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

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)

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Abstract

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.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: Sugeno-Fuzzy Expert System Modeling; non-linear laser lathing process
Subjects: T Technology > TJ Mechanical engineering and machinery
Faculty/Division: Faculty of Mechanical Engineering
Depositing User: Dr. Kumaran Kadirgama
Date Deposited: 25 Jan 2019 01:13
Last Modified: 25 Jan 2019 01:13
URI: http://umpir.ump.edu.my/id/eprint/23924
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