Karthik Venkitaraman Shankar

This work presents the design, modeling, and experimental evaluation of a biomimetic robotic fish actuated by shape memory alloy (SMA) wires integrated with a bistable polyethylene terephthalate glycol (PETG) caudal mechanism. Inspired by the general morphology and sub-carangiform-type swimming motion of Atlantic salmon, the robot employs a bistable elastic structure and a flexible caudal fin to convert SMA contraction into bidirectional tail motion through snap-through deformation. The kinematics, dynamics, and hydrodynamic characteristics of the robotic fish were investigated to evaluate the feasibility of mechanically amplifying the relatively slow SMA actuation into larger-amplitude tail motion. Water-tank experiments were conducted using a tethered prototype with a body length of 280 mm. Over 20 trials, the robot traveled 0.42 m with an average travel time of 39 s, corresponding to an average forward speed of 0.0108 m/s (0.038 BL/s), while the best observed trial required 31 s, corresponding to 0.0135 m/s (0.048 BL/s). The bistable mechanism produced approximately ±30° tail deflection, with a peak-to-peak oscillation frequency of approximately 1 Hz under the selected actuation condition. The measured acoustic output was approximately 45 dBA for the SMA-based system compared with 68 dBA for the motor-driven system, indicating a substantially lower measured acoustic output under the present experimental configuration. The swimming experiments also resulted in a high Strouhal number, primarily associated with the low forward speed and tether-induced drag. Overall, the results demonstrate the functional operation of bistable SMA-based actuation for compact robotic-fish propulsion and highlight its potential for reduced actuator-associated acoustic output. Further optimization of the SMA thermal response, bistable mechanism, tail kinematics, and untethered configuration is required to improve swimming performance and enable more complete evaluation of the propulsion system.

Additive manufacturing (AM) has emerged as a transformative manufacturing technology for producing complex components with unprecedented design flexibility. However, the widespread application of AM parts in tribological environments is often limited by inherent defects such as high surface roughness, porosity, residual stresses, anisotropy, and weak interlayer bonding, which adversely affect friction, wear resistance, and tribocorrosion performance. This review critically examines the tribological behavior of AM materials and components, emphasizing the influence of processing routes, material selection, secondary reinforcing phases, and microstructural evolution on tribological performance. Particular attention is given to surface engineering strategies, including thermal spray coatings, laser surface treatments, plasma electrolytic oxidation, vapor deposition technologies, and mechanical surface modification techniques for mitigating AM-induced defects and improving surface durability. Recent advances in machine learning (ML) and artificial intelligence (AI) for wear prediction, process optimization, and intelligent tribological monitoring are also discussed. The review highlights the relationships among manufacturing parameters, surface integrity, and wear mechanisms, while identifying key challenges associated with process variability, long-term reliability, and industrial implementation. Future research should focus on multifunctional surface systems, smart coatings, real-time condition monitoring, and data-driven design approaches to accelerate the deployment of tribologically optimized AM components in aerospace, biomedical, automotive, and energy applications.

Aluminium 6061 alloy is widely used in aerospace and automotive applications owing to its light weight, corrosion resistance, and machinability. Nevertheless, the poor hardness and limited surface integrity of Al 6061 alloys restrict its prolonged use under extreme conditions. In this work, the properties of Al 6061 alloy has been augmented by developing hard Titanium Carbide (TiC) coatings on its surface by tungsten inert gas (TIG) cladding technique. The developed TiC coatings enhanced the hardness of Al 6061 alloy up to 3 times, with average micro-hardness in the range of 431- 496 HV _0.05 . From this motivation, on the later stage of the work, titanium carbide particles are reinforced in to the weld joints of Al 6061 alloys by TIG arc heat under variable TIG current conditions. The integration of TiC reinforcement into the weld joints enhanced the ultimate tensile strength from 20–29 MPa to 59–83 MPa. Additionally, the maximum hardness attained as 173.72 HV _0.05 , depicting an improvement upto 1.5 times. Further, an Artificial Neural Network (ANN) model using backpropagation accurately predicted mechanical properties, showing strong agreement with experimental data and validating its forecasting capability. It achieved a high co-efficient of determination (R ² ≈ 0.99) and depicted minimal prediction error, signifying a strong correlation between the experimental data and the predicted outcomes.