Feedback-Based Anomaly Detection for Manufacturing Inspection
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Solution Overview
Problem
Human inspection for detecting anomalies in manufactured parts is slow, error-prone due to inconsistency, limited experience, and environmental factors, leading to costly production line shutdowns in the manufacturing industry.
Innovation Solution
An automated anomaly detection system using machine learning models trained with feedback, which analyzes images for anomalies by comparing them to baseline images and improves accuracy through user feedback, enabling detection of defects in 2D, 3D, color, and grayscale images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human inspection is used to detect anomalies in manufactured parts, then the system is simple and flexible, but the inspection speed is slow and errors are frequent due to inconsistency and limited experience
Solution Approach 1:
The patent implements a feedback mechanism where users review and correct anomaly detections made by the machine learning model. These corrections are fed back into the training data, allowing the model to learn from its mistakes and improve its detection accuracy over time. This resolves the reliability issue by continuously improving the model's performance through user feedback.
Solution Approach 2:
The patent replaces human inspection (mechanical/biological system) with an automated machine learning-based inspection system. The ML model processes images of manufactured parts to detect anomalies, significantly increasing inspection speed and consistency while reducing the impact of human factors like fatigue, bias, and limited experience.
2Reliability
If machine learning models are used to detect anomalies, then detection accuracy and speed improve, but the system complexity increases and requires continuous training and maintenance
Solution Approach 1:
The system performs self-improvement through automated retraining cycles. When new data becomes available or the model's performance degrades, the system can automatically retrain using updated training data without requiring manual intervention to redesign the entire system. This reduces the operational complexity of maintaining high accuracy over time.
Solution Approach 2:
The patent uses pre-trained models that have been trained on large datasets of normal and anomalous parts before being deployed. This preliminary training eliminates the need for users to create training data from scratch, significantly reducing the complexity barrier for implementing the system while maintaining high detection accuracy.
3Productivity
If automated inspection systems are implemented, then productivity increases and human error is reduced, but the initial cost and setup complexity increase
Solution Approach 1:
The machine learning model is designed to detect multiple types of anomalies (missing parts, misplaced components, surface defects, incorrect assembly) using a single unified system. This multi-functionality eliminates the need for separate inspection systems for different defect types, reducing overall implementation cost while maintaining high productivity across various inspection tasks.
Data Source
AI summary
Techniques for feedback-based training may include selecting a scoring machine learning model based at least in part on a test metric, and applying the model on an unlabeled dataset to generate, per dataset item of the unlabeled dataset, a prediction and an importance ranking score for the prediction. Techniques for feedback-based training may further include selecting, based on the importance ranking scores, a result of the application of the model on the unlabeled dataset, providing the result and requesting feedback on the result via a graphical user interface, receiving the feedback via the graphical user interface, adding data from the unlabeled dataset into a training dataset when the feedback indicates a verified result, and retraining the model using the training dataset with the data added from the unlabeled dataset to generate a retrained model.