Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition

Abstract This work establishes a reliable and accurate materials process–structure (PS) surrogate model that maps an 18-dimensional process parameter input domain to a high-dimensional space of single- and dual-phase microstructures. This was accomplished by employing the Materials Knowledge Systems...
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Autor*in:	Harrington, Grayson H. [verfasserIn] Kelly, Conlain Attari, Vahid Arroyave, Raymundo Kalidindi, Surya R.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Machine learning Chained-ANN Materials informatics Process–structure surrogate model Phase-field surrogate model

Anmerkung:	© The Minerals, Metals & Materials Society 2022

Übergeordnetes Werk:	Enthalten in: Integrating materials and manufacturing innovation - Berlin : SpringerOpen, 2012, 11(2022), 3 vom: Sept., Seite 433-449
Übergeordnetes Werk:	volume:11 ; year:2022 ; number:3 ; month:09 ; pages:433-449

Links:	Volltext

DOI / URN:	10.1007/s40192-022-00274-3

Katalog-ID:	SPR048147796

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520			\|a Abstract This work establishes a reliable and accurate materials process–structure (PS) surrogate model that maps an 18-dimensional process parameter input domain to a high-dimensional space of single- and dual-phase microstructures. This was accomplished by employing the Materials Knowledge Systems (MKS) framework (includes microstructure quantification via two-point statistics and dimensionality reduction using principal components analysis) for the feature engineering of the microstructures, and subsequently constructing a chained-artificial neural network (ANN) to learn the complex nonlinear mappings between the high-dimensional input domain and the MKS-derived low-dimensional representation of the corresponding microstructure space (includes both homogeneous and heterogeneous microstructures). The benefits of this workflow are demonstrated on a collection of ~ 10,000 final microstructures obtained from chemo-mechanical spinodal decomposition phase-field simulations in the $ Mg_{2} %$ Si_{x} %$ Sn_{1-x} $ material system. Specifically, it is shown that the complex phase-field process–structure relationships for the selected case study can be captured in a robust model with only 742 fittable parameters.
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allfields	10.1007/s40192-022-00274-3 doi (DE-627)SPR048147796 (SPR)s40192-022-00274-3-e DE-627 ger DE-627 rakwb eng Harrington, Grayson H. verfasserin aut Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Minerals, Metals & Materials Society 2022 Abstract This work establishes a reliable and accurate materials process–structure (PS) surrogate model that maps an 18-dimensional process parameter input domain to a high-dimensional space of single- and dual-phase microstructures. This was accomplished by employing the Materials Knowledge Systems (MKS) framework (includes microstructure quantification via two-point statistics and dimensionality reduction using principal components analysis) for the feature engineering of the microstructures, and subsequently constructing a chained-artificial neural network (ANN) to learn the complex nonlinear mappings between the high-dimensional input domain and the MKS-derived low-dimensional representation of the corresponding microstructure space (includes both homogeneous and heterogeneous microstructures). The benefits of this workflow are demonstrated on a collection of ~ 10,000 final microstructures obtained from chemo-mechanical spinodal decomposition phase-field simulations in the $ Mg_{2} %$ Si_{x} %$ Sn_{1-x} $ material system. Specifically, it is shown that the complex phase-field process–structure relationships for the selected case study can be captured in a robust model with only 742 fittable parameters. Machine learning (dpeaa)DE-He213 Chained-ANN (dpeaa)DE-He213 Materials informatics (dpeaa)DE-He213 Process–structure surrogate model (dpeaa)DE-He213 Phase-field surrogate model (dpeaa)DE-He213 Kelly, Conlain aut Attari, Vahid aut Arroyave, Raymundo aut Kalidindi, Surya R. (orcid)0000-0003-2710-5008 aut Enthalten in Integrating materials and manufacturing innovation Berlin : SpringerOpen, 2012 11(2022), 3 vom: Sept., Seite 433-449 (DE-627)726496499 (DE-600)2683170-3 2193-9772 nnns volume:11 year:2022 number:3 month:09 pages:433-449 https://dx.doi.org/10.1007/s40192-022-00274-3 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2022 3 09 433-449
