Xingyuan Sun will present his Pre-FPO "Using Machine Learning to Improve Fiber-Reinforced 3D Printed Objects" on Thursday, July 28, 2022 at 11:00 AM in CS 402 and Zoom. Zoom link: [ https://princeton.zoom.us/j/95907350060 | https://princeton.zoom.us/j/95907350060 ] Committee Members: Ryan P. Adams (Examiner, adviser), Szymon Rusinkiewicz (Examiner, adviser), Olga Russakovsky (Examiner), Sigrid Adriaenssens (reader), Felix Heide (reader). All are welcome to attend. Title: Using Machine Learning to Improve Fiber-Reinforced 3D Printed Objects Abstract: This dissertation studies fiber layout planning given a shape and external loads using machine learning, improving the object’s stiffness. However, simply planning fiber paths is not sufficient. Since fibers are stiff, their shape is smoothed after extrusion. We thus need another planning algorithm to generate an extruder path given any desired fiber path. To solve this task, we learn a two-stage neural network that we may consider an autoencoder. A decoder is first learned as a differentiable surrogate of the printing process, taking an extruder path and producing a resulting fiber path. An encoder is then trained to produce an extruder path given a desired fiber path, using the trained decoder to evaluate the quality of the planned extruder path. We compare this approach with direct supervised learning of the extruder path planning problem and direct optimization of the extruder path. Besides, we also study our algorithm in another case study on a soft robot, showing our approach can be applied to design problems with a similar structure. Results from simulation and real experiments show our algorithm generates higher quality solutions than direct supervised learning and solutions with competitive quality with direct optimization but greatly reduced running time. To plan fiber paths, we formalize the task as an optimization problem, solving the fiber layout to maximize the object’s stiffness with regularizers forcing paths to be feasible. We initialize every fiber path by iteratively using finite element analysis to calculate the stress field of the object and a greedy algorithm to “walk” on the field. We then use the adjoint method to calculate the gradient of the regularized stiffness with respect to the fiber layout, and optimize the layout using a gradient-based optimizer. We compare our algorithm with three baselines, including concentric fiber rings generated by Eiger, a leading digital manufacturing software, and two others producing greedy fiber paths on the stress field and a fiber orientation field calculated by smoothing the stress field, respectively. Experiments on simulation and real printings show objects with fiber paths from our algorithm improve the Pareto front, achieving larger stiffness while using less fiber. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014