Elsevier

Computer-Aided Design

Volume 115, October 2019, Pages 194-205
Computer-Aided Design

Generative Design Conversion to Editable and Watertight Boundary Representation,☆☆

https://doi.org/10.1016/j.cad.2019.05.016Get rights and content

Highlights

  • High-quality conversion method to obtain editable CAD models from generative designs.

  • Combines exact residual geometry of the input solids and editable T-NURCC surfaces.

  • Fully automatic algorithm produces watertight B-Reps.

  • Any combination of additive and subtractive in-synthesis processes and constraints.

Abstract

We present the first, to our knowledge, fully automatic method for the conversion of a general generative design to a watertight B-Rep composed of the exact residual geometry of the input solids and editable T-NURCC surfaces. The design can be synthesized by any combination of both additive and subtractive in-synthesis processes, and constrained by any number of keep-in and -out solids. Our method requires only the input generative setup solids, rudimentary data from the solve, and a boundary mesh of the optimized design.

Leveraging the generative solve data, we augment the boundary mesh with “incidence” attributes linking it to the input solids, and partition it into organic and incident regions. The organic regions are parameterized and approximated with T-NURCCs. Then, their boundaries are “pulled” into the adjacent input solids to construct clean intersection curves. Finally, the organic surfaces are combined with the input solids to compose a watertight solid B-Rep of the generative design.

Without such an automatic conversion, users seeking to exploit generative designs in downstream workflows (e.g. setting up assembly constraints, simulation analyses, performing parametric edits) have to manually convert the design to a B-Rep, slowing down the path to manufacturing and increasing the product time to market. As a result of our work, engineers can now quickly obtain useful CAD models of any number of generative designs computed by a variety of algorithms, settings and iterations.

Introduction

Generative design is a modern approach to synthesizing design alternatives by exploring the entire design space to achieve objectives such as minimal structural compliance, given a set of functional requirements and constraints based on geometry, material specifications, and manufacturing considerations. A precursor to this technology is topology optimization [1], a niche tool employed by a small number of expert users using specialized simulation software and know-how along with a significant budget. The availability of cloud as an “infinite computing” platform and novel manufacturing methods like additive manufacturing have opened up the possibility of using generative technology as a new approach to design. This capability is starting to become available in CAD packages such as [2], enabling users to leverage generative designs in diverse areas including aerospace, automotive, consumer products and architecture.

The generative design geometry constraints are defined as solid B-Reps and are placed in a R3 optimization domain to avoid interference (keep-out) and specify attachment joints (keep-in) with other parts in a CAD assembly. However, many generative solvers, e.g., [3], [4], [5], do not operate directly on the exact geometry constraint B-Reps. Instead, B-Reps are sampled and replaced with volumetric representations such as level sets or tetra-/hexahedral meshes, which are significantly more convenient and efficient for physical simulations and material synthesis.

Inevitably, this conversion introduces a loss of surface precision due to the limitations of the target representation (Fig. 1). While this loss is negligible in the context of the generative computations, in general it prevents the output of a generative design that accurately represents the input solid surface geometry away from the organic material synthesized by the solver. Subsequently, when integrating the design within the complete model assembly, these surface inaccuracies can cause issues such as part interferences and manufacturing deficiencies (Fig. 3).

To resolve these inaccuracies, our fully automatic method (Fig. 2) converts a generative design into an editable, watertight B-Rep by leveraging the generative solver input and representation to:

  • embed the exact input solid boundary surfaces where the design coincides with the input,

  • approximate everywhere else the design boundary with globally smooth, editable “organic” surfaces, and

  • join all surfaces to form a generative design output B-Rep.

Our method is applicable to generative designs that represent arbitrary combinations of material additions and subtractions, and leverages the solver geometry representation.

Section snippets

Related work

General B-Rep surface reconstruction from meshes and point clouds is a highly prominent and long-standing research topic with particularly important applications in the reverse engineering of existing physical objects [6], [7], [8], [9]. Reviewing this work is beyond the scope of our paper, so we refer to several recent comprehensive surveys: [10], [11], [12].

