HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API-Reference-Cited by-同舟云学术

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HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API

Published:2023-12-30 Issue: Volume:45 Page:1-4
ISSN:
Container-title:2023 International Conference on Electrical, Communication and Computer Engineering (ICECCE)
language:
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Author:

Olanipekun Ayorinde Tayo¹,Mashao Daniel²

Affiliation:

1. Institute for the Future of Knowledge, Faculty of Engineering and the Built Environment, University of Johannesburg,Data Science Across Disciplines Research Group,South Africa

2. University of Johannesburg,Faculty of Engineering and the Built Environment,South Africa

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10441786/10441790/10442446.pdf?arnumber=10442446

Reference23 articles.

1. Development of Vickers hardness prediction models via microstructural analysis and machine learning

2. An Evaluation of Mechanical Properties with the Hardness of Building Steel Structural Members for Reuse by NDT

3. MechElastic: A Python library for analysis of mechanical and elastic properties of bulk and 2D materials;Singh,2021

4. An materials informatics approach to Ni-based single crystal superalloys lattice misfit prediction

5. A strategy to apply machine learning to small datasets in materials science

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