Journal of Manufacturing Processes· 2026Q1
An efficient partitioning method for curved-surface laser texturing based on bipolar projection and multi-level pruning
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- Q1SCImago
- 2026year
Short summary
A new method partitions curved surfaces for laser texturing 69.5% more efficiently and 60% faster than existing techniques, reducing partition numbers and boundary lengths.
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Key points
- Proposes a Freeform Mesh Partitioning Method Based on Bipolar Projection and Multi-Level Manhattan-Euclidean Pruning.
- Achieves significant reductions in partition number (up to 69.5%) and boundary length (up to 21.8%) compared to Watershed and Random Seed.
- Reduces computation time by up to 60% while producing more complete regions and regular boundaries.
- Tested on Armadillo, Typical Surface, and Bunny models across 12 orthogonal cases.
AI-generated from the title and abstract; the full text is not read.
Abstract
Laser surface texturing (LST), as a highly promising technology, is applied to surface functional enhancement by forming special micron/nanometer-level texture structures. Curved-surface LST requires surface partitioning to satisfy laser process constraints. Dedicated partitioning methods for this application remain limited. This paper proposes a Freeform Mesh Partitioning Method Based on Bipolar Projection and Multi-Level Manhattan-Euclidean Pruning. Bipolar projection is employed for rapid angular candidate mapping, and multi-level Manhattan-Euclidean pruning is utilized for progressive pruning, thereby enabling the efficient and regular partitioning of complex freeform surfaces. Tests on Armadillo, Typical Surface, and Bunny models under 12 orthogonal cases show reductions of 69.5%/39.6% in partition number, 21.8%/16.9% in boundary length, and 60.0%/93.0% in computation time versus Watershed and Random Seed, while producing more complete regions and regular boundaries. Consequently, an effective solution for efficient partition planning in curved-surface LST is provided.
The authors' abstract, as published at the source. Journal of Manufacturing Processes, 2026 · DOI ↗
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Field: Computer Graphics and Computer-Aided Design
Computer Graphics and Computer-Aided DesignComputer Science