Generative Shape Optimization With Reliability Constraints
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Solution Overview
Problem
Current CAD software lacks the ability to directly optimize the shape and topology of physical structures for target part reliability during the generative design process, often requiring post-design simulations and manual editing to ensure acceptable component failure probabilities, which is time-consuming and prone to over-engineering.
Innovation Solution
A method that uses a statistical model, such as the Weibull model, to translate design criteria like acceptable likelihood of failure into structural performance metrics, iteratively modifying the 3D shape and topology to meet these criteria, allowing for direct generation of toolpath specifications for manufacturing without post-generative design editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If post-generative design simulations and manual editing are performed to ensure acceptable component failure probabilities, then part reliability is improved, but design time and complexity increase
Solution Approach 1:
The patent incorporates reliability constraints directly into the generative design process itself, performing reliability analysis and optimization during the initial design phase rather than as a subsequent post-processing step. This preliminary integration eliminates the need for separate post-generative design simulations and manual editing iterations, significantly reducing total design time while ensuring part reliability is achieved from the outset
Solution Approach 2:
The system performs automated reliability analysis and optimization within the generative design workflow, making the process self-sufficient. The software automatically evaluates failure probabilities, adjusts design parameters, and iterates to meet reliability targets without requiring manual intervention for post-design simulations or editing, thereby reducing both time and complexity
2Reliability
If post-generative design simulations and manual editing are performed to ensure acceptable component failure probabilities, then part reliability is improved, but device complexity increases
Solution Approach 1:
The patent merges the reliability analysis and optimization functions directly into the generative design process. By combining what were previously separate processes (generative design + post-design simulation + manual editing) into a single integrated workflow, the system improves part reliability while reducing process complexity through consolidation
Solution Approach 2:
The system performs automated reliability analysis and optimization within the generative design workflow, making the process self-sufficient. The software automatically evaluates failure probabilities, adjusts design parameters, and iterates to meet reliability targets without requiring manual intervention for post-design simulations or editing, thereby reducing both time and complexity
3Loss of substance
If generative design is used to minimize waste material or weight, then material efficiency is improved, but the risk of over-engineering increases when reliability constraints are not integrated
Solution Approach 1:
The patent changes the parameters used in generative design from purely geometric or mass-based optimization to include reliability-based parameters. By incorporating failure probability constraints and reliability metrics as optimization parameters, the system achieves material efficiency while preventing over-engineering, as the design is optimized to meet specific reliability targets rather than using excessive material
Solution Approach 2:
The system provides real-time feedback on component failure probabilities during the generative design process. This feedback mechanism allows the optimization algorithm to adjust design parameters iteratively, achieving material efficiency while ensuring reliability constraints are met, thereby preventing both over-engineering and under-engineering
Data Source
AI summary
Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using generative design processes. A method includes: obtaining a design space and design criteria for a modeled object including a design constraint on an acceptable likelihood of failure, wherein a statistical model that relates a structural performance metric to specific likelihoods of failure for material(s) is used to translate between the acceptable likelihood of failure and a value for the structural performance metric; iteratively modifying a generatively designed shape of the modeled object in the design space in accordance with the design criteria including the design constraint to stay under the acceptable likelihood of failure for the physical structure, wherein the numerical simulation includes computing the structural performance metric, which is evaluated against the design constraint; and providing the generatively designed shape of the modeled object for use in manufacturing a physical structure.