arXiv subjects

Prashil S. Joshi

Publications and source records attributed to Prashil S. Joshi.

2 recordsLinked to original sources

arXiv2026

Decoupling Strain-Rate Sensitivity and Deformation Length Scale Effects in Neutron-Irradiated Tungsten: A Coupled Nano-Indentation, HR-EBSD and Crystal Plasticity Study

Plastic deformation during strain-rate-controlled spherical nanoindentation is governed by the coupled evolution of constitutive strain-rate sensitivity and deformation length scale, making the intrinsic influence of strain rate difficult to isolate experimentally. This coupling is investigated in unirradiated and neutron-irradiated single-crystal tungsten using spherical nanoindentation, atomic force microscopy, high-resolution electron backscatter diffraction (HR-EBSD), and crystal plasticity finite element (CPFE) modeling. Nanoindentation experiments were performed at strain rates from 3.2e-5 to 3.2e-3 per second. AFM and HR-EBSD quantified surface pile-up, residual lattice strain, and geometrically necessary dislocation (GND) distributions. A strain-gradient CPFE framework incorporating thermally activated slip, GND hardening, irradiation-induced obstacle hardening, and strain-dependent softening was calibrated using a single experimental condition and validated across all remaining strain rates without further parameter adjustment. The validated model was then used to independently vary strain rate and indentation depth. Simulations show that strain rate primarily controls the stress required for thermally activated plastic flow, whereas indentation depth governs plastic-zone evolution, pile-up, and GND accumulation. Irradiation increases obstacle strength and promotes deformation localization while remaining consistent with a common thermally activated mechanism. The framework also predicts the compression response of a polycrystalline cube, demonstrating transferability across loading conditions and length scales, providing a robust basis for constitutive modeling of irradiation-hardened materials under transient loading.

cond-mat.mtrl-sci↗

arXiv2026

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $α+β$ Titanium Alloy

Accurate constitutive modeling of hot deformation behavior is essential for designing thermomechanical processes in advanced structural alloys. Conventional Arrhenius-type and empirical models do not adequately capture the combined effects of strain hardening, dynamic recovery (DRV), and dynamic recrystallization (DRX) across broad processing conditions. In this study, two Stacked Residual Physics-Informed Neural Networks (STAR-PINNs) were developed to simulate the hot deformation response of a Mo-rich $α+β$ titanium alloy (Ti-6Al-4Mo-1V-0.1Si). The Enhanced STAR-PINN incorporated thermomechanical constitutive constraints, while the DRX-Aware STAR-PINN employed a dual-output architecture to account for recrystallization kinetics. Both models used a shared residual encoder trained on experimental flow stress data collected at temperatures from 800 to 1050 degrees C and strain rates between 0.01 and 10 per second. Physics-informed constraints, including thermal softening, strain-rate sensitivity, strain hardening, and post-peak softening, were enforced through automatic differentiation. The DRX-Aware model further integrated JMAK-Avrami regularization, DRX saturation constraints, and Arrhenius-based consistency with tunable parameters, directly linking the predicted DRX fraction to stress output via latent-feature fusion. The DRX-Aware STAR-PINN achieved RMSE = 11.69 MPa, MAE = 4.83 MPa, R^2 = 0.9850, and a cross-validated RMSE of 12.47 +/- 0.26 MPa. This model accurately reproduced temperature-dependent flow curves, DRX kinetics, and Zener-Hollomon relationships, while maintaining physically consistent constitutive behavior. These results demonstrate that physics-informed deep learning provides a robust and interpretable framework for constitutive modeling, offering a practical approach for advanced process modeling of titanium alloys.

cond-mat.mtrl-sci↗