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A numerical framework was developed in this study to investigate the influence of relevant Extrusion Deposition Additive Manufacturing (EDAM) processing conditions on the final fiber orientation, inter-bead void and cross-sectional geometry of the bead laid down as the extrudate. Specifically, four key processes of the EDAM process are studied, namely: (i) flow of 90° angle turn as the extrudate exits the nozzle and is laid down on the previous layer or the substrate, (ii) flow of the bead during compaction, (iii) flow of an extrudate deposited adjacent to the previously compacted bead, and (iv) flow of the adjacent bead during compaction. The simulations utilize an anisotropic viscous flow model implemented using the smoothed particle hydrodynamics method in Abaqus and fiber orientation vectors are evolved under the assumption of affine motion. The simulation results are compared with experiments for printed bead geometries produced and this comparison was shown to validate the modeling approach as useful for predicting fiber orientation, bead geometry, and bead-to-bead contact interface geometry. The processing parameters considered are nozzle height from the substrate, the ratio of the print speed to the extrusion speed (V b / V e), and the bead-to-bead lateral overlap distance. A series of virtual experiments were conducted, and the following observations were noted: (i) the bead printed with a high nozzle height has significantly more fiber alignment in the printing direction than the lower nozzle height, (ii) the ratio of the print speed and extrudate speed, V b / V e , of greater than unity results in greater fiber alignment than for V b / V e <1. The developed model illuminates the roles of process parameters in determining the microstructure of the printed bead and bead geometry. [ABSTRACT FROM AUTHOR]

We present a Bayesian methodology to infer the elastic modulus of the constituent polymer and the fiber orientation state in a short-fiber reinforced polymer composite (SFRP). The properties are inversely determined using only a few experimental tests. Developing composite manufacturing digital twins for SFRP composite processes, including injection molding and extrusion deposition additive manufacturing (EDAM) requires extensive experimental material characterization. In particular, characterizing the composite mechanical properties is time consuming and therefore, micromechanics models are used to fully identify the elasticity tensor. Hence, the objective of this paper is to infer the fiber orientation and the effective polymer modulus and therefore, identify the elasticity tensor of the composite with minimal experimental tests. To that end, we develop a hierarchical Bayesian model coupled with a micromechanics model to infer the fiber orientation and the polymer elastic modulus simultaneously which we then use to estimate the composite elasticity tensor. We motivate and demonstrate the methodology for the EDAM process but the development is such that it is applicable to other SFRP composites processed via other methods. Our results demonstrate that the approach provides a reliable framework for the inference, with as few as three tensile tests, while accounting for epistemic and aleatory uncertainty. Posterior predictive checks show that the model is able to recreate the experimental data well. The ability of the Bayesian approach to calibrate the material properties and its associated uncertainties, make it a promising tool for enabling a probabilistic predictive framework for composites manufacturing digital twins. [Display omitted] [ABSTRACT FROM AUTHOR]

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 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. [Display omitted] [ABSTRACT FROM AUTHOR]

We introduce the concept of physics-guided transfer learning to predict the thermal conductivity of an additively manufactured short-fiber reinforced polymer (SFRP) using micro-structural characteristics extracted from tensile tests. Developing composite manufacturing digital twins for SFRP composite processes like extrusion deposition additive manufacturing (EDAM) require extensive experimental material characterization. Even the same material system printed on different EDAM systems can result in significant changes to the printed micro-structure, affecting the mechanical and transport properties. This, in turn, makes characterization efforts expensive and time-consuming. Therefore, the objective of the paper is to address this experimental bottleneck and use prior information about the material manufactured in one extrusion system to predict its properties when manufactured in another system. To enable this framework, we assume that changes in properties of the same material when manufactured in different systems arise solely due to microstructural changes. To that end, we present a Bayesian framework that can transfer thermal conductivity properties across extrusion deposition additive manufacturing systems. While we discuss the transfer of thermal conductivity properties, the development is such that the framework can be used for the transfer of other properties that depend on microstructural characteristics defined in the manufacturing process. These include the coefficient of thermal expansion, viscoelastic properties, etc. The framework begins by using thermal conductivity data of the composite printed in one extrusion system to probabilistically infer the constituent thermal properties of the fiber and the polymer. Next, by conducting limited tensile tests of the same material printed in another extrusion system, we infer its orientation tensor. Finally, the inferred constituent thermal conductivity properties and the inferred orientation tensor are coupled using a micromechanics model to predict the thermal conductivity properties of the composite printed in the second extrusion system. We experimentally verify the predictions and show that our method provides a reliable framework for transferring material properties while accounting for epistemic and aleatory uncertainties. • A probabilistic framework to transfer material information across manufacturing systems. • A framework to account for surrogate uncertainty in parameter inference. • Demonstrate transfer of thermal conductivity across additive manufacturing systems. • Applications to transfer other properties including coefficient of thermal expansion. [ABSTRACT FROM AUTHOR]