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Characteristics of machining data and machine learning models - A case study

Advancement of technologies in computing such as internet of things, cloud computing, and artificial intelligence drive manufacturing industries to adopt and implement automation in production. One of the key technologies or preferable methods to increase the productivity is implementing prediction...

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Main Authors: Natarajan, Elango, Fiorna, Vxynette, Al-Talib, Ammar Abdulaziz Majeed, Elango, Sangeetha, Gnanamuthu, Ezra Morris Abraham, Sarah Atifah, Saruchi
Format: Conference or Workshop Item
Language:English
English
Published: Institution of Engineering and Technology 2023
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
Online Access:http://umpir.ump.edu.my/id/eprint/41926/1/Characteristics%20of%20machining%20data%20and%20machine%20learning%20models.pdf
http://umpir.ump.edu.my/id/eprint/41926/2/Characteristics%20of%20machining%20data%20and%20machine%20learning%20models%20-%20A%20case%20study_ABS.pdf
http://umpir.ump.edu.my/id/eprint/41926/
https://doi.org/10.1049/icp.2023.1769
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spelling my.ump.umpir.419262024-08-30T00:17:57Z http://umpir.ump.edu.my/id/eprint/41926/ Characteristics of machining data and machine learning models - A case study Natarajan, Elango Fiorna, Vxynette Al-Talib, Ammar Abdulaziz Majeed Elango, Sangeetha Gnanamuthu, Ezra Morris Abraham Sarah Atifah, Saruchi T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Advancement of technologies in computing such as internet of things, cloud computing, and artificial intelligence drive manufacturing industries to adopt and implement automation in production. One of the key technologies or preferable methods to increase the productivity is implementing prediction models or machine learning (ML) algorithms in production. This article is aimed to show a comprehensive review on AI implementation in machining of materials, and to present methodology in prediction model development. The characteristic of experimental data and the key attributes in the model development are presented and discussed with a case study. Institution of Engineering and Technology 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41926/1/Characteristics%20of%20machining%20data%20and%20machine%20learning%20models.pdf pdf en http://umpir.ump.edu.my/id/eprint/41926/2/Characteristics%20of%20machining%20data%20and%20machine%20learning%20models%20-%20A%20case%20study_ABS.pdf Natarajan, Elango and Fiorna, Vxynette and Al-Talib, Ammar Abdulaziz Majeed and Elango, Sangeetha and Gnanamuthu, Ezra Morris Abraham and Sarah Atifah, Saruchi (2023) Characteristics of machining data and machine learning models - A case study. In: IET Conference Proceedings. 2023 International Conference on Green Energy, Computing and Intelligent Technology, GEn-CITy 2023 , 10 - 12 July 2023 , Hybrid, Iskandar Puteri. pp. 117-122., 2023 (11). ISSN 2732-4494 (Published) https://doi.org/10.1049/icp.2023.1769
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TS Manufactures
Natarajan, Elango
Fiorna, Vxynette
Al-Talib, Ammar Abdulaziz Majeed
Elango, Sangeetha
Gnanamuthu, Ezra Morris Abraham
Sarah Atifah, Saruchi
Characteristics of machining data and machine learning models - A case study
description Advancement of technologies in computing such as internet of things, cloud computing, and artificial intelligence drive manufacturing industries to adopt and implement automation in production. One of the key technologies or preferable methods to increase the productivity is implementing prediction models or machine learning (ML) algorithms in production. This article is aimed to show a comprehensive review on AI implementation in machining of materials, and to present methodology in prediction model development. The characteristic of experimental data and the key attributes in the model development are presented and discussed with a case study.
format Conference or Workshop Item
author Natarajan, Elango
Fiorna, Vxynette
Al-Talib, Ammar Abdulaziz Majeed
Elango, Sangeetha
Gnanamuthu, Ezra Morris Abraham
Sarah Atifah, Saruchi
author_facet Natarajan, Elango
Fiorna, Vxynette
Al-Talib, Ammar Abdulaziz Majeed
Elango, Sangeetha
Gnanamuthu, Ezra Morris Abraham
Sarah Atifah, Saruchi
author_sort Natarajan, Elango
title Characteristics of machining data and machine learning models - A case study
title_short Characteristics of machining data and machine learning models - A case study
title_full Characteristics of machining data and machine learning models - A case study
title_fullStr Characteristics of machining data and machine learning models - A case study
title_full_unstemmed Characteristics of machining data and machine learning models - A case study
title_sort characteristics of machining data and machine learning models - a case study
publisher Institution of Engineering and Technology
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/41926/1/Characteristics%20of%20machining%20data%20and%20machine%20learning%20models.pdf
http://umpir.ump.edu.my/id/eprint/41926/2/Characteristics%20of%20machining%20data%20and%20machine%20learning%20models%20-%20A%20case%20study_ABS.pdf
http://umpir.ump.edu.my/id/eprint/41926/
https://doi.org/10.1049/icp.2023.1769
_version_ 1822924596062453760
score 13.235362

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