Machine Vision Tool Configuration via ML Image Analysis
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Solution Overview
Problem
Setting up machine vision systems for product inspection is time-consuming and relies heavily on skilled operators, as it involves selecting and configuring appropriate tools and settings for evaluating products, which can be complex and labor-intensive.
Innovation Solution
Deploying machine learning models integrated into graphical user interfaces (GUIs) that provide recommendations for tools and settings based on image analysis, allowing users to efficiently create or modify jobs by suggesting tool combinations and settings based on previously trained models and data from similar products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine vision systems are set up using traditional methods with subject matter experts manually configuring tools and settings, then the system achieves accurate product inspection capability, but the setup process becomes time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary analysis of the product image to automatically identify features and pre-configure appropriate inspection tools and settings before the user completes the setup process. This preliminary action reduces the time required for manual configuration while maintaining inspection accuracy.
Solution Approach 2:
The machine vision system automatically configures itself by analyzing product images and selecting appropriate inspection parameters without requiring extensive manual intervention from subject matter experts. The system serves itself by making intelligent decisions about tool selection and parameter settings based on the analyzed product features.
2Reliability
If machine vision systems are set up with comprehensive manual configuration by skilled operators, then the system achieves proper tool and setting configuration, but the complexity and labor intensity of the setup process increases
Solution Approach 1:
The system automatically analyzes product images and configures appropriate inspection tools and settings without requiring skilled operators to manually navigate complex configuration options. This self-service capability maintains configuration reliability while significantly reducing setup complexity.
Solution Approach 2:
The patent replaces the mechanical process of manual configuration with an automated image analysis and machine learning-based system. Instead of operators manually selecting and configuring tools, the system uses computational algorithms to automatically determine appropriate settings based on product features.
3Ease of operation
If machine learning models are integrated into the GUI to provide automated recommendations, then the ease of use and setup speed improve, but the system complexity and computational requirements increase
Solution Approach 1:
The patent introduces machine learning models as an intermediary layer between the user interface and the machine vision system. This intermediary automatically analyzes product images and generates configuration recommendations, simplifying the user interface while managing system complexity through modular architecture.
Solution Approach 2:
The system performs preliminary image analysis and generates configuration recommendations before the user completes the setup process. This preliminary action using machine learning models provides automated suggestions that improve ease of use while containing complexity within the automated analysis component.
Data Source
AI summary
Machine learning based recommendations for user interactions with machine vision systems are provided via populating a graphical user interface (GUI) with a first instance of an image of a product captured by a machine vision system; identifying a feature of the product shown in the image that is associated with a criterion for analyzing the product according to a quality assurance test; identifying, via a machine learning model, a tool for assessing the criterion and settings for the tool based on the feature in the image; populating the GUI with a selectable icon that includes a second instance of the image with an overlay produced according to an assessment of the product via the tool configured according to the settings; and in response to receiving a selection of the selectable icon, adding the tool to a job comprising a series of processes for evaluating the product.