Development a multi-layer perceptron artificial neural network model to estimate the Vickers hardness of Mn–Ni–Cu–Mo austempered ductile iron

The hardness of austempered ductile irons is relative to its microstructure, strength, ductility, machinability and wear resistance properties. Therefore, hardness measurement can be used as a simple tool to control the heat treatment, chemical composition and mechanical properties of ADI parts during the production process. The aim of this study is to develop an Artificial Neural Network (ANN) model for estimating the Vickers hardness of ADIs after austempering treatment. A Multi-Layer Perceptron model (MLP–ANN) was used with Mo%, Cu%, austempering time and temperature as inputs and the Vickers hardness of samples after austempering as the output of the model. A variety of samples were prepared in different conditions of chemical composition and heat treatment cycle. The obtained experimental results were used for training the neural network. Efficiency test of the model showed reasonably good agreement between experimental and numerical results, so the synthesized ANN model can estimate the hardness of the castings with a small error in the range of the experimental results standard deviation.

► Austempering process strongly improves VHN of Mn–Ni–Cu–Mo alloyed ductile irons.
► Levenberg–Marquardt training algorithm results in a negligible estimation error.
► MLP–ANN can model VHN of alloyed ADIs with a great testing correlation coefficient.
► By some simple reported equations, VHN of alloyed ADIs can be estimated precisely.