Adina Srinivasa Vara Prasad

The recent rapid miniaturization of modern electronic products, along with their increasing power density, has heightened the need for an efficient thermal management solution. Microchannel heat sinks have emerged as a promising technology due to their high heat removal capabilities and compact design, making them suitable for cooling next-generation electronics. This review presents the evolution from conventional air and liquid cooling methods to microchannel-based systems and discusses the governing flow and heat transfer mechanisms that influence their performance. A comparative assessment of single-phase and two-phase cooling highlights essential trade-offs involving heat transfer efficiency, operational stability, and system complexity. Recent advancements in micromachining and additive manufacturing have enabled the fabrication of complex 3D architectures for improved system integration. Although enhancements such as geometric optimization, nanofluids, and two-phase cooling demonstrate significant potential, challenges persist in flow instability, manufacturability, and long-term durability. Future research directions emphasize AI-driven design frameworks, sustainable working fluids, hybrid cooling approaches, and holistic performance–cost–environment optimization.

In this study, cast Al–Mg–Zn and aging Al–Mg–Zn alloys were joined using the friction stir welding technique, with the welding parameters being the rotational speed of the tool, welding speed, tilt angle, and D/d ratio. The effects of these input parameters on output responses such as ultimate tensile strength, elongation percentage, hardness, and wear rate were investigated using Box–Behnken experimental design. Multi-response optimization was performed using the Box–Behnken Design (BBD) combined with Gray Relational Analysis (GRA). A rotational speed of 1200 rpm, welding speed of 100 mm/min, tilt angle of 1.5°, and D/d ratio of 2.25 were found to significantly enhance the mechanical properties and wear resistance. Furthermore, the Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed to predict outcomes based on the gray relational coefficient grades, leveraging its architecture to improve prediction accuracy. Finally, a comparative analysis was conducted between the BBD-GRA methods and the ANFIS technique. Results indicated that the ANFIS model was highly effective for predicting output parameters, achieving superior precision and accuracy with fewer iterative calculations. By selecting an appropriate ANFIS structure, the model’s performance is better than that of the BBD-GRA method.

This study focused on the electrochemical machining (ECM) of Al‐7Si‐0.4Mg alloy using a mixture electrolyte of NaNO ₃ and NaOH. Experiments were designed based on the Taguchi method, utilizing an L16 orthogonal array, and input parameters such as voltage (V), electrolyte concentration (E), feed rate (F) and duty cycle (D) were varied to evaluate the effects on material removal rate (MRR) and surface roughness (SR). Residual analysis, ANOVA and signal‐to‐noise (SN) ratio analysis were conducted to determine the optimal settings for single‐response optimization. For multi‐response optimization, a hybrid statistical approach combining Taguchi and TOPSIS was employed. The Taguchi–TOPSIS combination analysis identified the optimal parameter combination as V4–E3–F2–D3, corresponding to a voltage of 14 V, an electrolyte concentration of 80 g/L, a feed rate of 0.5 mm/min and a duty cycle of 75%, which achieved the highest MRR and lowest SR. ANOVA results revealed that voltage is the most influential factor, contributing 91.3% to MRR and 60.6% for SR. Validation tests under these optimal conditions (V4–E3–F2–D3) yielded an MRR of 33.25 mm ³ /min and a SR of 0.164 μm. Compared to prior experimental results, the optimized parameters achieved through the Taguchi–TOPSIS approach led to a 7.32% increase in MRR and an 11.35% reduction in SR, confirming the reliability and effectiveness of this hybrid method in significantly improving machining performance. Finally, the integration of the hybrid statistical optimization strategy with the dual‐electrolyte formulation made a significant advancement in the ECM of Al alloys, providing a highly efficient and minimal surface damage.