Information reuse to accelerate customized product slicing for additive manufacturing

Different from most traditional manufacturing processes, the productivity of additive manufacturing (AM) is independent of the geometric complexity from the object to be built. This characteristic opens up tremendous potentials to realize mass customization. However, the AM-specific parts, such as customized products, need to be represented by millions of triangle meshes. Moreover, a large number of sliced layers are needed with the increased resolution of AM machines. These together pose a fundamental challenge in slice generation. The slicing procedure for a single customized model can take tens of minutes or even hours to complete, and the time consumption becomes more prominent in the context of mass customization. We propose a new slicing paradigm which capitalizes upon the similarities among customized models, and it reuses information obtained from the template model slicing. The idea of information reuse is implemented at several different levels depending on variations between the customized model and the template model. Experimental results show that the proposed slicing paradigm can significantly reduce the time consumption on slicing process, and ultimately fulfill mass customization enabled by AM.