Prognostic Qualification Using Void Stress and Failure Prediction
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Solution Overview
Problem
Current methods for validating and qualifying new materials and manufacturing technologies, such as Additive Manufacturing, are cumbersome, time-consuming, and expensive, hindering the rapid introduction of new processes due to the difficulty in managing material and manufacturing process uncertainty.
Innovation Solution
A prognostic qualification system that employs a void filter to detect potential defects in manufactured parts, a stress analyzer to model expected stress forces, and deterministic models to predict failure, allowing for automated, analytical, and deterministic qualification of parts, thereby reducing the need for extensive testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional test-intensive methods are used to validate and qualify new manufacturing technologies, then reliability of qualification is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces physical testing mechanisms with computational modeling and simulation. Deterministic models predict void growth and part failure without requiring actual fatigue tests, while probabilistic models assess uncertainty. This substitution of mechanical testing with computational analysis dramatically reduces qualification time while maintaining reliability through rigorous mathematical frameworks.
Solution Approach 2:
The patent performs preliminary computational analysis during the design and manufacturing phase. By using sensor data from the manufacturing process to initialize deterministic models, the system predicts potential failures before the part is deployed. This preliminary action eliminates the need for extensive post-manufacturing testing, reducing overall qualification time.
2Reliability
If traditional test-intensive methods are used to validate and qualify new manufacturing technologies, then reliability of qualification is improved, but cost increases significantly
Solution Approach 1:
The patent replaces expensive physical testing infrastructure and materials with computational models. Deterministic models simulate void growth and failure mechanisms, while probabilistic models evaluate uncertainty, eliminating the need for costly repeated testing of physical specimens. This substitution maintains qualification reliability while dramatically reducing costs.
Solution Approach 2:
The patent creates virtual copies of the manufacturing process and part behavior through computational models. Instead of physically testing multiple specimens, the system uses sensor data to initialize models that replicate part behavior under various conditions. This virtual copying eliminates material costs and reduces infrastructure requirements while maintaining qualification reliability.
3Manufacturing precision
If extensive building block test series are repeated for new processes, then manufacturing precision is maintained, but productivity decreases
Solution Approach 1:
The patent performs preliminary computational validation using sensor data from the manufacturing process. Deterministic models predict void growth and failure for new processes before deployment, providing precision validation in advance. This preliminary action eliminates the need to repeat extensive building block test series, thereby increasing productivity while maintaining manufacturing precision.
Solution Approach 2:
The patent changes the validation approach from physical parameter testing to computational parameter analysis. Instead of physically testing materials and processes through repeated building block tests, the system uses sensor data to initialize deterministic models that analyze void growth and failure parameters computationally. This parameter change enables rapid validation of new processes while maintaining precision through rigorous mathematical analysis.
4Reliability
If traditional validation methods are used for additive manufacturing, then material uncertainty is managed conservatively, but device complexity increases
Solution Approach 1:
The patent replaces complex physical testing systems with computational models. Deterministic models predict void growth and failure mechanisms, while probabilistic models assess material uncertainty. This substitution manages material uncertainty through mathematical analysis rather than extensive physical testing, reducing system complexity while maintaining or improving reliability.
Solution Approach 2:
The patent introduces computational models as intermediaries between sensor data and qualification decisions. The deterministic models translate manufacturing sensor data into predictions of void growth and failure, while probabilistic models translate variability into uncertainty assessments. These intermediary models manage material uncertainty systematically without requiring complex physical testing infrastructure.
Data Source
AI summary
A system includes a void filter that receives sensor data employed to produce or inspect a manufactured part, the void filter generates a void data subset indicating voids detected in the manufactured part. A stress analyzer processes the void data subset from the void filter and determines coordinate data and force data for the respective detected voids in the manufactured part. At least one deterministic model analyzes the coordinate data and the force data from the stress analyzer determined for the detected voids from the void data subset. The deterministic model analyzes failure of the detected voids with respect to time and generates deterministic output data indicating failure over a deterministic timeframe. A prognostic analyzer processes the deterministic output data from the at least one deterministic model and generates a failure prediction for the as manufactured part.