Taguchi-Based and Intelligent Optimisation of a Multi-Response Process Using Historical Data

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ŠIBALIJA, Tatjana ;MAJSTOROVIĆ, Vidosav ;SOKOVIĆ, Mirko .
Taguchi-Based and Intelligent Optimisation of a Multi-Response Process Using Historical Data. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 57, n.4, p. 357-365, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/taguchi-based-and-intelligent-optimisation-of-a-multi-response-process-using-historical-data/>. Date accessed: 16 apr. 2021. 
doi:http://dx.doi.org/10.5545/sv-jme.2010.061.
Šibalija, T., Majstorović, V., & Soković, M.
(2011).
Taguchi-Based and Intelligent Optimisation of a Multi-Response Process Using Historical Data.
Strojniški vestnik - Journal of Mechanical Engineering, 57(4), 357-365.
doi:http://dx.doi.org/10.5545/sv-jme.2010.061
@article{sv-jmesv-jme.2010.061,
	author = {Tatjana  Šibalija and Vidosav  Majstorović and Mirko  Soković},
	title = {Taguchi-Based and Intelligent Optimisation of a Multi-Response Process Using Historical Data},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {57},
	number = {4},
	year = {2011},
	keywords = {optimisation; historical data; Taguchi method; neural networks; genetic algorithm},
	abstract = {Optimisation of manufacturing processes is typically performed by utilising mathematical process models or designed experiments. However, such approaches could not be used in the case when explicit quality function is unknown and when actual experimentation would be expensive and time-consuming. The paper presents an approach to optimisation of manufacturing processes with multiple potentially correlated responses, using historical process data. The integrated approach is consisted from two methods: the first relays on Taguchi’s quality loss function and multivariate statistical methods, the second method is based on the first one and employs artificial neural networks and a genetic algorithm to ensure global optimal settings of a critical parameters found in a continual space of solutions. The case study of a multi-response process with correlated responses was used to illustrate the effective application of the proposed approach, where historical data collected during normal production and stored in a control charts were used for process optimisation.},
	issn = {0039-2480},	pages = {357-365},	doi = {10.5545/sv-jme.2010.061},
	url = {https://www.sv-jme.eu/article/taguchi-based-and-intelligent-optimisation-of-a-multi-response-process-using-historical-data/}
}
Šibalija, T.,Majstorović, V.,Soković, M.
2011 June 57. Taguchi-Based and Intelligent Optimisation of a Multi-Response Process Using Historical Data. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 57:4
%A Šibalija, Tatjana 
%A Majstorović, Vidosav 
%A Soković, Mirko 
%D 2011
%T Taguchi-Based and Intelligent Optimisation of a Multi-Response Process Using Historical Data
%B 2011
%9 optimisation; historical data; Taguchi method; neural networks; genetic algorithm
%! Taguchi-Based and Intelligent Optimisation of a Multi-Response Process Using Historical Data
%K optimisation; historical data; Taguchi method; neural networks; genetic algorithm
%X Optimisation of manufacturing processes is typically performed by utilising mathematical process models or designed experiments. However, such approaches could not be used in the case when explicit quality function is unknown and when actual experimentation would be expensive and time-consuming. The paper presents an approach to optimisation of manufacturing processes with multiple potentially correlated responses, using historical process data. The integrated approach is consisted from two methods: the first relays on Taguchi’s quality loss function and multivariate statistical methods, the second method is based on the first one and employs artificial neural networks and a genetic algorithm to ensure global optimal settings of a critical parameters found in a continual space of solutions. The case study of a multi-response process with correlated responses was used to illustrate the effective application of the proposed approach, where historical data collected during normal production and stored in a control charts were used for process optimisation.
%U https://www.sv-jme.eu/article/taguchi-based-and-intelligent-optimisation-of-a-multi-response-process-using-historical-data/
%0 Journal Article
%R 10.5545/sv-jme.2010.061
%& 357
%P 9
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 57
%N 4
%@ 0039-2480
%8 2018-06-28
%7 2018-06-28
Šibalija, Tatjana, Vidosav  Majstorović, & Mirko  Soković.
"Taguchi-Based and Intelligent Optimisation of a Multi-Response Process Using Historical Data." Strojniški vestnik - Journal of Mechanical Engineering [Online], 57.4 (2011): 357-365. Web.  16 Apr. 2021
TY  - JOUR
AU  - Šibalija, Tatjana 
AU  - Majstorović, Vidosav 
AU  - Soković, Mirko 
PY  - 2011
TI  - Taguchi-Based and Intelligent Optimisation of a Multi-Response Process Using Historical Data
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2010.061
