Manufacturing Line Diagnosis Using Boundary-Triggered Feedback Data
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Solution Overview
Problem
Manufacturing and automation systems face difficulties in identifying the source or cause of defects due to their complexity and speed, making it challenging to diagnose and remedy issues effectively.
Innovation Solution
A method and apparatus that utilize sensor data from robotic elements in manufacturing lines to trigger feedback collection events when boundary conditions are exceeded, allowing for real-time identification and storage of feed-back data, which includes a time-frame analysis and multi-media format data capture, enabling efficient diagnosis without requiring reconfiguration of existing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manufacturing systems operate at high speed and complexity, then productivity is improved, but the ability to diagnose and identify defect sources deteriorates
Solution Approach 1:
The system performs preliminary actions by continuously collecting and storing feedback data (sensor readings, images, videos) before defects occur. When a boundary condition is exceeded, the pre-collected data is immediately available for analysis, eliminating the need for post-defect investigation and enabling rapid diagnosis without slowing production.
Solution Approach 2:
The system implements continuous feedback by monitoring sensor data against boundary conditions in real-time. When deviations are detected, feedback data is triggered and collected for analysis, creating a closed-loop system that enables rapid identification of defect sources while maintaining high-speed operation.
2Measurement precision
If comprehensive sensor data is collected for diagnosis, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system extracts only the necessary feedback data related to exceeded boundary conditions from the continuous sensor stream. By isolating and storing only relevant data segments (tagged with boundary condition identifiers), the system achieves high measurement precision without requiring complex processing of all sensor data, thereby reducing device complexity.
Solution Approach 2:
The continuous sensor data stream is segmented into discrete feedback events based on boundary condition exceedances. Each segment is independently tagged and stored with metadata identifying the specific boundary condition and time frame, allowing precise defect detection while simplifying data management and reducing system complexity.
3Loss of time
If real-time feedback data collection is implemented, then loss of time is reduced, but use of energy increases
Solution Approach 1:
Instead of continuous data collection, the system uses periodic action by triggering feedback data collection only when boundary conditions are exceeded. This event-driven approach minimizes energy consumption while ensuring that critical defect information is captured in real-time, reducing diagnosis time without excessive energy use.
Solution Approach 2:
The system applies partial action by collecting and storing only the specific feedback data necessary for diagnosing boundary condition exceedances, rather than continuously collecting all sensor data. This selective data collection reduces energy consumption while maintaining real-time diagnosis capability for critical events.
Data Source
AI summary
Disclosed herein is a method and apparatus for observing a manufacturing line, wherein the manufacturing line includes at least one robotic element for moving at least one part, the method comprising receiving, at a processor separate from the manufacturing line, sensor data about the at least one robotic element, and when the sensor data exceeds a boundary condition, triggering a feed-back collection event. The feed-back collection event includes identifying a stream of sensor data, capturing feed-back data at a data collection device associated with the at least one robotic element, tagging the feed-back data to reflect the at least one part associated with the boundary condition, identifying, within the feed-back data, when the boundary condition is exceeded by the at least one part in real time, and storing the feed-back data relating to the boundary condition in association with the sensor data and the boundary condition.