arXiv subjects

Jinling Liu

Publications and source records attributed to Jinling Liu.

3 recordsLinked to original sources

arXiv2025

Strengthening and toughening mechanisms in heterostructured laminates revealed by a phase field-enhanced crystal plasticity simulation

Heterostructured (HS) materials exhibit excellent mechanical properties, combining high strength and significant ductility. Hetero-deformation-induced (HDI) hardening and strain de-localization are key to their strength-ductility synergy. However, existing models often fall short in addressing these aspects. In this work, a coupled framework integrating strain gradient crystal plasticity and phase field damage models is developed. The interface dominated HDI hardening in HS laminates is handled by introducing a heterogeneity coefficient into the back stress. The phase field model accounts for defect energy-driven damage and accurately represents the materials ductile damage behavior by accounting for effects of microstructure on crack initiation and propagation. Simulation results on HS laminates align well with experimental results and reflect the distribution of geometrically necessary dislocations and back stresses at interfaces between regions with dissimilar microstructure. Crack initiation and propagation are accurately described, providing valuable insights into fracture behavior. The model can predict how strength and ductility change upon variations of the HS laminate microstructure, thus providing an essential tool for microstructure optimization. This work enhances the understanding of deformation mechanisms in HS laminates and provides valuable insights for design and optimization of this class of materials.

cond-mat.mtrl-sci↗

arXiv2020

Constitutive modeling of the tension-compression behavior of gradient structured materials

Gradient structured (GS) metals processed by severe plastic deformation techniques can be designed to achieve simultaneously high strength and high ductility. Significant kinematic hardening is key to their excellent strain hardening capacity which results in a favorable strength-ductility combination. Unfortunately, no constitutive model has been established to simulate and analyze the characteristic kinematic hardening behavior of GS metal to understand the relationship between their microstructure and macroscopic response. In this work, we developed a deformation-mechanismbased strain gradient plasticity model considering the plasticity heterogeneities from the grain to the sample scale. A back stress model, which accounts for the dependency of dislocation pile-ups on grain size, is established to describe the cyclic deformation properties of GS materials. The established model unified the geometrically necessary dislocations accommodating internal plasticity heterogeneities, the resulting back stress and reversible dislocations during reverse loading into a strain gradient plasticity framework, without introducing excessive numbers of independent material parameters. A finite element implementation of the model quantitatively predicts the uniaxial tensile and tensile-compressive responses of a GS copper bar as well as of a reference sample with homogeneous grain size. It is found that GS copper exhibits enhanced kinematic hardening which results mainly from fine grains in the GS layer and contributes to the considerable ductility of the GS material. The model allows to investigate the mechanical response and optimize the properties of materials with various types of spatially heterogeneous grain microstructures.

cond-mat.mtrl-sci↗

arXiv2019

Deep Contextualized Biomedical Abbreviation Expansion

Automatic identification and expansion of ambiguous abbreviations are essential for biomedical natural language processing applications, such as information retrieval and question answering systems. In this paper, we present DEep Contextualized Biomedical. Abbreviation Expansion (DECBAE) model. DECBAE automatically collects substantial and relatively clean annotated contexts for 950 ambiguous abbreviations from PubMed abstracts using a simple heuristic. Then it utilizes BioELMo to extract the contextualized features of words, and feed those features to abbreviation-specific bidirectional LSTMs, where the hidden states of the ambiguous abbreviations are used to assign the exact definitions. Our DECBAE model outperforms other baselines by large margins, achieving average accuracy of 0.961 and macro-F1 of 0.917 on the dataset. It also surpasses human performance for expanding a sample abbreviation, and remains robust in imbalanced, low-resources and clinical settings.

cs.CL↗