Enhancing the Structural Performance of Additively Manufactured Objects

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.