Split-Level Octree Modeling for High-Resolution 3D Printing
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Solution Overview
Problem
Additive manufacturing techniques face challenges in efficiently processing and storing large, complex three-dimensional object data models, particularly due to the exponential increase in data storage and processing resources required as resolution increases, which can exceed current hardware capabilities.
Innovation Solution
The use of a split-level octree representation method, where volumetric space is subdivided into a regular grid of sub-volume cells, and sub-volume octrees are built and merged to create a global volume octree, allowing for efficient data storage and processing by categorizing volumes as 'black', 'white', or 'grey' based on attribute presence, and using a 'Z-first' ordering for serializing data to facilitate layer-by-layer generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution data models are used for additive manufacturing, then manufacturing precision is improved, but data storage requirements and processing time increase exponentially
Solution Approach 1:
The patent applies segmentation by dividing the volumetric space into a regular grid of sub-volume cells and building separate sub-volume octrees for each cell. These sub-volume octrees are then merged to create a global volume octree. This segmentation approach allows the large complex data model to be broken down into manageable pieces that can be processed and stored more efficiently, reducing the exponential growth of data storage requirements while maintaining high-resolution manufacturing precision.
2Manufacturing precision
If high-resolution data models are used for additive manufacturing, then manufacturing precision is improved, but processing time increases
Solution Approach 1:
The patent segments the processing task by creating sub-volume octrees for individual grid cells and then merging them into a global octree structure. This segmentation enables more efficient processing of high-resolution models by organizing data in a hierarchical manner that reduces computational complexity and processing time while maintaining the required manufacturing precision.
3Ease of manufacture
If traditional data structures are used for volumetric data, then ease of implementation is maintained, but memory requirements exceed hardware capabilities
Solution Approach 1:
The patent implements the nested doll principle by creating a hierarchical octree structure where sub-volume octrees are nested within a global volume octree. Each sub-volume octree represents a portion of the total volumetric space, and these are subsequently merged into the global structure. This nested organization allows efficient memory management by only storing necessary volumetric data at appropriate levels of detail, significantly reducing memory requirements while remaining implementable with current hardware capabilities.
Data Source
AI summary
In an example, a method includes receiving a first data model of an object to be generated in additive manufacturing, at a processor. Using the processor, a second data model may be determined. Determining the second data model may include generating for each of plurality of contiguous, non-overlapping sub-volumes of a volume containing the object, a sub-volume octree characterising the sub-volume and having a root node. Determining the second data model may further include generating a volume octree characterising the volume containing the object, the volume octree characterising in its lowest nodes the root nodes of the sub-volume octrees.