Session: DAC-07-01-Design for Additive Manufacturing

Paper Number: 90050

90050 - Concurrent Build Direction, Part Segmentation, and Topology Optimization for Additive Manufacturing Using Neural Networks 

We propose a neural network-based approach to topology optimization that aims to reduce the use of support structures in additive manufacturing. Without an explicit formulation to minimize support structures, topology optimization may create complex shapes that require intensive use of support material when additively manufactured. Our approach uses a network architecture that allows the simultaneous determination of an optimized: (1) part segmentation, (2) the topology of each part, and (3) the build direction of each part that collectively minimize the amount of support structure. Through training, the network learns a material density and segment classification in the continuous 3D space. Given a problem domain with prescribed load and displacement boundary conditions, the neural network takes as input 3D coordinates of the voxelized domain as training samples and outputs a continuous density field. Since the neural network for topology optimization learns the density distribution field, analytical solutions to the density gradient can be obtained from the input-output relationship of the neural network. We demonstrate our approach on several compliance minimization problems with volume fraction constraints, where support volume minimization is added as an additional criterion to the objective function. We show that simultaneous optimization of part segmentation along with the topology and print angle optimization further reduces the support structure, compared to a combined print angle and topology optimization without segmentation.

Presenting Author: Hongrui Chen Carnegie Mellon University

Presenting Author Biography: Hongrui Chen is currently a Ph.D. student at Carnegie Mellon University.

Authors:

Hongrui Chen Carnegie Mellon University
Kate Whitefoot Carnegie Mellon University
Levent Burak Kara Carnegie Mellon University