Shape memory polymers (SMPs) have aroused much attention owing to their large deformation and programmability features. Nevertheless, the unsatisfactory toughness and brittleness of SMPs still restrict their practical intelligent applications, e.g., textiles, flexible electronics, and metamaterials. This study employed nature-derived nanocelluloses (NCs) as the reinforcement to fabricate shape memory epoxy-based nanocomposites (SMEPNs). An acetylation modification approach was further proposed to ameliorate the intrinsic incompatibility between NCs and epoxy matrix. The storage modulus increases, and the shape memory effect (SME) sustains after acetylated nanocelluloses (ANCs) incorporation. The SMEPNs with 0.06 wt.% ANCs loading perform the most exceptional toughness improvement over 42%, along with the enhanced fracture strain, elastic modulus, and ultimate strength. The incorporated nanoscale ANCs effectively impede crack propagation without deterioration of the macromolecular movability, resulting in excellent mechanical properties and SME.
The wear rate of a grinding wheel directly affects the workpiece surface integrity and tolerances. This paper summarizes a combined experimental-modeling framework for life expectancy of an electroplated Cubic Boron Nitride (CBN) grinding wheel, typically utilized in nickel-based superalloy grinding. The article presents an experimental framework to facilitate the formulation of a micromechanics based modeling framework. The presented study investigates the topological evolution of the grinding wheel surface and mechanisms of grit failure via depth profiling, digital microscopy, and scanning electron microscopy. The results are used to elucidate the statistical evolution of the grinding wheel surface. Different modes of grit failure, including grit attritious wear, fracture, and pull out haven been identified. The analysis of the surface topological features indicates a unique grit activation process, leading to a nonuniform spatial distribution of the grit wear. In addition, single grit pull out experiment has been conducted to assess the residual strength of the grit–wheel interface and the associated state of damage percolation. The experimental results are utilized in developing a life expectancy model for the CBN grinding wheel to assess the grit mean time to failure as well as grit surface topological evolution as a function of the process parameters.
Triply periodic minimal surface (TPMS) based lattices have drawn great attention in recent years due to their lightweight, design flexibility and compatibility with 3D printing. However, more effort should be made in the functionality enhancement of engineered sensors, especially with field-assisted 3D printing methods. In this study, magnetic field-assisted photopolymerisation (MF-VP) 3D printing technique was employed to fabricate magnetic Schwarz primitive TPMS (P-TPMS) lattice. Based on the fabricated magnetic P-TPMS lattices, a self-powered sensor was designed to achieve the velocity-to-electrical signal conversion process without the need for external power supply. MF-VP 3D printing technique significantly improved the mechanical compressive performance of the magnetic P-TPMS lattice in the carbonyl iron powder alignment direction, magnetic properties and sensing capabilities. The magnetic P-TPMS lattice self-powered sensor exhibited outstanding stability and durability, maintaining highly stable relative output voltage signals after 10,000 compression cycles. The developed sensor successfully detected toy car impact processes and foot movement states (stop/slow/fast), highlighting the practical application potential of the self-powered sensor.
Porous Cu-Sn-Ti alumina composites were fabricated by sintering Cu-Sn-Ti alloy powders, graphite particles, and alumina hollow particles agent. The effects of the pore structure and distribution on the composites strength were evaluated. Different pore distributions were modeled by using finite element analysis to investigate the tensile strength of the composites. Furthermore, a fractal analysis-based box-covering algorithm was used on the Cu-Sn-Ti alumina composites topology graphs to better investigate the pore structure and distribution. Results obtained show that different sizes and concentrations of alumina hollow particles could result in different porosities from 20% to 50%. A larger pore size and a higher pore concentration reduce the strength, but provide more space for chip formation as a bonding material of a grinding wheel. The body-centered pore structure of the composites shows the highest stress under a tension load. The original composites topology graphs have been transformed to ordered distributed pore graphs based on the total pore area conservation. The information dimension magnitude difference between the original topology graphs and the ordered distributed circulars graphs is found to be linear with the Cu-Sn-Ti alumina composites strength. A larger difference renders a lower flexural strength, which indicates that uniform ordered distributed pores could benefit the composites strength.
The meso-structure of woven fabrics plays a pivotal role in enabling significant deformations. To precisely characterize the deformation behavior of woven fabrics, a mesoscale model based on their meso-structure is developed. In this model, the fiber yarn is represented as a Timoshenko beam with fluctuations described by the shape function. A novel approach is proposed to establish the relationship between the macroscopic mechanical response and the infinitesimal strain of the beam, employing the principle of virtual work. The shearing behavior of woven fabrics is characterized by considering friction energy dissipation and transverse compression among fiber yarns. The general applicability of the proposed model is validated through its application to glass fiber woven fabrics and jute fiber woven fabrics. The model parameters are determined through uniaxial tensile and bias-extension experiments. The accuracy of the model is verified using bias-extension and picture frame experiments. The results demonstrate that the proposed model can effectively simulate the large deformation of woven fabrics.
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