A multiobjective topology optimization approach for cost and time minimization in additive manufacturing. (13th January 2019)

• Record Type: Journal Article
• Authors: Ryan, Luke; Kim, Il Yong
• Is Part Of: International journal for numerical methods in engineering. Volume 118:Number 7(2019)
• Page Start: 371
• Page End: 394
• Publication Date: 2019-01-13
• Subjects: additive manufacturing -- density gradient -- manufacturing constraints -- overhang angle -- support material -- topology optimization; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 620.001518
• DOI: 10.1002/nme.6017
• Languages: English
• ISSNs: 0029-5981

Abstract

Summary: The ever‐present drive for increasingly high‐performance designs realized on shorter timelines has fostered the need for computational design generation tools such as topology optimization. However, topology optimization has always posed the challenge of generating difficult, if not impossible to manufacture designs. The recent proliferation of additive manufacturing technologies provides a solution to this challenge. The integration of these technologies undoubtedly has the potential for significant impact in the world of mechanical design and engineering. This work presents a new methodology which mathematically considers additive manufacturing cost and build time alongside the structural performance of a component during the topology optimization procedure. Two geometric factors, namely, the surface area and support volume required for the design, are found to correlate to cost and build time and are controlled through the topology optimization procedure. A novel methodology to consider each of these factors dynamically during the topology optimization procedure is presented. The methodology, based largely on the use of the spatial gradient of the density field, is developed in such a way that it does not leverage the finite element discretization scheme. This work investigates a problem that has not yet been explored in the literature: direct minimization of support material volume in density‐based topology optimization. The entire methodology is formulated in a smooth and differentiable manner, and the sensitivity expressions required by gradient based optimization solvers are presented. A series of example problems are provided to demonstrate the efficacy of the proposed methodology.