M
Mathias Jäckel
Researcher at Fraunhofer Society
Publications - 17
Citations - 132
Mathias Jäckel is an academic researcher from Fraunhofer Society. The author has contributed to research in topics: Rivet & Process (computing). The author has an hindex of 5, co-authored 15 publications receiving 67 citations.
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Journal ArticleDOI
New Die Concept for Self-Pierce Riveting Materials with Limited Ductility
TL;DR: In this paper, a new tool concept for self-pierce riveting materials with limited ductility is proposed, where the riveting die is separated and a movable die element is used.
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Concept for Further Development of Self-pierce Riveting by Using Cyber Physical Systems☆
TL;DR: In this article, a numerical analysis of the most influential tool parameters as well as the influence of varying boundary conditions on the joining result for self-pierce riveting is presented, and a new concept for improving quality and flexibility of the process by using an advanced die system in combination with cyber physical systems is introduced.
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Overview of Current Challenges in Self-Pierce Riveting of Lightweight Materials
TL;DR: In this article, an overview of different analyses regarding current challenges at self-pierce riveting with solid rivets as well as semi-tubular rivets of lightweight materials like aluminum die casting, carbon fiber reinforced plastic and 7xxx series aluminum alloy is presented.
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Process-oriented Flow Curve Determination at Mechanical Joining
Mathias Jäckel,Sam Coppieters,Nelis Vandermeiren,Christian Kraus,Welf-Guntram Drossel,Noa Miyake,Toshihiko Kuwabara,Klaus Unruh,Heinrich Traphöner,A. Erman Tekkaya,Tudor Balan +10 more
TL;DR: In this paper, stress state analysis is presented for the mechanical joining techniques clinching as well as self-pierce riveting with semi-tubular rivet (SPR), based on which flow curves for DC04 material are determined by selected experimental materials tests in order to compare the influence of the flow curve determination method on the accuracy of the process simulation models.
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Gathering of Process Data through Numerical Simulation for the Application of Machine Learning Prognosis Algorithms
TL;DR: A method is shown by which application it is possible, that only on the basis the general mechanical properties and the use of data-based prognosis models of supervised machine learning to predict directly a result regarding suitable process parameters as well as expected forming result properties are shown.