Enhancing the Structural Performance of Additively Manufactured Objects
This addresses the need for cost-effective and structurally robust designs in additive manufacturing to enable broader industry adoption, representing an incremental improvement in optimization techniques.
The paper tackles the problem of quantifying and optimizing structural performance in additively manufactured objects, which is costly due to complex analyses for each unique design, and develops computationally tractable methods including geometry-preserving build orientation and data-driven shape optimization to enhance performance.
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.