Using design-of-experiments techniques for an efficient finite element study of the influence of changed parameters in design
(1998) International ANSYS Conference 2. p.63-72- Abstract
- All designs are marred by uncertainties and tolerances in dimen-
sions, load levels etc. Traditionally, one has often over-dimensioned
to take these uncertainties into account. The demand for optimized designs with high quality and reliability increases, which means that more sophisticated methods have been developed, see e.g. Lochner
and Matar (1990). By describing the fluctuations in design parame-
ters in terms of distributions with expectation and variance, the design can be examined with statistical methods, which results in a more op-timized design. This treatment of the design often demands several experiments, and to plan these experiments Design Of Experiments (DOE) techniques, see e.g.... (More) - All designs are marred by uncertainties and tolerances in dimen-
sions, load levels etc. Traditionally, one has often over-dimensioned
to take these uncertainties into account. The demand for optimized designs with high quality and reliability increases, which means that more sophisticated methods have been developed, see e.g. Lochner
and Matar (1990). By describing the fluctuations in design parame-
ters in terms of distributions with expectation and variance, the design can be examined with statistical methods, which results in a more op-timized design. This treatment of the design often demands several experiments, and to plan these experiments Design Of Experiments (DOE) techniques, see e.g. Montgomery (1991), are often used. By using DOE methods the design variables are systematically altered, which minimizes the number of experiments needed. The output of
the experiments is the results of a specified response function, giving
an indication of the influence of design variable fluctuations. A FEM system is a suitable tool when performing repeated, similar analyses. Examples exist where the DOE process has been performed external-
ly and then transferred to the FEM system in the form of parameter
sets defining the analysis cases that are to be solved, see e.g. Summers et al. (1996) and Billings (1996).
This paper describes a statistical DOE module based on Taguchi’s method that works within ANSYS. The module plans the FEM anal-ysis and calculates the standard statistical moments of the FEM result. This module serves as a powerful tool for the engineering designer
or analysts when examining the influence of variance and mean value of different design variables. It also serves as an exploration of where
to concentrate an optimization process. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1024333
- author
- Eriksson, Martin LU ; Andersson, Pär-Ola and Burman, Åke LU
- organization
- publishing date
- 1998
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- modified taguchi method, Design of experiment, Taguchi, Finite element
- host publication
- International ANSYS Conference
- volume
- 2
- pages
- 63 - 72
- publisher
- ANSYS Inc
- conference name
- International ANSYS Conference
- conference location
- Pittsburgh, United States
- conference dates
- 1998-08-17
- language
- English
- LU publication?
- yes
- id
- e89a9647-f302-4693-8523-d3f334acade8 (old id 1024333)
- date added to LUP
- 2016-04-04 10:14:05
- date last changed
- 2018-11-21 20:57:35
@inproceedings{e89a9647-f302-4693-8523-d3f334acade8,
abstract = {{All designs are marred by uncertainties and tolerances in dimen-<br/><br>
sions, load levels etc. Traditionally, one has often over-dimensioned<br/><br>
to take these uncertainties into account. The demand for optimized designs with high quality and reliability increases, which means that more sophisticated methods have been developed, see e.g. Lochner<br/><br>
and Matar (1990). By describing the fluctuations in design parame-<br/><br>
ters in terms of distributions with expectation and variance, the design can be examined with statistical methods, which results in a more op-timized design. This treatment of the design often demands several experiments, and to plan these experiments Design Of Experiments (DOE) techniques, see e.g. Montgomery (1991), are often used. By using DOE methods the design variables are systematically altered, which minimizes the number of experiments needed. The output of<br/><br>
the experiments is the results of a specified response function, giving<br/><br>
an indication of the influence of design variable fluctuations. A FEM system is a suitable tool when performing repeated, similar analyses. Examples exist where the DOE process has been performed external-<br/><br>
ly and then transferred to the FEM system in the form of parameter<br/><br>
sets defining the analysis cases that are to be solved, see e.g. Summers et al. (1996) and Billings (1996).<br/><br>
This paper describes a statistical DOE module based on Taguchi’s method that works within ANSYS. The module plans the FEM anal-ysis and calculates the standard statistical moments of the FEM result. This module serves as a powerful tool for the engineering designer<br/><br>
or analysts when examining the influence of variance and mean value of different design variables. It also serves as an exploration of where<br/><br>
to concentrate an optimization process.}},
author = {{Eriksson, Martin and Andersson, Pär-Ola and Burman, Åke}},
booktitle = {{International ANSYS Conference}},
keywords = {{modified taguchi method; Design of experiment; Taguchi; Finite element}},
language = {{eng}},
pages = {{63--72}},
publisher = {{ANSYS Inc}},
title = {{Using design-of-experiments techniques for an efficient finite element study of the influence of changed parameters in design}},
url = {{https://lup.lub.lu.se/search/files/5492762/1025056.pdf}},
volume = {{2}},
year = {{1998}},
}