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Potentials of Industrie 4.0 and Machine Learning for Mechanical Joining

Potenziale von Industrie 4.0 und Maschinellem Lernen für die Mechanische Fügetechnik
 
: Jäckel, Mathias

:
presentation urn:nbn:de:0011-n-4420455 (5.5 MByte PDF)
MD5 Fingerprint: e77df14423a31f1607b367aad3a57349
Created on: 20.4.2017


Automotive Circle:
Joining in Car Body Engineering 2017. Reducing Complexity - Enabling lightweight design : Module 1 - adhesive bonding and hybrid bonding; 4 to 6 April 2017, Bad Nauheim, Germany
Bad Nauheim, 2017
25 Folien
Conference "Joining in Car Body Engineering" <2017, Bad Nauheim>
English
Conference Paper, Electronic Publication
Fraunhofer IWU ()
mechanical joining; FEM-Simulation; machine learning; Industrie 4.0

Abstract
-Sensitivity analysis of the influence of component properties and joining parameters on the joining result for self-pierce riveting -Possibilities to link mechanical joining technologies with the automotive process chain for quality and flexibility improvements -Potential of using machine learning to reduce automotive product development cycles in relation to mechanical joining -Datamining for machine learning at mechanical joining

: http://publica.fraunhofer.de/documents/N-442045.html