AI Inspection of Custom Parts Using Real-Time Feature Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge in the machine inspection of customized part production lies in efficiently verifying features in real-time production against design specifications, especially during initial production where prior learning data is unavailable, and ensuring compliance across various subprocesses.
Innovation Solution
A computer-implemented method using a convolutional neural network (CNN) model that receives images from a video recording in real-time or near real-time to verify features in production against design information, generating alerts for non-compliance and identifying subprocesses, enabling automated AI inspection without prior learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated inspection systems are implemented for customized part production, then inspection accuracy and compliance verification are improved, but system complexity and initial setup requirements increase
Solution Approach 1:
The system performs preliminary actions by capturing images of the custom part during initial production and using these images to train the machine learning model before actual inspection begins. This preliminary training phase enables the system to learn the specific features and variations of custom parts without requiring complex pre-programming or extensive setup, thereby improving inspection accuracy while managing system complexity.
2Reliability
If machine learning models are trained on initial production data, then compliance verification capability is improved, but production time and processing duration increase
Solution Approach 1:
The system maintains continuity of useful action by performing compliance verification in near real-time during production. The machine learning model processes images as they are captured from the production line, enabling continuous inspection without significant interruption to the production flow. This approach ensures reliable compliance verification while minimizing impact on production duration.
Solution Approach 2:
The patent replaces traditional mechanical inspection systems with machine learning-based image analysis. Instead of using physical measurement devices and manual inspection processes that would slow down production, the system uses computer vision and neural networks to automatically verify compliance, thereby improving reliability without significantly increasing production time.
3Productivity
If real-time image processing is performed during production, then inspection speed and productivity are improved, but computational resource requirements and processing complexity increase
Solution Approach 1:
The system applies partial action by focusing the machine learning model's analysis on specific critical features of the custom part rather than processing the entire image in full detail. The model identifies and verifies only the most important compliance-critical features, enabling fast real-time inspection with reduced computational resource requirements while maintaining high inspection speed and productivity.
Data Source
AI summary
Aspects of the present disclosure relate generally to machine inspection of part production and, more particularly, to systems and methods of automated Al inspection of customized part production. For example, a computer-implemented method includes receiving, by a processor, design information for a custom part; extracting, by the processor, feature information of the custom part from the design information; receiving, by the processor, images of the custom part in production from a recording in near real time; and verifying, by the processor, using machine learning that features in the images of the custom part in production are in compliance with the feature information of the custom part.