Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25

Creep rupture extrapolation is crucial for high-temperature materials served in power plants. Many analytical models can be used for creep rupture analysis, and fundamental models are also available. Machine learning is also an alternative. However, unphysical prediction curves occur readily in comm...
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Autor*in:	Jun-Jing He [verfasserIn] Rolf Sandström [verfasserIn] Jing Zhang [verfasserIn] Hai-Ying Qin [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Soft constrained machine learning Creep rupture extrapolation Austenitic stainless steels Error analysis Remaining creep life Stability analysis

Übergeordnetes Werk:	In: Journal of Materials Research and Technology - Elsevier, 2015, 22(2023), Seite 923-937
Übergeordnetes Werk:	volume:22 ; year:2023 ; pages:923-937

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.jmrt.2022.11.154

Katalog-ID:	DOAJ081513216

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245	1	0	\|a Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25
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520			\|a Creep rupture extrapolation is crucial for high-temperature materials served in power plants. Many analytical models can be used for creep rupture analysis, and fundamental models are also available. Machine learning is also an alternative. However, unphysical prediction curves occur readily in common machine learning algorithms, where one must manipulate the best results or ignore the less satisfactory ones. Using just high regression coefficients and low errors is not enough to obtain high accuracy of the methods. Nevertheless, five soft constrained machine learning algorithms (SCMLAs), where soft constraints, stability analysis by culling long-time or low-stress data, extrapolation from short to long times, and errors of solutions and algorithms are considered, are used for creep rupture prediction in this work. The models can generate reasonable results for fitting all data, extrapolating from short to long times, and stability analysis for Sanicro 25 after a number of tests. The errors of solutions for all the analyses are in a quite reasonable range, including extrapolation and stability analysis. The average relative standard deviation of the five SCMLAs is less than 2.5% at three times the maximum experimental creep rupture time. Creep rupture strength of the austenitic stainless steel Sanicro 25 can be predicted quantitatively by taking the average predicted stresses of the five SCMLAs. The method can also be used for other high-temperature alloys with similar creep degradation mechanisms.
650		4	\|a Soft constrained machine learning
650		4	\|a Creep rupture extrapolation
650		4	\|a Austenitic stainless steels
650		4	\|a Error analysis
650		4	\|a Remaining creep life
650		4	\|a Stability analysis
653		0	\|a Mining engineering. Metallurgy
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700	0		\|a Jing Zhang \|e verfasserin \|4 aut
700	0		\|a Hai-Ying Qin \|e verfasserin \|4 aut
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allfields_unstemmed	10.1016/j.jmrt.2022.11.154 doi (DE-627)DOAJ081513216 (DE-599)DOAJ15d444b3e8bc469e8f8161691d74be11 DE-627 ger DE-627 rakwb eng TN1-997 Jun-Jing He verfasserin aut Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Creep rupture extrapolation is crucial for high-temperature materials served in power plants. Many analytical models can be used for creep rupture analysis, and fundamental models are also available. Machine learning is also an alternative. However, unphysical prediction curves occur readily in common machine learning algorithms, where one must manipulate the best results or ignore the less satisfactory ones. Using just high regression coefficients and low errors is not enough to obtain high accuracy of the methods. Nevertheless, five soft constrained machine learning algorithms (SCMLAs), where soft constraints, stability analysis by culling long-time or low-stress data, extrapolation from short to long times, and errors of solutions and algorithms are considered, are used for creep rupture prediction in this work. The models can generate reasonable results for fitting all data, extrapolating from short to long times, and stability analysis for Sanicro 25 after a number of tests. The errors of solutions for all the analyses are in a quite reasonable range, including extrapolation and stability analysis. The average relative standard deviation of the five SCMLAs is less than 2.5% at three times the maximum experimental creep rupture time. Creep rupture strength of the austenitic stainless steel Sanicro 25 can be predicted quantitatively by taking the average predicted stresses of the five SCMLAs. The method can also be used for other high-temperature alloys with similar creep degradation mechanisms. Soft constrained machine learning Creep rupture extrapolation Austenitic stainless steels Error analysis Remaining creep life Stability analysis Mining engineering. Metallurgy Rolf Sandström verfasserin aut Jing Zhang verfasserin aut Hai-Ying Qin verfasserin aut In Journal of Materials Research and Technology Elsevier, 2015 22(2023), Seite 923-937 (DE-627)768093163 (DE-600)2732709-7 22140697 nnns volume:22 year:2023 pages:923-937 https://doi.org/10.1016/j.jmrt.2022.11.154 kostenfrei https://doaj.org/article/15d444b3e8bc469e8f8161691d74be11 kostenfrei http://www.sciencedirect.com/science/article/pii/S2238785422018592 kostenfrei https://doaj.org/toc/2238-7854 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_151 GBV_ILN_161 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_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_2034 GBV_ILN_2038 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_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 22 2023 923-937
