Showing papers by "Shanyi Du published in 2021"

Photosensitive Composite Inks for Digital Light Processing Four-Dimensional Printing of Shape Memory Capture Devices.

Abstract: High-performance shape memory thermosetting polymers and their composites for four-dimensional (4D) printing are essential in practical applications. To date, most printable thermosets suffer from complicated processes, poor thermodynamic performances, and low printing speed. Here, photosensitive composite inks for fast photocuring printing are developed. The inks consist of epoxy acrylate (EPAc), polyethylene glycol dimethacrylate (PEGDMA), and carbon fillers, which form a firm network structure when exposed to UV light. EPAc is synthesized via addition esterification of epoxy resin and acrylic acid under mild conditions. It is worth noting that raw materials for the reaction are diverse, including not only various epoxy resins but also molecules with epoxy groups. The 4D printing speed of up to 180 mm/h is mainly attributed to the exothermic reaction initiated by free radicals, which accelerates the polymerization of EPAc and PEGDMA. Most importantly, by increasing the exposure time of each layer from 1 s to 3 s during the printing process, the epoxy composite-infilled carbon nanotubes and carbon fibers are printed to ensure the integrity of the microlayer structure. Furthermore, we design a claw-like catcher device based on the above printable composite inks to demonstrate its potential applications in aerospace, such as grasping end-of-service spacecraft or explosive debris. Undoubtedly, 4D printing technology opens up a new portal for the manufacturing of thermoset epoxy composites and complex structures, which make the shape memory thermosetting epoxy resins and their composites possess excellent properties and good engineering application prospects.

ZrW2O8/ZrO2 Composites with Low/Near-Zero Coefficients of Thermal Expansion Fabricated at Ultralow Temperature: An Integration of Hydrothermal Assembly and a Cold Sintering Process.

Abstract: ZrW2O8/ZrO2 composites with tunable low/near-zero coefficients of thermal expansion (CTE) are promising candidates in several fields including aerospace, precision manufacturing and measurement, electronic circuit, etc., for counteracting the thermal expansion effect. However, bottleneck issues (such as the unstable decomposition of ZrW2O8 phase, manufacturing size limitation, etc.) caused by conventional high-temperature sintering impede the development and application of ZrW2O8/ZrO2. To solve these scientific issues, a methodology integrating hydrothermal assembly with a cold sintering process (CSP) is exploited. The ZrW2O8/ZrO2 composite powders with a mace-like structure, in which the spherical ZrO2 nanoparticles peripherally embed on the rod-like ZrW2O8 matrix particles, are hydrothermally assembled. Then, the relatively dense ZrW2O8/ZrO2 composites with excellent low or even near-zero CTE are successfully achieved by CSP (as low as 190 °C) with a postannealing treatment (550 °C). The evolution of sintering densification, phase composition, and microstructure followed by the fundamental mechanism regarding the hydrothermal assembly of the mace-like structure and densification of CSP are investigated in detail. This research not only effectively overcomes the bottleneck issues of ZrW2O8/ZrO2 via integrating the hydrothermal assembly with the sintering technology at ultralow temperature but also develops a promising prospect for the fabrication of a broader range of metastable functional materials.

Machine learning-based prediction of the translaminar R-curve of composites from simple tensile test of pre-cracked samples

Abstract: A novel approach to determine the translaminar crack resistance curve of composite laminates by means of a machine learning model is presented in this paper. The main objective of the proposed meth...

An improved model for predicting stiffness of single-lap composites bolted joints using Matlab/Simulink

Abstract: A novel model for predicting the stiffness of single-lap composites bolted joints is introduced on the basis of the analytical spring-mass model. While the previous spring-mass model shows three ph...

An even-load-distribution design for composite bolted joints using a novel circuit model and artificial neural networks.

Abstract: Due to the brittle feature of carbon fiber reinforced plastic laminates, mechanical multi-joint within these composite components show uneven load distribution for each bolt, which weaken the strength advantage of composite laminates. In order to reduce this defect and achieve the goal of even load distribution in mechanical joints, we propose a machine learning-based framework as an optimization method. Since that the friction effect has been proven to be a significant factor in determining bolt load distribution, our framework aims at providing optimal parameters including bolt-hole clearances and tightening torques for a minimum unevenness of bolt load. A novel circuit model is established to generate data samples for the training of artificial networks at a relatively low computational cost. A database for all the possible inputs in the design space is built through the machine learning model. The optimal dataset of clearances and torques provided by the database is validated by both the finite element method, circuit model, and an experimental measurement based on the linear superposition principle, which shows the effectiveness of this general framework for the optimization problem. Then, our machine learning model is further compared and worked in collaboration with commonly used optimization algorithms, which shows the potential of greatly increasing computational efficiency for the inverse design problem.