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

Florian Nürnberger,Olga Perederieieva,Anna Halikova,Mirko Schaper,Friedrich-Wilhelm Bach,Iryna Mozgova,Kostjantyn Kuznetsov

doi:10.1155/2009/582739

Abstract

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.

Highlights

Precision forging is a technology for the production of components with near-net-shape geometry such as automotive gears
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
Deformation CCT diagrams for several tempering steels from previous works have been investigated taking into consideration the process conditions of precision forging

Summary

Introduction

Precision forging is a technology for the production of components with near-net-shape geometry such as automotive gears. The deformation CCT diagrams for the tempering steels 34CrMo4 (SAE 4135), 42CrMo4 (SAE 4140), 50CrMo4 (SAE 4150), 51CrV4 (SAE 6150), and 34CrNiMo6 (1.6582) were determined in a previous work [7] according to the standards SEP 1680 [8], SEP 1681 [9], and PN-68/H-04500 [10], respectively. Since such physical experiments are time-consuming and costly, the capabilities of artificial neural networks were investigated for predicting deformation CCT diagrams with regard to the particular processing in precision forging. The capabilities for designing new steels using neural networks have been demonstrated by Trzaska and Dobrzanski [17,18,19]

Deformation-Dependent Microstructural Transformations

Modeling and Implementation of Artificial Neural Networks

Data Extension

Results

Summary