<p>Extrusion deposition additive manufacturing (EDAM) has enabled upscaling the dimensions of the objects that can be additively manufactured from the desktop scale to the size of a full vehicle. The EDAM process consists of depositing beads of molten material in a layer-by-layer manner, thereby giving rise to temperature gradients during part manufacturing. To investigate the phenomena involved in EDAM, the Composites Additive Manufacturing Research Instrument (CAMRI) was developed as part of this project. CAMRI provided unparalleled flexibility for conducting controlled experiments with carbon fiber reinforced semi-crystalline polymers and served as a validation platform for the work presented in this dissertation. </p> <p>Since the EDAM process is highly non-isothermal, modeling heat transfer in EDAM is of paramount importance for predicting interlayer bonding and evolution of internal stresses during part manufacturing. Hence, local heat transfer mechanisms were characterized and implemented in a framework for EDAM process simulations. These include local convection conditions, heat losses in material compaction as well as heat of crystallization or melting. Numerical predictions of the temperature evolution during the printing process of a part were in great agreement with experimental measurements by only calibrating the radiation ambient temperature. </p> In the absence of fibers reinforcing the interface between adjacent layers, the bond developed through the polymer is the primary mechanisms governing the interlayer fracture properties in printed parts. Hence, a fusion bonding model was extended to predict the evolution of interlayer fracture properties in EDAM with semi-crystalline polymer composites. The fusion bonding model was characterized and implemented in the framework for EDAM process simulation. Experimental verification of numerical predictions obtained with the fusion bonding model for interlayer fracture properties is provided. Finally, this fusion bonding model bridges the gap between processing conditions and interlayer fracture properties which is extremely valuable for predicting regions with frail interlayer bond within a part.

<p>Extrusion Deposition Additive Manufacturing is a process by which short fiber-reinforced polymers are extruded in a screw and deposited onto a build platform using a set of instructions specified in the form of a machine code. The highly non-isothermal process can lead to undesired effects in the form of residual deformation and part delamination. Process simulations that can predict residual deformation and part delamination have been a thrust area of research to prevent the repeated trial and error process before a useful part has been produced. However, populating the material properties required for the process simulations require extensive characterization efforts. Tackling this experimental bottleneck is the focus of the first half of this research.</p><p>The first contribution is a method to infer the fiber orientation state from only tensile tests. While measuring fiber orientation state using computed tomography and optical microscopy is possible, they are often time-consuming, and limited to measuring fibers with circular cross-sections. The knowledge of the fiber orientation is extremely useful in populating material properties using micromechanics models. To that end, two methods to infer the fiber orientation state are proposed. The first is Bayesian methodology which accounts for aleatoric and epistemic uncertainty. The second method is a deterministic method that returns an average value of the fiber orientation state and polymer properties. The inferred orientation state is validated by performing process simulations using material properties populated using the inferred orientation state. A different challenge arises when dealing with multiple extrusion systems. Considering even the same material printed on different extrusion systems requires an engineer to redo the material characterization efforts (due to changes in microstructure). This, in turn, makes characterization efforts expensive and time-consuming. Therefore, the objective of the second contribution 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. A framework that can transfer thermal conductivity data while accounting for uncertainties arising from different sources is presented. The predicted properties are compared to experimental measurements and are found to be in good agreement.</p><p>While the process simulations using finite element methods provide a reliable framework for the prediction of residual deformation and part delamination, they are often computationally expensive. Tackling the fundamental challenges regarding this computational bottleneck is the focus of the second half of this dissertation. To that end, as the third contribution, a neural network based solver is developed that can solve parametric partial differential equations. This is attained by deriving the weak form of the governing partial differential equation. Using this variational form, a novel loss function is proposed that does not require the evaluation of the integrals arising out of the weak form using Gauss quadrature methods. Rather, the integrals are identified to be expectation values for which an unbiased estimator is developed. The method is tested for parabolic and elliptical partial differential equations and the results compare well with conventional solvers. Finally, the fourth contribution of this dissertation involves using the new solver to solve heat transfer problems in additive manufacturing, without the need for discretizing the time domain. A neural network is used to solve the governing equations in the evolving geometry. The weak form based loss is altered to account for the evolving geometry by using a novel sequential collocation sampling method. This work forms the foundational work to solve parametric problems in additive manufacturing.</p>