Date of Award

2021

Degree Type

Thesis

Degree Name

Master of Science in Mechanical Engineering (MSME)

Department

Mechanical, Industrial and Systems Engineering

First Advisor

David G. Taggart

Abstract

A rising demand for lightweight structures and the advance of additive manufacturing led to an increased importance of topology optimization tools and research. One developed scheme is the “Prescribed Material Redistribution" (PMR) method, which heuristically solves minimum compliance problems under a single volume constraint.

This method has not yet been fully evaluated and benchmarked. In this research, the current standalone PMR code is enhanced by using the commercial finite element software Abaqus to improve performance as well as ease of use. The novel code is then validated for 2D and 3D cases. Subsequently, a benchmark is conducted to compare the PMR approach with the “Solid Isotropic Material with Penalization" (SIMP) and \Rational Approximation of Material Properties" (RAMP) formulations commercially implemented in Simulia's Tosca Structure. The benchmark framework and 6 sample cases are defined based on a literature review. The schemes' efficiency and solution quality are investigated quantitatively and visually-qualitatively in a parametric study on 306 test cases. The analysis includes checkerboarding, mesh distortion, mesh density, and different element formulations.

Hypotheses on the effects of individual parameters are checked, and reasonable settings for the methods are derived from the findings. The benchmark findings indicate that the novel PMR code is less efficient than the commercial SIMP and RAMP implementations and the initial academic PMR version. The ease of use and the functionality is, however, significantly enhanced. The PMR and the SIMP method generated visually better results than the RAMP scheme. Based on these findings, the commercial viability of the PMR method is discussed in reference to recent topology optimization literature.

Available for download on Sunday, July 30, 2023

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