Laminate Nonconformance Detection Using AI Layup Inspection Data
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Solution Overview
Problem
Current methods for detecting nonconformances in laminates are tedious, error-prone, and rely heavily on human skill, resulting in excessive scrap due to inefficient visual inspection processes.
Innovation Solution
A sensor system records layup and inspection information, with an AI-powered analyzer identifying nonconformances and requesting user verification, especially when confidence levels are below a threshold, to improve accuracy and reduce scrap rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection by human operators is used to detect nonconformances, then the process is simple to implement, but the detection accuracy is low and error-prone
Solution Approach 1:
The patent replaces the mechanical visual inspection process performed by human operators with an automated optical inspection system using cameras and image processing algorithms. This substitution eliminates human error and fatigue while maintaining operational simplicity through automated defect detection and classification.
Solution Approach 2:
The system creates digital copies (images) of the laminate surface and analyzes these copies using image processing techniques. This allows multiple analyses of the same surface without physical contact, enabling accurate nonconformance detection while keeping the inspection process non-intrusive and repeatable.
2Loss of substance
If more scrap is generated from laminates with nonconformances, then the inspection process is simpler, but the loss of material increases
Solution Approach 1:
The system implements feedback loops where inspection results are continuously analyzed, and adjustments are made to inspection parameters and thresholds. This feedback mechanism improves the reliability of nonconformance identification over time, reducing false positives that lead to unnecessary scrap while maintaining high detection accuracy for actual defects.
Solution Approach 2:
The inspection system dynamically adjusts parameters such as lighting conditions, camera exposure, and defect detection thresholds based on the specific laminate being inspected. This adaptability allows the system to optimize detection sensitivity for different materials and defect types, minimizing unnecessary scrap while maintaining high reliability in nonconformance identification.
3Loss of time
If the inspection process is made more thorough to reduce errors, then the detection accuracy improves, but the inspection time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing images with enhancement algorithms and pre-identifying regions of interest before conducting detailed defect analysis. This preliminary processing reduces the computational burden during actual inspection, enabling thorough analysis without significantly increasing inspection time.
Solution Approach 2:
The inspection process is segmented into multiple stages: initial scanning for obvious defects, detailed analysis of suspicious regions, and final verification. This segmentation allows the system to focus computational resources on areas most likely to contain nonconformances, maintaining high detection accuracy while minimizing overall inspection time by not applying exhaustive analysis to every area uniformly.
Data Source
AI summary
A method for managing nonconformances in laminates. The method comprises recording, by a sensor system, layup information about a layup of layers on a workpiece platform, wherein the layup of layers forms a workpiece and recording inspection information about the laminate on an inspection platform, wherein the laminate is formed from curing the workpiece. An analyzer in a computer system identifies a laminate nonconformance in the laminate using the inspection information and a user input verifies the laminate nonconformance in the laminate is present. An artificial intelligence system is trained by the computer system using the layup information, the inspection information, and the user input verifying the laminate nonconformance.