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What potential does Artificial Intelligence have in engineering? The AI marketplace has developed AI solutions for specific challenges in engineering in six use cases.
From intelligent product monitoring to AI-supported manufacturability analysis: On this page, you can learn more about each application case and the respective potentials of AI.
The goal of Ubermetrics’ project is an AI application that extracts relevant information from unstructured or semi-structured texts (e.g. online ratings, complaint emails, service reports, complaint reports), analyzes it and makes it systematically available to a developer. The application can be used, for example, in the context of targeted optimization of system components.
How can service technicians be optimally supported? A pilot project by Diebold Nixdorf Systems together with Fraunhofer IOSB-INA is dedicated to this question. The aim is to develop an AI application that reads and analyzes service and sensor data from ATMs in the field. Based on this, differentiated repair instructions for service technicians are to be generated. An existing service platform will serve as a database.
Artificial Intelligence has the potential to fundamentally change the way we work and operate. CLAAS GmbH & Co. KGaA, an internationally active manufacturer of agricultural machinery, has recognised this potential and is testing a special use case for the integration of AI in Computer Aided Design (CAx) in the AI marketplace together with the Fraunhofer Institutes IEM and IPK.
The project of Westaflex in cooperation with the Fraunhofer IEM focuses on an AI application that helps to optimize the sequence planning of production orders. For this purpose, real-time data from production control as well as from machines are to be evaluated in order to extract hints for optimal machine allocations.
A set-up process in an automated manufacturing process that is optimized with the help of AI and replaces the previously manual “teaching” – this is the core of a project by düspohl Maschinenbau GmbH and the Fraunhofer IEM. In addition, the probable producibility of new product specifications is to be assessed automatically.
At present, vehicle diagnosis in a workshop requires comprehensive automotive knowledge and involves a great deal of work. Hella Gutmann’s aim is therefore to develop an AI application for AI-supported diagnosis and identification of potentially defective vehicle components on the basis of historical vehicle data (e.g. fault codes or sensor readings) and to integrate a further variety of data sources (e.g. invoice data, repair information etc.).