V. Sampath

Bio: V. Sampath is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Shape-memory alloy & Microstructure. The author has an hindex of 13, co-authored 45 publications receiving 705 citations. Previous affiliations of V. Sampath include Ruhr University Bochum.

Effect of Thermal Processing on Microstructure and Shape-Memory Characteristics of a Copper–Zinc–Aluminum Shape-Memory Alloy

Abstract: Although Cu-based shape-memory alloys possess higher transformation temperatures compared with Ni–Ti shape-memory alloys and a higher magnitude of strain recovery, they are still not used extensive...

Modeling of Liquid Metal Infiltration of Porous Fiber Preform During Squeeze Casting

Abstract: The present work adopts a new approach to the analytical modeling of infiltration of porous fiber preforms by liquid metal in the squeeze casting of metal matrix composites, with the assumption that the process is adiabatic and that the flow is unidirectional. Fluid dynamics is described on the basis of Darcy's law, while separate equations are derived to explain the thermal behavior of the liquid metal and the fiber, assuming that the thermal interactions between the two are interfacial. Unlike earlier models, this approach does not consider the thermal behavior of a “composite,” but instead studies the behavior of the liquid metal and the fiber preform separately. In addition to the conventional application of heat balance techniques and development of partial differential equations involving temperatures, this work introduces supplementary conditions for temperature calculations, specifically at the entry and front points during infiltration. Differential equations are solved by a method of finite diff...

Prediction of high temperature deformation characteristics of an Fe-based shape memory alloy using constitutive and artificial neural network modelling

Abstract: The high temperature deformation characteristics of an Fe-28Ni-17Co-11.5Al-2.5Ta-0.05B (at.%) shape memory alloy (SMA) were studied by high temperature compression testing under large temperature (1323–1473 K) and strain rate (0.01–10 s−1) ranges. These were predicted by applying the Arrhenius-type and strain-compensated Arrhenius-type constitutive models, and the artificial neural network (ANN) model to the results obtained from the experiments. The capability of the models for prediction was assessed as a function of the correlation coefficient (R) and the relative percentage error. The results reveal that the true stress prediction by the strain-compensated Arrhenius-type constitutive model is more precise at a lower strain rate (0.01 s−1) than at a higher strain rate (10 s−1). Moreover, it yields better results in comparison with those obtained from Arrhenius-type model. They further reveal ANN model shows higher efficiency and preciseness in forecasting the high temperature flow characteristics of the SMA as compared to the strain-compensated Arrhenius-type and Arrhenius-type models.

Nano-Manipulation, Nano-Manufacturing, Nano-Measurements by New Smart Material-Based Mechanical Nanotools

Abstract: Recent progress in the study of new functional materials, such as Ti(NiCu) intermetallic with shape memory effect (SME), opens up exciting possibilities for the design reconfigurable micro- and nano-structures and for operating mechanical nanotools controlled by external fields or heat. This report gives an overview of physical effects, in particular, solid state phase transitions and accompanying phenomena in alloys and composites exhibiting SME. The limitations pertaining to the minimum size of the nanomechanical devices exhibiting shape memory effect that arise due to the solid state phase transitions are now under discussion and have not been completely understood yet. The modern nanotechnologies allow designing of the mechanical micro- and nanotools, such as nanotweezers, nanopinchers etc., with an active layer thickness of about several tenths of nm, and whose overall size is below 1 μm. The nanotools with SME can be controlled by heating as well as by magnetic field activation in ferromagnetic alloys exhibiting SME, such as Ni 2 MnGa. 3D nanomanipulation is demonstrated by composite nanotweezers with SME in different nanoobjects, such as CNTs, nanowires, nanowhiskers, bionanoobjects, DNA, etc. In these devices, the surface interactions and Casimir and van der Waals forces affect the process of nanomanipulation. The prospects of nanorobotics and manufacturing on nanoscale adapting the principle of mechanical bottom-up nanoassembly are discussed. In addition, nanoscale measurements can take advantage of 3D mechanical nanomanipulation, including transportation of analytes to nanosensors, elasticity measurements by nanotools with calibrated force, etc.

Identification of Quaternary Shape Memory Alloys with Near‐Zero Thermal Hysteresis and Unprecedented Functional Stability

Abstract: Improving the functional stability of shape memory alloys (SMAs), which undergo a reversible martensitic transformation, is critical for their applications and remains a central research theme driving advances in shape memory technology. By using a thin-film composition-spread technique and high-throughput characterization methods, the lattice parameters of quaternary Ti-Ni-Cu-Pd SMAs and the thermal hysteresis are tailored. Novel alloys with near-zero thermal hysteresis, as predicted by the geometric nonlinear theory of martensite, are identified. The thin-film results are successfully transferred to bulk materials and near-zero thermal hysteresis is observed for the phase transformation in bulk alloys using the temperature-dependent alternating current potential drop method. A universal behavior of hysteresis versus the middle eigenvalue of the transformation stretch matrix is observed for different alloy systems. Furthermore, significantly improved functional stability, investigated by thermal cycling using differential scanning calorimetry, is found for the quaternary bulk alloy Ti 50.2 Ni 34.4 Cu 12.3 Pd 3.1 .