Hybrid 3D Printed Structures With COTS Part Replacement
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Solution Overview
Problem
Designing multi-component structures that effectively combine 3-D printed and commercial-off-the-shelf parts is challenging due to the difficulty in identifying optimal arrangements considering performance, cost, assembly, material requirements, and durability factors, often resulting in sub-optimal design choices that fail to account for subtle but important design aspects.
Innovation Solution
A method and apparatus for determining multi-component structure models that involve obtaining a 3-D print model based on load case criteria, identifying portions that can be replaced with commercial-off-the-shelf parts, and replacing those portions to create an optimized multi-component structure model, utilizing modules for 3-D print model generation, COTS part identification, and optimization techniques such as topology and multi-objective optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If 3-D printed parts are used for complex structures, then design flexibility and geometric complexity are improved, but manufacturing cost and production time increase
Solution Approach 1:
The design system segments the overall structure into multiple components, identifying which portions should be 3-D printed and which should be COTS parts. This segmentation allows the complex, geometry-critical portions to be printed while simpler portions use conventional manufacturing, optimizing the balance between design flexibility and manufacturing cost.
Solution Approach 2:
Different manufacturing approaches are applied to different portions of the structure based on local requirements. High-complexity regions requiring design flexibility use 3-D printing, while regions with standard geometries use COTS parts, achieving local optimization of both adaptability and manufacturing efficiency.
2Shape
If 3-D printed parts are used for complex structures, then geometric complexity is improved, but assembly complexity increases
Solution Approach 1:
The system segments the structure into modular components that can be independently manufactured and then assembled. By identifying discrete portions suitable for COTS replacement, the system reduces assembly complexity while preserving geometric complexity where needed through 3-D printing.
3Reliability
If design optimization is performed considering multiple factors, then structure performance is improved, but design time and computational resources increase
Solution Approach 1:
The system performs preliminary identification of COTS replacement opportunities and load case criteria before detailed optimization. This preliminary action filters the design space early, reducing computational requirements and design time while still achieving optimal structure performance through subsequent targeted optimization.
Solution Approach 2:
The optimization system evaluates multiple design parameters including material properties, geometric constraints, load cases, and manufacturing considerations simultaneously. By changing and optimizing these parameters together, the system achieves superior structure performance while managing design time through efficient computational algorithms.
Data Source
AI summary
Aspects of methods, apparatuses, and computer-readable media for performing multi-material selection optimization (MMSO) to provide topologically and geometrically optimized multi-component structures (MCSs) across a plurality of design inputs and constraints are proposed. In some embodiments, a 3-D print model of an object based on load case criteria is obtained. A portion of the 3-D print model is determined that can be replaced with a commercial-off-the-shelf (COTS) part model such that the load case criteria remain satisfied. The portion or the 3-D print model can then be replaced with the COTS part model to determine the MCS model. In various embodiments, a mesh representation of the model can be generated, and plurality of optimization techniques can be used to determine the MCS model.