Manufacturing Process Data Linking for Real-Time Machine Analytics
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Solution Overview
Problem
Current manufacturing systems lack the capability to transmit and receive continuous, high-frequency data streams in real-time, making real-time monitoring and control of machines impossible, and previous systems collected data separately without connections for analytics, limiting geospatial and process data integration.
Innovation Solution
A method involving a high precision counter to associate real-time and non-real-time data through a data collection system, allowing for contextual linking and storage of data based on counter values, enabling advanced analytics and 3D visualizations of manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machines are equipped to transmit and receive continuous high-frequency data streams in real-time, then real-time monitoring and control capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces an external data collection system as an intermediary between the existing machines and the monitoring infrastructure. This mediator captures high-frequency data streams from machines through their existing communication interfaces (Ethernet, serial ports, fieldbus protocols) without requiring modifications to the machines themselves, thereby achieving real-time monitoring capability while avoiding increased machine complexity
Solution Approach 2:
The data collection system leverages existing machine communication capabilities and self-associates data with NC code sentences through automatic parsing and matching algorithms. The system serves itself by autonomously collecting, timestamping, and correlating data from multiple sources without requiring additional machine intelligence or complex integration hardware
2Ease of manufacture
If data is collected separately without connections, then data collection simplicity is maintained, but analytics capability and data integration are worsened
Solution Approach 1:
The data collection system performs multiple functions through a single unified platform: it collects data from diverse machine sources using various protocols, timestamps all data with high-precision counters, associates data with NC code sentences, and provides analytics capabilities. This multi-functional approach maintains collection simplicity while eliminating data silos and enabling comprehensive analytics
Solution Approach 2:
The system segments the data collection process into distinct functional modules: data acquisition from multiple sources, timestamping with precision counters, NC code sentence association, and analytics processing. This segmentation allows each module to operate independently with simple interfaces while the integrated system achieves comprehensive data correlation and analytics capability
3Device complexity
If tracing durations are limited to short durations, then data storage and processing complexity is reduced, but diagnostic capability is worsened
Solution Approach 1:
The system dynamically adjusts trace duration based on diagnostic needs and data volume. It implements configurable trace lengths that can extend indefinitely for long-duration processes, with automatic management of data retention policies. The dynamic approach allows extended tracing for comprehensive diagnostics while maintaining system efficiency through selective data archiving and retrieval capabilities
Data Source
AI summary
Techniques are described for viewing a plurality of related presentation areas linked to a set of machine-related data. In one example, a machine-related data set associated with a manufacturing process session for manufacturing a particular workpiece is presented, the data set representing a common data set presented in a plurality of presentation areas, each associated with a separate view on the machine-related data set. In one of the presentation areas, a selection of a particular group of data points is identified and that presentation area is updated. Reference values associated with the common data set are identified based on the selected group of data points. For each of the other presentation areas, (1) a particular set of data included in the particular other presentation area corresponding to the identified reference values is identified and (2) the corresponding presentation area is updated based on that identified data set.