AI Defect Detection for Moving Manufactured Parts
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Solution Overview
Problem
Conventional machine vision systems for object detection in manufacturing are costly, require extensive configuration and maintenance, and struggle with detecting objects that move within the field of view, leading to inefficiencies and production delays.
Innovation Solution
A system utilizing AI deep learning for object detection, which includes a system controller connected to image capture devices, a programmable logic controller, and a trained object detection model, enabling rapid setup and accurate detection of defects with minimal human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine vision systems are used for object detection, then defect detection capability is achieved, but deployment cost and configuration time increase significantly
Solution Approach 1:
The patent replaces traditional machine vision systems with rule-based algorithms and manual configuration with deep learning-based object detection models. The system uses neural networks to automatically learn defect patterns from training data, eliminating the need for manual rule creation and complex system configuration. This substitution reduces deployment complexity while maintaining or improving detection reliability.
Solution Approach 2:
The deep learning model performs self-learning from training images to automatically identify defect patterns. The system continuously improves its detection capability by learning from new data without requiring manual reconfiguration. This self-service approach reduces maintenance requirements and deployment costs compared to conventional systems that need expert configuration and ongoing manual tuning.
2Reliability
If conventional machine vision systems are used for object detection, then defect detection is achieved, but deployment time and human intervention requirements increase
Solution Approach 1:
The system performs preliminary training of the deep learning model using a dataset of labeled defect images before actual deployment. This preliminary action creates a pre-trained model that can be quickly deployed without requiring time-consuming manual configuration during production setup. The model learns defect patterns in advance, enabling rapid deployment while maintaining high detection accuracy.
Solution Approach 2:
The patent replaces manual configuration processes with automated deep learning model training and deployment. Instead of requiring experts to manually configure detection rules, the system automatically trains models on labeled data and deploys them seamlessly, dramatically reducing deployment time while maintaining or improving detection accuracy.
3Ease of operation
If traditional machine vision is used for moving objects, then detection is attempted, but reliability decreases due to object movement in field of view
Solution Approach 1:
The patent employs dynamic object detection approaches that can track and identify objects regardless of their position or movement within the field of view. The deep learning model is trained to recognize objects in various positions and orientations, making the detection system adaptive to moving objects. This dynamic capability maintains high reliability even when objects move during the detection process.
Data Source
AI summary
A system for object detection of a manufactured part. The system comprises a system controller electronically coupled to an image acquisition device and delivery mechanism. An electronically stored ordered object detection map is provided which comprises predetermined detectable objects associated with the manufactured part. The ordered object detection map is generated from output created by execution of a trained object detection model and by processing such output according to predetermined calibration criteria. The system causing a visual image of the manufactured part to be captured with image data being extracted and processed to render at least one of a pass determination and a fail determination. The system being configured to process the manufactured based upon the rendered pass/fail determination.