spelling	10.1007/s40192-022-00274-3 doi (DE-627)SPR048147796 (SPR)s40192-022-00274-3-e DE-627 ger DE-627 rakwb eng Harrington, Grayson H. verfasserin aut Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Minerals, Metals & Materials Society 2022 Abstract This work establishes a reliable and accurate materials process–structure (PS) surrogate model that maps an 18-dimensional process parameter input domain to a high-dimensional space of single- and dual-phase microstructures. This was accomplished by employing the Materials Knowledge Systems (MKS) framework (includes microstructure quantification via two-point statistics and dimensionality reduction using principal components analysis) for the feature engineering of the microstructures, and subsequently constructing a chained-artificial neural network (ANN) to learn the complex nonlinear mappings between the high-dimensional input domain and the MKS-derived low-dimensional representation of the corresponding microstructure space (includes both homogeneous and heterogeneous microstructures). The benefits of this workflow are demonstrated on a collection of ~ 10,000 final microstructures obtained from chemo-mechanical spinodal decomposition phase-field simulations in the $ Mg_{2} %$ Si_{x} %$ Sn_{1-x} $ material system. Specifically, it is shown that the complex phase-field process–structure relationships for the selected case study can be captured in a robust model with only 742 fittable parameters. Machine learning (dpeaa)DE-He213 Chained-ANN (dpeaa)DE-He213 Materials informatics (dpeaa)DE-He213 Process–structure surrogate model (dpeaa)DE-He213 Phase-field surrogate model (dpeaa)DE-He213 Kelly, Conlain aut Attari, Vahid aut Arroyave, Raymundo aut Kalidindi, Surya R. (orcid)0000-0003-2710-5008 aut Enthalten in Integrating materials and manufacturing innovation Berlin : SpringerOpen, 2012 11(2022), 3 vom: Sept., Seite 433-449 (DE-627)726496499 (DE-600)2683170-3 2193-9772 nnns volume:11 year:2022 number:3 month:09 pages:433-449 https://dx.doi.org/10.1007/s40192-022-00274-3 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2022 3 09 433-449
allfields_unstemmed	10.1007/s40192-022-00274-3 doi (DE-627)SPR048147796 (SPR)s40192-022-00274-3-e DE-627 ger DE-627 rakwb eng Harrington, Grayson H. verfasserin aut Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Minerals, Metals & Materials Society 2022 Abstract This work establishes a reliable and accurate materials process–structure (PS) surrogate model that maps an 18-dimensional process parameter input domain to a high-dimensional space of single- and dual-phase microstructures. This was accomplished by employing the Materials Knowledge Systems (MKS) framework (includes microstructure quantification via two-point statistics and dimensionality reduction using principal components analysis) for the feature engineering of the microstructures, and subsequently constructing a chained-artificial neural network (ANN) to learn the complex nonlinear mappings between the high-dimensional input domain and the MKS-derived low-dimensional representation of the corresponding microstructure space (includes both homogeneous and heterogeneous microstructures). The benefits of this workflow are demonstrated on a collection of ~ 10,000 final microstructures obtained from chemo-mechanical spinodal decomposition phase-field simulations in the $ Mg_{2} %$ Si_{x} %$ Sn_{1-x} $ material system. Specifically, it is shown that the complex phase-field process–structure relationships for the selected case study can be captured in a robust model with only 742 fittable parameters. Machine learning (dpeaa)DE-He213 Chained-ANN (dpeaa)DE-He213 Materials informatics (dpeaa)DE-He213 Process–structure surrogate model (dpeaa)DE-He213 Phase-field surrogate model (dpeaa)DE-He213 Kelly, Conlain aut Attari, Vahid aut Arroyave, Raymundo aut Kalidindi, Surya R. (orcid)0000-0003-2710-5008 aut Enthalten in Integrating materials and manufacturing innovation Berlin : SpringerOpen, 2012 11(2022), 3 vom: Sept., Seite 433-449 (DE-627)726496499 (DE-600)2683170-3 2193-9772 nnns volume:11 year:2022 number:3 month:09 pages:433-449 https://dx.doi.org/10.1007/s40192-022-00274-3 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2022 3 09 433-449
allfieldsGer	10.1007/s40192-022-00274-3 doi (DE-627)SPR048147796 (SPR)s40192-022-00274-3-e DE-627 ger DE-627 rakwb eng Harrington, Grayson H. verfasserin aut Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Minerals, Metals & Materials Society 2022 Abstract This work establishes a reliable and accurate materials process–structure (PS) surrogate model that maps an 18-dimensional process parameter input domain to a high-dimensional space of single- and dual-phase microstructures. This was accomplished by employing the Materials Knowledge Systems (MKS) framework (includes microstructure quantification via two-point statistics and dimensionality reduction using principal components analysis) for the feature engineering of the microstructures, and subsequently constructing a chained-artificial neural network (ANN) to learn the complex nonlinear mappings between the high-dimensional input domain and the MKS-derived low-dimensional representation of the corresponding microstructure space (includes both homogeneous and heterogeneous microstructures). The benefits of this workflow are demonstrated on a collection of ~ 10,000 final microstructures obtained from chemo-mechanical spinodal decomposition phase-field simulations in the $ Mg_{2} %$ Si_{x} %$ Sn_{1-x} $ material system. Specifically, it is shown that the complex phase-field process–structure relationships for the selected case study can be captured in a robust model with only 742 fittable parameters. Machine learning (dpeaa)DE-He213 Chained-ANN (dpeaa)DE-He213 Materials informatics (dpeaa)DE-He213 Process–structure surrogate model (dpeaa)DE-He213 Phase-field surrogate model (dpeaa)DE-He213 Kelly, Conlain aut Attari, Vahid aut Arroyave, Raymundo aut Kalidindi, Surya R. (orcid)0000-0003-2710-5008 aut Enthalten in Integrating materials and manufacturing innovation Berlin : SpringerOpen, 2012 11(2022), 3 vom: Sept., Seite 433-449 (DE-627)726496499 (DE-600)2683170-3 2193-9772 nnns volume:11 year:2022 number:3 month:09 pages:433-449 https://dx.doi.org/10.1007/s40192-022-00274-3 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2022 3 09 433-449
