Recently, there has been growing interest in water‐assisted injection molding (WAIM) not only for its advantages over gas‐assisted molding (GAIM) and conventional injection molding (CIM), but also for its great potential advantages in industrial applications. To understand the formation mechanism of water penetration induced fiber orientation in overflow water‐assisted injection molding (OWAIM) parts of short glass fiber‐reinforced polypropylene (SGF/PP), in this work, the external fields and water penetration process within the mold cavity were investigated by experiments and numerical simulations. The results showed that the difference of fiber orientation distribution in thickness direction between WAIM moldings and CIM moldings was mainly ascribed to the great external fields generated by water penetration. Besides, fiber orientation depended on the position both across the part thickness and along the flow direction. Especially in the radial direction, fiber orientation varied considerably. The results also showed that the melt temperature is the principal parameter affecting the fiber orientation along the flow direction, and a higher melt temperature significantly facilitated more fibers to be oriented along the flow direction, which is quite different from the results as previously reported in short‐shot water‐assisted injection molding (SSWAIM). A higher water pressure, shorter water injection delay time, and higher melt temperature significantly induced more fibers to be orderly oriented in OWAIM moldings, which may improve their mechanical performances and broaden their application scope.

Developing stretchable strain sensors with high stretchability as well as sensitivity via a scalable technique is still a challenge that remains to be addressed. Herein, high‐performance stretchable strain sensors based on Ag nanoparticles (NPs) sandwiched between two thermoplastic polyurethane (TPU) fibrous textiles are fabricated by combining 3D‐printed TPU elastomer and solution‐coated Ag NP pulp, in which a conductive network is formed at a low loading of 4.3 wt% Ag NPs. The printed TPU fibers in each textile are arranged in parallel, while perpendicular to each other between the upper and lower textiles, forming an oblique lattice structure with their diagonal being the same direction as the external force. Under stretching, the deformed TPU fibers in the upper and lower textiles squeezed Ag NPs to undergo shearing motion, such that the disconnected electrical circuit can be compensated by some Ag NPs to retain the electrical pathway. Consequently, the printed TPU/Ag strain sensors showed high sensitivity (gauge factor of 38220 at a strain of 120–138%), wide working strain range (0–138%), and long‐term durability (>2500 cycles at 50% strain). Their promise as flexible electronic components is demonstrated in wearable motion detection systems for monitoring human motions and recognizing facial expressions.

Electromagnetic (EM) shielding materials have attracted significant attention, owing to their widespread potential in preventing EM irradiation in electrical devices and human bodies. In this study, a hierarchical porous carbon nanotube (CNT) skeleton with electrical continuity is presented, which is rapidly fabricated via the facile microwave pyrolysis of CNT‐coated organic templates, for constructing high‐performance EM shielding materials. Furthermore, the CNT skeleton, which comprises countless intertwined CNTs, can be easily designed into various configurations, such as CNT foams and CNT sheets, with varying conductivities and pore densities. In the as‐constructed CNT‐skeleton‐supported polydimethylsiloxane (PDMS)/CNT‐foam composites, the continuity of intertwined CNTs leads to a high conductivity of 271.2 S m−1 at a CNT loading of 2 wt%. Owing to the multiple reflections and reabsorption of the EM waves in the hierarchical porous CNT skeleton with macroporous, microporous, and hollow structures, the PDMS/CNT‐foam composites exhibit a high EM shielding effectiveness (SE) of 43 dB, mainly via absorption. Additionally, the electrical continuity of the CNT skeleton allows the dissipation of heat in the PDMS/CNT‐skeleton.

Abstract Based on the effects of natural cooling on the warpage of the injection-molded parts, a concept of total warpage deformation was proposed. A three-dimensional numerical model of total warpage for the injection-molded parts of short-glass-fiber-reinforced polypropylene (PP) composites was established using coupled finite element method (FEM). The total warpage deformation is composed of two parts: stress-induced deformation during injection molding and thermally induced shrinkage deformation after ejection. The residual stress, temperature, and anisotropic thermal and mechanical properties formed in injection molding were subsequently transferred into the thermal stress analysis package as initial conditions. On account of the difference between the fluid and structural mesh, the tetrahedral and hexahedral mesh types were used in injection molding simulation and thermal stress analysis, respectively. The comparison between the simulation and experimental results showed that the simulated warpage deformation agreed well with the experimental measurements quantitatively and qualitatively, suggesting the validation of the proposed numerical model.

This study aimed at improving the residual wall thickness uniformity (RWTU), which was closely related to the mechanical properties of plastic parts with a hollow cross-section, in short-fiber reinforced composites (SFRC) overflow water-assisted injection molding (OWAIM). The influences of five independent process parameters (melt temperature, mold temperature, delay time, water pressure, and water temperature) on RWTU were investigated through the methods such as central composite design, regression equation, and analyses of variance. Response surface methodology (RSM) and artificial neural network (ANN) optimized by genetic algorithm (GA) were employed to map the relationship between the process parameters and the standard deviation (SD) depicting the RWTU. Comparison assessments of three models (RSM, ANN, and ANN-GA) were carried out through some statistical indexes. It was concluded that the effect of melt temperature, delay time, and water temperature were significant to RWTU; the hybrid ANN-GA model had the best performance for predicting SD compared with RSM and ANN; the least SD obtained in optimization using ANN-GA as a fitness function was 0.0972.