Machine learning-based predictions and analyses of the creep rupture life of the Ni-based single crystal superalloy

The evaluation of creep rupture life is complex due to its variable formation mechanism. In this paper, machine learning algorithms are applied to explore the creep rupture life span as a function of 27 physical properties to address this issue. By training several classical machine learning models...

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Vydáno v:	Scientific reports Ročník 14; číslo 1; s. 20716 - 16
Hlavní autoři:	Zou, Fan, Liu, Pengjie, Chen, Yanzhan, Zhao, Yaohua
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 05.09.2024 Nature Publishing Group Nature Portfolio
Témata:	639/166/988 639/301/1034/1037 Accuracy Algorithms Alloys Artificial intelligence Creep property prediction Datasets Humanities and Social Sciences Learning algorithms Life span Machine learning multidisciplinary Neural networks Nickel Optimization algorithms Physical properties Prediction models R&D Research & development Rupture Science Science (multidisciplinary) Shapley additive explanations Single crystals Sparrow optimization algorithm Superalloys Temperature XGBoost Sparrow optimization algorithm Creep property prediction Shapley additive explanations XGBoost
ISSN:	2045-2322, 2045-2322
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Abstract	The evaluation of creep rupture life is complex due to its variable formation mechanism. In this paper, machine learning algorithms are applied to explore the creep rupture life span as a function of 27 physical properties to address this issue. By training several classical machine learning models and comparing their prediction performance, XGBoost is finally selected as the predictive model for creep rupture life. Moreover, we introduce an interpretable method, Shapley additive explanations (SHAP), to explain the creep rupture life predicted by the XGBoost model. The SHAP values are then calculated, and the feature importance of the creep rupture life yielded by the XGBoost model is discussed. Finally, the creep fracture life is optimized by using the chaotic sparrow optimization algorithm. We then show that our proposed method can accurately predict and optimize creep properties in a cheaper and faster way than other approaches in the experiments. The proposed method can also be used to optimize the material design across various engineering domains.
AbstractList	The evaluation of creep rupture life is complex due to its variable formation mechanism. In this paper, machine learning algorithms are applied to explore the creep rupture life span as a function of 27 physical properties to address this issue. By training several classical machine learning models and comparing their prediction performance, XGBoost is finally selected as the predictive model for creep rupture life. Moreover, we introduce an interpretable method, Shapley additive explanations (SHAP), to explain the creep rupture life predicted by the XGBoost model. The SHAP values are then calculated, and the feature importance of the creep rupture life yielded by the XGBoost model is discussed. Finally, the creep fracture life is optimized by using the chaotic sparrow optimization algorithm. We then show that our proposed method can accurately predict and optimize creep properties in a cheaper and faster way than other approaches in the experiments. The proposed method can also be used to optimize the material design across various engineering domains. The evaluation of creep rupture life is complex due to its variable formation mechanism. In this paper, machine learning algorithms are applied to explore the creep rupture life span as a function of 27 physical properties to address this issue. By training several classical machine learning models and comparing their prediction performance, XGBoost is finally selected as the predictive model for creep rupture life. Moreover, we introduce an interpretable method, Shapley additive explanations (SHAP), to explain the creep rupture life predicted by the XGBoost model. The SHAP values are then calculated, and the feature importance of the creep rupture life yielded by the XGBoost model is discussed. Finally, the creep fracture life is optimized by using the chaotic sparrow optimization algorithm. We then show that our proposed method can accurately predict and optimize creep properties in a cheaper and faster way than other approaches in the experiments. The proposed method can also be used to optimize the material design across various engineering domains.The evaluation of creep rupture life is complex due to its variable formation mechanism. In this paper, machine learning algorithms are applied to explore the creep rupture life span as a function of 27 physical properties to address this issue. By training several classical machine learning models and comparing their prediction performance, XGBoost is finally selected as the predictive model for creep rupture life. Moreover, we introduce an interpretable method, Shapley additive explanations (SHAP), to explain the creep rupture life predicted by the XGBoost model. The SHAP values are then calculated, and the feature importance of the creep rupture life yielded by the XGBoost model is discussed. Finally, the creep fracture life is optimized by using the chaotic sparrow optimization algorithm. We then show that our proposed method can accurately predict and optimize creep properties in a cheaper and faster way than other approaches in the experiments. The proposed method can also be used to optimize the material design across various engineering domains. Abstract The evaluation of creep rupture life is complex due to its variable formation mechanism. In this paper, machine learning algorithms are applied to explore the creep rupture life span as a function of 27 physical properties to address this issue. By training several classical machine learning models and comparing their prediction performance, XGBoost is finally selected as the predictive model for creep rupture life. Moreover, we introduce an interpretable method, Shapley additive explanations (SHAP), to explain the creep rupture life predicted by the XGBoost model. The SHAP values are then calculated, and the feature importance of the creep rupture life yielded by the XGBoost model is discussed. Finally, the creep fracture life is optimized by using the chaotic sparrow optimization algorithm. We then show that our proposed method can accurately predict and optimize creep properties in a cheaper and faster way than other approaches in the experiments. The proposed method can also be used to optimize the material design across various engineering domains.
