Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and...

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Bibliographic Details
Published in:Archives of computational methods in engineering Vol. 29; no. 5; pp. 3341 - 3385
Main Authors: Sharma, A., Mukhopadhyay, T., Rangappa, S. M., Siengchin, S., Kushvaha, V.
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.08.2022
Springer Nature B.V
Subjects:
Algorithms
Artificial intelligence
Composite materials
Engineering
Literature reviews
Machine learning
Marine technology
Mathematical and Computational Engineering
Mechanical properties
Modelling
Optimization
Polymer matrix composites
Polymers
Reliability analysis
Review Article
Sensitivity analysis
Uncertainty quantification
AI and ML in polymer science
Curse of dimensionality
Prediction and characterization
Machine learning in polymer composites
Optimization
ISSN:1134-3060, 1886-1784
Online Access:Get full text
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Abstract The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites. Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.
AbstractList The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites. Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.
Author Mukhopadhyay, T.
Kushvaha, V.
Sharma, A.
Siengchin, S.
Rangappa, S. M.
Author_xml – sequence: 1
  givenname: A.
  surname: Sharma
  fullname: Sharma, A.
  organization: Department of Civil Engineering, Indian Institute of Technology Jammu
– sequence: 2
  givenname: T.
  orcidid: 0000-0002-0778-6515
  surname: Mukhopadhyay
  fullname: Mukhopadhyay, T.
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  organization: Department of Aerospace Engineering, Indian Institute of Technology Kanpur
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  givenname: S. M.
  surname: Rangappa
  fullname: Rangappa, S. M.
  organization: Natural Composites Research Group Lab, King Mongkut’s University of Technology North Bangkok
– sequence: 4
  givenname: S.
  surname: Siengchin
  fullname: Siengchin, S.
  organization: Natural Composites Research Group Lab, King Mongkut’s University of Technology North Bangkok
– sequence: 5
  givenname: V.
  orcidid: 0000-0001-6021-4981
  surname: Kushvaha
  fullname: Kushvaha, V.
  email: vinod.kushvaha@iitjammu.ac.in
  organization: Department of Civil Engineering, Indian Institute of Technology Jammu
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Keywords Uncertainty quantification
AI and ML in polymer science
Curse of dimensionality
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Machine learning in polymer composites
Optimization
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Snippet The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other...
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SubjectTerms Algorithms
Artificial intelligence
Composite materials
Engineering
Literature reviews
Machine learning
Marine technology
Mathematical and Computational Engineering
Mechanical properties
Modelling
Optimization
Polymer matrix composites
Polymers
Reliability analysis
Review Article
Sensitivity analysis
Title Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design
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