Simulation of twist extrusion process parameters of AA6061-T6 aluminum alloy by artificial neural network

Modern fabrication is to a large extent based on deformation processing. Plastic deformation process is a technique capable of producing metal products with high strength and good ductility. Using the parameters of load, temperature and the number of passes in twist extrusion, it is possible to produce an alloy with good properties and characteristics. Plastic deformation of AA6061-T6 aluminum alloy by twist extrusion is an important issue. In this study, we investigated the effect of load, temperature and the number of passes of twist extrusion on AA6061-T6. Using the input and output data, the process was modeled by the neural network method. In order to train the neural network, Neuro Solution software was used and for reducing the mean square error, the gradient descent momentum algorithm was implemented. Results showed that the effect of the number of passes and the load on tensile strength and hardness were maximum and minimum respectively.