A visualization of mechanical Kansei using the DNN approach

Abstract

It is illustrated in general that the decision-making in conceptual design is highly dependent on the designer's aesthetic sense. However, even in a subjective phase, objective aspects such as mechanical rationality are implicitly or explicitly considered, and these two perspectives are inseparable. A concept of mechanical Kansei defined as “the capability to evaluate and judge impressions in terms of unconscious, intuitive, and integrative information in mechanics,” has been proposed to elucidate how this capability is utilized for design. In this article, we focus on the capability to unconsciously recall the force flow inside a structure; and take the topology-optimized structures as the representation of such force flow. Through learning the structural shapes with a variational autoencoder (VAE), dimensionality compression from the large design variable space to the lower dimensional latent variable space is performed. As a result, it was shown that the two-dimensional latent space reflected the geometric positions of randomly located supports on the design domain. The obtained mappings from the structural conditions to the representation of force flow in structure within the encoder and decoder of VAE can help to understand the mechanical Kansei.