Generation of deviated geometry based on manufacturing process simulations

Beitrag bei einer Tagung
(Konferenzbeitrag)


Details zur Publikation

Autorinnen und Autoren: Schleich B, Wartzack S
Herausgeber: Dynardo GmbH
Jahr der Veröffentlichung: 2012
Tagungsband: Proceedings of the 9th Optimization and Stochastic Days 2012
Sprache: Englisch


Abstract


Every process underlies fluctuations of input and ambient parameters. In manufacturing processes, these fluctuations manifest i.a. in geometric deviations of manufactured workpieces which in turn hugely decrease the function and quality of technical products. Therefore, these deviations have to be limited by geometric tolerances. In this regard, tolerance simulations aim at determining and quantifying the effects of deviations on the product quality. However, in order to obtain resilient predictions about the impacts of process fluctuations on the quality of technical products realistic samples of deviated geometries have to be available which reflect the observable geometric manufacturing deviations. The proposed approach employs methods from computer vision and regression analysis for generating realistic geometry samples based on a limited set of observations, e.g. gathered from manufacturing process simulations or measurements, considering manufacturing process parameters. An exemplary application using the software "Statistics on Structure" (SoS) and "optiSLang" by dynardo GmbH is given.



FAU-Autorinnen und Autoren / FAU-Herausgeberinnen und Herausgeber

Schleich, Benjamin Dr.-Ing.
Lehrstuhl für Konstruktionstechnik
Wartzack, Sandro Prof. Dr.-Ing.
Lehrstuhl für Konstruktionstechnik


Zitierweisen

APA:
Schleich, B., & Wartzack, S. (2012). Generation of deviated geometry based on manufacturing process simulations. In Dynardo GmbH (Eds.), Proceedings of the 9th Optimization and Stochastic Days 2012. Weimar, DE.

MLA:
Schleich, Benjamin, and Sandro Wartzack. "Generation of deviated geometry based on manufacturing process simulations." Proceedings of the 9th Optimization and Stochastic Days 2012, Weimar Ed. Dynardo GmbH, 2012.

BibTeX: 

Zuletzt aktualisiert 2018-07-08 um 05:10