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APPROACH FOR THE AUTOMATED AND DATA-BASED DESIGN OF MECHANICAL JOINTS.

Published online by Cambridge University Press:  27 July 2021

Christoph Zirngibl ,
Benjamin Schleich  and
Sandro Wartzack
Christoph Zirngibl*
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Benjamin Schleich
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
Sandro Wartzack
Affiliation:
Friedrich-Alexander-Universität Erlangen-Nürnberg
*
Zirngibl, Christoph, Friedrich-Alexander-Universität Erlangen-Nürnberg Engineering Design, KTmfk, Germany, zirngibl@mfk.fau.de

Abstract

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As a result of the increasing challenges in the field of lightweight constructions, the demand for efficient joining technologies is continuously rising. For this purpose, cold forming processes offer an environmental friendly and fast alternative to established joining methods (e.g. welding). However, to ensure a high reliability, not only the selection of an appropriate procedure, but also the dimensioning of the individual joint is essential. While product designers can rely on a wide range of design principles for thermal processes, the dimensioning and evaluation of mechanical joining processes is mainly based on expert knowledge and a few experimental tests. Although few studies already investigated the numerical analysis of mechanical joints, an approach for the sustainable and consistent optimization of the strength and reliability of joining connections for varying use-cases is not available yet. Motivated by this lack, this paper presents an approach for the automated transfer of information within the process chain and the data-based analysis of mechanical joints by using clinching as an example. Therefore, the CRISP-DM reference model is used for the systematic data mining.

Keywords

Lightweight designOptimisationMachine learningClinchingCRISP-DM
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 521 - 530
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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