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Variation Simulation of Stresses Using the Method of Influence Coefficients

[+] Author Affiliations
Samuel Lorin, Lars Lindkvist, Rikard Söderberg

Chalmers University of Technology, Gothenburg, Sweden

Paper No. DETC2013-12814, pp. V004T05A013; 9 pages
doi:10.1115/DETC2013-12814
From:
  • ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 4: 18th Design for Manufacturing and the Life Cycle Conference; 2013 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications
  • Portland, Oregon, USA, August 4–7, 2013
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5591-1
  • Copyright © 2013 by ASME

abstract

In every manufacturing situation there are geometric deviations leading to variation in properties of the manufactured products. Variation affects the manufacturability, functions and aesthetics of the products. Therefore, a number of methods and tools have been developed during the last 20 years in order to assure the geometric quality and to minimize the effect of variability. These methods and tools have mainly been developed for rigid bodies or sheet metal components.

Plastics or composites have been an increasingly popular material due to their flexible mechanical properties and their relative ease in manufacturing. However, their mechanical properties are introducing challenges that have not often been addressed in the process of geometry assurance. One challenge is to assure that the stresses introduced, as a consequence of non-nominal assembly in the positioning system, are kept well below critical limits during the conditions of use.

In this paper, we are proposing the use of the method of influencing coefficients (MIC) to simulate the distribution of von Mises stresses in assembled components. This method will be compared to the more flexible but computationally much heavier Direct Monte Carlo (DMC) method, which is not suitable for variation simulation due to the large number of runs required for statistical inference.

Two industrial case studies are presented to elicit the need of the proposed method.

Copyright © 2013 by ASME
Topics: Simulation , Stress

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