Predicting surface roughness in turning operation using extreme learning machine

Ahmad Nooraziah, Tiagrajah V. Janahiraman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Prediction model allows the machinist to determine the values of the cutting performance before machining. According to literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Recently, Extreme Learning Machine (ELM) has been introduced as the alternative to overcome the limitation from the previous methods. ELM has similar structure as single hidden layer feedforward neural network with analytically to determine output weight. By comparing to Response Surface Methodology, Support Vector Machine and Neural Network, this paper proposed the prediction of surface roughness using ELM method. The result indicates that ELM can yield satisfactory solution for predicting surface roughness in term of training speed and parameter selection.

Original languageEnglish
Title of host publicationMechanical and Materials Engineering
PublisherTrans Tech Publications Ltd
Number of pages5
ISBN (Print)9783038351092
Publication statusPublished - 01 Jan 2014

Publication series

NameApplied Mechanics and Materials
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482


All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Nooraziah, A., & V. Janahiraman, T. (2014). Predicting surface roughness in turning operation using extreme learning machine. In Mechanical and Materials Engineering (pp. 431-435). (Applied Mechanics and Materials; Vol. 554). Trans Tech Publications Ltd.