On the effect of numerical noise in approximate optimization of forming processes using numerical simulations
Abstract The coupling of Finite Element (FE) simulations with approximate optimization techniques is becoming increasingly popular in forming industry. By doing so, it is implicitly assumed that the optimization objective and possible constraints are smooth functions of the design variables and, in...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wiebenga, J. H. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag France 2013 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of material forming - Paris [u.a.] : Springer, 2008, 7(2013), 3 vom: 07. Apr., Seite 317-335 |
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| Übergeordnetes Werk: |
volume:7 ; year:2013 ; number:3 ; day:07 ; month:04 ; pages:317-335 |
| Links: |
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| DOI / URN: |
10.1007/s12289-013-1130-2 |
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| Katalog-ID: |
SPR024817899 |
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| 100 | 1 | |a Wiebenga, J. H. |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a On the effect of numerical noise in approximate optimization of forming processes using numerical simulations |
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| 520 | |a Abstract The coupling of Finite Element (FE) simulations with approximate optimization techniques is becoming increasingly popular in forming industry. By doing so, it is implicitly assumed that the optimization objective and possible constraints are smooth functions of the design variables and, in case of robust optimization, design and noise variables. However, non-linear FE simulations are known to introduce numerical noise caused by the discrete nature of the simulation algorithms, e.g. errors caused by re-meshing, time-step adjustments or contact algorithms. The subsequent usage of metamodels based on such noisy data reduces the prediction quality of the optimization routine and is known to even magnify the numerical errors. This work provides an approach to handle noisy numerical data in approximate optimization of forming processes, covering several fundamental research questions in dealing with numerical noise. First, the deteriorating effect of numerical noise on the prediction quality of several well-known metamodeling techniques is demonstrated using an analytical test function. Next, numerical noise is quantified and its effect is minimized by the application of local approximation and regularization techniques. A general approximate optimization strategy is subsequently presented and coupling with a sequential update algorithm is proposed. The strategy is demonstrated by the sequential deterministic and robust optimization of 2 industrial metal forming processes i.e. a V-bending application and a cup-stretching application. Although numerical noise is often neglected in practice, both applications in this work show that the general awareness of its presence is highly important to increase the overall accuracy of optimization results. | ||
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| 650 | 4 | |a Regularization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Sequential optimization |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a van den Boogaard, A. H. |4 aut | |
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10.1007/s12289-013-1130-2 doi (DE-627)SPR024817899 (SPR)s12289-013-1130-2-e DE-627 ger DE-627 rakwb eng Wiebenga, J. H. verfasserin aut On the effect of numerical noise in approximate optimization of forming processes using numerical simulations 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag France 2013 Abstract The coupling of Finite Element (FE) simulations with approximate optimization techniques is becoming increasingly popular in forming industry. By doing so, it is implicitly assumed that the optimization objective and possible constraints are smooth functions of the design variables and, in case of robust optimization, design and noise variables. However, non-linear FE simulations are known to introduce numerical noise caused by the discrete nature of the simulation algorithms, e.g. errors caused by re-meshing, time-step adjustments or contact algorithms. The subsequent usage of metamodels based on such noisy data reduces the prediction quality of the optimization routine and is known to even magnify the numerical errors. This work provides an approach to handle noisy numerical data in approximate optimization of forming processes, covering several fundamental research questions in dealing with numerical noise. First, the deteriorating effect of numerical noise on the prediction quality of several well-known metamodeling techniques is demonstrated using an analytical test function. Next, numerical noise is quantified and its effect is minimized by the application of local approximation and regularization techniques. A general approximate optimization strategy is subsequently presented and coupling with a sequential update algorithm is