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Modeling of Surface Roughness in Turning Operation Using Extreme Learning Machine

  • Research Article - Mechanical Engineering
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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.

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Authors and Affiliations

  1. College of Engineering, Universiti Tenaga Nasiona, Jln IKRAM-UNITEN, 43000, Kajang, Selangor, Malaysia

    Nooraziah Ahmad

  2. Department of Electronics and Communication Engineering, Centre for Signal Processing and Control Systems, College of Engineering, Universiti Tenaga Nasional, Jln IKRAM-UNITEN, 43000, Kajang, Selangor, Malaysia

    Tiagrajah V. Janahiraman

  3. College of Engineering, Qatar University, Al Tarfa, Doha, 2713, Qatar

    Faris Tarlochan

Authors
  1. Nooraziah Ahmad
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  2. Tiagrajah V. Janahiraman
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  3. Faris Tarlochan
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Correspondence to Nooraziah Ahmad.

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Ahmad, N., Janahiraman, T.V. & Tarlochan, F. Modeling of Surface Roughness in Turning Operation Using Extreme Learning Machine. Arab J Sci Eng 40, 595–602 (2015). https://doi.org/10.1007/s13369-014-1420-0

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  • DOI: https://doi.org/10.1007/s13369-014-1420-0

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