  The ability to accurately quantify the performance an additively manufactured
(AM) product is important for a widespread industry adoption of AM as the
design is required to: (1) satisfy geometrical constraints, (2) satisfy
structural constraints dictated by its intended function, and (3) be cost
effective compared to traditional manufacturing methods. Optimization
techniques offer design aids in creating cost-effective structures that meet
the prescribed structural objectives. The fundamental problem in existing
approaches lies in the difficulty to quantify the structural performance as
each unique design leads to a new set of analyses to determine the structural
robustness and such analyses can be very costly due to the complexity of in-use
forces experienced by the structure. This work develops computationally
tractable methods tailored to maximize the structural performance of AM
products. A geometry preserving build orientation optimization method as well
as data-driven shape optimization approaches to structural design are
presented. Proposed methods greatly enhance the value of AM technology by
taking advantage of the design space enabled by it for a broad class of
problems involving complex in-use loads.
