Kayaroganam Palanikumar

Adaptive Neuro-Fuzzy Inference System (ANFIS)-based inverse kinematics (IK) is highly accurate for trained poses but often yields approximations for unseen inputs due to non-standardized training data. This research addresses these limitations through two novel contributions designed for any generic Degrees of Freedom (DoF) serial revolute robotic arm. First, A structured training methodology is introduced using workspace decomposition and cubic path planning. Instead of random sampling, the workspace is partitioned into cubic regions where 28 unique trajectories (12 edges, 12 face diagonals, four space diagonals) connect the eight vertices using cubic polynomial interpolation. This ensures physically consistent data mirroring real world point to point (PTP) movements. Even though validated on an ABB IRB-1200 robotic arm, this modular design is inherently scalable, allowing the local cubic expertise to be extended to cover the entire reachable workspace. Second, a two-stage hybrid IK framework is proposed, where an initial ANFIS approximation is refined via Jacobian-based iterative methods. Three Hybrid Frame works were evaluated, Framework-1 (ANFIS + Jacobian Gradient), Framework-2 (ANFIS + Jacobian Pseudoinverse/Newton–Raphson), and Framework-3 (ANFIS + Damped Least Squares). The results show that all three hybrid IK frameworks achieve reliable convergence, while the DLS-based hybrid provides the best trade-off between accuracy, convergence speed, and numerical stability. This generic, analytical free architecture provides a computationally efficient solution even in a hybrid scenario, bridging the gap between offline structured training and online, real-time refinement for digital twin synchronization and industrial automation.

The building industry has recently experienced a surge in the use of wood plastic composite (WPC) materials, which are lightweight, easy to work with, and visually appealing. This study aims to analyze and optimize drilling parameters to minimize delamination at the entrance and exit of the hole, leading to defect-free products. To analyze delamination, three input parameters for the drilling process are selected to measure the delamination factor (F _d ) at both the entrance and exit of the hole. The experimental data are validated using the developed regression and fuzzy models. Results show that feed rate (FR) and spindle speed (SS) are the primary factors in reducing delamination. Desirability-based optimization suggests an optimal SS of 3000 rpm, a FR of 75 mm/min, and a drill diameter of 6 mm, with output responses of F _d , 1.04454 (entry), and 1.42694 (exit). Additionally, metaheuristic algorithms such as particle swarm and hippopotamus optimization algorithms are employed to determine the optimal drilling conditions. This paper highlights the importance of optimizing drilling parameters when creating holes in WPC panels for various construction uses.

This study uses friction stir processing (FSP) to enhance the mechanical characteristics and corrosion resistance of AA2014‐T6, an area that has often been overlooked in earlier research. Taguchi‐Grey Relational Analysis and advanced multi‐objective optimization are used to determine the optimal balance under different process conditions. The research focuses on grain refinement and reduced grain boundary precipitates to enhance tensile strength, hardness, and elongation while improving corrosion resistance via fine grains and reduced secondary phase content. Machinability under drilling is also evaluated. Ideal FSP parameters (1000 rpm, 60 mm/min feed, 2° pivot) produced a GRG value of 0.876, confirming performance improvements.

Natural composites find application in various fields because of their low specific weight and low investment cost. But due to their inherent nature, natural composites have lower strength and tend to absorb moisture, which makes them weak. In this work, woven glass, mono-bi-filament polypropylene, and polyester fibers in an epoxy matrix were developed with four and five different stacking layers of texture utilizing the hand-layup procedure. However, understanding the directional dependence of material properties is necessary for the application of these new materials. Three distinctive plates were fabricated for the purpose of the investigation. The laminated plates were tested on a universal testing machine (UTM) and a flexible test setup to examine the mechanical properties of the polymer fiber. By adding short fibers such as polypropylene, polyester fibers in a random manner improved the mechanical strength of the polymer composite compared to the other fiber types such as woven glass fiber sheets and woven polypropylene sheets placed in the middle of the composite. This is because short polymer fibers bond well with epoxy resin and have very good bonding strength.

Friction stir processing (FSP) is used for changing the microstructure and material properties improvement. In modern years, special attention is exposed by governing the temperature level, and is applied by various coolants spraying on the top surface of the sample during FSP. This review paper explores the impact of several cooling mediums such as normal water, cold water, hot water, and cryogenic on the temperature distribution, grain refinement, mechanical characteristics, and superplastic deformation behavior of FSP using various grades of magnesium (Mg) alloys. It also includes the corrosion rate and wear behavior of FSP on Mg alloys. Through the systematic review, it has been observed that refined grains were formed and the generation of intermetallic compounds was suppressed after FSP in underwater and cryogenic conditions, leading to a notable increase in strength. The higher tensile strength and elongation are exhibited while using cryogenic compared to water medium. This is due to a rapid cooling process, where the high temperature was short for the newly-formed grains to fully develop. This review not only concludes the key findings of the preceding research but also suggests future recommendations.