Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Meghana R. Ransing"'
Publikováno v:
Computers & Industrial Engineering. 93:1-11
The clause 6.1 of the ISO9001:2015 quality standard requires organisations to take specific actions to determine and address risks and opportunities in order to minimize undesired effects in the process and achieve process improvement. This paper pro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af20d75656356722e02d1d78d695fed8
https://doi.org/10.1016/j.cie.2015.12.008
https://doi.org/10.1016/j.cie.2015.12.008
Autor:
Cinzia Giannetti, Meghana R. Ransing, David T. Gethin, David Bould, Johann Sienz, Rajesh S. Ransing
Publikováno v:
Computers & Industrial Engineering. 72:217-229
In the last two decades the application of statistical techniques to process control has gained popularity due to the widespread adoption of quality management systems such as ISO9001. Demonstration of continual process improvement by monitoring proc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e8ffd6489ee887281794b2fbd8add9c
https://doi.org/10.1016/j.cie.2014.03.017
https://doi.org/10.1016/j.cie.2014.03.017
Publikováno v:
Computers in Industry. 64:514-523
Foundry process is a complex process with more than 100 parameters that influence the quality of final cast component. It is a process with multiple optimal conditions. For two foundries manufacturing the same alloy and cast geometry, the process and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c551b88fdd4766badcd1e45c3795c11
https://doi.org/10.1016/j.compind.2013.02.009
https://doi.org/10.1016/j.compind.2013.02.009
A risk based tolerance synthesis approach is based on ISO9001:2015 quality standard’s risk based thinking. It analyses in-process data to discover correlations among regions of input data scatter and desired or undesired process outputs. Recently,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::32e96ff86d48dcb8dbc52af03f7997f2
https://cronfa.swan.ac.uk/Record/cronfa29741/Download/0029741-05092016092231.pdf
https://cronfa.swan.ac.uk/Record/cronfa29741/Download/0029741-05092016092231.pdf
Autor:
David T. Gethin, Meghana R. Ransing, Rajesh S. Ransing, David Bould, Johann Sienz, Cinzia Giannetti
Despite many advances in the field of casting technologies the foundry industry still incurs significant losses due to the cost of scrap and rework with adverse effects on profitability and the environment. Approaches such as Six Sigma, DoE, FMEA are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::704413438473a04733d5c34ec9a9b4ca
https://cronfa.swan.ac.uk/Record/cronfa21931/Download/0021931-17112015151802.pdf
https://cronfa.swan.ac.uk/Record/cronfa21931/Download/0021931-17112015151802.pdf
Publikováno v:
Asia International Conference on Modelling and Simulation
Neural-network techniques, particularly back- propagation algorithms, have been widely used as a tool for discovering a mapping function between a known set of input and output examples. Neural networks learn from the known example set by adjusting i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::773e937e1fe9a11ebd11d600f16da2a7
https://doi.org/10.1109/ams.2008.70
https://doi.org/10.1109/ams.2008.70
Publikováno v:
Research and Development in Intelligent Systems XXIII ISBN: 9781846286629
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e49251f6c719b66149e20d932f36f962
https://doi.org/10.1007/978-1-84628-663-6_4
https://doi.org/10.1007/978-1-84628-663-6_4
Publikováno v:
ISDA (1)
The Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm usually used for nonlinear least squares is presented and is combined with the modified back propagation algorithm yielding a new fast training multilayer perceptron (MLP) algorithm (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::252ed510cf68a0b9f18e091e7835a344
https://doi.org/10.1109/isda.2006.95
https://doi.org/10.1109/isda.2006.95