Lattice Structure Optimization for Additive Manufacturing Readiness
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Solution Overview
Problem
Topology optimization in CAD systems is inflexible, deterministic, and resource-intensive, often resulting in similar design solutions and inadequate models for additive layer manufacturing without intensive manual processing.
Innovation Solution
A computer-implemented method that generates and optimizes a lattice structure for object design, allowing for multiple design options by using structural analysis and converting it into a 3D model, which can be efficiently processed for additive manufacturing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If topology optimization is used to generate design solutions, then structural integrity is improved, but the design process becomes deterministic and limited in exploring diverse solutions
Solution Approach 1:
The patent applies dynamics by transitioning from a deterministic topology optimization process to a dynamic evolutionary algorithm-based approach. The system allows the design process to adapt and evolve through multiple generations, exploring diverse solutions while maintaining structural integrity. The evolutionary algorithm enables the design to dynamically adjust and explore different design spaces rather than following a fixed optimization path.
Solution Approach 2:
The patent utilizes parameter changes by allowing modification of design parameters during the evolutionary process. Different design variables, constraints, and optimization criteria can be changed across generations to explore diverse design solutions. This enables the system to maintain structural integrity while achieving varied design outcomes by adjusting parameters such as material distribution, geometric configuration, and loading conditions.
2Reliability
If traditional topology optimization is applied, then structural performance is optimized, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent applies preliminary action by pre-defining design spaces, constraints, and evaluation criteria before the optimization process begins. The system prepares computational models, material properties, and structural requirements in advance, enabling more efficient execution of the evolutionary algorithm. This preliminary preparation reduces the time required during the actual optimization and design iteration phases.
Solution Approach 2:
The patent utilizes copying by creating virtual replicas of the design problem through computational models. The evolutionary algorithm works with digital copies of the structure, allowing rapid iteration and evaluation without physical prototyping. This virtual copying enables extensive design exploration and optimization in a fraction of the time required for traditional design methodologies.
3Loss of substance
If topology optimization generates design solutions, then material efficiency is improved, but the resulting 3D models become inadequate for additive manufacturing without intensive manual processing
Solution Approach 1:
The patent applies segmentation by dividing the design process into distinct phases: structural optimization, manufacturability assessment, and design refinement. This segmentation allows the system to first optimize material efficiency through evolutionary algorithms, then separately address manufacturing requirements. The segmented approach enables targeted modification of the 3D model to ensure additive manufacturing suitability while maintaining material efficiency gains.
Solution Approach 2:
The patent introduces an intermediary step between structural optimization and final manufacturing preparation. This intermediary phase includes automated checks and transformations that convert the optimized design into a manufacturing-ready format. The intermediary process adds necessary manufacturing features, adjusts geometries for additive manufacturing capabilities, and prepares support structures, thereby bridging the gap between optimized design and manufacturable product.
Data Source
AI summary
A design engine for designing an object using structural analysis. The design engine generates a lattice structure for the object comprising a plurality of nodes and a plurality of lines connecting the nodes. The lattice structure is optimized to remove one or more lines using structural analysis based on at least one load-related design requirement. Several design options are provided for generating and optimizing the lattice structure. The design engine then generates a 3D model of the object by thickening each line of the lattice structure into a pipe volume. The thickness of each pipe is determined using structural analysis based on the at least one load-related design requirement. The 3D model represents the volume of the object and is exportable to a fabrication device.