Benguluri Surekha

This present study epitomises the fabrication of Cu-15%WC-X%Gr (X = 0, 3, 6, 9, 12) hybrid composites through a microwave sintering process. The synthesised composites were evaluated for hardness and compression strength as per ASTM standards. The composite corresponding to Cu-15%WC-9%Gr shows the optimal compression strength of 395 MPa. Based on this, the composite corresponding to the maximum compression strength was selected for subsequent wear investigations under dry, oil, and SiC nanofluid lubrication conditions. The SiC nanofluids were prepared by dispersing 1 wt% SiC, 1.5 wt% SiC, and 2 wt% SiC nanoparticles in soluble oils. Increasing the nanoparticle content enhanced both the thermal conductivity and zeta potential, indicating an improved heat transfer and dispersion stability. The wear test under different lubricating regimes demonstrates that the lubricating type had a pronounced influence on the wear rate and C.O.F. The minimum rate of wear of 0.0235 mm³/m and C.O.F. of 0.28 were achieved for the 2 wt% SiC nanofluid lubrication. The worn surfaces under dry and oil-lubricated regimes revealed prominent microcracks and delamination wear. In contrast, surfaces tested under nanofluid lubrication exhibited smoother grooves with minimal surface damage and an absence of microcracking.

This study presents the experimental investigations on modeling of the compression strength and permeability of the resin-bonded sand mold system through Machine Learning approaches. The process of constructing the Fuzzy Logic system was automated by utilizing a data-base and rule-base optimized through genetic algorithms, and the recorded datasets. This research employed three AI models, namely Artificial Neural Networks (ANN), Decision Tree (DT), and Random Forests (RF), using the datasets produced by the GA-tuned Fuzzy model. The objective of this study was to assess and compare the predictive capabilities of three different AI models (Artificial Neural Networks, Decision Tree, and Random Forests) in terms of predicting the values of Compression Strength and Permeability. The complete dataset was divided into two separate subsets, referred to as training data and testing data. Based on the findings, it appears that Random Forest (RF) model exhibits promising potential in accurately predicting the desired mold qualities. The model achieved a high R² value of 0.9487, indicating a strong correlation with the target values. Additionally, the model demonstrated impressively low Mean Squared Error (MSE) and Mean Absolute Error (MAE) values of 117 and 17.6 points, respectively. Expanding the dataset size may further enhance the efficacy of the models.

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.

With the significant growth of cutting tool technology, the machining of hard to cut material is the main discussed area among academic researchers and metal-cu

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 present paper attempts to fabricate the functionally graded materials (FGMs) with two dissimilar metals using Gravity Die Casting (GDC) with and without an interfacing layer. AZ91D and Al7075 alloys are used in this study as base metals. The Zn layer is used as interfacing material to prepare the FGMs. The interfacial microstructures of the said FGMs reveal α-Mg, Al₅Mg₁₁Zn₄, and MgZn₂ compounds. The absence of a Zn interface layer revealed harmful Al-Mg intermetallics such as Al₁₂Mg₁₇, Al₃Mg_2, and Mg₅Al₈. The microstructural examination reveals that the presence of the Zn interface protects the diffusion of Al particles with Mg particles. Further studies on the Zn interfacing layer increased the microhardness value, whereas FGMs without interface showed increased shear and impact strength.