The multi-objective non-probabilistic interval optimization of the loading paths for T-shape tube hydroforming
Abstract The focus of presented paper is to find a reliable Pareto front of the loading paths for T-shape tube hydroforming (THF) process using multi-objective non-probabilistic interval optimization method. Three indicators are included to measure the forming quality of THF process: the contact are...
Ausführliche Beschreibung
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| Autor*in: |
Huang, Tianlun [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Ltd. 2017 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - Springer London, 1985, 94(2017), 1-4 vom: 16. Aug., Seite 677-686 |
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| Übergeordnetes Werk: |
volume:94 ; year:2017 ; number:1-4 ; day:16 ; month:08 ; pages:677-686 |
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| DOI / URN: |
10.1007/s00170-017-0927-4 |
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OLC2026111278 |
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| 520 | |a Abstract The focus of presented paper is to find a reliable Pareto front of the loading paths for T-shape tube hydroforming (THF) process using multi-objective non-probabilistic interval optimization method. Three indicators are included to measure the forming quality of THF process: the contact area is used to evaluate the calibration degree; bursting failure is estimated by the maximum thinning ratio and the protrusion height is also taken into consideration. The reliability-based degree of interval of constraints is employed to guarantee the reliability of THF process. A validated finite element model is adopted to conduct virtual experiments. The percentage contributions of the loading parameters for each indicators are calculated by the Taguchi method, and some significant parameters are identified and the dimensionality of the design variables can be reduced. The support vector regression model whose accuracy is calculated by leave-one-out cross-validated method is adopted to improve the optimization efficiency for the determination of the Pareto front. The Pareto fronts of uncertain optimization and deterministic optimization are compared, and the results show that more reliable solutions can be achieved by the presented method. | ||
| 650 | 4 | |a Loading path | |
| 650 | 4 | |a Tube hydroforming | |
| 650 | 4 | |a Support vector regression | |
| 650 | 4 | |a Interval optimization | |
| 700 | 1 | |a Song, Xuewei |4 aut | |
| 700 | 1 | |a Liu, Min |4 aut | |
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10.1007/s00170-017-0927-4 doi (DE-627)OLC2026111278 (DE-He213)s00170-017-0927-4-p DE-627 ger DE-627 rakwb eng 670 VZ Huang, Tianlun verfasserin aut The multi-objective non-probabilistic interval optimization of the loading paths for T-shape tube hydroforming 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract The focus of presented paper is to find a reliable Pareto front of the loading paths for T-shape tube hydroforming (THF) process using multi-objective non-probabilistic interval optimization method. Three indicators are included to measure the forming quality of THF process: the contact area is used to evaluate the calibration degree; bursting failure is estimated by the maximum thinning ratio and the protrusion height is also taken into consideration. The reliability-based degree of interval of constraints is employed to guarantee the reliability of THF process. A validated finite element model is adopted to conduct virtual experiments. The percentage contributions of the loading parameters for each indicators are calculated by the Taguchi method, and some significant parameters are identified and the dimensionality of the design variables can be reduced. The support vector regression model whose accuracy is calculated by leave-one-out cross-validated method is adopted to improve the optimization efficiency for the determination of the Pareto front. The Pareto fronts of uncertain optimization and deterministic optimization are compared, and the results show that more reliable solutions can be achieved by the presented method. Loading path Tube hydroforming Support vector regression Interval optimization Song, Xuewei aut Liu, Min aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 94(2017), 1-4 vom: 16. Aug., Seite 677-686 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:94 year:2017 number:1-4 day:16 month:08 pages:677-686 https://doi.org/10.1007/s00170-017-0927-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 94 2017 1-4 16 08 677-686 |
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10.1007/s00170-017-0927-4 doi (DE-627)OLC2026111278 (DE-He213)s00170-017-0927-4-p DE-627 ger DE-627 rakwb eng 670 VZ Huang, Tianlun verfasserin aut The multi-objective non-probabilistic interval optimization of the loading paths for T-shape tube hydroforming 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract The focus of presented paper is to find a reliable Pareto front of the loading paths for T-shape tube hydroforming (THF) process using multi-objective non-probabilistic interval optimization method. Three indicators are included to measure the forming quality of THF process: the contact area is used to evaluate the calibration degree; bursting failure is estimated by the maximum thinning ratio and the protrusion height is also taken into consideration. The reliability-based degree of interval of constraints is employed to guarantee the reliability of THF process. A validated finite element model is adopted to conduct virtual experiments. The percentage contributions of the