Abstract
Austempered ductile iron (ADI), because of its attractive properties, for example, high tensile strength along with good ductility is widely used in automotive industries. Such properties of ADI primarily depend on two factors: addition of a delicate proportion of several chemical compositions during the production of ductile cast iron and an isothermal heat treatment process, called austempering process. The chemical compositions, depending on the austempering temperature and its time duration, interact in a complex manner that influences the microstructure of ADI, and determines its hardness and ductility. Vickers hardness number (VHN) is commonly used as a measure of the hardness of a material. In this paper, an artificial neural network (ANN)-based modeling technique is proposed to predict the VHN of ADI by taking experimental data from literature. Extensive simulations showed that the ANN-based model can predict the VHN with a maximum mean absolute error (MAPE) of 0.22%, considering seven chemical compositions, in contrast to 0.71% reported in the recent paper considering only two chemical compositions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Putatunda, S.K.: Development of austempered ductile cast iron (ADI) with simultaneous high yield strength and fracture toughness by a novel two-step austempering process. Mater. Sci. Eng. A 315, 70–80 (2001)
Yazdani, S., Elliott, R.: Influence of molybdenum on austempering behaviour of ductile iron Part 1 – Austempering kinetics and mechanical properties of ductile iron containing 0.13% Mo. Mater. Sci. Technol. 15, 531–540 (1999)
Panneerselvam, S., Martis, C.J., Putatunda, S.K., Boileau, J.M.: An investigation on the stability of austenite in austempered ductile cast iron (ADI). Mater. Sci. Eng. A 626, 237–246 (2015)
C.M.D. Ltd.: Austempered ductile-iron castings — advantages, production, properties and specifications. Mater. Des. 13, 285–297 (1992)
Harding, R.: Austempered ductile irons-gears. Mater. Des. 6, 177–184 (1985)
Cakir, M.C., Bayram, A., Isik, Y., Salar, B.: The effects of austempering temperature and time onto the machinability of austempered ductile iron. Mater. Sci. Eng. A 407, 147–153 (2005)
Angus, H.T.: Cast Iron: Physical and Engineering Properties. Butterworth-Heinemann, Oxford (1978)
Trudel, A., Gagne, M.: Effect of composition and heat treatment parameters on the characteristics of austempered ductile irons. Can. Metall. Q. 36, 289–298 (1997)
de Albuquerque Vicente, A., Moreno, J.R.S., de Abreu Santos, T.F., Espinosa, D.C.R., Tenório, J.A.S.: Nucleation and growth of graphite particles in ductile cast iron. J. Alloys Compd. 775, 1230–1234 (2019)
Giannakopoulos, A.E., Larsson, P.L., Vestergaard, R.: Analysis of Vickers indentation. Int. J. Solids Struct. 31, 2679–2708 (1994)
Haykin, S.: Neural Networks. Prentice Hall, Upper Saddle River (1999)
Patra, J.C., Kot, A.C.: Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks. IEEE Trans. Syst. Man Cybern. Part B 32, 505–511 (2002)
Patra, J.C.: Neural network-based model for dual-junction solar cells. Prog. Photovoltaics Res. Appl. 19, 33–44 (2011)
Patra, J.C., Chakraborty, G.: e-MLP-based modeling of high-power PEM fuel cell stacks. In: 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Anchorage, AK, pp. 802–807 (2011)
Patra, J.C., Maskell, D.L.: Artificial neural network-based model for estimation of EQE of multi-junction solar cells. In: 2011 37th IEEE Photovoltaic Specialists Conference (PVSC), Seattle, WA, pp. 002279–002282 (2011)
Patra, J.C.: Chebyshev neural network-based model for dual-junction solar cells. IEEE Trans. Energy Convers. 26, 132–139 (2011)
Jiang, L.L., Maskell, D.L., Patra, J.C.: Chebyshev functional link neural network-based modeling and experimental verification for photovoltaic arrays. In: 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, QLD, pp. 1–8 (2012)
Patra, J.C., Maskell, D.L.: Modeling of multi-junction solar cells for estimation of EQE under influence of charged particles using artificial neural networks. Renew. Energy. 44, 7–16 (2012)
Cool, T., Bhadeshia, H.K.D.H., MacKay, D.J.C.: The yield and ultimate tensile strength of steel welds. Mater. Sci. Eng. A 223, 186–200 (1997)
Malinov, S., Sha, W., McKeown, J.J.: Modeling and correlation between processing parameters and properties in titanium alloys using artificial network. Comput. Mater. Sci. 21, 375–394 (2001)
Yilmaz, M., Ertunc, H.M.: The prediction of mechanical behavior for steel wires and cord materials using neural networks. Mater. Des. 28, 599–608 (2007)
Reddy, N.S., Krishnaiah, J., Young, H.B., Lee, J.S.: Design of medium carbon steels by computational intelligence techniques. Comput. Mater. Sci. 101, 120–126 (2015)
Yescas, M.A.: Prediction of the Vickers hardness in austempered ductile irons using neural networks. Int. J. Cast Met. Res. 15, 513–521 (2003)
PourAsiabi, H., PourAsiabi, H., AmirZadeh, Z., BabaZadeh, M.: Development a multi-layer perceptron artificial neural network model to estimate the Vickers hardness of Mn–Ni–Cu–Mo austempered ductile iron. Mater. Des. 35, 782–789 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Savangouder, R.V., Patra, J.C., Bornand, C. (2019). Artificial Neural Network-Based Modeling for Prediction of Hardness of Austempered Ductile Iron. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1143. Springer, Cham. https://doi.org/10.1007/978-3-030-36802-9_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-36802-9_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36801-2
Online ISBN: 978-3-030-36802-9
eBook Packages: Computer ScienceComputer Science (R0)