R. Byron Pipes's research while affiliated with Purdue University and other places

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.

Compression molding with long discontinuous fiber-reinforced thermoplastics enables replacing traditionally machined metallic components with geometrical complexity and with reductions in weight and potential enhancements in structural characteristics like durability, fatigue, and serviceability. Fiber length is critical in fiber-reinforced composites. While long continuous fibers limit the geometrical complexity that can be fabricated but provide exceptional mechanical properties, discontinuous fibers provide manufacturing flexibility but with a penalty in strength. This work demonstrates enhancement in strength and reduction in strength variability achieved by compression molding of long discontinuous fiber platelets and continuous fiber preforms. This approach was demonstrated for an overhead bin pin bracket geometry. Continuous fiber preforms were manufactured with 60% by volume of carbon fiber-reinforced Poly Ether Ketone Ketone (PEKK) using the 9T Labs continuous fiber Additive Fusion Technology (AFT). Similarly, fiber platelets with 60% by volume of carbon fiber reinforced PEKK were utilized. Continuous fiber preforms were designed considering both the concurrent flow of continuous and discontinuous fibers with the desired mesostructure of continuous and discontinuous fibers. The results presented in this work showed an increase of 99.6% in the load at the onset of damage by reinforcing the pin bracket with about 17% by weight of continuous fiber preforms. Similarly, the coefficient of variance of the load at the onset of failure decreased by 46%. Finally, reinforcing the pin bracket with continuous fiber preforms not only enhanced the strength characteristics but also decreased the variability in strength characteristics.

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 (), 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, , of greater than unity results in greater fiber alignment than for <1. The developed model illuminates the roles of process parameters in determining the microstructure of the printed bead and bead geometry.