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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


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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  1. Murcinkova Z, Baron P, Pollak M (2018) Study of the press fit bearing-shaft joint dimensional parameters by analytical and numerical approach. Adv Mater Sci Eng

  2. Obeidi M, McCarthy E, Kailas L, Brabazon D (2018) Laser surface texturing of stainless steel 316L cylindrical pins for interference fit applications. J Mater Process Technol 252:58–68

    Article  Google Scholar 

  3. Hüyük H, Music O, Koç A, Kardogan C, Bayram C (2014) Analysis of elastic-plastic interference-fit joints. Procedia Engineering 81:2030–2035

    Article  Google Scholar 

  4. H. Sohrabpoor, A. Issa, A. Hamaoy, I. Ahad, E. Chikarakara, K. Bagga, D. Brabazon, Development of laser processing technologies via experimental design, Chapter 24, pp. 707–730, 2nd edn, 2017

    Google Scholar 

  5. Aminian M, Teimouri R (2015) Application of soft computing techniques for modeling and analysis of MRR and taper in laser machining process as well as weld strength and weld width in laser welding process. Soft Comput 19:793–810

    Article  Google Scholar 

  6. Biswas A, Rajat S, Gupta R (2018) Application of artificial neural network for performance evaluation of vertical axis wind turbine rotor. Int J Ambient Energy 37(2):1–10

    Google Scholar 

  7. sohrabpoor H (2016) Analysis of laser powder deposition parameters: ANFIS modeling and ICA optimization. Optik 127(8):4031–4038

    Article  Google Scholar 

  8. Sohrabpoor H, Negi S, Shaiesteh H, Ahad IU, Brabazon D (2018) Optimizing selective laser sintering process by grey relational analysis and soft computing techniques. Optik 174:185–194

    Article  Google Scholar 

  9. Umrao R, Sharma L, Singh R, Singh T (2018) Determination of strength and modulus of elasticity of heterogenous sedimentary rocks: An ANFIS predictive technique. Measurement 126:194–201

    Article  Google Scholar 

  10. Teimouri R, Shrabpoor H (2013) Application of adaptive neuro-fuzzy inference system and cuckoo optimization algorithm for analyzing electro chemical machining process. Front Mech Eng 8(4):429–442

    Article  Google Scholar 

  11. Gholami A, Bonakdari H, Ebtehaj I, Mohammadi M, Gharabaghi B, Khodashenase S (2018) Uncertainty analysis of intelligent model of hybrid genetic algorithm and particle swarm optimization with ANFIS to predict threshold bank profile shape based on digital laser approach sensing. Measurement 121:294–303

    Article  Google Scholar 

  12. Fister I, Perc M, Kamal S, Fister I (2015) A review of chaos-based firefly algorithms: Perspectives and research challenges. Appl Math Comput 252(1):155–165

    MathSciNet  MATH  Google Scholar 

  13. Pandremenos J, Chryssolouris G (2011) A neural network approach for the development of modular product architectures. Int J Comput Integr Manuf 24(10):879–887

    Article  Google Scholar 

  14. Karagiannis S, Stavropoulos P, Ziogas C, Kechagias J (2013) Prediction of surface roughness magnitude in computer numerical controlled end milling processes using neural networks, by considering a set of influence parameters: an aluminium alloy 5083 case study. Proc Inst Mech Eng B J Eng Manuf 228(2):233–244

    Article  Google Scholar 

  15. Ojha V, Abraham A, Snášel V (2017) Metaheuristic design of feedforward neural networks: a review of two decades of research. Eng Appl Artif Intell 60:97–116

    Article  Google Scholar 

  16. Baseri H, Damirchi H (2011) Rediction of the ferrite-Core probe performance using a neural network approach. Mater Manuf Process

  17. Shamsipour M, Pahlevani Z, Ostad M, Mazahery S (2016) Optimization of the EMS process parameters in compocasting of high-wear-resistant Al-nano-TiC composites. Appl Phys A 122

  18. Acı M (2016) Artificial neural network approach for atomic coordinate prediction of carbon nanotubes. Appl Phys A 122

  19. Obeidi MA, McCarthy E, Brabazon D (2016) Methodology of laser processing for precise control of surface micro-topology. Surf Coat Technol 307(Part A)

  20. Tsamardinos I, Greasidou E, Borboudakis G (2018) Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation. Mach Learn 107(12):1895–1922

    MathSciNet  Article  Google Scholar 

  21. Yurdakul M, Tansel İÇ Y (2009) Application of correlation test to criteria selection for multi criteria decision making (MCDM) models. Int J Adv Manuf Technol 40:403–412

    Article  Google Scholar 

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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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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).

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