Abstract
Mass customization and increasing product complexity require new methods to ensure a continuously high product quality. In the case of product failures it has to be determined what distinguishes flawed products. The data generated by cybertronic products over their lifecycle offers new possibilities to find such distinctions. To manage this data for individual product instances the concept of a Digital Twin has been proposed. This paper introduces the elements of a Digital Twin for root cause analysis and product quality monitoring and suggests a data structure that enables data analytics.
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