AI Quality Control Modules for Composite Fabrication Defects
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Solution Overview
Problem
Existing techniques for monitoring and refining the fabrication of composite materials have higher failure rates than desired, necessitating the development of systems and methods to improve the integrity and reliability of composite materials.
Innovation Solution
Implementing a controller with AI quality control (AIQC) modules at various process steps to monitor and generate quality control data, including pass or fail indicators, and update models based on monitoring and testing data, with human or robotic operators providing verification and overrides.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional monitoring techniques are used for composite fabrication, then the system complexity remains low, but the reliability and detection precision of quality control deteriorate
Solution Approach 1:
The quality control system is divided into multiple AIQC modules, each responsible for specific process steps (e.g., ply placement, resin application, curing). Each module independently monitors and evaluates its designated step, enabling distributed intelligence that improves reliability without requiring a monolithic complex system.
Solution Approach 2:
AI models are trained in advance on historical monitoring data and testing data to learn patterns of defects and quality issues. This preliminary training enables the system to perform real-time quality assessment with high reliability, as the AI models are pre-equipped with knowledge from extensive prior learning.
2Measurement precision
If AIQC modules are implemented at multiple process steps, then the detection precision and quality control improve, but the device complexity increases
Solution Approach 1:
The monitoring system is segmented into multiple specialized AIQC modules, each focused on detecting specific defect types at specific process steps. This segmentation allows each module to achieve high detection precision for its specific function while the overall system manages complexity through modular architecture.
Solution Approach 2:
The controller serves as an intermediary that coordinates between multiple AIQC modules, collecting their outputs and integrating quality assessments. This intermediary role simplifies the management of multiple monitoring modules by providing a centralized coordination point without requiring direct complex interactions between all modules.
3Adaptability or versatility
If real-time monitoring and AI model updates are performed continuously, then the quality control adapts and improves, but the computational energy consumption increases
Solution Approach 1:
AI model updates are performed periodically using batch processing of accumulated monitoring data and testing data, rather than continuous real-time updates. This periodic action maintains system adaptability while significantly reducing computational energy consumption compared to continuous online learning.
Solution Approach 2:
The system maintains continuous quality monitoring using the trained AI models, ensuring real-time adaptability to quality issues. The useful action of quality assessment continues uninterrupted, while model retraining occurs in periodic batches to balance adaptability with energy efficiency.
Data Source
AI summary
A quality control system may include a controller configured to be communicatively coupled with a monitoring assembly including one or more detectors. The controller may implement two or more AI quality control (AIQC) modules associated with two or more process steps for fabricating a composite material, where each of the two or more AIQC modules is associated with a different one of the two or more process steps. A particular AIQC module may receive monitoring data associated with the particular process step for a workpiece, generate quality control data using a particular AI model, and update the particular AI model based on testing data associated with the workpiece from one or more testing tools after at least the particular process step.