Intelligent Robotic Inspection for Foundation Industry Optimisation - IRIFIO

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This collaborative, industrial R&D project will utilise machine learning to enhance artificial intelligence, robotics and vision systems when applied to foundation industry production processes. This project builds on previous work inspecting defects in metals production through digitised inspection sensor technology to enhance industrial productivity and significantly reduce energy in both glass and ceramic manufacturing. This includes the integration into factory processes visual inspection with machine learning and AI-driven automated design for the non-destructive testing of fabricated parts. Current manufacturing methods are inflexible, often requiring the time-intensive pre-programming or manual intervention of production tasks responding to unexpected occurrences or production errors. This means that foundation industries are unable to respond to the demands of future environmental requirements, and can not make further improvements within the manufacturing process until the production methods are updated. This project aims to use vision sensors to feed information into machine learning and AI algorithms to monitor and improve the metals, glass and ceramic production process. To guarantee the repeatability and accuracy of measurement, automation through the flexibility offered by modern multi-axis robotic systems will be explored. The ultimate output of the system will result in foundation industry-wide benefits in glass, ceramics and metals production. This project will address specific needs in these foundation industries by offering an augmented, existing manufacturing process thorough digitised inspection & learning. It is anticipated that a reduction in energy costs and improved production yields associated with the manufacture of tempered glass & dense ceramic materials will be significantly and positively impacted, as is the case in the steel industry.