Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning

With the rapid development of the synchrotron radiation X-ray characterization techniques, the preprocessing of large small-angle X-ray scattering (SAXS) datasets and the data mining become urgent requirements for researchers. In this work, we apply the variational autoencoder (VAE) and the conditio...
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Autor*in:	Chenhao Zhao [verfasserIn] Wancheng Yu [verfasserIn] Liangbin Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Übergeordnetes Werk:	In: Materials & Design - Elsevier, 2019, 228(2023), Seite 111828-
Übergeordnetes Werk:	volume:228 ; year:2023 ; pages:111828-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.matdes.2023.111828

Katalog-ID:	DOAJ087666758

Internformat


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520			\|a With the rapid development of the synchrotron radiation X-ray characterization techniques, the preprocessing of large small-angle X-ray scattering (SAXS) datasets and the data mining become urgent requirements for researchers. In this work, we apply the variational autoencoder (VAE) and the conditional variational autoencoder (cVAE) to visualize a large SAXS dataset of hard-elastic isotactic polypropylene (iPP) films in 2- and 1-dimensional latent spaces. The low-dimensional representations enable us to capture key features of the dataset rapidly, such as the similarity among SAXS patterns and the structural evolution trends. The preprocessing of the dataset points out the further direction of data analysis so that researchers can focus on the most valued regions in the dataset. Then, we develop a hybrid VAE-multilayer perceptron (MLP) neural network to realize the processing-structure mapping of iPP films. The robustness of the hybrid VAE-MLP network is verified. Finally, SAXS patterns in the temperature-strain space are generated, which allows us to explore the processing parameter space not involved by previous experiments. These capabilities indicate that the developed machine-learning methods are valuable artificial intelligence toolset to assist in the preprocessing of large-scale SAXS datasets and the establishment of comprehensive processing-structure relationship of hard-elastic iPP films.
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allfields	10.1016/j.matdes.2023.111828 doi (DE-627)DOAJ087666758 (DE-599)DOAJ754b59957af0427aae5756fcf2953fbd DE-627 ger DE-627 rakwb eng TA401-492 Chenhao Zhao verfasserin aut Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of the synchrotron radiation X-ray characterization techniques, the preprocessing of large small-angle X-ray scattering (SAXS) datasets and the data mining become urgent requirements for researchers. In this work, we apply the variational autoencoder (VAE) and the conditional variational autoencoder (cVAE) to visualize a large SAXS dataset of hard-elastic isotactic polypropylene (iPP) films in 2- and 1-dimensional latent spaces. The low-dimensional representations enable us to capture key features of the dataset rapidly, such as the similarity among SAXS patterns and the structural evolution trends. The preprocessing of the dataset points out the further direction of data analysis so that researchers can focus on the most valued regions in the dataset. Then, we develop a hybrid VAE-multilayer perceptron (MLP) neural network to realize the processing-structure mapping of iPP films. The robustness of the hybrid VAE-MLP network is verified. Finally, SAXS patterns in the temperature-strain space are generated, which allows us to explore the processing parameter space not involved by previous experiments. These capabilities indicate that the developed machine-learning methods are valuable artificial intelligence toolset to assist in the preprocessing of large-scale SAXS datasets and the establishment of comprehensive processing-structure relationship of hard-elastic iPP films. Materials of engineering and construction. Mechanics of materials Wancheng Yu verfasserin aut Liangbin Li verfasserin aut In Materials & Design Elsevier, 2019 228(2023), Seite 111828- (DE-627)32052857X (DE-600)2015480-X 18734197 nnns volume:228 year:2023 pages:111828- https://doi.org/10.1016/j.matdes.2023.111828 kostenfrei https://doaj.org/article/754b59957af0427aae5756fcf2953fbd kostenfrei http://www.sciencedirect.com/science/article/pii/S0264127523002435 kostenfrei https://doaj.org/toc/0264-1275 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 228 2023 111828-
