Why are we doing it?
This use case seeks to explore solutions to a number of challenges:
- Uncertainty in some manufacturing processes
- Reactive versus adaptive controls
- Latency challenges of high frequency data
Application 01
Real-time Monitoring and Adaptive Closed-Loop Control
Challenge: Reduce cost and time associated with defects and quality issues. Create a no-fault forward manufacturing system.
Result: Estimated 15-25% reduction in the number of defects, amount of waste generated and machine downtime arising from improved process precision and predictive maintenance strategies and fewer errors.
What are we doing?
This use case will monitor high speed machining and manufacturing processes using the decreased latency capabilities of 5G. It will explore real-time control of machines in high-frequency, high-throughput scenarios by adding sensors to machines. The Internet of Things (IoT) data generated will be used to intervene with operations and improve quality, insight, and efficiencies.
It will investigate the challenges of adaptive control such as uncertainty, latency in high data throughput/volume applications and the reactive versus adaptive behaviours using 5G.
The high frequency IoT data will pass via 5G to an MTT controller. This will establish the performance capabilities of 5G in a Real Time Control Systems (RTCS) use case to measure bandwidth and latency when using 5G in real-time control in a small-scale manufacturing process using CNC machines.
What are the expected benefits?
- Prove real-time control in a small-scale CNC process;
- Measure latency via 5G;
- Determine bandwidth and performance of 5G;
- Reduce downtime with data-driven preventative maintenance;
- Improve quality by reducing waste/ scrap;
- Improve insights;
- Improve efficiencies;
- Provide an architecture and platform to develop more advanced adaptive control algorithms which can be applied to the CNC machine tools of the future.
Industrial Applications
5G Factory of the Future has developed five use cases for 5G in manufacturing, delivering innovation with measurable outcomes.
Challenge: Increasing visibility across the supply chain network through all tiers for assets and products, guaranteeing operational efficiency and delivery to customers.
Result: Estimated 30% reduction in lost and damaged assets, improved schedule accuracy, and providing supply-chain transparency and real-time condition monitoring for assets tracked by the system.
Factory Ecosystem Monitoring (FEM)
Challenge: Reduce infrastructure and through-life operational costs via real-time, agile monitoring of critical production environments.
Result: Estimated 5-10% improved machine utilisation, reduction in energy use (10-15%) and maintenance time (20%), arising from performance optimisation and enhanced resource utilisation.
Challenge: Enable a business-winning paradigm via data-driven digital twins through the product lifecycle.
Result: Estimated 15-20% machine utilisation improvement (reduction in idle time, improved scheduling) and factory efficiency, arising from data-driven decision-making, real-time asset location and inventory accuracy, efficient scheduling, asset performance optimisation, and improved predictive maintenance.
Supported By
