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Fuzzy regression integrated with genetic-tabu algorithm for prediction and optimization of a turning process

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Abstract

Prediction of surface roughness is a key element for an automated machining center. In this regard, it is important to optimize the machining process. In this paper, fuzzy linear regression approach is employed to predict the surface roughness for a turning process in an uncertain condition. The important process parameters such as cutting speed, cutting depth, speed, and tool tip radius are considered as inputs to determine their significance for prediction. To handle uncertainty, fuzzy theory is employed. Thus, fuzzy liner regression is modeled. To optimize the estimated values of prediction errors, a genetic algorithm (GA) is developed. In addition, tabu search is used to facilitate GA for better performance. A numerical example is worked out to show the effectiveness of the proposed method.

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Authors and Affiliations

  1. Department of Industrial Engineering, Mazandaran University of Science and Technology, Babol, Iran

    Hadi Gholizadeh & Nikbakhsh Javadian

  2. Department of Industrial Engineering, College of Engineering, Damghan University, Damghan, Iran

    Hamed Fazlollahtabar

Authors
  1. Hadi Gholizadeh
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  2. Nikbakhsh Javadian
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  3. Hamed Fazlollahtabar
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Correspondence to Nikbakhsh Javadian.

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Gholizadeh, H., Javadian, N. & Fazlollahtabar, H. Fuzzy regression integrated with genetic-tabu algorithm for prediction and optimization of a turning process. Int J Adv Manuf Technol 96, 2781–2790 (2018). https://doi.org/10.1007/s00170-018-1655-0

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  • DOI: https://doi.org/10.1007/s00170-018-1655-0

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