Approach for the Reliable and Virtual Design of Mechanical Joints in an Uncertain Environment
DS 133: Proceedings of the 35th Symposium Design for X (DFX2024)
Year: 2024
Editor: Dieter Krause; Kristin Paetzold-Byhain; Sandro Wartzack
Author: Jonathan-Markus Einwag; Stefan Goetz; Sandro Wartzack
Series: DfX
Institution: Engineering Design (KTmfk), Friedrich-Alexander-Universitat Erlangen-Nurnberg (FAU), Germany
Page(s): 222-230
DOI number: 10.35199/dfx2024.23
Abstract
The demand for lightweight assemblies necessitates appropiate joining processes, such as cold forming processes enabling multi-material joints. The absence of universally applicable approaches for the design of mechanical joints makes their initial design iterative and time-consuming. Machine learning based approaches already partly solve this problem, but the impact of uncertainties, is usually neglected. Thus, this contribution proposes the concept of a novel computer-aided approach, supporting the initial design of clinch joints, taking into account uncertainties and varying conditions utilizing numerical simulations, data-driven methods and ontologies. This aims for a high-quality joint design demonstrated using an application scenario where a hat profile and a sheet are joined.
Keywords: Mechanical Joining, Clinching, Machine Learning, Finite Element Method, Uncertainties