ArticleNumber	20716
Author	Chen, Yanzhan Liu, Pengjie Zou, Fan Zhao, Yaohua
Author_xml	– sequence: 1 givenname: Fan surname: Zou fullname: Zou, Fan organization: School of Traffic & Transportation Engineering, Central South University, School of Intelligent Manufacturing and Mechanical Engineering, Hunan Institute of Technology – sequence: 2 givenname: Pengjie surname: Liu fullname: Liu, Pengjie email: pengjieliu_1112@163.com organization: School of Traffic & Transportation Engineering, Central South University – sequence: 3 givenname: Yanzhan surname: Chen fullname: Chen, Yanzhan organization: School of Traffic & Transportation Engineering, Central South University – sequence: 4 givenname: Yaohua surname: Zhao fullname: Zhao, Yaohua organization: School of Traffic & Transportation Engineering, Central South University
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Cites_doi	10.1007/978-3-319-03200-9_2 10.1006/game.1994.1008 10.1007/978-1-84996-098-4_4 10.1016/j.jappgeo.2016.03.027 10.1080/21642583.2019.1708830 10.1007/s00521-019-04620-z 10.1016/j.ins.2013.12.060 10.1007/s12540-020-00713-w 10.1007/s11837-020-04344-9 10.1007/s00521-020-05390-9 10.1016/j.advengsoft.2013.12.007 10.1016/j.jallcom.2018.11.417 10.1016/j.imavis.2020.104024 10.1007/s12540-020-00883-7 10.1007/s12540-020-00809-3 10.1016/S0921-5093(01)01965-7 10.1179/1878641315Y.0000000010 10.1145/3065386 10.1016/j.asoc.2019.105743 10.1016/j.asoc.2018.10.036 10.1016/j.actamat.2020.05.001 10.1016/j.jmat.2017.08.002 10.1016/j.tcs.2005.05.020 10.1007/BF01897163 10.1007/s12540-019-00435-8 10.1016/j.frl.2018.12.032 10.1016/S1369-7021(05)71123-8 10.1007/s10032-018-0310-5 10.1023/A:1010933404324 10.1016/j.asoc.2020.106198 10.1016/j.ijpvp.2017.08.001 10.1016/j.matdes.2017.06.007 10.1016/S0142-1123(98)00071-1 10.1109/4235.687885 10.1016/j.actamat.2009.08.018 10.1007/s12540-017-7089-7 10.3390/s19132969 10.1016/j.jallcom.2010.05.108 10.1007/978-1-4842-4470-8_18 10.1145/2939672.2939785 10.1145/3292500.3330701
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References	Jain, Jain, Upadhyay, Kathuria, Lan (CR15) 2020; 32 Li, Wang, Shi, Yang, Qi (CR6) 2019; 783 Rutkowski, Jaworski, Pietruczuk, Duda (CR27) 2014; 266 de Sousa (CR36) 2019; 19 Jain, Lan, Manikandan (CR14) 2020; 103 Zhang, Xu (CR1) 2020 Thankachan, Soorya Prakash, Kavimani, Silambarasan (CR10) 2020; 27 Dang, Zhao, Yuan (CR7) 2016; 33 Rajan (CR3) 2005; 8 Milligan, Cooper (CR22) 1988; 5 CR35 Liu, Zhao, Ju, Shi (CR12) 2017; 3 CR33 CR32 Reed, Tao, Warnken (CR4) 2009; 57 Sibalija (CR39) 2019; 84 Sun, Liu, Sima (CR31) 2020; 32 Hong, Kwon, Yim (CR9) 2020 Rudolph (CR43) 1997; 1 Liu, Wu, Wang, Lu, Avdeev, Shi, Wang, Yu (CR19) 2020; 195 Nowak, Radzik (CR37) 1994; 6 Venkatesh, Rack (CR17) 1999; 21 Xue, Shen (CR38) 2020; 8 Rao, Shi, Rodrigue, Feng, Xia, Elhoseny, Yuan, Gu (CR24) 2019; 74 Abe, Abe (CR28) 2010 Lin, Yang, Chen, He (CR11) 2019 Weber, Pinz, Ghosh (CR26) 2020; 72 Lisboa, Masulli, Pasi, Yager (CR34) 2013 Liu, Fink, Govindaraju, Jin (CR29) 2018; 21 Conduit, Jones, Stone, Conduit (CR20) 2017; 131 Liu, Sun, Yu, Yue, Zhang (CR42) 2016; 129 Jain, Jain, Lan, Upadhyay, Thareja (CR13) 2020; 1 Breiman (CR30) 2001; 45 Bolton (CR8) 