Advances in nano research

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Application of adaptive neuro-fuzzy system in prediction of nanoscale and grain size effects on formability

  • Nan Yang (School of Architecture and Civil Engineering, Qiqihar University) ;
  • Meldi Suhatril (Department of Civil Engineering, Faculty of Engineering, Universiti Malaya) ;
  • Khidhair Jasim Mohammed (Air conditioning and Refrigeration Techniques Engineering Department, Al-Mustaqbal University College) ;
  • H. Elhosiny Ali (Department of Physics, Faculty of Science, King Khalid University)
  • Received : 2022.02.13
  • Accepted : 2022.08.29
  • Published : 2023.02.25
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Abstract

Grain size in sheet metals in one of the main parameters in determining formability. Grain size control in industry requires delicate process control and equipment. In the present study, effects of grain size on the formability of steel sheets is investigated. Experimental investigation of effect of grain size is a cumbersome method which due to existence of many other effective parameters are not conclusive in some cases. On the other hand, since the average grain size of a crystalline material is a statistical parameter, using traditional methods are not sufficient for find the optimum grain size to maximize formability. Therefore, design of experiment (DoE) and artificial intelligence (AI) methods are coupled together in this study to find the optimum conditions for formability in terms of grain size and to predict forming limits of sheet metals under bi-stretch loading conditions. In this regard, a set of experiment is conducted to provide initial data for training and testing DoE and AI. Afterwards, the using response surface method (RSM) optimum grain size is calculated. Moreover, trained neural network is used to predict formability in the calculated optimum condition and the results compared to the experimental results. The findings of the present study show that DoE and AI could be a great aid in the design, determination and prediction of optimum grain size for maximizing sheet formability.

Keywords

Acknowledgement

This research was funded by Scientific Research Project of Qiqihar University (145209130).

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