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Jun Wu

Researcher at Delft University of Technology

Publications -  73
Citations -  2257

Jun Wu is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Topology optimization & Computer science. The author has an hindex of 21, co-authored 66 publications receiving 1494 citations. Previous affiliations of Jun Wu include Technische Universität München & Shanghai University.

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Infill Optimization for Additive Manufacturing—Approaching Bone-Like Porous Structures

TL;DR: This paper presents a method to generate bone-like porous structures as lightweight infill for additive manufacturing and proposes upper bounds on the localized material volume in the proximity of each voxel in the design domain.
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Self-supporting rhombic infill structures for additive manufacturing

TL;DR: A novel method for generating application-specific infill structures on rhombic cells so that the resultant structures can automatically satisfy manufacturing requirements on overhang-angle and wall-thickness and is demonstrated in the applications of improving mechanical stiffness and static stability.
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Minimum Compliance Topology Optimization of Shell-Infill Composites for Additive Manufacturing

TL;DR: In this article, a method for generating simultaneously optimized shell and infill in the context of minimum compliance topology optimization is presented. But this method is not suitable for additive manufacturing.
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Topology optimization of multi-scale structures: a review

TL;DR: In this paper, the authors present a review of existing approaches for topology optimization of multi-scale structures, explaining the principles of each category, analyzing their strengths and applicability, and discussing open research questions.
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A System for High-Resolution Topology Optimization

TL;DR: A scalable system for generating 3D objects using topology optimization, which allows to efficiently evolve the topology of high-resolution solids towards printable and light-weight-high-resistance structures and shed light on the question how to incorporate geometric shape constraints, such as symmetry and pattern repetition, in the optimization process.