Neural Network Conformance Testing for Manufactured Parts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for managing and tracking manufactured parts are inefficient, often failing to ensure complete separation or requiring burdensome physical markings, which can be lost or impractical.
Innovation Solution
Utilizing deep learning neural networks to analyze images of manufactured parts, inferring the fabrication source without strict separation protocols or physical marks, by leveraging unique fabrication fingerprints perceptible to computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If strict part separation protocols are used to track manufactured parts, then part identification accuracy is improved, but manufacturing complexity and operational burden increase
Solution Approach 1:
The patent creates a digital copy (image) of the manufactured part and analyzes it using a neural network to identify the manufacturing machine, replacing the need for physical separation protocols and digital tagging systems with direct visual analysis of the part itself
Solution Approach 2:
The patent replaces mechanical separation protocols and physical tracking systems with an automated neural network system that uses images to identify part origins, eliminating the need for complex physical separation infrastructure
2Measurement precision
If physical markings are applied to manufactured parts for tracking, then part identification capability is improved, but reliability decreases due to marking loss or impracticality
Solution Approach 1:
Instead of relying on physical markings that can be lost, the patent creates a digital image copy of the part and extracts identification information from the image through neural network analysis, eliminating the reliability issues associated with physical markings
Solution Approach 2:
The patent introduces an image as an intermediary carrier of identification information, which is more reliable than direct physical markings on the part. The image serves as a stable digital record that can be analyzed without affecting the part itself
3Device complexity
If traditional tracking methods are used for manufactured parts, then system simplicity is maintained, but productivity decreases due to inefficiency
Solution Approach 1:
The patent enables the manufacturing machine itself to be identified through its unique visual characteristics captured in part images, eliminating the need for external tagging systems or complex tracking infrastructure while achieving reliable part identification
Solution Approach 2:
The patent replaces inefficient mechanical tracking systems with an automated neural network-based system that rapidly analyzes images to identify part origins, significantly improving management efficiency without requiring complex infrastructure
Data Source
AI summary
Various embodiments may involve obtaining an image of at least a section of a manufactured part; determining, based on executing a neural network on the image, that the manufactured part was not fabricated according to a specification for the manufactured part, wherein the neural network was trained to associate images of manufactured parts with corresponding indicators of specifications for the manufactured parts; and, in response to determining that the manufactured part was not fabricated according to the specification, generating an electronic alert indicating that the manufactured part was improperly fabricated.