Probabilistic Metamaterial Design via Program Code
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Solution Overview
Problem
Metamaterials with complex geometries are challenging to represent and manufacture accurately using traditional CAD systems, as they require millions of geometric elements and are prone to manufacturing variances, leading to costly and time-consuming redesigns due to limited probabilistic engineering capabilities.
Innovation Solution
Implementing probabilistic design techniques in program code by assigning code parameters as probability distributions, allowing for efficient representation and analysis of metamaterials, which accounts for manufacturing variances and tolerances, thereby improving design efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional CAD systems are used to represent metamaterials with complex geometries, then the design can be created, but the representation requires millions of geometric elements and is prone to manufacturing variances
Solution Approach 1:
The patent replaces traditional mechanical CAD geometric representations with a programmatic/mathematical system that defines metamaterial structures through code parameters and probability distributions. This substitution eliminates the need to manually represent millions of geometric elements while maintaining manufacturing precision through probabilistic modeling of manufacturing variances.
Solution Approach 2:
The patent changes the fundamental parameters from fixed geometric coordinates to probability distributions that model manufacturing uncertainties. By representing metamaterial geometry through probabilistic parameters rather than deterministic coordinates, the system achieves both reduced complexity and improved manufacturing precision.
2Productivity
If traditional CAD systems are used, then design can proceed, but costly and time-consuming redesigns are required due to limited probabilistic engineering capabilities
Solution Approach 1:
The patent performs preliminary probabilistic analysis during the design phase by incorporating probability distributions that model manufacturing variances. This preliminary action identifies potential manufacturing issues before production, eliminating the need for costly post-manufacturing redesigns and improving both productivity and reliability.
Solution Approach 2:
The system implements feedback by using probabilistic analysis results to refine design parameters and predict manufacturing outcomes. This feedback loop allows designers to optimize metamaterial structures for manufacturability before production, reducing redesign cycles and improving design robustness.
3Reliability
If deterministic values are assigned to code parameters, then the design is simple, but it does not account for manufacturing variances and tolerances
Solution Approach 1:
The patent creates a composite parameter representation that combines deterministic base values with probabilistic variance components. This composite approach maintains the simplicity of deterministic design while incorporating manufacturing uncertainty modeling, achieving both reliability and manageable complexity.
Data Source
AI summary
A computing system may include a metamaterial representation engine configured to represent a metamaterial of a three-dimensional (3D) object as program code. The metamaterial may define an internal geometry of the 3D object and may be configured to be physically constructed via additive manufacturing. Representation of the metamaterial as program code may include assigning a value of a code parameter of the metamaterial as a probability distribution. The computing system may also include a metamaterial analysis engine configured to analyze the metamaterial through the probability distribution assigned for the value of the code parameter of the program code.