Ductile fracture for clustered shape matching

Jones, Ben; Martin, April; Levine, Joshua A.; Shinar, Tamar; Bargteil, Adam W.

doi:10.1145/2856400.2856415

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…Shape matching has been extended in various ways before e.g. to simulate ductile fracture [JML*16] to handle large plastic deformation using cluster resampling [FJL*17], [CMM16]. These extensions are orthogonal to our work and will benefit from a physically based variant.…”

Section: Related Workmentioning

confidence: 99%

Physically Based Shape Matching

Müller¹,

Macklin²,

Chentanez

et al. 2022

Computer Graphics Forum

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The shape matching method is a popular approach to simulate deformable objects in interactive applications due to its stability and simplicity. An important feature is that there is no need for a mesh since the method works on arbitrary local groups within a set of particles. A major drawback of shape matching is the fact that it is geometrically motivated and not derived from physical principles which makes calibration difficult. The fact that the method does not conserve volume can yield visual artifacts, e.g. when a tire is compressed but does not bulge. In this paper we present a new meshless simulation method that is related to shape matching but derived from continuous constitutive models. Volume conservation and stiffness can be specified with physical parameters. Further, if the elements of a tetrahedral mesh are used as groups, our method perfectly reproduces FEM based simulations.

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Section: Related Workmentioning

confidence: 99%

Physically Based Shape Matching

Müller¹,

Macklin²,

Chentanez

et al. 2022

Computer Graphics Forum

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“…We would also like to explicitly state an observation regarding the range of values for the spring stiffness α which, despite being used to generate results in prior research [Falkenstein et al 2017;Jones et al 2015Jones et al , 2016, has not been formally addressed. More specifically, Müller and colleagues [2005] bound α to the range 0 ≤ α ≤ 1.…”

Section: Applying Multi-resolution Clusters To Dynamics Computationsmentioning

confidence: 99%

“…The armadillo is sampled with roughly 125K particles and our approach computes six resolution levels (with 8192, 1024, 128, 16, 2, and 1 cluster(s), respectively). This is our largest example, using a particle set roughly five times larger than that used in recent clustered shape matching research [Jones et al 2016]. Like previous examples, we begin by considering six baseline animations.…”

Section: Examplesmentioning

confidence: 99%

Multi-resolution Clustering for Enhanced Elastic Behavior in Clustered Shape Matching

Kaliappan

Bargteil

2020

Motion, Interaction and Games

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Clustered shape matching is an approach for physics-based animation of deformable objects, which breaks an object into overlapping clusters of particles. At each timestep, it computes a best-fit rigid transformation between a cluster's rest state and current particle configuration and Hookean springs are used to pull particles toward desired goal positions. In this paper, we present multi-resolution clustering as an extension to clustered shape matching. We iteratively construct fine-to-coarse sets of clusters and weights over the set of particles and compute dynamics in a single coarse-to-fine pass. We demonstrate that our approach enhances the possible elastic behavior available to artists and provides an intuitive parameterization to blend between stiffness and deformation richness, which are in contention in the traditional clustered shape matching approach that operates at a single spatial scale. We can specify a different stiffness value for each resolution level, where a greater weight at coarser levels result in a stiffer object while a greater weight at finer levels yield richer deformation; we evaluate a number of approaches for choosing these stiffness values and demonstrate the differences in the accompanying video.

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“…Early approaches to meshless fracture simulation include element-free Galerkin (EFG) Lu et al 1995;Sukumar et al 1997] which requires node-visibility algorithms; CRAMP which augments MPM to track cracks with massless particles and requires grid nodes to have multiple velocity fields near crack geometry [Nairn 2003]; Moving Least Squares with volume sampling ; and, expanding on the latter, resampling during crack propagation and adapting the shape functions dynamically [Pauly et al 2005] to simulate both brittle and ductile fracture. More recent works incorporate clustered shape matching for ductile fracture [Jones et al 2016]; the local Petrov-Galerkin method (MLPG) to avoid lengthy neighbor searches [Liu et al 2011]; and, similarly, smoothed particle hydrodynamics (SPH) has shown success in simulating brittle fracture [Chen et al 2013]. Though successful, some of these methods produce notable directional artifacts due to element removal.…”

Section: Related Work 21 Fracture Simulationmentioning

confidence: 99%