AI Edge Inspection Model Prioritization for Defect Detection
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Solution Overview
Problem
Current quality inspection systems in manufacturing environments face inefficiencies in model prioritization and resource utilization, leading to increased time and computational costs, as well as potential human errors in defect detection and management across distributed edge devices.
Innovation Solution
A computer-implemented method and system that utilizes an AI-driven defect prioritization algorithm to select and deploy the most suitable defect identification models on edge devices, leveraging edge computing for real-time inspection and centralized management, which reduces computational resources and improves defect detection accuracy by assigning scores to models based on historical data and product types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple defect identification models are run to check all product attributes, then measurement precision is improved, but use of energy and computational resources increases
Solution Approach 1:
The system performs preliminary actions by scoring and ranking defect identification models based on historical data and product attributes before actual inspection. This pre-evaluation allows the system to select only the most relevant models for each inspection task, avoiding the need to run all possible models and thereby reducing computational resource consumption while maintaining detection accuracy.
Solution Approach 2:
The system changes the parameter of model selection by introducing a scoring mechanism that dynamically ranks models based on product attributes, defect types, and historical performance. This parameter change enables adaptive model selection where only the top-scoring models are deployed for specific inspection scenarios, optimizing the balance between detection precision and resource usage.
2Measurement precision
If multiple defect identification models are deployed on edge devices, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system implements feedback mechanisms where inspection results, defect patterns, and performance metrics are continuously fed back to update model scoring and selection. This feedback loop enables automated model management and optimization, reducing the complexity of managing multiple models on edge devices by allowing the system to adaptively adjust model deployment based on actual performance and changing production conditions.
Solution Approach 2:
The system performs preliminary scoring and ranking of models based on historical data and product attributes before deployment to edge devices. This pre-configuration reduces on-device complexity by pre-determining which models should be deployed for specific product types and inspection scenarios, simplifying the model management burden on edge devices.
3Ease of operation
If defect inspection is performed manually, then ease of operation is maintained, but productivity decreases and human errors occur
Solution Approach 1:
The system enables self-service by implementing automated model selection and defect detection algorithms that operate autonomously on edge devices. The AI models automatically select appropriate inspection models based on product attributes and perform defect detection without human intervention, dramatically increasing productivity while maintaining ease of operation through centralized configuration and automated workflows.
Solution Approach 2:
The system replaces manual mechanical inspection processes with automated AI-based computer vision systems deployed on edge devices. This substitution eliminates human errors, increases inspection throughput and productivity, while maintaining ease of operation through centralized model management and automated decision-making algorithms.
4Reliability
If centralized model management is implemented across distributed edge devices, then reliability is improved, but device complexity and network requirements increase
Solution Approach 1:
The system implements a universal centralized model management platform that serves multiple distributed edge devices through a single unified interface. This multi-functional platform handles model scoring, selection, deployment, and updates across all edge devices, improving reliability and detection consistency while reducing overall system complexity by consolidating management functions into a single system.
Data Source
AI summary
An approach alerting users based on a detected defect during manufacturing quality inspection based on graphical images is disclosed. The approach initiates a device inspection, wherein a model controller collects metadata about a product to be inspected and select a first model with a highest score to identify defects in the device. The approach utilizes an API to obtain results from the inspection and after determining that another model is available, initiating the second model run via an edge device performing the inspection of the device. And the algorithm awaits a response in detecting a defect during either the first model run or the second model run, providing an alert detailing the defect detected in the device.