An adaptive slicing approach for processing STL massive data model in batches based on layer merging

Abstract

Abstract Slicing is one of the core parts of the additive manufacturing software system, which completes the function of transforming the 3D model into a 2D profile. The adaptive slicing algorithm uses different layer thicknesses for layering in different areas according to the changes in the geometry of the mesh model. Where the model is more complex and the curvature changes a lot, a smaller layer thickness is used to approximate the model. In areas of large curvature of the model, maximum layer thickness is used to improve printing efficiency. With the development of additive manufacturing technology, grid model files such as STL have become larger and larger, and the amount of data needed to be processed by slicing software has increased dramatically. Existing slicing software is limited by computer hardware and cannot process massive data model. It greatly limits the development of additive manufacturing technology, so it is urgent to propose a new method to solve the rapid stratification of mass data model. In this paper, we proposed an adaptive slicing approach for processing STL massive data model in batches based on layer merging. At the same time, it is compared with the fixed-layer thickness slicing method, which shows that the algorithm can improve printing efficiency under the premise of ensuring accuracy.