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

被引：0

作者：

Sivaraos ^{[1
]}

Khalim, A. Z. ^{[1
]}

Salleh, M. S. ^{[1
]}

Sivakumar, D. ^{[2
]}

Kadirgama, K. ^{[3
]}

机构：

[1] Univ Tech Malaysia Melaka, Fac Mfg Engn, Durian Tunggal, Melaka, Malaysia

[2] Univ Tech Malaysia Melaka, Fac Mech Engn, Durian Tunggal, Melaka, Malaysia

[3] Univ Malaysia Pahang, Fac Mech Engn, Pekan, Pahang, Malaysia

来源：

MALAYSIAN TECHNICAL UNIVERSITIES CONFERENCE ON ENGINEERING AND TECHNOLOGY 2017 (MUCET 2017) | 2018年 / 318卷

关键词：

IDENTIFICATION;

D O I：

10.1088/1757-899X/318/1/012066

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页数：8