spelling	10.1016/j.matdes.2023.111828 doi (DE-627)DOAJ087666758 (DE-599)DOAJ754b59957af0427aae5756fcf2953fbd DE-627 ger DE-627 rakwb eng TA401-492 Chenhao Zhao verfasserin aut Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of the synchrotron radiation X-ray characterization techniques, the preprocessing of large small-angle X-ray scattering (SAXS) datasets and the data mining become urgent requirements for researchers. In this work, we apply the variational autoencoder (VAE) and the conditional variational autoencoder (cVAE) to visualize a large SAXS dataset of hard-elastic isotactic polypropylene (iPP) films in 2- and 1-dimensional latent spaces. The low-dimensional representations enable us to capture key features of the dataset rapidly, such as the similarity among SAXS patterns and the structural evolution trends. The preprocessing of the dataset points out the further direction of data analysis so that researchers can focus on the most valued regions in the dataset. Then, we develop a hybrid VAE-multilayer perceptron (MLP) neural network to realize the processing-structure mapping of iPP films. The robustness of the hybrid VAE-MLP network is verified. Finally, SAXS patterns in the temperature-strain space are generated, which allows us to explore the processing parameter space not involved by previous experiments. These capabilities indicate that the developed machine-learning methods are valuable artificial intelligence toolset to assist in the preprocessing of large-scale SAXS datasets and the establishment of comprehensive processing-structure relationship of hard-elastic iPP films. Materials of engineering and construction. Mechanics of materials Wancheng Yu verfasserin aut Liangbin Li verfasserin aut In Materials & Design Elsevier, 2019 228(2023), Seite 111828- (DE-627)32052857X (DE-600)2015480-X 18734197 nnns volume:228 year:2023 pages:111828- https://doi.org/10.1016/j.matdes.2023.111828 kostenfrei https://doaj.org/article/754b59957af0427aae5756fcf2953fbd kostenfrei http://www.sciencedirect.com/science/article/pii/S0264127523002435 kostenfrei https://doaj.org/toc/0264-1275 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 228 2023 111828-
allfields_unstemmed	10.1016/j.matdes.2023.111828 doi (DE-627)DOAJ087666758 (DE-599)DOAJ754b59957af0427aae5756fcf2953fbd DE-627 ger DE-627 rakwb eng TA401-492 Chenhao Zhao verfasserin aut Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of the synchrotron radiation X-ray characterization techniques, the preprocessing of large small-angle X-ray scattering (SAXS) datasets and the data mining become urgent requirements for researchers. In this work, we apply the variational autoencoder (VAE) and the conditional variational autoencoder (cVAE) to visualize a large SAXS dataset of hard-elastic isotactic polypropylene (iPP) films in 2- and 1-dimensional latent spaces. The low-dimensional representations enable us to capture key features of the dataset rapidly, such as the similarity among SAXS patterns and the structural evolution trends. The preprocessing of the dataset points out the further direction of data analysis so that researchers can focus on the most valued regions in the dataset. Then, we develop a hybrid VAE-multilayer perceptron (MLP) neural network to realize the processing-structure mapping of iPP films. The robustness of the hybrid VAE-MLP network is verified. Finally, SAXS patterns in the temperature-strain space are generated, which allows us to explore the processing parameter space not involved by previous experiments. These capabilities indicate that the developed machine-learning methods are valuable artificial intelligence toolset to assist in the preprocessing of large-scale SAXS datasets and the establishment of comprehensive processing-structure relationship of hard-elastic iPP films. Materials of engineering and construction. Mechanics of materials Wancheng Yu verfasserin aut Liangbin Li verfasserin aut In Materials & Design Elsevier, 2019 228(2023), Seite 111828- (DE-627)32052857X (DE-600)2015480-X 18734197 nnns volume:228 year:2023 pages:111828- https://doi.org/10.1016/j.matdes.2023.111828 kostenfrei https://doaj.org/article/754b59957af0427aae5756fcf2953fbd kostenfrei http://www.sciencedirect.com/science/article/pii/S0264127523002435 kostenfrei https://doaj.org/toc/0264-1275 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 228 2023 111828-
allfieldsGer	10.1016/j.matdes.2023.111828 doi (DE-627)DOAJ087666758 (DE-599)DOAJ754b59957af0427aae5756fcf2953fbd DE-627 ger DE-627 rakwb eng TA401-492 Chenhao Zhao verfasserin aut Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of the synchrotron radiation X-ray characterization techniques, the preprocessing of large small-angle X-ray scattering (SAXS) datasets and the data mining become urgent requirements for researchers. In this work, we apply the variational autoencoder (VAE) and the conditional variational autoencoder (cVAE) to visualize a large SAXS dataset of hard-elastic isotactic polypropylene (iPP) films in 2- and 1-dimensional latent spaces. The low-dimensional representations enable us to capture key features of the dataset rapidly, such as the similarity among SAXS patterns and the structural evolution trends. The preprocessing of the dataset points out the further direction of data analysis so that researchers can focus on the most valued regions in the dataset. Then, we