Shape Parametrization &amp; Morphing in Sheet-Metal Forming

Thomas, Anoop Ebey; Guevelou, Simon; Pasquale, E. Di; Chambard, Anne; Duval, Jean‐Louis; Chinesta, Francisco; Limousin, Victor; Delgerie, Xavier; Leroy, Emmanuel

doi:10.1016/j.promfg.2020.04.216

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Our proposed shape blending scheme is heavily inspired by the computer graphics field, where morphing one geometric model into another has long been studied and utilized in, e.g., animation films and video games (Sanchez et al, 2015;Rohra and Kulkarni, 2019). These methods have also supported applications like medical imaging (Carballido-Gamio et al, 2005) and metalforming manufacturing simulations (Thomas et al, 2020). In fact, evolving geometries through surface representations (Breen and Whitaker, 2001) and partial differential equations (Bojsen-Hansen et al, 2012) is the foundation of the popular level-set TO method, while combining shapes using distance fields is the bedrock of TO algorithms that use geometry projection (Smith and Norato, 2020) and Movable Morphing Components (Zhang et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Remixing Functionally Graded Structures: Data-Driven Topology Optimization with Multiclass Shape Blending

Chan,

Da,

Wang

et al. 2021

Preprint

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To create heterogeneous, multiscale structures with unprecedented functionalities, recent topology optimization approaches design either fully aperiodic systems or functionally graded structures, which compete in terms of design freedom and efficiency. We propose to inherit the advantages of both through a data-driven framework for multiclass functionally graded structures that mixes several families, i.e., classes, of microstructure topologies to create spatially-varying designs with guaranteed feasibility. The key is a new multiclass shape blending scheme that generates smoothly graded microstructures without requiring compatible classes or connectivity and feasibility constraints. Moreover, it transforms the microscale problem into an efficient, low-dimensional one without confining the design to predefined shapes. Compliance and shape matching examples using common truss geometries and diversitybased freeform topologies demonstrate the versatility of our framework, while studies on the effect of the number and diversity of classes illustrate the effectiveness. The generality of the proposed methods supports future extensions beyond the linear applications presented.

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