Using Topology Optimization to Improve Design for Additive Manufacture

Open Access
Ferguson, Ian
Graduate Program:
Mechanical Engineering
Master of Science
Document Type:
Master Thesis
Date of Defense:
July 06, 2015
Committee Members:
  • Timothy William Simpson, Thesis Advisor
  • Mary I Frecker, Thesis Advisor
  • additive manufacturing
  • 3d printing
  • FDM
  • DMLS
  • topology optimization
  • design
  • optimization
Additive manufacturing (AM) offers new design freedom to create topologies with complex surfaces and internal structures that could not be produced by traditional manufacturing processes. Due to this design flexibility, parts designed for AM have the potential to withstand the same structural loads as traditionally manufactured parts at lower masses. In an attempt to reduce the mass of structural parts to a minimum, optimization techniques such as topology optimization can be employed to achieve geometries that may be unintuitive to designers. While in many cases AM is the only means to realize such an optimized design, the constraints of the particular AM process may require a design to be modified before it can be produced. This thesis examines the current state of topology optimization technology and investigates how topology optimization software fits into the workflow of design for AM. This is achieved by exploring the problem of minimizing the mass of a mounting plate for an aerospace vehicle. Optimization is performed with varying boundary conditions and materials to observe their effect on resulting topologies and design performance. The results are then manually interpreted to conform to AM constraints. A 60% weight savings was achieved over the current mounting plate design, but the optimization software did not take AM constraints into account. Manual design modifications were required to ensure that the design was one continuous part and that a suitable prototype of the optimized design could be produced. In the context of this problem, the benefits and limitations of incorporating topology optimization into design for AM are presented. It was found that manual design workflow for AM requires the designer to iterate design around performance, while incorporating topology optimization into the workflow requires the designer to iterate design around manufacturability.