Software-Code-Defined Digital Threads for AI-Assisted Model Integration
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Solution Overview
Problem
Existing digital engineering tools are siloed, requiring massive teams to integrate disparate engineering models and human-readable documentation, leading to costly and redundant physical testing, and lack an efficient method to create and maintain interconnected digital threads.
Innovation Solution
An interconnected digital engineering platform (IDEP) generates software-code-defined digital threads using machine learning models to link engineering models and data sources, enabling seamless integration and certification processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If disparate engineering models from multiple software tools are integrated manually, then integration capability is achieved, but team size and cost increase significantly
Solution Approach 1:
The patent introduces a digital thread as an intermediary data structure that connects disparate engineering models from different software tools. This digital thread serves as a mediator that enables integration without requiring large teams of specialists, as it standardizes the connection mechanism between models from CAD, FEA, CFD, and other tools.
Solution Approach 2:
The digital thread is designed as a universal integration mechanism that can connect multiple types of engineering models from different disciplines and software tools. It provides a multi-functional platform that handles various model types (geometric, physical, simulation data) through a single standardized interface, eliminating the need for separate integration teams for each tool combination.
2Adaptability or versatility
If traditional integration methods are used, then model connection is achieved, but maintenance cost and time increase
Solution Approach 1:
The digital thread enables self-service integration by providing automated mechanisms for connecting and maintaining engineering models. The system automatically manages the relationships between models from different tools, reducing the need for manual intervention and ongoing maintenance by large teams of specialists.
Solution Approach 2:
The patent establishes the digital thread structure in advance, creating a predefined framework for model integration. This preliminary setup includes standardized connection points and data exchange protocols that are prepared before the actual integration occurs, significantly reducing the time and effort required for ongoing maintenance and updates.
3Reliability
If physical testing is performed extensively, then validation accuracy is achieved, but cost and time consumption increase
Solution Approach 1:
The patent creates digital copies of physical systems through engineering models that are interconnected via digital threads. These digital twins and virtual models serve as accurate replicas that can be used for validation and verification, reducing the need for extensive physical testing while maintaining validation accuracy.
Solution Approach 2:
The system performs preliminary validation through simulation and analysis in the digital domain before physical testing is conducted. By using the interconnected engineering models to predict and verify system behavior upfront, the need for time-consuming physical testing is reduced, as many validation activities are completed in advance in the virtual environment.
Data Source
AI summary
Methods and systems for generating digital threads that connect models and software tools for an interconnected digital engineering and certification ecosystem are provided. In one embodiment, a process for generating a software-code-defined digital thread includes receiving a first model representation of a first engineering model, a second model representation of a second engineering model, and an intent input. Determining, using a machine learning (ML) model, one or more model endpoints in the first and the second model representations. Generating, using a script-generating ML model, a platform orchestration script that accomplishes the intent input by connecting the first model representation and the second model representation, using the one or more model endpoints. Finally, storing the platform orchestration script as the software-code-defined digital thread. The digital threading of digital engineering models, with and without AI assistance, leads to exponential increases in efficiency of designing, testing, certifying, manufacturing, and operating physical products, allowing physical products to be made at digital speeds.