Ankur Jaiswal

Silicon‐containing refractory high‐entropy alloys (RHEAs) provide a promising platform for combining a refractory body‐centered cubic (BCC) matrix with thermally stable silicide phases. However, the effects of the processing route on phase evolution and elemental partitioning in Si‐rich RHEAs remain insufficiently understood. An equiatomic MoNbSiTiW alloy was processed by mechanical alloying followed by hot‐press sintering (MA + HPS) and vacuum arc melting (VAM). Both processing routes produced BCC + Nb ₅ Si ₃ phases but differed significantly in microstructural morphology and elemental distribution. The MA + HPS route resulted in a refined microstructure, with uniformly dispersed Nb‐ and Si‐rich silicide regions within the BCC matrix, whereas VAM produced a coarse dendritic structure with pronounced chemical segregation. These results demonstrate that configurational entropy alone does not suppress silicide formation in the present Si‐rich composition, owing to strong chemical interactions involving Si and refractory elements. Instead, the phase scale and spatial distribution are strongly influenced by solid‐state diffusion and solidification kinetics. Empirical thermodynamic parameters provided qualitative guidance; however, calculation of phase diagrams (CALPHAD) would be required for more rigorous prediction of phase stability and evolution. Overall, the results demonstrate that processing history strongly influences phase architecture and elemental partitioning in Si‐rich MoNbSiTiW RHEAs.

Traditional alloys are commonly based around a single dominant alloying element, and other alloying elements are added in lesser amounts to alter their properties. In contrast, high-entropy alloys ...

This study evaluates the performance of a multi-walled carbon nanotube (MWCNT)-based nanofluid as an alternative cutting fluid in the turning of EN31 steel. Experiments were conducted under dry, conventional coolant, and nanofluid-assisted conditions using a gravity-feed lubrication system with a flow rate of 1 L/h. The results indicate that the nanofluid improves machining performance, with reductions of 5–20% in cutting force, 11–37% in thrust force, 4–33% in feed force, and 2–34% in surface roughness compared to conventional cooling. Response Surface Methodology (RSM) with Central Composite Design was applied to develop predictive models for surface roughness and resultant cutting force. The models showed good statistical significance and prediction accuracy. Multi-objective optimization was performed to minimize surface roughness and cutting force while maximizing material removal rate. The optimal parameters were identified as 1500 rpm cutting speed, 0.10 mm/rev feed rate, and 1 mm depth of cut, resulting in surface roughness of approximately 0.47 μm, cutting force of 165 N, and material removal rate of 7.54 mm³/min. Comparative analysis of optimization algorithms showed that NSGA-II provided superior convergence and stability, achieving better performance than other methods. The findings confirm the effectiveness of nanofluid-assisted machining combined with intelligent optimization for improved machining outcomes.

This study presents an integrated optimisation method for CNC milling of heat-treated YXR7 tool steel using carbide cutting inserts under varying lubrication and process parameters. A full factorial experimental design comprising 27 runs was employed to assess the influence of depth of cut ( d _c ), feed per tooth ( f _t ), cutting speed ( C _s ), and nano-cutting fluid ( C _f ) on critical performance responses such as surface roughness ( Ra ), material removal rate ( MRR ), and tool wear rate ( TWR ). An advanced modelling through regression and ANOVA showed complex interactive and non-linear effects among process parameters. To effectively navigate these interdependencies, a novel hybrid decision-making model combining the Full Consistency Method (FUCOM) and fuzzy-MARCOS was employed. This multi-criteria decision-making (MCDM) method was described for uncertainties in machining performance and successfully ranked experimental alternatives based on their proximity to ideal performance. The optimal configuration (Experiment 21) accomplished a superior balance across all criteria, notably achieving a low surface roughness (Ra ≈ 0.42 µm) and TWR (~0.148 mm³/min) while maintaining a high MRR (~109.4 mm³/min). The proposed fuzzy-FUCOM-MARCOS method reveals high robustness, adaptability, and decision reliability, contributing a valuable strategy for precision machining of hard-to-cut steels. This work bridges experimental understandings with intelligent optimisation, fostering sustainable and high-performance manufacturing practices in the tooling industry.

High-entropy alloys (HEAs) based on CoCrFeMnNi continue to attract attention to their balanced mechanical properties and corrosion resistance; however, their tribological response remains strongly path-dependent. This review combines processing routes such as casting, powder metallurgy/spark plasma sintering (SPS), additive manufacturing (SLM/LPBF), and coating methods such as PVD/thermal spray with the resultant phase constitution (FCC, BCC, σ, and Laves) and defect structures to describe their trends in hardness, friction, and wear at room temperature up to approximately 800 o C. To balance different literature reports, we standardize the units of wear and cluster complete test data (counterface, load, kinematics, atmosphere, and temperature) so that quantitative comparison between studies is possible. Determining (i) systematic decreases when the FCC changes to BCC or intermetallic-reinforced state and (ii) a crossover in temperature at which many coating types can minimize wear at approximately 400 o C before increasing in temperature, a causal map was constructed linking route-controlled phase selection and secondary reinforcers (e.g., carbides/solid lubricants) to effect sizes in selective wear and friction. A route-selection guide, important information gaps in tribocorrosion (sliding-electrochemistry coupling), and template reporting tools that improve design and reproducibility are included at the conclusion of the review. All these factors lead to a consistent foundation on which to build the engineering of Cantor based HEAs and coating to meet desired wear windows in commercial environments.

Nanofluid-assisted machining is a promising sustainable alternative to the conventional cutting fluids; however, the correct prediction and optimization of the latter process is still difficult due to nonlinearities inherent to it and the lack of experimental data. Hence, the current study presents an innovative hybrid generative deep-learning model that synergistically integrates Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and a Generative Adversarial Network (GAN) to predict intelligently and optimize multi-fold in the nanofluid-assisted turning of EN31 steel. It has been experimentally shown that MWCNT based gravity fed nanofluid decreases the surface roughness by 12.83% and cutting force components by up to 23.4% when compared to traditional coolant. The RSM model has a moderate predictive power (R ² ≈ 0.82–0.88) and ANN model outperforms the RSM model (R ² ≈ 0.95–0.97). Under the limited data condition, the GAN-ANN hybrid has higher predictive robustness with R ² ≈ 0.97–0.99 and lowest mean squared error (1.49 for roughness and 7.89 for cutting forces) than any of the other models tested. Using a hybrid ANN-RSM strategy based on desirability, best machining parameters predict a cutting force of about 205 N, a surface roughness of 0.45 μm and a maximum rate of material removal of 16.6 m ³ /min, which are later confirmed in follow-up experiments with an error of less than 5%. Therefore, the suggested framework will provide a scalable, interpretable and environmentally friendly decision support system to intelligent machining applications.