An interaction-aware, perceptual model for non-linear elastic objects

Abstract

Force-displacement Force histogramPhysically based simulation Stiff Soft Figure 1: 3D printing allows us to print objects with varying deformation properties. The question that we want to answer is: Given a set of printing materials and a 3D object with desired elasticity properties, which material should be used to print the object? For example, given sample ducks (left) with desired elasticity properties (e.g., measured), our system considers several candidate materials that can be used for replicating the ducks (right), and chooses materials that will best match compliance properties when examined by an observer (red and green outlines). Moreover, we can sort all possible materials by their perceived compliance as predicted by our model. The measured compliance is indicated with colors ranging from stiff (blue) to soft (red). AbstractEveryone, from a shopper buying shoes to a doctor palpating a growth, uses their sense of touch to learn about the world. 3D printing is a powerful technology because it gives us the ability to control the haptic impression an object creates. This is critical for both replicating existing, real-world constructs and designing novel ones. However, each 3D printer has different capabilities and supports different materials, leaving us to ask: How can we best replicate a given haptic result on a particular output device? In this work, we address the problem of mapping a real-world material to its nearest 3D printable counterpart by constructing a perceptual model for the compliance of nonlinearly elastic objects. We begin by building a perceptual space from experimentally obtained user comparisons of twelve 3D-printed metamaterials. By comparing this space to a number of hypothetical computational models, we identify those that can be used to accurately and efficiently evaluate human-perceived differences in nonlinear stiffness. Furthermore, we demonstrate how such models can be applied to complex geometries in an interaction-aware way where the compliance is influenced not only by the material properties from which the object is made but also its geometry. We demonstrate several applications of our method in the context of fabrication and evaluate them in a series of user experiments.

Tactile perception of randomly rough surfaces

“…The objective of our study was to quantify the relative importance of topography or shape and Hurst exponent or micro-scale roughness in the perception of randomly rough surfaces. We therefore investigated perceived similarity in a forced choice scheme with two alternatives and one reference in order to minimize bias in the decisions 4,[19][20][21] . We will discuss below how our implicit question about similarity still allows to estimate just noticeable differences in friction or micro-roughness.…”

Mechanical Metamaterials on the Way from Laboratory Scale to Industrial Applications: Challenges for Characterization and Scalability

“…As this community is very interested in human perception, experiments are created with human touch in mind, for example recreating a finger pushing a material locally, similarly to local indentation experiments. [87] The main focus of mechanical experiments in publications is showcasing the deformation behavior of the fabricated unit cells or unit cell networks as a proof-of-concept, even though the latter are less thoroughly studied compared to the behavior of single unit cells. Very few publications target failure mechanisms and the impact of defects [70,88].…”

Decoupling simulation accuracy from mesh quality

“…These methods exploit almost exclusively h-refinement (i.e., the size of the elements changes, not their order), and are used for problems involving large deformations, including the emergence of geometric features or discontinuities. Examples include tearing and cracking [Pfaff et al 2014], resolving features on cloth [Simnett et al 2009], cutting [Seiler et al 2011], image retargeting [Kaufmann et al 2013], elastoplasticity [Wicke et al 2010;Piovarči et al 2016], and viscoelasticity [Wojtan and Turk 2008]. At the same time, a sampling of recent works using FEM, [Xu and Barbič 2016;Kim et al 2017;Liu et al 2017;Wang et al 2017] shows that adaptivity is commonly avoided whenever possible, as simulation on a fixed mesh offers substantial benefits, especially for interactive applications, enabling precomputation and avoiding resampling of quantities stored on the mesh (e.g., skinning weights, UV coordinates, colors).…”