Predicting mechanical properties of elastomeric modified nylon blend using adaptive neuro-fuzzy interference system and neural network

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• 作者：M. Ghatarband (1)
  Z. A. Asadi (2)
  S. Mazinani (3)
  M. R. Kalaee (4)
  M. E. Shiri (5)
  
  1. Department of Polymer Engineering ; Islamic Azad University ; South Tehran Branch ; 1777613651 ; Tehran ; Iran
  2. Department of Computer Science ; University of Bojnord ; Bojnord ; Iran
  3. Amirkabir Nanotechnology Research Institute (ANTRI) ; Amirkabir University of Technology ; 15875-4413 ; Tehran ; Iran
  4. Polymer Engineering Group ; Department of Engineering ; Qom University of Technology ; Qom ; Iran
  5. Math and Computer Science Department ; Amirkabir University of Technology ; 15875-4413 ; Tehran ; Iran
  
• 关键词：ANFIS ; ANN ; Blend ; Mechanical properties ; Polyamide ; 6
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：76
• 期：5-8
• 页码：961-970
• 全文大小：1,507 KB
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• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering
  Production and Logistics
  Mechanical Engineering
  Computer-Aided Engineering and Design
  
• 出版者：Springer London
• ISSN：1433-3015

文摘

Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) are employed to predict the final mechanical properties of Polyamide 6 (PA6)/ ethylene-propylene-rubber (EPR)-grafted blend at different compositions. An individual ANFIS model is developed to predict the mechanical property at different processing condition. The results of this study are suggestive that both ANFIS and ANN could be employed quite effectively for this aim in such a polymer blend system. Therefore, the mechanical properties such as yield strength, modulus, and Izod impact strength are well predicted employing this method including an acceptable range of errors.