Digital Part Modeling Using Field Data for Manufacturing Tuning
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Solution Overview
Problem
Industrial manufacturing processes often fail to account for the discrepancy between intended and actual performance of manufactured components, particularly in high-temperature applications like aircraft engines, where traditional dimensional tolerances do not guarantee robustness and functional performance.
Innovation Solution
Integrating field performance measurements into manufacturing processes by using a system that fetches performance data from in-field scoring systems to construct digital models and generate manufacturing functions tailored to specific performance metrics, such as thermal robustness, rather than solely relying on geometric tolerances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional dimensional tolerances are used to manufacture parts, then manufacturing precision is improved, but functional performance and robustness deteriorate
Solution Approach 1:
The patent changes the optimization parameters from geometric dimensions to functional performance metrics. Instead of controlling manufacturing based on dimensional tolerances, the system uses performance data (e.g., thermal performance, cooling efficiency) as the basis for adjusting manufacturing parameters, thereby improving functional reliability while maintaining manufacturing precision
Solution Approach 2:
The patent implements a feedback loop where in-field performance measurements are collected, analyzed, and used to update digital models that guide subsequent manufacturing processes. This closed-loop feedback system allows continuous improvement of functional performance by learning from actual field data and translating it back into manufacturing parameters
2Reliability
If field performance measurements are integrated into manufacturing processes, then functional performance is improved, but device complexity increases
Solution Approach 1:
The patent creates a multi-functional system where a single integrated platform performs multiple functions: collecting field data, analyzing performance, updating digital models, and generating manufacturing parameters. This universal system consolidates what would otherwise be separate functions into one cohesive system, managing complexity through integration rather than proliferation of separate components
Solution Approach 2:
The patent introduces a digital model as an intermediary between field performance measurements and manufacturing processes. This digital twin acts as a mediator that translates complex field data into actionable manufacturing parameters, simplifying the interface between measurement systems and manufacturing equipment while enabling functional optimization
3Manufacturing precision
If manufacturing processes are optimized for geometric tolerances, then manufacturing precision is improved, but service life and robustness deteriorate
Solution Approach 1:
The patent fundamentally changes the optimization parameters from geometric to functional. By using performance metrics (thermal performance, cooling efficiency, structural integrity under load) as the basis for manufacturing optimization, the system produces parts that are designed for longevity and robustness rather than just dimensional accuracy, thereby extending service life
Solution Approach 2:
The patent performs preliminary analysis of field performance data to identify critical factors affecting service life before manufacturing. By pre-processing field measurements and extracting key performance indicators, the system prepares optimized manufacturing parameters in advance, ensuring that parts are manufactured with built-in robustness for extended service life rather than relying on post-manufacturing adjustments
Data Source
AI summary
There are provided a system and a method of use thereof for executing a manufacturing process. For example, a method can include executing, by a system configured to drive the manufacturing process, a set of manufacturing functions based on a digital model of a first part. The method can include fetching, by the system, from an in-field scoring system, performance data relating to a second part. The method can further include constructing the digital model based on the performance data relating to the second part. The method can further include generating, based on the digital model, a forecast representative of a performance of the first part and generating the set of manufacturing functions based on the digital model and the forecast. The method further includes manufacturing the first part according to the set of manufacturing functions.