4.7 Article

Experimental and theoretical investigation of prestressed natural fiber-reinforced polylactic acid (PLA) composite materials

Journal

COMPOSITES PART B-ENGINEERING
Volume 95, Issue -, Pages 346-354

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.compositesb.2016.03.089

Keywords

Fibers; Mechanical properties; Analytical modeling; 3-D printing

Funding

  1. National Science Foundation [1537194]
  2. Directorate For Engineering
  3. Div Of Civil, Mechanical, & Manufact Inn [1537194] Funding Source: National Science Foundation

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In this work we demonstrate that the specific (weight-normalized) mechanical properties of polylactic acid (PLA) can be enhanced by leveraging a combination of (a) additive manufacturing (3D printing) and (b) initial post-tensioning of continuous natural-fiber reinforcement. In this study both tensile and flexural PLA specimens with different cross-sectional geometries were 3D-printed with and without post-tensioning ducts. The mechanical properties of two continuous reinforcing fiber strands (i.e., jute, flax) were experimentally characterized prior to threading, post-tensioning to a prescribed level of stress, and securing in place with 3D-printed anchors. The effect of fiber type, matrix cross-sectional geometry, number of reinforcing strands, and degree of post-tensioning on the specific mechanical properties (i.e., strength-, stiffness-, rigidity-to-weight) of PLA were investigated using both tensile and flexural mechanical testing. Experimental results confirmed that additive manufacturing alone can improve the specific tensile and flexural mechanical properties of PLA and that these properties are further improved via initial mechanical prestressing of natural fiber reinforcement. Data indicate increases of 116% and 62% for tensile specific strength and stiffness and 14% and 10% for flexural specific strength and rigidity, respectively, compared to solid, unreinforced PLA. A theoretical model of the prestressed composite tensile response was employed and found to accurately predict (<10% error) improvements in mechanical behavior. (C) 2016 Elsevier Ltd. All rights reserved.

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