2017; 157 CR23 CR21 Terada, Sato (CR5) 2010; 504 Yoo, Jo, Jones (CR18) 2002; 336 Mirjalili, Mirjalili, Lewis (CR41) 2014; 69 Sutskever, Hinton, Krizhevsky (CR25) 2012 Jain, Kumar, Garg (CR16) 2020; 91 Dorigo, Blum (CR40) 2005; 344 Choi, Kim, Hong, Do, Jung, Jo (CR2) 2017; 23 DK Jain (71431_CR13) 2020; 1 C-L Liu (71431_CR29) 2018; 21 IP de Sousa (71431_CR36) 2019; 19 71431_CR33 S Li (71431_CR6) 2019; 783 71431_CR32 AS Nowak (71431_CR37) 1994; 6 71431_CR35 G Weber (71431_CR26) 2020; 72 S Mirjalili (71431_CR41) 2014; 69 PJG Lisboa (71431_CR34) 2013 YC Lin (71431_CR11) 2019 Y Terada (71431_CR5) 2010; 504 Y Liu (71431_CR19) 2020; 195 D Jain (71431_CR16) 2020; 91 BD Conduit (71431_CR20) 2017; 131 D Jain (71431_CR15) 2020; 32 71431_CR23 71431_CR21 L Rutkowski (71431_CR27) 2014; 266 X Sun (71431_CR31) 2020; 32 Y Liu (71431_CR12) 2017; 3 J Bolton (71431_CR8) 2017; 157 J Xue (71431_CR38) 2020; 8 Y Zhang (71431_CR1) 2020 BG Choi (71431_CR2) 2017; 23 YS Yoo (71431_CR18) 2002; 336 GW Milligan (71431_CR22) 1988; 5 S Abe (71431_CR28) 2010 RC Reed (71431_CR4) 2009; 57 H Rao (71431_CR24) 2019; 74 D Hong (71431_CR9) 2020 TV Sibalija (71431_CR39) 2019; 84 K Rajan (71431_CR3) 2005; 8 YY Dang (71431_CR7) 2016; 33 L Breiman (71431_CR30) 2001; 45 L Liu (71431_CR42) 2016; 129 I Sutskever (71431_CR25) 2012 DK Jain (71431_CR14) 2020; 103 M Dorigo (71431_CR40) 2005; 344 G Rudolph (71431_CR43) 1997; 1 T Thankachan (71431_CR10) 2020; 27 V Venkatesh (71431_CR17) 1999; 21
References_xml	– start-page: 15 year: 2013 end-page: 21 ident: CR34 article-title: Interpretability in machine learning—Principles and practice publication-title: Fuzzy Logic and Applications doi: 10.1007/978-3-319-03200-9_2 – volume: 6 start-page: 150 year: 1994 end-page: 161 ident: CR37 article-title: The Shapley value for n-Person games in generalized characteristic function form publication-title: Games Econom. Behav. doi: 10.1006/game.1994.1008 – start-page: 163 year: 2010 end-page: 226 ident: CR28 article-title: Variants of support vector machines publication-title: Support Vector Machines for Pattern Classification doi: 10.1007/978-1-84996-098-4_4 – volume: 129 start-page: 28 year: 2016 end-page: 35 ident: CR42 article-title: A modified fuzzy C-means (FCM) clustering algorithm and its application on carbonate fluid identification publication-title: J. Appl. Geophys. doi: 10.1016/j.jappgeo.2016.03.027 – volume: 8 start-page: 22 year: 2020 end-page: 34 ident: CR38 article-title: A novel swarm intelligence optimization approach: Sparrow search algorithm publication-title: Syst. Sci. Control Eng. doi: 10.1080/21642583.2019.1708830 – volume: 32 start-page: 1 year: 2020 ident: CR15 article-title: Deep refinement: Capsule network with attention mechanism-based system for text classification publication-title: Neural Comput. Appl. doi: 10.1007/s00521-019-04620-z – volume: 266 start-page: 1 year: 2014 end-page: 15 ident: CR27 article-title: The CART decision tree for mining data streams publication-title: Inf. Sci. doi: 10.1016/j.ins.2013.12.060 – ident: CR33 – year: 2020 ident: CR9 article-title: Exploration of machine learning to predict hot ductility of cast steel from chemical composition