develop a hybrid VAE-multilayer perceptron (MLP) neural network to realize the processing-structure mapping of iPP films. The robustness of the hybrid VAE-MLP network is verified. Finally, SAXS patterns in the temperature-strain space are generated, which allows us to explore the processing parameter space not involved by previous experiments. These capabilities indicate that the developed machine-learning methods are valuable artificial intelligence toolset to assist in the preprocessing of large-scale SAXS datasets and the establishment of comprehensive processing-structure relationship of hard-elastic iPP films. Materials of engineering and construction. Mechanics of materials Wancheng Yu verfasserin aut Liangbin Li verfasserin aut In Materials & Design Elsevier, 2019 228(2023), Seite 111828- (DE-627)32052857X (DE-600)2015480-X 18734197 nnns volume:228 year:2023 pages:111828- https://doi.org/10.1016/j.matdes.2023.111828 kostenfrei https://doaj.org/article/754b59957af0427aae5756fcf2953fbd kostenfrei http://www.sciencedirect.com/science/article/pii/S0264127523002435 kostenfrei https://doaj.org/toc/0264-1275 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 228 2023 111828-
allfieldsSound	10.1016/j.matdes.2023.111828 doi (DE-627)DOAJ087666758 (DE-599)DOAJ754b59957af0427aae5756fcf2953fbd DE-627 ger DE-627 rakwb eng TA401-492 Chenhao Zhao verfasserin aut Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier With the rapid development of the synchrotron radiation X-ray characterization techniques, the preprocessing of large small-angle X-ray scattering (SAXS) datasets and the data mining become urgent requirements for researchers. In this work, we apply the variational autoencoder (VAE) and the conditional variational autoencoder (cVAE) to visualize a large SAXS dataset of hard-elastic isotactic polypropylene (iPP) films in 2- and 1-dimensional latent spaces. The low-dimensional representations enable us to capture key features of the dataset rapidly, such as the similarity among SAXS patterns and the structural evolution trends. The preprocessing of the dataset points out the further direction of data analysis so that researchers can focus on the most valued regions in the dataset. Then, we develop a hybrid VAE-multilayer perceptron (MLP) neural network to realize the processing-structure mapping of iPP films. The robustness of the hybrid VAE-MLP network is verified. Finally, SAXS patterns in the temperature-strain space are generated, which allows us to explore the processing parameter space not involved by previous experiments. These capabilities indicate that the developed machine-learning methods are valuable artificial intelligence toolset to assist in the preprocessing of large-scale SAXS datasets and the establishment of comprehensive processing-structure relationship of hard-elastic iPP films. Materials of engineering and construction. Mechanics of materials Wancheng Yu verfasserin aut Liangbin Li verfasserin aut In Materials & Design Elsevier, 2019 228(2023), Seite 111828- (DE-627)32052857X (DE-600)2015480-X 18734197 nnns volume:228 year:2023 pages:111828- https://doi.org/10.1016/j.matdes.2023.111828 kostenfrei https://doaj.org/article/754b59957af0427aae5756fcf2953fbd kostenfrei http://www.sciencedirect.com/science/article/pii/S0264127523002435 kostenfrei https://doaj.org/toc/0264-1275 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 228 2023 111828-
language	English
source	In Materials & Design 228(2023), Seite 111828- volume:228 year:2023 pages:111828-
sourceStr	In Materials & Design 228(2023), Seite 111828- volume:228 year:2023 pages:111828-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Materials of engineering and construction. Mechanics of materials
isfreeaccess_bool	true
container_title	Materials & Design
authorswithroles_txt_mv	Chenhao Zhao @@aut@@ Wancheng Yu @@aut@@ Liangbin Li @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	32052857X
id	DOAJ087666758
language_de	englisch
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callnumber-first	T - Technology
author	Chenhao Zhao
spellingShingle	Chenhao Zhao misc TA401-492 misc Materials of engineering and construction. Mechanics of materials Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning
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topic_title	TA401-492 Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning
topic	misc TA401-492 misc Materials of engineering and construction. Mechanics of materials
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title	Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning
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title_full	Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning
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author_browse	Chenhao Zhao Wancheng Yu Liangbin Li
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title_sort	visualization of small-angle x-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning
callnumber	TA401-492
title_auth	Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning
abstract	With the rapid development of the synchrotron radiation X-ray characterization techniques, the preprocessing of large small-angle X-ray scattering (SAXS) datasets and the data mining become urgent requirements for researchers. In this work, we apply the variational autoencoder (VAE) and the conditional variational autoencoder (cVAE) to visualize a large SAXS dataset of hard-elastic isotactic polypropylene (iPP) films in 2- and 1-dimensional latent spaces. The low-dimensional representations enable us to capture key features of the dataset rapidly, such as the similarity among SAXS patterns and the structural evolution trends. The preprocessing of the dataset points out the further direction of data analysis so that researchers can focus on the most valued regions in the dataset. Then, we develop a hybrid VAE-multilayer perceptron (MLP) neural network to realize the processing-structure mapping of iPP films. The robustness of the hybrid VAE-MLP network is verified. Finally, SAXS patterns in the temperature-strain space are generated, which allows us to explore the processing parameter space not involved by previous experiments. These capabilities indicate that the developed machine-learning methods are valuable artificial intelligence toolset to assist in the preprocessing of large-scale SAXS datasets and the establishment of comprehensive processing-structure relationship of hard-elastic iPP films.
abstractGer	With the rapid development of the synchrotron radiation X-ray characterization techniques, the preprocessing of large small-angle X-ray scattering (SAXS) datasets and the data mining become urgent requirements for researchers. In this work, we apply the variational autoencoder (VAE) and the conditional variational autoencoder (cVAE) to visualize a large SAXS dataset of hard-elastic isotactic polypropylene (iPP) films in 2- and 1-dimensional latent spaces. The low-dimensional representations enable us to capture key features of the dataset rapidly, such as the similarity among SAXS patterns and the structural evolution trends. The preprocessing of the dataset points out the further direction of data analysis so that researchers can focus on the most valued regions in the dataset. Then, we develop a hybrid VAE-multilayer perceptron (MLP) neural network to realize the processing-structure mapping of iPP films. The robustness of the hybrid VAE-MLP network is verified. Finally, SAXS patterns in the temperature-strain space are generated, which allows us to explore the processing parameter space not involved by previous experiments. These capabilities indicate that the developed machine-learning methods are valuable artificial intelligence toolset to assist in the preprocessing of large-scale SAXS datasets and the establishment of comprehensive processing-structure relationship of hard-elastic iPP films.
abstract_unstemmed	With the rapid development of the synchrotron radiation X-ray characterization techniques, the preprocessing of large small-angle X-ray scattering (SAXS) datasets and the data mining become urgent requirements for researchers. In this work, we apply the variational autoencoder (VAE) and the conditional variational autoencoder (cVAE) to visualize a large SAXS dataset of hard-elastic isotactic polypropylene (iPP) films in 2- and 1-dimensional latent spaces. The low-dimensional representations enable us to capture key features of the dataset rapidly, such as the similarity among SAXS patterns and the structural evolution trends. The preprocessing of the dataset points out the further direction of data analysis so that researchers can focus on the most valued regions in the dataset. Then, we develop a hybrid VAE-multilayer perceptron (MLP) neural network to realize the processing-structure mapping of iPP films. The robustness of the hybrid VAE-MLP network is verified. Finally, SAXS patterns in the temperature-strain space are generated, which allows us to explore the processing parameter space not involved by previous experiments. These capabilities indicate that the developed machine-learning methods are valuable artificial intelligence toolset to assist in the preprocessing of large-scale SAXS datasets and the establishment of comprehensive processing-structure relationship of hard-elastic iPP films.
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title_short	Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning
url	https://doi.org/10.1016/j.matdes.2023.111828 https://doaj.org/article/754b59957af0427aae5756fcf2953fbd http://www.sciencedirect.com/science/article/pii/S0264127523002435 https://doaj.org/toc/0264-1275
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Visualization of small-angle X-ray scattering datasets and processing-structure mapping of isotactic polypropylene films by machine learning

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