allfieldsSound	10.1007/s40192-022-00274-3 doi (DE-627)SPR048147796 (SPR)s40192-022-00274-3-e DE-627 ger DE-627 rakwb eng Harrington, Grayson H. verfasserin aut Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Minerals, Metals & Materials Society 2022 Abstract This work establishes a reliable and accurate materials process–structure (PS) surrogate model that maps an 18-dimensional process parameter input domain to a high-dimensional space of single- and dual-phase microstructures. This was accomplished by employing the Materials Knowledge Systems (MKS) framework (includes microstructure quantification via two-point statistics and dimensionality reduction using principal components analysis) for the feature engineering of the microstructures, and subsequently constructing a chained-artificial neural network (ANN) to learn the complex nonlinear mappings between the high-dimensional input domain and the MKS-derived low-dimensional representation of the corresponding microstructure space (includes both homogeneous and heterogeneous microstructures). The benefits of this workflow are demonstrated on a collection of ~ 10,000 final microstructures obtained from chemo-mechanical spinodal decomposition phase-field simulations in the $ Mg_{2} %$ Si_{x} %$ Sn_{1-x} $ material system. Specifically, it is shown that the complex phase-field process–structure relationships for the selected case study can be captured in a robust model with only 742 fittable parameters. Machine learning (dpeaa)DE-He213 Chained-ANN (dpeaa)DE-He213 Materials informatics (dpeaa)DE-He213 Process–structure surrogate model (dpeaa)DE-He213 Phase-field surrogate model (dpeaa)DE-He213 Kelly, Conlain aut Attari, Vahid aut Arroyave, Raymundo aut Kalidindi, Surya R. (orcid)0000-0003-2710-5008 aut Enthalten in Integrating materials and manufacturing innovation Berlin : SpringerOpen, 2012 11(2022), 3 vom: Sept., Seite 433-449 (DE-627)726496499 (DE-600)2683170-3 2193-9772 nnns volume:11 year:2022 number:3 month:09 pages:433-449 https://dx.doi.org/10.1007/s40192-022-00274-3 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 11 2022 3 09 433-449
language	English
source	Enthalten in Integrating materials and manufacturing innovation 11(2022), 3 vom: Sept., Seite 433-449 volume:11 year:2022 number:3 month:09 pages:433-449
sourceStr	Enthalten in Integrating materials and manufacturing innovation 11(2022), 3 vom: Sept., Seite 433-449 volume:11 year:2022 number:3 month:09 pages:433-449
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Machine learning Chained-ANN Materials informatics Process–structure surrogate model Phase-field surrogate model
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container_title	Integrating materials and manufacturing innovation
authorswithroles_txt_mv	Harrington, Grayson H. @@aut@@ Kelly, Conlain @@aut@@ Attari, Vahid @@aut@@ Arroyave, Raymundo @@aut@@ Kalidindi, Surya R. @@aut@@
publishDateDaySort_date	2022-09-01T00:00:00Z
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id	SPR048147796
language_de	englisch
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author	Harrington, Grayson H.
spellingShingle	Harrington, Grayson H. misc Machine learning misc Chained-ANN misc Materials informatics misc Process–structure surrogate model misc Phase-field surrogate model Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition
authorStr	Harrington, Grayson H.
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topic_title	Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition Machine learning (dpeaa)DE-He213 Chained-ANN (dpeaa)DE-He213 Materials informatics (dpeaa)DE-He213 Process–structure surrogate model (dpeaa)DE-He213 Phase-field surrogate model (dpeaa)DE-He213
topic	misc Machine learning misc Chained-ANN misc Materials informatics misc Process–structure surrogate model misc Phase-field surrogate model
topic_unstemmed	misc Machine learning misc Chained-ANN misc Materials informatics misc Process–structure surrogate model misc Phase-field surrogate model
topic_browse	misc Machine learning misc Chained-ANN misc Materials informatics misc Process–structure surrogate model misc Phase-field surrogate model
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title	Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition
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title_full	Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition
author_sort	Harrington, Grayson H.
