Cover Image

Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization

—End milling process is widely used in various industrial applications, including health, aerospace and manufacturing industries. Over the years, machine technology of end milling has grown exponentially to attain the needs of various fields especially in manufacturing industry. The main concern of...

Full description

Saved in:
Bibliographic Details
Main Authors: Mat Deris, Ashanira, Ali, Rozniza, Ahmad Sabri, Ily Amalina, Zainal, Nurezayana
Format: Article
Language:English
Published: ijacsa 2024
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/11874/1/J17506_2cb6e65dff7bb76453771737108ea341.pdf
http://eprints.uthm.edu.my/11874/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uthm.eprints.11874
record_format eprints
spelling my.uthm.eprints.118742024-10-28T07:49:56Z http://eprints.uthm.edu.my/11874/ Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization Mat Deris, Ashanira Ali, Rozniza Ahmad Sabri, Ily Amalina Zainal, Nurezayana T Technology (General) —End milling process is widely used in various industrial applications, including health, aerospace and manufacturing industries. Over the years, machine technology of end milling has grown exponentially to attain the needs of various fields especially in manufacturing industry. The main concern of manufacturing industry is to obtain good quality products. The machined products quality is commonly correlated with the value of surface roughness (Ra), representing vital aspect that can influence overall machining performance. However, finding the optimal value of surface roughness is remain as a challenging task because it involves a lot of considerations on the cutting process especially the selection of suitable machining parameters and also cutting materials and workpiece. Hence, this study presents a support vector machine (SVM) prediction model to obtain the minimum Ra for end milling machining process. The prediction model was developed with three input parameters, namely feed rate, depth of cut and spindle speed, while Ra is the output parameter. The data of end milling is collected from the case studies based on the machining experimental with titanium alloy, workpiece and three types of cutting tools, namely uncoated carbide WC-Co (uncoated), common PVD-TiAlN (TiAlN) and Supernitride coating (SNTR). The prediction result has found that SVM is an effective prediction model by giving a better Ra value compared with experimental and regression results. ijacsa 2024 Article PeerReviewed text en http://eprints.uthm.edu.my/11874/1/J17506_2cb6e65dff7bb76453771737108ea341.pdf Mat Deris, Ashanira and Ali, Rozniza and Ahmad Sabri, Ily Amalina and Zainal, Nurezayana (2024) Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization. International Journal of Advanced Computer Science and Applications,, 15 (2). pp. 570-577.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Mat Deris, Ashanira
Ali, Rozniza
Ahmad Sabri, Ily Amalina
Zainal, Nurezayana
Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization
description —End milling process is widely used in various industrial applications, including health, aerospace and manufacturing industries. Over the years, machine technology of end milling has grown exponentially to attain the needs of various fields especially in manufacturing industry. The main concern of manufacturing industry is to obtain good quality products. The machined products quality is commonly correlated with the value of surface roughness (Ra), representing vital aspect that can influence overall machining performance. However, finding the optimal value of surface roughness is remain as a challenging task because it involves a lot of considerations on the cutting process especially the selection of suitable machining parameters and also cutting materials and workpiece. Hence, this study presents a support vector machine (SVM) prediction model to obtain the minimum Ra for end milling machining process. The prediction model was developed with three input parameters, namely feed rate, depth of cut and spindle speed, while Ra is the output parameter. The data of end milling is collected from the case studies based on the machining experimental with titanium alloy, workpiece and three types of cutting tools, namely uncoated carbide WC-Co (uncoated), common PVD-TiAlN (TiAlN) and Supernitride coating (SNTR). The prediction result has found that SVM is an effective prediction model by giving a better Ra value compared with experimental and regression results.
format Article
author Mat Deris, Ashanira
Ali, Rozniza
Ahmad Sabri, Ily Amalina
Zainal, Nurezayana
author_facet Mat Deris, Ashanira
Ali, Rozniza
Ahmad Sabri, Ily Amalina
Zainal, Nurezayana
author_sort Mat Deris, Ashanira
title Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization
title_short Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization
title_full Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization
title_fullStr Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization
title_full_unstemmed Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization
title_sort integrating taguchi method and support vector machine for enhanced surface roughness modeling and optimization
publisher ijacsa
publishDate 2024
url http://eprints.uthm.edu.my/11874/1/J17506_2cb6e65dff7bb76453771737108ea341.pdf
http://eprints.uthm.edu.my/11874/
_version_ 1814937237730623488
score 13.211869

Similar Items