arXiv subjects

Zhenwei Ma

Publications and source records attributed to Zhenwei Ma.

3 recordsLinked to original sources

arXiv2026

Deep Indentation of Hyperelastic Materials Reveals Tip Independent Parabolic Force Depth Response via Strain Energy Delocalization

Indentation is a practical route for probing soft materials when standard tests are difficult, destructive, or cannot be performed in situ. Conventional indentation is usually interpreted in the shallow-depth regime, where the indentation depth D is small compared with the indenter radius R. In this limit, the response is controlled by local contact geometry and primarily identifies the small-strain Young's modulus E. Here, we show that at deep indentation, D >> R, flat and spherical indenters converge to the same parabolic force-depth law, F = beta E D^2. The coefficient beta is independent of indenter radius and tip shape, only mildly affected by interfacial friction, and controlled by the hyperelastic strain-stiffening response. Finite-element simulations show that this scaling arises from strain-energy delocalization: the region where SED/mu > 0.01 expands into a spheroidal domain whose size scales with D. The activated volume therefore scales as D^3, giving stored elastic energy U ~ E D^3 and force F = dU/dD ~ E D^2. Far from contact, the strain-energy-density fields collapse toward the Boussinesq far-field solution when distances are normalized by a = sqrt(F/E), which scales as D in the deep-indentation regime. These results provide a mechanistic basis for tip-shape independence and link beta to the Ogden strain-stiffening parameter alpha, enabling hyperelastic parameter extraction from deep-indentation data.

cond-mat.soft↗

arXiv2024

Cutting Soft Matter: Scaling relations controlled by toughness, friction, and wear

Cutting mechanics of soft solids is gaining rapid attention thanks to its promising benefits in material characterization and other applications. However, a full understanding of the physical phenomena is still missing, and several questions remain outstanding. E.g.: How can we directly and reliably measure toughness from cutting experiments? What is the role of blade sharpness? In this paper, we explore the simple problem of wire cutting, where blade sharpness is only defined by the wire radius. Through finite element analysis, we obtain a simple scaling relation between the wire radius and the steady-state cutting force per unit sample thickness. The cutting force is independent of the wire radius if the latter is below a transition length, while larger radii produce a linear force-radius correlation. The minimum cutting force, for small radii, is given by cleavage toughness, i.e., the surface energy required to break covalent bonds in the crack plane. The force-radius slope is instead given by the wear shear strength in the material. Via cutting experiments on polyacrylamide gels, we find that the magnitude of shear strength is close to the work of fracture of the material, i.e., the critical strain energy density required to break a pristine sample in uniaxial tension. The work of fracture characterizes the toughening contribution from the fracture process zone (FPZ), which adds to cleavage toughness. Our study provides two important messages, that answer the above questions: Toughness can be estimated from wire-cutting experiments from the intercept of the force-radius linear correlation, as previously explored. However, as we discovered, this only estimates cleavage toughness. Additionally, the force-radius slope is correlated with the work of fracture, giving an estimation of the dissipative contributions from the FPZ.

cond-mat.soft↗

arXiv2019

Dynamic Region Division for Adaptive Learning Pedestrian Counting

Accurate pedestrian counting algorithm is critical to eliminate insecurity in the congested public scenes. However, counting pedestrians in crowded scenes often suffer from severe perspective distortion. In this paper, basing on the straight-line double region pedestrian counting method, we propose a dynamic region division algorithm to keep the completeness of counting objects. Utilizing the object bounding boxes obtained by YoloV3 and expectation division line of the scene, the boundary for nearby region and distant one is generated under the premise of retaining whole head. Ulteriorly, appropriate learning models are applied to count pedestrians in each obtained region. In the distant region, a novel inception dilated convolutional neural network is proposed to solve the problem of choosing dilation rate. In the nearby region, YoloV3 is used for detecting the pedestrian in multi-scale. Accordingly, the total number of pedestrians in each frame is obtained by fusing the result in nearby and distant regions. A typical subway pedestrian video dataset is chosen to conduct experiment in this paper. The result demonstrate that proposed algorithm is superior to existing machine learning based methods in general performance.

cs.CV↗