AI-based computer vision technology will help reduce waste/scrap
Visionify.ai, a computer vision technology company that provides turnkey solutions to its customers, announced it is now a solutions provider for one of General Shale’s brick plants. General Shale’s family of brands manufacture and supplies high-quality thin bricks for residential and commercial construction. They use state-of-the-art manufacturing and packaging facilities in the United States and Canada.
Common problems in any brick manufacturing operation include damaged, cracked or non-conforming bricks. General Shale currently uses ultrasound sensors to detect damaged bricks and remove them from their production line. However, that solution has proven not to be accurate enough and generates more false negatives, creating unnecessary wastage, and impacting the production unit’s bottom line.
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“Technologies like ultrasound and infrared are great, but are also very rigid in nature. Building tolerances and acceptable deviations in inspections are not readily possible using those technologies,” said Priyesh Sanghvi, Co-Founder and CEO of Visionify.ai.
Harsh Murari, CTO of Visionify.ai, added, “AI-based computer vision technology is best suited for such scenarios because such systems can be trained to mimic human-like inspection behavior which inherently allows for acceptable deviations in the inspection system. The performance of such a system improves over time as it sees and learns from more and more samples.”
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Visionify.ai will deploy an artificial intelligence-based computer vision system that will learn the difference between good and bad bricks. Based on the learning, the vision system will instantly classify the bricks and discard the bad ones. Visionify.ai’s proprietary algorithm will reduce the wastage at the facility by using past heuristics to accurately determine acceptable bricks. The scalable solution will also detect other characteristics like brick color, composition, etc.
Visionify.ai is a vision AI solutions provider that detects anomalies, contaminants, etc. on products passing through belts. Its systems support a wide variety of products and are fully customizable. Factories receive real-time feedback to take necessary actions. The company uses pre-built models, inference engines and extensible architecture for customized integrations, allowing for complete working solutions to be deployed in less than four weeks.
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