Global ETD Search

1	A Framework for Generative Product Design Powered by Deep Learning and Artificial Intelligence : Applied on Everyday Products Nilsson, Alexander, Thönners, Martin January 2018 (has links) 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. Generative Design Deep Learning Machine Learning Artificial Intelligence Variational Auto Encoder Generative Adversarial Network VAE GAN Design Variations Windows Mullions Framework Windows Dataset Computer Engineering Datorteknik

Search results

A Framework for Generative Product Design Powered by Deep Learning and Artificial Intelligence : Applied on Everyday Products