Big Data Mining and Classification of Intelligent Material Science Data Using Machine Learning

There is a high need for a big data repository for material compositions and their derived analytics of metal strength, in the material science community. Currently, many researchers maintain their own excel sheets, prepared manually by their team by tabulating the experimental data collected from s...

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Bibliographic Details
Published in:Applied sciences Vol. 11; no. 18; p. 8596
Main Authors: Chittam, Swetha, Gokaraju, Balakrishna, Xu, Zhigang, Sankar, Jagannathan, Roy, Kaushik
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2021
Subjects:
Algorithms
Alloys
Automation
Big Data
Classification
classification algorithms
Data analysis
Data mining
Data models
Datasets
Ductility
Electronic health records
Literature reviews
logistic regression
Machine learning
Metals
mongodb
No-SQL database
Science
Structured Query Language-SQL
support vector machine SVM
Support vector machines
Tensile strength
Variables
Yield stress
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:There is a high need for a big data repository for material compositions and their derived analytics of metal strength, in the material science community. Currently, many researchers maintain their own excel sheets, prepared manually by their team by tabulating the experimental data collected from scientific journals, and analyzing the data by performing manual calculations using formulas to determine the strength of the material. In this study, we propose a big data storage for material science data and its processing parameters information to address the laborious process of data tabulation from scientific articles, data mining techniques to retrieve the information from databases to perform big data analytics, and a machine learning prediction model to determine material strength insights. Three models are proposed based on Logistic regression, Support vector Machine SVM and Random Forest Algorithms. These models are trained and tested using a 10-fold cross validation approach. The Random Forest classification model performed better on the independent dataset, with 87% accuracy in comparison to Logistic regression and SVM with 72% and 78%, respectively.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app11188596