Manufacturing settings produce significant volumes of operational data. In the case of powder metallurgy, such data can be obtained from machinery, RFID, BLE, sensors, inventory management platforms, manufacturing records, quality control procedures and other sources.
However, the issue is rarely one of data collection. The main problem lies in data connectivity.
For example, a powder metallurgy factory can use different databases, spreadsheets, manuals, machinery, monitoring systems, etc., to store its operational data. With this setup, it can become difficult for the team to track production status, traceability, equipment state, and material consumption.
But here the idea of AIoT architecture can be useful.
How Does AIoT Work?
AIoT includes the combination of connected devices, data integration, analytics, and artificial intelligence.
Machines / Sensors / RFID / BLE
↓
Connectivity Layer
↓
Edge / Integration
↓
Data Normalization
↓
Analytics + AI
↓
Operational Insights
↓
Human Decision-Making
AI does not have to replace the existing manufacturing solutions. It can work on top of connected data instead.
Connecting The Factory
Various technology types solve various manufacturing problems.
RFID helps to track tooling and asset management. BLE assists in workforce tracking and location-based services. Industrial IoT sensors can help monitor machinery and environment, whereas LoRaWAN will allow long-range connection in large factories.
True value lies in integrating all of these sources.
For instance, information about the location of the die is useful. Integrating information about location with history of usage, production cycles, and maintenance can give far more information.
From Data to Intelligence
Connected data can assist in monitoring equipment, equipment maintenance, tooling usage, inventory prediction, WIP, production analysis, and traceability.
The aim is not to automate every decision-making process. The idea is to provide manufacturing professionals with information when decision-making is needed.
Connected production history could look like this:
Powder Lot → Mixing → Compaction → Sintering → Inspection → Certification
Connecting these stages will help build the foundation for digital genealogy and quality investigation.
Start With One Use Case
Manufacturers are not required to instantly connect their entire factory floor. They can start with one clear use case:
- Where are the key tools?
- How are the materials being used up?
- Where is the production batch?
- Which equipment needs attention?
- How does process history connect?
Once one use case proves itself valuable, others can be added.
AIoT is not merely about putting sensors everywhere. It is about connecting people, machines, materials, process, and information.
PowderForge AI uses a connected strategy to help manage the powder metal factory floor, from tooling to inventory, WIP, traceability, workforces, and analytics.
The future of intelligent manufacturing might hinge not on collecting even more data but in making existing data valuable.
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