Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process

Modeling can be categorised into four main domains: prediction, optimisation, estimation and calibration. In this paper, the Takagi-Sugeno-Kang (TSK) fuzzy logic method is examined as a prediction modelling method to investigate the taper quality of laser lathing, which seeks to replace traditional...

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Main Authors:	Sivaraos, ., Khalim, A. Z., Salleh, M. S., D., Sivakumar, K., Kadirgama
Format:	Conference or Workshop Item
Language:	English
Published:	IOP Publishing 2018
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/23924/ http://umpir.ump.edu.my/id/eprint/23924/ http://umpir.ump.edu.my/id/eprint/23924/1/Sivaraos_2018_IOP_Conf._Ser.__Mater._Sci._Eng._318_012066.pdf

id	ump-23924
recordtype	eprints
spelling	ump-239242019-01-25T01:13:53Z http://umpir.ump.edu.my/id/eprint/23924/ Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process Sivaraos, . Khalim, A. Z. Salleh, M. S. D., Sivakumar K., Kadirgama TJ Mechanical engineering and machinery Modeling can be categorised into four main domains: prediction, optimisation, estimation and calibration. In this paper, the Takagi-Sugeno-Kang (TSK) fuzzy logic method is examined as a prediction modelling method to investigate the taper quality of laser lathing, which seeks to replace traditional lathe machines with 3D laser lathing in order to achieve the desired cylindrical shape of stock materials. Three design parameters were selected: feed rate, cutting speed and depth of cut. A total of twenty-four experiments were conducted with eight sequential runs and replicated three times. The results were found to be 99% of accuracy rate of the TSK fuzzy predictive model, which suggests that the model is a suitable and practical method for non-linear laser lathing process. IOP Publishing 2018 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/23924/1/Sivaraos_2018_IOP_Conf._Ser.__Mater._Sci._Eng._318_012066.pdf Sivaraos, . and Khalim, A. Z. and Salleh, M. S. and D., Sivakumar and K., Kadirgama (2018) Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process. In: IOP Conference Series: Materials Science and Engineering, Malaysian Technical Universities Conference on Engineering and Technology 2017 (MUCET 2017), 6-7 December 2017 , Penang, Malaysia. pp. 1-8., 318 (012066). ISSN 1757-8981 (Print), 1757-899X (Online) https://doi.org/10.1088/1757-899X/318/1/012066
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Sivaraos, . Khalim, A. Z. Salleh, M. S. D., Sivakumar K., Kadirgama Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process
description	Modeling can be categorised into four main domains: prediction, optimisation, estimation and calibration. In this paper, the Takagi-Sugeno-Kang (TSK) fuzzy logic method is examined as a prediction modelling method to investigate the taper quality of laser lathing, which seeks to replace traditional lathe machines with 3D laser lathing in order to achieve the desired cylindrical shape of stock materials. Three design parameters were selected: feed rate, cutting speed and depth of cut. A total of twenty-four experiments were conducted with eight sequential runs and replicated three times. The results were found to be 99% of accuracy rate of the TSK fuzzy predictive model, which suggests that the model is a suitable and practical method for non-linear laser lathing process.
format	Conference or Workshop Item
author	Sivaraos, . Khalim, A. Z. Salleh, M. S. D., Sivakumar K., Kadirgama
author_facet	Sivaraos, . Khalim, A. Z. Salleh, M. S. D., Sivakumar K., Kadirgama
author_sort	Sivaraos, .
title	Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process
title_short	Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process
title_full	Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process
title_fullStr	Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process
title_full_unstemmed	Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process
title_sort	sugeno-fuzzy expert system modeling for quality prediction of non-contact machining process
publisher	IOP Publishing
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/23924/ http://umpir.ump.edu.my/id/eprint/23924/ http://umpir.ump.edu.my/id/eprint/23924/1/Sivaraos_2018_IOP_Conf._Ser.__Mater._Sci._Eng._318_012066.pdf
first_indexed	2023-09-18T22:36:02Z
last_indexed	2023-09-18T22:36:02Z
_version_	1777416601881018368

Sugeno-Fuzzy Expert System Modeling for Quality Prediction of Non-Contact Machining Process

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