KW  - optimisation; historical data; Taguchi method; neural networks; genetic algorithm
N2  - Optimisation of manufacturing processes is typically performed by utilising mathematical process models or designed experiments. However, such approaches could not be used in the case when explicit quality function is unknown and when actual experimentation would be expensive and time-consuming. The paper presents an approach to optimisation of manufacturing processes with multiple potentially correlated responses, using historical process data. The integrated approach is consisted from two methods: the first relays on Taguchi’s quality loss function and multivariate statistical methods, the second method is based on the first one and employs artificial neural networks and a genetic algorithm to ensure global optimal settings of a critical parameters found in a continual space of solutions. The case study of a multi-response process with correlated responses was used to illustrate the effective application of the proposed approach, where historical data collected during normal production and stored in a control charts were used for process optimisation.
UR  - https://www.sv-jme.eu/article/taguchi-based-and-intelligent-optimisation-of-a-multi-response-process-using-historical-data/
@article{{sv-jme}{sv-jme.2010.061},
	author = {Šibalija, T., Majstorović, V., Soković, M.},
	title = {Taguchi-Based and Intelligent Optimisation of a Multi-Response Process Using Historical Data},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {57},
	number = {4},
	year = {2011},
	doi = {10.5545/sv-jme.2010.061},
	url = {https://www.sv-jme.eu/article/taguchi-based-and-intelligent-optimisation-of-a-multi-response-process-using-historical-data/}
}
TY  - JOUR
AU  - Šibalija, Tatjana 
AU  - Majstorović, Vidosav 
AU  - Soković, Mirko 
PY  - 2018/06/28
TI  - Taguchi-Based and Intelligent Optimisation of a Multi-Response Process Using Historical Data
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 57, No 4 (2011): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2010.061
KW  - optimisation, historical data, Taguchi method, neural networks, genetic algorithm
N2  - Optimisation of manufacturing processes is typically performed by utilising mathematical process models or designed experiments. However, such approaches could not be used in the case when explicit quality function is unknown and when actual experimentation would be expensive and time-consuming. The paper presents an approach to optimisation of manufacturing processes with multiple potentially correlated responses, using historical process data. The integrated approach is consisted from two methods: the first relays on Taguchi’s quality loss function and multivariate statistical methods, the second method is based on the first one and employs artificial neural networks and a genetic algorithm to ensure global optimal settings of a critical parameters found in a continual space of solutions. The case study of a multi-response process with correlated responses was used to illustrate the effective application of the proposed approach, where historical data collected during normal production and stored in a control charts were used for process optimisation.
UR  - https://www.sv-jme.eu/article/taguchi-based-and-intelligent-optimisation-of-a-multi-response-process-using-historical-data/
Šibalija, Tatjana, Majstorović, Vidosav, AND Soković, Mirko.
"Taguchi-Based and Intelligent Optimisation of a Multi-Response Process Using Historical Data" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 57 Number 4 (28 June 2018)

Authors

Affiliations

  • University of Belgrade, Faculty of Mechanical Engineering, Kraljice Marije 16, 11000 Belgrade 1
  • University of Ljubljana, Faculty of Mechanical Engineering 2

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 57(2011)4, 357-365

https://doi.org/10.5545/sv-jme.2010.061

Optimisation of manufacturing processes is typically performed by utilising mathematical process models or designed experiments. However, such approaches could not be used in the case when explicit quality function is unknown and when actual experimentation would be expensive and time-consuming. The paper presents an approach to optimisation of manufacturing processes with multiple potentially correlated responses, using historical process data. The integrated approach is consisted from two methods: the first relays on Taguchi’s quality loss function and multivariate statistical methods, the second method is based on the first one and employs artificial neural networks and a genetic algorithm to ensure global optimal settings of a critical parameters found in a continual space of solutions. The case study of a multi-response process with correlated responses was used to illustrate the effective application of the proposed approach, where historical data collected during normal production and stored in a control charts were used for process optimisation.

optimisation; historical data; Taguchi method; neural networks; genetic algorithm