TSN Transfer Learning for Adaptive Machine Configuration
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Solution Overview
Problem
Current industrial automation systems in manufacturing, such as auto manufacturing, face challenges due to static configurations that do not account for dynamic variations in machines, materials, and environmental factors, leading to poor quality products and inefficient processes.
Innovation Solution
Implementing Self-Learning Time-Sensitive Networking (TSN) Gossip Based Autonomous Agents that dynamically adjust configurations based on real-time data and machine learning algorithms, using community-based TSN to synchronize operations and propagate optimal configurations across machines, leveraging Platform Trusted Execution Environment (TEE) and Wireless Credential Exchange (WCE) for secure and efficient synchronization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If static configurations are used for all controllers and autonomous agents, then device complexity is reduced and ease of operation is improved, but manufacturing precision and reliability deteriorate due to inability to account for dynamic variations
Solution Approach 1:
The patent implements dynamic configurations that automatically adapt to varying operational conditions, environmental factors, and machine states. Controllers and autonomous agents transition from static to dynamic configuration models, enabling real-time adjustment of parameters to maintain manufacturing precision while managing complexity through systematic adaptation rules
Solution Approach 2:
The system enables autonomous agents and controllers to self-adjust their configurations based on real-time data from sensors and performance metrics. Each agent independently modifies its own configuration parameters within predefined boundaries, eliminating the need for complex external coordination while maintaining high manufacturing precision through distributed intelligence
2Productivity
If parallel processes are run in a cell to minimize product spend time, then productivity is improved, but manufacturing precision deteriorates due to dynamic variations in operation, environment, and inter-process effects
Solution Approach 1:
The patent implements real-time feedback mechanisms where sensors continuously monitor operational parameters, environmental conditions, and process interactions. This feedback is fed back to controllers and autonomous agents, which dynamically adjust configuration parameters to compensate for variations introduced by parallel processes, thereby maintaining manufacturing precision while preserving high productivity
Solution Approach 2:
The system performs preliminary analysis of potential interactions between parallel processes and pre-adjusts configurations to prevent quality degradation. By anticipating dynamic variations and environmental effects before they impact product quality, the system maintains precision while running parallel processes at high speed
3Adaptability or versatility
If homogeneous configurations are used for all machines, then device complexity is reduced, but adaptability deteriorates due to inability to account for dynamic variations in machines, materials, and environment
Solution Approach 1:
The patent implements local quality by allowing each controller and autonomous agent to have customized configuration parameters tailored to its specific machine, process, and environmental context. Instead of uniform homogeneous configurations, each component adapts its local settings to optimize performance for its particular operating conditions while maintaining overall system coherence through standardized communication protocols
Data Source
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AI summary
Methods and apparatus for Time-Sensitive Networking Coordinated Transfer Learning in industrial settings are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to cause performance of an operation by a first machine according to a first configuration, process a performance metric of the performance of the operation by the first machine to determine whether the performance metric is within a threshold range, and in response to a determination that the performance metric is not within the threshold range, cause performance of the operation by a second machine according to a second configuration different from the first configuration.