In this work the Warm Hydroforming (WHF) process for the production of a 6xxx series Al alloy component has been investigated using a numerical/experimental approach: both experimental and numerical hydroforming tests were carried out using the alloy AC170PX, a pre aged (T4 condition) Al alloy often adopted for automotive applications. In order to evaluate both the mechanical and strain behaviour of the material, tensile tests were carried out at different temperature and strain rate levels using the Gleeble system 3180, keeping also into account the ageing effect; in addition, formability (Nakazima) tests in warm conditions were performed by means of a specific equipment and the Forming Limit Curves at different temperature levels were evaluated according to the ISO standard 12004-2. Hydroforming experiments were carried out using a prototypal press machine specifically designed for WHF and SuperPlastic Forming tests. Such tests, scheduled by a DoE approach, were aimed at investigating the suitability of using the investigated Al alloy in the WHF process: attention was thus focused on those parameters mainly affecting the aging phenomenon (temperature, heating time and cycle time). In order to overcome the actual physical limitation of the hydroforming facilities, a Finite Element (FE) model of the WHF process was also created implementing experimental data (flow stress curves and FLCs) and tuned using data from preliminary WHF tests. In particular, after setting the Coefficient Of Friction (COF) according to temperature and verifying the robustness of numerical simulations, the FE model was used for investigating: (i) the influence of the Blank Holder Force (neglected in the experimental campaign); (ii) the adoption of quite smaller values of the parameter cycle time (being the aim to determine higher strain rates in the material). Through the definition of proper response variables (Flatness, Bursting Pressure and Thickness Ratio) both experimental and numerical results were analyzed by means of polynomial Response Surfaces in order to evaluate the optimal process conditions.
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