Materials Science

Computational Intelligence for Polymer Composite Materials
Modeling, Analysis, and Design

Editors: Dr. Pijush Dutta
Prof. Mahuya Das
Dr. Barun Haldar
Dr. Korhan Cengiz

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Computational Intelligence for Polymer Composite Materials

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Short description of the volume:


In this era of advanced material, polymer composites are the focus of attraction due to their versatile properties. They are suitable for application in the fields of structural, automotive, aerospace, biomedical and so many as their property can be tailor-made. Polymer composites exhibit high strength, low density, easy processibility, good corrosion resistance, acoustic damping, thermal insulation properties, easy processability, etc. Like other composites, polymer composites have two parts;: matrix and reinforcement. The matrix part of polymer composites is always polymers, whereas the reinforcement part may be ceramic, metal, natural fiber, polymeric fiber, and nanoparticles depending upon the nature of the matrix and reinforcement the property of the composite varies. The property of polymer composites will reach its highest value when the interfacial adhesion or linkage between the two different phases of matrix and reinforcement is highest.

Again, there are different types of polymers like thermoplastics, thermosetting, hydrophilic, hydrophobic, etc. Then the quality of interfacial adhesion will be controlled by the nature of the polymer, reinforcements, and amount of reinforcement loading. Reinforcement may further be long continuous or particulate in nature. Hence, the wetting of reinforcement with liquid or molten matrix is also very important for forming good interfacial bonding. High loading of reinforcement may hamper proper wetting leading to improper interfacial bonding. Therefore, it is observed that to fabricate a composite with an optimum combination of properties we have to consider a lot of variables which is very tedious as well as time-consuming when attempts are to be made physically to find the suitable composition of constituents for polymer composites.

Computational intelligence has introduced a paradigm shift in the field of technology in the last two decades. Similarly, the field of materials science is also getting enriched to design and develop new and complex materials with the advantage of this computational technology. The result is an increase in the reliability of methodology which a combination of computation intelligence and experimental validation has developed. Therefore, researchers are applying computation intelligence in the form of machine learning and artificial intelligence to design polymer composites with optimum properties. As editors, we will do our best to bring the important and essential elements of computational intelligence for composite materials under a single title.

Coverage in this book:

We request potential authors to consider submitting their scholarly findings on this topic to enrich the content of this intended volume. The book will cover the following topics but not limited to:

  • Introduction: An overview of polymer composites
  • Natural fiber-reinforced polymer composites and their shortcomings
  • Effect of interfacial adhesion on the properties of thermoplastic polymers
  • Polymeric fiber reinforced polymer composite and optimization of their properties
  • Surface treatment of reinforcement for optimization of properties of polymer composites
  • Improvement of interfacial adhesion of polymer composites
  • Fiber-reinforced polymer composites and their defect modification
  • Particulate polymer composites and optimization of their properties
  • Polymer composites of thermoset polymers and their defects related to improper wetting over thermoset.
  • Framework for polymer matrix composite engineering materials design applications.
  • Widely adopted AI algorithms in polymer composites
  • Multifaceted applications of AI in polymer composites
  • AI-assisted multi-scale uncertainty quantification of polymer composites.
  • Perspective on the development of biomedical polymer materials in the AI age.


Important Dates:
Initial Proposal / Abstract Submission (400–600 words) Deadline: March 15th, 2024
Notification of Acceptance: March 30th, 2024
Full Chapter (8,000 - 10,000 words) Submission Due: On or before August 31st, 2024

Submission procedure:
Researchers and practitioners are invited to submit a one-page chapter proposal or abstract on or before March 15th, 2024, clearly mentioning the title of the paper and author(s) details. Author(s) will be notified about the status of their proposals by March 30th, 2024. Full chapters (15-20 pages) are expected to be submitted by August 31st, 2024. All submitted chapters will be peer-reviewed.

Prospective authors are requested to submit their proposals / full-length chapters as an email attachment in a Word file to:

Dr. Pijush Dutta, Editor
Email: pijushdutta009@gmail.com


Authors must refer to the following link for detailed guidelines for chapter preparation:
https://appleacademicpress.com/download/AAP_MS_INSTRUX.pdf

Note: There is no publication fee for manuscripts submitted to this book publication. Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere.


About the Authors / Editors:
Editors: Dr. Pijush Dutta
Assistant Professor, Department of Electronics & Communication Engineering, Greater Kolkata College of Engineering and Management, West Bengal, India


Prof. Mahuya Das
Professor, Department of Basic Science & Humanity, Greater Kolkata College of Engineering and Management, West Bengal, India


Dr. Barun Haldar
Assistant Professor, Department of Mechanical Engineering, Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia


Dr. Korhan Cengiz
Associate Professor, College of Information Technology, University of Fujairah, UAE





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