More relevant to our method is research work which focuses on partitioning and approximating a mesh with piece-wise smooth surfaces [13],

Input data

The primary input to our method consists of the generative solver input solid B-Reps {Si} and output mesh M representing the generative design G. To link M to {Si}, we can also process the intermediate solver representation (level set or volumetric mesh). Alternatively, the solver representation accuracy error, e.g., the level set voxel grid edge length, can be supplied instead.

{Si} designate sub-spaces of the optimization domain. We use the standard notation Si and Sio to denote the solid

Mesh partition

The first stage of our algorithm partitions M into a pair of disjoint triangle subsets T=O, where represents the input solid B-Reps in M, and O represents the boundary surface of the “organic” material synthesized by the solver. We use to denote the union of disjoint sets. To compute this partition, our method builds an “incidence” relation I:MP(Si) using the solver geometry representation, where P(Si) is the power set of Si. I can map a mesh element to zero, one or multiple input solids.

Organic boundary

Let B be the subset of edges separating O and . We refer to B as the organic boundary and to its elements as the organic boundary edges, or just boundary edges: this shorthand notation is not ambiguous since M is closed by definition.

Organic surfaces

We construct an editable smooth surface Oi for each organic component Oi. As the construction is identical Oi, in this section the component index i is omitted to simplify the notation.

Our representation of choice is T-NURCCs [56] due to the following useful properties:

  • The surface is defined by a quad control mesh, a very popular tool for shape editing employed by industry designers.

  • Local refinement [57] allows for error reduction by adding control points only where needed [27], [28].

  • General

Boundary contact

Typically, the constructed organic surface boundary O=Oi is within a small distance ( the generative solver accuracy) away from the input solid constraints S. For the composed output B-Rep to be watertight, it is necessary that OS. Hence, we apply a small “pull” modification to each organic surface boundary Oi to move it in the interior of S (Fig. 9). Then, we construct the corresponding B-Rep contact intersection curves Ci,j=OiSj to ensure the successful outcome of our subsequent

Composition

The final stage of our method composes the watertight B-Rep representing the generative design by combining the organic surfaces Oi and the input solids Si. Contact curves for Oi are now constructed, so it only remains to determine what volume must be assigned to the output generative B-Rep.

So far, we did not disambiguate in our algorithms between keep-in and keep-out input solids. We now define the subsets KLP=S where K, L and P are respectively the keep-in, keep-out and seed input solids.

Results

We include five more generative designs conversions output by our method (Fig. 10, Fig. 11, right column). In all illustrations, the input mesh is shown after being partitioned into incident (yellow) and organic (white) regions. Input keep-in (transparent, red tint) and keep-out (transparent, blue tint) bodies are overlaid on the organic T-NURCC surfaces (white). In all results, T-spline patches have been merged where possible to produce a simpler patch layout for the output B-Rep.

All

Conclusion

We propose a fully automatic method for generative design to B-Rep conversion capable of processing extremely complex and intricate shapes, implemented and tested over a wide range of data. We hope it can be a driver for growth of the popularity and adoption of generative design models. Engineers and designers looking to incorporate generative designs in their workflows are no longer constrained by the inaccuracy and detail loss of the solver output: instead they can work with an editable,

Acknowledgments

We would like to thank the anonymous reviewers for their helpful feedback, Max Lyon, David Bommes and Leif Kobbelt for their support with integrating IGM, Andreas Bastian, Bryce Heventhal, Karl Willis and Michael Smell for providing generative design test data, and our colleagues in the Autodesk modeling components, simulation, Fusion 360, platform and research groups for supporting the development of our algorithms. This work is funded by Autodesk, Inc, United States .

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    This paper has been recommended for acceptance by Pierre Alliez, Yong-Jin Liu & Xin Li.

    ☆☆

    One or more of the authors of this paper have disclosed potential or pertinent conflicts of interest, which may include receipt of payment, either direct or indirect, institutional support, or association with an entity in the biomedical field which may be perceived to have potential conflict of interest with this work. For full disclosure statements refer to https://doi.org/10.1016/j.cad.2019.05.016.

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