Methodological approach to incorporate Deep Generative and Natural Language Processing models in the engineering design process

Abstract

The engineering design process allows to create or enhance designs to fulfill any particular need methodologically. With the advances in artificial intelligence, mainly in Deep Learning, a new perspective is coming to systems engineering design, aided by intelligent algorithms. However, a downfall of engineering design is the lack of quantitative outcomes, making troublesome the use of artificial intelligence. For this, one of the solutions is using axiomatic design (AD) in the design process of systems. The work presented in this thesis, introduces a methodology that involves classic engineering methodologies and the proposal of incorporating current state-of-the-art algorithms and models for the conceptual design, as the main contribution of the research work. This research work and methodology proposal is meant to reduce the time of the design process and understand the needs/requirements implicated in the same design process, with the possibility of developing more robust designs closer to the original needs/requirements.