Appearance-preserving tactile optimization

Abstract

Textures are encountered often on various common objects and surfaces. Many textures combine visual and tactile aspects, each serving important purposes; most obviously, a texture alters the object's appearance or tactile feeling as well as serving for visual or tactile identification and improving usability. The tactile feel and visual appearance of objects are often linked, but they may interact in unpredictable ways. Advances in high-resolution 3D printing enable highly flexible control of geometry to permit manipulation of both visual appearance and tactile properties. In this paper, we propose an optimization method to independently control the tactile properties and visual appearance of a texture. Our optimization is enabled by neural network-based models, and allows the creation of textures with a desired tactile feeling while preserving a desired visual appearance at a relatively low computational cost, for use in a variety of applications.

Neural Metamaterial Networks for Nonlinear Material Design

“…Different from their work, which requires re-meshing and native-scale simulation per optimization step, we propose a novel learning-based approach that removes the need for native-scale simulation and re-meshing during inverse design. Similar in spirit to previous work that replaced simulation with a learned model for optimization [Tymms et al 2020;, we leverage deep neural networks as analytically differentiable surrogates for nonlinear metamaterial design.…”

Mixed Integer Neural Inverse Design

“…Perhaps photonic design is the front-runner field in using neural surrogate models for computational design [Jiang et al 2020]. Also, in computational fabrication we are seeing a surge in the use of NSMs, such as in computational design of cold-bent glass façades [Gavriil et al 2020], appearance-preserving tactile design [Tymms et al 2020], or fine art reproduction [Shi et al 2018].…”