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Publication: IIP Proceedings Year: 2022, Month: November Page No: 1-18, ISSN/ISBN: 978-93-95632-70-6, DOI/Link: https://rsquarel.org/assets/docupload/rsl202302F2F06C8FBB0DC.pdf
SUPERVISED LEARNING MODELS FOR THE PREDICTION OF MATERIAL PROPERTIES
Area/Stream: Artificial Intelligence, Authors: Arivarasi A, Gayathri S, Sathya Sree J Keywords: : supervised learning, material properties, prediction, application of machine learning Book Name /series: Futuristic Trends in Artificial Intelligence,Volume 2, Book 16, Chapter 1Publication: IIP Proceedings Year: 2022, Month: November Page No: 1-18, ISSN/ISBN: 978-93-95632-70-6, DOI/Link: https://rsquarel.org/assets/docupload/rsl202302F2F06C8FBB0DC.pdf
Abstract:
Machine learning (ML) techniques play a major role in engineering world. In this sequence the manufacturing industries also utilize the ML techniques for the various applications. Among them material properties prediction or forecasting is a noticeable process of manufactures using ML techniques. The ML techniques are broadly categorized into three types such as supervised; semi supervised and unsupervised learning techniques. The learning approach can be preferred based on the problem to solve using ML technique. In this chapter, the supervised learning for the prediction of material properties is presented. Initially the properties of materials and the necessity of ML technique for the prediction of material properties is described. Then four different supervised learning such as Random Forest (RF), Naive Bayesian (NB), Support Vector Machine (SVM), and Artificial Neural Network (ANN) are described for the prediction of material properties. Finally, the performance of these four techniques is evaluated based on accuracy. The performance analysis shows that the ANN with accuracy of 98% provides better than other techniques.
Cite this: Arivarasi A, Gayathri S, Sathya Sree J,"SUPERVISED LEARNING MODELS FOR THE PREDICTION OF MATERIAL PROPERTIES", Futuristic Trends in Artificial Intelligence,Volume 2, Book 16, Chapter 1, November, 2022, 1-18, 978-93-95632-70-6, https://rsquarel.org/assets/docupload/rsl202302F2F06C8FBB0DC.pdf
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