Name of the participant: Lea Mayer
Description of the IT-research project:In today’s process flows, new technologies are increasingly being used, which generate an ever increasing amount of process and product data. This information is already being fed into various systems and made available to users, but is not used to a sufficient extent. In the context of Industry 4.0, Process Analytics can be further expanded in today’s applications – for example, added value can be generated from the integration of different data formats by using additional information to optimize processes.
In the automotive industry, the test phase of new products is becoming more and more difficult due to high cost pressure, increased technology changes and shorter market introduction times. Although possibilities for error analysis and status monitoring already exist in the individual domains, there is a lack of a cross-domain system for complex processes. Any irregularities occurring within the product life cycle of a product are detected late, so that only reactive measures can be taken. This can result in minor effects, such as a small amount of scrap, but also in major damage. An example would be the installation of a defective component in a vehicle that has been handed over to the customer.
In order to solve the problems described above, a tool based on Artificial Intelligence (AI) is to be developed, which enables cross-domain status monitoring and inherent process optimization – this is to be carried out over the entire product life cycle. Anomaly detection, failure analysis and root cause identification shall help to limit the production of defective products and to minimize possible damages. In order to strengthen the user’s confidence in the system, the results are to be presented to the quality engineer in an understandable way using Explainable AI (XAI) methods.
Software Campus partners: DFKI, IAV
Implementation period: 01/2020 – 12/2021
