Design of experiment algorithms for assembled products
Design of experiment algorithms for assembled products
Designing experiments to identify improvement in products that are assembled from manufactured components does not readily fit into conventional design of experiments methods and can be costly. Efficient methods are explored for determining designs for engineering problems where some, or all, of the factors of interest are (a) not easily set to prescribed values and (b) are dependent on a combination of properties of several components. The methods involve taking a sample of each type of component, measuring the relevant features and then finding a design that specifies an optimal set of assembled products for experiment. Three examples from manufacturing industry are presented to illustrate the approach. Two different algorithms for finding designs are described, an exchange algorithm and a genetic algorithm, and a comparison of their performances is made on the three examples.
298-308
Sexton, C.J.
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Anthony, D.K.
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Lewis, C.M.
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Please, C.P.
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Keane, A.J.
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October 2006
Sexton, C.J.
d8e295f8-6491-4952-942c-835d0343bce7
Anthony, D.K.
68b00ebe-cbfb-498b-aa24-c287bab1f875
Lewis, C.M.
018dcc08-5a60-4ae7-91a1-e6f9eb313cc7
Please, C.P.
118dffe7-4b38-4787-a972-9feec535839e
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Sexton, C.J., Anthony, D.K., Lewis, C.M., Please, C.P. and Keane, A.J.
(2006)
Design of experiment algorithms for assembled products.
Journal of Quality Technology, 38 (4), .
Abstract
Designing experiments to identify improvement in products that are assembled from manufactured components does not readily fit into conventional design of experiments methods and can be costly. Efficient methods are explored for determining designs for engineering problems where some, or all, of the factors of interest are (a) not easily set to prescribed values and (b) are dependent on a combination of properties of several components. The methods involve taking a sample of each type of component, measuring the relevant features and then finding a design that specifies an optimal set of assembled products for experiment. Three examples from manufacturing industry are presented to illustrate the approach. Two different algorithms for finding designs are described, an exchange algorithm and a genetic algorithm, and a comparison of their performances is made on the three examples.
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Published date: October 2006
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Local EPrints ID: 43834
URI: http://eprints.soton.ac.uk/id/eprint/43834
PURE UUID: 56a84c81-940c-4239-853c-9fadcdb7327b
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Date deposited: 02 Feb 2007
Last modified: 16 Mar 2024 02:53
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Author:
C.J. Sexton
Author:
D.K. Anthony
Author:
C.M. Lewis
Author:
C.P. Please
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