Improving precision in the prediction of laser texturing and surface interference of 316L assessed by neural network and adaptive neuro-fuzzy inference models
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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.
KeywordsLaser texturing Artificial neural network Adaptive neuro-fuzzy inference system
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.
- 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 EngGoogle 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, 2017Google 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):1–10Google 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 121:294–303CrossRefGoogle 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):233–244CrossRefGoogle Scholar
- 16.Baseri H, Damirchi H (2011) Rediction of the ferrite-Core probe performance using a neural network approach. Mater Manuf ProcessGoogle Scholar
- 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 122Google Scholar
- 18.Acı M (2016) Artificial neural network approach for atomic coordinate prediction of carbon nanotubes. Appl Phys A 122Google Scholar
- 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)Google Scholar