Inadequate and inefficient quality controls can impact production or the business in very negative ways. Examples include low customer satisfaction, damage to corporate image, and rising costs.
The use of AI-based methods can help ensure that quality assurance is comprehensive and end-to-end. This makes quality traceable in the long run and saves costs at the same time.


As a rule, image recognition and classification methods as well as time series analyses of machine or process data are used here. This allows anomalies and deviations to be detected or trends to be identified. Predictive models are created that can forecast quality problems before they occur. Correlation analyses and data-based root cause analyses allow the cause of defects to be identified.


AI-based methods can be used to detect the variability of raw materials or materials, the deviation of process or product parameters, or errors in production planning. Not only are errors detected automatically, but emerging problems can be identified at an early stage. And all this around the clock. By linking data along the production process, AI can help determine the causes of quality problems.

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Through our research in Explainable AI, we have developed AI systems that enable humans to make informed decisions under supervision. We also use state-of-the-art privacy methods to protect sensitive corporate data.
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In the course of the H2020 project AI4DI, the Know Center delivers AI components for the realization of an intelligent FMEA (Failure Mode and Effects Analysis) wizard to preventively detect and fix potential problems or failures. more