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Improving precision in the prediction of laser texturing and surface interference of 316L assessed by neural network and adaptive neuro-fuzzy inference models

The International Journal of Advanced Manufacturing Technology volume 104, pages 4571–4580 (2019)Cite this article

Abstract

Laser-based surface texturing provides highly controlled interference fit between two parts. In this work, artificial intelligence-based models were used to predict the surface properties of laser processed stainless steel 316 samples. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict the characteristics of laser surface texturing. The models based on feedforward neural network (FFNN) were developed to examine the effect of the laser process parameters for surface texturing on 316L cylindrical pins. The accuracy of the models was measured by calculating the root mean square error and mean absolute error. The reliability of the ANFIS and FFNN models for the output prediction of the laser surface texturing (LST) system were investigated by using the data measured from experiments based on a 3^3 factorial design, with main processing parameters set as laser power, pulse repetition frequency, and percentage of laser spot overlap. The relative assessment of the models was performed by comparing percentage error prediction. Finally, the impact of input data was examined using predicted response surface plots. Results showed that ANFIS prediction was 48% more accurate compared with that provided by the FFNN model.

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Funding

This research is supported by a research grant from the Science Foundation Ireland (SFI) under Grant Number 16/RC/3872 and is co-funded under the European Regional Development Fund and by I-Form industry partners. This work is also supported by Irish Research Council Government of Ireland Scholarship.

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Authors and Affiliations

  1. Advanced Processing Technology Research Centre, Dublin City University, Dublin, Ireland

    H. Sohrabpoor, R. Taherzadeh Mousavian, M. Obeidi, I. U. Ahad & D. Brabazon

  2. I-Form, Advanced Manufacturing Research Centre, Dublin City University, Dublin, Ireland

    H. Sohrabpoor, R. Taherzadeh Mousavian, M. Obeidi, I. U. Ahad & D. Brabazon

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  1. H. Sohrabpoor
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  2. R. Taherzadeh Mousavian
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  3. M. Obeidi
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  4. I. U. Ahad
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  5. D. Brabazon
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Correspondence to H. Sohrabpoor.

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Sohrabpoor, H., Mousavian, R.T., Obeidi, M. et al. Improving precision in the prediction of laser texturing and surface interference of 316L assessed by neural network and adaptive neuro-fuzzy inference models. Int J Adv Manuf Technol 104, 4571–4580 (2019). https://doi.org/10.1007/s00170-019-04291-z

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  • DOI: https://doi.org/10.1007/s00170-019-04291-z

Keywords

  • Laser texturing
  • Artificial neural network
  • Adaptive neuro-fuzzy inference system