loading parameters for each indicators are calculated by the Taguchi method, and some significant parameters are identified and the dimensionality of the design variables can be reduced. The support vector regression model whose accuracy is calculated by leave-one-out cross-validated method is adopted to improve the optimization efficiency for the determination of the Pareto front. The Pareto fronts of uncertain optimization and deterministic optimization are compared, and the results show that more reliable solutions can be achieved by the presented method. Loading path Tube hydroforming Support vector regression Interval optimization Song, Xuewei aut Liu, Min aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 94(2017), 1-4 vom: 16. Aug., Seite 677-686 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:94 year:2017 number:1-4 day:16 month:08 pages:677-686 https://doi.org/10.1007/s00170-017-0927-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 94 2017 1-4 16 08 677-686 |
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10.1007/s00170-017-0927-4 doi (DE-627)OLC2026111278 (DE-He213)s00170-017-0927-4-p DE-627 ger DE-627 rakwb eng 670 VZ Huang, Tianlun verfasserin aut The multi-objective non-probabilistic interval optimization of the loading paths for T-shape tube hydroforming 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract The focus of presented paper is to find a reliable Pareto front of the loading paths for T-shape tube hydroforming (THF) process using multi-objective non-probabilistic interval optimization method. Three indicators are included to measure the forming quality of THF process: the contact area is used to evaluate the calibration degree; bursting failure is estimated by the maximum thinning ratio and the protrusion height is also taken into consideration. The reliability-based degree of interval of constraints is employed to guarantee the reliability of THF process. A validated finite element model is adopted to conduct virtual experiments. The percentage contributions of the loading parameters for each indicators are calculated by the Taguchi method, and some significant parameters are identified and the dimensionality of the design variables can be reduced. The support vector regression model whose accuracy is calculated by leave-one-out cross-validated method is adopted to improve the optimization efficiency for the determination of the Pareto front. The Pareto fronts of uncertain optimization and deterministic optimization are compared, and the results show that more reliable solutions can be achieved by the presented method. Loading path Tube hydroforming Support vector regression Interval optimization Song, Xuewei aut Liu, Min aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 94(2017), 1-4 vom: 16. Aug., Seite 677-686 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:94 year:2017 number:1-4 day:16 month:08 pages:677-686 https://doi.org/10.1007/s00170-017-0927-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 94 2017 1-4 16 08 677-686 |
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10.1007/s00170-017-0927-4 doi (DE-627)OLC2026111278 (DE-He213)s00170-017-0927-4-p DE-627 ger DE-627 rakwb eng 670 VZ Huang, Tianlun verfasserin aut The multi-objective non-probabilistic interval optimization of the loading paths for T-shape tube hydroforming 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract The focus of presented paper is to find a reliable Pareto front of the loading paths for T-shape tube hydroforming (THF) process using multi-objective non-probabilistic interval optimization method. Three indicators are included to measure the forming quality of THF process: the contact area is used to evaluate the calibration degree; bursting failure is estimated by the maximum thinning ratio and the protrusion height is also taken into consideration. The reliability-based degree of interval of constraints is employed to guarantee the reliability of THF process. A validated finite element model is adopted to conduct virtual experiments. The percentage contributions of the loading parameters for each indicators are calculated by the Taguchi method, and some significant parameters are identified and the dimensionality of the design variables can be reduced. The support vector regression model whose accuracy is calculated by leave-one-out cross-validated method is adopted to improve the optimization efficiency for the determination of the Pareto front. The Pareto fronts of uncertain optimization and deterministic optimization are compared, and the results show that more reliable solutions can be achieved by the presented method. Loading path Tube hydroforming Support vector regression Interval optimization Song, Xuewei aut Liu, Min aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 94(2017), 1-4 vom: 16. Aug., Seite 677-686 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:94 year:2017 number:1-4 day:16 month:08 pages:677-686 https://doi.org/10.1007/s00170-017-0927-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 94 2017 1-4 16 08 677-686 |
