Role of machine learning in the field of Fiber reinforced polymer composites: A preliminary discussion

• Published in: Materials today : proceedings Vol. 44; pp. 4703 - 4708
• Main Authors: Pattnaik, Punyasloka, Sharma, Ankush, Choudhary, Mahavir, Singh, Vijander, Agarwal, Pankaj, Kukshal, Vikas
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2021
• Subjects: Artificial intelligence; Machine learning; Numerical analysis; Polymer composite; Soft computing; Numerical analysis; Soft computing; Polymer composite; Artificial intelligence; Machine learning
• ISSN: 2214-7853; 2214-7853
• DOI: 10.1016/j.matpr.2020.11.026

Artificial Intelligence has become the backbone of almost every domain of science and engineering. Machine learning, the branch of AI adopts probabilistic and statistical methods to learn from the past experience based upon the experimental output data set and detect the possible solution. In this paper, an overview of various machine learning algorithm used so far for the prediction of various problems such as optimization of process parameters, ranking of materials, validation is discussed. The process of design and optimization of the fibre reinforcement in polymer composites with distinguished properties has been redefined by the machine learning approach. This paper also highlights the role of machine learning algorithm, solution techniques and their data bases used in the different stages starting from the selection of raw materials to the end user application for the fiber reinforced polymer composites. This paper also supports readers to understand the future course of action to implement for the development of new product generation in an industry. At the end, a comparison has been made to understand the functionality of machine learning with respective to other technical tools used in the real-world problem.