arXiv subjects

Bhanugoban Maheswaran

Publications and source records attributed to Bhanugoban Maheswaran.

3 recordsLinked to original sources

arXiv2026

Decoupling dislocation multiplication and velocity effects in metals at extreme strain rates

The dynamic behavior of metals is governed by collective dislocation motion and interactions that strongly depend on the applied strain rate. Metals exhibit weak strain rate sensitivity (SRS) below a certain threshold, followed by a distinct SRS upturn at higher loading rates. While this upturn is typically attributed to increased glide resistance at high dislocation velocity due to mechanisms such as phonon drag, the role of strain-rate-dependent dislocation multiplication and microstructural evolution under these extreme conditions remains elusive. Here, we decouple these two strengthening effects and show that, while dislocation velocity primarily governs the SRS upturn, the hardening due to microstructure evolution depends strongly on the initial dislocation density. Our investigation of hardness evolution across ten decades of strain rates in a quenched and tempered martensitic low-carbon steel (LCS) using laser-induced projectile impact tests (LIPIT) and nanoindentation reveals SRS upturn around 10^7 1/s. By performing in situ re-indentation of the formed craters, we probe the contribution of dislocations generated during initial deformation at different strain rates. We show that while dislocation multiplication plays a negligible role in fine-grained LCS with high dislocation density, a pronounced dislocation multiplication contributes to the hardness increase in pure iron with lower initial dislocation density. Our results show that, depending on the initial microstructure of metals, dislocation multiplication significantly governs high-strain-rate plasticity, in addition to dislocation velocity effects.

cond-mat.mtrl-sci↗

arXiv2025

Enduring mechanical memory from the constitutive response of elastically recoverable nanostructured materials

Mechanical memory and computing are gaining significant traction as means to augment traditional electronics for robust and energy efficient performance in extreme environments. However, progress has largely focused on bistable metamaterials, while traditional constitutive memory effects have been largely overlooked, primarily due to the absence of compelling experimental demonstrations in elastically recoverable materials. Here, we report constitutive return point memory (RPM) in elastically recoverable, vertically aligned carbon nanotube (VACNT) foams, analogous to magnetic hysteresis-based RPM utilized in hard drives. Unlike viscoelastic fading memory, VACNTs exhibit non-volatile memory arising from rate-independent nanoscale friction. We find that the interplay between RPM and frictional dissipation enables independent tunability of the VACNT dynamic modulus, allowing for both on-demand softening and stiffening. We leverage this property to experimentally demonstrate tunable wave speed in a VACNT array with rigid interlayers, paving the way for novel shock limiters, elastodynamic lensing, and wave-based analog mechanical computing.

cond-mat.mtrl-sci↗

arXiv2025

Implicit Geometric Descriptor-Enabled ANN Framework for a Unified Structure-Property Relationship in Architected Nanofibrous Materials

Hierarchically architected nanofibrous materials, such as the vertically aligned carbon nanotube (VACNT) foams, draw their exceptional mechanical properties from the interplay of nanoscale size effects and inter-nanotube interactions within and across architectures. However, the distinct effects of these mechanisms, amplified by the architecture, on different mechanical properties remain elusive, limiting their independent tunability for targeted property combinations. Reliance on architecture-specific explicit design parameters further inhibits the development of a unified structure-property relationship rooted in those nanoscale mechanisms. Here, we introduce two implicit geometric descriptors -- multi-component shape invariants (MCSI) -- in an artificial neural network (ANN) framework to establish a unified structure-property relationship that governs diverse architectures. The MCSIs effectively capture the key nanoscale mechanisms that give rise to the bulk mechanical properties such as specific-energy absorption, peak stress, and average modulus. Exploiting their ability to predict mechanical properties for designs that are even outside of the training data, we propose generalized design strategies to achieve desired mechanical property combinations in architected VACNT foams. Such implicit descriptor-enabled ANN frameworks can guide the accelerated and tractable design of complex hierarchical materials for applications ranging from shock-absorbing layers in extreme environments to functional components in soft robotics.

cond-mat.mtrl-sci↗