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A Framework for Generative Product Design Powered by Deep Learning and Artificial Intelligence: Applied on Everyday Products
Linköping University, Department of Management and Engineering, Machine Design.
Linköping University, Department of Management and Engineering, Machine Design.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this master’s thesis we explore the idea of using artificial intelligence in the product design process and seek to develop a conceptual framework for how it can be incorporated to make user customized products more accessible and affordable for everyone.

We show how generative deep learning models such as Variational Auto Encoders and Generative Adversarial Networks can be implemented to generate design variations of windows and clarify the general implementation process along with insights from recent research in the field.

The proposed framework consists of three parts: (1) A morphological matrix connecting several identified possibilities of implementation to specific parts of the product design process. (2) A general step-by-step process on how to incorporate generative deep learning. (3) A description of common challenges, strategies andsolutions related to the implementation process. Together with the framework we also provide a system for automatic gathering and cleaning of image data as well as a dataset containing 4564 images of windows in a front view perspective.

Place, publisher, year, edition, pages
2018. , p. 112
Keywords [en]
Generative Design, Deep Learning, Machine Learning, Artificial Intelligence, Variational Auto Encoder, Generative Adversarial Network, VAE, GAN, Design Variations, Windows, Mullions, Framework, Windows Dataset
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-149454ISRN: LIU-IEI-TEK-A--18/03082—SEOAI: oai:DiVA.org:liu-149454DiVA, id: diva2:1229712
External cooperation
SkyMaker AB
Subject / course
Machine Design
Supervisors
Examiners
Available from: 2018-12-03 Created: 2018-07-02 Last updated: 2018-12-03Bibliographically approved

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3233343536373835 of 88
CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf