Process Modelling of Aluminium Propeller Shaft by Integrated Computational Materials Engineering Approach

Integrated Computational Materials Engineering (ICME) simulation is an approach associated with materials processing methods by linking materials models at multiple length scales. AA6061 aluminum alloys are used popularly for its high strength-to-weight ratio so, enhancement of its mechanical properties by optimized thermomechanical process parameters using Matcalc is carried out. Simulation for different thermo mechanical processing is carried out and compare with the experimental results and analyzed. Initially, cold forging is carried out with artificial aging and then warm forging is carried out with and without artificial aging. Analyzed the simulation result of all three thermo mechanical treatment and correlated with simulation further to get the optimum process parameter for the iterations. In cold forging, dislocation density increment is observed and also refinement of grain. A refine grain form more grain boundary area which impedes dislocation motion and dislocations tend to pile up at grain boundaries which introduce stress concentrations ahead of their slip planes, which require more stress for deformation of material so increases the strength of the material. Experimentally yield strength value after cold forging is 330 MPa and correlated this value with matcalc simulation result get 85% accuracy. In warm forged condition at 100 °C resulted in less dislocations density due to dynamic recovery and coarsening of grain and this decreased the yield strength as compared to cold-forged conditions. When warm forged with artificial aging at 200 °C lead to increase in the strength due to high fraction phase β'' form which is coherent with the matrix. Additional stress is required for a dislocation to pass β’’ precipitates which increases the strength of the material. Experimentally yield strength value after warm forging with artificial aging is 290 MPa and correlated this value with matcalc simulation result get 90% accuracy.