Data Aggregation for Digital Manufacturing via Queued Messaging
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Solution Overview
Problem
Digital manufacturing systems, such as 3D printers, face challenges in data aggregation and analytics due to disparate communication protocols and reliance on non-real-time operating systems, leading to inefficiencies and difficulties in monitoring and managing multiple machines across a multivendor environment.
Innovation Solution
The implementation of a client-centric queued messaging system, like Apache Kafka, to manage and distribute data queries and responses, allowing for real-time data aggregation and analysis across multiple digital manufacturing machines, thereby minimizing computing load and enabling seamless communication across different vendor systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is collected from multiple digital manufacturing machines using disparate communication protocols, then data coverage and comprehensiveness are improved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent introduces a message queue system as an intermediary component between diverse manufacturing machines and the data processing system. This mediator standardizes data collection by receiving messages from various machines through unified interfaces, eliminating the need for complex direct integrations with each machine's proprietary protocol while maintaining comprehensive data coverage
Solution Approach 2:
The system implements a universal data collection architecture using standardized message queues that can handle multiple types of manufacturing machines simultaneously. The message queue infrastructure provides multi-functional capabilities to process data from different vendors and machine types through a single unified interface, reducing integration complexity
2Productivity
If real-time data aggregation is implemented across multiple machines, then production efficiency and monitoring capability are improved, but computing load and resource requirements increase
Solution Approach 1:
The patent extracts data collection and initial processing functions from the central computing system and places them at the edge devices (manufacturing machines themselves). Each machine publishes its own data to message queues, eliminating the need for a centralized system to actively query or collect data from every machine, thereby reducing computing load on central systems while maintaining real-time aggregation capabilities
Solution Approach 2:
The system implements self-service data publishing where each manufacturing machine autonomously publishes its own operational data to the message queue infrastructure. This eliminates the need for external systems to actively manage data collection, reducing computational overhead while enabling real-time data aggregation across all machines
3Adaptability or versatility
If standardized data formats are implemented across multivendor machines, then data interoperability and accessibility are improved, but adaptation effort and initial setup time increase
Solution Approach 1:
The patent changes the data format parameters at the source by implementing standardized message structures at each manufacturing machine. Each machine transforms its proprietary data into a universal message queue format before publication, ensuring interoperability without requiring complex translation layers. The standardization is built into the data publishing process itself, reducing long-term adaptation needs
Data Source
AI summary
Systems and methods for aggregating and analyzing digital manufacturing data are disclosed. An aggregator can collect output data generated by a number of digital manufacturing machines. The output data can be filtered and transmitted to a server for storage in a database. One or more clients can access the data in the database via an API. This can extract 3D printer data, transform it into a canonical form, and upload it to a network-based database. These services can interact using a client-centric messaging system like Kafka or, more generally, a message manager. When a client's producer sends a message, the messaging system responds that the message request either can or cannot be processed. If the message can be processed, then the aggregator sends the message; otherwise the producer can retry at a future time.