Prediction of Stress-strain Behavior for Polyurethane Elastomers Based on Machine Learning.
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| Title: | Prediction of Stress-strain Behavior for Polyurethane Elastomers Based on Machine Learning. |
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| Authors: | Zhou, Li1 (AUTHOR), Wang, Mei-Fang2 (AUTHOR), Huang, Chao-Kun1 (AUTHOR), Song, Meng3 (AUTHOR) chengsimengyin@126.com, Wang, Xiu-Juan1 (AUTHOR) wangxj@qust.edu.cn |
| Source: | Chinese Journal of Polymer Science (Springer Science & Business Media B.V.). May2026, Vol. 44 Issue 5, p1562-1573. 12p. |
| Subjects: | Polyurethane elastomers, Machine learning, Strains & stresses (Mechanics), Shapley Additive Explanations, Mechanical behavior of materials, Molecular structure, Phase separation |
| Abstract: | Predicting stress-strain behavior is key to facilitating the design of polymer materials and their products with tailored mechanical responses. However, polyurethane elastomers (PUE) often exhibit highly nonlinear mechanical responses owing to their tunable molecular structure and the complexity of microphase separation for hard segments, which poses challenges for developing models for predicting stress-strain properties. In this study, four machine learning models were constructed to predict the stress-strain curve of PUE, mainly by investigating the influence of molecular hard segment content and molecular structure characteristics on the mechanical properties of PUE. Based on the Pearson correlation analysis, key variables were screened to effectively capture the evolution law of the mechanical behavior of PUE. The results show that the Transformer model performs the best and can effectively predict the stress-strain behavior of the PUE (coefficient of determination (R2) = 0.79, root mean square error (RMSE) = 5.82). Cross-validation was adopted to evaluate the generalization ability of the model. The experimental data further confirmed that this model can effectively fit the stress-strain curve of PUE. The Shapley additive explanation (SHAP) method was adopted to analyze the contribution of key descriptors to the stress response, and the intrinsic correlation between molecular structure characteristics and macroscopic mechanical behavior was revealed. Among them, descriptors such as SlogP_VSA10 were used as structural proxies for soft segments, whereas descriptors such as RingCount quantified the impact of hard segments. In addition, BCUT2D_CHGHI directly affects intermolecular forces (such as hydrogen bonds), which are crucial for microphase separation and the mechanical properties of elastomers. In conclusion, by using machine-learning algorithms to establish quantitative relationships between these descriptors and mechanical properties, we can adjust the molecular structures related to the descriptors to achieve PUE with customized mechanical responses. [ABSTRACT FROM AUTHOR] |
| Copyright of Chinese Journal of Polymer Science (Springer Science & Business Media B.V.) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194042260 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prediction of Stress-strain Behavior for Polyurethane Elastomers Based on Machine Learning. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Li%22">Zhou, Li</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Mei-Fang%22">Wang, Mei-Fang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huang%2C+Chao-Kun%22">Huang, Chao-Kun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Meng%22">Song, Meng</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> chengsimengyin@126.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Xiu-Juan%22">Wang, Xiu-Juan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wangxj@qust.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Chinese+Journal+of+Polymer+Science+%28Springer+Science+%26+Business+Media+B%2EV%2E%29%22">Chinese Journal of Polymer Science (Springer Science & Business Media B.V.)</searchLink>. May2026, Vol. 44 Issue 5, p1562-1573. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Polyurethane+elastomers%22">Polyurethane elastomers</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Strains+%26+stresses+%28Mechanics%29%22">Strains & stresses (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Shapley+Additive+Explanations%22">Shapley Additive Explanations</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+behavior+of+materials%22">Mechanical behavior of materials</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+structure%22">Molecular structure</searchLink><br /><searchLink fieldCode="DE" term="%22Phase+separation%22">Phase separation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Predicting stress-strain behavior is key to facilitating the design of polymer materials and their products with tailored mechanical responses. However, polyurethane elastomers (PUE) often exhibit highly nonlinear mechanical responses owing to their tunable molecular structure and the complexity of microphase separation for hard segments, which poses challenges for developing models for predicting stress-strain properties. In this study, four machine learning models were constructed to predict the stress-strain curve of PUE, mainly by investigating the influence of molecular hard segment content and molecular structure characteristics on the mechanical properties of PUE. Based on the Pearson correlation analysis, key variables were screened to effectively capture the evolution law of the mechanical behavior of PUE. The results show that the Transformer model performs the best and can effectively predict the stress-strain behavior of the PUE (coefficient of determination (R2) = 0.79, root mean square error (RMSE) = 5.82). Cross-validation was adopted to evaluate the generalization ability of the model. The experimental data further confirmed that this model can effectively fit the stress-strain curve of PUE. The Shapley additive explanation (SHAP) method was adopted to analyze the contribution of key descriptors to the stress response, and the intrinsic correlation between molecular structure characteristics and macroscopic mechanical behavior was revealed. Among them, descriptors such as SlogP_VSA10 were used as structural proxies for soft segments, whereas descriptors such as RingCount quantified the impact of hard segments. In addition, BCUT2D_CHGHI directly affects intermolecular forces (such as hydrogen bonds), which are crucial for microphase separation and the mechanical properties of elastomers. In conclusion, by using machine-learning algorithms to establish quantitative relationships between these descriptors and mechanical properties, we can adjust the molecular structures related to the descriptors to achieve PUE with customized mechanical responses. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Chinese Journal of Polymer Science (Springer Science & Business Media B.V.) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10118-026-3606-9 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1562 Subjects: – SubjectFull: Polyurethane elastomers Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Strains & stresses (Mechanics) Type: general – SubjectFull: Shapley Additive Explanations Type: general – SubjectFull: Mechanical behavior of materials Type: general – SubjectFull: Molecular structure Type: general – SubjectFull: Phase separation Type: general Titles: – TitleFull: Prediction of Stress-strain Behavior for Polyurethane Elastomers Based on Machine Learning. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhou, Li – PersonEntity: Name: NameFull: Wang, Mei-Fang – PersonEntity: Name: NameFull: Huang, Chao-Kun – PersonEntity: Name: NameFull: Song, Meng – PersonEntity: Name: NameFull: Wang, Xiu-Juan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02567679 Numbering: – Type: volume Value: 44 – Type: issue Value: 5 Titles: – TitleFull: Chinese Journal of Polymer Science (Springer Science & Business Media B.V.) Type: main |
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