By Topic

Automating and accelerating the additive manufacturing design process with multi-objective constrained evolutionary optimization and HPC/Cloud computing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Buckner, M.A. ; RF, Commun. & Intell. Syst. Group, Oak Ridge Nat. Lab., Oak Ridge, TN, USA ; Love, L.J.

The ultimate objective of additive manufacturing is the implementation of techniques that can be used throughout the full manufacturing cycle. However, since its introduction, the additive manufacturing process has been used for little more than pre-production prototyping. The goal of some current work at ORNL is to change that reality. ORNL recently completed the first step towards optimizing the final design and manufacture of a component part (a cantilever in this case) using computer-aided design (CAD) tools, finite element analysis and simulations, and internally-developed optimization software. This paper will describe the present design process, the tools used, and the progress made thus far. It will also discuss the recent porting of ORNL's Multi-Objective Constrained Evolutionary Optimization (MOCEO) algorithms to ORNL's high performance computing (HPC) resources and to other resources available for Cloud computing, and the path forward for implementing additive manufacturing designs on these resources.

Published in:

Future of Instrumentation International Workshop (FIIW), 2012

Date of Conference:

8-9 Oct. 2012