Partition-based Print Sequence Planning and Adaptive Slicing for Scalar Field-based Multi-axis Additive Manufacturing - Lau, Tak Yu; Chen, Li; He, Dong; Li, Zhaoyu; Tang, Kai (Computer aided design, 2023)
While multi-axis additive manufacturing is found to be a good solution to the inherent limitations of conventional 2.5D additive manufacturing, it is a much more sophisticated process. Among different existing multi-axis process planning algorithms, we are interested in those based on a scalar field, in which print slices are the iso-surfaces of a scalar field embedded in the 3D model. In this paper, we propose a partitioned-based print sequence planning algorithm and an adaptive slicing algorithm, which together determine a complete multi-axis printing process for an arbitrary solid model. The first algorithm iteratively subdivides the model into a set of components such that a collision-free print sequence can be established among the components. The second algorithm then extracts print slices from each component such that all these slices satisfy the self-support condition. Since an arbitrary model may not satisfy both the self-support and collision-free requirements, we also define certain critical printability rules at the beginning to check whether a given input model with its associated scalar field is printable. The generated print slices and print sequence by the proposed two algorithms are guaranteed to be printable. Furthermore, a shorter total fabrication time and a better surface quality are achieved. Physical experiments of four test models are performed on a homebuilt multi-axis FDM printer, whose results verify the capabilities of the proposed algorithms. •A new print sequence planning algorithm based on partitioning for a 3D model associated with a scalar field is proposed.•An adaptive slicing paradigm for scalar field-based multi-axis printing is proposed to reduce the print time.•The developed algorithms guarantee printability, i.e., self-support and collision-free.•Computer simulations and physical experiments are performed on four models to validate the correctness of our algorithms.