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

Ayorinde Tayo Olanipekun; Daniel Mashao

doi:10.1109/ICECCE61019.2023.10442446

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

Ayorinde Tayo Olanipekun
, Daniel Mashao

University of Johannesburg

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This research presents the HardnessTesterV app- a web application for predicting the Vicker hardness of Laser welded Metallic alloy using Flask API, HTML, and CSS to build the front end and back end. Vickers hardness is an important property in the manufacturing industry, and predicting it accurately is vital for material selection and design. The Web application takes some of the important processing parameters as the input features and uses a random forest Machine learning model to predict its Vickers hardness. Flask API was used to create a RESTful interface that provides a user-friendly platform for users to submit the combinations of the processing parameters and receive predictions. HTML and CSS were used to design the front end of the web application to provide a visually appealing interface for users. The application was deployed on a PythonAnywhere cloud server, making it easily accessible to users. The application was deployed on a cloud server, making it easily accessible to users worldwide. The accuracy of the predictions was evaluated using various metrics and the results showed that the developed model can accurately predict the Vickers hardness of metallic alloys. HardnessTesterV web app measures the Vickers hardness of 2507 Duplex stainless steel material. The web application is expected to be a valuable tool for engineers and researchers in the manufacturing industry for material selection and design.

Original language	English
Title of host publication	4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350369694
DOIs	https://doi.org/10.1109/ICECCE61019.2023.10442446
Publication status	Published - 2023
Externally published	Yes
Event	4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023 - Dubai, United Arab Emirates Duration: 30 Dec 2023 → 31 Dec 2023

Publication series

Name	4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023

Conference

Conference	4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023
Country/Territory	United Arab Emirates
City	Dubai
Period	30/12/23 → 31/12/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Flask API
Vicker Hardness
Web applications
machine learning app
mechanical properties

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Signal Processing
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Instrumentation

Access to Document

10.1109/ICECCE61019.2023.10442446

Cite this

Olanipekun, A. T., & Mashao, D. (2023). HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API. In 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023 (4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECCE61019.2023.10442446

Olanipekun, Ayorinde Tayo ; Mashao, Daniel. / HardnessTesterV : A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API. 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023).

@inproceedings{dd6a1ba9061c4b79b4821bd934192b7c,

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

abstract = "This research presents the HardnessTesterV app- a web application for predicting the Vicker hardness of Laser welded Metallic alloy using Flask API, HTML, and CSS to build the front end and back end. Vickers hardness is an important property in the manufacturing industry, and predicting it accurately is vital for material selection and design. The Web application takes some of the important processing parameters as the input features and uses a random forest Machine learning model to predict its Vickers hardness. Flask API was used to create a RESTful interface that provides a user-friendly platform for users to submit the combinations of the processing parameters and receive predictions. HTML and CSS were used to design the front end of the web application to provide a visually appealing interface for users. The application was deployed on a PythonAnywhere cloud server, making it easily accessible to users. The application was deployed on a cloud server, making it easily accessible to users worldwide. The accuracy of the predictions was evaluated using various metrics and the results showed that the developed model can accurately predict the Vickers hardness of metallic alloys. HardnessTesterV web app measures the Vickers hardness of 2507 Duplex stainless steel material. The web application is expected to be a valuable tool for engineers and researchers in the manufacturing industry for material selection and design.",

keywords = "Flask API, Vicker Hardness, Web applications, machine learning app, mechanical properties",

author = "Olanipekun, \{Ayorinde Tayo\} and Daniel Mashao",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023 ; Conference date: 30-12-2023 Through 31-12-2023",

year = "2023",

doi = "10.1109/ICECCE61019.2023.10442446",

language = "English",

series = "4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Olanipekun, AT & Mashao, D 2023, HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API. in 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023. 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023, Institute of Electrical and Electronics Engineers Inc., 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023, Dubai, United Arab Emirates, 30/12/23. https://doi.org/10.1109/ICECCE61019.2023.10442446

HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API. / Olanipekun, Ayorinde Tayo; Mashao, Daniel.
4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - HardnessTesterV

T2 - 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023

AU - Olanipekun, Ayorinde Tayo

AU - Mashao, Daniel

PY - 2023

Y1 - 2023

N2 - This research presents the HardnessTesterV app- a web application for predicting the Vicker hardness of Laser welded Metallic alloy using Flask API, HTML, and CSS to build the front end and back end. Vickers hardness is an important property in the manufacturing industry, and predicting it accurately is vital for material selection and design. The Web application takes some of the important processing parameters as the input features and uses a random forest Machine learning model to predict its Vickers hardness. Flask API was used to create a RESTful interface that provides a user-friendly platform for users to submit the combinations of the processing parameters and receive predictions. HTML and CSS were used to design the front end of the web application to provide a visually appealing interface for users. The application was deployed on a PythonAnywhere cloud server, making it easily accessible to users. The application was deployed on a cloud server, making it easily accessible to users worldwide. The accuracy of the predictions was evaluated using various metrics and the results showed that the developed model can accurately predict the Vickers hardness of metallic alloys. HardnessTesterV web app measures the Vickers hardness of 2507 Duplex stainless steel material. The web application is expected to be a valuable tool for engineers and researchers in the manufacturing industry for material selection and design.

AB - This research presents the HardnessTesterV app- a web application for predicting the Vicker hardness of Laser welded Metallic alloy using Flask API, HTML, and CSS to build the front end and back end. Vickers hardness is an important property in the manufacturing industry, and predicting it accurately is vital for material selection and design. The Web application takes some of the important processing parameters as the input features and uses a random forest Machine learning model to predict its Vickers hardness. Flask API was used to create a RESTful interface that provides a user-friendly platform for users to submit the combinations of the processing parameters and receive predictions. HTML and CSS were used to design the front end of the web application to provide a visually appealing interface for users. The application was deployed on a PythonAnywhere cloud server, making it easily accessible to users. The application was deployed on a cloud server, making it easily accessible to users worldwide. The accuracy of the predictions was evaluated using various metrics and the results showed that the developed model can accurately predict the Vickers hardness of metallic alloys. HardnessTesterV web app measures the Vickers hardness of 2507 Duplex stainless steel material. The web application is expected to be a valuable tool for engineers and researchers in the manufacturing industry for material selection and design.

KW - Flask API

KW - Vicker Hardness

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KW - mechanical properties

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DO - 10.1109/ICECCE61019.2023.10442446

M3 - Conference contribution

AN - SCOPUS:85187216819

T3 - 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023

BT - 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 30 December 2023 through 31 December 2023

ER -

Olanipekun AT, Mashao D. HardnessTesterV: A Web Machine Learning application for Vickers Hardness Prediction of a Metallic Alloy Using Flask API. In 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023). doi: 10.1109/ICECCE61019.2023.10442446

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

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this