allfieldsGer	10.1016/j.jmrt.2022.11.154 doi (DE-627)DOAJ081513216 (DE-599)DOAJ15d444b3e8bc469e8f8161691d74be11 DE-627 ger DE-627 rakwb eng TN1-997 Jun-Jing He verfasserin aut Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Creep rupture extrapolation is crucial for high-temperature materials served in power plants. Many analytical models can be used for creep rupture analysis, and fundamental models are also available. Machine learning is also an alternative. However, unphysical prediction curves occur readily in common machine learning algorithms, where one must manipulate the best results or ignore the less satisfactory ones. Using just high regression coefficients and low errors is not enough to obtain high accuracy of the methods. Nevertheless, five soft constrained machine learning algorithms (SCMLAs), where soft constraints, stability analysis by culling long-time or low-stress data, extrapolation from short to long times, and errors of solutions and algorithms are considered, are used for creep rupture prediction in this work. The models can generate reasonable results for fitting all data, extrapolating from short to long times, and stability analysis for Sanicro 25 after a number of tests. The errors of solutions for all the analyses are in a quite reasonable range, including extrapolation and stability analysis. The average relative standard deviation of the five SCMLAs is less than 2.5% at three times the maximum experimental creep rupture time. Creep rupture strength of the austenitic stainless steel Sanicro 25 can be predicted quantitatively by taking the average predicted stresses of the five SCMLAs. The method can also be used for other high-temperature alloys with similar creep degradation mechanisms. Soft constrained machine learning Creep rupture extrapolation Austenitic stainless steels Error analysis Remaining creep life Stability analysis Mining engineering. Metallurgy Rolf Sandström verfasserin aut Jing Zhang verfasserin aut Hai-Ying Qin verfasserin aut In Journal of Materials Research and Technology Elsevier, 2015 22(2023), Seite 923-937 (DE-627)768093163 (DE-600)2732709-7 22140697 nnns volume:22 year:2023 pages:923-937 https://doi.org/10.1016/j.jmrt.2022.11.154 kostenfrei https://doaj.org/article/15d444b3e8bc469e8f8161691d74be11 kostenfrei http://www.sciencedirect.com/science/article/pii/S2238785422018592 kostenfrei https://doaj.org/toc/2238-7854 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_151 GBV_ILN_161 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_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_2034 GBV_ILN_2038 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_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 22 2023 923-937
allfieldsSound	10.1016/j.jmrt.2022.11.154 doi (DE-627)DOAJ081513216 (DE-599)DOAJ15d444b3e8bc469e8f8161691d74be11 DE-627 ger DE-627 rakwb eng TN1-997 Jun-Jing He verfasserin aut Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Creep rupture extrapolation is crucial for high-temperature materials served in power plants. Many analytical models can be used for creep rupture analysis, and fundamental models are also available. Machine learning is also an alternative. However, unphysical prediction curves occur readily in common machine learning algorithms, where one must manipulate the best results or ignore the less satisfactory ones. Using just high regression coefficients and low errors is not enough to obtain high accuracy of the methods. Nevertheless, five soft constrained machine learning algorithms (SCMLAs), where soft constraints, stability analysis by culling long-time or low-stress data, extrapolation from short to long times, and errors of solutions and algorithms are considered, are used for creep rupture prediction in this work. The models can generate reasonable results for fitting all data, extrapolating from short to long times, and stability analysis for Sanicro 25 after a number of tests. The errors of solutions for all the analyses are in a quite reasonable range, including extrapolation and stability analysis. The average relative standard deviation of the five SCMLAs is less than 2.5% at three times the maximum experimental creep rupture time. Creep rupture strength of the austenitic stainless steel Sanicro 25 can be predicted quantitatively by taking the average predicted stresses of the five SCMLAs. The method can also be used for other high-temperature alloys with