FAIR is a service-based solution that applies AI Deep Learning to achieve greater automation of inspection processes. It offers significantly more than previous machine vision capabilities, as a comprehensive audit captures everything, and quality control decisions are compiled into a report, along with images of the defects. Furthermore, it can be deployed on-premises or in the cloud, integrates with existing manufacturing systems, and adapts to unique environments. Regarding its advantages over older Automated Optical Inspection (AOI), FAIR offers five key benefits: it learns from images, rather than programmed rules; it adapts to changing specifications and product variations; it replicates worker insights; it highlights anything unexpected and is easy to apply to more complex images; and it classifies all types of defects.
Some examples of FAIR applications:
Railway maintenance
Analysis by combining thermal and optical images, captured from the front cameras of a train, which are used to identify assets and their condition, reducing the presence of people in the control of the tracks and offering improved safety.
Water pipe maintenance
Video inspection captures images from inside water pipes, identifying problems such as air pockets, dirt, and gaps in pipe sections. FAIR allows for the analysis of selected sections instead of the entire pipe length, reducing inspection time and service interruptions.
Inspection of invasive plants
Analysis of large amounts of drone-captured images, a project carried out in Australia, confirmed that FAIR identifies invasive plant species, provides a comprehensive view of the location of the most critical areas, and offers intelligence to support preventative action.
Wind turbine manufacturing
Ultrasonic quality inspection generates a wealth of data on wind turbine blades for review. This inspection process is now fully automated, reducing inspection time by 80% and allowing inspectors to focus on higher-value activities. FAIR was able to save 32,000 man-hours annually, resulting in a dramatic reduction in costs and production lead times.
