3D Part Packing by Subset Classification for Build Volume Density
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Solution Overview
Problem
Determining an optimal or near-optimal packing of three-dimensional (3D) parts in a build volume for additive manufacturing is computationally intensive due to the large solution space of possible packings, especially when part sizes are comparable to or larger than the build volume, leading to costly and potentially sub-optimal results.
Innovation Solution
The method involves classifying parts into subsets based on attribute values such as dimension and density ratios, using a multi-stage approach to determine a first subset of significant parts and a second subset, where the first subset is packed optimally and serves as a starting point for packing the second subset using a genetic algorithm or randomized search to achieve near-optimal packing density.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a comprehensive search of the solution space is performed to find optimal packing, then packing density is improved, but computational time and cost increase exponentially
Solution Approach 1:
The patent segments the solution space by classifying parts into different subsets based on their dimensions and properties. This segmentation allows the algorithm to handle different subsets with different strategies, reducing the overall computational complexity while maintaining packing density optimization.
Solution Approach 2:
The patent performs preliminary classification of parts into subsets before the main packing optimization. This preliminary action organizes the problem structure in advance, enabling more efficient subsequent optimization steps and reducing the computational burden of searching the entire solution space.
2Adaptability or versatility
If part sizes are comparable to or larger than the build volume, then the number of possible packings increases, but the computational complexity grows exponentially
Solution Approach 1:
The patent applies different processing strategies to different subsets of parts based on their local characteristics (size relative to build volume). Large parts that are comparable to the build volume are handled with specialized algorithms, while smaller parts use different optimization approaches, thereby managing computational complexity effectively.
Solution Approach 2:
The patent changes the parameter representation and classification criteria for parts based on their size relative to the build volume. By dynamically adjusting how parts are categorized and processed based on size parameters, the system efficiently handles the increased complexity of packing large parts.
Data Source
AI summary
Examples of methods for part packing are described herein. In some examples, a first subset and a second subset of a set of parts are determined. In some examples a first packing of the first subset is determined. In some examples, a second packing of the second subset is determined based on the first packing.