proposed. The strategy is demonstrated by the sequential deterministic and robust optimization of 2 industrial metal forming processes i.e. a V-bending application and a cup-stretching application. Although numerical noise is often neglected in practice, both applications in this work show that the general awareness of its presence is highly important to increase the overall accuracy of optimization results. Finite element simulations (dpeaa)DE-He213 Approximate optimization (dpeaa)DE-He213 Numerical noise (dpeaa)DE-He213 Regularization (dpeaa)DE-He213 Sequential optimization (dpeaa)DE-He213 van den Boogaard, A. H. aut Enthalten in International journal of material forming Paris [u.a.] : Springer, 2008 7(2013), 3 vom: 07. Apr., Seite 317-335 (DE-627)565515306 (DE-600)2423930-6 1960-6214 nnns volume:7 year:2013 number:3 day:07 month:04 pages:317-335 https://dx.doi.org/10.1007/s12289-013-1130-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 7 2013 3 07 04 317-335 |
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10.1007/s12289-013-1130-2 doi (DE-627)SPR024817899 (SPR)s12289-013-1130-2-e DE-627 ger DE-627 rakwb eng Wiebenga, J. H. verfasserin aut On the effect of numerical noise in approximate optimization of forming processes using numerical simulations 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag France 2013 Abstract The coupling of Finite Element (FE) simulations with approximate optimization techniques is becoming increasingly popular in forming industry. By doing so, it is implicitly assumed that the optimization objective and possible constraints are smooth functions of the design variables and, in case of robust optimization, design and noise variables. However, non-linear FE simulations are known to introduce numerical noise caused by the discrete nature of the simulation algorithms, e.g. errors caused by re-meshing, time-step adjustments or contact algorithms. The subsequent usage of metamodels based on such noisy data reduces the prediction quality of the optimization routine and is known to even magnify the numerical errors. This work provides an approach to handle noisy numerical data in approximate optimization of forming processes, covering several fundamental research questions in dealing with numerical noise. First, the deteriorating effect of numerical noise on the prediction quality of several well-known metamodeling techniques is demonstrated using an analytical test function. Next, numerical noise is quantified and its effect is minimized by the application of local approximation and regularization techniques. A general approximate optimization strategy is subsequently presented and coupling with a sequential update algorithm is proposed. The strategy is demonstrated by the sequential deterministic and robust optimization of 2 industrial metal forming processes i.e. a V-bending application and a cup-stretching application. Although numerical noise is often neglected in practice, both applications in this work show that the general awareness of its presence is highly important to increase the overall accuracy of optimization results. Finite element simulations (dpeaa)DE-He213 Approximate optimization (dpeaa)DE-He213 Numerical noise (dpeaa)DE-He213 Regularization (dpeaa)DE-He213 Sequential optimization (dpeaa)DE-He213 van den Boogaard, A. H. aut Enthalten in International journal of material forming Paris [u.a.] : Springer, 2008 7(2013), 3 vom: 07. Apr., Seite 317-335 (DE-627)565515306 (DE-600)2423930-6 1960-6214 nnns volume:7 year:2013 number:3 day:07 month:04 pages:317-335 https://dx.doi.org/10.1007/s12289-013-1130-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 7 2013 3 07 04 317-335 |
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10.1007/s12289-013-1130-2 doi (DE-627)SPR024817899 (SPR)s12289-013-1130-2-e DE-627 ger DE-627 rakwb eng Wiebenga, J. H. verfasserin aut On the effect of numerical noise in approximate optimization of forming processes using numerical simulations 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag France 2013 Abstract The coupling of Finite Element (FE) simulations with approximate optimization techniques is becoming increasingly popular in forming industry. By doing so, it is implicitly assumed that the optimization objective and possible constraints are smooth functions of the design variables and, in case of robust optimization, design and noise variables. However, non-linear FE simulations are known to introduce numerical noise caused by the discrete nature of the simulation algorithms, e.g. errors caused by re-meshing, time-step adjustments or contact algorithms. The subsequent usage of metamodels based on such noisy data reduces the prediction quality of the optimization routine and is known to even magnify the numerical errors. This work provides an approach to handle noisy numerical data in approximate optimization of forming processes, covering several fundamental research questions in dealing with numerical noise. First, the deteriorating effect of numerical noise on the prediction quality of several well-known metamodeling techniques