The reliability and performance qualification of additively manufactured metal parts is critical for their successful and safe use in engineering applications. In current powder-bed fusion type metal additive manufacturing processes, local thermal accumulations affect material microstructure features, overall part quality and integrity, as well as bulk mechanical behavior. To address such challenges, the investigation presented in this manuscript describes a novel digital design approach combining topology optimization, process simulations, and lattice size optimization to address local thermal effects caused during manufacturing. Specifically, lattices are introduced in regions of topology optimized geometries where local thermal accumulations are predicted using the process simulations with the overall goal to mitigate high thermal gradients. The results presented demonstrate that the proposed digital design approach reduces local thermal accumulations while achieving target mechanical performance metrics. A discussion on how post-manufacturing heat treatment effects could be also considered, as well as comments on the computational implementation of the proposed approach are provided.

The identification of early stages of damage is an important consideration for determining remaining useful life in critical structures such as aircrafts. In this context, previous experimental investigations on precipitate-hardened aluminum alloys, which is a widely used class of aerospace materials, confirmed the acoustic emission resulting from fracture of second phase particles, which in fact occurs early in the deformation process so that it can be considered a damage precursor. The experiments also identified correlations between crystallographic (e.g. grain boundaries) and property mismatches (e.g. of elastic modulus and hardness) at the microscale with fracture initiation sites. Motivated by these findings, the research work presented in this thesis develops a computational modeling approach targeting the investigation of fracture at the microscale of aluminum alloys. In addition, the resulting wave propagation occurring because of the acoustic emission following the fracture events at that scale, is explicitly modeled using the finite element method. Specifically, a crystal plasticity model is employed to model strain localizations near particles in the aluminum matrix. Then a numerical approach is developed to transfer the crystal plasticity results to an enriched continuum domain. Finally, the resulting wave propagation is investigated by linking implicit and explicit analysis. The results from this investigation confirmed the effect of the fracture initiation sites on the resulting wave characteristics while they also provide theoretical validation on the related experimental investigations of recording acoustic emission at the microscale.

Metal Additive Manufacturing (AM) has become a viable method of fabricating complex geometries that are difficult and even impossible to manufacture using some of the traditional techniques. Design for Additive Manufacturing (DfAM) approaches leverages the vast potential of AM approaches for getting optimal and novel part designs. However, the inherent cyclic thermal nature of metal AM processes creates thermal gradients in the printed part resulting in geometry challenges such as residual stress induced distortions which compromise the overall part performance. Therefore, the reliability and performance qualification of metal AM parts is critical for their safe and successful adoption in engineering applications. The thermal profile in metal AM parts is dependent on the process parameters used for printing, the geometry of the part, and the orientation used while printing. For these reasons, a 3D-printed part optimized using standard design optimization formulations that do not account for these manufacturing parameters may not provide the desired part performance. The work presented in this dissertation proposes a potential approach that may help address this critical issue. Design considerations for AM rely on thermomechanical process simulations; which although provide valuable insights on the role of manufacturing parameters and overall geometry on the print quality, do not offer direct ways to adapt the target part geometry into the specific AM process used and thus create a disconnect between the model and the produced part. To address this issue, this dissertation presents an approach to directly couple In-Situ Monitoring (ISM) data of the AM process with a gradient-based topology optimization approach. To accomplish this goal, a database of thermal data is generated by printing candidate parts for which in-situ monitoring data is collected. Neural network-based surrogate models are then used to predict temperature and temperature gradients for novel part geometries. A bi-objective topology optimization with mechanical and thermal constraints that queries the surrogate model for the thermal data and runs a static structural finite element solver for the mechanical objective evaluation is used. The proposed data-driven approach is much faster than current trial-and-error DfAM approaches. This approach is also expected to provide more optimal design outcomes than by previous DfAM approaches proposed in literature.