A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites

This study aims to develop a high-generalizability machine learning framework for predicting the homogenized mechanical properties of short fiber-reinforced polymer composites. The ensemble machine learning model (EML) employs a stacking algorithm using three base models of Extra Trees (ET), eXtreme...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yunmei Zhao [verfasserIn] Zhenyue Chen [verfasserIn] Xiaobin Jian [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	short fiber-reinforced polymer composite ensemble machine learning homogenized properties SHAP interpretation model generalizability

Übergeordnetes Werk:	In: Polymers - MDPI AG, 2011, 15(2023), 3962, p 3962
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:3962, p 3962

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/polym15193962

Katalog-ID:	DOAJ093206887

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spelling	10.3390/polym15193962 doi (DE-627)DOAJ093206887 (DE-599)DOAJad3574f8ffea48a699b8b0d08d009adc DE-627 ger DE-627 rakwb eng QD241-441 Yunmei Zhao verfasserin aut A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study aims to develop a high-generalizability machine learning framework for predicting the homogenized mechanical properties of short fiber-reinforced polymer composites. The ensemble machine learning model (EML) employs a stacking algorithm using three base models of Extra Trees (ET), eXtreme Gradient Boosting machine (XGBoost), and Light Gradient Boosting machine (LGBM). A micromechanical model of a two-step homogenization algorithm is adopted and verified as an effective approach to composite modeling with randomly distributed fibers, which is integrated with finite element simulations for providing a high-quality ground-truth dataset. The model performance is thoroughly assessed for its accuracy, efficiency, interpretability, and generalizability. The results suggest that: (1) the EML model outperforms the base members on prediction accuracy, achieving <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mi<R</mi<<mn<2</mn<</msup<</semantics<</math<</inline-formula< values of 0.988 and 0.952 on the train and test datasets, respectively; (2) the SHapley Additive exPlanations (SHAP) analysis identifies the Young’s modulus of matrix, fiber, and fiber content as the top three factors influencing the homogenized properties, whereas the anisotropy is predominantly determined by the fiber orientations; (3) the EML model showcases good generalization capability on experimental data, and it has been shown to be more effective than high-fidelity computational models by significantly lowering computational costs while maintaining high accuracy. short fiber-reinforced polymer composite ensemble machine learning homogenized properties SHAP interpretation model generalizability Organic chemistry Zhenyue Chen verfasserin aut Xiaobin Jian verfasserin aut In Polymers MDPI AG, 2011 15(2023), 3962, p 3962 (DE-627)61409612X (DE-600)2527146-5 20734360 nnns volume:15 year:2023 number:3962, p 3962 https://doi.org/10.3390/polym15193962 kostenfrei https://doaj.org/article/ad3574f8ffea48a699b8b0d08d009adc kostenfrei https://www.mdpi.com/2073-4360/15/19/3962 kostenfrei https://doaj.org/toc/2073-4360 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 15 2023 3962, p 3962
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callnumber-first	Q - Science
author	Yunmei Zhao
spellingShingle	Yunmei Zhao misc QD241-441 misc short fiber-reinforced polymer composite misc ensemble machine learning misc homogenized properties misc SHAP interpretation misc model generalizability misc Organic chemistry A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites
authorStr	Yunmei Zhao
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topic_title	QD241-441 A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites short fiber-reinforced polymer composite ensemble machine learning homogenized properties SHAP interpretation model generalizability
topic	misc QD241-441 misc short fiber-reinforced polymer composite misc ensemble machine learning misc homogenized properties misc SHAP interpretation misc model generalizability misc Organic chemistry
topic_unstemmed	misc QD241-441 misc short fiber-reinforced polymer composite misc ensemble machine learning misc homogenized properties misc SHAP interpretation misc model generalizability misc Organic chemistry
topic_browse	misc QD241-441 misc short fiber-reinforced polymer composite misc ensemble machine learning misc homogenized properties misc SHAP interpretation misc model generalizability misc Organic chemistry
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title	A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites
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title_full	A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites
author_sort	Yunmei Zhao
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author_browse	Yunmei Zhao Zhenyue Chen Xiaobin Jian
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title_sort	high-generalizability machine learning framework for analyzing the homogenized properties of short fiber-reinforced polymer composites
callnumber	QD241-441
title_auth	A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites
