Development of surface roughness prediction model for high speed end milling of hardened tool steel
The quality of the surface plays a very important role performance of milling as a good-quality milled surface in a variety of manufacturing industries including the aerospace and automotive sectors where good quality surface significantly improves fatigue strength, corrosion resistance, or creep li...
| Principais autores: | , , , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
Asian Network for Scientific Information
2011
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| Assuntos: | |
| Acesso em linha: | http://irep.iium.edu.my/7367/1/AJAS_255-263.pdf |
| _version_ | 1825644898010791936 |
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| author | Mohd Ali , Afifah Adesta, Erry Yulian Triblas Agusman, Delvis Mohamad Badari, Siti Norbahiyah Al Hazza, Muataz Hazza Faizi |
| author_facet | Mohd Ali , Afifah Adesta, Erry Yulian Triblas Agusman, Delvis Mohamad Badari, Siti Norbahiyah Al Hazza, Muataz Hazza Faizi |
| author_sort | Mohd Ali , Afifah |
| collection | IIUM |
| description | The quality of the surface plays a very important role performance of milling as a good-quality milled surface in a variety of manufacturing industries including the aerospace and automotive sectors where good quality surface significantly improves fatigue strength, corrosion resistance, or creep life. This study discussed the issue of surface machined quality and the effort taken to predict surface roughness. For thus purpose , hardened materials AISI H13 tool steel with hardness of 48 Rockwell Hardness (HRC) was chosen for work material. Machining was done at High Cutting speed (Vc) from 150 up to 250 m/min, feedrate (Vf) 0005-0.15 mm/rev and depth of cut (DOC) 0.1-0.5mm. The analysisi and observation of the surface roughness were done by using optical surface roughness machine. Response Surface Methodology (RSM) Model was used to design the prediction model with parameters generated by using Central Composite Face (CCF) methods. A prediction model developed with 90% accuracy with the conclusion of feedrate as the main contributor to surface roughness followed by cutting speed. Therefore, RSM has been proven to be an efficient method to predict the surface finish during end-milling of H13 tool steel using TiAIN coated carbide tool inserts under dry conditions. |
| first_indexed | 2024-03-05T22:39:37Z |
| format | Article |
| id | oai:generic.eprints.org:7367 |
| institution | International Islamic University Malaysia |
| language | English |
| last_indexed | 2024-03-05T22:39:37Z |
| publishDate | 2011 |
| publisher | Asian Network for Scientific Information |
| record_format | dspace |
| spelling | oai:generic.eprints.org:73672013-05-29T07:06:32Z http://irep.iium.edu.my/7367/ Development of surface roughness prediction model for high speed end milling of hardened tool steel Mohd Ali , Afifah Adesta, Erry Yulian Triblas Agusman, Delvis Mohamad Badari, Siti Norbahiyah Al Hazza, Muataz Hazza Faizi TS200 Metal manufactures. Metalworking The quality of the surface plays a very important role performance of milling as a good-quality milled surface in a variety of manufacturing industries including the aerospace and automotive sectors where good quality surface significantly improves fatigue strength, corrosion resistance, or creep life. This study discussed the issue of surface machined quality and the effort taken to predict surface roughness. For thus purpose , hardened materials AISI H13 tool steel with hardness of 48 Rockwell Hardness (HRC) was chosen for work material. Machining was done at High Cutting speed (Vc) from 150 up to 250 m/min, feedrate (Vf) 0005-0.15 mm/rev and depth of cut (DOC) 0.1-0.5mm. The analysisi and observation of the surface roughness were done by using optical surface roughness machine. Response Surface Methodology (RSM) Model was used to design the prediction model with parameters generated by using Central Composite Face (CCF) methods. A prediction model developed with 90% accuracy with the conclusion of feedrate as the main contributor to surface roughness followed by cutting speed. Therefore, RSM has been proven to be an efficient method to predict the surface finish during end-milling of H13 tool steel using TiAIN coated carbide tool inserts under dry conditions. Asian Network for Scientific Information 2011 Article PeerReviewed application/pdf en http://irep.iium.edu.my/7367/1/AJAS_255-263.pdf Mohd Ali , Afifah and Adesta, Erry Yulian Triblas and Agusman, Delvis and Mohamad Badari, Siti Norbahiyah and Al Hazza, Muataz Hazza Faizi (2011) Development of surface roughness prediction model for high speed end milling of hardened tool steel. Asian Journal of Scientific Research, 4 (3). pp. 255-263. ISSN 1992-1454 http://www.doaj.org/doaj?func=openurl&issn=19921454&genre=journal DOI : 10.3923/ajsr.2011.255.263 |
| spellingShingle | TS200 Metal manufactures. Metalworking Mohd Ali , Afifah Adesta, Erry Yulian Triblas Agusman, Delvis Mohamad Badari, Siti Norbahiyah Al Hazza, Muataz Hazza Faizi Development of surface roughness prediction model for high speed end milling of hardened tool steel |
| title | Development of surface roughness prediction model for high speed end milling of hardened tool steel |
| title_full | Development of surface roughness prediction model for high speed end milling of hardened tool steel |
| title_fullStr | Development of surface roughness prediction model for high speed end milling of hardened tool steel |
| title_full_unstemmed | Development of surface roughness prediction model for high speed end milling of hardened tool steel |
| title_short | Development of surface roughness prediction model for high speed end milling of hardened tool steel |
| title_sort | development of surface roughness prediction model for high speed end milling of hardened tool steel |
| topic | TS200 Metal manufactures. Metalworking |
| url | http://irep.iium.edu.my/7367/1/AJAS_255-263.pdf |
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