is demonstrated using an analytical test function. Next, numerical noise is quantified and its effect is minimized by the application of local approximation and regularization techniques. A general approximate optimization strategy is subsequently presented and coupling with a sequential update algorithm is proposed. The strategy is demonstrated by the sequential deterministic and robust optimization of 2 industrial metal forming processes i.e. a V-bending application and a cup-stretching application. Although numerical noise is often neglected in practice, both applications in this work show that the general awareness of its presence is highly important to increase the overall accuracy of optimization results. Finite element simulations (dpeaa)DE-He213 Approximate optimization (dpeaa)DE-He213 Numerical noise (dpeaa)DE-He213 Regularization (dpeaa)DE-He213 Sequential optimization (dpeaa)DE-He213 van den Boogaard, A. H. aut Enthalten in International journal of material forming Paris [u.a.] : Springer, 2008 7(2013), 3 vom: 07. Apr., Seite 317-335 (DE-627)565515306 (DE-600)2423930-6 1960-6214 nnns volume:7 year:2013 number:3 day:07 month:04 pages:317-335 https://dx.doi.org/10.1007/s12289-013-1130-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 7 2013 3 07 04 317-335 |
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10.1007/s12289-013-1130-2 doi (DE-627)SPR024817899 (SPR)s12289-013-1130-2-e DE-627 ger DE-627 rakwb eng Wiebenga, J. H. verfasserin aut On the effect of numerical noise in approximate optimization of forming processes using numerical simulations 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag France 2013 Abstract The coupling of Finite Element (FE) simulations with approximate optimization techniques is becoming increasingly popular in forming industry. By doing so, it is implicitly assumed that the optimization objective and possible constraints are smooth functions of the design variables and, in case of robust optimization, design and noise variables. However, non-linear FE simulations are known to introduce numerical noise caused by the discrete nature of the simulation algorithms, e.g. errors caused by re-meshing, time-step adjustments or contact algorithms. The subsequent usage of metamodels based on such noisy data reduces the prediction quality of the optimization routine and is known to even magnify the numerical errors. This work provides an approach to handle noisy numerical data in approximate optimization of forming processes, covering several fundamental research questions in dealing with numerical noise. First, the deteriorating effect of numerical noise on the prediction quality of several well-known metamodeling techniques is demonstrated using an analytical test function. Next, numerical noise is quantified and its effect is minimized by the application of local approximation and regularization techniques. A general approximate optimization strategy is subsequently presented and coupling with a sequential update algorithm is proposed. The strategy is demonstrated by the sequential deterministic and robust optimization of 2 industrial metal forming processes i.e. a V-bending application and a cup-stretching application. Although numerical noise is often neglected in practice, both applications in this work show that the general awareness of its presence is highly important to increase the overall accuracy of optimization results. Finite element simulations (dpeaa)DE-He213 Approximate optimization (dpeaa)DE-He213 Numerical noise (dpeaa)DE-He213 Regularization (dpeaa)DE-He213 Sequential optimization (dpeaa)DE-He213 van den Boogaard, A. H. aut Enthalten in International journal of material forming Paris [u.a.] : Springer, 2008 7(2013), 3 vom: 07. Apr., Seite 317-335 (DE-627)565515306 (DE-600)2423930-6 1960-6214 nnns volume:7 year:2013 number:3 day:07 month:04 pages:317-335 https://dx.doi.org/10.1007/s12289-013-1130-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 7 2013 3 07 04 317-335 |
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10.1007/s12289-013-1130-2 doi (DE-627)SPR024817899 (SPR)s12289-013-1130-2-e DE-627 ger DE-627 rakwb eng Wiebenga, J. H. verfasserin aut On the effect of numerical noise in approximate optimization of forming processes using numerical simulations 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag France 2013 Abstract The coupling of Finite Element (FE) simulations with approximate optimization techniques is becoming increasingly popular in forming industry. By doing so, it is implicitly assumed that the optimization objective and possible constraints are smooth functions of the design variables and, in case of robust optimization, design and noise variables. However, non-linear FE simulations are known to introduce numerical noise caused by the discrete nature of the simulation algorithms, e.g. errors caused by re-meshing, time-step adjustments or contact algorithms. The subsequent usage of metamodels based on such noisy data reduces the prediction quality of the optimization routine and is known to even magnify the numerical errors. This