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University of Ontario Institute of Technology

Customized topology optimization for additive manufacturing

Abstract

dc:description.abstract

One of the biggest limitations of additive manufacturing (AM) is the resulting production times. Due to the layer-based method of material deposition, the time to produce a single part is substantial compared to techniques like injection molding or casting. However, the level of part complexity that can be achieved using AM processes is also unrivaled. This is a perfect match for the structural design method of topology optimization. It often produces parts with complex organic features that can perform substantially better in terms of weight and stiffness compared to their conventionally designed counterparts. Thus, an AM topology optimization constraint is developed to address the limitations of these processes while maintaining the advantages of the optimization. This is achieved through a penalization scheme applied to boundary contours identified through a slicing mechanism. The result is parts that print substantially faster, while only losing some stiffness compared to the normal topology optimization.

Degree

thesis:*
Name thesis:degree_name
Master of Applied Science (MASc)
Discipline thesis:degree_discipline
Mechanical Engineering
Grantor
University of Ontario Institute of Technology
Year dc:date.issued
2019

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Jankovics, Davin
Advisor dc:contributor.advisor
  • Barari, Ahmad

Subjects

dc:subject × 5

Rights

Language dc:language.iso
en

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/10155/1105
OAI identifier oai:identifier
oai:ontariotechu.scholaris.ca:10155/1105

Chain of custody

source
Harvested from
Ontario Institute of Technology
Base URL
ontariotechu.scholaris.ca/server/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

Jankovics, Davin. Customized topology optimization for additive manufacturing. University of Ontario Institute of Technology, 2019. https://hdl.handle.net/10155/1105