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

Thomas, Akshay J.; Barocio, Eduardo; Pipes, R. Byron

doi:10.1016/j.compscitech.2022.109293

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

Journal Article · Tue Jan 25 23:00:00 EST 2022 · Composites Science and Technology

DOI:https://doi.org/10.1016/j.compscitech.2022.109293· OSTI ID:1977021

Thomas, Akshay J. ^[1]; ^[2]; ^[2]

Purdue University, West Lafayette, IN (United States); OSTI
Purdue University, West Lafayette, IN (United States)

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. Here 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 composite 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.

View Accepted Manuscript (DOE)

Research Organization:: Collaborative Composite Solutions Corporation, Knoxville, TN (United States); Purdue University, West Lafayette, IN (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

Grant/Contract Number:: EE0006926

OSTI ID:: 1977021

Journal Information:: Composites Science and Technology, Journal Name: Composites Science and Technology Journal Issue: C Vol. 220; ISSN 0266-3538

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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36 MATERIALS SCIENCE
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