Mixed-layer adaptive slicing for robotic Additive Manufacturing (AM) based on decomposing and regrouping

Donghua Zhao () and Weizhong Guo ()
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Donghua Zhao: Shanghai Jiao Tong University
Weizhong Guo: Shanghai Jiao Tong University

Journal of Intelligent Manufacturing, 2020, vol. 31, issue 4, No 12, 985-1002

Abstract: Abstract AM, generally known as 3D printing, is a promising technology. Robotic AM enables the direct fabrication of products possessing complex geometry and high performance without extra support structures. Process planning of slicing and tool path generation has been a challenging issue due to geometric complexity, material property, etc. Simple and robust planar slicing has been widely researched and applied. However, support structures usually result in time-consuming and cost-expensive. Notwithstanding multi-direction slicing and non-planar slicing (curved layer slicing) have been proposed respectively to decrease support structures, capture some minute but critical features and improve the surface quality and part strength. There is no slicing method aiming at features of part’s sub-volumes. A comprehensive literature review is given first to illustrate the problems and features of available slicing methods better. Then, in order to combine the merits of planar and non-planar slicing to realize intelligent manufacturing further, this paper reports the concept and implementation of a mixed-layer adaptive slicing method for robotic AM. Different from applying planar slicing in any cases or adopting the decomposing and regrouping based multi-direction planar slicing for finding the optimal slicing directions, the proposed method mainly focuses on how to apply planar and non-planar slicing for each sub-volume according to the geometrical features. Additionally, the requirements for robotic AM equipment in possessing multi-mode of printing and slicing are investigated.

Keywords: Mixed-layer adaptive slicing; Robotic AM; Planar slicing; Non-planar slicing; Decomposing and regrouping (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10845-019-01490-z

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