On Neural Networks’ Ability to Approximate Geometrical Variation Propagation in Assembly
| Autor: | Loïc Andolfatto, Marc Douilly, François Thiébaut, Claire Lartigue |
|---|---|
| Přispěvatelé: | Laboratoire Universitaire de Recherche en Production Automatisée (LURPA), École normale supérieure - Cachan (ENS Cachan)-Université Paris-Sud - Paris 11 (UP11), EADS Innovation Works [Suresnes] (EADS IW), EADS - European Aeronautic Defense and Space |
| Rok vydání: | 2013 |
| Předmět: |
assembly
0209 industrial biotechnology Engineering Sequence 021103 operations research Artificial neural network Tolerance analysis neural network contact influence business.industry Simulation modeling 0211 other engineering and technologies Process (computing) 02 engineering and technology Variation (game tree) Finite element method [SPI.MECA.GEME]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Mechanical engineering [physics.class-ph] 020901 industrial engineering & automation Quality (physics) General Earth and Planetary Sciences business geometrical variation propagation Algorithm Simulation General Environmental Science |
| Zdroj: | 12th CIRP Conference on Computer Aided Tolerancing 12th CIRP Conference on Computer Aided Tolerancing, Apr 2012, Huddersfield, United Kingdom. pp.224-232, ⟨10.1016/j.procir.2013.08.035⟩ |
| ISSN: | 2212-8271 |
| DOI: | 10.1016/j.procir.2013.08.035 |
| Popis: | International audience; Tolerance analysis is an important step to validate assembly process planning scenario. Simulations are generally performed to evaluate the expected geometrical variations of the assembled product. When the simulation models take into account part compliance, assembly sequence and contact interaction, the resulting behaviour of the assembly are generally non-linear and simulations – mainly performed using finite element analysis – require high computing efforts. This paper investigates the ability to approximate the non-linear propagation of geometrical variations in assembly with artificial neural networks. The aim is to drastically reduce the computing efforts required for the simulation and therefore allow its use for the geometrical tolerances allocation optimisation. The influence of the neural network design parameters on the approximation quality is presented in a case study. The quality of the neural network approximation is also evaluated and discussed. |
| Databáze: | OpenAIRE |
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