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About this Research Topic

Manuscript Submission Deadline 14 December 2022

The superior multi-functional properties of polymer composites have made them a suitable candidate for biomedical, defense, automobile, agriculture, and domestic applications. The growing demand for these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Characterization techniques are very vital considering the extensive investigation. The criticality and consideration of a number of parameters for the characterization make this investigation more complex. The self-learning ability of machine learning algorithms makes this investigation more accurate and accommodates all the complex requirements. The recent development in neural codes can accommodate the data in all the forms such as numerical values as well as images.

The aim of the Research Topic is to address the design, characterization, and development of prediction analysis of sustainable polymer composites using machine learning algorithms. This topic encourages the development of sustainable polymer composites using machine learning algorithms before actually manufacturing them. Recent development in AI & ML techniques help in the development of sustainable development polymers. This research topic also encourages the development of AI & ML technique for effective characterization based on the research data available for different polymers. Considering the capability of AI&ML techniques, various properties such as physical, mechanical, chemical, thermal, and electrical can be predicted for desired sustainable polymer composite. This research topic also provides the scope of possible combination of reinforcement in the polymer with the ability to predict the life span of the composite, energy appropriation using local density, manufacturing optimization, phase diagrams, precision machining, micrograph analysis, and damage assessment.

Topics covered under this Research Topic, include but are not limited to:
- Types of reinforcement used for development of sustainable composite polymer
- Type of modeling, analysis, design and manufacturing process
- Characterization of sustainable polymer composites
- Development of machine learning based algorithm for property predication and novel material discovery
- Development of machine learning based algorithm for material and structure optimization design
- Use of Machine learning algorithm to addressed issues related to environmental and economics
- Area of industrial and domestic applications

Keywords: Sustainable, Reinforcement, Matrix, Environment, Economics, Machine Learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The superior multi-functional properties of polymer composites have made them a suitable candidate for biomedical, defense, automobile, agriculture, and domestic applications. The growing demand for these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Characterization techniques are very vital considering the extensive investigation. The criticality and consideration of a number of parameters for the characterization make this investigation more complex. The self-learning ability of machine learning algorithms makes this investigation more accurate and accommodates all the complex requirements. The recent development in neural codes can accommodate the data in all the forms such as numerical values as well as images.

The aim of the Research Topic is to address the design, characterization, and development of prediction analysis of sustainable polymer composites using machine learning algorithms. This topic encourages the development of sustainable polymer composites using machine learning algorithms before actually manufacturing them. Recent development in AI & ML techniques help in the development of sustainable development polymers. This research topic also encourages the development of AI & ML technique for effective characterization based on the research data available for different polymers. Considering the capability of AI&ML techniques, various properties such as physical, mechanical, chemical, thermal, and electrical can be predicted for desired sustainable polymer composite. This research topic also provides the scope of possible combination of reinforcement in the polymer with the ability to predict the life span of the composite, energy appropriation using local density, manufacturing optimization, phase diagrams, precision machining, micrograph analysis, and damage assessment.

Topics covered under this Research Topic, include but are not limited to:
- Types of reinforcement used for development of sustainable composite polymer
- Type of modeling, analysis, design and manufacturing process
- Characterization of sustainable polymer composites
- Development of machine learning based algorithm for property predication and novel material discovery
- Development of machine learning based algorithm for material and structure optimization design
- Use of Machine learning algorithm to addressed issues related to environmental and economics
- Area of industrial and domestic applications

Keywords: Sustainable, Reinforcement, Matrix, Environment, Economics, Machine Learning


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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