Predicting surface roughness with respect to process parameters using Regression Analysis Models in end milling

Predicting surface roughness with respect to process parameters using Regression Analysis Models in end milling

Surface roughness affects the functional attributes of finished parts. Therefore, predicting the finish surface is important to select the cutting levels in order to reach the required quality. In this research an experimental investigation was conducted to predict the surface roughness in the fi...

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Main Authors: Adesta, Erry Yulian Triblas, Al Hazza, Muataz Hazza Faizi, Suprianto, Mohamad Yuhan, Riza, Muhammad
Format: Article
Language:English
Published: Trans Tech Publications, Switzerland 2012
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://irep.iium.edu.my/29225/
http://irep.iium.edu.my/29225/
http://irep.iium.edu.my/29225/1/Predicting_Surface_Roughness_with_Respect_to_Process_Parameters.pdf
id iium-29225
recordtype eprints
spelling iium-292252013-05-30T05:11:37Z http://irep.iium.edu.my/29225/ Predicting surface roughness with respect to process parameters using Regression Analysis Models in end milling Adesta, Erry Yulian Triblas Al Hazza, Muataz Hazza Faizi Suprianto, Mohamad Yuhan Riza, Muhammad TA Engineering (General). Civil engineering (General) Surface roughness affects the functional attributes of finished parts. Therefore, predicting the finish surface is important to select the cutting levels in order to reach the required quality. In this research an experimental investigation was conducted to predict the surface roughness in the finish end milling process with higher cutting speed. Twenty sets of data for finish end milling on AISI H13 at hardness of 48 HRC have been collected based on five-level of Central Composite Design (CCD). All the experiments done by using indexable tool holder Sandvick Coromill R490 and the insert was PVD coated TiAlN carbide. The experimental work performed to predict four different roughness parameters; arithmetic mean roughness (Ra), total roughness (Rt), mean depth of roughness (Rz) and the root mean square (Rq). Trans Tech Publications, Switzerland 2012 Article PeerReviewed application/pdf en http://irep.iium.edu.my/29225/1/Predicting_Surface_Roughness_with_Respect_to_Process_Parameters.pdf Adesta, Erry Yulian Triblas and Al Hazza, Muataz Hazza Faizi and Suprianto, Mohamad Yuhan and Riza, Muhammad (2012) Predicting surface roughness with respect to process parameters using Regression Analysis Models in end milling. Advanced Materials Research, 576. pp. 99-102. ISSN 1022-6680 10.4028/www.scientific.net/AMR.576.99
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Adesta, Erry Yulian Triblas
Al Hazza, Muataz Hazza Faizi
Suprianto, Mohamad Yuhan
Riza, Muhammad
Predicting surface roughness with respect to process parameters using Regression Analysis Models in end milling
description Surface roughness affects the functional attributes of finished parts. Therefore, predicting the finish surface is important to select the cutting levels in order to reach the required quality. In this research an experimental investigation was conducted to predict the surface roughness in the finish end milling process with higher cutting speed. Twenty sets of data for finish end milling on AISI H13 at hardness of 48 HRC have been collected based on five-level of Central Composite Design (CCD). All the experiments done by using indexable tool holder Sandvick Coromill R490 and the insert was PVD coated TiAlN carbide. The experimental work performed to predict four different roughness parameters; arithmetic mean roughness (Ra), total roughness (Rt), mean depth of roughness (Rz) and the root mean square (Rq).
format Article
author Adesta, Erry Yulian Triblas
Al Hazza, Muataz Hazza Faizi
Suprianto, Mohamad Yuhan
Riza, Muhammad
author_facet Adesta, Erry Yulian Triblas
Al Hazza, Muataz Hazza Faizi
Suprianto, Mohamad Yuhan
Riza, Muhammad
author_sort Adesta, Erry Yulian Triblas
title Predicting surface roughness with respect to process parameters using Regression Analysis Models in end milling
title_short Predicting surface roughness with respect to process parameters using Regression Analysis Models in end milling
title_full Predicting surface roughness with respect to process parameters using Regression Analysis Models in end milling
title_fullStr Predicting surface roughness with respect to process parameters using Regression Analysis Models in end milling
title_full_unstemmed Predicting surface roughness with respect to process parameters using Regression Analysis Models in end milling
title_sort predicting surface roughness with respect to process parameters using regression analysis models in end milling
publisher Trans Tech Publications, Switzerland
publishDate 2012
url http://irep.iium.edu.my/29225/
http://irep.iium.edu.my/29225/
http://irep.iium.edu.my/29225/1/Predicting_Surface_Roughness_with_Respect_to_Process_Parameters.pdf
first_indexed 2023-09-18T20:42:53Z
last_indexed 2023-09-18T20:42:53Z
_version_ 1777409482489331712

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