AI Surface Inspection Using Pre-Trained Models
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Solution Overview
Problem
Existing automated surface inspection systems are time-consuming and expensive due to the need for extensive training of models and acquisition of large datasets, and they often suffer from reliability shortcomings in mapping detected light to surface anomalies.
Innovation Solution
The use of a pre-trained model and a decentralized network of nodes for model training, combined with a decentralized blockchain for data storage and integrity, allows for the generation of targeted models for surface anomaly detection, reducing the burden of data acquisition and processing power while maintaining data privacy and integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional automated inspection systems are used with extensive model training and large datasets, then mapping accuracy can be improved, but training time and expense increase substantially
Solution Approach 1:
The system performs preliminary actions by using pre-trained models that have already been trained on large datasets beforehand. These pre-trained models can be applied directly to surface inspection tasks without requiring extensive retraining, thus achieving accurate anomaly detection while significantly reducing training time and computational resources.
Solution Approach 2:
The system creates and uses copies of pre-trained models that can be deployed across multiple inspection tasks. Instead of training new models from scratch for each application, the system replicates and adapts existing pre-trained models, maintaining detection accuracy while eliminating the need for repetitive extensive training processes.
2Measurement precision
If traditional automated inspection systems are used with extensive model training and large datasets, then mapping accuracy can be improved, but training expense increases substantially
Solution Approach 1:
The system performs preliminary actions by using pre-trained models that have already been trained on large datasets beforehand. These pre-trained models can be applied directly to surface inspection tasks without requiring extensive retraining, thus achieving accurate anomaly detection while significantly reducing training time and computational resources.
Solution Approach 2:
The system creates and uses copies of pre-trained models that can be deployed across multiple inspection tasks. Instead of training new models from scratch for each application, the system replicates and adapts existing pre-trained models, maintaining detection accuracy while eliminating the need for repetitive extensive training processes.
3Reliability
If manual visual inspection is used to monitor surface anomalies, then operator judgment can be applied, but inspection time increases and reliability decreases due to operator errors
Solution Approach 1:
The system replaces the mechanical human visual inspection process with an automated AI-based inspection system. The pre-trained models automatically detect and classify surface anomalies, eliminating operator errors and fatigue while maintaining high inspection speeds. This substitution provides both the reliability of consistent automated detection and the productivity of rapid high-speed inspection.
4Loss of time
If pre-trained models are used for surface inspection, then training time and expense are reduced, but model adaptability to specific industries may be limited
Solution Approach 1:
The system applies local quality by allowing different pre-trained models to be selected and applied to different industry-specific inspection tasks. Each model can be optimized for particular materials, defect types, or manufacturing processes while maintaining the benefits of pre-training. This enables industry-specific adaptability without requiring extensive retraining of each model.
Data Source
AI summary
The disclosed computer-implemented method for artificial-intelligence-based automated surface inspection can include receiving customer data, a request for a targeted model, and compensation for the requested targeted model. The compensation can include an agreement to contribute the customer data and/or targeted model to be available for other third-party entities. The method can also include retrieving the pre-trained model from a pre-trained model pool. The pre-trained model can be related to objects in a second industry. The method can include generating the targeted model from the pre-trained model and the customer data. The targeted model can be related to mapping sensor data to surface anomalies. The method can also include providing the targeted model to the third-party entity. The method can further include updating a distributed blockchain structure to include the at least one of the customer data and the targeted model.