ORCID Profile
0000-0003-0058-2349
Current Organisation
Deakin University
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Publisher: Elsevier BV
Date: 04-2020
Publisher: Informa UK Limited
Date: 24-07-2019
Publisher: Elsevier BV
Date: 12-2020
Publisher: Elsevier BV
Date: 11-2017
DOI: 10.1016/J.CARBPOL.2017.08.072
Abstract: Nanostarches were successfully prepared by high speed jet (HSJ) after pretreatment of micronization. The nanostarches were obtained at the conditions of micronization treatment for 60min, and then one cycle at 240MPa of HSJ (188.1nm). Moreover, after HSJ treated for three cycles, the particle size could reach the level of nanometer materials (66.94nm). The physicochemical properties of nanostarches had been characterized. Rapid Visco-Analysis (RVA) showed that the viscosity of nanostarches significantly decreased compared with native tapioca starch and slightly decreased with increasing processing cycles of HSJ. Steady shear analysis indicated that all s les displayed pseudoplastic, shear-thinning behavior, while the flow curves of nanostarches were little impact by the processing cycles of HSJ. X-ray diffraction analysis showed that the complete destruction of tapioca starch crystalline structure was obtained after HSJ treatment. Molecular characteristics determination suggested that the degradation of amylopectin chains occurred after the treatment of micronization and HSJ, which was proved by the decrease of weight-average molar mass. The results demonstrated that nanostarches were obtained due to the breakdown of starch molecules. This study will provide useful information of the nanostarches for its potential industrial application.
Publisher: SAGE Publications
Date: 11-04-2022
DOI: 10.1177/10567895221089655
Abstract: This paper presents a systemic calibration methodology to efficiently simulate progressive damage evolution in four different pultruded glass fiber reinforced polymer (GFRP) composites using the strain-based COMposite DAMage Model (CODAM2) in the commercial finite element software LS-DYNA. In particular, Compact Tension (CT), scaled-up CT, and wide CT tests are simulated to find the best set of input parameters by considering four distinct indicators obtained from experimental and numerical load vs displacement data. By combining these indicators into a physically meaningful equivalent deviation value via a linear weighted-sum method, the results show that the most suited input damage variables yield physically accurate crack length predictions which underlines the robustness and accuracy of the proposed method. Furthermore, it is shown that the incorporation of bi-linear softening laws improves CODAM2 simulation results by up to 90%, however it also increases the number of parameters to be calibrated.
Publisher: Science Publications
Date: 04-2016
Publisher: Science Publications
Date: 04-2015
Publisher: MDPI AG
Date: 29-12-2020
DOI: 10.3390/APP11010238
Abstract: The smooth design of self-supporting topologies has attracted great attention in the design for additive manufacturing (DfAM) field as it cannot only enhance the manufacturability of optimized designs but can obtain light-weight designs that satisfy specific performance requirements. This paper integrates Langelaar’s AM filter into the Smooth-Edged Material Distribution for Optimizing Topology (SEMDOT) algorithm—a new element-based topology optimization method capable of forming smooth boundaries—to obtain print-ready designs without introducing post-processing methods for smoothing boundaries before fabrication and adding extra support structures during fabrication. The effects of different build orientations and critical overhang angles on self-supporting topologies are demonstrated by solving several compliance minimization (stiffness maximization) problems. In addition, a typical compliant mechanism design problem—the force inverter design—is solved to further demonstrate the effectiveness of the combination between SEMDOT and Langelaar’s AM filter.
Publisher: Informa UK Limited
Date: 23-07-2019
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2020
DOI: 10.3934/MBE.2020255
Publisher: Informa UK Limited
Date: 04-10-2020
Publisher: Elsevier BV
Date: 03-2023
Publisher: ACM
Date: 22-06-2020
Publisher: Elsevier BV
Date: 10-2019
No related grants have been discovered for FU Yun-Fei.