surface plots. The micrograph of the worn surface of RZ5/TiB ₂ composites is studied using field emission scanning electron microscope (FESEM), indicating the formation of an oxide layer on the worn surface.