Indexing Cells of N-Dimensional Objects for Additive Manufacturing
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Solution Overview
Problem
Existing methods for processing n-dimensional objects, such as 2D and 3D models, are inefficient in representing and processing data with varying properties and resolutions, particularly in additive manufacturing, as they require uniform grids or tree structures like octrees that are not well-suited for high-speed processing and efficient data representation.
Innovation Solution
The method involves indexing cells of n-dimensional objects by grouping indexing digits for each orthogonal axis, allowing for efficient encoding and processing of property data, and generating control data for printing, which enables varying resolutions and fast processing by subdividing objects into cells with different properties and indexing them for efficient storage and transfer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform grids or tree structures like octrees are used to represent n-dimensional objects, then data representation is achieved, but processing speed and efficiency deteriorate
Solution Approach 1:
The patent segments the n-dimensional object into multiple cells with different resolutions, allowing different parts of the object to be represented at different levels of detail. This segmentation enables efficient processing by focusing computational resources on regions requiring higher precision while using coarser representation for less critical areas, thereby resolving the contradiction between data representation accuracy and processing speed.
Solution Approach 2:
The patent applies local quality by assigning different resolutions to different cells within the n-dimensional object. Each cell can have its own resolution level, allowing high-resolution representation where needed and low-resolution representation elsewhere. This local differentiation maintains data representation accuracy in critical regions while improving overall processing speed by reducing the computational burden in less critical regions.
2Productivity
If varying resolutions are implemented for different parts of n-dimensional objects, then data efficiency improves, but data structure complexity increases
Solution Approach 1:
The patent divides the n-dimensional object into a grid of cells, where each cell can independently specify its resolution level. This segmentation approach simplifies the data structure compared to complex tree structures like octrees, while still enabling varying resolutions across different regions. The grid-based segmentation with per-cell resolution specification achieves data processing efficiency without introducing excessive structural complexity.
3Manufacturing precision
If high-resolution data is used for entire n-dimensional objects, then manufacturing precision improves, but memory usage and processing time increase
Solution Approach 1:
The patent applies local quality by assigning different resolution levels to different cells within the n-dimensional object. Regions requiring high manufacturing precision are represented with high resolution, while other regions use lower resolution. This approach maintains print quality in critical areas while significantly reducing the overall data volume and associated memory usage and processing time.
Solution Approach 2:
The patent segments the object into cells with variable resolution, allowing high-resolution data to be used only where necessary for manufacturing precision. This segmentation strategy reduces the total quantity of data stored and processed while maintaining the required print quality in regions where high precision is needed, thereby resolving the contradiction between manufacturing precision and data volume.
Data Source
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AI summary
Cells of an n-dimensional object are indexed. A plurality of indexing digits are grouped for each axis of the n dimensions of the n-dimensional object. A first indexing digit of a given group is allocated to each of at least two cells resulting from at least one subdivision of the dimension of the object along the axis of the given group; and an mth subsequent indexing digit of the given group is allocated to any subsequent at least two cells resulting from any subsequent at least one subdivision of at least one cell of the at least two cells resulting from the m-1th subdivision of the dimension of the object along the axis of the given group.