arXiv subjects

Yogesh C. Chandrashekar

Publications and source records attributed to Yogesh C. Chandrashekar.

2 recordsLinked to original sources

arXiv2026

Process-fracture mapping of a DLP-printed photopolymer using Bayesian active learning and surrogate-based sensitivity analysis

Digital light processing (DLP) enables rapid fabrication of polymer structures, but fracture performance depends on multiple interacting processing variables, making exhaustive experimental characterization impractical. This work presents a data-efficient framework for process-fracture mapping of a DLP-printed photopolymer using Bayesian active learning and digital image correlation (DIC)-assisted Mode I fracture experiments. Four processing parameters were considered: layer angle, UV exposure time, layer height, and print temperature. Fracture resistance was quantified by the critical J-integral, $J_c$, obtained from three-point-bending tests with DIC-based evaluation of crack-mouth opening displacement and hinge-point kinematics. Beginning with two randomly selected conditions, Gaussian process regression (GPR) and a modified upper confidence bound (UCB)-style acquisition function selected 26 additional experiments, yielding 28 processing conditions with three replicates each. The final GPR surrogate reproduced the training data with $R^2=0.99$ and achieved leave-one-out cross-validation performance of $R^2=0.63$ and Pearson $r=0.81$. Surrogate-based sensitivity analysis quantified parameter effects and global contributions. One-at-a-time response curves revealed nonlinear conditional trends, while global Sobol analysis identified UV exposure time as the dominant processing variable, with first-order and total-order indices of 0.6780 and 0.7581, respectively. Based on total-order influence, the parameters ranked as UV exposure time, layer angle, print temperature, and layer height. The first-order Sobol indices summed to 0.8058, indicating non-negligible interaction and higher-order effects. These results demonstrate that Bayesian-active-learning-guided experimentation can efficiently recover process-fracture relationships and parameter interactions from a sparse experimental campaign.

stat.AP↗

arXiv2026

A constitutive framework for distortional-mode-dependent failure in soft materials: Tension-compression asymmetry and beyond

Soft materials exhibit pronounced tension-compression asymmetry (TCA) in their softening and failure, a feature that conventional hyperelastic and continuum-damage formulations fail to capture in a unified framework. We present a Lode-invariant-based hyperelastic softening model for distortional-mode-dependent failure in soft materials, where mode dependence is introduced through a bi-failure construction with distinct tensile and compressive energy limiters. The proposed model extends Volokh's classical energy-limiting approach by embedding a Lode-angle-dependent weighting function, ensuring a smooth and physically consistent transition in failure across distortion modes within the constitutive description of the bulk response, without introducing internal damage variables. Agarose hydrogels (1, 2, and 3 % w/v) serve as the validation system. The framework reproduces experimental stress-stretch responses in uniaxial tension and compression, capturing concentration-dependent stiffness and failure energetics. Using parameters calibrated solely from combined uniaxial data, the model predicts pure shear behavior, including softening and failure, thereby demonstrating strong cross-mode predictive capability. To further assess thermodynamic consistency and distortion-mode sensitivity, the model's free-energy landscape is analyzed across the full Lode-invariant space, confirming a smooth and physically consistent response under diverse loading conditions. Parameter evolution with concentration follows power-law scaling, enabling interpolation and predictive validation at intermediate concentrations (evaluated at 2.5 % w/v). Overall, the proposed formulation provides a physically interpretable constitutive framework for tension-compression-asymmetric softening and distortional-mode-dependent failure, and establishes a foundation for three-dimensional failure mapping in soft materials.

cond-mat.soft↗