3D Model Partitioning Using Analytic Hierarchy Processing
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Solution Overview
Problem
Current 3D printing technologies face challenges in partitioning large models into smaller pieces that fit within printing volumes, as existing methods are tedious and do not adequately consider engineering constraints like thermal deformations and structural integrity, limiting the potential of additive manufacturing.
Innovation Solution
A method and system that use Analytic Hierarchy Processing (AHP) to identify optimal cutting planes in a binary tree structure, considering partition sensitive locations, structural integrity, and thermal deformations, to divide 3D models into manageable parts for printing, leveraging parallel processing and user input for efficient partitioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional binary space partitioning techniques are used to divide large 3D models, then partitioning efficiency is improved, but engineering constraints such as thermal deformations, structural deformations, and support generation are ignored
Solution Approach 1:
The patent segments the partitioning process into multiple evaluation dimensions including structural integrity, thermal deformation, and support generation. Each partition candidate is evaluated against multiple criteria rather than a single efficiency metric, allowing the system to divide models while maintaining engineering constraints.
Solution Approach 2:
The patent changes the evaluation parameters from simple geometric partitioning metrics to comprehensive engineering parameters including stress analysis, thermal deformation, and support structure requirements. This allows the partitioning algorithm to optimize for both efficiency and reliability simultaneously.
2Ease of operation
If user-assisted partitioning tools are used to divide large 3D models, then partitioning control is improved, but the process becomes tedious and challenging when there are several pieces and engineering constraints
Solution Approach 1:
The patent performs preliminary automated analysis of engineering constraints (structural integrity, thermal deformation, support requirements) before the user interacts with the partitioning interface. This preliminary evaluation prepares multiple candidate partitions that already satisfy engineering constraints, reducing the iterative back-and-forth between user and system.
Solution Approach 2:
The patent implements feedback mechanisms where the system evaluates partition candidates against engineering constraints and provides guidance to users on which partitions satisfy requirements. This feedback loop reduces the number of manual adjustments needed while maintaining user control over the partitioning process.
3Speed
If arbitrary partitions are used in binary space partitioning trees, then partitioning speed is improved, but additive manufacturing challenges such as thermal deformations, structural deformations, and support generation are not considered
Solution Approach 1:
The patent changes the partition evaluation from purely geometric parameters to include manufacturing-specific parameters such as thermal deformation, structural stress, and support generation. The system evaluates candidate partitions based on these manufacturing parameters while maintaining efficient search algorithms.
Solution Approach 2:
The patent performs preliminary evaluation of partition candidates against manufacturing constraints before finalizing the partitioning structure. This preliminary filtering ensures that only partitions satisfying thermal and structural requirements are considered, avoiding the need for post-processing adjustments.
Data Source
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AI summary
A method of partitioning a model to facilitate printing of the model on a 3D printer includes identifying partition sensitive locations on the model and creating a binary tree with a root note representative of the model. An iterative partitioning process is applied to divide the model into objects by selecting a node of the binary tree without any children nodes, identifying a portion of the model corresponding to the node, and determining candidate cutting planes on the portion of the model based on the partition sensitive locations. During the process, analytic hierarchical processing (AHP) is applied to select an optimal cutting plane from the candidate cutting planes based on partitioning criteria. The optimal cutting plane is used to segment the portion of the model into sub-portions, and two children nodes representative of these subportions are created on the node of the binary tree.