Digital Twin Modeling for Additive Manufacturing Variance Validation
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Solution Overview
Problem
Additive manufacturing processes face challenges in optimizing and validating components due to manufacturing variances, leading to scrap material and uncertainty in performance, as traditional destructive testing methods fail to account for aggregate influences on operational regimes.
Innovation Solution
Integration of operational characteristics throughout a component's life cycle with design and manufacturing, using a digital integrated process to create models that mitigate performance and manufacturing variances, enabling automated, quantitative assessments and optimizing material usage and testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If destructive testing is used to validate manufactured components, then manufacturing precision can be confirmed, but significant quantities of scrap material are produced
Solution Approach 1:
The patent creates digital twins (virtual copies) of physical components that replicate manufacturing processes and predict performance outcomes. Instead of destroying physical components for testing, the digital twin simulations provide validation, thereby reducing scrap material while maintaining manufacturing precision verification.
Solution Approach 2:
The patent performs preliminary digital simulations and predictions before physical manufacturing or testing. By using digital twins to predict component performance and identify potential issues beforehand, the need for extensive destructive testing is reduced, thereby minimizing scrap material generation.
2Reliability
If multiple tolerance tests are conducted to account for aggregate variances, then reliability improves, but productivity decreases due to increased testing time
Solution Approach 1:
The patent uses digital twins to simulate and evaluate multiple tolerance scenarios and aggregate variance effects virtually. This allows comprehensive reliability assessment without requiring multiple physical prototypes or extensive testing cycles, thereby maintaining productivity while improving reliability validation.
Solution Approach 2:
The patent performs preliminary digital simulations of aggregate variance effects and operational regimes before physical testing. By predicting how multiple tolerances interact and affect performance in advance, the number of required physical tests is reduced, improving productivity while maintaining reliability assessment quality.
3Ease of manufacture
If traditional manufacturing processes are used with predetermined tolerances, then ease of manufacture is maintained, but reliability decreases due to unquantified variance effects
Solution Approach 1:
The patent introduces digital twins as an intermediary between traditional manufacturing processes and reliability assessment. The digital twin captures manufacturing variances and predicts their aggregate effects on component performance, providing reliability quantification without requiring changes to the actual manufacturing process, thus maintaining ease of manufacture while improving reliability.
4Manufacturing precision
If components are scrapped due to unquantifiable fitness for operation, then manufacturing precision is maintained, but loss of time increases due to reduced useful output
Solution Approach 1:
The patent creates digital twins that track and predict the fitness for operation of components throughout their useful life. By monitoring variance accumulation and predicting when components will no longer meet performance requirements, the system extends the usable life of components before scrapping, reducing time loss while maintaining manufacturing precision standards.
Solution Approach 2:
The patent implements feedback mechanisms through digital twins that continuously monitor component performance and predict remaining useful life. This feedback allows for optimized maintenance and replacement scheduling, preventing premature scrapping of components that still have useful life, thereby reducing time loss while maintaining quality standards.
Data Source
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AI summary
There are provided methods and systems for making or repairing a specified part. For example, there is provided a method for creating a manufacturing process to make or repair the specified part. The method includes receiving data from a plurality of sources, the data including as-designed, as-manufactured, as-simulated, as-operated, as-inspected, and as-tested data relative to one or more parts similar to the specified part. The method includes updating, in real time, a surrogate model corresponding with a physics-based model of the specified part, wherein the surrogate model forms a digital twin of the specified part. The method includes generating a multi-variant distribution including component performance and manufacturing variance, the manufacturing variance being associated with at least one of an additive manufacturing process step and a reductive manufacturing process step. The method includes comparing a performance from the multi-variant distribution with an expected performance of the new part based on the surrogate model. The method includes executing, based on the digital twin, the optimized process to either repair or make the specified part.