The operations in manufacturing generate streams of operational data in the form of equipment condition, environmental parameters, material movement, and production processes. But data collection is not the only thing here; the real problem lies in how fast this data is processed to aid decision-making.
This is where edge computing becomes an integral element of IIoT and AIoT infrastructure.
Why Do We Need Edge Computing?
Cloud computing-based platforms collect operational data and send it to the central servers in order to analyze it further. Such approach provides the most reliable solution for reports and enterprise-wide analysis. But, at the same time, it can create delays when manufacturing processes are time-sensitive.
Edge computing provides an opportunity to process part of the data on-site, analyzing selected events and responding instantly if required conditions are detected.
Common Edge Operations Are:
- Filtering sensor information
- Abnormal machine behavior detection
- Alert generation
- Processing RFID events
- Device management
- Syncing information summaries with cloud systems
This decentralized architecture leads to faster reactions and less network traffic.
The Importance of Latency in Manufacturing Processes
In powder metallurgy, manufacturing processes are highly interconnected. A delay in one area often impacts others very quickly.
Specific examples are:
- Problems with tooling availability
- Inventory differences during manufacturing
- Work in progress delays
- Environmental conditions
- Machine operation
Manufacturing staff can discover problems earlier when operational information is analyzed closer to its origin.
Combining Various Data Streams
Todayβs factories usually donβt depend on just one type of technology. Typically, various technologies are used in a single facility and provide different kinds of information.
An AIoT system can combine the following data sources:
- Radio Frequency Identification (RFID)
- Bluetooth Low Energy (BLE)
- Sensors for industrial IoT devices
- Data from manufacturing execution system
- Data about maintenance procedures
- Inventory data
What really matters is the correlation between all these data streams rather than their separate analysis.
From Information Gathering to Operational Intelligence
Gathering information is the first step, but using it with AI and IoT can result in much more.
For example:
- Recognizing recurrent bottlenecks in manufacturing processes
- Forecasting the rate of wear of tools
- Analyzing inventory consumption patterns
- Detecting workflow inefficiencies
- Enabling digital traceability in production process
Rather than fixing problems after they occurred, manufacturers will be able to detect them using operational intelligence.
Issues of Security and Scalability
With the emergence of more connected industrial machines, architectural decisions gain relevance.
Common elements of successful implementations of AIoT include:
- Secure connection between devices
- Segmented industrial network
- Access based on roles in the system
- Synchronization of edge and cloud environments
- Infrastructure capable of scaling with the increase in production
Achieving balance between performance and cybersecurity is key to ensuring continuity of operations and integrity of the data.
Future Prospects
In terms of AI in the industrial sphere, it will most likely not rely exclusively on bigger data sets in the future. The real value of it will be in processing relevant data at the right place and at the right time.
For powder metallurgy manufacturers, integration of edge computing together with AIoT allows achieving increased visibility in production, inventories, tooling, worker operation, and traceability without reliance on delayed reports.
Interested readers can find an example of such implementation in powder metallurgy manufacturing on the website of PowderForge AI
As the concept of connected manufacturing matures, the combination of edge computing and AIoT is likely to become an important component in industrial organization's decision-making process.
Top comments (0)