Author Description

"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 behaviour under different exposure conditions. Characterization techniques are very vital considering the extensive investigation. The criticality and consideration of several 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 manufacturing them. Recent development 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 combinations 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"-- Provided by publisher.