Interactive design space exploration and optimization for CAD models

Abstract

Fig. 1. Our method harnesses data from CAD systems, which are parametric from construction and capture the engineer's design intent, but require long regeneration times and output meshes with different combinatorics. We sample the parametric space in an adaptive grid and propose techniques to smoothly interpolate this data. We show how this can be used for shape optimization and to drive interactive exploration tools that allow designers to visualize the shape space while geometry and physical properties are updated in real time.Computer Aided Design (CAD) is a multi-billion dollar industry used by almost every mechanical engineer in the world to create practically every existing manufactured shape. CAD models are not only widely available but also extremely useful in the growing field of fabrication-oriented design because they are parametric by construction and capture the engineer's design intent, including manufacturability. Harnessing this data, however, is challenging, because generating the geometry for a given parameter value requires time-consuming computations. Furthermore, the resulting meshes have different combinatorics, making the mesh data inherently discontinuous with respect to parameter adjustments. In our work, we address these challenges and develop tools that allow interactive exploration and optimization of parametric CAD data. To achieve interactive rates, we use precomputation on an adaptively sampled grid and propose a novel scheme for interpolating in this domain where each sample is a mesh with different combinatorics. Specifically, we extract partial correspondences from CAD representations for local mesh morphing and propose a novel interpolation method for adaptive grids that is both continuous/smooth and local (i.e., the influence of each sample is constrained to the local regions where mesh morphing can be computed). We show examples of how our method can be used to interactively visualize and optimize objects with a variety of physical properties.

SimuLearn

“…For elastic materials, [9,23,48] used variations of FEA to enable users to predict and design shape-changing interfaces with complex deformation behaviors. For rigid materials, Forte [11], AutoConnect [19], and [36,45] used physical simulations to augment design tools and produce structurally optimized objects. In architectural scales, TrussFab [18], and TrussFormer [17] also used interactive simulation to guide users to design pavilions that met structural demands.…”

InverseCSG

“…Koyama et al [2015] use CAD to parametrize the space of manufacturable connectors. Similarly, Shugrina et al [2015] and Schulz et al [2017] use parametric CAD models for fabricationoriented design optimization. These recent works argue that the advantage of directly using CAD models over algorithms for automatic parametrization of an input 3D mesh [Bokeloh et al 2012;Jacobson et al 2011;Zheng et al 2011] include: (1) greater and more meaningful geometric variations (as opposed to some dimensions) and (2) automatic exposure of relational constraints that can be used to understand function and support multiple fabrication methods.…”

“…This enables engineers to iterate over the design parameters to improve the performance of objects or to adapt existing designs so that they can be reused in new scenarios. For this reason, there has been a growing interest in using parametric CAD representations for fabrication-oriented design exploration and optimization algorithms [Schulz et al 2017;Shugrina et al 2015].…”

GenYacht: An interactive generative design system for computer-aided yacht hull design

“…The interaction with a human evaluator facilitates the generation of a solution that incorporates human intuition without explicitly codifying them into the design process. In interactive design systems, user carry out the design exploration either with interactive interfaces [13,14,15,16] or by integrating the meta-heuristics with the interactive interfaces to semi-automate the exploration process [5,17,18,19]. In the latter approaches, users are interactively involved at each iteration/generation of an optimiser and guide the optimisation process towards the promising regions of the design space.…”