Machine Tool Camera View Selection for Faster Fault Localization
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Solution Overview
Problem
Existing methods for monitoring machine tool production processes struggle to efficiently identify and display relevant camera images for inexperienced users, particularly in the event of machine tool failures, making it difficult to quickly locate and address issues.
Innovation Solution
A method and device that utilize multiple cameras with different fields of view, an evaluation unit, and an algorithm to prioritize and display relevant images on a monitor based on criteria such as machine tool status/error messages, optical flow, and moving parts, allowing for efficient tracking of the region of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple cameras with different fields of view are used to monitor the machine tool, then the coverage and detail of monitoring is improved, but the complexity of selecting and displaying relevant images increases
Solution Approach 1:
An evaluation unit with algorithm acts as an intermediary between multiple cameras and the user interface. This automated intermediary analyzes images from multiple cameras, applies relevance criteria (error messages, optical flow, moving parts), and selects appropriate images for display, eliminating the need for users to manually navigate through multiple camera feeds
Solution Approach 2:
The system performs self-service by automatically evaluating and selecting relevant images without user intervention. The algorithm autonomously processes images from multiple cameras, determines relevance based on predefined criteria, and presents only the most relevant images to users, making the complex multi-camera system as easy to use as a single camera
2Loss of information
If all camera images are displayed on the monitor, then complete information is provided to the user, but the user cannot quickly locate relevant information during failures
Solution Approach 1:
The evaluation unit extracts only the relevant images from the complete set of camera images based on relevance criteria. Instead of displaying all images, the system extracts and displays only those images that contain error-related information, high optical flow, or moving parts, allowing users to quickly focus on problematic areas without sifting through irrelevant footage
Solution Approach 2:
Different images are treated with different quality levels based on their relevance. The system identifies and prioritizes display of images showing error-prone areas, high-activity regions, and moving parts, while reducing or excluding less relevant images, ensuring that critical information receives premium display resources
3Reliability
If multiple camera images are processed and displayed, then comprehensive monitoring is achieved, but computational resources are consumed
Solution Approach 1:
The system applies partial processing to the complete set of camera images. Rather than fully processing and displaying all images at equal quality, the evaluation unit selectively processes only relevant images in full detail while reducing or skipping processing of less relevant images, achieving comprehensive monitoring coverage with reduced computational overhead
Solution Approach 2:
The system dynamically changes processing parameters based on image relevance. Images are evaluated against multiple criteria (error message relevance, optical flow magnitude, detection of moving parts), and processing resources are allocated according to these parameter assessments, with higher-resolution processing applied only to relevant images
Data Source
AI summary
A method for monitoring the manufacture of a component with a machine tool. A plurality of cameras cover different fields of view. An algorithm creates a ranking of the relevance of images from the cameras and features only the most relevant image(s) on a monitor. The algorithm can assign a higher relevance to images that: a) depict a known machine tool part that is mentioned in a status or error message; b) have a high optical flow; and/or c) depict an identified machine tool part that is moving. In the event of c), images can be successively assigned higher relevance if an identified machine tool part moves from one field of view to the next. Images assigned lower relevance may be deleted or reduced in size. The algorithm can take the form of artificial intelligence.