Search results

This paper aims to design a composite, aiming to improve the static puncture resistance through polyurethane impregnated treatment with five concentration gradients.

The relationship between polyurethane concentration, the number of polyester nonwovens, component fiber type (filament or staple) and the static puncture resistance is explored respectively and elaborately.

This study provides a new perspective to design flexible composites with better static puncture resistance, feasible preparation process as well as low cost.

The results show that the static puncture resistance of nonwovens impregnated by polyurethane is improved obviously. Meanwhile the puncture strength-T of nonwovens increases first and then decreases with the increase of the number of layers, and the maximum puncture strength-T is found at 20 layers. Moreover, the composite with filaments illustrates better mechanical resistance.

This paper aims to estimate the performance of protective clothing used to resist puncture (anti-stab property).

Seven single-layer (one layer) samples were investigated in this research. The first three samples were already used for the purpose of (anti-stab property), manufactured from Du-Pont product (commercial samples). The rest of the samples were locally designed and manufactured for the same purpose. These seven samples have then been examined after been added in conjunction with WL Kevlar XP (S 104) witness multilayers (eight layers) panel to create which are called multilayer samples.

The results of the statistical analysis for one-way ANOVA illustrated significant effect for single layer samples for all properties. While for multi-layer samples, the results showed a significant difference for all variables except displacement. The Tukey post hoc test observed a significant effect for some samples; also, other samples show a non-significant effect between samples.

It was observed that the locally manufactured samples serve the purpose as (anti-stab samples) compared with the commercial samples. The radar chart shows that for single-layer sample, the fifth sample fulfill the highest radar chart area, whereas for multi-layer samples, the sixth sample achieved the highest radar chart area.

As a high-performance engineering plastic, polyimide (PI) is widely used in the aerospace, electronics and automotive industries. This paper aims to review the latest progress in the tribological properties of PI-based composites, especially the effects of nanofiller selection, composite structure design and material modification on the tribological and mechanical properties of PI-matrix composites.

The preparation technology of PI and its composites is introduced and the effects of carbon nanotubes (CNTs), carbon fibers (CFs), graphene and its derivatives on the mechanical and tribological properties of PI-based composites are discussed. The effects of different nanofillers on tensile strength, tensile modulus, coefficient of friction and wear rate of PI-based composites are compared.

CNTs can serve as the strengthening and lubricating phase of PI, whereas CFs can significantly enhance the mechanical properties of the matrix. Two-dimensional graphene and its derivatives have a high modulus of elasticity and self-lubricating properties, making them ideal nanofillers to improve the lubrication performance of PI. In addition, copolymerization can improve the fracture toughness and impact resistance of PI, thereby enhancing its mechanical properties.

The mechanical and tribological properties of PI matrix composites vary depending on the nanofiller. Compared with nanofibers and nanoparticles, layered reinforcements can better improve the friction properties of PI composites. The synergistic effect of different composite fillers will become an important research system in the field of tribology in the future.

The rock drill’s drill tail experiences high-frequency fretting simultaneously in the rotational and axial directions. Due to the complex working characteristics and the low viscosity of the water medium, the pure water seal is susceptible to damage and failure. The purpose of this paper is to enhance the water seal’s performance.

The Y-shaped seal ring is modeled and simulated using orthogonal testing. Through analysis of the impact of various seal section parameters on sealing performance, the maximum contact stress and maximum Von Mises stress are selected as indicators of sealing effectiveness.

The maximum contact stress is proportional to lip thickness and chamfer length but inversely proportional to lip length. Meanwhile, the maximum Von Mises stress is directly influenced by lip depth and the included angle of the lip and drill tail but is inversely proportional to the lip thickness. The enhanced Y-shaped water seal sees reductions of 15% and 45% in maximum contact stress and maximum Von Mises stress, respectively.

This paper used analytical method and model that is helpful for design of the water seal’s structure in complex working characteristics and the low viscosity of the water medium.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2023-0366/

The purpose of this study is to evaluate the 3D printability of a multimaterial, fully self-supporting auxetic structure. This will contribute to expanding the application of additive manufacturing (AM) to new products, such as automotive suspensions.

An experimental approach for sample fabrication on a multiextruder 3D printer and characterization by compression testing was conducted along with numerical simulations, which were used to support the design of different auxetic configurations for the jounce bumper.

The effect of stacking different auxetic cell modules was discussed, and the findings demonstrated that a one-piece printed structure has a better performance than one composed of multiple single modules stacked on top of each other.

The quality of the 3D printing process affected the performance of the final components and reproducibility of the results. Therefore, researchers are encouraged to further study component fabrication optimization to achieve a more reliable process.

This research work can help improve the manufacturing and functionality of a critical element of automotive suspension systems, such as the jounce bumper, which can efficiently reduce noise, vibration and harshness by absorbing impact energy.

In previous research, auxetic structures for the application of jounce bumpers have already been suggested. However, to the best of the authors’ knowledge, in this work, an AM approach was used for the first time to fabricate multimaterial auxetic structures, not only by co-printing a flexible thermoplastic polymer with a stiffer one but also by continuously extruding multilevel structures of auxetic cell modules.

In this paper the aim is that Aramid/alginate blended nonwoven fabrics were prepared, and the flame retardancy of the blended nonwoven fabrics was studied by thermogravimetric analysis, vertical flame test, limiting oxygen index (LOI) and cone calorimeter test.

The advantages of different fibers can be combined by blending, and the defects may be remedied. The study investigates whether incorporating alginate fibers into aramid fibers can enhance the flame retardancy and reduce the smoke production of prepared aramid/alginate blended nonwoven fabrics.

Thermogravimetric analysis indicated that alginate fibers could effectively inhibit the combustion performance of aramid fibers at a higher temperature zone, leaving more residual chars for heat isolation. And vertical flame test, LOI and cone calorimeter test testified that the incorporation of alginate fibers improved the flame retardancy and fire behaviors. When the ratio of alginate fibers for aramid/alginate blended nonwoven fabrics reached 80%, the incorporation of alginate fibers could notably decreased peak-heat release rate (54%), total heat release (THR) (29%), peak-smoke production rate (93%) and total smoke production (86%). What is more, the lower smoke production rate and lower THR of the blends vastly reduced the risk of secondary injury in fires.

This study proposes to inhibit the flue gas release of aramid fiber and enhance the flame retardant by mixing with alginate fiber, and proposes that alginate fiber can be used as a biological smoke inhibitor, as well as a flame retardant for aramid fiber.

This paper aims to investigate the tribological properties of polytetrafluoroethylene (PTFE) composites modified by nano-serpentine and nano-lanthanum oxide in a seawater environment.

In this paper, seven PTFE composites were prepared by unified design method and vacuum thermoforming method, and their hardness, water absorption and tribological properties were measured under seawater environment. The modification effects and thermal stability of the materials were analyzed by Fourier transform infrared spectroscopy, thermal gravimetry and differential scanning calorimetry. This paper analyzed the wear mechanism of PTFE composites by scanning electron microscopy and energy spectroscopy.

The results showed that the hardness of the PTFE composites were all improved, but the water absorption was increased with the increase of additives. The modification of nano-serpentine was successful and the thermal stability of PTFE composites was better. The lowest coefficient and minimum wear rate are 0.0267 and 8.67 × 10⁻⁵ · mm³ · (N · m)⁻¹ respectively, which is 34.9% and 76% less than the pure PTFE.

The analysis showed that the wear mechanism of PTFE composites was abrasive wear and a small amount of adhesive wear, and when the additive content was appropriate, it easily formed a transfer film on the surface mating parts.

In recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites.

Microstructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared.

The comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models.

This study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials.