Prediction of Flow Stress in Cadmium Using Constitutive Equation and Artificial Neural Network Approach

A model is developed to predict the constitutive flow behavior of cadmium during compression test using artificial neural network (ANN). The inputs of the neural network are strain, strain rate, and temperature, whereas flow stress is the output. Experimental data obtained from compression tests in the temperature range −30 to 70 °C, strain range 0.1 to 0.6, and strain rate range 10⁻³ to 1 s⁻¹ are employed to develop the model. A three-layer feed-forward ANN is trained with Levenberg-Marquardt training algorithm. It has been shown that the developed ANN model can efficiently and accurately predict the deformation behavior of cadmium. This trained network could predict the flow stress better than a constitutive equation of the type \( \dot{\upvarepsilon } = A\sinh (\upalpha /\upsigma )^{n} \exp ( - Q/RT) \).