Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant
Grinding is often an important finishing process for many engineering components and for some components it is even a major production process. In this study, prediction model have been developed to find the effect of grinding condition in term of depth of cut and type of grinding coolant. Zinc Oxid...
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| Format: | Article |
| Language: | English |
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Iceland Journal of Life Sciences
2014
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| Online Access: | http://umpir.ump.edu.my/id/eprint/5271/ http://umpir.ump.edu.my/id/eprint/5271/1/paper.pdf |
| _version_ | 1848817565211557888 |
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| author | K., Kadirgama M., Yogeswaran S. , Thiruchelvam M. M., Rahman |
| author_facet | K., Kadirgama M., Yogeswaran S. , Thiruchelvam M. M., Rahman |
| author_sort | K., Kadirgama |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Grinding is often an important finishing process for many engineering components and for some components it is even a major production process. In this study, prediction model have been developed to find the effect of grinding condition in term of depth of cut and type of grinding coolant. Zinc Oxide (ZnO) nano-coolant was used as a coolant with water as a based liquid. The experiments
conducted with grinding depth in the range of 5 to 21μm. Silicon Carbide wheel are used to grind the AISI P20 tool work piece. Artificial intelligence model has been developed using Artificial Neural Network(ANN). Result shows that the lower surface roughness and wheel wear obtain at the lowest cutting depth which is 5 μm. Besides that, grind using ZnO nano-coolant gives best surface roughness and minimum wheel wears compared to grind using normal soluble coolant. The surface roughness have been reduced approximately 47.84% for single pass experiment and 126.1% for multi pass experiment. However, there is no wheel wheel wear obtain for grinding using ZnO nanocoolant. From the prediction of ANN, it can predict the surface roughness closely with the experimental value. |
| first_indexed | 2025-11-15T01:23:47Z |
| format | Article |
| id | ump-5271 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T01:23:47Z |
| publishDate | 2014 |
| publisher | Iceland Journal of Life Sciences |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-52712018-01-25T03:16:13Z http://umpir.ump.edu.my/id/eprint/5271/ Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant K., Kadirgama M., Yogeswaran S. , Thiruchelvam M. M., Rahman TJ Mechanical engineering and machinery Grinding is often an important finishing process for many engineering components and for some components it is even a major production process. In this study, prediction model have been developed to find the effect of grinding condition in term of depth of cut and type of grinding coolant. Zinc Oxide (ZnO) nano-coolant was used as a coolant with water as a based liquid. The experiments conducted with grinding depth in the range of 5 to 21μm. Silicon Carbide wheel are used to grind the AISI P20 tool work piece. Artificial intelligence model has been developed using Artificial Neural Network(ANN). Result shows that the lower surface roughness and wheel wear obtain at the lowest cutting depth which is 5 μm. Besides that, grind using ZnO nano-coolant gives best surface roughness and minimum wheel wears compared to grind using normal soluble coolant. The surface roughness have been reduced approximately 47.84% for single pass experiment and 126.1% for multi pass experiment. However, there is no wheel wheel wear obtain for grinding using ZnO nanocoolant. From the prediction of ANN, it can predict the surface roughness closely with the experimental value. Iceland Journal of Life Sciences 2014 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5271/1/paper.pdf K., Kadirgama and M., Yogeswaran and S. , Thiruchelvam and M. M., Rahman (2014) Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant. Jokull Journal, 66 (5). pp. 1-15. ISSN 0449-0576. (Published) http://jokulljournal.com/index.html |
| spellingShingle | TJ Mechanical engineering and machinery K., Kadirgama M., Yogeswaran S. , Thiruchelvam M. M., Rahman Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant |
| title | Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant |
| title_full | Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant |
| title_fullStr | Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant |
| title_full_unstemmed | Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant |
| title_short | Prediction of Grinding Machinability when Grind P20 Tool Steel Using Water Based Zno Nano-Coolant |
| title_sort | prediction of grinding machinability when grind p20 tool steel using water based zno nano-coolant |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/5271/ http://umpir.ump.edu.my/id/eprint/5271/ http://umpir.ump.edu.my/id/eprint/5271/1/paper.pdf |