Modeling of Surface Roughness in Turning Operation Using Extreme Learning Machine

Nooraziah Ahmad, Tiagrajah V. Janahiraman, Faris Tarlochan

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Prediction model allows the machinist to determine the values of the cutting performance before machining. According to the literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Response surface methodology (RSM) is a statistical method that only predicts effectively within the observed data provided. Most artificial intelligent systems mostly had an issue with user-defined data and long processing time. Recently, the extreme learning machine (ELM) method has been introduced, combining the single hidden layer feed- forward neural network with analytically determined output weights. The advantage of this method is that it can overcome the limitations due to the previous methods which include too many engineers’ judgment and slow iterative learning phase. Therefore, in this study, the ELM was proposed to model the surface roughness based on RSM design of experiment. The results indicate that ELM can yield satisfactory solution for predicting the response within a few seconds and with small amount of error.

Original languageEnglish
Pages (from-to)595-602
Number of pages8
JournalArabian Journal for Science and Engineering
Volume40
Issue number2
DOIs
Publication statusPublished - 01 Jan 2014

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