Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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Geometric Design Process Automation with Artificial Intelligence

2022 , Brünnhäußer, Jörg , Lünnemann, Pascal , Bisang, Ursina Saskia , Novikov, Ruslan , Flachmeier, Florian , Wolff, Mario

Design tasks are largely performed manually by engineers, while machine learning is increasingly able to support and partially automate this process to save time or costs. The prerequisite for this is that the necessary data for training is available. This paper investigates whether it is possible to use data-driven methods to support the design of jounce bumpers at BASF. Based on the analysis of the use case, the geometry of the jounce bumper is approximated with a spline to generate suitable data for training. Based on this, data for training the machine learning model is generated and simulated. In the training process, the appropriate feedforward neural network and the best combination of hyperparameters are determined. In the subsequent evaluation process, it is shown that it is possible to predict the geometries of jounce bumpers with our proof of concept. Finally, the results are discussed, the limitations are shown and the next steps to further improve ssthe results are reflected.

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Assembly Issue Resolution System Using Machine Learning in Aero Engine Manufacturing

2020 , Brünnhäußer, Jörg , Gogineni, Sonika , Nickel, Jonas , Witte, Heiko , Stark, Rainer

Companies are progressively gathering data within the digitalization of production processes. By actively using these production data sets operational processes can be improved, hence empowering businesses to compete with other corporations. One way to achieve this is to use data from production processes to develop and offer smart services that enable companies to continuously improve and to become more efficient. In this paper, the authors present an industrial use case of how machine learning can be implemented into smart services in production processes to decrease the time for resolving upcoming issues in manufacturing. The implementation is carried out by using an assistance system that aids a team which attends to problems in the assembling of turbines. Therefore, the authors have analyzed the assembly problems from an issue management system that the team had to resolve. Subsequently three different approaches based upon natural language processing, regression and clustering were selected. This paper also presents the development and evaluation of the assistance system.

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