Fixture Image Recognition for Accurate Test System Identification
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Solution Overview
Problem
Current test systems face challenges in accurately identifying fixtures used in mechanical testing, leading to potential errors in data interpretation and equipment damage due to manual identification methods, which can be cumbersome and prone to mistakes.
Innovation Solution
Implementing an image recognition system using machine learning or neural networks to identify fixtures by capturing and processing image data from cameras integrated into the test systems, allowing for automated recognition of fixtures without additional hardware on the fixtures themselves.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification methods are used for fixtures, then the system structure remains simple, but identification accuracy decreases and errors increase
Solution Approach 1:
The patent replaces manual identification methods with an automated image recognition system using machine learning or neural networks. The system captures images of fixtures with a camera and uses processed to automatically identify fixture types, substituting human visual inspection and manual input with automated optical detection and AI-based recognition, thereby improving identification accuracy while managing system complexity through software-based solutions.
2Extent of automation
If automated image recognition is implemented, then identification accuracy improves, but device complexity increases
Solution Approach 1:
The system enables self-service automation where the image recognition system automatically captures images, processes them through machine learning models, and identifies fixtures without human intervention. The processed automatically selects appropriate test parameters and configurations based on identified fixture types, allowing the system to serve itself in the identification and setup process, thereby achieving high automation while keeping the operational complexity manageable.
3Reliability
If additional hardware is added to fixtures for identification, then identification reliability improves, but the quantity of components increases
Solution Approach 1:
The patent uses optical copying by capturing images of fixtures with a camera instead of adding physical identification hardware to each fixture. The image recognition system creates a digital representation (copy) of the fixture's visual characteristics and uses machine learning to identify the fixture type from this copy, eliminating the need for additional physical components like RFID tags, barcodes, or sensors on the fixtures themselves, thereby maintaining reliability while minimizing component quantity.
Data Source
AI summary
Described are systems and methods for fixture identification in test systems. A method for fixture identification in a test system may include capturing image data representative of a first fixture of the test system with an imaging device. The method may further include transmitting the image data representative of the first fixture from the imaging device to a processor running an image recognition application. The method may also include identifying the first fixture based on the image data with the processor running the image recognition application.