2018/11/01 by Erva Ulu, Ulu, Erva
Computer Science · #Computational Engineering #Computational Geometry (cs.CG) #FOS: Computer and information sciences #Finance #Graphics (cs.GR) #Machine Learning (cs.LG) #and Science (cs.CE) #cs.CE #cs.CG #cs.GR #cs.LG
paper · pdf · doi:10.48550/arxiv.1811.00548
PhD Thesis 2018, Carnegie Mellon University
arxiv created 2018/11/01 · arxiv updated 2018/11/05
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.