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/sl/article/taguchi-based-and-intelligent-optimisation-of-a-multi-response-process-using-historical-data/>. Date accessed: 20 apr. 2024. 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/sl/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/sl/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. 20 Apr. 2024
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/sl/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/sl/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/sl/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)
Strojniški vestnik - Journal of Mechanical Engineering 57(2011)4, 357-365
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
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.