Balaji Vasudevan

The transient thermal behavior of natural fiber hybrid sandwich composites governs their suitability for thermally loaded structural applications, yet spatially resolved transient response across a single panel is rarely documented. In this study, infrared thermography was used to characterize the location-dependent transient surface-temperature response of an areca–glass fiber hybrid epoxy sandwich composite under localized hot-air heating. A controlled protocol (60 s heating, 180 s cooling) was applied at central, corner, and edge locations of a 30 cm × 30 cm panel, and the surface temperature was monitored at 16 grid points using a FLIR One Pro LT camera. Exponential heating and Newtonian cooling expressions were fitted to extract effective time constants at each location. The corner location reached the highest peak surface temperature (42.0 °C) and the slowest response (heating time constant τh = 24.4 s; cooling τc = 45.1 s), whereas the edge location responded fastest (τh = 9.8 s, τc = 19.6 s), an approximately 2.5-fold variation attributable to the differing lateral heat-dissipation pathways available at each location. Heating proceeded 2.7–3.1 times faster than cooling at all locations. The fitted expressions described the measured curves with R² > 0.95 for heating and R² > 0.80 for cooling (RMSE < 0.6 °C). The extracted time constants are configuration- and geometry-dependent descriptors of the surface response under the imposed boundary condition rather than intrinsic material properties, and independent measurement of thermophysical properties would be required for material-level characterization. This study indicated that the proposed infrared thermography procedure provided a valuable reference for the reproducible, spatially resolved comparison of transient thermal response in natural fiber hybrid sandwich composites.

The growing need to machine challenging materials, especially nickel-based superalloys found in vital aerospace and automotive parts, is evident. This study examines the machining of 15.5 mm Nimonic-263 superalloy using Abrasive Waterjet Machining (AWJM). Nine trial sets, repeated thrice, are comprehensively evaluated using an L9 orthogonal array design to analyze crucial machining variables: the impact of Stand-off Distance, Waterjet Pressure, and Traverse Speed on Kerf Angle, Machining Time, and Material Removal Rate. Statistical significance is evaluated through multi-parametric analysis of variance, and quadratic multiple linear regression models are formulated to correlate output responses with machining variables. The JAYA optimization algorithm is introduced to optimize the machining process, aiming to minimize Kerf Angle and Machining Time while maximizing Material Removal Rate. A comparison with Ant-Lion, Genetic, and Multi-Verse Optimization algorithms highlights JAYA’s effectiveness. A confirmation test validates the JAYA algorithm’s output, confirming its superior performance. This research aids in optimizing machining parameters for challenging materials, benefiting critical components in various industries, especially aerospace.

The purpose of this research is to ascertain the optimal abrasive waterjet drilling parameters for making holes on Inconel 718 superalloy coated with yttrium-stabilized zirconia, namely waterjet pressure (J_P), standoff distance (S_D), abrasive flow rate (F_R), and angle of impact (A_I). The study explores the predictive modeling of the entry diameter (D_N) and exit diameter (D_X) of the drilled holes using an amalgamation of experimental analysis, response optimization and machine learning approaches. Eight different ML techniques are used to predict D_N and D_X. Better values of D_N = 1.31352 mm and D_X = 1.00515 mm are obtained through Random Forest for the setting of J_P = 175 MPa, F_R = 250 g min⁻¹, S_D = 1.45455 mm, and A_I = 0.909091 deg. Further, the tuning of hyperparameters of Random Forest algorithm is performed to study the improvement in measuring D_N and D_X. The least value of total absolute error = 0.468 is observed while finding the D_N and D_X using Random Forest algorithm than the traditional response optimization method with reference to the confirmation test results. The work provides important insights for enhancing the machinability of YSZ-coated Inconel 718 superalloy utilizing the AWJ drilling process by bridging the gap between manufacturing research, machine learning, and real-world applications.

The demand for high-performance materials in aerospace, automotive, and power generation industries has led to the development and utilization of advanced superalloys. Nimonic C-263/263, a nickel-based superalloy, is widely employed in critical components of jet engines, such as combustion chambers, heat exchangers, reformer tubes, gas turbine power generation components like turbine discs, shafts, and blades, due to its exceptional high-temperature strength, corrosion resistance, and creep properties. However, the inherent hardness and toughness of this alloy pose significant challenges for conventional machining processes. Abrasive waterjet cutting (AWJC) emerges as a promising alternative for the efficient machining of such difficult-to-cut superalloy materials. This paper presents an in-depth investigation on the machinability of thick Nimonic 263 superalloy plate material using AWJC, aiming to provide insights into the effects of process parameters, cutting performances, and resulting surface integrity due to change in declination angle measurement. An examination was also conducted on the pattern of striation formation under varying process parameters, utilizing digital microscopic images. The goal of the study is to establish a link between the parameters of striation production and the angle of jet impingement. The results indicate that a decrease in striation during the water jet processing of Nimonic 263 superalloy is caused by an increase in the declination angle.