Universal and efficient hybrid modeling and direct slicing method for additive manufacturing processes

Sen-Lin Wang, Li-Chao Zhang, Chao Cai, Ming-Kai Tang, Si Chen, Jiang Huang, Yu-Sheng Shi

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (2) : 300-316.

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (2) : 300-316. DOI: 10.1007/s40436-023-00468-8
Article

Universal and efficient hybrid modeling and direct slicing method for additive manufacturing processes

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Abstract

Model design and slicing contour generation in additive manufacturing (AM) data processing face challenges in terms of efficiency and scalability when stereolithography files generated by complex functionally graded structures have millions of faces. This paper proposes a hybrid modeling and direct slicing method for AM to efficiently construct and handle complex three-dimensional (3D) models. All 3D solids, including conformal multigradient structures, were uniformly described using a small amount of data via signed distance fields. The hybrid representations were quickly discretized into numerous disordered directed lines using an improved marching squares algorithm. By establishing a directional HashMap to construct the topological relationship between lines, a connecting algorithm with linear time complexity is proposed to generate slicing contours for manufacturing. This method replaces the mesh reconstruction and Boolean operation stages and can efficiently construct complex conformal gradient models of arbitrary topologies through hybrid modeling. Moreover, the time and memory consumption of direct slicing are much lower than those of previous methods when handling hybrid models with hundreds of millions of faces after mesh reconstruction.

Keywords

Additive manufacturing (AM) / Hybrid modeling / Direct slicing / Signed distance field

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Sen-Lin Wang, Li-Chao Zhang, Chao Cai, Ming-Kai Tang, Si Chen, Jiang Huang, Yu-Sheng Shi. Universal and efficient hybrid modeling and direct slicing method for additive manufacturing processes. Advances in Manufacturing, 2024, 12(2): 300‒316 https://doi.org/10.1007/s40436-023-00468-8

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Funding
Key Area R&D Program of Guangdong Province(2020B090924002); National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809(51790174)

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