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author_browse	Harrington, Grayson H. Kelly, Conlain Attari, Vahid Arroyave, Raymundo Kalidindi, Surya R.
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author-letter	Harrington, Grayson H.
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title_sort	application of a chained-ann for learning the process–structure mapping in $ mg_{2} %$ si_{x} %$ sn_{1−x} $ spinodal decomposition
title_auth	Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition
abstract	Abstract This work establishes a reliable and accurate materials process–structure (PS) surrogate model that maps an 18-dimensional process parameter input domain to a high-dimensional space of single- and dual-phase microstructures. This was accomplished by employing the Materials Knowledge Systems (MKS) framework (includes microstructure quantification via two-point statistics and dimensionality reduction using principal components analysis) for the feature engineering of the microstructures, and subsequently constructing a chained-artificial neural network (ANN) to learn the complex nonlinear mappings between the high-dimensional input domain and the MKS-derived low-dimensional representation of the corresponding microstructure space (includes both homogeneous and heterogeneous microstructures). The benefits of this workflow are demonstrated on a collection of ~ 10,000 final microstructures obtained from chemo-mechanical spinodal decomposition phase-field simulations in the $ Mg_{2} %$ Si_{x} %$ Sn_{1-x} $ material system. Specifically, it is shown that the complex phase-field process–structure relationships for the selected case study can be captured in a robust model with only 742 fittable parameters. © The Minerals, Metals & Materials Society 2022
abstractGer	Abstract This work establishes a reliable and accurate materials process–structure (PS) surrogate model that maps an 18-dimensional process parameter input domain to a high-dimensional space of single- and dual-phase microstructures. This was accomplished by employing the Materials Knowledge Systems (MKS) framework (includes microstructure quantification via two-point statistics and dimensionality reduction using principal components analysis) for the feature engineering of the microstructures, and subsequently constructing a chained-artificial neural network (ANN) to learn the complex nonlinear mappings between the high-dimensional input domain and the MKS-derived low-dimensional representation of the corresponding microstructure space (includes both homogeneous and heterogeneous microstructures). The benefits of this workflow are demonstrated on a collection of ~ 10,000 final microstructures obtained from chemo-mechanical spinodal decomposition phase-field simulations in the $ Mg_{2} %$ Si_{x} %$ Sn_{1-x} $ material system. Specifically, it is shown that the complex phase-field process–structure relationships for the selected case study can be captured in a robust model with only 742 fittable parameters. © The Minerals, Metals & Materials Society 2022
abstract_unstemmed	Abstract This work establishes a reliable and accurate materials process–structure (PS) surrogate model that maps an 18-dimensional process parameter input domain to a high-dimensional space of single- and dual-phase microstructures. This was accomplished by employing the Materials Knowledge Systems (MKS) framework (includes microstructure quantification via two-point statistics and dimensionality reduction using principal components analysis) for the feature engineering of the microstructures, and subsequently constructing a chained-artificial neural network (ANN) to learn the complex nonlinear mappings between the high-dimensional input domain and the MKS-derived low-dimensional representation of the corresponding microstructure space (includes both homogeneous and heterogeneous microstructures). The benefits of this workflow are demonstrated on a collection of ~ 10,000 final microstructures obtained from chemo-mechanical spinodal decomposition phase-field simulations in the $ Mg_{2} %$ Si_{x} %$ Sn_{1-x} $ material system. Specifically, it is shown that the complex phase-field process–structure relationships for the selected case study can be captured in a robust model with only 742 fittable parameters. © The Minerals, Metals & Materials Society 2022
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container_issue	3
title_short	Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition
url	https://dx.doi.org/10.1007/s40192-022-00274-3
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author2	Kelly, Conlain Attari, Vahid Arroyave, Raymundo Kalidindi, Surya R.
author2Str	Kelly, Conlain Attari, Vahid Arroyave, Raymundo Kalidindi, Surya R.
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doi_str	10.1007/s40192-022-00274-3
up_date	2024-07-03T17:19:21.908Z
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Application of a Chained-ANN for Learning the Process–Structure Mapping in $ Mg_{2} %$ Si_{x} %$ Sn_{1−x} $ Spinodal Decomposition

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