4.7 Article

A machine learning approach to determine the elastic properties of printed fiber-reinforced polymers

期刊

COMPOSITES SCIENCE AND TECHNOLOGY
卷 220, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.compscitech.2022.109293

关键词

Inverse determination; Short fiber composites; Elastic properties; Fiber orientation state; Extrusion deposition additive manufacturing; Support vector regression

资金

  1. U.S. Department of Energy, Institute for Advanced Composites Manufacturing Innovation (IACMI) [DOE DE-EE0006926, IACMI PA16-0349-3.12-01]

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This study focuses on determining the elastic constants and fiber orientation state of a short fiber-reinforced polymer composite using minimal experimental tests. A methodology is introduced to identify the fiber orientation state and polymer properties by performing tensile tests on the composite coupon level. The proposed approach can be applied to different processing methods of short fiber-reinforced polymer systems.
This work focuses on the simultaneous determination of the elastic constants and the fiber orientation state for a short fiber-reinforced polymer composite by performing a minimum of experimental tests. We introduce a methodology that enables the inverse determination of fiber orientation state and the in-situ polymer properties by performing tensile tests at the composite coupon level. We demonstrate the approach for the extrusion deposition additive manufacturing (EDAM) process to illustrate one application of the methodology, but the development is such that it can be applied to short fiber-reinforced polymer (SFRP) systems processed via other methods. Currently, developing composites additive manufacturing digital twins require extensive material characterization. In particular, the mechanical characterization of the orthotropic elastic properties of a com-posite involves extensive sample preparation and testing, therefore the elasticity tensor is generally populated using a micromechanics model. This, however, requires measuring the fiber orientation state in addition to knowing the constituent material properties. Experimentally measuring the fiber orientation state can be tedious and time consuming. Further, optical methods are limited to resolving the orientation of cylindrical fibers or cluster of non-cylindrical fibers, and computed tomography (CT) methods scan regions of volume that are much smaller than a full printed bead. Therefore, we propose a methodology, accelerated by machine learning, to identify the anisotropic mechanical properties and fiber orientation state at the same time. Early results show that inference of the fiber orientation and composite properties is possible with as few as three tensile tests. Our results show that a combination of the choice of the micromechanics model and reliable set of experiments can yield the nine elastic constants, as well as, the fiber orientation state.

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