Robust Design of Active Systems - An Approach to ...

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International Symposium on Robust Design (ISoRD'14) Technical University of Denmark, DTU

Robust Design of Active Systems - An Approach to Considering Disturbances within the Selection of Sensors
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Author:Tillman Freund (Institute for Product Development and Machine Elements, Technische Universität Darmstadt, Germany)
Jan Würtenberger (Institute for Product Development and Machine Elements, Technische Universität Darmstadt, Germany)
Stefan Calmano (Institute for Production Engineering and Forming Machines, Technische Universität Darmstadt, Germany)
Daniel Hesse (Institute for Product Development and Machine Elements, Technische Universität Darmstadt, Germany)
Hermann Kloberdanz (Institute for Product Development and Machine Elements, Technische Universität Darmstadt, Germany)
Date: 2014-08-15     Track: Main     Session: 13:00-14:30 Oral Session 2 - Robustness in Production
DOI:10.4122/dtu:2102

Plain spherical bearings are precision assemblies with a low frictional moment finding wide application in industry where they operate in harsh environments. They are manufactured using a cold forming process known as ‘nosing’. An experiential approach is currently used by a manufacturer to develop new bearings and determine associated process settings. Typically, inefficiencies can be observed for the bearing assembly post-nosing where any one of nine different failure modes may occur leading to rework or scrap costs due to a number of component and process inconsistencies. The initial focus is the outer bearing shell component and the geometrical relationships of the end chamfer features. Process capability measures are developed for a bearing model, with parts individually tracked through the nosing process to examine the influence out-of-tolerance variation on process integrity, measured forming loads and frictional moment. A validated Finite Element (FE) model is used to predict the complex elastic-plastic material behaviour at high strain-rates in the nosing process to support the simulation of in-process failure modes. These models take into account the geometrical and dimensional variations of the chamfers, material property variation and coefficient of friction. Predictions are made for feature process capabilities which produce lower failure rates in production.