Artificial neural network-based modeling for preditction of hardness of austempered ductile iron

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

Prediction of hardness of austempered ductile iron using enhanced multilayer perceptron based on Chebyshev expansion

In various industries, e.g., manufacturing, railways, and automotive, austempered ductile iron (ADI), is extensively used because of its desirable characteristics for example, high tensile strength with good ductility. The hardness and ductility of ADI can be tailor-made for a specific application by following an appropriate process. Such characteristics can be achieved by (i) adding a delicate proportion of several chemical compositions during the production of ductile cast iron and then followed by (ii) 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, we propose a computationally efficient enhanced multilayer perceptron (eMLP)-based technique to model the austempering process of ADI for prediction of VHN by taking experimental data reported in literature. By comparing the performance of the eMLP model with an MLP-based model, we have shown that the proposed model provides similar performance but with less computational complexity