Wieland needed a more efficient way to inspect visually demanding copper products where manual review was slowed by reflective surfaces and round test objects.
Reported impact
Wieland improved inspection efficiency and scalability, supported over 20 test cells and 50 products, and established a centrally managed quality-control setup that can expand to further use cases.
Wieland Group partnered with Capgemini to replace labor-intensive visual inspection of reflective copper products with a scalable AI-driven computer vision setup. After reviewing current operations and visiting production environments, the team designed and tested a deep-learning-based inspection approach that could cope with round test objects and challenging metallic surfaces. The solution automates quality inspection across multiple production scenarios while keeping central management in the cloud, which makes rollout and reuse easier across new manufacturing contexts. Once implemented, the system supported more than 20 test cells and over 50 products, giving Wieland a standardized inspection capability that can expand as additional use cases are added. The project combines machine-learning inference on shop-floor imagery with centralized governance so the manufacturer can improve consistency, reduce scrap, and scale automated quality control independently over time.
Images from production test cells are evaluated by a deep learning model inside the AI-driven computer vision solution, and centrally managed cloud controls help Wieland reuse and scale the inspection setup across additional products and use cases.
"Centralized management from the cloud"
"Support for more than 20 test cells and 50 products with a scrap rate of 1.5%"
"Wieland worked with Capgemini to develop, implement, and test a scalable computer vision solution based on a deep learning model"
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The page says “10,279 employees” and notes this is the “Average number of employees of the Wieland Group in the 2024/25 fiscal year.”
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