A multiobjective topology optimization approach for cost and time minimization in additive manufacturing

Abstract

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.

Simultaneous single‐loop multimaterial and multijoint topology optimization

“…This has been extended to design shell‐infill structures for additive manufacturing (AM) by Wu et al Spatial gradients have also been used to enforce overhang constraints when performing TO for AM. This has been demonstrated by Qian and more recently by Ryan and Kim . Spatial gradients have also been used previously by Peterson and Sigmund to enforce solution existence through slope constraints on the design variable field.…”

“…Thus, the field must be first projected into discrete material choices using a Heaviside scheme. In the literature, this approach was proven by Ryan and Kim and Guest et al to be effective in enforcing binary choices in a smooth and numerically stable fashion. The projection of the design variable field can be written using the approximation given by Qian where is the projected field such that is now a highly discretized material selection (values close to 0 or 1).…”

“…Mirzendehdel and Suresh and Langelaar both propose methods for support minimization. Another area of interest is in the elimination of unwanted void regions with solutions proposed by Liu et al and Li et al A holistic approach for cost and compliance minimization in AM was proposed by Ryan and Kim . Thompson et al performed a thorough review of TO for AM.…”

Void region restriction for additive manufacturing via a diffusion physics approach

“…Cost and time minimization of AM processes are continually being improved, encouraging the proliferation of AM into low and mid-volume production runs. [1][2][3][4][5] As a result, the advantages of AM technology will offer an attractive alternative to traditional manufacturing methods. As the number of academic papers focusing on improving AM technology by way of design optimization continues to exponentially increase, fundamental roadblocks that had previously gone unchallenged present themselves as the next frontier.…”

“…Historically, the first instance of a TO-based solution for a fundamental challenge in AM involved addressing the minimization of overhung areas requiring support material; many authors have since presented elegant solutions to this issue. 3,[6][7][8][9][10] Other topics including lattice infill optimization, design rules for surface finish control, support structure optimization, structural-thermal coupled optimization, orientation optimization, and parts consolidation are but a few issues that have been endeavored thus far and continue to be active areas of research. 11,12 One challenge that has received little attention is that of increasing post-processing tool accessibility to support material or unused build material powder in powder bed fusion AM techniques.…”