work provides an approach to handle noisy numerical data in approximate optimization of forming processes, covering several fundamental research questions in dealing with numerical noise. First, the deteriorating effect of numerical noise on the prediction quality of several well-known metamodeling techniques is demonstrated using an analytical test function. Next, numerical noise is quantified and its effect is minimized by the application of local approximation and regularization techniques. A general approximate optimization strategy is subsequently presented and coupling with a sequential update algorithm is proposed. The strategy is demonstrated by the sequential deterministic and robust optimization of 2 industrial metal forming processes i.e. a V-bending application and a cup-stretching application. Although numerical noise is often neglected in practice, both applications in this work show that the general awareness of its presence is highly important to increase the overall accuracy of optimization results. Finite element simulations (dpeaa)DE-He213 Approximate optimization (dpeaa)DE-He213 Numerical noise (dpeaa)DE-He213 Regularization (dpeaa)DE-He213 Sequential optimization (dpeaa)DE-He213 van den Boogaard, A. H. aut Enthalten in International journal of material forming Paris [u.a.] : Springer, 2008 7(2013), 3 vom: 07. Apr., Seite 317-335 (DE-627)565515306 (DE-600)2423930-6 1960-6214 nnns volume:7 year:2013 number:3 day:07 month:04 pages:317-335 https://dx.doi.org/10.1007/s12289-013-1130-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 7 2013 3 07 04 317-335 |
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H.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">On the effect of numerical noise in approximate optimization of forming processes using numerical simulations</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2013</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag France 2013</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The coupling of Finite Element (FE) simulations with approximate optimization techniques is becoming increasingly popular in forming industry. By doing so, it is implicitly assumed that the optimization objective and possible constraints are smooth functions of the design variables and, in case of robust optimization, design and noise variables. However, non-linear FE simulations are known to introduce numerical noise caused by the discrete nature of the simulation algorithms, e.g. errors caused by re-meshing, time-step adjustments or contact algorithms. The subsequent usage of metamodels based on such noisy data reduces the prediction quality of the optimization routine and is known to even magnify the numerical errors. This work provides an approach to handle noisy numerical data in approximate optimization of forming processes, covering several fundamental research questions in dealing with numerical noise. First, the deteriorating effect of numerical noise on the prediction quality of several well-known metamodeling techniques is demonstrated using an analytical test function. 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Wiebenga, J. H. |
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Wiebenga, J. H. misc Finite element simulations misc Approximate optimization misc Numerical noise misc Regularization misc Sequential optimization On the effect of numerical noise in approximate optimization of forming processes using numerical simulations |
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On the effect of numerical noise in approximate optimization of forming processes using numerical simulations Finite element simulations (dpeaa)DE-He213 Approximate optimization (dpeaa)DE-He213 Numerical noise (dpeaa)DE-He213 Regularization (dpeaa)DE-He213 Sequential optimization (dpeaa)DE-He213 |
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On the effect of numerical noise in approximate optimization of forming processes using numerical simulations |
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on the effect of numerical noise in approximate optimization of forming processes using numerical simulations |
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On the effect of numerical noise in approximate optimization of forming processes using numerical simulations |
| abstract |
