Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
Please note that all times are shown in the time zone of the conference. The current conference time is: 17th May 2024, 11:29:21am CEST
1Hochschule Niederrhein University of Applied Sciences, Germany; 2Chalmers University of Technology, Sweden; 3Intended Future AB, Sweden
Sustainability is influencing the automotive industry. Car manufacturers are increasingly prioritizing sustainable materials. The production of these still pose environmental challenges. This paper addresses the trends related to sustainable car interiors. It emphasizes the importance of understanding how customers perceive these new materials. Seven in-depth interviews were conducted, followed by a survey with 106 participants to obtain generalized insights. The results will reduce the uncertainty regarding customer expectations for sustainable car interiors.
Future-robust product portfolio development: insights into the advancement of product portfolios in companies – an interview study
Michael Schlegel1, Markus Just1, Ingrid Wiederkehr2, Carsten Thümmel1, Christoph Kempf1, Christian Koldewey2, Roman Dumitrescu2, Albert Albers1
1Karlsruhe Institute of Technology, Germany; 2Heinz Nixdorf Institute, Paderborn University, Germany
A volatile environment and an increasing number of products along with a growing range of functions pose a challenge for companies when it comes to further development. Existing methods are no longer sufficient to cope with these challenges. In order to develop new methods, the process and challenges in the advancement of product portfolios must be understood. In this paper we conduct an interview study with ten experts to gain a better understanding of the advancement of product portfolios. Triggers, changes and actions are examined and goals and requirements for new methods are derived.
Decision making support for designers at the early design stage regarding narrowing down the range values of design variables
1Meiji University, Japan; 2Mazda Motor Corporation, Japan; 3The University of Tokyo, Japan
This study presents a search method for a solution space that aligns with a designer’s design intent. The proposed method uses multiobjective optimization to determine the size of the narrowed solution space and the weakness of the constraint relationships between the design variables. The suitability of the proposed method is tested by applying it to the design problem of an electric motor for an EV, aiming to provide designers with solution spaces that offer a high degree of freedom in the later design stages and that have weaker constraint relationships among the design variables.
Using cluster analysis to enhance a method for the management of disturbance factors via product structures
Richard Breimann, Laura Luran Sun, Eckhard Kirchner
Technische Universität Darmstadt, Germany
To achieve higher functionality in mechatronic systems, the management of disturbance factors plays a crucial role. For this purpose, a method was developed in prior works to address this management via the optimisation of product structures. However, this method lacks applicability due to the complexity of one step of the method. It is the goal of this paper to present a software tool, utilizing cluster-analysis to sort components into assemblies, with which this step is supported. Additionally, the method is generally adapted to address a wider spectrum of phenomena in mechatronic systems.
Modeling uncertain requirements
Lukas Block
Fraunhofer IAO, Germany
Anticipating all technical requirements that a product must meet throughout its lifespan has become difficult due to a rise in market, regulatory, and technological uncertainty. As a result, the attribute values of these requirements may be highly uncertain at the start of product development. We propose a mathematical model that captures and quantifies this uncertainty in a clear and comprehensive manner. We evaluate the approach by encoding uncertain requirements for an automotive project. Misconceptions regarding probabilities are alleviated and the requirements are unambiguously defined.
Data- and simulation-based material behaviour prediction
Anton Dybov, Carina Fresemann, Rainer Stark
Technische Universität Berlin, Germany
In research environments and laboratories e.g. for material sciences the in- and output of simulation data is manually managed. Therefore, physical experiments as well as simulations might be carried out several times, learnings are not systematically gathered, and experiments do not systematically build on learnings from data. This paper proposes to engage an ontology in conjunction with a simulation to use data from already carried out experiments and on that basis predict material behaviour under certain condition and plan further physical experiments.
