ResourceNet: a collaboration network among decentralised manufacturing resources for autonomous exception‐handling in smart manufacturing

Qian, Cheng; Sun, Wen; Zhang, Yingfeng

doi:10.1049/iet-cim.2019.0066

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…Delaunay meshes are a specific type of manifold triangle meshes [1][2][3] that offer benefits such as the empty geodesic circumcircle property and the ability to maximise the minimum angle. Therefore, Delaunay meshes have been applied in many industrial domains such as augmented reality, virtual reality, and 3D game production [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Delaunay meshes simplification with multi‐objective optimisation and fine tuning

Fan,

Wu,

et al. 2023

IET Collab Intel Manufact

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Abstract3D meshes simplification plays an important role in many industrial domains. The two goals of Delaunay mesh simplification are maintaining high geometric fidelity and reducing mesh complexity. However, they are conflicting and cannot solved by gradient. Such limitation prevents existing Delaunay mesh simplification to obtain a small enough number of vertices and promising fidelity at the same time. To address these issues, this paper proposes an evolutionary multi‐objective approach for Delaunay mesh simplification. Firstly, the authors replace the previous fixed error‐bound threshold by the designed adaptive segment‐specific thresholds. Secondly, a constrained simplification is performed through a series of edge collapses that satisfy both Delaunay and error constraints. Next, the non‐dominated sorting genetic algorithm II (NSGA‐II) is employed to solve the multi‐objective problem to search for the optimal trade‐off threshold sequences. Finally, a fine‐tuning method is designed to further enhance the geometric fidelity of the simplified mesh. Experimental results demonstrate that the authors’ method consistently achieves a satisfactory balance between the approximation error and number of vertices, outperforming existing state‐of‐the‐art methods.

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Section: Introductionmentioning

confidence: 99%

Delaunay meshes simplification with multi‐objective optimisation and fine tuning

Fan,

Wu,

et al. 2023

IET Collab Intel Manufact

View full text Add to dashboard Cite

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Self-Organization in Smart Manufacturing— Background, Systematic Review, Challenges and Outlook

et al. 2023

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The concept of smart manufacturing has attracted huge attention in the last years as an answer to the increasing complexity, heterogeneity, and dynamism of manufacturing ecosystems. This vision embraces the notion of autonomous and self-organized elements, capable of self-management and self-decision-making under a context-aware and intelligent infrastructure. While dealing with dynamic and uncertain environments, these solutions are also contributing to generating social impact and introducing sustainability into the industrial equation thanks to the development of task-specific resources that can be easily adapted, re-used, and shared. A lot of research under the context of self-organization in smart manufacturing has been produced in the last decade considering different methodologies and developed under different contexts. Most of these works are still in the conceptual or experimental stage and have been developed under different application scenarios. Thus, it is necessary to evaluate their design principles and potentiate their results. The objective of this paper is threefold. First, to introduce the main ideas behind self-organization in smart manufacturing. Then, through a systematic literature review, describe the current status in terms of technological and implementation details, mechanisms used, and some of the potential future research directions. Finally, the presentation of an outlook that summarizes the main results of this work and their interrelation to facilitate the development of self-organized manufacturing solutions. By providing a holistic overview of the field, we expect that this work can be used by academics and practitioners as a guide to generate awareness of possible requirements, industrial challenges, and opportunities that future self-organizing solutions can have towards a smart manufacturing transition.

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ResourceNet: a collaboration network among decentralised manufacturing resources for autonomous exception‐handling in smart manufacturing

Cited by 2 publications

References 15 publications

Delaunay meshes simplification with multi‐objective optimisation and fine tuning

Delaunay meshes simplification with multi‐objective optimisation and fine tuning

Self-Organization in Smart Manufacturing— Background, Systematic Review, Challenges and Outlook

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