and thermal conditions publication-title: Met. Mater. Int. doi: 10.1007/s12540-020-00713-w – volume: 72 start-page: 4404 year: 2020 end-page: 4419 ident: CR26 article-title: Machine learning-aided parametrically homogenized crystal plasticity model (PHCPM) for single crystal Ni-based superalloys publication-title: JOM doi: 10.1007/s11837-020-04344-9 – ident: CR35 – volume: 1 start-page: 1 year: 2020 end-page: 14 ident: CR13 article-title: Driver distraction detection using capsule network publication-title: Neural Comput. Appl. doi: 10.1007/s00521-020-05390-9 – volume: 69 start-page: 46 year: 2014 end-page: 61 ident: CR41 article-title: Grey wolf optimizer publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2013.12.007 – ident: CR23 – volume: 783 start-page: 565 year: 2019 end-page: 573 ident: CR6 article-title: A physically based model for correlating the microstructural degradation and residual creep lifetime of a polycrystalline Ni-based superalloy publication-title: J. Alloys Compd. doi: 10.1016/j.jallcom.2018.11.417 – volume: 103 start-page: 104024 year: 2020 ident: CR14 article-title: Fusion of iris and sclera using phase intensive rubbersheet mutual exclusion for periocular recognition publication-title: Image Vis. Comput. doi: 10.1016/j.imavis.2020.104024 – year: 2020 ident: CR1 article-title: Lattice misfit predictions via the Gaussian process regression for Ni-based single crystal superalloys publication-title: Met. Mater. Int. doi: 10.1007/s12540-020-00883-7 – ident: CR21 – volume: 27 start-page: 220 year: 2020 ident: CR10 article-title: Machine learning and statistical approach to predict and analyze wear rates in copper surface composites publication-title: Metals Mater. Int. doi: 10.1007/s12540-020-00809-3 – volume: 336 start-page: 22 year: 2002 end-page: 29 ident: CR18 article-title: Compositional prediction of creep rupture life of single crystal Ni base superalloy by Bayesian neural network publication-title: Mater. Sci. Eng. A doi: 10.1016/S0921-5093(01)01965-7 – volume: 33 start-page: 1 issue: 1 year: 2016 end-page: 5 ident: CR7 article-title: Predicting long-term creep-rupture property of Inconel 740 and 740H publication-title: Mater. High Temp. doi: 10.1179/1878641315Y.0000000010 – year: 2012 ident: CR25 article-title: Imagenet classification with deep convolutional neural networks publication-title: Adv. Neural. Inf. Process. Syst. doi: 10.1145/3065386 – volume: 84 start-page: 105743 year: 2019 ident: CR39 article-title: Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008–2018) publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105743 – volume: 74 start-page: 634 year: 2019 end-page: 642 ident: CR24 article-title: Feature selection based on artificial bee colony and gradient boosting decision tree publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2018.10.036 – volume: 195 start-page: 454 year: 2020 end-page: 467 ident: CR19 article-title: Predicting creep rupture life of Ni-based single crystal superalloys using divide-and-conquer approach based machine learning publication-title: Acta Mater. doi: 10.1016/j.actamat.2020.05.001 – volume: 3 start-page: 159 year: 2017 end-page: 177 ident: