A Numerical Approach to the Prediction of Hardness at Different Points of a Heat-Treated Steel

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• 作者：M. Kianezhad (1)
  S. A. Sajjadi (1)
  H. Vafaeenezhad (2)
  
  1. Department of Materials Science and Engineering ; Engineering Faculty ; Ferdowsi University of Mashhad ; Mashhad ; Iran
  2. Materials Processing Simulation Laboratory (MPS - LAB) ; School of Materials and Metallurgical Engineering ; Iran University of Science and Technology (IUST) ; Narmak ; Tehran ; Iran
  
• 关键词：artificial neural networks ; CK60 steel ; hardness ; prediction ; quench factor analysis
• 刊名：Journal of Materials Engineering and Performance
• 出版年：2015
• 出版时间：April 2015
• 年：2015
• 卷：24
• 期：4
• 页码：1516-1521
• 全文大小：960 KB
• 参考文献：1. Willey, LA, Fink, WL (1948) Quenching of 75S Aluminum Alloy. Trans AIME 175: pp. 414-428
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  5. G.E. Totten, L.C.F. Canale, A.C. Canale, and C.E. Bates, Quench Factor Analysis to Quantify Steel Quench Severity and its Successful Use in Steel Hardness Predictions: Quench Factor Analysis of AISI 1045 Steel, / Proc. Int. conf. on 鈥楩ederation for Heat Treatment and Surface Engineering鈥?/em>, 2006, p 384
  6. G.E. Totten, Y.H. Sun, and C.E. Bates, Simplified Property Predictions for AISI 1045 Based on Quench Factor Analysis, / Presented at: Proc. 3rd. Int. Conf. on Quenching and Control of Distortion, 1999, p 219鈥?25
  7. G.E. Totten, Y.H. Sun, G.M. Webster, C.E. Bates, and L.M. Jarvis, Computerized Steel Hardness Predictions Based on Cooling Curve Analysis, / Proc. Conf. on Quenching and Distortion Control Technology, Chicago, 1998, p 183鈥?91
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• 刊物类别：Chemistry and Materials Science
• 刊物主题：Chemistry
  Characterization and Evaluation Materials
  Materials Science
  Tribology, Corrosion and Coatings
  Quality Control, Reliability, Safety and Risk
  Engineering Design
  
• 出版者：Springer New York
• ISSN：1544-1024

文摘

Accurate prediction of the mechanical properties in quenched steel parts has been considered by many recent researchers. For this purpose, different methods have been introduced. One of them is the quench factor analysis (QFA) which is based on continuous cooling rate during quenching. Another method for prediction of the mechanical properties in heat-treated alloys is artificial neural networks (ANNs). In the present research, QFA and ANN approaches have been used to predict the hardness of quenched steel parts in several different quench media. Then for the two methods, the predicted values have been compared with the experimental data. Results showed that the two methods are suitable in prediction of the hardness at different points of the quenched steel parts.