Literature DB >> 33596449

Experimental study on microstructure and mechanical properties of stalk for Glycyrrhiza Glabra.

Bao-Qin Wen1, Yang Li1, Za Kan2, Jing-Bin Li1, Liqiao Li1, Jianbing Ge1, Longpeng Ding1, Kaifei Wang1, Yameng Shi3.   

Abstract

In this paper, a year-old stalk of Glycyrrhiza glabra was used as the research object. The electronic universal testing machine was used to test the mechanical properties of shearing and bending. The microstructure of the stalk of Glycyrrhiza glabra was observed with a microscope. Mechanical test research indicated that the shearing process included an elastic phase, a yield phase, and a plastic deformation phase. The bending process was divided into elastic deformation stage and plastic deformation stage. In addition, the shearing force, shearing energy, bending force and bending energy all increased with the increase in diameter. As the water content increased, the shearing force and bending force decreased at first, reached the minimum when the water content was about 45%, and then had an upward trend. The shearing energy increased with the water content, and the bending energy, decreased with the water content. The two test factors were statistically significant for both shearing and bending properties. The microscopic test results showed that the phloem, fiber, and pith constitute the microstructure of the licorice stalk. The linear regression model could reflect the correlation between the cross-sectional area of each part and the shearing force and bending force (P < 0.05). Through analysis, it was concluded that the change of the cross-sectional area of the stalk microstructure had an important influence on the mechanical properties of shearing and bending. The results can provide theoretical basis for the design of Glycyrrhiza Glabra stalk harvesting, crushing and processing equipment.
Copyright © 2020. Published by Elsevier Ltd.

Entities:  

Keywords:  Bending; Diameter; Glycyrrhiza glabra; Microstructure; Shearing; Water content

Year:  2020        PMID: 33596449     DOI: 10.1016/j.jbiomech.2020.110198

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  1 in total

1.  Semi-supervised few-shot learning approach for plant diseases recognition.

Authors:  Yang Li; Xuewei Chao
Journal:  Plant Methods       Date:  2021-06-27       Impact factor: 4.993

  1 in total

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