CR12 article-title: Materials discovery and design using machine learning publication-title: J. Materiom. doi: 10.1016/j.jmat.2017.08.002 – volume: 344 start-page: 243 year: 2005 end-page: 278 ident: CR40 article-title: Ant colony optimization theory: A survey publication-title: Theoret. Comput. Sci. doi: 10.1016/j.tcs.2005.05.020 – volume: 5 start-page: 181 year: 1988 end-page: 204 ident: CR22 article-title: A study of standardization of variables in cluster analysis publication-title: J. Classif. doi: 10.1007/BF01897163 – year: 2019 ident: CR11 article-title: Stacked auto-encoder network to predict tensile deformation behavior of a typical nickel-based superalloy considering Portevin–Le Chatelier effects publication-title: Met. Mater. Int. doi: 10.1007/s12540-019-00435-8 – volume: 32 start-page: 101084 year: 2020 ident: CR31 article-title: A novel cryptocurrency price trend forecasting model based on LightGBM publication-title: Financ. Res. Lett. doi: 10.1016/j.frl.2018.12.032 – ident: CR32 – volume: 8 start-page: 38 year: 2005 end-page: 45 ident: CR3 article-title: Materials informatics publication-title: Mater. Today doi: 10.1016/S1369-7021(05)71123-8 – volume: 21 start-page: 159 issue: 3 year: 2018 end-page: 160 ident: CR29 article-title: Special issue on deep learning for document analysis and recognition publication-title: Int. J. Doc. Anal. Recogn. doi: 10.1007/s10032-018-0310-5 – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: CR30 article-title: Random Forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 91 start-page: 106198 year: 2020 ident: CR16 article-title: Sarcasm detection in mash-up language using soft-attention based bi-directional LSTM and feature-rich CNN publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106198 – volume: 157 start-page: 1 year: 2017 end-page: 19 ident: CR8 article-title: Reliable analysis and extrapolation of creep rupture data publication-title: Int. J. Press. Vessels Pip. doi: 10.1016/j.ijpvp.2017.08.001 – volume: 131 start-page: 358 year: 2017 end-page: 365 ident: CR20 article-title: Design of a nickel-base superalloy using a neural network publication-title: Mater. Des. doi: 10.1016/j.matdes.2017.06.007 – volume: 21 start-page: 225 year: 1999 end-page: 234 ident: CR17 article-title: A neural network approach to elevated temperature creep–fatigue life prediction publication-title: Int. J. Fatigue doi: 10.1016/S0142-1123(98)00071-1 – volume: 1 start-page: 249 year: 1997 end-page: 258 ident: CR43 article-title: Local convergence rates of simple evolutionary algorithms with Cauchy mutations publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.687885 – volume: 57 start-page: 5898 year: 2009 end-page: 5913 ident: CR4 article-title: Alloys-by-design: Application to nickel-based single crystal superalloys publication-title: Acta Mater. doi: 10.1016/j.actamat.2009.08.018 – volume: 23 start-page: 877 year: 2017 end-page: 883 ident: CR2 article-title: Effect of Ti content on creep properties of Ni-base single crystal superalloys publication-title: Met. Mater. Int. doi: 10.1007/s12540-017-7089-7 – volume: 19 start-page: 969 year: 2019 ident: CR36 article-title: Local interpretable