Abstract The coupling of Finite Element (FE) simulations with approximate optimization techniques is becoming increasingly popular in forming industry. By doing so, it is implicitly assumed that the optimization objective and possible constraints are smooth functions of the design variables and, in case of robust optimization, design and noise variables. However, non-linear FE simulations are known to introduce numerical noise caused by the discrete nature of the simulation algorithms, e.g. errors caused by re-meshing, time-step adjustments or contact algorithms. The subsequent usage of metamodels based on such noisy data reduces the prediction quality of the optimization routine and is known to even magnify the numerical errors. This work provides an approach to handle noisy numerical data in approximate optimization of forming processes, covering several fundamental research questions in dealing with numerical noise. First, the deteriorating effect of numerical noise on the prediction quality of several well-known metamodeling techniques is demonstrated using an analytical test function. Next, numerical noise is quantified and its effect is minimized by the application of local approximation and regularization techniques. A general approximate optimization strategy is subsequently presented and coupling with a sequential update algorithm is proposed. The strategy is demonstrated by the sequential deterministic and robust optimization of 2 industrial metal forming processes i.e. a V-bending application and a cup-stretching application. Although numerical noise is often neglected in practice, both applications in this work show that the general awareness of its presence is highly important to increase the overall accuracy of optimization results. © Springer-Verlag France 2013 |
| abstractGer |
Abstract The coupling of Finite Element (FE) simulations with approximate optimization techniques is becoming increasingly popular in forming industry. By doing so, it is implicitly assumed that the optimization objective and possible constraints are smooth functions of the design variables and, in case of robust optimization, design and noise variables. However, non-linear FE simulations are known to introduce numerical noise caused by the discrete nature of the simulation algorithms, e.g. errors caused by re-meshing, time-step adjustments or contact algorithms. The subsequent usage of metamodels based on such noisy data reduces the prediction quality of the optimization routine and is known to even magnify the numerical errors. This work provides an approach to handle noisy numerical data in approximate optimization of forming processes, covering several fundamental research questions in dealing with numerical noise. First, the deteriorating effect of numerical noise on the prediction quality of several well-known metamodeling techniques is demonstrated using an analytical test function. Next, numerical noise is quantified and its effect is minimized by the application of local approximation and regularization techniques. A general approximate optimization strategy is subsequently presented and coupling with a sequential update algorithm is proposed. The strategy is demonstrated by the sequential deterministic and robust optimization of 2 industrial metal forming processes i.e. a V-bending application and a cup-stretching application. Although numerical noise is often neglected in practice, both applications in this work show that the general awareness of its presence is highly important to increase the overall accuracy of optimization results. © Springer-Verlag France 2013 |
| abstract_unstemmed |
Abstract The coupling of Finite Element (FE) simulations with approximate optimization techniques is becoming increasingly popular in forming industry. By doing so, it is implicitly assumed that the optimization objective and possible constraints are smooth functions of the design variables and, in case of robust optimization, design and noise variables. However, non-linear FE simulations are known to introduce numerical noise caused by the discrete nature of the simulation algorithms, e.g. errors caused by re-meshing, time-step adjustments or contact algorithms. The subsequent usage of metamodels based on such noisy data reduces the prediction quality of the optimization routine and is known to even magnify the numerical errors. This work provides an approach to handle noisy numerical data in approximate optimization of forming processes, covering several fundamental research questions in dealing with numerical noise. First, the deteriorating effect of numerical noise on the prediction quality of several well-known metamodeling techniques is demonstrated using an analytical test function. Next, numerical noise is quantified and its effect is minimized by the application of local approximation and regularization techniques. A general approximate optimization strategy is subsequently presented and coupling with a sequential update algorithm is proposed. The strategy is demonstrated by the sequential deterministic and robust optimization of 2 industrial metal forming processes i.e. a V-bending application and a cup-stretching application. Although numerical noise is often neglected in practice, both applications in this work show that the general awareness of its presence is highly important to increase the overall accuracy of optimization results. © Springer-Verlag France 2013 |
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On the effect of numerical noise in approximate optimization of forming processes using numerical simulations |
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https://dx.doi.org/10.1007/s12289-013-1130-2 |
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van den Boogaard, A. H. |
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|
| score |
7.399967 |