abstract	This study aims to develop a high-generalizability machine learning framework for predicting the homogenized mechanical properties of short fiber-reinforced polymer composites. The ensemble machine learning model (EML) employs a stacking algorithm using three base models of Extra Trees (ET), eXtreme Gradient Boosting machine (XGBoost), and Light Gradient Boosting machine (LGBM). A micromechanical model of a two-step homogenization algorithm is adopted and verified as an effective approach to composite modeling with randomly distributed fibers, which is integrated with finite element simulations for providing a high-quality ground-truth dataset. The model performance is thoroughly assessed for its accuracy, efficiency, interpretability, and generalizability. The results suggest that: (1) the EML model outperforms the base members on prediction accuracy, achieving <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mi<R</mi<<mn<2</mn<</msup<</semantics<</math<</inline-formula< values of 0.988 and 0.952 on the train and test datasets, respectively; (2) the SHapley Additive exPlanations (SHAP) analysis identifies the Young’s modulus of matrix, fiber, and fiber content as the top three factors influencing the homogenized properties, whereas the anisotropy is predominantly determined by the fiber orientations; (3) the EML model showcases good generalization capability on experimental data, and it has been shown to be more effective than high-fidelity computational models by significantly lowering computational costs while maintaining high accuracy.
abstractGer	This study aims to develop a high-generalizability machine learning framework for predicting the homogenized mechanical properties of short fiber-reinforced polymer composites. The ensemble machine learning model (EML) employs a stacking algorithm using three base models of Extra Trees (ET), eXtreme Gradient Boosting machine (XGBoost), and Light Gradient Boosting machine (LGBM). A micromechanical model of a two-step homogenization algorithm is adopted and verified as an effective approach to composite modeling with randomly distributed fibers, which is integrated with finite element simulations for providing a high-quality ground-truth dataset. The model performance is thoroughly assessed for its accuracy, efficiency, interpretability, and generalizability. The results suggest that: (1) the EML model outperforms the base members on prediction accuracy, achieving <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mi<R</mi<<mn<2</mn<</msup<</semantics<</math<</inline-formula< values of 0.988 and 0.952 on the train and test datasets, respectively; (2) the SHapley Additive exPlanations (SHAP) analysis identifies the Young’s modulus of matrix, fiber, and fiber content as the top three factors influencing the homogenized properties, whereas the anisotropy is predominantly determined by the fiber orientations; (3) the EML model showcases good generalization capability on experimental data, and it has been shown to be more effective than high-fidelity computational models by significantly lowering computational costs while maintaining high accuracy.
abstract_unstemmed	This study aims to develop a high-generalizability machine learning framework for predicting the homogenized mechanical properties of short fiber-reinforced polymer composites. The ensemble machine learning model (EML) employs a stacking algorithm using three base models of Extra Trees (ET), eXtreme Gradient Boosting machine (XGBoost), and Light Gradient Boosting machine (LGBM). A micromechanical model of a two-step homogenization algorithm is adopted and verified as an effective approach to composite modeling with randomly distributed fibers, which is integrated with finite element simulations for providing a high-quality ground-truth dataset. The model performance is thoroughly assessed for its accuracy, efficiency, interpretability, and generalizability. The results suggest that: (1) the EML model outperforms the base members on prediction accuracy, achieving <inline-formula<<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"<<semantics<<msup<<mi<R</mi<<mn<2</mn<</msup<</semantics<</math<</inline-formula< values of 0.988 and 0.952 on the train and test datasets, respectively; (2) the SHapley Additive exPlanations (SHAP) analysis identifies the Young’s modulus of matrix, fiber, and fiber content as the top three factors influencing the homogenized properties, whereas the anisotropy is predominantly determined by the fiber orientations; (3) the EML model showcases good generalization capability on experimental data, and it has been shown to be more effective than high-fidelity computational models by significantly lowering computational costs while maintaining high accuracy.
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container_issue	3962, p 3962
title_short	A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites
url	https://doi.org/10.3390/polym15193962 https://doaj.org/article/ad3574f8ffea48a699b8b0d08d009adc https://www.mdpi.com/2073-4360/15/19/3962 https://doaj.org/toc/2073-4360
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up_date	2024-07-03T15:55:05.378Z
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A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites

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