Scanned Digital Twin Correction for Geometric Drift
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Solution Overview
Problem
Existing digital models of structures often contain inaccuracies due to geometrical drift, which can negatively impact downstream applications such as simulations and navigation, necessitating a method for correction.
Innovation Solution
Utilizing gradient descent optimization methods and constraint-based solving to correct digital twins by creating keypaths, identifying structural constraints, and optimizing structural properties through a cost function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If digital models are created using standard capture tools, then ease of operation is improved, but measurement precision deteriorates due to geometrical drift and inaccuracies
Solution Approach 1:
The system performs preliminary actions by establishing a cost function that encodes structural constraints before the actual correction process. This cost function is prepared in advance and guides the optimization process to correct geometrical drift and inaccuracies while maintaining ease of operation through automated constraint-based solving
Solution Approach 2:
The system implements feedback mechanisms through iterative optimization processes. The cost function continuously evaluates structural properties against defined constraints and provides feedback to adjust the digital twin, progressively reducing measurement errors while preserving the user-friendly capture process
2Measurement precision
If optimization methods are applied to correct digital twins, then measurement precision is improved, but device complexity increases due to constraint optimization processes
Solution Approach 1:
The system extracts the complexity of constraint optimization into a separate, dedicated correction module. By isolating the optimization processes into distinct functional components, the overall system maintains high measurement precision through sophisticated correction while presenting a simplified interface to users
Solution Approach 2:
The cost function serves as an intermediary that translates complex structural constraints into an optimized correction process. This intermediary layer manages the computational complexity internally while delivering precise structural corrections through a streamlined workflow
3Manufacturing precision
If constraint-based correction is implemented, then manufacturing precision is improved for digital models, but loss of time increases due to optimization computation
Solution Approach 1:
The system applies partial correction actions by selectively optimizing only the structural properties that violate defined constraints, rather than reprocessing the entire digital twin. This targeted approach maintains manufacturing precision for critical structural elements while reducing overall computation time through selective constraint-based correction
Data Source
AI summary
Various embodiments relate to a method, apparatus, and machine-readable storage medium including one or more of the following: creating keypaths into the digital twin that accesses a structural property stored in the digital twin; identifying a constraint for the physical structure associated with the keypaths, creating a cost function from the keypaths and the constraint; and optimizing a value for structural property using the cost function, where the structural property stored in the digital twin is corrected.