Distributed Additive Manufacturing With Edge Query Processing
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Solution Overview
Problem
The proliferation of data from IoT sensors and other sources in value chain networks overwhelms traditional centralized data collection methods, leading to complexity and inefficiencies in data transmission and automated decision-making.
Innovation Solution
A method for processing queries in a distributed database using edge devices, where queries are stored on a dynamic ledger, generating approximate responses based on summary data, and transmitting these responses, with the option to use a probability distribution model or neural network for prediction, optimizing data management and reducing network overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data from thousands of IoT sensors is collected and transmitted through centralized systems, then complete data availability is achieved, but network bandwidth is overwhelmed and system complexity increases
Solution Approach 1:
The patent divides the centralized data collection system into distributed edge devices that each independently process and store data locally. Edge devices segment the overall system functionality, allowing data to be processed at multiple locations rather than funneling everything through a central point, thereby reducing network bandwidth pressure and system complexity.
Solution Approach 2:
The patent introduces a new dimension of data processing by implementing edge computing capabilities at distributed devices. This adds a spatial dimension to data handling, where data can be processed, stored, and queried locally at edge locations rather than only through centralized cloud systems, effectively distributing the computational burden.
2Loss of information
If all sensor data is transmitted to centralized systems for processing, then comprehensive analysis is possible, but response time increases due to network transmission delays
Solution Approach 1:
The patent implements preliminary data processing and storage at edge devices before centralized retrieval is needed. By pre-processing data locally and maintaining it in distributed databases, the system avoids the need to transmit raw data from sensors to centralized systems for every query, significantly reducing response time while preserving data completeness.
Solution Approach 2:
The patent introduces distributed edge devices as intermediaries between sensors and centralized systems. These edge devices act as mediators that can independently handle data queries and only communicate with centralized systems when necessary, reducing network transmission delays while maintaining comprehensive data access.
3Loss of time
If distributed edge devices process queries independently, then response time improves, but data consistency across the network may deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where edge devices periodically synchronize with centralized systems and with each other. Query results and data updates from distributed edge devices are fed back to the centralized system, which maintains a master view of the data, ensuring consistency across the distributed network while allowing independent local processing.
Solution Approach 2:
The patent combines distributed edge processing with centralized coordination. While edge devices operate independently for fast local queries, they are merged into a unified system through the centralized database that aggregates data from all edges and maintains global data consistency, achieving both speed and coherence.
Data Source
AI summary
An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. A modeling stage of a process workflow includes a digital twin modeling system defined by a product instruction or a control tower instruction to encode a set of digital twins representing a product for use by the additive manufacturing management platform. The information technology system includes an artificial intelligence system executable by a data processing system. The artificial intelligence system is trained to generate process parameters for the process workflows managed by the additive manufacturing management platform using data collected from the distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the distributed manufacturing network entities.