Manufacturing Line Diagnostics With Triggered Robot Feedback Capture
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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 remedy issues effectively.
Innovation Solution
A method and apparatus that involve receiving sensor data from robotic elements in manufacturing lines, triggering a feedback collection event when boundary conditions are exceeded, and capturing, tagging, and storing feedback data to diagnose issues in real-time, allowing for the identification of the time frame and cause of defects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sophisticated defect identification technologies are implemented in manufacturing systems, then defect detection capability is improved, but the ability to determine the source or cause of defects deteriorates due to system complexity and speed
Solution Approach 1:
The patent segments the continuous manufacturing system into discrete functional zones along the production line. Each zone is monitored independently for specific boundary conditions (temperature, pressure, vibration, etc.), allowing the complex system to be analyzed in manageable segments. When a defect is detected, the segmented monitoring data enables traceability to specific zones and machines, resolving the source identification problem despite overall system complexity.
Solution Approach 2:
The patent introduces an intermediary diagnostic system that sits between the manufacturing execution system and the defect detection technologies. This intermediary layer collects, correlates, and analyzes data from multiple sensors and machines, translating complex system data into actionable diagnostic information. The intermediary enables cause determination by bridging the gap between sophisticated detection capabilities and the underlying system complexity.
2Loss of information
If continuous monitoring of all manufacturing parameters is implemented, then defect source identification is improved, but data processing complexity and computational requirements worsen
Solution Approach 1:
The patent applies local quality by monitoring and analyzing data with different levels of detail at different locations in the manufacturing system. Critical boundary conditions at specific machines or zones receive focused, detailed monitoring, while other areas use standard monitoring. This localized approach to data collection and analysis improves defect source identification accuracy without requiring exhaustive monitoring of every parameter everywhere, thereby reducing overall data processing complexity.
Solution Approach 2:
The patent implements partial monitoring by focusing computational resources on monitoring and analyzing only those parameters and zones that are relevant to current production conditions and defect risks. Rather than continuously processing all possible data streams, the system dynamically adjusts monitoring intensity based on operational context, achieving adequate defect source identification with reduced computational burden.
3Loss of time
If real-time feedback collection is implemented when boundary conditions are exceeded, then diagnostic speed is improved, but system response time and processing overhead worsen
Solution Approach 1:
The patent implements periodic feedback collection by continuously monitoring boundary conditions and triggering detailed feedback data collection only when specific threshold violations occur. Instead of collecting all feedback data continuously, the system uses periodic checks against predefined boundary conditions to determine when intensive data collection is necessary. This approach accelerates diagnostic speed by focusing resources on critical events while maintaining acceptable system response time during normal operation.
Solution Approach 2:
The diagnostic system operates autonomously by automatically detecting boundary condition violations and initiating feedback collection without requiring external intervention. The system self-manages the diagnostic process, triggering data collection, analysis, and alert generation only when necessary, thereby improving diagnostic speed while minimizing processing overhead during normal production operations.
Data Source
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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.