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10.1007/s00170-017-0927-4 doi (DE-627)OLC2026111278 (DE-He213)s00170-017-0927-4-p DE-627 ger DE-627 rakwb eng 670 VZ Huang, Tianlun verfasserin aut The multi-objective non-probabilistic interval optimization of the loading paths for T-shape tube hydroforming 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd. 2017 Abstract The focus of presented paper is to find a reliable Pareto front of the loading paths for T-shape tube hydroforming (THF) process using multi-objective non-probabilistic interval optimization method. Three indicators are included to measure the forming quality of THF process: the contact area is used to evaluate the calibration degree; bursting failure is estimated by the maximum thinning ratio and the protrusion height is also taken into consideration. The reliability-based degree of interval of constraints is employed to guarantee the reliability of THF process. A validated finite element model is adopted to conduct virtual experiments. The percentage contributions of the loading parameters for each indicators are calculated by the Taguchi method, and some significant parameters are identified and the dimensionality of the design variables can be reduced. The support vector regression model whose accuracy is calculated by leave-one-out cross-validated method is adopted to improve the optimization efficiency for the determination of the Pareto front. The Pareto fronts of uncertain optimization and deterministic optimization are compared, and the results show that more reliable solutions can be achieved by the presented method. Loading path Tube hydroforming Support vector regression Interval optimization Song, Xuewei aut Liu, Min aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 94(2017), 1-4 vom: 16. Aug., Seite 677-686 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:94 year:2017 number:1-4 day:16 month:08 pages:677-686 https://doi.org/10.1007/s00170-017-0927-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2018 GBV_ILN_2333 AR 94 2017 1-4 16 08 677-686 |
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Abstract The focus of presented paper is to find a reliable Pareto front of the loading paths for T-shape tube hydroforming (THF) process using multi-objective non-probabilistic interval optimization method. Three indicators are included to measure the forming quality of THF process: the contact area is used to evaluate the calibration degree; bursting failure is estimated by the maximum thinning ratio and the protrusion height is also taken into consideration. The reliability-based degree of interval of constraints is employed to guarantee the reliability of THF process. A validated finite element model is adopted to conduct virtual experiments. The percentage contributions of the loading parameters for each indicators are calculated by the Taguchi method, and some significant parameters are identified and the dimensionality of the design variables can be reduced. The support vector regression model whose accuracy is calculated by leave-one-out cross-validated method is adopted to improve the optimization efficiency for the determination of the Pareto front. The Pareto fronts of uncertain optimization and deterministic optimization are compared, and the results show that more reliable solutions can be achieved by the presented method. © Springer-Verlag London Ltd. 2017 |
| abstractGer |
Abstract The focus of presented paper is to find a reliable Pareto front of the loading paths for T-shape tube hydroforming (THF) process using multi-objective non-probabilistic interval optimization method. Three indicators are included to measure the forming quality of THF process: the contact area is used to evaluate the calibration degree; bursting failure is estimated by the maximum thinning ratio and the protrusion height is also taken into consideration. The reliability-based degree of interval of constraints is employed to guarantee the reliability of THF process. A validated finite element model is adopted to conduct virtual experiments. The percentage contributions of the loading parameters for each indicators are calculated by the Taguchi method, and some significant parameters are identified and the dimensionality of the design variables can be reduced. The support vector regression model whose accuracy is calculated by leave-one-out cross-validated method is adopted to improve the optimization efficiency for the determination of the Pareto front. The Pareto fronts of uncertain optimization and deterministic optimization are compared, and the results show that more reliable solutions can be achieved by the presented method. © Springer-Verlag London Ltd. 2017 |
| abstract_unstemmed |
Abstract The focus of presented paper is to find a reliable Pareto front of the loading paths for T-shape tube hydroforming (THF) process using multi-objective non-probabilistic interval optimization method. Three indicators are included to measure the forming quality of THF process: the contact area is used to evaluate the calibration degree; bursting failure is estimated by the maximum thinning ratio and the protrusion height is also taken into consideration. The reliability-based degree of interval of constraints is employed to guarantee the reliability of THF process. A validated finite element model is adopted to conduct virtual experiments. The percentage contributions of the loading parameters for each indicators are calculated by the Taguchi method, and some significant parameters are identified and the dimensionality of the design variables can be reduced. The support vector regression model whose accuracy is calculated by leave-one-out cross-validated method is adopted to improve the optimization efficiency for the determination of the Pareto front. The Pareto fronts of uncertain optimization and deterministic optimization are compared, and the results show that more reliable solutions can be achieved by the presented method. © Springer-Verlag London Ltd. 2017 |
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The multi-objective non-probabilistic interval optimization of the loading paths for T-shape tube hydroforming |
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