similar creep degradation mechanisms. Soft constrained machine learning Creep rupture extrapolation Austenitic stainless steels Error analysis Remaining creep life Stability analysis Mining engineering. Metallurgy Rolf Sandström verfasserin aut Jing Zhang verfasserin aut Hai-Ying Qin verfasserin aut In Journal of Materials Research and Technology Elsevier, 2015 22(2023), Seite 923-937 (DE-627)768093163 (DE-600)2732709-7 22140697 nnns volume:22 year:2023 pages:923-937 https://doi.org/10.1016/j.jmrt.2022.11.154 kostenfrei https://doaj.org/article/15d444b3e8bc469e8f8161691d74be11 kostenfrei http://www.sciencedirect.com/science/article/pii/S2238785422018592 kostenfrei https://doaj.org/toc/2238-7854 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_151 GBV_ILN_161 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_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_2034 GBV_ILN_2038 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_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 22 2023 923-937
language	English
source	In Journal of Materials Research and Technology 22(2023), Seite 923-937 volume:22 year:2023 pages:923-937
sourceStr	In Journal of Materials Research and Technology 22(2023), Seite 923-937 volume:22 year:2023 pages:923-937
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Soft constrained machine learning Creep rupture extrapolation Austenitic stainless steels Error analysis Remaining creep life Stability analysis Mining engineering. Metallurgy
isfreeaccess_bool	true
container_title	Journal of Materials Research and Technology
authorswithroles_txt_mv	Jun-Jing He @@aut@@ Rolf Sandström @@aut@@ Jing Zhang @@aut@@ Hai-Ying Qin @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	768093163
id	DOAJ081513216
language_de	englisch
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callnumber-first	T - Technology
author	Jun-Jing He
spellingShingle	Jun-Jing He misc TN1-997 misc Soft constrained machine learning misc Creep rupture extrapolation misc Austenitic stainless steels misc Error analysis misc Remaining creep life misc Stability analysis misc Mining engineering. Metallurgy Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25
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topic_title	TN1-997 Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25 Soft constrained machine learning Creep rupture extrapolation Austenitic stainless steels Error analysis Remaining creep life Stability analysis
topic	misc TN1-997 misc Soft constrained machine learning misc Creep rupture extrapolation misc Austenitic stainless steels misc Error analysis misc Remaining creep life misc Stability analysis misc Mining engineering. Metallurgy
topic_unstemmed	misc TN1-997 misc Soft constrained machine learning misc Creep rupture extrapolation misc Austenitic stainless steels misc Error analysis misc Remaining creep life misc Stability analysis misc Mining engineering. Metallurgy
topic_browse	misc TN1-997 misc Soft constrained machine learning misc Creep rupture extrapolation misc Austenitic stainless steels misc Error analysis misc Remaining creep life misc Stability analysis misc Mining engineering. Metallurgy
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title	Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25
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title_full	Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25
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title_sort	application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel sanicro 25
callnumber	TN1-997
title_auth	Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25
abstract	Creep rupture extrapolation is crucial for high-temperature materials served in power plants. Many analytical models can be used for creep rupture analysis, and fundamental models are also available. Machine learning is also an alternative. However, unphysical prediction curves occur readily in common machine learning algorithms, where one must manipulate the best results or ignore the less satisfactory ones. Using just high regression coefficients and low errors is not enough to obtain high accuracy of the methods. Nevertheless, five soft constrained machine learning algorithms (SCMLAs), where soft constraints, stability analysis by culling long-time or low-stress data, extrapolation from short to long times, and errors of solutions and algorithms are considered, are used for creep rupture prediction in this work. The models can generate reasonable results for fitting all data, extrapolating from short to long times, and stability analysis for Sanicro 25 after a number of tests. The errors of solutions for all the analyses are in a quite reasonable range, including extrapolation and stability analysis. The average relative standard deviation of the five SCMLAs is less than 2.5% at three times the maximum experimental creep rupture time. Creep rupture strength of the austenitic stainless steel Sanicro 25 can be predicted quantitatively by taking the average predicted stresses of the five SCMLAs. The method can also be used for other high-temperature alloys with similar creep degradation mechanisms.
