Now showing 1 - 6 of 6
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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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Entscheidende Veränderungen in der zukünftigen kollaborativen Produktentwicklung

2016 , Lünnemann, Pascal , Fresemann, Carina , Neumeyer, Sebastian , Wang, W.M. , Stark, R.

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Potentials and challenges of Smart Products and related business models

2020 , Wang, Wei Min , Lünnemann, Pascal , Klemichen, Antje , Blüher, Till , Stark, Rainer

Smart products are increasingly penetrating the market. They extend cyber-physical systems with Internet-based services and thus enable customer-specific adaptations and updates of products in the field. For industrial companies, smart product offering bear the potential to expand their product portfolios and increase their sales. In this article, the results of a survey among German engineers are presented that evaluated the actual state of adoption and future expectations regarding smart products in German industrial companies. The results show that the majority of German companies have recognized the potentials of smart products and also introduced such offerings in their portfolio. In this context, it is also expected that the product-related usage data will lead to a more individual communication with the customer. On the other hand, there is a high degree of uncertainty among companies regarding the adaptation of new business models, cooperation with an increasing number of partners, data management and the necessary technological infrastructure. These uncertainties can result in companies leaving potentials unexploited and opening up opportunities for new market participants. In summary, there seems to be a remaining gap between the willingness to abandon traditional business models and the expectations and strategies for future value creation.

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Product development in collaborative networks - An expert view on current challenges and future trends

2016 , Wang, Wei Min , Lünnemann, Pascal , Neumeyer, Sebastian , Hayka, Haygazun , Stark, Rainer

Cross-enterprise collaboration in product development has become a common practice in industry. Companies increasingly depend on collaborations with networks of partners from different tiers of value creation, various geographic locations and even from outside of their own sector. Moreover, there is a growing tendency to involve parties from later stages of the product's lifecycle in the product development phase. To face the challenges of the increasing complexity of collaborative product development a holistic approach of Product Lifecycle Management (PLM) has evolved. Although methods, processes and IT-systems of PLM proved to be beneficial in supporting the internal collaboration of industrial companies, they seem to have only little effect in the context of cross-company collaboration. Reasons and solution approaches for that were evaluated in an expert study with 40 experts from industry and academia. Results from that study and practical implications are presented in this paper.

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Qualitätskriterien für den Datenfluss im Advanced System Engineering

2019 , Müller, Helena , Riedelsheimer, Theresa , Lünnemann, Pascal , Blüher, Till , Stark, Rainer

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Systems Engineering im Kontext der unternehmensübergreifenden Produktentwicklung

2016 , Neumeyer, Sebastian , Lünnemann, Pascal , Woll, Robert