Lightweight porous support structure design for additive manufacturing via knowledge-based bio-inspired volume generation and lattice configuration

ABSTRACT

Support structure plays an important role on sustaining overhang areas, resistingshape deformation and reducing thermal stress in many additive manufacturing (AM) processes. However, design of support structures in the preparation stage and removing of those structures in the post-processing stage are still time-consuming and costly. To reduce support structure volume, post-processing time and improve the printing quality, this paper proposes a novel enhanced bio-inspired generative design method, integrating parametric L-system rules and lattice structure configuration, to generate lightweight, easy-to-remove and heat-diffusion-friendly biomimetic support structures. A complex dental component with freeform geometries and discontinuous support areas is selected as a case study to compare with existing popular support design methods. The comparison results show the proposed method exhibits a good support performance for complex dental overhang areas. Hence it has potential to be adopted for other AM processes where support structures are required.

KEYWORDS:

• Support structure
• bio-inspired design
• lattice structure configuration
• knowledge-based method
• additive manufacturing

Acknowledgements

This research work is partially supported by the xxx, under grant number xxx. The authors also would like to thank Mr. xxx, PhD candidate, from xxx. for their support in providing dental models and AM process parameters for case study experiments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the China Scholarship Council [grant number 201806950079].

Notes on contributors

Zhiping Wang

ZhipingWang, is a post-doctoral research fellow in LS2N at the Ecole Centrale de Nantes. His research interest focuses on developing a set of Constructive Generative Design methods for Additive Manufacturing with ensured manufacturability.

Yicha Zhang

Yicha Zhang, Email: [email protected], Université de Technologie Belfort-Montbéliard, ICB-COMM, CNRS UMR 6303, Sevenans, France

Yicha Zhang, is now an Associate Professor at the Université de Technologie Belfort-Montbéliard (UTBM). His main research topics include design, planning and optimization for additive manufacturing (AM). He was elected as an associate member of CIRP (International Academy for Production Engineering) in 2020 and awarded the CIRP Taylor Medal for the contribution to the design & planning for AM in 2021.

Alain Bernard

Alain Bernard, is an Emeritus Professor at the Ecole Centrale de Nantes. He is engaged in the research of additive manufacturing, industrial engineering and system engineering.He is currently the Fellow Member of Academy of Technologies in France, CIRP Fellow, and Vice-President of France Additive association.