Prediction of Continuous Cooling Diagrams for the Precision Forged Tempering Steel 50CrMo4 by Means of Artificial Neural Networks | Zendy

Florian Nürnberger | Zendy; Mirko Schaper | Zendy; FriedrichWilhelm Bach | Zendy; Iryna Mozgova | Zendy; Kostjantyn Kuznetsov | Zendy; Anna Halikova | Zendy; Olga Perederieieva | Zendy

Open Access

Prediction of Continuous Cooling Diagrams for the Precision Forged Tempering Steel 50CrMo4 by Means of Artificial Neural Networks

Author(s) -

Florian Nürnberger,

Mirko Schaper,

FriedrichWilhelm Bach,

Iryna Mozgova,

Kostjantyn Kuznetsov,

Anna Halikova,

Olga Perederieieva

Publication year - 2009

Publication title -

advances in materials science and engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.356

H-Index - 42

eISSN - 1687-8442

pISSN - 1687-8434

DOI - 10.1155/2009/582739

Subject(s) - forging , materials science , tempering , artificial neural network , quenching (fluorescence) , continuous cooling transformation , isothermal transformation diagram , process (computing) , isothermal process , deformation (meteorology) , matlab , work (physics) , mechanical engineering , metallurgy , computer science , artificial intelligence , thermodynamics , composite material , microstructure , martensite , engineering , bainite , physics , quantum mechanics , fluorescence , operating system

Quenching and tempering of precision forged components using their forging heat leads to reduced process energy and shortens the usual process chains. To design such a process, neither the isothermal transformation diagrams (TTT) nor the continuous cooling transformation (CCT) diagrams from literature can be used to predict microstructural transformations during quenching since the latter diagrams are significantly influenced by previous deformations and process-related high austenitising temperatures. For this reason, deformation CCT diagrams for several tempering steels from previous works have been investigated taking into consideration the process conditions of precision forging. Within the scope of the present work, these diagrams are used as input data for predicting microstructural transformations by means of artificial neural networks. Several artificial neural network structures have been examined using the commercial software MATLAB. Predictors have been established with satisfactory capabilities for predicting CCT diagrams for different degrees of deformation within the analyzed range of data

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