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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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institution of Engineering and Technology
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/41926/ 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 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:45:20Z |
| format | Conference or Workshop Item |
| id | ump-41926 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:45:20Z |
| publishDate | 2023 |
| publisher | Institution of Engineering and Technology |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 |
| 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 |
| title | 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_short | Characteristics of machining data and machine learning models - A case study |
| title_sort | characteristics of machining data and machine learning models - a case study |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/41926/ http://umpir.ump.edu.my/id/eprint/41926/ 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 |