model-agnostic explanations for classification of lymph node metastases publication-title: Sensors doi: 10.3390/s19132969 – volume: 504 start-page: 261 year: 2010 end-page: 264 ident: CR5 article-title: Assessment of creep rupture life of heat resistant Mg–Al–Ca alloys publication-title: J. Alloys Compd. doi: 10.1016/j.jallcom.2010.05.108 – volume: 32 start-page: 1 year: 2020 ident: 71431_CR15 publication-title: Neural Comput. Appl. doi: 10.1007/s00521-019-04620-z – volume: 266 start-page: 1 year: 2014 ident: 71431_CR27 publication-title: Inf. Sci. doi: 10.1016/j.ins.2013.12.060 – volume: 103 start-page: 104024 year: 2020 ident: 71431_CR14 publication-title: Image Vis. Comput. doi: 10.1016/j.imavis.2020.104024 – volume: 504 start-page: 261 year: 2010 ident: 71431_CR5 publication-title: J. Alloys Compd. doi: 10.1016/j.jallcom.2010.05.108 – volume: 69 start-page: 46 year: 2014 ident: 71431_CR41 publication-title: Adv. Eng. Softw. doi: 10.1016/j.advengsoft.2013.12.007 – volume: 45 start-page: 5 year: 2001 ident: 71431_CR30 publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 8 start-page: 22 year: 2020 ident: 71431_CR38 publication-title: Syst. Sci. Control Eng. doi: 10.1080/21642583.2019.1708830 – volume: 33 start-page: 1 issue: 1 year: 2016 ident: 71431_CR7 publication-title: Mater. High Temp. doi: 10.1179/1878641315Y.0000000010 – volume: 131 start-page: 358 year: 2017 ident: 71431_CR20 publication-title: Mater. Des. doi: 10.1016/j.matdes.2017.06.007 – volume: 21 start-page: 225 year: 1999 ident: 71431_CR17 publication-title: Int. J. Fatigue doi: 10.1016/S0142-1123(98)00071-1 – volume: 84 start-page: 105743 year: 2019 ident: 71431_CR39 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105743 – volume: 783 start-page: 565 year: 2019 ident: 71431_CR6 publication-title: J. Alloys Compd. doi: 10.1016/j.jallcom.2018.11.417 – volume: 336 start-page: 22 year: 2002 ident: 71431_CR18 publication-title: Mater. Sci. Eng. A doi: 10.1016/S0921-5093(01)01965-7 – volume: 74 start-page: 634 year: 2019 ident: 71431_CR24 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2018.10.036 – year: 2012 ident: 71431_CR25 publication-title: Adv. Neural. Inf. Process. Syst. doi: 10.1145/3065386 – volume: 72 start-page: 4404 year: 2020 ident: 71431_CR26 publication-title: JOM doi: 10.1007/s11837-020-04344-9 – start-page: 163 volume-title: Support Vector Machines for Pattern Classification year: 2010 ident: 71431_CR28 doi: 10.1007/978-1-84996-098-4_4 – year: 2020 ident: 71431_CR1 publication-title: Met. Mater. Int. doi: 10.1007/s12540-020-00883-7 – year: 2020 ident: 71431_CR9 publication-title: Met. Mater. Int. doi: 10.1007/s12540-020-00713-w – ident: 71431_CR32 doi: 10.1007/978-1-4842-4470-8_18 – volume: 6 start-page: 150 year: 1994 ident: 71431_CR37 publication-title: Games Econom. Behav. doi: 10.1006/game.1994.1008 – volume: 195 start-page: 454 year: 2020 ident: 71431_CR19 publication-title: Acta Mater. doi: 10.1016/j.actamat.2020.05.001 – volume: 27 start-page: 220 year: 2020 ident: 71431_CR10 publication-title: Metals Mater. Int. doi: 10.1007/s12540-020-00809-3 – volume: 129 start-page: 28 year: 2016 ident: 71431_CR42 publication-title: J. Appl. Geophys. doi: 10.1016/j.jappgeo.2016.03.027 – volume: 5 start-page: 181 year: 1988 ident: 71431_CR22 publication-title: J. Classif. doi: 10.1007/BF01897163 – year: 2019 ident: 71431_CR11 publication-title: Met. Mater. Int. doi: 10.1007/s12540-019-00435-8 – volume: 21 start-page: 159 issue: 3 year: 2018 ident: 71431_CR29 publication-title: Int. J. Doc. Anal. Recogn. doi: 10.1007/s10032-018-0310-5 – volume: 57 start-page: 5898 year: 2009 ident: 71431_CR4 publication-title: Acta Mater. doi: 10.1016/j.actamat.2009.08.018 – volume: 32 start-page: 101084 year: 2020 ident: 71431_CR31 publication-title: Financ. Res. Lett. doi: 10.1016/j.frl.2018.12.032 – ident: 71431_CR35 – ident: 71431_CR23 doi: 10.1145/2939672.2939785 – volume: 91 start-page: 106198 year: 2020 ident: 71431_CR16 publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106198 – volume: 8 start-page: 38 year: 2005 ident: 71431_CR3 publication-title: Mater. Today doi: 10.1016/S1369-7021(05)71123-8 – ident: 71431_CR21 – volume: 23 start-page: 877 year: 2017 ident: 71431_CR2 publication-title: Met. Mater. Int. doi: 10.1007/s12540-017-7089-7 – volume: 1 start-page: 1 year: 2020 ident: 71431_CR13 publication-title: Neural Comput. Appl. doi: 10.1007/s00521-020-05390-9 – volume: 3 start-page: 159 year: 2017 ident: 71431_CR12 publication-title: J. Materiom. doi: 10.1016/j.jmat.2017.08.002 – volume: 1 start-page: 249 year: 1997 ident: 71431_CR43 publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.687885 – volume: 19 start-page: 969 year: 2019 ident: 71431_CR36 publication-title: Sensors doi: 10.3390/s19132969 – start-page: 15 volume-title: Fuzzy Logic and Applications year: 2013 ident: 71431_CR34 doi: 10.1007/978-3-319-03200-9_2 – volume: 344 start-page: 243 year: 2005 ident: 71431_CR40 publication-title: Theoret. Comput. Sci. doi: 10.1016/j.tcs.2005.05.020 – volume: 157 start-page: 1 year: 2017 ident: 71431_CR8 publication-title: Int. J. Press. Vessels Pip. doi: 10.1016/j.ijpvp.2017.08.001 – ident: 71431_CR33 doi: 10.1145/3292500.3330701
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Snippet	The evaluation of creep rupture life is complex due to its variable formation mechanism. In this paper, machine learning algorithms are applied to explore the... Abstract The evaluation of creep rupture life is complex due to its variable formation mechanism. In this paper, machine learning algorithms are applied to...
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SubjectTerms	639/166/988 639/301/1034/1037 Accuracy Algorithms Alloys Artificial intelligence Creep property prediction Datasets Humanities and Social Sciences Learning algorithms Life span Machine learning multidisciplinary Neural networks Nickel Optimization algorithms Physical properties Prediction models R&D Research & development Rupture Science Science (multidisciplinary) Shapley additive explanations Single crystals Sparrow optimization algorithm Superalloys Temperature XGBoost
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Title	Machine learning-based predictions and analyses of the creep rupture life of the Ni-based single crystal superalloy
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