abstractGer	Creep rupture extrapolation is crucial for high-temperature materials served in power plants. Many analytical models can be used for creep rupture analysis, and fundamental models are also available. Machine learning is also an alternative. However, unphysical prediction curves occur readily in common machine learning algorithms, where one must manipulate the best results or ignore the less satisfactory ones. Using just high regression coefficients and low errors is not enough to obtain high accuracy of the methods. Nevertheless, five soft constrained machine learning algorithms (SCMLAs), where soft constraints, stability analysis by culling long-time or low-stress data, extrapolation from short to long times, and errors of solutions and algorithms are considered, are used for creep rupture prediction in this work. The models can generate reasonable results for fitting all data, extrapolating from short to long times, and stability analysis for Sanicro 25 after a number of tests. The errors of solutions for all the analyses are in a quite reasonable range, including extrapolation and stability analysis. The average relative standard deviation of the five SCMLAs is less than 2.5% at three times the maximum experimental creep rupture time. Creep rupture strength of the austenitic stainless steel Sanicro 25 can be predicted quantitatively by taking the average predicted stresses of the five SCMLAs. The method can also be used for other high-temperature alloys with similar creep degradation mechanisms.
abstract_unstemmed	Creep rupture extrapolation is crucial for high-temperature materials served in power plants. Many analytical models can be used for creep rupture analysis, and fundamental models are also available. Machine learning is also an alternative. However, unphysical prediction curves occur readily in common machine learning algorithms, where one must manipulate the best results or ignore the less satisfactory ones. Using just high regression coefficients and low errors is not enough to obtain high accuracy of the methods. Nevertheless, five soft constrained machine learning algorithms (SCMLAs), where soft constraints, stability analysis by culling long-time or low-stress data, extrapolation from short to long times, and errors of solutions and algorithms are considered, are used for creep rupture prediction in this work. The models can generate reasonable results for fitting all data, extrapolating from short to long times, and stability analysis for Sanicro 25 after a number of tests. The errors of solutions for all the analyses are in a quite reasonable range, including extrapolation and stability analysis. The average relative standard deviation of the five SCMLAs is less than 2.5% at three times the maximum experimental creep rupture time. Creep rupture strength of the austenitic stainless steel Sanicro 25 can be predicted quantitatively by taking the average predicted stresses of the five SCMLAs. The method can also be used for other high-temperature alloys with similar creep degradation mechanisms.
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title_short	Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25
url	https://doi.org/10.1016/j.jmrt.2022.11.154 https://doaj.org/article/15d444b3e8bc469e8f8161691d74be11 http://www.sciencedirect.com/science/article/pii/S2238785422018592 https://doaj.org/toc/2238-7854
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doi_str	10.1016/j.jmrt.2022.11.154
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The errors of solutions for all the analyses are in a quite reasonable range, including extrapolation and stability analysis. The average relative standard deviation of the five SCMLAs is less than 2.5% at three times the maximum experimental creep rupture time. Creep rupture strength of the austenitic stainless steel Sanicro 25 can be predicted quantitatively by taking the average predicted stresses of the five SCMLAs. The method can also be used for other high-temperature alloys with similar creep degradation mechanisms.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Soft constrained machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Creep rupture extrapolation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Austenitic stainless steels</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Error analysis</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Remaining creep life</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Stability analysis</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Mining engineering. Metallurgy</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Rolf Sandström</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Jing Zhang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Hai-Ying Qin</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">In</subfield><subfield code="t">Journal of Materials Research and Technology</subfield><subfield code="d">Elsevier, 2015</subfield><subfield code="g">22(2023), Seite 923-937</subfield><subfield code="w">(DE-627)768093163</subfield><subfield code="w">(DE-600)2732709-7</subfield><subfield code="x">22140697</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" 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Application of soft constrained machine learning algorithms for creep rupture prediction of an austenitic heat resistant steel Sanicro 25

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