The Learning Factory Digital Twin
A proof-of-concept to apply digital twinning to the manufacturing process of aerospace components.
Updated March 31, 2023
Even the latest advancements in manufacturing processes and intelligent materials still rely on a form of trial and error: parts are produced and tested, and then refined and redeveloped in a costly, iterative process.
The Learning Factory Digital Twin project is integrating advanced materials research with emerging manufacturing technologies to make products lighter, stronger, smarter, more durable and energy efficient, while minimizing production costs.
How We Are Solving It
The simulation of traditional factory processes in a virtual environment is creating, in essence, a digital twin of a physical production facility. Sensors are being deployed to collect real-time data that will be used in combination with physics-based simulations of the production line to detect problems faster, predict results more accurately, and ultimately lead to the manufacturing of better products.
The Learning Factory Digital Twin project helps position British Columbia as a leader in digitally enhanced advanced manufacturing, leveraging the province’s growing technology sector and existing relationships within the Cascadia Corridor.
Avcorp Industries Inc. is leading this project in partnership with Convergent Manufacturing Technologies Inc., AMPD Technologies, Boeing Research & Technology, LlamaZOO Interactive, Microsoft and the University of British Columbia. Together, the project team is digitizing segments of two existing industrial production lines for complex Boeing aircraft parts, bridging the knowledge and talent gaps between research, education and full-scale industrial production to create digitally driven, industrial tools.
These tools will have a dramatic impact on spatial planning, asset tracking, asset state determination, data collection, aggregation, physics-based simulation, digital architecture and process automation, benefiting diverse customers and industries.
The project will also demonstrate the benefits of data-driven collaboration, enabling advanced computational and modelling approaches and commercial technology development opportunities.
This project aimed to demonstrate a functional blueprint of a digital twin solution for the manufacturing processes of aerospace components, allowing for hands-on learning and research to drive continuous improvements through predictive planning, real-time monitoring and quality control. Ultimately, the digital twin developed through this project will inform future work and create a new approach to advanced aerospace manufacturing. The project has created simulation models of production processes and layout modelling to evaluate capacity planning and resource allocation for optimizing workflows and demonstrated capabilities in physics-based models, probabilistic predictions and sensitivity and spatial analysis. The project achieved 80-90% improvement in productivity or cycle times where its technologies were applied in manufacturing operations.
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