Challenge: Enabling real-time rich information and AI assistance to be exploited by people at the point-of-use; reducing cost of downtime, interpretation and uncertainty.
Result: Estimated reduction in travel costs (65%) and maintenance time (15%) arising from real-time, worldwide collaboration and increased ease of training and maintenance support.
This industrial application use case investigates the use of augmented and mixed reality technologies for assembly, maintenance and inspection activities, for example manufacturing instructions. The digital instructions will be fed directly to a worker at the point of use via devices such as wireless hand-held tablets and personal headsets.
It will integrate the digital work instructions with CAD designs to help engineers carry out production and maintenance activities. Low latency instructions, enabled by 5G technology, could save about five seconds per instruction read compared with traditional methods. In addition to augmented reality, this industrial application use case will also investigate the review and approval of CAD designs.
This industrial application will test the use of visual inspection to validate the positioning of labels and the parts they attach to in order to validate that a label has been positioned correctly and attached to the correct part; defect detection of misaligned labels; augmented and mixed reality connected via 5G; sequential digital work instructions; and simulated integration with planning systems using synthetic data.
To understand the impact of 5G, all data will flow through the 5G network to realise the effects, impact, benefits, and constraints of 5G on the manufacturing processes.
This use case seeks to explore solutions to a number of challenges.
Opportunities exist to improve efficiencies around cycle times. These areas are:
5G Factory of the Future has developed five use cases for 5G in manufacturing, delivering innovation with measurable outcomes.
Introducing Hybrid Reality Spaces
Challenge: Enabling real-time rich information and AI assistance to be exploited by people at the point-of-use; reducing cost of downtime, interpretation and uncertainty.
Result: Estimated reduction in travel costs (65%) and maintenance time (15%) arising from real-time, worldwide collaboration and increased ease of training and maintenance support.
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.
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.
